[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"systematic-review\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849171</article-id><article-id pub-id-type=\"pmc\">PMC7431762</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00639</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Systematic Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Autism and Epilepsy in Patients With Tuberous Sclerosis Complex</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Specchio</surname><given-names>Nicola</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/585567/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Pietrafusa</surname><given-names>Nicola</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Trivisano</surname><given-names>Marina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/808359/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Moavero</surname><given-names>Romina</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/521889/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>De Palma</surname><given-names>Luca</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/985787/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ferretti</surname><given-names>Alessandro</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/886687/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Vigevano</surname><given-names>Federico</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Curatolo</surname><given-names>Paolo</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/456138/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Rare and Complex Epilepsy Unit, Division of Neurology, Department of Neurosciences, Bambino Gesù Children's Hospital, IRCCS</institution>, <addr-line>Rome</addr-line>, <country>Italy</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Member of European Reference Network EpiCARE</institution></aff><aff id=\"aff3\"><sup>3</sup><institution>Child Neurology and Psychiatry Unit, Systems Medicine Department, Tor Vergata University</institution>, <addr-line>Rome</addr-line>, <country>Italy</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Neuroscience, Bambino Gesù Children's Hospital, IRCCS</institution>, <addr-line>Rome</addr-line>, <country>Italy</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Brahim Tabarki Melaiki, University of Sousse, Tunisia</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Iliyana Pacheva, Plovdiv Medical University, Bulgaria; Masashi Mizuguchi, The University of Tokyo, Japan</p></fn><corresp id=\"c001\">*Correspondence: Nicola Specchio <email>nicola.specchio@opbg.net</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Pediatric Neurology, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>639</elocation-id><history><date date-type=\"received\"><day>11</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>29</day><month>5</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Specchio, Pietrafusa, Trivisano, Moavero, De Palma, Ferretti, Vigevano and Curatolo.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Specchio, Pietrafusa, Trivisano, Moavero, De Palma, Ferretti, Vigevano and Curatolo</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Introduction:</bold> Individuals with Tuberous Sclerosis Complex (TSC) are at increased risk of developing both epilepsy and autism spectrum disorder (ASD), but the relationship between these conditions is little understood. We reviewed published reports to elucidate the relationship between ASD, epilepsy, and TSC, and to define the genetic and neurological risk factors.</p><p><bold>Methods:</bold> Articles (January 2004–May 2019) were identified via PubMed, EMBASE, and CENTRAL databases. Article inclusion required report on individuals with TSC-associated ASD and epilepsy with prevalence, odds ratio, or rate report on the comorbidity of ASD in epileptic patients due to TSC.</p><p><bold>Results:</bold> A total of 841 abstracts were identified in the original search. Thirty-six articles were included, which identified study populations, ASD measures used, and study confounders as bias factors. This review included 2,666 TSC patients, with a mean age of 15.9 years (range 1.94–30.3 years). The percentage of TSC patients with epilepsy <italic>and</italic> autism was 33.7%. Patients with TSC <italic>and</italic> autism showed more frequent seizures and earlier epilepsy onset than TSC patients without autism. ASD and intractable epilepsy were both predicted by a higher number of areas with dysplastic features revealed in brain MR scans. ASD, the onset of seizures in children <2 years of age, and >3 tubers have all been associated with an increased risk of refractory epilepsy in TSC patients. However, the direction of the relationship is not clear because a history of epilepsy, or infantile spasms in patients with TSC is also associated with an increased likelihood of ASD. Overall, 73.2% of patients carried <italic>TSC2</italic> genetic variant and, among patients with TSC and autism, the percentage of <italic>TSC2</italic> individuals was 85.6%.</p><p><bold>Conclusions:</bold> The complex interrelationship between TSC, autism, and epilepsy, coupled with limited knowledge on the neurobiological basis for the interrelationship, limits overall understanding and opportunities for management. The results of this review highlight the need for early identification and management to optimize favorable outcomes in the most vulnerable individuals with TSC. Regardless of whether studies are considered individually or collectively, interpretation is made difficult due to the differences between the studies, most notably between methods and diagnostic criteria used to assess intellectual ability.</p></abstract><kwd-group><kwd>tuberous sclerosis complex</kwd><kwd>epilepsy</kwd><kwd>autism spectrum disorder</kwd><kwd>prognostic factors</kwd><kwd>age at onset</kwd><kwd>genetic</kwd><kwd>TSC1</kwd><kwd>TSC2</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"4\"/><equation-count count=\"0\"/><ref-count count=\"64\"/><page-count count=\"13\"/><word-count count=\"9023\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Tuberous Sclerosis Complex (TSC) is a rare genetic multisystem disorder characterized by hamartoma formation in several organs and systems (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>), with an estimated birth incidence of 1 in 5,800 (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). TSC is caused by mutation in either <italic>TSC1</italic> (chromosome 9q34) or <italic>TSC2</italic> (16p13.3) gene, encoding for hamartin and tuberin, respectively (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). These two proteins, along with TBC1D7, form a heterotrimeric complex regulating the activity of mTOR complex 1 (mTORC1), which is a key regulator of cell metabolism and proliferation. mTORC1 dysregulation is the main reason for aberrant growth and differentiation underlying the formation of TSC-related lesions, either in the brain or other organs (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>).</p><p>Neurologic and developmental issues such as epilepsy, autism spectrum disorder (ASD), and developmental delay (DD), are major sources of morbidity in people of all ages with TSC and typically present in infancy or early childhood (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Epilepsy is estimated to occur in about 80% of TSC patients, typically within the first 3 years of life, and considered to be a result of the genetic mutation leading to an imbalance between excitation and inhibition of gamma-amino-butyric acid (GABA) receptors. Dysregulation of the neurotransmission of GABA has also been proposed as a neurobiological link between epilepsy and ASD in TSC patients (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>).</p><p>ASD is an early onset, lifelong, neurobiological disorder characterized by impairments in communication and social interaction along with the presence of restricted and repetitive patterns of behavior, interests or activities, and is prevalent in 1.85% of children aged 8 years (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). In the last 5 years, some longitudinal studies have explored the early emerging symptoms and prompt intervention in infants with high familial risk of ASD (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). In contrast, very few studies have addressed this topic in ASD associated with specific syndromes or genetic conditions (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>–<xref rid=\"B13\" ref-type=\"bibr\">13</xref>).</p><p>TSC is one of the major syndromes associated with ASD. The prevalence of ASD in TSC ranges from 26 to 45%, depending on the sample, ASD criteria, and the testing methodologies employed (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Some autistic features are present in about half of patients with TSC. A number of factors have been identified as being associated with ASD in TSC, including brain lesion load, prominent lesion type, the size and location of the tubers, cyst-like tubers, <italic>TSC2</italic> mutation, early onset and refractory seizures, and the presence and severity of cognitive impairment (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Prompt cessation of early seizures can, in at least some cases, improve neuropsychiatric outcome (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>).</p><p>To our knowledge, no review has yet examined the relationship between ASD and epilepsy in patients with TSC. We performed a review of the literature to assess the prevalence and risk factors for ASD in patients with TSC and epilepsy, and to investigate the relationship and comorbidity between these conditions. The main aims of this review were: to identify the frequency of both ASD and epilepsy within the TSC population, and to elucidate the relationship between ASD and epilepsy in individuals with TSC.</p></sec><sec sec-type=\"methods\" id=\"s2\"><title>Methods</title><p>The results of the present review were reported according to the preferred reporting items for reviews and meta-analyses (PRISMA) and adheres to a structured review protocol (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>).</p><sec><title>Search Strategy and Article Selection</title><p>Two authors (NP and NS) performed a search of PubMed, EMBASE, and CENTRAL databases using the following search strategy: “autism” OR “autistic” OR “asperger” OR “autism spectrum disorder” OR “pervasive” OR “pervasive developmental disorder” OR “PDD” OR “ASD” AND “epilepsy” OR “seizure” OR “epileptic” OR “convulsion” AND “tuberous sclerosis complex” OR “tuberous sclerosis” OR “TSC.”</p><p>Studies were initially included if they:</p><list list-type=\"order\"><list-item><p>Involved individuals with ASD and epilepsy symptomatic of TSC.</p></list-item><list-item><p>Reported prevalence, odds ratio, or numerical report of the comorbidity of ASD in patients with epilepsy due to TSC.</p></list-item><list-item><p>Were written in English.</p></list-item><list-item><p>Were based on human research.</p></list-item><list-item><p>Were published within 15 years of the search date (January 2004–May 2019), which was considered a sufficient period to capture publications with the most reliable and appropriate diagnostic and management procedures.</p><p>Two authors independently screened all titles and abstracts of studies identified by the initial search. The full text of an article was obtained when either reviewer thought that it might fulfill the inclusion criteria. Upon uncertainty for inclusion of a publication, an additional author was consulted (LDP).</p></list-item></list><p>Full articles were reviewed for relevance and articles were excluded if they did not include data relating to the prevalence of epilepsy/seizures in the TSC population. Articles also had to contain a reported or calculable prevalence for ASD in the text (if not provided in the abstract).</p><p>Based on the Quality in Prognosis Strategy (QUIPS) tool, the most commonly found risk factors for bias in the studies reviewed included study participation, ASD measure, and study confounders. Many [14] of the reviewed articles included participants drawn from one clinic or hospital (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>–<xref rid=\"B32\" ref-type=\"bibr\">32</xref>); others [5] had a specific age range (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>–<xref rid=\"B35\" ref-type=\"bibr\">35</xref>) or a particular subset of the TSC population (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>–<xref rid=\"B47\" ref-type=\"bibr\">47</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Only 18 of the included articles reported the diagnostic criteria for ASD (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Large variations were noted in the measures and criteria used to define ASD and many of the articles relied on reports of ASD by parents and caregivers. Comparisons between various studies were subject to a number of potential confounders, including a failure to report seizure onset, type, and frequency for epilepsy, antiseizure medication (ASM), genetic susceptibility, or other relevant baseline measures. Only articles that unequivocally reported the above-mentioned information were included in <xref rid=\"T2\" ref-type=\"table\">Tables 2</xref>–<xref rid=\"T4\" ref-type=\"table\">4</xref>. From <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>, eight articles were excluded because of no mention of onset, type, or frequency of epileptic seizures; 16 articles were excluded from <xref rid=\"T3\" ref-type=\"table\">Table 3</xref> because of no mention of number of patients with epilepsy, TSC and autism; 14 articles were excluded from <xref rid=\"T4\" ref-type=\"table\">Table 4</xref> because of no mention of genetic mutation in <italic>TSC1</italic> and <italic>TSC2</italic>. In this review we have used the terminology “infantile spasms” for infants with ES (with or without hypsarrhythmia), who may or may not have had cognitive regression. This operational definition was chosen because it was not always possible to determine whether the infants had hypsarrhythmia or cognitive regression. In the tables and figures, however, the term “epileptic spasms” has been used because this refers to that specific type of seizure.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Demographic information and prevalence rates of autism and epilepsy/seizures in Tuberous Sclerosis Complex patients reported within each of the articles included in this review.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Article</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>TSC patients, <italic>n</italic></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Male, <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Mean age (unless median reported)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>TSC patients with epilepsy/seizures, <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>TSC patients with autism, <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Autism assessment</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>TSC patients with epilepsy and autism, <italic>n</italic> (%)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Baumer et al. (<xref rid=\"B36\" ref-type=\"bibr\">36</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective cohort</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (59%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7.2 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (59%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5 (29%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Baumer et al. (<xref rid=\"B37\" ref-type=\"bibr\">37</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective clinical records (MRI)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31 (61%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9.25 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36 (71%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19 (37%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DSM IV/V and ADOS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 (35%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Benova et al. (<xref rid=\"B38\" ref-type=\"bibr\">38</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prospective imaging</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 (59%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.3 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (91%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9 (41%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ADI-R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (41%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Capal et al. (<xref rid=\"B51\" ref-type=\"bibr\">51</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TACERN Prospective longitudinal study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">130</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">68 (52%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23.3 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95 (73%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Symptoms only studied</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AOSI and ADOS-2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Caylor et al. (<xref rid=\"B39\" ref-type=\"bibr\">39</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Exome sequencing in 3 families</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (67%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16.3 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 (33%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (33%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chopra et al. (<xref rid=\"B40\" ref-type=\"bibr\">40</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cohort</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 (49%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14.8 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35 (78%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15 (33%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chou et al. (<xref rid=\"B20\" ref-type=\"bibr\">20</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cohort MRI</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14 (56%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23 (92%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5 (20%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cusmai et al. (<xref rid=\"B41\" ref-type=\"bibr\">41</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective cohort</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19 (43%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13.8 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13 (30%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 (30%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">de Vries et al. (<xref rid=\"B33\" ref-type=\"bibr\">33</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Postal survey</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">265</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">106 (40%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reported age in groups (<5 and >18 were excluded)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">238 (90%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">119 (45%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Doherty et al. (<xref rid=\"B42\" ref-type=\"bibr\">42</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (48%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9 (20%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PDD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (20%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Eluvathingal et al. (<xref rid=\"B43\" ref-type=\"bibr\">43</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MRI and PET scans of consecutive patients</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44 (56%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Symptoms only studied</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gilliam Asperger's Disorder Scale (GADS)<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>*</sup></xref> and VABS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gül Mert et al. (<xref rid=\"B21\" ref-type=\"bibr\">21</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case study of clinical records</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43 (53%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">33.5 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">28 (34%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28 (34%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Huang et al. (<xref rid=\"B22\" ref-type=\"bibr\">22</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Medical records</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 (50%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26 (81%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6 (19%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Iscan et al. (<xref rid=\"B23\" ref-type=\"bibr\">23</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Brain imaging</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (59%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9.5 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15 (88%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1 (6%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Jeste et al. (<xref rid=\"B13\" ref-type=\"bibr\">13</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Longitudinal study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 (62%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">32.1 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34 (94%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18 (50%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ADOS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 (50%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kilincaslan et al. (<xref rid=\"B44\" ref-type=\"bibr\">44</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case study of patients with refractory epilepsy</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (67%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16.25 y<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3 (50%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CARS and AuBC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (50%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kingswood et al. (<xref rid=\"B46\" ref-type=\"bibr\">46</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective longitudinal cohort</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">334</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">157 (47%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">30.3 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">257 (77%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">41 (13%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kopp et al. (<xref rid=\"B28\" ref-type=\"bibr\">28</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Clinical records</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45 (45%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7.7 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">87 (88%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">31 (31%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kosac and Jovic (<xref rid=\"B25\" ref-type=\"bibr\">25</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective cohort (clinical records)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 (41%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19.4y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39 (89%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6 (14%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (11%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Metwellay et al. (<xref rid=\"B32\" ref-type=\"bibr\">32</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cross sectional observational study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 (75%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6.2 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (88%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11 (46%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ADI-R and ADOS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mizuguchi et al. (<xref rid=\"B45\" ref-type=\"bibr\">45</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Randomized trial</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 (59%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8.76 y<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20 (69%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PARS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (69%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moavero et al. (<xref rid=\"B34\" ref-type=\"bibr\">34</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Epistop prospective study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45 (55%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r evaluated at 6, 12, and 18 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51 (62%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25 (30%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ADOS and BSID</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19 (23%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Muzykewicz et al. (<xref rid=\"B52\" ref-type=\"bibr\">52</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective chart review</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">241</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">118 (49%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">208 (86%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">86 (36%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Neuropsychological exam or clinical opinion</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Numis et al. (<xref rid=\"B22\" ref-type=\"bibr\">22</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective cohort (clinical records)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">103</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47 (46%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13.05 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91 (88%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">41 (40%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DSM-IV, Child symptom inventory-4, BASC-2 and Gilliam Asperger's Disorder Scale (GADS)<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>*</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40 (39%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Overwater et al. (<xref rid=\"B47\" ref-type=\"bibr\">47</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 (50%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12 y<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25 (78%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17 (53%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ADOS and CANTAB</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pascual-Castroviejo (<xref rid=\"B26\" ref-type=\"bibr\">26</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective review of MRI data</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23 (51%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16 (36%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 (36%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Saltik et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective study of clinical records</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (52%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7.5 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2 (10%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DSM-IV</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (10%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Samir et al. (<xref rid=\"B35\" ref-type=\"bibr\">35</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prospective EEG and MRI</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 (53%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4.66 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12 (40%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ADIR and ADOS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (40%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Spurling Jeste et al. (<xref rid=\"B12\" ref-type=\"bibr\">12</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prospective study as part of a multisite longitudinal study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">data reported at 6 mo intervals</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36 (90%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22 (55%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AOSI and ADOS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 (55%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Staley et al. (<xref rid=\"B49\" ref-type=\"bibr\">49</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective review of clinical records</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">257</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">210 (82%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23 (9%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gilliam Asperger's Disorder Scale (GADS)<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>*</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Toldo et al. (<xref rid=\"B28\" ref-type=\"bibr\">28</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective and prospective cohort study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 (50%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9.75 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24 (75%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22 (69%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vignoli et al. (<xref rid=\"B29\" ref-type=\"bibr\">29</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cohort Study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 (43%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19.3 y<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17 (40%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SCQ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 (40%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wataya-Kaneada et al. (<xref rid=\"B30\" ref-type=\"bibr\">30</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Comparison study of current vs. historical data from patients with TSC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70 (42%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">26.6 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">138 (83%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">35 (21%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pediatric and pychiatric departments (no diagnostic criteria) in Japan</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wilbur et al. (<xref rid=\"B31\" ref-type=\"bibr\">31</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective review of clinical records</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41 (51%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10 y<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>a</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74 (91%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20 (25%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (25%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wong and Khong (<xref rid=\"B53\" ref-type=\"bibr\">53</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MRI records</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (45%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15.25 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (95%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7 (32%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DSM-IV/ADIR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 (32%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yang et al. (<xref rid=\"B50\" ref-type=\"bibr\">50</xref></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Systematic analysis of genotypic and clinical data of Chinese patients</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">117</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60 (51%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5.17 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">113 (97%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">27 (23%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr></tbody></table><table-wrap-foot><p><italic>ADI-R, The Autism Diagnostic Interview-Revised; ADOS, The Autism Diagnostic Observation Schedule; AOSI, Autism Observation Scale for Infants; AuBC, Autism Behavior Checklist; BASC, Behavioral Assessment System for Children; BSID, Bayley Scales of Infant Development; CARS, Childhood Autism Rating Scale; DSM, Diagnostic Statistics Manual; MRI, magnetic resonance imaging; n/r, not reported; PARS, Pervasive Developmental Disorders Autism Society Japan Rating Scale; PET, Positron emission tomography; SCQ, Social Communication Questionnaire; TSC, Tuberous Sclerosis Complex; VABS, Vineland Adaptive Behavior Scales</italic>.</p><fn id=\"TN1\"><label>a</label><p><italic>Median age reported</italic>.</p></fn><fn id=\"TN2\"><label>*</label><p><italic>(<xref rid=\"B54\" ref-type=\"bibr\">54</xref>)</italic>.</p></fn></table-wrap-foot></table-wrap><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Summary of history of epilepsy in patients with Tuberous Sclerosis Complex.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Article</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Epilepsy/seizures present in TSC</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Age at onset, mean</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Epileptic spasms, <italic>n</italic></bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Epilepsy/seizure type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Refractory epilepsy (%)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Seizure frequency</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Benova et al. (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.1 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Daily (<italic>n</italic> = 14); weekly (<italic>n</italic> = 2); monthly (<italic>n</italic> = 4)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Capal et al. (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Focal szs (<italic>n</italic> = 21); mixed (<italic>n</italic> = 42) Generalized szs (<italic>n</italic> = 4); unclassified (<italic>n</italic> = 6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Caylor et al. (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal lobe epilepsy (<italic>n</italic> = 1); Focal szs (<italic>n</italic> = 1);</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (33%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chou et al. (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1 y (<italic>n</italic> = 13); <2 y (<italic>n</italic> = 19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (48%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cusmai et al. (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Focal motor szs (<italic>n</italic> = 19); generalized szs (<italic>n</italic> = 1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14 (32%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Doherty et al. (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gul Mert et al. (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.46 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Focal (<italic>n</italic> = 23); multifocal (<italic>n</italic> = 12); generalized (<italic>n</italic> = 26)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15 (18%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Huang et al. (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"> ≤ 6 mo (<italic>n</italic> = 11); 7–12 mo (<italic>n</italic> = 8); ≥12 mo (<italic>n</italic> = 4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Complex partial (<italic>n</italic> = 4); simple partial (<italic>n</italic> = 4); generalized (<italic>n</italic> = 7); clonic (<italic>n</italic> = 1); tonic (<italic>n</italic> = 1; myoclonic (<italic>n</italic> = 1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Iscan et al. (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.7 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Generalized (<italic>n</italic> = 3); mixed (<italic>n</italic> = 4); Complex partial (<italic>n</italic> = 2) myoclonic (<italic>n</italic> = 1); febrile (<italic>n</italic> = 1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Jeste et al. (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.75 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (18%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Monthly (26%); weekly (7%); daily (27%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kilincaslan et al. (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><6 mo (<italic>n</italic> = 3); <2 y (<italic>n</italic> = 2); 7 y (<italic>n</italic> = 1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Complex partial (<italic>n</italic> = 2); simple partial (<italic>n</italic> = 2); atonic/atypical absence (<italic>n</italic> = 1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (100%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">>1 a day (<italic>n</italic> = 4); >1 a week (<italic>n</italic> = 2)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kopp et al. (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.9 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Complex partial history (<italic>n</italic> = 78); mixed seizures history (<italic>n</italic> =18)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mean per month 39.9 (<italic>n</italic> = 66)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kosac and Jovic (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.8 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Focal szs (84.6%); Secondary generalized szs (39.3%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Metwellay et al. (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><6 mo (<italic>n</italic> = 12); >6 mo (<italic>n</italic> = 9)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Generalized (<italic>n</italic> = 3); Focal (<italic>n</italic> = 4); Partial with secondary generalization (<italic>n</italic> = 1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 (76%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mizuguchi et al. (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moavero et al. (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1 y (<italic>n</italic> = 38); <2 y (<italic>n</italic> = 13)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32 (63%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Muzykewicz et al. (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">208</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">92</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">141 (68%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Numis et al. (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.9 y</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60 (66%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1.75 per week</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Overwater et al. (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14 (56%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pascual-Castroviejo et al. (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Saltik et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1 y (76.1%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Focal szs (<italic>n</italic> = 20); diffuse tonic-clonic (<italic>n</italic> = 3); atonic (<italic>n</italic> = 3); absence (<italic>n</italic> = 1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 (62%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Samir et al. (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><6 mo (<italic>n</italic> = 16); ≥6 mo (<italic>n</italic> = 14)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Focal szs (<italic>n</italic> = 5); secondary generalization (<italic>n</italic> = 8)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19 (63%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Spurling Jeste et al. (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.8 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vignoli et al. (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.9 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (26%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Monthy (<italic>n</italic> = 7); Weekly (<italic>n</italic> = 10)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wataya-Kaneada et al. (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">143</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (14%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wilbur et al. (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 mo median</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Focal (66%); epileptic spasms (26%); generalized (5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wong et al. (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33 mo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (14%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yang et al. (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">113</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr></tbody></table><table-wrap-foot><p><italic>n/r, not reported; Szs, seizures; TCS, Tuberous Sclerosis Complex</italic>.</p></table-wrap-foot></table-wrap><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Summary of family history of Tuberous Sclerosis Complex (TSC) and genetic mutations in patients with TSC.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Article</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>TSC patients, <italic>n</italic></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>TSC patients with epilepsy and autism, <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Seizure/epilepsy in patients with ASD</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Baumer et al. (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 (35%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Benova et al. (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (41%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2/9 ES</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Caylor et al. (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (33%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1/1 focal to bilateral seizure</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cusmai et al. (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 (30%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8/13 ES, 5/13 focal motor</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Doherty et al. (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (20%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gül Mert et al. (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28 (34%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Iscan et al. (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Jeste et al. (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 (50%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13/18 ES</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kilincaslan et al. (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (50%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2/3 ES, 3/3 focal seizure, 2/3 tonic seizure</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kosac and Jovic (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (11%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mizuguchi et al. (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (69%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moavero et al. (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19 (23%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2/15 ES</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Numis et al. (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">103</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40 (39%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24/40 ES</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pascual-Castroviejo et al. (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 (36%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Saltik et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (10%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Samir et al. (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (40%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11/12 ES</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Spurling Jeste et al. (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 (55%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14/22 ES</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vignoli et al. (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 (40%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wilbur et al. (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (25%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wong and Khong (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 (32%)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">n/r</td></tr></tbody></table><table-wrap-foot><p><italic>n/r, not reported; TSC, Tuberous Sclerosis Complex; ASD, Autism spectrum disorder; ES, epileptic spasms</italic>.</p></table-wrap-foot></table-wrap><table-wrap id=\"T4\" position=\"float\"><label>Table 4</label><caption><p>Summary of family history of Tuberous Sclerosis Complex (TSC) and genetic mutations in patients with TSC.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Article</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>TSC pts, <italic>n</italic></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>TSC1</italic> (all patients), <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>TSC1</italic> (patients with autism), <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>TSC2</italic> (all patients), <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>TSC2</italic> (patients with autism), <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>No mutation identified (all patients), <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>No mutation identified (patients with autism), <italic>n</italic> (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Family history of TSC, <italic>n</italic> (%)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Benova et al. (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 (32%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (22%) (<italic>n</italic> = 9)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (55%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (56%) (<italic>n</italic> = 9)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Caylor et al. (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (67%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (100%) (<italic>n</italic> = 1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (33%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (<italic>n</italic> = 1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chopra et al. (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (20%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (7%) (<italic>n</italic> = 15)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24 (53%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (80%) (<italic>n</italic> = 15)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (24%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (11%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chou et al. (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (8%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cusmai et al. (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (9%) (<italic>n</italic> = 23)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (87%) (<italic>n</italic> = 23)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (4%) (<italic>n</italic> = 23)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Doherty et al. (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (23%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26 (59%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Huang et al. (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (19%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (17%) (<italic>n</italic> = 6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26 (81%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (83%) (<italic>n</italic> = 6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Iscan et al. (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Jeste et al. (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (16%) (<italic>n</italic> = 31)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (27%) (<italic>n</italic> = 15)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26 (84%) (<italic>n</italic> = 31)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (73%) (<italic>n</italic> = 15)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kopp et al. (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15 (16%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58 (62%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21(22%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (20%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Kosac and Jovic (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (30%) (<italic>n</italic> = 10)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5 (50%) (<italic>n</italic> = 10)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (25%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moavero et al. (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (24%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59 (72%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (4%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Muzykewicz et al. (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">241</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50<xref ref-type=\"table-fn\" rid=\"TN3\"><sup>a</sup></xref> (27%) (<italic>n</italic> = 191)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">106<xref ref-type=\"table-fn\" rid=\"TN3\"><sup>a</sup></xref> (55%) (<italic>n</italic> = 191)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34 (18%) (<italic>n</italic> = 191)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Numis et al. (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">103</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24 (23%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (7%) (<italic>n</italic> = 41)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58 (56%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27 (66%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (10%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (22%) (<italic>n</italic> = 41)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Overwater et al. (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 (22%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (66%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (13%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Saltik et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 (33%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Samir et al. (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (13%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Staley et al. (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">257</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51 (27%) (<italic>n</italic> = 192)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">109 (57%) (<italic>n</italic> = 192)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vignoli et al. (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (24%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (71%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (5%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wataya-Kaneada et al. (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">166</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21 (28%) (<italic>n</italic> = 75)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24 (32%) (<italic>n</italic> = 75)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (39%) (<italic>n</italic> = 75)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 (23%) (<italic>n</italic> = 75)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wilbur et al. (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (33%) (<italic>n</italic> = 6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (67%) (<italic>n</italic> = 6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">n/r</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (7%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yang et al. (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">117</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 (14%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>) (<italic>n</italic> = 27%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">101 (86)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25 (93%) (<italic>n</italic> = 27)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14 (12%)</td></tr></tbody></table><table-wrap-foot><p><italic>n/r, not reported; TSC, Tuberous Sclerosis Complex; pts, patients</italic>.</p><fn id=\"TN3\"><label>a</label><p><italic>One patient had both TSC1 and TSC2 mutations</italic>.</p></fn></table-wrap-foot></table-wrap></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><p>A total of 841 abstracts were identified in the original search. Of these, 673 were duplicates or congress abstracts only. The remaining abstracts and articles were reviewed for inclusion/exclusion criteria, and a total of 36 articles were considered suitable for inclusion (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). Included articles are presented in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. In total, 2,666 patients with TSC were included in this review, with a mean age of 15.9 years (range 1.94–30.3 years). TSC populations included within the selected articles were predominantly male; males represented 52.5% of overall participants, ranging from 41 to 75% of patients in articles (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Search strategy.</p></caption><graphic xlink:href=\"fneur-11-00639-g0001\"/></fig><sec><title>Prevalence of Autism and Epilepsy in Patients With TSC</title><p>Of the patients with TSC included with available data in this review, the overall percentage of patients with autism was 29.8% (732 of 2,458 patients with available data), ranging from 6% (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>) to 69% (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>), and those with epilepsy/seizures was 88.2% (2,352 of 2,666 patients), ranging from 59 to 100% (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>–<xref rid=\"B45\" ref-type=\"bibr\">45</xref>) (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Patients with epilepsy <italic>and</italic> autism are also reported where available (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>), with the overall percentage being 33.7% (279 of 828 patients with available data) and ranging from 10% (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>) to 69% (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>).</p></sec><sec><title>Epilepsy</title><p>The mean age for onset of epilepsy was below 33 months; however, data were available for 859 patients only. Infantile spasms were reported in 42.8% of TSC populations studied (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>), ranging from 20% (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>) to 67% (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). Other epilepsy types were less frequently reported within the articles reviewed, but Huang et al. (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>) suggested that focal seizures were also frequent in infants with TSC under 1 year of age. Reports of refractory epilepsy in patients with TSC ranged from 14% to 100% (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>), although the latter specifically focused on 6 TSC patients with refractory epilepsy.</p><p>The relationship between epilepsy, ASD, and TSC is complex. Autism, the onset of seizures in children <2 years of age and with >3 tubers (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>) have all been associated with an increased risk of refractory epilepsy in TSC patients. However, the direction of the relationship is unclear because a history of epilepsy (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>) or infantile spasms (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>) in patients with TSC is also associated with an increased likelihood of ASD. Patients with TSC <italic>and</italic> autism showed more frequent seizures than TSC patients without autism (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>) and an earlier age of onset of epilepsy has been associated with ASD (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>), delayed language, intellectual disability (ID), and poor cognitive flexibility (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). In <xref rid=\"T3\" ref-type=\"table\">Table 3</xref> are reported the epilepsy features in patients with TSC and autism.</p></sec><sec><title>Phenotype/Behavior</title><p>Clinically significant behavioral problems and social withdrawal are common in young children with TSC (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Conditions including mood disorder, anxiety, ADHD, and aggressive behavior were reported in 66% of a pediatric population with TSC (<italic>n</italic> = 241) (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>). Aggressive behavior was associated with both increased severity of epilepsy and features of autism/pervasive developmental disorder (PDD) (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>).</p><p>Early identifiers of autism or autistic-like features in patients with TSC include early DD or a slowing in nonverbal cognition (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Studies of very young infants with TSC suggest early delay in visual reception (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>) and under-developed fine-motor skills to be markers of the development of autism traits (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Deficits across all domains of the Bayley Scales of Infant Development (BSDI) at 1 year of age were predictive of higher autism traits on the Autism Diagnostic Observation Schedule (ADOS) at 2 years within a prospective study of infants with TSC (<italic>n</italic> = 82) in 10 sites across Europe and Australia (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). ID is often more common in TSC patients with ASD than those with TSC alone (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). Behavioral problems have been reported to be exacerbated by seizure frequency and a mixed seizure profile (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Results of a study exploring the relationship between cognitive delay and clinical features of TSC in Egypt reported that the age of seizure onset (<italic>p</italic> = 0.044) and number of brain tubers (<italic>p</italic> = 0.06) increased the odds for cognitive delay in 24 children with TSC (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Similarly, ID has been associated with early onset of seizures, infantile spasms (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>), and intractable epilepsy (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Early seizure onset was the most significant predictor of DD at 2 years of age in a longitudinal prospective analysis of developmental outcomes in infants (0–3 years) with TSC (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Since the data in most of the reported studies did not specify infantile spasms, many of the early onset seizures could have been infantile spasms. The neurologic symptoms of TSC, refractory epilepsy, ASD, and ID have all shown an interrelationship (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>), making specific relationships between ASD, ID, and epilepsy difficult to discern.</p><p>Self-injurious behavior in patients with TSC was associated with a history of infantile spasms and seizures, ID, ASD, and <italic>TSC2</italic> mutations (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Aggressive behavior was also associated with ID and <italic>TSC2</italic> mutations (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>), suggesting a potential genetic link.</p><p>Data on severity of autism and developmental delay were sparse and therefore not reported.</p></sec><sec><title>Genotype</title><p>Refractory epilepsy (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>), ID (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>), and autism in TSC patients have all been associated with the <italic>TSC2</italic> genotype (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>). The <italic>TSC2</italic> genotype was more common than <italic>TSC1</italic> genotype among TSC patients overall (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>), with the exception of one study that focused on individuals from three families, in which three individuals had a diagnosis of TSC: two with the <italic>TSC1</italic> genotype and one with the <italic>TSC2</italic> genotype (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Overall, 73.2% of TSC individuals had the <italic>TSC2</italic> genotype—ranging from 32% (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>) to 89% (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>)—and 26.8% had the <italic>TSC1</italic> genotype—ranging from 9% (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>) to 67% (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>) (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>). Among patients with TSC and autism, 85.6% had the <italic>TSC2</italic> genotype. Autistic behavior correlated with nonsense mutations in the <italic>TSC2</italic> gene group in a retrospective review of medical records from patients with TSC in Taiwan (<italic>n</italic> = 32) (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>).</p></sec><sec><title>Neuroimaging</title><p>A magnetic resonance imaging (MRI) study including 25 children (aged >2 years) reported that lesion load within the left temporal lobe was positively correlated with the neurological severity score (<italic>r</italic> = 0.609; <italic>p</italic> = 0.001). This finding was supported in an electroencephalogram (EEG) study that found greater interictal epileptiform features in the left temporal lobe only (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>).</p><p>Two studies exploring potential impact of TSC proteins on white-matter tract pathways have identified abnormal diffusion characteristics, which are believed to arise from abnormal neuronal and axonal organization and hypomyelination (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>). Furthermore, these effects were each associated with TSC, epilepsy, and autism (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>). In a diffusion MRI study exploring the directionality of water movement [fractional anisotropy (FA)], TSC alone was related to lower callosal FA values than controls—and this difference was greater in the TSC patients with autism than without—when comparing study groups of TSC patients with either epilepsy (with and without comorbid autism; <italic>n</italic> = 19 and <italic>n</italic> = 32, respectively) or autism alone (<italic>n</italic> = 46) with a healthy control group (<italic>n</italic> = 89) (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). A positron emission tomography (PET) study comparing TSC patients with and without a cerebellar lesion (<italic>n</italic> = 20 vs. <italic>n</italic> = 57, respectively) reported that the group with cerebellar lesions had higher overall autistic symptomology (i.e., social isolation and communicative/developmental disturbance) and that these deficits were associated with right-sided cerebellar lesions (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>).</p><p>The size, number, and anatomical location of tubers have all independently been linked to autism and/or epilepsy in TSC (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>), although this relationship has not always been established (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). The number of tubers is strongly associated with infantile spasms (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>) and ASD (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Tubers of larger size were associated with increased likelihood of seizures and autism (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>), and higher prevalence of cyst-like tubers was associated with ASD (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). ASD and intractable epilepsy were both predicted by a higher number of areas with dysplastic features (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). ASD or PDD have been linked with tubers in the frontal areas of the brain (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>), increased tuber count in the occipital lobe (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>), cystic-like tubers, and tubers in insular and temporal areas (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Infantile spasms are more likely to occur in children with cortical tubers in the parietal lobes (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>).</p></sec><sec><title>Pharmacological Treatment</title><p>Data relating to ASM use was not commonly provided in the studies included in this review. Where reported, the mean number of ASMs per patient with TSC ranged from 1.46 to 3.95 (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Combination treatment with two ASMs or more was common and, where reported, the number of TSC patients using polytherapy ranged from 52 to 100% (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Common ASMs included valproic acid, carbamazepine, topiramate, lamotrigine, and vigabatrin (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Only two of the reviewed studies reported individual use of ASMs among TSC patients, and these data are summarized in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Proportion of Tuberous Sclerosis Complex (TSC) participants using antiseizure medications (ASMs) (<italic>n</italic> = 66) (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>).</p></caption><graphic xlink:href=\"fneur-11-00639-g0002\"/></fig><p>Early treatment with ASMs may be of importance, since better long-term epileptic encephalopathy outcomes were reported in those treated early in a randomized trial of early vs. later treatment with vigabatrin (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). In general, studies should distinguish between early and later treatment of epilepsy in TSC, considering that later treatment of seizures in TSC is often disappointing and research reports that the development of ID is predicted by the number of ASMs used (potentially related to delay in effective treatment) to treat epilepsy in children with TSC (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>).</p><p>The studies in our review with data on individuals with uncontrolled epilepsy reported these to represent 14–100% of patients with a history of epilepsy, with the majority of studies reporting >40% of the epilepsy population still having seizures (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Although these data suggest a greater proportion of TSC patients with difficult-to-treat epilepsy than is typical of a general population, the bias in study participation remains a caveat to such speculation.</p><p>Three studies evaluated the effects of an mTOR kinase inhibitor, everolimus, which can be used to reduce tumor size (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>, <xref rid=\"B47\" ref-type=\"bibr\">47</xref>, <xref rid=\"B48\" ref-type=\"bibr\">48</xref>). The first, a three-armed randomized trial in Japan (<italic>n</italic> = 29), reported adjunctive everolimus treatment to significantly reduce seizure frequency in TSC patients with refractory epilepsy, with a trend for improvements in ASD symptoms (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). A similar finding was reported in a small case study evaluating everolimus for refractory epilepsy in six TSC patients with refractory epilepsy (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). This second study also reported improvement in ASD symptoms, such as social contact, language, and repetitive behavior (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). However, the third study—a recent randomized controlled trial conducted in the Netherlands including 32 children with TSC—found no benefit of everolimus on cognitive or neuropsychological functioning, or autism traits, in comparison with placebo (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). In this third study, age at enrollment was high—the median age was 11.5 years for patients on placebo and 12.2 years for patients on everolimus—therefore, firm conclusions cannot be drawn (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). However, early treatment with everolimus might be required for improvement in features such as social contact, language and repetitive behavior; there is a need for formal studies to determine whether this is the case.</p></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>Based on the 36 articles included in this review, our findings were consistent with previous reports of high rates of epilepsy in patients with TSC (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Interestingly, epilepsy was reported in 83.6% of patients with TSC in an international TuberOus SClerosis registry to increase disease Awareness (TOSCA); however, data on the prevalence of ASD in this population were not reported (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>). The prevalence of autism in patients with TSC in the subjects included in this review is high, but is consistent with previous estimates of syndromic ASD in TSC (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref>).</p><p>The risk of autism is increased by early onset seizures (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>) and by DD and ID (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>), which in turn have been associated with early onset epilepsy and infantile spasms (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Existence of phenotypic variability should be acknowledged: TSC is also associated with high-functioning autism, normal intelligence, hypercalculia, and drug-resistant epilepsy with an EEG pattern characterized by hypsarrhythmia and electrical status epilepticus during sleep (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>).</p><p>The relationship between TSC, epilepsy, and ASD is highly complex. A poor prognosis of epilepsy outcomes is largely reported to be exacerbated by ASD (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>). An additive neuroanatomical impact of TSC, epilepsy, and autism has been proposed that is predominantly evident in white-matter pathways (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>), supporting the association between autism, epilepsy, and DD/ID in patients with TSC.</p><p>Evidence suggests both epilepsy and autism are linked with mutations on the <italic>TSC1</italic> and <italic>TSC2</italic> genes. Mutations in the <italic>TSC2</italic> gene are more prevalent in association with epilepsy and autism (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Early genotyping may, therefore, help identify TSC patients at increased risk of poorer long-term outcomes.</p><p>In terms of autism, neuroimaging studies report that tuber features, such as larger size or increased number of cyst-like tubers, are associated with increased risk (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>). It is also demonstrated that diffusion imaging abnormalities correlate with reduced myelination in TSC patients (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>) and the effect of mTOR overactivation on white matter might be modified by pharmacological inhibition (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>). Moreover, TSC patients with autism have been documented to have a reduction of fractional anisotropy in different white-matter regions, and this happens over the first 2 years of life (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Since size, type, and location of tubers influence the longer-term risk of autism and epilepsy in TSC, early characterization of such features could assist in determining the focus of early intervention.</p><p>Cells in the central nervous system express TSC1 and TSC2 proteins throughout childhood and into adulthood. These proteins help regulate myelination, axon guidance, and dendritic arborization, promoting normal synaptic formation and function (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). Dysregulation of the neurotransmission of GABA, resulting from genetic mutations of TSC, has previously been argued to underlie development of epilepsy and autism in this population (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Limited evidence suggests that treatment with everolimus, particularly if commenced early, may improve epilepsy outcomes and reduce the risk of autism in TSC patients (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>). Data coming from the EXIST-3 trial confirm that adjunctive everolimus might reduce seizure frequency in pediatric patients with treatment-refractory seizures associated with tuberous sclerosis complex also in patients younger than 6 years (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). However, these findings were based on few trials and contradictory evidence also exists (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). Additional research into alternative treatment strategies and an increased focus on the longer-term outcomes would help elucidate whether size, type, and location of tubers influence the longer-term risk of autism and epilepsy in TSC.</p><p>Early treatment with ASMs to control epilepsy is reported to improve longer-term epilepsy outcomes (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>), and controlled epilepsy is associated with reduced symptoms of autism (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>). ID and DD are in turn associated with increased presence of autism (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>), so the number and choice of ASMs in infants with TSC needs to be managed with care. <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref> is a diagrammatic overview of the complex relationship between the phenotypic features of TSC and polytherapy treatment with ASMs based on the evidence reviewed here.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Diagram of interrelationship between phenotypic profiles and prognostic risk factors among patients with TSC.</p></caption><graphic xlink:href=\"fneur-11-00639-g0003\"/></fig></sec><sec id=\"s5\"><title>Limitations</title><p>Although we identified 36 articles reporting autism and epilepsy in TSC, only approximately half of these articles indicated which patients were experiencing either of these comorbid conditions. Very few of the included studies summarized the potential prognostic features of patients with all three conditions (TSC, epilepsy, and autism). This review has therefore identified a need for future studies to focus on common associative factors.</p><p>A second limitation was that, because the studies were conducted in different settings across different countries, practices were not standardized with respect to identification of TSC, epilepsy, and—above all—autism. Different diagnostic criteria were used to identify patients with TSC according to the clinical practice of the country or region. Likewise, the tools used to define the presence of autism varied considerably. In some cases, the diagnosis of autism was not confirmed, but relied on reports from parents and caregivers. In populations that only focused on very young infants, in whom a clinical diagnosis of autism was not possible, the conclusions regarding risk of autism were based on autistic features, which do not necessarily indicate a later clinical outcome.</p><p>The methodological approaches of the included articles also varied widely and ranged from small clinical series to large retrospective studies, each with differing strengths and limitations. One of the challenges of establishing a representative sample of individuals with TSC is the rarity of the disease. The TSC populations within the included articles ranged from infants to adults, sometimes within the same study. Consequently, the core features of TSC and age of onset of the conditions may not have been reliable.</p><p>Lastly, the quality of the available data does not allow a meaningful review to be performed.</p></sec><sec sec-type=\"conclusions\" id=\"s6\"><title>Conclusions</title><p>Early onset epilepsy, frequently represented by epileptic encephalopathy, can be considered one of the risk factors for ID in TSC patients. However, the role of genetic variations should be highlighted as the major player in determining both epilepsy and intellectual disability due to mTOR overactivation (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>).</p><p>In terms of further defining the prognostic features of epilepsy and autism within TSC, large prospective studies, such as TACERN or those conducted by the EPISTOP group (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>, <xref rid=\"B64\" ref-type=\"bibr\">64</xref>), are helping to identify early biomarkers for treatment.</p><p>The prevalence of autism and epilepsy in TSC is much higher than that in the general population, both alone and as comorbid features. We summarized the phenotypic, genetic, and neurological risk factors for the association of autism and epilepsy in TSC patients from available data, but the inherent limitations of the source studies should be noted.</p><p>The relationship between these three conditions is complex. Early identification of the risk factors, together with early use of m-TOR inhibitors might be a priority to optimize favorable outcomes in this vulnerable population.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>NS conceptualized and designed the study, drafted the initial manuscript, supervised data collection, and reviewed and revised the manuscript. NP, MT, and LD designed the data collection instruments, collected data, carried out the initial analyses, and reviewed and revised the manuscript. AF and RM collected data, carried out the initial analyses, and reviewed and revised the manuscript. FV and PC conceptualized and designed the study and critically reviewed the manuscript for important intellectual content. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>NS has received support from Livanova and Biomarin, and has served as a paid consultant for Livanova. PC has served as a paid consultant for Novartis. FV has served as paid consultant for Zogenix, Eisai, and GW Pharma. MT has served as paid consultant for Biomarin. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> Medical writing support was provided by Dr. Brenda J. Meyer. English text editing was provided by Dr. David Macari. 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] |
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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"brief-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Med (Lausanne)</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Med (Lausanne)</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Med.</journal-id><journal-title-group><journal-title>Frontiers in Medicine</journal-title></journal-title-group><issn pub-type=\"epub\">2296-858X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850919</article-id><article-id pub-id-type=\"pmc\">PMC7431763</article-id><article-id pub-id-type=\"doi\">10.3389/fmed.2020.00450</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Medicine</subject><subj-group><subject>Brief Research Report</subject></subj-group></subj-group></article-categories><title-group><article-title>Intrahepatic Expression of Fatty Acid Translocase CD36 Is Increased in Obstructive Sleep Apnea</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Rey</surname><given-names>Esther</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/936283/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>del Pozo-Maroto</surname><given-names>Elvira</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Marañón</surname><given-names>Patricia</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1025042/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Beeler</surname><given-names>Brittany</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>García-García</surname><given-names>Yaiza</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Landete</surname><given-names>Pedro</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Isaza</surname><given-names>Stephania C.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Farré</surname><given-names>Ramón</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/23393/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>García-Monzón</surname><given-names>Carmelo</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/857244/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Almendros</surname><given-names>Isaac</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>‡</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/117243/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>González-Rodríguez</surname><given-names>Águeda</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>‡</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/521165/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Research Unit, Hospital Universitario Santa Cristina, Instituto de Investigación Sanitaria Hospital Universitario de La Princesa, CIBERehd</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Respiratory Medicine Department, Hospital Universitario de La Princesa, Instituto de Investigación Sanitaria Princesa Hospital Universitario de La Princesa</institution>, <addr-line>Madrid</addr-line>, <country>Spain</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Unitat de Biofísica i Bioenginyeria, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, CIBERES, IDIBAPS</institution>, <addr-line>Barcelona</addr-line>, <country>Spain</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Angel Lanas, University of Zaragoza, Spain</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Javier González-Gallego, Institute of Biomedicine, University of León, Spain; Zuzana Macek Jilkova, Service d'Hépato-Gastroentérologie, Centre Hospitalier Universitaire de Grenoble, France</p></fn><corresp id=\"c001\">*Correspondence: Carmelo García-Monzón <email>garciamonzon@hotmail.com</email></corresp><corresp id=\"c002\">Águeda González-Rodríguez <email>aguedagr.phd@gmail.com</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Gastroenterology, a section of the journal Frontiers in Medicine</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†These authors have contributed equally to this work</p></fn><fn fn-type=\"other\" id=\"fn003\"><p>‡These authors share senior authorship</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>7</volume><elocation-id>450</elocation-id><history><date date-type=\"received\"><day>30</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>07</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Rey, del Pozo-Maroto, Marañón, Beeler, García-García, Landete, Isaza, Farré, García-Monzón, Almendros and González-Rodríguez.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Rey, del Pozo-Maroto, Marañón, Beeler, García-García, Landete, Isaza, Farré, García-Monzón, Almendros and González-Rodríguez</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Nocturnal intermittent hypoxia (IH) featuring obstructive sleep apnea (OSA) dysregulates hepatic lipid metabolism and might contribute to the development of non-alcoholic fatty liver disease (NAFLD) observed in OSA patients. However, further research is required to better understanding the molecular mechanisms underlying IH-induced hepatic lipid accumulation. Therefore, the aim of the present study was to determine the effects of OSA on hepatic CD36 expression and the impact of IH by using a mouse model of OSA. Histological analysis, lipid content and CD36 expression were assessed in livers from subjects who underwent liver biopsy and polygraphic study during sleep, and in livers from mice submitted to chronic IH mimicking OSA. Among those who presented OSA features, NAFLD were significantly more frequent than in control subjects with normal respiratory function (77.8 vs. 36.4%, respectively), and showed more severe liver disease. Interestingly, CD36 expression was significantly overexpressed within the liver of OSA patients with respect to controls, and a significant positive correlation was observed between hepatic levels of CD36 and the values of two well-known respiratory parameters that characterized OSA: apnea/hypopnea index (AHI) and oxygen desaturation index (ODI). Moreover, hepatic lipid accumulation as well as induction of hepatic lipogenic genes, and CD36 mRNA and protein expression were significantly higher in livers from mice exposed to IH conditions for 8 weeks than in their corresponding littermates. This study provides novel evidence that IH featuring OSA could contribute to NAFLD setup partly by upregulating hepatic CD36 expression.</p></abstract><kwd-group><kwd>obstructive sleep apnea</kwd><kwd>intermittent hypoxia</kwd><kwd>CD36</kwd><kwd>steatosis</kwd><kwd>NAFLD</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"30\"/><page-count count=\"10\"/><word-count count=\"5685\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Non-alcoholic fatty liver disease (NAFLD) is characterized by metabolic dysfunction and accumulation of lipid deposits in the livers of patients in whom alcohol abuse is not the causal agent of disease onset (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). NAFLD encompasses a wide range of histologic findings from simple steatosis to non-alcoholic steatohepatitis (NASH) with fibrosis and, ultimately, liver cirrhosis, and hepatocellular carcinoma (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>). The number of individuals affected by some clinical form of this chronic liver disease is steadily increasing because NAFLD is highly associated with obesity and type 2 diabetes, being considered the hepatic manifestation of the metabolic syndrome (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>).</p><p>It is well-known that the liver maintains bodily lipid homeostasis by regulating hepatic free fatty acid (FFA) uptake, lipid synthesis, lipid oxidation, and lipid export; however, an imbalance between these metabolic pathways can lead to an excessive lipid accumulation within the liver (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>), being an increased <italic>de novo</italic> lipogenesis and largely an enhanced uptake of FFAs released from insulin resistant-adipocytes the main sources of these lipid accumulates (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). The uptake of FFAs into hepatocytes is mainly dependent on the fatty acid translocase CD36 which, under physiological conditions, is weakly expressed in the liver and its expression increases by a number of different stimuli, such as insulin and lipid metabolites, facilitating the process of FFA uptake (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Some experimental studies have demonstrated that CD36 plays an important role in NAFLD setup in rodents (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>) and, reinforcing this notion, it has been observed that fatty liver attenuates in mice fed high fat diet (HFD) upon either systemic or hepatocyte-specific deletion of CD36 (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Moreover, a growing clinical evidence suggests that this FFA transporter could play a relevant role in NAFLD pathogenesis in humans as well. In particular, Greco et al. showed that hepatic CD36 mRNA levels correlated with liver fat content in morbidly obese patients (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). In addition, different clinical studies have convincingly shown that the amount of both CD36 mRNA and protein was higher in the livers of biopsy-proven NAFLD patients than in subjects with histologically normal liver (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>–<xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><p>An increasing number of clinical studies point out to a potential link between obstructive sleep apnea (OSA), a respiratory disorder featured by nocturnal intermittent hypoxia (IH) and sleep fragmentation, and NAFLD (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>–<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). To highlight, both OSA and NAFLD are especially prevalent among obese individuals and, more interestingly, the severity of nocturnal IH positively correlates with histological features of NASH in OSA patients (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). Although the underlying molecular mechanisms are not fully understood, it has been reported that IH exacerbated fatty liver in obese mice by inducing hepatic lipid biosynthesis (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>) likely due to the upregulation of the HIF1α/SREBP1c signaling pathway (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>), and that promoted liver inflammation and fibrosis in mice fed with a HFD (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). However, further research is required to unveil the pathophysiological interplays between IH and lipid accumulation. In that regard, whether IH is able to regulate CD36 gene expression in hepatocytes still remains to be elucidated.</p><p>Therefore, the primary objective of this study was to determine the impact of IH on CD36 expression as well as on lipid content in livers from OSA patients with biopsy-proven NAFLD and in livers from mice exposed to IH.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>Patients</title><p>This study was performed in agreement with the Declaration of Helsinki, and with local and national laws. The Human Ethics Committee of the Hospital Universitario Santa Cristina (HUSC, Madrid, Spain) approved all procedures (PI-688A). This cross-sectional study included 20 patients with gallstones to whom a programmed laparoscopic cholecystectomy was performed in the HUSC. All participants gave a written consent for a perioperative liver biopsy and a postoperative respiratory polygraphy as part of an experimental protocol designed to evaluate the relationship between sleep disturbances and liver disease. All subjects included drank <20 g/day of alcohol, had no previous respiratory disorders, were not having potentially hepatotoxic drugs, had no analytical evidence of iron overload, and were seronegative for autoantibodies, for hepatitis B virus, hepatitis C virus, and human immunodeficiency virus.</p></sec><sec><title>Sleep Study</title><p>The polygraphic studies were performed at night in the Sleep Laboratory of the HUSC (Madrid, Spain). For interpretation, the recommendations of the American Academy of Sleep Medicine (AASM) for the diagnosis of OSA were followed. The apnea/hypopnea index (AHI) was used as diagnostic criteria for severity of OSA: AHI <5, no OSA; AHI 5–14, mild OSA; AHI 15–29, moderate OSA; AHI ≥30, severe OSA. In addition, nocturnal hypoxemia parameters including oxygen desaturation index (ODI), cumulative sleep time percentage with oxyhemoglobin saturation (SpO<sub>2</sub>) <90% (Tc90) and minimum SpO<sub>2</sub> were analyzed.</p></sec><sec><title>Animal Care and Intermittent Hypoxia Protocol</title><p>Twelve-weeks-old C57BL/6J mice were purchased from Charles River Laboratories (Saint Germain sur L'Arbresle, France) and divided into two groups of 10 mice. The control mice (C mice) were placed in conditions of normoxia while the experimental group (IH mice) was subjected to IH conditions. Every minute, IH mice received air containing an oxygen fraction of 5% for 20 s, followed by 40 s of room air, during 6 h per day, 5 days a week for a total of 8 weeks. Control mice were only exposed to room air (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). At the end of the experiment, mice were anesthetized, sacrificed and livers were harvested. All experimental procedures were approved by the Ethical Committee of the University of Barcelona (174/18−10268).</p></sec><sec><title>Histopathology Assessment</title><p>Liver sections (5 μm) were embedded in paraffin and cut using a Microm microtome (Midland, ON, Canada). After cutting, sections were stained with hematoxylin (1.09235.0500, PanReac AppliChem, Barcelona, Spain) and eosin (71211, Thermo Fisher Scientific, Inc., Madrid, Spain) and with Masson's Trichrome Solution (Masson Trichome Kit with Aniline Blue 04-010802, Milan, Italy). Once stained, the severity of steatosis was quantified by a single-blind hepatopathologist. Specifically, Kleiner's histological scoring system was employed to evaluate the degree of steatosis, lobular inflammation, hepatocellular ballooning, and the stage of fibrosis (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). The following percentages of steatotic hepatocytes were used in the histological assessment: 0–5% hepatocytes, grade 0; 5–33%, grade 1; 33–66%, grade 2; and >66%, grade 3. Histologic diagnosis of liver biopsies was classified into two groups: simple steatosis without hepatocellular ballooning nor lobular inflammation, also termed NAFL, and NASH. Minimal criteria for NASH included the combined presence of grade 1 steatosis, lobular inflammation and hepatocellular ballooning with or without fibrosis. NAFLD activity score was also calculated for each liver biopsy (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). To this end, three different lobular areas were analyzed in each sample. Representative images were taken using a Nikon Eclipse E400 optical microscope (Nikon, Tokyo, Japan) and the NIS Elements Imaging Software (Melville, NY, USA).</p></sec><sec><title>Assessment of Lipid Accumulation</title><p>Liver tissue was embedded in Tissue-Tek® O.C.T.™ Compound (Sakura Finetek Europe, Netherlands). Sections (10 μm) were then cut using a Leica CM1510S cryostat (Leica Microsistemas S.L.U, Barcelona, Spain), stained using an Oil Red O biological stain (Sigma-Aldrich, St. Louis, MO, USA) working solution (60% ORO/isopropanol w:v), and counterstained with hematoxylin (1.09235.0500, PanReac AppliChem). Three different lobular areas were analyzed in each sample and photographed using a Nikon Eclipse E400 optical microscope (Nikon) and the NIS Elements Imaging Software (Melville). Intensity of red stain was quantified using ImageJ Biological Image Analysis (NIH) and reported as the average value in arbitrary units (a.u.).</p></sec><sec><title>Quantitative Analysis of Hepatic Triglycerides</title><p>Triglycerides (TGs) were extracted as described previously (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Briefly, liver biopsy samples (15–20 μg) were homogenized in distilled water. Chloroform (−20°C) and methanol (−20°C) were added to each sample. Samples were centrifuged and the triglyceride-containing layer was collected. Once purified, TGs were suspended in isopropanol and analyzed using a colorimetric kit (SpinReact, Girona, Spain). Absorbance values were obtained using a Dynex Spectra MR Microplate spectrophotometer/computer software (Chantilly, VA, USA) and graphically expressed as mg/dl.</p></sec><sec><title>Protein Extraction and Western Blot Analysis</title><p>Liver biopsy samples (15–20 μg) were homogenized in an extraction buffer containing the following: 10 mM ethylene-diamine-tetraacetic acid (EDTA), 50 mM Hepes, 50 mM sodium pyrophosphate, 0.1 mM NaF, 10 mM Na<sub>3</sub>VO<sub>4</sub>, 1% Triton X-100 and protease inhibitors. Protein extracts were stored at −80°C after centrifugation. A small aliquot of sample was used for protein quantification (Bradford method). The samples were then prepared to be loaded into 8% SDS-PAGE gels. After running, proteins were further transferred to Inmunoblot nitrocellulose membranes (BioRad Inc., Madrid, Spain), blocked with 5% non-fat dry milk and incubated overnight with primary antibodies: CD36/SR-B3 (1:1000, NB400-144, Novus Biotechne, Abingdon, United Kingdom) and anti-βactin (1:5000, A-5441, Sigma Aldrich). Then, the corresponding secondary antibodies were added (Santa Cruz Biotechnology Inc., Heidelberg, Germany). Using the Bio-Rad Clarity™ Western ECL Substrate (BioRad Inc.), the immunoreactive bands were visualized by the ImageQuant LASD 4000 digital imaging system (GE Healthcare Europe, Barcelona, Spain). Densitometric analysis of the band was performed using ImageJ Biological Image Analysis (NIH), normalized against the loading control (βactin), and graphically expressed as fold change relative to control condition (1).</p></sec><sec><title>Quantitative Real-Time PCR (RT-qPCR)</title><p>RNA was extracted from liver samples using the TRIzol® reagent (Vitro, Sevilla, Spain). Samples were then reverse transcribed using the Reverse Transcription System kit (Promega Inc., Madison, WI, USA). A BioRad T100™ Thermal Cycler was used to carry out the reverse transcription. Quantitative real-time polymerase chain reaction (RT-qPCR) was performed to assess gene expression using a StepOnePlus™ Real Time PCR System Sequence Detector (Thermo Fisher Scientific Inc.). Samples were prepared using a SYBER Green qPCR Kit (Promega Inc.) and d(N)6 random primers were purchased from Metabion (Planegg, Germany). Primer sequences are detailed in <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 1</xref>. Each sample was run in duplicated, normalized in comparison to <italic>36B4</italic> gene expression, and graphically expressed as fold change relative to control condition (1).</p></sec><sec><title>CD36 Immunohistochemistry</title><p>Paraffin-embedded liver biopsy sections (5 μm) were deparaffinized and rehydrated. Sections were then placed in antigen retrieval buffer (10 mM sodium citrate, pH 6–7), boiled for 20 min at 95°C and incubated with a blocking solution for 1 h before being immunostained with the CD36 antibody (1:200, NB400-144, Novus) for 16 h in a moisture chamber. The EnVision™ FLEX Mini Kit, High pH (Link) (Agilent, Santa Clara, CA, USA) was used for visualization according to the manufacturer's instructions. Three different lobular areas were analyzed in each sample and images were captured using a Nikon Eclipse E400 optical microscope (Nikon) and the NIS Elements Imaging Software (Melville). Intensity of CD36 stain was quantified using the FIJI software (NIH) and reported as the average value in arbitrary units (a.u.).</p></sec><sec><title>Statistical Analysis</title><p>Categorical variables were presented as percentage and were compared by the Pearson χ<sup>2</sup> test. Continuous data were shown as standard deviation (<italic>SD</italic>) or standard error of mean (SEM), and were compared using the unpaired <italic>t</italic>-test or Mann-Whitney <italic>U</italic>-test, as indicated. The Spearman's <italic>r</italic>-test was used to evaluate correlations. All statistical analyses were performed using the GraphPad Prism 6 software (GraphPad Software Inc., San Diego, CA, USA) and SPSS statistical software version 24.0 (IBM SPSS Statistics, Armonk, NY), with a <italic>p</italic> < 0.05 considered statistically significant.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>Characteristics of the Study Patients</title><p>Twenty patients undergoing programmed cholecystectomy had both a liver biopsy and a sleep study. Overall, the mean age of the study population was 46.5 years, 14 (70%) were female and 9 (45%) had a diagnosis of OSA by polygraphy (AHI > 5). Patient characteristics from the cohort included in this study are presented in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Demographic, metabolic, biochemical, respiratory, and histological characteristics of the study population.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Feature</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Patients without OSA (<italic>n</italic> = 11)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Patients with OSA (<italic>n</italic> = 9)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>p-</italic>value</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (years)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39.9 ± 9.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">54.6 ± 10.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.004</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BMI (kg/m<sup>2</sup>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26.5 ± 6.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.6 ± 5.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.201</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Waist circunference (cm)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.8 ± 14.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">105.5 ± 11.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.034</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Glucose (mg/dL)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">92.2 ± 9.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">96.2 ± 11.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.359</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Insulin (μU/L)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.2 ± 2.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.2 ± 4.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.245</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HOMA-IR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6 ± 0.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.1 ± 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.192</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Triglycerides (mg/dL)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">111.2 ± 54.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">136.4 ± 68.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.467</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Total Cholesterol (mg/dL)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">207 ± 32.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">210 ± 29.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.832</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HDL-cholesterol (mg/dL)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53.5 ± 12.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46.9 ± 9.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.212</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ALT (IU/L)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24.6 ± 17.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.2 ± 19.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.489</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AST (IU/L)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.1 ± 9.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21.3 ± 5.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.808</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">γGT (IU/L)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.4 ± 24.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.4 ± 14.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.601</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Alkaline phosphatase (IU/L)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70.3 ± 22.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61 ± 13.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.359</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bilirubin (mg/dL)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.7 ± 0.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63 ± 0.32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.780</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Average oxygen saturation (%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">94 ± 1.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">93.3 ± 1.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.225</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Minimum oxygen saturation (%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">88.2 ± 4.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83.7 ± 5.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.073</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AHI (events/hour)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.9 ± 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 ± 9.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.002</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ODI (events/hour)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.6± 0.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.2 ± 9.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tc90 (%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.5 ± 16.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 ± 8.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.053</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>NAS Score (%)</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> 0</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.2%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> 2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.2%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> 3</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.2%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> 4</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Steatosis (%)</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63.6%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.3%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.1%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44.5%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 3</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.1%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Lobular Inflammation (%)</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77.8%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Ballooning (%)</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77.8%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 1</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.2%</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Grade 2</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Fibrosis (%)</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Stage 0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100%</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100%</td><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><p>Data are shown as mean ± standard deviation.</p><p><italic>OSA, obstructive sleep apnea; BMI, body mass index; HOMA-IR, homeostatic model assessment-insulin resistance; HDL, high density lipoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; γGT, gamma-glutamiltranspeptidase; AHI, apnea-hipopnea index; ODI, oxygen desatutaion index; Tc90, sleep time with oxygen saturation <90%</italic>.</p></table-wrap-foot></table-wrap><p>OSA patients were significantly older than those without OSA (<italic>p</italic> = 0.004) and women predominated in both groups. In order to evaluate the presence of obesity in the entire study population, body mass index (BMI) was calculated and waist circumference was measured in each participant. Regarding BMI, the study population showed an overweight status and no significant differences were found among the different patient groups studied (<italic>p</italic> = 0.201), whereas waist perimeter was significantly higher in patients with OSA than in those without (<italic>p</italic> = 0.034).</p><p>As expected, OSA patients had a significantly higher rate of oxygen desaturation per hour of sleep (ODI) and percentage of sleep time with oxygen saturations lower than 90% (Tc90) with respect to patients without OSA (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><p>Regarding metabolic parameters, basal glucose levels did not significantly differ between groups, while insulin levels and the degree of insulin resistance assessed by HOMA-IR index were higher in OSA patients than in those without OSA, but these differences were not statistically significant (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><p>Although no significant differences were observed in liver enzymes between the two groups, there was a higher prevalence of NAFLD and evidence of more severe disease among patients with OSA (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> and <xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). Surprisingly, 36.4% of patients without OSA exhibited NAFLD, all of them featuring simple steatosis. In the OSA group, however, 55.6% of them had simple steatosis and 22.2% showed histological features of steatohepatitis (NASH) (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> and <xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). Hepatic fibrosis was not observed. All other variables did not significantly differ between groups.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Prevalence of NAFLD and hepatic CD36 expression is higher in patients diagnosed with OSA. <bold>(A)</bold> NAFLD activity score. <bold>(B)</bold>\n<italic>CD36</italic> mRNA levels. <bold>(C)</bold> Correlation in the study population of matched mRNA values for CD36 with the indicated respiratory parameters, evaluated by Spearman's <italic>r</italic>-test. <bold>(D)</bold> Representative 20X and 60X images of CD36 immunostaining, and quantification of CD36-expressing cells. Scale bar 100 and 50 μm, respectively. Study population: control group (No-OSA) (<italic>n</italic> = 11) and OSA patients (<italic>n</italic> = 9). *<italic>p</italic> < 0.05, OSA vs. Control, compared using the Mann-Whitney <italic>U</italic>-test.</p></caption><graphic xlink:href=\"fmed-07-00450-g0001\"/></fig></sec><sec><title>Expression of CD36 Is Increased Within the Liver of OSA Patients</title><p>Next, we wanted to investigate whether OSA might alter CD36 expression in the liver. Interestingly, hepatic mRNA levels of CD36 were significantly higher in patients with OSA when compared with control patients (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>), and its expression significant positively correlated with both AHI and ODI values in the entire study population (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>), but not with Tc90 (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 1A</xref>). In parallel, an increase of CD36 protein expression was also observed in the livers from OSA patients compared with those from the control group detected by immunostaining (<xref ref-type=\"fig\" rid=\"F1\">Figure 1D</xref>).</p></sec><sec><title>Intermittent Hypoxia (IH) Triggers Hepatic Steatosis in Mice</title><p>In order to investigate whether intermittent hypoxia (IH), one of the main features of OSA, contributes to liver steatosis and to the increase of CD36 expression observed in OSA patients, a mouse experimental model of OSA was used. After 2 months of IH exposure, histological examinations of liver tissue revealed that 60% of the mice submitted to IH exhibited signs of mild hepatic steatosis while control mice displayed normal liver features (<xref ref-type=\"fig\" rid=\"F2\">Figures 2A,B</xref>). Liver fibrosis was not detected in any of the groups (<xref ref-type=\"supplementary-material\" rid=\"SM3\">Supplementary Figure 2</xref>).</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Intermittent Hypoxia (IH) is associated with increased hepatic lipid content in mice. <bold>(A)</bold> Representative 20X and 60X images of liver sections stained with hematoxylin and eosin (H&E). Scale bar 100 and 50 μm, respectively. <bold>(B)</bold> NAFLD activity score. <bold>(C)</bold>\n<italic>(left panel)</italic> Representative 40X and 60X images of Oil Red O stained liver sections. Scale bar 50 μm. <italic>(right panel)</italic> Quantification of red-stain intensity. <bold>(D)</bold> Analysis of hepatic triglyceride (TG) levels. Experimental groups: mice maintained in normoxic conditions (Control, C) and mice exposed to intermittent hypoxia (IH) (<italic>n</italic> = 10 mice in each group). **<italic>p</italic> < 0.01 and ***<italic>p</italic> < 0.005, IH vs. C, compared using the unpaired <italic>t</italic>-test.</p></caption><graphic xlink:href=\"fmed-07-00450-g0002\"/></fig><p>Next, we investigated the amount of lipids performing an Oil Red O staining on sections of liver biopsy samples from all mice. The results indicated that there was a significant increase in average red-stain intensity (directly proportional to lipid content) among the IH mice when compared to control mice (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>). To further evaluate hepatic lipid content, triglycerides were extracted and quantified from liver biopsies of both IH and C mice. Triglyceride levels of the IH mice were greater than those observed in the C mice (<xref ref-type=\"fig\" rid=\"F2\">Figure 2D</xref>).</p></sec><sec><title>Intermittent Hypoxia (IH) Induces Hepatic CD36 Expression</title><p>tHEN, we analyzed the hepatic expression of genes involved in the regulation of lipid metabolism. We observed a significant increase in the expression of genes implicated in lipid synthesis, such as <italic>Fasn</italic> (fatty acid synthase) and <italic>Scd1</italic> (stearoyl-CoA desaturase 1), among livers from mice exposed to IH (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>); however, no differences were observed in the expression of genes implicated in β-oxidation, such as <italic>Cpt1a</italic> (carnitine palmitoyltransferase I) and <italic>Ppara</italic> (peroxisome proliferator activated receptor alpha) (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>).</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Intermittent Hypoxia (IH) induces hepatic CD36 expression in mice. <bold>(A)</bold> mRNA levels of <italic>Fasn, Scd1, Cpt1a</italic>, and <italic>Ppara</italic>. <bold>(B)</bold>\n<italic>Cd36</italic> mRNA levels. <bold>(C)</bold>\n<italic>(left panel)</italic> Representative blots with the indicated antibodies. <italic>(right panel)</italic> Quantification of all blots with respect to loading control, βactin. <bold>(D)</bold>\n<italic>(left panel)</italic> Representative 20X and 60X images from CD36 immunochemistry. Scale bar 100 and 50 μm, respectively. <italic>(right panel)</italic> Quantification of CD36-stain intensity. Experimental groups: mice maintained in normoxic conditions (Control, C) and mice exposed to intermittent hypoxia (IH) (<italic>n</italic> = 10 mice in each group). *<italic>p</italic> < 0.05 and ***<italic>p</italic> < 0.005, IH vs. C, compared using the unpaired <italic>t</italic>-test.</p></caption><graphic xlink:href=\"fmed-07-00450-g0003\"/></fig><p>With respect to CD36, its hepatic mRNA expression was significantly increased in mice submitted to IH compared to those maintained in normoxic conditions (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>), which was also found in OSA patients. In parallel, its protein expression determined by both Western blot (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>) and immunohistochemistry (<xref ref-type=\"fig\" rid=\"F3\">Figure 3D</xref>) was elevated in the livers of IH mice.</p></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>Distinct clinical studies have reported that OSA is significantly associated with NAFLD severity (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>) and there is an increasing experimental evidence that chronic IH, the best characterized OSA manifestation, is a major trigger for oxidative stress and inflammatory liver injury leading to NAFLD progression (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). In agreement with these previous studies, we found that 22.2% of OSA patients had NASH whereas none of those without OSA had histological features of NASH, supporting the assumption that OSA is a risk factor for progression from simple steatosis to NASH (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Interestingly, our findings showed that there were no differences regarding BMI between the two study groups, but waist circumference was significantly higher in patients with OSA, suggesting that is abdominal obesity, but not overall obesity, what actually has a clinical impact on the features of metabolic syndrome, including OSA and NAFLD.</p><p>To the best of our knowledge, this is the first study revealing that CD36 expression is significantly elevated in livers from patients with OSA. Moreover, both AHI and ODI positively correlated with hepatic <italic>CD36</italic> mRNA levels, indicating a potential role for nocturnal IH in the upregulation of this FFA transporter. An intriguing question regarding our findings showed herein is whether age might influence the hepatic CD36 expression pattern observed in OSA patients because they were significantly older (54.6 ± 10.6 years) than those without OSA (39.9 ± 9.6 years). Indeed, we have reported that hepatic CD36 expression increased with aging in mice and humans (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>), but the age-dependent increases in hepatic CD36 expression were observed comparing young (20–38 years old) with aged individuals (50–83 years), thus we believe that age differences seen in our study population are not sufficient to explain the hepatic CD36 upregulation observed in OSA patients. Supporting this assumption, no correlation was found between <italic>CD36</italic> mRNA expression and age in our study population (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 1B</xref>).</p><p>In agreement with our findings in OSA patients, the majority of mice exposed to IH displayed simple steatosis as well as a higher hepatic TG content and CD36 expression than in control mice breathing normal oxygen concentrations. Collectively, our results indicate that IH may contribute to hepatosteatosis setup, partly by the upregulation of hepatic CD36 expression, but the underlying molecular mechanisms still remain to be elucidated.</p><p>It is well-known that cellular adaptive responses to hypoxia are tightly regulated by hypoxia-inducible transcription factors (HIFs), being HIF1α and HIF2α the best characterized (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). In that regard, Li et al. demonstrated that IH exacerbated hepatosteatosis in mice in parallel with an upregulation of key genes for hepatic lipid biosynthesis, such as <italic>Srebp1</italic> and <italic>Scd1</italic> (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>) and that this effect is mediated through HIF1α (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). In line with these results, our study also found increased mRNA levels of relevant genes for <italic>de novo</italic> lipogenesis, such as <italic>Fasn</italic> and <italic>Scd1</italic>, along with an upregulation of <italic>Cd36</italic> gene expression in livers of mice exposed to IH, suggesting that HIF1α might regulate <italic>Cd36</italic> gene expression as well. There is convincing evidence, however, indicating that the regulation of CD36 expression is not largely linked to the HIF1α/SREBP1c signaling pathway in hepatocytes. Notably, it has been recently reported that hepatocyte-specific <italic>Srebp1</italic> downregulation did not affect expression of genes involved in FFA uptake as <italic>Cd36</italic> in mouse livers (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). In addition, we have just demonstrated that both CD36 expression and triglyceride content increased in mouse and human liver cells under hypoxic conditions and that silencing <italic>HIF2A</italic> gene markedly suppressed both <italic>CD36</italic> gene upregulation and lipid accumulation in hepatocytes (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). The novelty of our present study is that both CD36 expression and the degree of steatosis are increased in livers from animal models of IH and in patients with OSA featured by nocturnal IH, supporting the notion that CD36 could be a key factor driving hepatosteatosis in OSA patients.</p><p>In conclusion, the results of the present study demonstrate that CD36 expression is increased within the liver of patients with OSA and in mice exposed to IH, the clinical hallmark featuring OSA. Moreover, our results point out that the excessive lipid accumulation observed in livers of mice under IH conditions is likely due to the upregulation of CD36, which is involved in FFA uptake into hepatocytes, along with that of genes implicated in <italic>de novo</italic> lipogenesis, thus leading to the onset of hepatosteatosis, the earliest phase of NAFLD. Collectively, our findings shed light on the molecular mechanisms underlying IH-induced hepatosteatosis helping to understand better the NAFLD pathogenesis and identifying CD36 as a potential target for new pharmacological therapies to NAFLD patients.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>All datasets presented in this study are included in the article/<xref ref-type=\"sec\" rid=\"s9\">Supplementary Material</xref>.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Human Ethics Committee of the Hospital Universitario Santa Cristina. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Ethical Committee of the University of Barcelona.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>CG-M, IA, and ÁG-R designed the study. EP-M, PL, and CG-M carried out and analyzed the clinical study. ER, BB, PM, and SI carried out the experimental study. RF, CG-M, IA, and ÁG-R analyzed and discussed data. IA and ÁG-R wrote the manuscript. All authors were involved in editing the paper and had final approval of the submitted and published versions.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We thankfully acknowledge Esther Fuertes Yebra for helpful technical assistance.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by PI13/01299, PI17/00535 and CIBEREHD from Instituto de Salud Carlos III (ISCIII/FEDER, Spain) to CG-M; PI16/00823 and PI19/00123 (ISCIII/FEDER, Spain), and Beca Eduardo Gallego 2016 (Fundación Francisco Cobos, Spain) to ÁG-R. RF was supported by the Spanish Ministry of Science, Innovation and Universities (SAF2017-85574-R) and CIBERES.</p></fn></fn-group><sec sec-type=\"supplementary-material\" id=\"s9\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fmed.2020.00450/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fmed.2020.00450/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><label>Supplementary Table 1</label><caption><p>Primer sequences for RT-qPCR.</p></caption><media xlink:href=\"Table_1.DOCX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM2\"><label>Supplementary Figure 1</label><caption><p>Correlation in the study population of matched mRNA values for CD36 with Tc90 values <bold>(A)</bold> and age <bold>(B)</bold>, evaluated by Spearman's <italic>r</italic>-test. Study population: control group (No-OSA) (<italic>n</italic> = 11) and OSA patients (<italic>n</italic> = 9).</p></caption><media xlink:href=\"Image_1.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM3\"><label>Supplementary Figure 2</label><caption><p>Representative images of liver sections stained with Masson's trichrome solution. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Psychol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Psychol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Psychol.</journal-id><journal-title-group><journal-title>Frontiers in Psychology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-1078</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848963</article-id><article-id pub-id-type=\"pmc\">PMC7431764</article-id><article-id pub-id-type=\"doi\">10.3389/fpsyg.2020.01379</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Psychology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Mandarin–Italian Dual-Language Children’s Comprehension of Head-Final and Head-Initial Relative Clauses</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Hu</surname><given-names>Shenai</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/561778/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Costa</surname><given-names>Francesca</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1025090/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Guasti</surname><given-names>Maria Teresa</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/368974/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Foreign Language Education, Xiamen University</institution>, <addr-line>Xiamen</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Psychology, University of Milano-Bicocca</institution>, <addr-line>Milan</addr-line>, <country>Italy</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Itziar Laka, University of the Basque Country, Spain</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Maria Garraffa, Heriot-Watt University, United Kingdom; Cristiano Chesi, University of Pavia, Italy; Irene De La Cruz Pavía, Université Paris Descartes, France</p></fn><corresp id=\"c001\">*Correspondence: Shenai Hu, <email>shenai.hu@xmu.edu.cn</email></corresp><corresp id=\"c002\">Maria Teresa Guasti, <email>mariateresa.guasti@unimib.it</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Language Sciences, a section of the journal Frontiers in Psychology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1379</elocation-id><history><date date-type=\"received\"><day>03</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>5</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Hu, Costa and Guasti.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Hu, Costa and Guasti</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The acquisition of languages by children using two languages is a matter of debate as many factors contribute to the success of this type of acquisition. We focus on how the competence of dual-language children changes in their two languages as a function of length of exposure and establish whether there are reciprocal influences during language development. We examined the comprehension of subject and object relative clauses in a group of 6-year-old (younger) and 8-year-old (older) Mandarin–Italian dual-language children. After 3 years of regular and intensive exposure to Italian, the younger group reached the same level of competence in the comprehension of relative clauses in their two languages, and after 5 years of exposure to Italian, the older group had a better comprehension of relative clauses in Italian than in Mandarin. Acquiring two languages leads to bidirectional influence, beyond a reciprocal support. Finally, some penalty may be observed in the acquisition of subject head-final relative clauses, which is not evident in that of subject head-initial relative clauses.</p></abstract><kwd-group><kwd>dual-language development</kwd><kwd>Mandarin</kwd><kwd>Italian</kwd><kwd>relative clause comprehension</kwd><kwd>head-directionality</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"5\"/><equation-count count=\"0\"/><ref-count count=\"45\"/><page-count count=\"15\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>The unique way in which dual-language children<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup> develop is difficult to characterize because many variables contribute to shaping their competence. First, the age of onset of dual-language acquisition impacts on several aspects of late language competence (e.g., <xref rid=\"B18\" ref-type=\"bibr\">Flege et al., 1999</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Kovelman et al., 2008</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Unsworth, 2013</xref>). Second, the input, such as which language is most commonly spoken around the child, may determine language dominance (<xref rid=\"B16\" ref-type=\"bibr\">David and Wei, 2008</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Hoff et al., 2012</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Unsworth et al., 2014</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Unsworth, 2016</xref>). Third, the characteristics of the surrounding community speaking the child’s L1 may also determine aspects of language development (<xref rid=\"B23\" ref-type=\"bibr\">He, 2006</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Montrul, 2012</xref>). Finally, cross-linguistic influences have often been observed in dual-language children (<xref rid=\"B36\" ref-type=\"bibr\">Müller and Hulk, 2001</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Serratrice et al., 2004</xref>), with different transfer effects depending on the types of languages involved (<xref rid=\"B11\" ref-type=\"bibr\">Blom et al., 2012</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Zdorenko and Paradis, 2012</xref>). In the light of this complicated array of factors at play in the development of dual-language children, we would like to focus on how the competence of these children changes as the length of exposure to the majority language increases. In addition, as in other studies, we intend to establish whether there are reciprocal influences between two typologically distant languages and how these manifest. To achieve these goals, we investigated the comprehension of a complex structure: relative clauses (RCs) in Mandarin–Italian dual-language children. The children were born in Italy; they were first exposed to Mandarin at home and in the Chinese community and started to be regularly exposed to Italian between 2 and 4 years of age in public preschools. At the time of testing, they were all attending an Italian public school and receiving formal instruction in Italian. On weekends (for one or two full days) and during vacation (for 2 months with five full days per week), they attended the community school, where they spoke Mandarin and learned Chinese characters. Two groups of children were involved: the younger group consisted of children attending the last year of preschool or grade 1 in Italian primary school and had a mean age of 6 years; the older group, comprised of children attending grade 2 or 3 in Italian primary school, had a mean age of 8 years. Thus, the first group had less exposure to Italian than the second and was less literate in both languages.</p><p>The article is organized as follows. First, we describe Mandarin and Italian RCs and provide a brief review of previous studies with monolingual children and dual-language children. Then, we introduce the current study, report the results, and offer a general discussion.</p><sec id=\"S1.SS1\"><title>Typological Differences Between Mandarin and Italian Relative Clauses</title><p>Both Mandarin and Italian have the same canonical word order, namely, subject-verb-object (SVO), but their RCs have different word orders. Mandarin RCs are head-final, with the RC linearly preceding the relativizer <italic>de</italic> and the relative head, while Italian RCs are head-initial, with the RC linearly following the relative head and the relativizer <italic>che</italic> (that/who), as in (1–2).</p><p><inline-graphic xlink:href=\"fpsyg-11-01379-i001.jpg\"/></p><p>In Mandarin, subject RCs have a verb-object-subject (VOS) order (1a), while object RCs have the SVO order, which corresponds to the canonical order in declarative sentences. In Italian, subject RCs have the SVO order, which is the canonical order in declarative sentences, while object RCs (2b) display the object-subject-verb (OSV) order<sup><xref ref-type=\"fn\" rid=\"footnote2\">2</xref></sup>.</p><p>Although the linear order of Mandarin and Italian RCs is different, at the hierarchical level, they present similarities. In (3a) we have the structure of a Mandarin subject RC and in (3b) that of an object RC. In these structures, the relative head, <italic>xiaomao</italic> “cat” must be connected to its copy (indicated with <italic>t</italic>) to be properly interpreted. This dependency in the object RC (3b) crosses the embedded subject <italic>xiaogou</italic> “dog,” namely, the subject structurally intervenes between the relative head and its copy. In contrast, in the subject RC (3a), nothing intervenes between the relative head and its copy. It is also the case that in object RCs, the structural distance between the relative head and its copy is longer than in subject RCs.</p><p><inline-graphic xlink:href=\"fpsyg-11-01379-i002.jpg\"/></p><p>Similarly, in Italian object RCs (4b), the subject intervenes in the dependency between the relative head <italic>i gatti</italic> “the cats” and its copy, while nothing intervenes in the dependency between the relative head and its copy in subject RCs (4a). In addition, the relative head and its copy are hierarchically more distant in object RCs than in subject RCs.</p><p><inline-graphic xlink:href=\"fpsyg-11-01379-i003.jpg\"/></p></sec><sec id=\"S1.SS2\"><title>Comprehension of Relative Clauses by Monolingual Chinese and Italian Children</title><p>In this section, we concentrate on the comprehension of RCs in monolingual Mandarin-speaking children (e.g., <xref rid=\"B34\" ref-type=\"bibr\">Lee, 1992</xref>; <xref rid=\"B12\" ref-type=\"bibr\">Cao et al., 2005</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Hu, 2014</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Hu et al., 2016</xref>; <xref rid=\"B24\" ref-type=\"bibr\">He et al., 2017</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Yang et al., 2020</xref>) and monolingual Italian-speaking children (e.g., <xref rid=\"B3\" ref-type=\"bibr\">Arosio et al., 2009</xref>, <xref rid=\"B5\" ref-type=\"bibr\">2011</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Adani, 2011</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Belletti et al., 2012</xref>; <xref rid=\"B14\" ref-type=\"bibr\">Contemori and Belletti, 2014</xref>). We will show that in Italian a subject RC advantage is uniformly observed; in Mandarin, the same advantage is observed in most studies, and its absence is likely due to methodological choices.</p><p>A subject RC advantage has been reported in the comprehension of Mandarin RCs (e.g., <xref rid=\"B28\" ref-type=\"bibr\">Hu et al., 2016</xref>). <xref rid=\"B28\" ref-type=\"bibr\">Hu et al. (2016)</xref> tested Mandarin-speaking children aged 3–8, using a character-sentence matching task (i.e., a referent selection task), in which children were asked to point out a character in a set of two pictures including four characters (in one picture character A was acting on character B and in the other, B was acting on A). The results showed that, at least up to 7 years of age, children comprehended subject RCs significantly better than object RCs (e.g., age 7: 99.4% vs. 45.6%), and children at age 8 achieved ceiling performance in both structures. Moreover, the error pattern found in the comprehension of subject RCs differed from that observed in the comprehension of object RCs. In the first case, children aged 3–5 made three different types of errors with equal frequency. In other words, when they failed to choose the correct character, they chose one of the other three characters, which suggests that they were performing randomly. In object RC comprehension, the most common mistake made by children from age 3–7 was the Embedded Error, i.e., children chose the correct picture, but the wrong character. In linguistic terms, this error consists in taking the embedded subject, i.e., the first NP encountered, as the Agent and misinterpreting the sentence (1b), repeated here, <italic>the cat that the dog hits</italic> as <italic>the dog that hits the cat</italic>, that is, they turned an object RC into a subject RC. In some studies, this error is called Head Error (e.g., <xref rid=\"B31\" ref-type=\"bibr\">Kidd et al., 2015</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al., 2019</xref>). The subject advantage has been replicated in other studies that we will discuss in the next section with monolingual Mandarin-speaking control children (<xref rid=\"B13\" ref-type=\"bibr\">Chan et al., 2017</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al., 2019</xref>).</p><p>However, some other studies on Mandarin RCs report contrasting results regarding the subject advantage, likely due to the types of tasks employed (<xref rid=\"B12\" ref-type=\"bibr\">Cao et al., 2005</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Hu, 2014</xref>; <xref rid=\"B24\" ref-type=\"bibr\">He et al., 2017</xref>). Using a picture-sentence matching task (i.e., a picture selection task), in which children were asked to point to one picture out of two, either an object RC preference or no preference was found (<xref rid=\"B27\" ref-type=\"bibr\">Hu, 2014</xref>; <xref rid=\"B24\" ref-type=\"bibr\">He et al., 2017</xref>). As pointed out in the literature (<xref rid=\"B2\" ref-type=\"bibr\">Arnon, 2005</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Adani, 2011</xref>), the use of the picture-sentence matching task is inadequate, as the RC is used to select a character and not a picture. Moreover, this task overestimates children’s comprehension of RCs, especially in head-final ones as in Mandarin (<xref rid=\"B27\" ref-type=\"bibr\">Hu, 2014</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Hu et al., 2016</xref>). To find the matching picture, children could simply rely on the linear order of the embedded prenominal RC, which is VO for subject RCs and SV for object RCs. In this way, they can choose the correct picture even if they do not choose the correct character. Finally, the picture-sentence matching task does not offer the possibility of observing the error types found in the character-sentence matching task.</p><p>In the comprehension of Italian RCs, a subject RC advantage is uniformly reported in children at least up to age 6, regardless of the task used, and afterward ceiling effects of object RCs are reported (<xref rid=\"B3\" ref-type=\"bibr\">Arosio et al., 2009</xref>, <xref rid=\"B5\" ref-type=\"bibr\">2011</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Adani, 2011</xref>). This preference is found in many studies on the acquisition of head-initial RCs (e.g., <xref rid=\"B20\" ref-type=\"bibr\">Friedmann et al., 2009</xref>). <xref rid=\"B1\" ref-type=\"bibr\">Adani (2011)</xref> tested Italian-speaking children aged 3–7 with a character-sentence matching task, in which children were asked to point out a character in a picture involving three characters. Italian-speaking children until age 6 comprehended subject RCs more accurately than object RCs (e.g., age 3: 91% vs. 53%). Note that children at age 3 achieved almost ceiling performance in subject RCs, whereas children until age 7 did so in object RCs. If children failed to understand RCs, they mainly made Reversal Error. In the case of object RCs, Reversal Error consists in taking the relative head, i.e., the first NP encountered, as the Agent and misinterpreting the sentence (2b) <italic>the cats that the dog hits</italic> by pointing to the characters described by <italic>the cats that hit the dog</italic>. In this case, the theta-roles are reversed and again an object RC is turned into a subject RC.</p><p>One interpretation of the subject advantage is that it results from the fact that the structural distance between the relative head and its copy is shorter in subject RCs (see 3a and 4a) than in object RCs, both in Mandarin and Italian. In addition, in subject RCs nothing intervenes in this dependency, while in object RCs the embedded subject intervenes in this dependency (i.e., it c-commands the object copy) (<xref rid=\"B20\" ref-type=\"bibr\">Friedmann et al., 2009</xref>). As subject RCs are less complex than object RCs, it is not surprising that children’s mistakes in both languages consist of turning object RCs into subject RCs. In fact, Reversal Errors in Italian occur because children take the relative head, which comes first in the sentence and in the hierarchical structure, as the Agent of the action and the Subject of the sentence. In Mandarin, Embedded Errors come about because children take the embedded subject as the Agent and Subject: given the order of Mandarin object RCs (S V < O > REL O), the first NP is encountered linearly and hierarchically.</p><p>These similarities notwithstanding, comparing the Mandarin and the Italian studies, two differences stand out. First, Italian-speaking children showed ceiling performance in the comprehension of subject RCs as of age 3 (<xref rid=\"B1\" ref-type=\"bibr\">Adani, 2011</xref>), while Mandarin-speaking children did so only at age 7 (<xref rid=\"B28\" ref-type=\"bibr\">Hu et al., 2016</xref>). Second, while contrasting results are reported for the comprehension of Mandarin RCs, this is not the case in the comprehension of Italian RCs. In our view, this asymmetry is mainly due to the different tasks used, which have an impact when it comes to the comprehension of head-final RCs (as in Mandarin), but not when the comprehension of head-initial RCs is at stake.</p></sec><sec id=\"S1.SS3\"><title>Relative Clauses in Dual-Language Children</title><p>The comprehension of RCs by dual-language children has been investigated in a number of studies (e.g., <xref rid=\"B21\" ref-type=\"bibr\">Garraffa et al., 2015</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Hopp et al., 2019</xref>). Here, we concentrate on the studies focused on language pairs whose RCs have opposite orders, namely, head-initial RCs (such as English and Italian) vs. head-final RCs (such as Mandarin, Cantonese, and Korean) (e.g., <xref rid=\"B33\" ref-type=\"bibr\">Lee and Lee, 2004</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Kidd et al., 2015</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Chan et al., 2017</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Hu and Guasti, 2017</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al., 2019</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Jia and Paradis, 2020</xref>). As we will see, these studies provide an inconsistent picture, either a subject/object asymmetry or no asymmetry being reported in dual-language acquisition; the error patterns of dual-language children are different from that of monolingual children, but it is not clear how this difference is related to length of exposure and thus to transfer effects.</p><p><xref rid=\"B31\" ref-type=\"bibr\">Kidd et al. (2015)</xref> investigated the comprehension of RCs in simultaneous Cantonese–English-speaking children (<italic>M</italic> = 8;11, age range: 4;10–11;11) living in Australia, using a character-sentence matching task. The results revealed a subject RC advantage in the dual-language children, but no advantage in monolingual Cantonese-speaking children. The difficulty that the dual-language children experienced with Cantonese object RCs was attributed to the fact that Cantonese object RCs display the canonical SVO word order, which competes with the canonical SVO word order of declarative sentences in Cantonese and, crucially, in English. This competition was responsible for the subject advantage. Typically, the dual-language children made more Embedded Errors than the monolingual children when they were presented with an object RC. This means that when listening to an object RC, the children assigned the Agent thematic role to the first NP in Cantonese and stuck to this interpretation, which is also supported by their English. One weakness of the study is the large age range of the children tested, namely, from age 4 to age 11, which may conceal different patterns across ages and thus length of exposure to the languages. In other words, it is difficult to see how languages influence each other and whether the dual-language effect is consistent across ages (<xref rid=\"B29\" ref-type=\"bibr\">Hu and Guasti, 2017</xref>).</p><p>Investigating the comprehension of Mandarin and Italian RCs by Mandarin–Italian dual-language children (aged 6;0–9;11) and their age-matched monolingual peers, <xref rid=\"B29\" ref-type=\"bibr\">Hu and Guasti (2017)</xref> found a subject RC advantage in both languages, similar to their monolingual peers, but lower accuracy rates than the monolingual children. The authors proposed that learning two languages may slow down the acquisition of complex structures such as RCs. However, they did not provide any independent measure of language competence to discard the conjecture that dual-language children’s competence was generally weaker than that of monolingual children.</p><p>Two other relevant studies investigating Mandarin RC comprehension are <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al. (2019)</xref> and <xref rid=\"B30\" ref-type=\"bibr\">Jia and Paradis (2020)</xref>, both testing Mandarin–English dual-language children, but showing a different comprehension pattern. <xref rid=\"B30\" ref-type=\"bibr\">Jia and Paradis (2020)</xref> showed that dual-language school-aged children (<italic>M</italic> = 8;0) in Canada were comparable to monolingual Mandarin-speaking children (<italic>M</italic> = 7;1) in comprehending subject and object RCs. However, their dual-language children were older than their monolingual children. In addition, the authors used a picture-sentence matching task, which, as mentioned earlier, may overestimate children’s abilities in the comprehension of RCs, especially in Mandarin. <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al. (2019)</xref> tested two groups of Mandarin–English dual-language children living in Australia, and compared them with language-matched (receptive vocabulary) monolingual Mandarin-speaking children. For Mandarin, they found a subject RC advantage in all the groups, consistent with the results of the aforementioned studies (<xref rid=\"B28\" ref-type=\"bibr\">Hu et al., 2016</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Hu and Guasti, 2017</xref>). In English, the younger dual-language children (<italic>M</italic> = 6;1) had more difficulties with RCs than the older ones (<italic>M</italic> = 8;9); in addition, a subject advantage was evident in the younger group, but not in the older one, due to ceiling effects. Regarding the issue of reciprocal influence and based on the fact that monolingual Mandarin-speaking children displayed a subject advantage, <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al. (2019)</xref> concluded that the subject RC advantage in Mandarin-speaking dual-language children cannot be attributed to the effect of dual-language use, contra <xref rid=\"B31\" ref-type=\"bibr\">Kidd et al. (2015)</xref>. However, <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al. (2019)</xref> noted that influences between the languages were evident in that the error pattern of the dual-language children was different from that of the monolinguals. Embedded Errors were the most common type of error, both in Mandarin and English. In both languages, the younger children made more Embedded Errors than the older ones and they made them more often in object RCs than subject RCs. Embedded Errors are rare in monolingual children acquiring head-initial RCs, but were found when the dual-language children failed to comprehend English RCs. In Mandarin, Reversal Errors were also found more frequently in the dual-language children than the monolinguals, and more in the younger children than the older children. Notice that Reversal Errors are typically found in the monolingual acquisition of head-initial RCs, as we said earlier (<xref rid=\"B1\" ref-type=\"bibr\">Adani, 2011</xref>).</p><p>Relative clauses comprehension has also been studied in multilingual acquisition contexts. <xref rid=\"B13\" ref-type=\"bibr\">Chan et al. (2017)</xref> compared a group of trilingual Cantonese (L1)-English (L2)-Mandarin (L3) children (<italic>M</italic> = 5;8) to monolingual Mandarin-speaking children and monolingual Cantonese-speaking children, and found a subject advantage in Mandarin for the trilingual and the monolingual Mandarin speakers. In Cantonese, again, the trilingual children displayed a subject advantage, but their monolingual Cantonese-speaking peers displayed an object advantage, contrary to the lack of advantage reported in <xref rid=\"B31\" ref-type=\"bibr\">Kidd et al. (2015)</xref>. Embedded Errors were more frequent than Reversal Errors, both in Mandarin and Cantonese, in line with studies on monolingual Mandarin and Cantonese speakers.</p><p>In sum, in the comprehension of RCs in dual-language children with typologically different languages, some contrasts are apparent. First, <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al.’s (2019)</xref> study calls into question the claim that the subject RC advantage found in dual-language children is due to the effect of acquiring a language with head-initial RCs. However, there are contrasting results (<xref rid=\"B31\" ref-type=\"bibr\">Kidd et al., 2015</xref>) and few studies have focused on pairs of languages with RCs displaying different orders, and thus, this claim needs further support. Second, the effect of dual-language learning seems to be evident in the types of errors, but to understand better how this influence shapes acquisition, it is important to see how the type of error changes as a response to length of exposure. Third, dual-language children may have a weaker competence than monolingual children in understanding RCs in one or both of their languages, and this may be due to the influence of one language on the other or to the quantity of input. As we observed in the previous section, head-final subject RCs seem to be more difficult than head-initial RCs, in spite of a subject advantage.</p></sec><sec id=\"S1.SS4\"><title>Aims of the Current Study and Predictions</title><p>In this study, we aim at (i) investigating the role of the length of exposure to the majority language in shaping the comprehension of RCs, (ii) examining the reciprocal influence of the two typologically distant languages, especially at the level of errors, again as a function of the length of exposure, and (iii) gaining insight into the delay in comprehending subject RCs in Mandarin. To achieve these goals, we recruited two groups of Mandarin–Italian dual-language children, one who had been exposed to the majority language for about 3 years (the younger group) and the other for about 5 years (the older group). While the former had little formal education in Italian, the latter had more. We chose RCs as this structure is a late acquisition even in monolingual children and, therefore, we could be sure that it was still developing in the participating children. In addition, since RCs present an “opposite” word order in the two languages (i.e., head-final in Mandarin and head-initial in Italian), they allow us to investigate the issue of reciprocal influence. The first two aims have been addressed in other studies, but having two groups will better shed light on the role of length of exposure and transfer effects. Besides, given the contrasting results, replication is needed. Finally, we will contribute new data from an L2, Italian, rarely investigated in dual-language acquisition, and focus more on error analysis.</p><p>We expect to replicate previous findings concerning the subject RC advantage in both languages, especially in the younger group. Given that 5–7 years of formal education are needed to achieve a literacy level comparable to those of monolingual children (<xref rid=\"B15\" ref-type=\"bibr\">Cummins, 1979</xref>) and that RCs are formally taught in Italian primary school, we may anticipate that the older group would not display a subject RC advantage in Italian because of ceiling performance. It is possible that the advantage is still evident in Mandarin. In fact, although the older group of children speak Mandarin at home and are literate in Mandarin, their competence is likely lower than that of monolingual children, as they attend Mandarin classes only on weekends and vacations. We also anticipate that Mandarin RCs will lag behind Italian RCs, as children are more often exposed to Italian, which is the majority language.</p><p>The reciprocal influence may be evident in the fact that better comprehension in one language correlates with better comprehension in the other. This influence will also manifest at the level of errors. As we pointed out in the earlier discussion, two main types of errors have been reported in the literature: Reversal Errors, which consist of reversing the thematic roles; and Embedded Errors, which consist of choosing the embedded argument as the relative head. First, we expect the younger group to behave as monolingual Mandarin-speaking children, as far as Mandarin is concerned, as this may still be their dominant language and they had less experience with Italian than the older group. Specifically, for Mandarin subject RCs, we expect the younger group to make an equal number of both types of errors, in line with the previous studies. Both types of errors may also be evident in Italian. Although Italian subject RCs display the canonical order (SVO), this order in Mandarin is that of an object RC and this may lead children to err and choose the referent randomly. Second, in the case of object RCs, both types of errors result in a subject RC. If the subject advantage is the result of subject RCs being structurally less complex, we expect the younger group to adopt the processing strategy of taking the first NP as the Agent, in line with the fact that both Italian and Mandarin are SVO languages. As a result, dual-language children will make the typical errors found in monolingual comprehension of object RCs: Reversal Errors in Italian and Embedded Errors in Mandarin. Third, negative influence from Italian to Mandarin may be observed in the older group, where Reversal Errors may be evident in Mandarin. Becoming more familiar (and dominant) with Italian, our dual-language children will also be more familiar with the fact that the SVO order is typical of subject RCs in Italian and this will lead them to analyze a Mandarin object RC as a subject RC.</p><p>With respect to our third research aim, we expect to observe a delay in the comprehension of subject RCs in Mandarin as compared with that in Italian. As introduced earlier, it has been found that subject head-initial RCs are almost at ceiling in monolingual 3-year-old Italian-speaking children (and this holds for several other languages with head-initial RCs), while ceiling performance on subject head-final RCs is reached only at age 7 in monolingual Mandarin-speaking children. This different developmental pattern seems to indicate that head-final RCs may have an additional component of difficulty not present in head-initial RCs. Dual-language children are an ideal group of children to examine this conjecture.</p></sec></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Participants</title><p>Thirty-seven Mandarin–Italian dual-language children participated in the study in two groups: younger children (<italic>N</italic> = 19, <italic>M</italic> = 6;2, age range: 5;3–6;11) and older children (<italic>N</italic> = 18, <italic>M</italic> = 8;4, age range: 7;6–8;11). Data from three additional children were collected, but excluded because they did not finish all the tests reported below.</p><p>The children were recruited from the Chinese community in Milan, Italy. The criteria for the selection of the children were the following: no history of language impairment or hearing loss, regular exposure to Mandarin from birth, intensive and regular exposure to Italian starting in nursery or kindergarten, and use of both languages daily. After an initial screening done by the first author together with parents and teachers, all the parents of selected children completed a questionnaire that was an adaptation of the one used in <xref rid=\"B31\" ref-type=\"bibr\">Kidd et al. (2015)</xref> and the UBiLEC (<xref rid=\"B41\" ref-type=\"bibr\">Unsworth, 2013</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Unsworth et al., 2014</xref>) to measure children’s language use and background. They were asked to indicate: (i) whether their child was born in Italy (if not, when they arrived in Italy); (ii) the amount of time per year they had visited China since birth; (iii) the first time they attended Italian schools (and how long for); (iv) the average amount of time the child spent in Mandarin- and Italian-speaking environments; (v) how often the child spoke each language at home on a 5-point scale (1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always); (vi) how well the child understood each language on a 7-point Likert scale (1 = poor, 7 = excellent); and (vii) whether they were able to read Chinese (if so, judge their literacy level compared with age-matched children in China). See <xref rid=\"T1\" ref-type=\"table\">Table 1</xref> for participant characteristics.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Mean age, summaries of language experience, and performance on PPVT.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Younger Mean (<italic>SD</italic>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Older Mean (<italic>SD</italic>)</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age<sup>a</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74.42 (6.29)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.33 (5.40)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age of first exposure to Italian<sup>a</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.21 (3.28)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40.33 (8.17)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Length of exposure to Italian<sup>a</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.26 (5.74)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59.33 (9.03)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Time spent in Mandarin-speaking environment<sup>b</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.03 (15.92)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.28 (15.37)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Time spent in Italian-speaking environment<sup>b</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.11 (10.99)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39.33 (9.16)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frequency of speaking Mandarin<sup>c</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.79 (0.98)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.89 (1.02)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frequency of speaking Italian<sup>c</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.74 (0.93)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.06 (1.11)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rating of Mandarin comprehension<sup>d</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.05 (1.68)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.00 (1.94)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rating of Italian comprehension<sup>d</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.95 (1.68)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.28 (1.49)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mandarin PPVT<sup>e</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40.68 (19.30)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83.89 (34.59)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Italian PPVT<sup>e</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41.90 (18.84)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83.44 (31.14)</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup>a</sup>Age, age of first exposure to Italian, and length of exposure to Italian are expressed in months; <sup>b</sup>time spent in Mandarin- or Italian-speaking environments are expressed in hours per week; <sup>c</sup>frequency of speaking Mandarin and Italian at home is expressed on a 5-point scale; <sup>d</sup>rating of how well children understood Mandarin and Italian is expressed on a 7-point Likert scale; <sup>e</sup>PPVT are expressed in raw scores (and standard deviation).</italic></attrib></table-wrap-foot></table-wrap><p>All the children reported in the present study were born in Italy and grew up in households where Mandarin was spoken. All the parents were Mandarin native speakers and predominately used their native language with their children; the children also predominately spoke Mandarin with their parents. In addition, the children had access to Mandarin through other native Mandarin speakers in their extended social networks in Italy and from short visits to China. They attended weekend classes and summer camps in a Chinese cultural center in Milan where Mandarin was the medium of instruction. They were all able to read Chinese as they learned it in Mandarin classes, and according to their parents’ judgment, their literacy was much lower than that of age-matched children in China. All the children went to Italian nursery or kindergarten between age 2 and age 4, and all were educated in Italian public schools. The younger group were regularly exposed to Italian at the mean age of 37.21 months and, at the time of testing, had been exposed to Italian for 37.26 months. The older group was regularly exposed to Italian at the mean age of 40.33 months and had been exposed to Italian for 59.33 months. There was no significant difference between groups in terms of age of first exposure to Italian, while there was a significant difference in terms of length of exposure [<italic>t</italic>(35) = −8.92, <italic>p</italic> < 0.001]. The older group had been exposed to Italian 2 years longer than the younger group.</p><p>In addition, there were significant differences between groups on the average amount of time the children spent in the Italian-speaking environment at the time of the study [<italic>t</italic>(35) = −2.17, <italic>p</italic> < 0.05] and on parents’ rating of how well their children understood Italian [<italic>t</italic>(35) = −2.54, <italic>p</italic> < 0.05]. No other difference was found. Overall, the children in the older group spent more time in an Italian-speaking environment than the children in the younger group, and the older children’s comprehension of Italian was rated higher than that of the younger children.</p><p>Then, we compared the language experience of the two groups. First, there was no significant difference within each group regarding the average amount of time the children spent in a Mandarin- or Italian-speaking environment. Second, children spoke Mandarin more frequently than Italian at home: the younger group [<italic>t</italic>(18) = 3.40, <italic>p</italic> < 0.01] and the older group [<italic>t</italic>(17) = 3.40, <italic>p</italic> < 0.05]. Third, parents of the younger group rated their children’s comprehension of Mandarin as higher than their comprehension of Italian [<italic>t</italic>(18) = 2.02, <italic>p</italic> = 0.0503]; no difference was observed in the older group.</p><p>Children’s linguistic competence was also measured using the Peabody Picture Vocabulary Test (PPVT; <xref rid=\"B17\" ref-type=\"bibr\">Dunn and Dunn, 1981</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Stella et al., 2000</xref>). The difference between children’s Mandarin PPVT and Italian PPVT scores was not significant in the younger group or the older group. This is a hint that the children’s vocabulary knowledge was balanced.</p></sec><sec id=\"S2.SS2\"><title>Materials</title><p>We examined the comprehension of subject and object RCs in Mandarin and Italian using a character-sentence matching task. Two sets of the materials were constructed, each with a Mandarin and an Italian version. Each set of the materials consisted of 8 subject RCs and 8 object RCs, as exemplified in (5a-b) for Mandarin RCs, and in (6a-b) for Italian RCs.</p><p><inline-graphic xlink:href=\"fpsyg-11-01379-i004.jpg\"/></p><p>The task consisted of 16 black and white pictures with the same structure (i.e., one animal X on the left, a pair of animals Y in the middle, and another X on the right). The pictures depicted eight actions including bite, chase, follow, hit, push, smell, splash, and wipe. They were presented an equal number of times, i.e., each action appears in the four RCs exemplified in (5) and (6). To avoid priming effects, each picture was used only once in each version of the test. <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> is a sample of the experimental pictures. For sentences (5a) and (6a) the correct answer is the horse on the right, and for sentences (5b) and (6b) the correct answer is the horse on the left.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>An experimental picture used in the Mandarin and Italian experiments on RC comprehension. This picture can be used to test comprehension of both subject RCs and object RCs such as (5) and (6).</p></caption><graphic xlink:href=\"fpsyg-11-01379-g001\"/></fig><p>In addition, there were 8 filler sentences involving intransitive verbs (e.g., <italic>sleep</italic>) or actional irreversible verbs (e.g., <italic>drink</italic>), and 3 practice items. In total, there were 27 items in each version of the RC test (see <xref ref-type=\"supplementary-material\" rid=\"DS1\">Supplementary Material</xref> for the materials). All the sentences were recorded by female native speakers of Mandarin or Italian.</p></sec><sec id=\"S2.SS3\"><title>Procedure</title><p>Before testing, written consent and the questionnaires were collected from parents. The study was approved by the Ethics Committee of the University of Milano-Bicocca according to the standards of the Helsinki Declaration (1964).</p><p>Participants were tested individually in Chinese schools or university. They were given the Mandarin tests and the Italian tests in two sessions, with a 1-or-2-week interval between the sessions. The order of testing language was counterbalanced: half of the children first completed the tests in Mandarin and then in Italian, and half vice versa. The experimental materials of the tests were presented on a laptop using Microsoft PowerPoint. Each task was explained to the children. For the RC tests, each child was instructed to point to the character referred to in the sentence, and was given practice items to familiarize themselves with the task.</p></sec><sec id=\"S2.SS4\"><title>Scoring and Error Coding</title><p>In the RC comprehension tests, the dependent variable was the proportion of accurate responses, namely, the accuracy in identifying the correct character. When participants did not choose the correct character(s), we coded the response as an Error. Errors were labeled as Reversal Errors and Embedded Errors.<sup><xref ref-type=\"fn\" rid=\"footnote3\">3</xref></sup></p><p>Consider (5b), i.e., <italic>the horse that the lions are chasing</italic>, and <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>. A Reversal Error consisted of choosing the horse on the right, i.e., <italic>the horse that is chasing the lions</italic>, rather than the horse on the left. Here, the theta-roles are reversed, i.e., in (5b) the relative head <italic>the horse</italic> is a Patient, but the child interpreted it as an Agent. An Embedded Error was coded when children pointed to the middle characters corresponding to the embedded NP in the RC. For example, for the sentence <italic>the horse that the lions are chasing</italic>, children pointed to the characters in the middle, i.e., <italic>the lions</italic>, which is the subject of the RC.</p></sec><sec id=\"S2.SS5\"><title>Statistical Analysis</title><p>In this study, we used R (<xref rid=\"B37\" ref-type=\"bibr\">R Core Team, 2018</xref>) and <italic>lme4</italic> (<xref rid=\"B7\" ref-type=\"bibr\">Bates et al., 2013</xref>) to perform linear mixed-effects analyses of the relationship between different fixed factors. Models were constructed with a maximal random effects structure and were successively simplified when they failed to converge (<xref rid=\"B6\" ref-type=\"bibr\">Barr et al., 2013</xref>). Language (Mandarin vs. Italian), Sentence Type (subject RCs vs. object RCs), and Group (younger vs. older) were categorical variables, while Age was a continuous variable. Reference levels were Italian for Language, object RCs for Sentence Type, and older for Group. For simplicity, we mainly report the details in each analysis for significant effects. In addition, we explored individual differences and error types, using Pearson’s Chi-squared tests with Yates’ continuity correction and Poisson regression models, respectively. In the end, correlations among measures of linguistic background and RC comprehension were computed to assess the relation between language experience and RC comprehension.</p></sec></sec><sec id=\"S3\"><title>Results</title><p>We report the results of RC comprehension, with the order of the analyses of correct responses, individual performance, the error analyses, and the correlation analyses.</p><sec id=\"S3.SS1\"><title>Correct Responses</title><p><xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> reports the frequencies of correct responses in the younger and older groups from dual-language children. The younger children comprehend RCs less well than the older children did and they displayed a clear advantage in the comprehension of subject RCs with respect to object RCs. In addition, comprehension of Italian RCs was higher than that of Mandarin RCs in the older group.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Correct responses of Mandarin–Italian dual-language children in the comprehension of subject and object RCs (bars indicate a standard error). Two groups of children participated in the study: younger (<italic>M</italic> = 6;2) and older (<italic>M</italic> = 8;4). On the <italic>X</italic>-axis, we have the type of relative clause (RC): subject (S) and object (O) RCs. On the <italic>Y</italic>-axis, we have frequencies of correct responses.</p></caption><graphic xlink:href=\"fpsyg-11-01379-g002\"/></fig><p>We first analyzed the dual-language children’s correct responses, considering Age a continuous variable, and Language (Mandarin vs. Italian) and Sentence Type (subject RCs vs. object RCs) as categorical variables. As random effects, the models presented by-subject and by-item intercepts. There were significant effects of Age, Language, and Sentence Type (see <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>), and no interaction between Language and Sentence Type. We visualized the main findings in <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>. Both lines have a positive slope, indicating that accuracy increased with age, but the slope of the Mandarin RC line is less steep than that of the Italian RC line, indicating that accuracy of Italian RC comprehension increased faster over age than that of Mandarin RC comprehension.</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Summary of the significant fixed effects in the mixed-effects model (<italic>N</italic> = 1184, log likelihood = −606.5) in the RC comprehension.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Estimate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SE</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wald <italic>Z</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(Intercept)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−3.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−3.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000 ***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Language</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−3.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000 ***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sentence type</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000 ***</td></tr></tbody></table><table-wrap-foot><attrib><italic>Age is expressed in months; reference level for Language = Italian; reference level for Sentence Type = object RCs; ***p < 0.001.</italic></attrib></table-wrap-foot></table-wrap><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Graph of the relationship between age (in months) and percentage of correct responses in the comprehension of Mandarin and Italian RCs.</p></caption><graphic xlink:href=\"fpsyg-11-01379-g003\"/></fig><p>To better understand the dual-language children’s performance, we further analyzed their correct responses, adopting a factorial approach to their age (i.e., the younger and the older groups). Group (younger vs. older), Language (Mandarin vs. Italian), and Sentence Type (subject RCs vs. object RCs) were initially entered into a factorial model, and all significantly contributed to the model fit [χ<sup>2</sup>(3) = 107.25, <italic>p</italic> < 0.001]. Notably, there were significant Group and Language effects qualified by a Group × Language interaction. See <xref rid=\"T3\" ref-type=\"table\">Table 3</xref> for a summary of the significant effects of the full analysis.</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Summary of the significant fixed effects in the mixed-effects model (<italic>N</italic> = 1184, log likelihood = −587.2) in the RC comprehension.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Estimate</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>SE</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Wald <italic>Z</italic></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>p</italic></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(Intercept)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Group</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−3.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.49</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−6.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Language</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−2.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−5.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000***</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Group × Language</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.000***</td></tr></tbody></table><table-wrap-foot><attrib><italic>Reference level for Group = older; reference level for Language = Italian; ***p < 0.001.</italic></attrib></table-wrap-foot></table-wrap><p>To explore the interaction, we analyzed each group separately and each language separately. For each group, we analyzed the data with a mixed-effects model including Language (Mandarin vs. Italian) and Sentence Type (subject RCs vs. object RCs) as fixed factors, and by-subject and by-item random intercepts. We analyzed each language separately and used a mixed-effects model including Sentence Type (subject RCs vs. object RCs) and Group (younger vs. older) as fixed factors, and by-subject and by-item random intercepts.</p><sec id=\"S3.SS1.SSS1\"><title>The Younger Group</title><p>Only the main effect of Sentence Type was significant (β = 1.48, Wald <italic>Z</italic> = 5.63, <italic>p</italic> < 0.001). The results suggest that the younger dual-language children comprehended subject RCs significantly better than object RCs, in line with previous findings on monolingual children that there is a subject/object asymmetry in the comprehension of RCs (at least up to a certain age).</p></sec><sec id=\"S3.SS1.SSS2\"><title>The Older Group</title><p>Only the main effect of Language was significant (β = −2.10, Wald <italic>Z</italic> = −5.43, <italic>p</italic> < 0.001). These results prove that after about 5 years of exposure to Italian, the dual-language children comprehended Italian RCs significantly better than Mandarin RCs. Comprehension of Italian RCs is almost at ceiling. As it happens, in older monolingual children no subject/object asymmetry is evident (at least with this type of task).<sup><xref ref-type=\"fn\" rid=\"footnote4\">4</xref></sup></p></sec><sec id=\"S3.SS1.SSS3\"><title>Mandarin</title><p>We found main effects of Sentence Type (β = 0.64, Wald <italic>Z</italic> = 2.19, <italic>p</italic> = 0.03) and Group (β = −1.43, Wald <italic>Z</italic> = −3.01, <italic>p</italic> = 0.003), and a marginally significant interaction between them (β = 0.73, Wald <italic>Z</italic> = 1.84, <italic>p</italic> = 0.07). The results suggest that the older group performed better than the younger group, as we had already established in the general analysis, and this was especially true for object RCs. As the inspection of <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> reveals, the increase in subject RCs is numerically much smaller than in object RCs, which, added to the marginally significant interaction, suggests that the older group’s improvement was mainly observed in object RCs.</p></sec><sec id=\"S3.SS1.SSS4\"><title>Italian</title><p>Only the main effect of Group was significant (β = −3.52, Wald <italic>Z</italic> = −5.58, <italic>p</italic> < 0.001). These results suggest that the older group performed better than the younger group, as we also established in the general analysis.</p><p>To wrap up, the younger group displayed a clear subject/object asymmetry in both languages. The performance of younger Mandarin–Italian dual-language children is similar in the two languages, as far as RC comprehension is concerned. This similarity is remarkable. Although these children had been exposed to Italian for about 3 years, their level was comparable to that of their Mandarin. Recall that this similarity was also evident in their PPVT scores in the two languages. The younger group understood RCs less well than the older group, both in Mandarin and in Italian, that is, with more exposure, comprehension of RCs improved.</p><p>Interestingly, the older group displayed a higher comprehension of RCs in Italian than in Mandarin; that is, their improvement was higher for the majority language. In fact, in Italian, their performance was at ceiling. Notice that the older group did not display the subject/object asymmetry in either of their two languages. Recall that this asymmetry typically disappears when children grow older and is not evident in adults, at least if comprehension is measured with the character-sentence matching task (see footnote 4). This lack of asymmetry in Italian is understandable, as children are at ceiling. It is more surprising in Mandarin, as children are not at ceiling. In general, during development we observe an asymmetry until children reach ceiling performance and often this ceiling performance is reached first for subject RCs. This was not the situation for our children. Together with the observation that in Mandarin, object RCs improved more than subject RCs from the younger to the older group (the marginal significant interaction), this lack of asymmetry suggests that Mandarin subject RCs display some aspects of complexity, as do Mandarin object RCs.</p></sec></sec><sec id=\"S3.SS2\"><title>Individual Performance</title><p>We further ran an individual analysis by examining the number of participants performing above chance in each condition. In the task, participants have to choose one character out of three and answer 8 items in each condition. According to the binomial distribution, the performance was considered as above chance when six responses (out of eight) in each condition were correct. <xref rid=\"T4\" ref-type=\"table\">Table 4</xref> reports the percentages of participants performing above chance level in comprehending Mandarin and Italian RCs.</p><table-wrap id=\"T4\" position=\"float\"><label>TABLE 4</label><caption><p>Percentages of participants who performed above chance (i.e., six correct responses out of eight) in the comprehension of subject and object RCs.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Group</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">Mandarin<hr/></td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">Italian<hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Subject RCs</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Object RCs</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Subject RCs</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Object RCs</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Younger</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">68% (13/19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32% (6/19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">63% (12/19)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26% (5/19)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Older</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61% (11/18)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50% (9/18)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">94% (17/18)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">94% (17/18)</td></tr></tbody></table></table-wrap><p>We explored these results by running Pearson’s Chi-squared tests with Yates’ continuity correction, comparing the number of children who performed above chance level with the number of children that did not.</p><p>We first compared the number of children who performed above chance in the two languages, according to their age. In the younger group, there was no significant difference between the two languages in subject RCs or in object RCs. This result is in line with the previous analysis showing no language difference in the younger group. In the older group, a significant difference between the two languages was evident, in both subject [χ<sup>2</sup>(1) = 4.02, <italic>p</italic> = 0.045] and object RCs [χ<sup>2</sup>(1) = 6.78, <italic>p</italic> = 0.009].</p><p>In addition, we counted the number of participants who performed above chance in both subject and object RCs. In the case of Mandarin, 16% (3 out of 19) of the children in the younger group and 44% (8 out of 18) of those in the older group did so, but the difference between the two groups did not reach significance [χ<sup>2</sup>(1) = 2.39, <italic>p</italic> = 0.12]. In the case of Italian, 21% (4 out of 19) of the children in the younger group and 89% (16 out of 18) of those in the older group did so. This difference was significant [χ<sup>2</sup>(1) = 14.50, <italic>p</italic> < 0.001].</p><p>To sum up, the results of individual analyses confirm that the older dual-language children comprehended subject and object RCs in Italian better than those in Mandarin, although they had been exposed to Mandarin from birth and continued to use it. In addition, more children in the older group performed above chance on both RC types in Italian than the younger group. This was not the case in Mandarin; the number of children performing above chance across the two groups did not significantly differ. It appears clear that both at the group and individual levels, improvement is more evident in Italian than in Mandarin. In addition, the improvement in Mandarin is more evident in object RCs.</p></sec><sec id=\"S3.SS3\"><title>Error Analyses</title><p>We further investigated what children did when they failed to understand RCs, by examining the distribution of errors. <xref rid=\"T5\" ref-type=\"table\">Table 5</xref> summarizes means and standard deviation of error types (i.e., Reversal Errors and Embedded Errors) for each group in the comprehension of RCs in the two languages.</p><table-wrap id=\"T5\" position=\"float\"><label>TABLE 5</label><caption><p>Means (and standard deviation) of incorrect responses in the comprehension of subject and object RCs (means are calculated over the total number of responses).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Group</td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">Mandarin<hr/></td><td valign=\"top\" align=\"center\" colspan=\"2\" rowspan=\"1\">Italian<hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Subject RCs</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Object RCs</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Subject RCs</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Object RCs</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Younger</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Correct response</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68 (0.30)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41 (0.32)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74 (0.27)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.43 (0.38)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reversal error</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.13 (0.23)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18 (0.21)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12 (0.18)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36 (0.33)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Embedded error</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19 (0.18)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41 (0.36)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.14 (0.16)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21 (0.24)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Older</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Correct response</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77 (0.21)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67 (0.32)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96 (0.10)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92 (0.16)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reversal error</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.17 (0.21)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21 (0.24)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03 (0.09)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06 (0.11)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Embedded error</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06 (0.12)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12 (0.24)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01 (0.04)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02 (0.06)</td></tr></tbody></table></table-wrap><p>We counted numbers of each type of error that each child made, and treated it as a count variable to run a Poisson regression model.</p><p>Let us first consider the errors that the younger group made. In subject RC comprehension, no difference between Reversal and Embedded Errors was found in Mandarin or in Italian (β = −0.34, Wald <italic>Z</italic> = −1.15, <italic>p</italic> = 0.25, and β = −0.15, Wald <italic>Z</italic> = −0.48, <italic>p</italic> = 0.63, respectively). In the comprehension of Mandarin object RCs, Embedded Errors were significantly more frequent than Reversal Errors (β = −0.85, Wald <italic>Z</italic> = −3.68, <italic>p</italic> < 0.001). By contrast, in the comprehension of Italian object RCs, the opposite pattern was found, namely, Reversal Errors were significantly more frequent than Embedded Errors (β = 0.52, Wald <italic>Z</italic> = 2.35, <italic>p</italic> = 0.019).</p><p>Second, consider the errors made by the older group. In subject RC comprehension, Reversal Errors were significantly more frequent than Embedded Errors in Mandarin (β = 0.98, Wald <italic>Z</italic> = 2.51, <italic>p</italic> = 0.012), but no significant difference between the two error types was observed in Italian (β = 0.69, Wald <italic>Z</italic> = 0.80, <italic>p</italic> = 0.42). In the comprehension of Mandarin object RCs, Reversal Errors were more frequent than Embedded Errors in Mandarin (β = 0.60, Wald <italic>Z</italic> = 2.00, <italic>p</italic> = 0.047), while in the comprehension of Italian object RCs, no significant difference between the two types of errors was evident (β = 0.98, Wald <italic>Z</italic> = 1.45, <italic>p</italic> = 0.15).</p><p>To sum up, when the younger children were not able to comprehend subject RCs, they made Embedded or Reversal Errors both in Mandarin and in Italian. In other words, they chose another character randomly. When they did not comprehend object RCs, they were more likely to make Embedded Errors in Mandarin, but Reversal Errors in Italian. Notice that, as stated earlier, given the different structures of Mandarin and Italian object RCs, the two different types of errors led to the choice of the first NP heard in both languages. As for the older children, they still had difficulties in the comprehension of Mandarin RCs. Unlike the younger children, they were more likely to make Reversal Errors than Embedded Errors in Mandarin subject RCs and object RCs.</p></sec><sec id=\"S3.SS4\"><title>Correlation Analyses</title><p>Several correlations were found between the comprehension of Mandarin RCs and Italian RCs. The children who were better at understanding object RCs in Italian were better at doing the same thing in Mandarin. This holds true both for the younger (Pearson correlation coefficient = 0.60, <italic>p</italic> < 0.01) and the older groups (Pearson correlation coefficient = 0.48, <italic>p</italic> < 0.05).</p><p>In addition, we found a correlation between the comprehension of Italian RCs and the length of exposure to Italian (Pearson correlation coefficient = 0.63, <italic>p</italic> < 0.001), which holds true for both subject RCs (Pearson correlation coefficient = 0.43, <italic>p</italic> < 0.01) and object RCs (Pearson correlation coefficient = 0.57, <italic>p</italic> < 0.001).</p><p>Interestingly, we found a correlation between the comprehension of Mandarin RCs and the frequency with which children spoke Mandarin in the home (Pearson correlation coefficient = 0.44, <italic>p</italic> < 0.01). In particular, this holds true for subject RCs in the younger group (Pearson correlation coefficient = 0.66, <italic>p</italic> < 0.01) and there is a marginal significant difference for object RCs in the older group (Pearson correlation coefficient = 0.45, <italic>p</italic> = 0.06). By contrast, we found a correlation between the comprehension of Italian RCs and the average amount of time the child spent in an Italian-speaking environment (Pearson correlation coefficient = 0.37, <italic>p</italic> < 0.05). This holds particularly true for object RCs in the younger group (Pearson correlation coefficient = 0.46, <italic>p</italic> < 0.05). The results indicate that children’s comprehension of Mandarin RCs is more related to how often they spoke the language, while their comprehension of Italian RCs is more related to the average amount of time they spent in an Italian-speaking environment.</p><p>Moreover, in the older group, we found a correlation between comprehension of Mandarin object RCs and Mandarin vocabulary score (Pearson correlation coefficient = 0.51, <italic>p</italic> < 0.01). By contrast, we did not observe any significant correlations between children’s comprehension of Italian subject and object RCs and their vocabulary knowledge in Italian, suggesting that comprehension of Italian RC does not depend on vocabulary knowledge. Recall that the level of Italian and Mandarin vocabulary were similar in the older group, but their comprehension of Italian RCs was more advanced than that of Mandarin RCs.</p></sec></sec><sec id=\"S4\"><title>Discussion</title><p>Our findings revealed a complex picture of RC comprehension in dual-language children speaking Mandarin and Italian. For convenience, we discuss the findings in distinct subsections.</p><sec id=\"S4.SS1\"><title>The Role of Length of Exposure to the Majority Language</title><p>Regarding our first aim, we found that the length of exposure to the majority language contributed to shaping comprehension of RCs and this was evident in both groups, although in different ways.</p><p>First, the younger group comprehended subject RCs better than object RCs in both of their languages. This was the only significant effect found in the younger group, suggesting that 3 years of exposure to Italian were enough to put these children at the same level as they were in their Mandarin, as far as RC comprehension is concerned. We attribute this subject RC advantage to subject RCs being structurally simpler than object RCs in both languages. The dependency between the relative head and its copy is shorter in subject RCs than in object RCs. In other words, nothing intervenes between the two in the former case, while it does in the latter case; specifically, the subject intervenes in the dependency. Based on a corpus analysis of Mandarin child-directed speech from CHILDES, <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al. (2019)</xref> attributes the Mandarin subject RC advantage to the frequency of RC-like structures (e.g., possessive structures). In Mandarin, <italic>de</italic> has different functions beyond being a relativizer. <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al. (2019)</xref> found that the subject RC-like structures [VN de (N)] are more frequent than the object RC-like structures [NV de (N)]. However, as they notice, if RCs are extrapolated from these RC-like structures, the opposite holds: object RCs are more frequent than subject RCs. Accordingly, they claim that frequency of information matters, but various levels of frequency may be differently relevant. We are skeptical about this conclusion, as there are several studies on RC comprehension, which found that Italian passive object RCs (i.e., an object RC turned into a subject one through passivization), like in (7a), are easier to acquire than active object RCs, as in (7b) (see <xref rid=\"B14\" ref-type=\"bibr\">Contemori and Belletti, 2014</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Belletti and Guasti, 2015</xref>; <xref rid=\"B4\" ref-type=\"bibr\">Arosio et al., 2017</xref>).</p><p><inline-graphic xlink:href=\"fpsyg-11-01379-i005.jpg\"/></p><p>Interestingly, in corpora, passive object RCs are extremely rare and less frequent than object RCs (<xref rid=\"B8\" ref-type=\"bibr\">Belletti and Chesi, 2011</xref>). The reason why passive object RCs are easier than active ones is that they are structurally simpler, either in terms of distance or in terms of lack of intervention effects.</p><p>It is worth pointing out that our results converge with those reported by <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al. (2019)</xref> in showing that the subject RC advantage in Mandarin dual-language children as found in our younger group is not due to influence from a language with head-initial RCs, be it English or Italian. However, this influence seems active in multilingual children speaking Cantonese, based on <xref rid=\"B31\" ref-type=\"bibr\">Kidd et al. (2015)</xref> and <xref rid=\"B13\" ref-type=\"bibr\">Chan et al. (2017)</xref>.</p><p>Second, the older group had a better comprehension of RCs in Italian than in Mandarin. We conclude that after about 5 years of intensive exposure to Italian (and 2 years of formal instruction), better performance is found in the majority language than in Mandarin. In the older group, the subject/object asymmetry in the comprehension of RCs disappeared in Italian, because children were at ceiling. It also disappeared in Mandarin, but in this language, we observed that the older group of children improved in the comprehension of object RCs slightly more than in the comprehension of subject RCs, although this interaction was only marginally significant. The comprehension of Mandarin RCs was poorer than that of Italian ones. Thus, concerning our first aim, we can say that after 3 years of immersion in the majority language children catch up with their minority language (the younger group) and with 5 years of exposure and formal education they attain native-like competence and are similar to the monolingual 7-year-olds studied in <xref rid=\"B1\" ref-type=\"bibr\">Adani (2011)</xref>, as far as comprehension of RCs is concerned. Concerning the younger group, it is quite remarkable that children reach the same level of comprehension in Italian as in Mandarin, in spite of just 3 years of exposure to Italian, in contrast with 6 years to Mandarin. No correlation between vocabulary comprehension and RC comprehension emerges in Italian, suggesting that the two competencies develop separately in Italian. In fact, while in the older group the comprehension of Italian RCs was at ceiling, their vocabulary comprehension corresponded to a standard score of 79 (raw scores = 83.44) based on a monolingual Italian norm. This is not surprising, as learning the vocabulary depends on the contexts of use of a language (if Italian is rarely spoken at home, words used in the family context may be absent). In contrast, once the RC structure is acquired, there is nothing more to do.<sup><xref ref-type=\"fn\" rid=\"footnote5\">5</xref></sup></p><p>With respect to monolingual Mandarin-speaking age-matched children (who achieve adult levels at age 8), based on <xref rid=\"B28\" ref-type=\"bibr\">Hu et al. (2016)</xref>, our older group performed less well numerically. At the same time, we found a correlation between the frequency with which children spoke Mandarin in the home and the comprehension of Mandarin RCs, suggesting that active use of a language is important to letting the language grow. We also uncovered a correlation between the comprehension of RCs in the two languages: children who were better in one language were also better in the other language, suggesting reciprocal support. Thus, using two languages, even with structures that are quite different, as RCs, promotes comprehension of these structures in both languages. This is a hint that at some levels Mandarin and Italian RCs share the same representational structure.</p></sec><sec id=\"S4.SS2\"><title>Bidirectional Influence</title><p>We turn now to our second aim. Beyond the positive and general reciprocal support, the bidirectional influence was especially evident in the error analysis. From the quantitative point of view, the younger group made a similar number of errors in the comprehension of subject RCs in their two languages: 32% in Mandarin and 26% in Italian (Embedded and Reversal Errors together). This result is in line with what the monolingual Mandarin-speaking children tested by <xref rid=\"B28\" ref-type=\"bibr\">Hu et al. (2016)</xref> did at the age of 6 years: they produced 24% errors. However, there is a sharp contrast with what the monolingual Italian-speaking children studied by <xref rid=\"B1\" ref-type=\"bibr\">Adani (2011)</xref> did: they only made 9% of errors at age 3 (and 4% at age 6). This contrast may be due to the fact that although dual-language children were regularly exposed to Italian for about 3 years (i.e., the same time as the Italian monolinguals in <xref rid=\"B1\" ref-type=\"bibr\">Adani, 2011</xref>), they received less Italian input than monolinguals. Alternatively, the contrast may be a hint that acquiring RCs with opposite directionality is challenging. It is possible that children, at least in the initial stage of exposure to Italian, have trouble dealing with two structures displaying dependencies that go in opposite directions (the relative head precedes the gap in Italian and follows it in Mandarin). If it is the different directionality that matters, we expect children acquiring an L2, in which RCs have the same word order, not to experience difficulties in the comprehension of subject RCs. On the contrary, if it is insufficient input that matters, then we expect these children to experience the same difficulties observed in our Mandarin–Italian speaking children. Another alternative is that the “SV <italic>de</italic> O” structure of Mandarin object RCs misleads children as it is superficially similar to that of Italian subject RCs with the structure “S that VO.” Under this view, the high number of errors with Italian subject RCs could be a manifestation of negative influence from Mandarin. This conjecture is supported by a qualitative analysis of the errors in subject RCs. Our Mandarin–Italian dual-language children, like monolingual Mandarin-speaking children (<xref rid=\"B28\" ref-type=\"bibr\">Hu et al., 2016</xref>), Mandarin–English dual-language children (<xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al., 2019</xref>), and Cantonese–English–Mandarin trilingual children (<xref rid=\"B13\" ref-type=\"bibr\">Chan et al., 2017</xref>), made Embedded and Reversal Errors in Mandarin when they had to comprehend a subject RC. Interestingly, they did the same in Italian, unlike monolingual Italian-speaking children, who, according to <xref rid=\"B1\" ref-type=\"bibr\">Adani (2011)</xref>, made very few Reversal Errors. We interpret the presence of both Reversal and Embedded Errors in Italian as an indication that the Mandarin–Italian dual-language children were confused and randomly chose either the first NP or the last NP heard, as the monolingual Mandarin-speaking children did in <xref rid=\"B28\" ref-type=\"bibr\">Hu et al. (2016)</xref>.</p><p>The errors displayed in the comprehension of object RCs do not seem to result from any bidirectional influence between the two languages in the younger group. The types of errors are similar to those made by the monolingual Mandarin-speaking children (<xref rid=\"B28\" ref-type=\"bibr\">Hu et al., 2016</xref>) and the monolingual Italian-speaking children (<xref rid=\"B1\" ref-type=\"bibr\">Adani, 2011</xref>): Embedded Errors in Mandarin and Reversal Errors in Italian. Embedded Errors were also found in Mandarin–English dual-language children (<xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al., 2019</xref>) and Cantonese–English–Mandarin trilingual children (<xref rid=\"B13\" ref-type=\"bibr\">Chan et al., 2017</xref>) in the comprehension of Mandarin object RCs. As we said, given the structure of RCs in the two languages, an Embedded Error in Mandarin amounts to choosing the first NP heard as the Agent of the action and similarly for Reversal Errors in Italian. Thus, this error may be the result of a common strategy in the two languages: the first NP is the chosen referent and Agent of the action.</p><p>The influence between the two languages re-emerges in the older group. First, the older group was almost perfect in Italian, behaving as monolingual Italian-speaking children at the age of 7. They still made many errors in the comprehension of Mandarin subject and object RCs. However, Embedded Errors were significantly less frequent than Reversal Errors both in subject and object RCs, that is, the older children preferred reversing the thematic roles. This shift in the type of error was not observed in the monolinguals by <xref rid=\"B28\" ref-type=\"bibr\">Hu et al. (2016)</xref>, where Embedded Errors were the most common type in object RC comprehension for all age groups investigated (from 3 to 7 years). This finding can be attributed to negative influence from Italian to Mandarin. As we said earlier, Italian subject RCs display the order “S that VO,” which superficially corresponds to the surface order of Mandarin object RCs. This similarity may mislead dual-language children toward a subject RC analysis of Mandarin object RCs.</p><p>Thus, the influence of one language on the other is evident in three ways. There is negative influence from Mandarin to Italian in the younger group (evident in the comprehension of Italian subject RCs), negative influence in the older group from Italian to Mandarin (evident in the error analysis concerning Mandarin object RCs), and reciprocal support in that better comprehension of RCs in one language correlated with better comprehension in the other one.</p></sec><sec id=\"S4.SS3\"><title>Is There a Delay in the Comprehension of Head-Final Subject Relative Clauses?</title><p>Our third aim was to gain insight into the delay in the comprehension of subject RCs in Mandarin with respect to languages with head-initial RCs. Although subject RCs are easier to understand than object RCs, they were challenging for the younger children and elicited 32% errors; so were subject RCs in Italian (26% errors), which we attributed to the influence from Mandarin and/or lack of sufficient input in Italian. This evidence notwithstanding, we may remark that the younger children had been exposed to Mandarin, from birth, for 6 years, and to Italian for half of that time, though they had less exposure to each language compared with monolinguals. Hence, the fact that the percentages of errors are almost the same in the two languages suggests that Mandarin subject RCs are more challenging than Italian ones. This observation is further corroborated by the 5–6-year-old trilingual children, whose rate of correct responses for Cantonese (L1) and Mandarin (L3) subject RCs was 29% and 34%, respectively (<xref rid=\"B13\" ref-type=\"bibr\">Chan et al., 2017</xref>). For their L2 English, this rate was 90%. For the Mandarin–English bilinguals (<italic>M</italic> = 6;1), the rate of correct responses was 48% in Mandarin and 80% in English (<xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al., 2019</xref>). Taken together, these findings support the conclusion that subject head-final RCs contain some elements of complexity, which are not present in subject head-initial RCs.</p><p>Earlier, we attributed the subject advantage to their structural simplicity. Under the explanation that we adopted, this simplicity consists in the fact that in subject RCs nothing intervenes hierarchically between the relative head and its copy (<xref rid=\"B20\" ref-type=\"bibr\">Friedmann et al., 2009</xref>). Along these lines, what may be challenging in head-final subject RCs is the fact that the object intervenes linearly between the relative head and its backward copy (<xref rid=\"B28\" ref-type=\"bibr\">Hu et al., 2016</xref>). Linear intervention is less disruptive than structural intervention, as shown in <xref rid=\"B19\" ref-type=\"bibr\">Franck et al. (2006)</xref>, hence the subject advantage also evident in Mandarin. However, it contributes some penalty, which causes trouble for children acquiring head-final RCs. This delay notwithstanding, we have to acknowledge that the older group is weak in its comprehension of Mandarin RCs and this is likely due to the lower input to which they are exposed. We mentioned that RCs are explicitly taught at school in Italy. This may suggest that similar teaching would be helpful in Mandarin classes, as it would exploit the kind of competence that is already developed in Italian classes.</p></sec></sec><sec id=\"S5\"><title>Conclusion</title><p>We replicated the subject advantage in Mandarin (younger group), as found in several other studies. We showed that the length of exposure to the majority language affects comprehension of RCs, even in a situation in which the minority language is supported both at the oral and the written level. In particular, the time spent in the majority language environment correlated with RC comprehension. Nevertheless, we found reciprocal support between the two languages in the comprehension of RCs, suggesting that double language use must be sustained. The use of two languages leads to bidirectional influence, and this is evident in the errors that are made as a function of length of exposure: the younger and the older group behaved differently, likely due to which language was more frequently used. Finally, the penalty that subject head-final RCs seems to have with respect to subject head-initial RCs is likely due to linear intervention, a process less disruptive than structural intervention, but that still affects comprehension.</p></sec><sec sec-type=\"data-availability\" id=\"S6\"><title>Data Availability Statement</title><p>The datasets generated for this study are available on request to the corresponding author.</p></sec><sec id=\"S7\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by University of Milano-Bicocca. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>SH and MG conceived the research question, developed the experimental task, and drafted the manuscript. SH recruited and tested the participants and performed the statistical analyses. FC developed the experimental task and tested the participants. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This research was supported by the National Social Science Fund of China under grant agreement no. 18BYY080.</p></fn></fn-group><ack><p>We are grateful to all the children, parents and teachers who participated in the study, to Li Qunlai and Zhao Tongyu for their help in obtaining permission to work in schools, to Irene Benassi, Roberta Bettoni, Claudia Perri, and Benedetta Rubino for their help in data collection, to Zhong Lin for her help in entering data in Excel, to Natale Stucchi for his help in data analyses, and to Ding Xiaojun, Lian Zeyu, Marco Marelli, Daniel Mirman, and Zhao Jing for suggestions.</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p>Our children started to learn the L2 between 2 and 4 years of age. Some authors refer to these children as bilingual, while for others they are early L2 learners. To avoid entering into this debate, we used the term “dual-language children” in line with some articles in the literature to refer to children who use two languages, as this is a more neutral term.</p></fn><fn id=\"footnote2\"><label>2</label><p>In Italian, object RCs can occur in another order, i.e., the object-verb-subject (OVS) order. As in (i), the embedded subject <italic>il cane</italic> “the dog” follows the verb <italic>colpisce</italic> “hits,” with which it agrees in number. For the relevant discussion, see <xref rid=\"B1\" ref-type=\"bibr\">Adani (2011)</xref>.</p><p><inline-graphic xlink:href=\"fpsyg-11-01379-i006.jpg\"/></p></fn><fn id=\"footnote3\"><label>3</label><p>What we call an Embedded Error is called a Middle Error in <xref rid=\"B1\" ref-type=\"bibr\">Adani (2011)</xref>. It should be noticed that the task of our study has three choices as in <xref rid=\"B1\" ref-type=\"bibr\">Adani (2011)</xref>, but differs from the task which has four choices (e.g., <xref rid=\"B28\" ref-type=\"bibr\">Hu et al., 2016</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Tsoi et al., 2019</xref>).</p></fn><fn id=\"footnote4\"><label>4</label><p>A subject RC advantage is still observed in adults and older children if reading times are measured (see the comparison of the comprehension of RCs by children and adults in <xref rid=\"B22\" ref-type=\"bibr\">Guasti et al., 2018</xref>).</p></fn><fn id=\"footnote5\"><label>5</label><p>The correlation between vocabulary and object RC comprehension was found in Mandarin, though. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Beilstein J Nanotechnol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Beilstein J Nanotechnol</journal-id><journal-title-group><journal-title>Beilstein Journal of Nanotechnology</journal-title></journal-title-group><issn pub-type=\"epub\">2190-4286</issn><publisher><publisher-name>Beilstein-Institut</publisher-name><publisher-loc>Trakehner Str. 7-9, 60487 Frankfurt am Main, Germany</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32832314</article-id><article-id pub-id-type=\"pmc\">PMC7431765</article-id><article-id pub-id-type=\"doi\">10.3762/bjnano.11.103</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Full Research Paper</subject></subj-group><subj-group subj-group-type=\"topic\"><subject>Nanoscience</subject></subj-group><subj-group subj-group-type=\"topic\"><subject>Nanotechnology</subject></subj-group></article-categories><title-group><article-title>High permittivity, breakdown strength, and energy storage density of polythiophene-encapsulated BaTiO<sub>3</sub> nanoparticles</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Khan</surname><given-names>Adnanullah</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-4774-9444</contrib-id><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Habib</surname><given-names>Amir</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-3391-3677</contrib-id><email>amirhabib@uhb.edu.sa</email><xref ref-type=\"aff\" rid=\"A2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Afzal</surname><given-names>Adeel</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-6528-7300</contrib-id><email>aa@aafzal.com</email><xref ref-type=\"aff\" rid=\"A3\">3</xref></contrib></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Motta</surname><given-names>Nunzio</given-names></name><role>Associate Editor</role></contrib></contrib-group><aff id=\"A1\"><label>1</label>School of Chemical and Materials Engineering, National University of Science and Technology, Sect. H-12, Islamabad, 44000, Pakistan</aff><aff id=\"A2\"><label>2</label>Department of Physics, College of Science, University of Hafr Al Batin, PO Box 1803, Hafr Al Batin, 39524, Saudi Arabia</aff><aff id=\"A3\"><label>3</label>Department of Chemistry, College of Science, University of Hafr Al Batin, PO Box 1803, Hafr Al Batin, 39524, Saudi Arabia</aff><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>10</day><month>8</month><year>2020</year></pub-date><volume>11</volume><fpage>1190</fpage><lpage>1197</lpage><ext-link ext-link-type=\"doi\" xlink:href=\"10.3762/bjnano.11.103\">10.3762/bjnano.11.103</ext-link><history><date date-type=\"received\"><day>29</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, Khan et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Khan et al.</copyright-holder><ali:free_to_read xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\"/><license license-type=\"Beilstein\"><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">https://creativecommons.org/licenses/by/4.0</ali:license_ref><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">https://www.beilstein-journals.org/bjnano/terms</ali:license_ref><license-p>This is an Open Access article under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0\">https://creativecommons.org/licenses/by/4.0</ext-link>). Please note that the reuse, redistribution and reproduction in particular requires that the authors and source are credited.</license-p><license-p>The license is subject to the Beilstein Journal of Nanotechnology terms and conditions: (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.beilstein-journals.org/bjnano/terms\">https://www.beilstein-journals.org/bjnano/terms</ext-link>)</license-p></license></permissions><abstract><p>High permittivity and breakdown strength are desired to improve the energy storage density of dielectric materials based on reinforced polymer composites. This article presents the synthesis of polythiophene-encapsulated BaTiO<sub>3</sub> (BTO-PTh) nanoparticles via an in situ Cu(II)-catalyzed chemical oxidative polymerization of thiophene monomer on hydrothermally obtained tetragonal BTO nanocrystals. The formed core–shell-type BTO-PTh nanoparticles exhibit excellent dielectric properties with high permittivity (25.2) and low loss (0.04) at high frequency (10<sup>6</sup> Hz). A thick PTh encapsulation layer on the surface of the BTO nanoparticles improves their breakdown strength from 47 to 144 kV/mm and the energy storage density from 0.32 to 2.48 J/cm<sup>3</sup>. A 7.75-fold increase in the energy storage density of the BTO-PTh nanoparticles is attributed to simultaneously high permittivity and breakdown strength, which are excellent for potential energy storage applications.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>barium titanate (BaTiO<sub>3</sub>) nanoparticles</kwd><kwd>breakdown strength</kwd><kwd>dielectric materials</kwd><kwd>energy storage</kwd><kwd>polythiophene</kwd></kwd-group></article-meta></front><body><sec><title>Introduction</title><p>The fast-paced progress and constantly growing demand of microelectronic devices and energy storage technologies have led to extensive research on the development of new dielectric materials [<xref rid=\"R1\" ref-type=\"bibr\">1</xref>–<xref rid=\"R3\" ref-type=\"bibr\">3</xref>]. High-κ ceramic-based dielectric materials such as BaTiO<sub>3</sub> (BTO) have been prepared and used in actuators, capacitors, and communication devices [<xref rid=\"R4\" ref-type=\"bibr\">4</xref>–<xref rid=\"R6\" ref-type=\"bibr\">6</xref>]. However, the ceramic-based dielectrics are often brittle, and possess low electrical breakdown strength and energy storage density [<xref rid=\"R7\" ref-type=\"bibr\">7</xref>], which hampers their practical applications in energy storage devices such as dielectric capacitors. To overcome these problems, BTO-polymer composite-based dielectric materials have been developed and extensively investigated. Organic polymers offer many advantages including higher breakdown strength, lighter mass, greater flexibility, processability, and cost efficiency [<xref rid=\"R8\" ref-type=\"bibr\">8</xref>]. However, they are inherently poor dielectrics with very low permittivity (values in the range of 2–10) [<xref rid=\"R2\" ref-type=\"bibr\">2</xref>]. Thus, organic polymers are often reinforced with ceramic-based dielectric materials such as BTO.</p><p>The dielectric properties of BTO–polymer composites are known to improve considerably with the inclusion of BTO nanoparticles. For instance, Dang et al. [<xref rid=\"R9\" ref-type=\"bibr\">9</xref>] achieved a permittivity value of ca. 40 at 10 kHz with 50 vol % loading of BTO in polyvinylidene fluoride (PVDF). However, such a high content of BTO nanoparticles has severe effects on the overall performance of the composite dielectric materials [<xref rid=\"R9\" ref-type=\"bibr\">9</xref>–<xref rid=\"R10\" ref-type=\"bibr\">10</xref>], which are largely attributed to the interphase inhomogeneity, poor distribution, and agglomeration of BTO nanoparticles in the polymer matrix. You et al. [<xref rid=\"R10\" ref-type=\"bibr\">10</xref>] observed an increase in the permittivity (from 4 to 14) of poly(arylene ether nitrile) filled with 40 wt % polyaniline-functional-BTO nanoparticles, but they noticed that breakdown strength of the composite was critically affected at a high concentration of filler due to free charge accumulation at the interface.</p><p>Therefore, to improve breakdown strength and energy storage density of BTO, we propose the design of polythiophene (PTh)-encapsulated BaTiO<sub>3</sub> nanoparticles with a 9:1 mass ratio of BTO/PTh, and a facile method for the synthesis of inverted [<xref rid=\"R11\" ref-type=\"bibr\">11</xref>] core–shell-type BTO-PTh nanostructures, which yields a uniform PTh coating on the BTO surface. BTO-PTh nanoparticles are prepared by Cu(II)-catalyzed oxidative polymerization of PTh on the BTO surface. This procedure yields a high BTO content in the PTh shell, which not only results in superior dielectric properties such as high permittivity and low loss, but also significantly increases breakdown strength. Consequently, core–shell BTO-PTh nanoparticles exhibit greatly improved energy storage density. We believe BTO-PTh nanoparticles are a promising material and this design is a noteworthy strategy for future research and advanced microelectronic and energy storage applications.</p></sec><sec><title>Experimental</title><p>Barium hydroxide octahydrate (Ba(OH)<sub>2</sub>·8H<sub>2</sub>O, ≥98%, Sigma-Aldrich) and fine-grained titanium dioxide (TiO<sub>2</sub> Tronox, 99.5%, Tronox Pigments GmbH) are used as Ba and Ti precursors for the hydrothermal synthesis of BTO nanoparticles. Ba(OH)<sub>2</sub>·8H<sub>2</sub>O also acts as the mineralizer and prevents the use of NaOH or KOH for controlling the pH value of the reaction mixture [<xref rid=\"R12\" ref-type=\"bibr\">12</xref>]. Equimolar amounts of Ba and Ti precursors are added to the double-distilled water in a PTFE-lined pressure vessel. The hydrothermal reaction is carried out in an autoclave (Berghof Instruments GmbH) at 120 °C and autogenous pressure for 24 h under stirring. The product is washed with dilute formic acid solution and double-distilled water to dissolve and remove impurities and is dried in an oven at 120 °C. The obtained dried product is pulverized to get BTO nanoparticles.</p><p>In the next step, core–shell BTO-PTh nanoparticles are synthesized via in situ chemical oxidative polymerization of thiophene on hydrothermally produced BTO nanoparticles. BTO nanoparticles are first dispersed in double-distilled water containing sodium dodecyl sulfate (SDS, ACS reagent, ≥99.0%, Sigma Aldrich) and thiophene (Th, synthesis grade 99%, Scharlab, S.L.) monomer through ultrasonication for 1 h. The mass ratio of BTO/Th is adjusted to 9:1. Subsequently, hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>, solution 30% w/w, Scharlab, S.L.) and copper(II) sulfate pentahydrate (CuSO<sub>4</sub>·5H<sub>2</sub>O, reagent grade, ≥98.0%, Honeywell) solution are added to the BTO-Th mixture. The reaction is performed by stirring the mixture for 7 h at 50 °C. The product is washed with double-distilled water and ethanol and is dried in a vacuum oven at 50 °C for 24 h. <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> shows a schematic of the synthesis of core–shell BTO-PTh nanoparticles.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Schematic of the formation of core–shell BaTiO<sub>3</sub>-thiophene (BTO-PTh) nanoparticles. BTO nanoparticles and thiophene (Th) monomer are dispersed in water. Th-monomer molecules adsorb on the surface of BTO nanoparticles through weak intermolecular interactions. The chemical oxidative polymerization of Th initiated by H<sub>2</sub>O<sub>2</sub> and catalyzed by Cu<sup>2+</sup> ions yields the core–shell BTO-PTh nanoparticles.</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1190-g002\"/></fig><p>To compare dielectric properties and energy efficiency, a similar approach is used to prepare pristine polythiophene (PTh) without introducing BTO nanoparticles in the mixture. All materials including BTO, BTO-PTh, and PTh are characterized using Fourier-transform infrared spectroscopy (Nicolet 520 FTIR spectrophotometer) and X-ray diffraction (STOE STADI P X-ray diffractometer). The morphology of BTO and BTO-PTh nanoparticles is studied using scanning electron microscopy (JEOL JSM 6490LA SEM) and atomic force microscopy (JSPM-5200 scanning probe microscope). Electrical properties of the bulk materials are measured under ambient conditions with a Wayne Kerr 6505B precision impedance analyzer and a Hipotronics HD103 3kV DC Hipot Tester.</p></sec><sec><title>Results and Discussion</title><p>Chemical structure and surface morphology of the as-prepared BTO nanoparticles, core–shell-type BTO-PTh nanoparticles, and pristine PTh are characterized and reported herein. <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> shows the FTIR spectra of PTh, BTO, and BTO-PTh nanoparticles. A distinct peak at 560 cm<sup>−1</sup> is the characteristic stretching vibration (νTi–O) of BTO structure [<xref rid=\"R13\" ref-type=\"bibr\">13</xref>–<xref rid=\"R14\" ref-type=\"bibr\">14</xref>]. PTh is characterized by the symmetric and asymmetric stretching vibrations (νC=C) of the aromatic ring at 1628 and 1385 cm<sup>−1</sup>, a sharp aromatic ring deformation (δC–S–C) at 610 cm<sup>−1</sup>, and a typical stretching vibration of the aromatic β-hydrogens (νC<sub>β</sub>–H) at 3060 cm<sup>−1</sup> [<xref rid=\"R15\" ref-type=\"bibr\">15</xref>–<xref rid=\"R16\" ref-type=\"bibr\">16</xref>]. The broad transmittance peak at 3418 cm<sup>−1</sup> and weak bands in the range of 2845–2935 cm<sup>−1</sup> are attributed to the adsorbed water molecules (νO–H) and the carbonaceous impurities (νCH<sub>3</sub>, νCH<sub>2</sub>) in PTh, respectively. A peak at 1123 cm<sup>−1</sup> that is also characteristic of PTh corresponds to the C<sub>α</sub>–C<sub>α</sub> resonance absorption between two thiophene rings [<xref rid=\"R17\" ref-type=\"bibr\">17</xref>]. It demonstrates that PTh is predominantly formed by C<sub>α</sub>–C<sub>α</sub> conjunction during the low-temperature oxidative polymerization. The transmittance peaks at 1035 cm<sup>−1</sup> and 788 cm<sup>−1</sup> further prove this point as they indicate the out-of-plane bending (τC<sub>β</sub>–H) and in-plane bending vibrations (ρC<sub>β</sub>–H) of PTh, respectively [<xref rid=\"R17\" ref-type=\"bibr\">17</xref>–<xref rid=\"R18\" ref-type=\"bibr\">18</xref>]. According to Wu et al. [<xref rid=\"R17\" ref-type=\"bibr\">17</xref>], the intensity of C<sub>β</sub>–H transmissions would considerably decrease if PTh had C<sub>β</sub>–C<sub>β</sub> conjunction. In BTO-PTh spectrum, the out-of-plane and in-plane bending of C<sub>β</sub>–H shift to 1077 cm<sup>−1</sup> and 983 cm<sup>−1</sup>, respectively, which is attributed to the interactions between β-hydrogens of PTh and oxygen atoms on the BTO surface.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>FTIR spectra of the as-prepared BTO nanoparticles, pristine PTh, and core–shell BTO-PTh nanoparticles.</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1190-g003\"/></fig><p>X-ray diffraction patterns of BTO, PTh, and core–shell BTO-PTh nanoparticles are presented in <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>. Pristine PTh is amorphous in nature and shows a low-intensity broad peak at around 23°, which corresponds to the intermolecular π–π stacking structure and amorphous packing of the polymer [<xref rid=\"R19\" ref-type=\"bibr\">19</xref>]. The XRD pattern of hydrothermally prepared BTO nanoparticles shows good agreement with the tetragonal perovskite structure (JCPDS No. 05-0626) with the <italic>P</italic>4<italic>mm</italic> space group [<xref rid=\"R20\" ref-type=\"bibr\">20</xref>–<xref rid=\"R21\" ref-type=\"bibr\">21</xref>]. The major diffraction peaks at around 22.1°, 31.6°, 38.5°, 44.8°, 45.2°, 55.7°, and 56.0° are indexed as (100), (110), (111), (002), (200), (112), and (211), respectively. These diffractions are used to identify the tetragonal BTO lattice. A distinct peak splitting around 45° corresponding to the Miller indexes (002) and (200) differentiates tetragonal BTO from cubic BTO [<xref rid=\"R20\" ref-type=\"bibr\">20</xref>]. Thus, as-prepared BTO nanoparticles are monocrystalline and exhibit a tetragonal lattice structure. Also, there is no sign of any impurity due to the repeated washing of BTO nanoparticles with dilute formic acid solution, which removes common impurities such as BaCO<sub>3</sub> [<xref rid=\"R22\" ref-type=\"bibr\">22</xref>].</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>XRD patterns of the as-prepared BTO nanoparticles, pristine PTh, and core–shell BTO-PTh nanoparticles. The diffraction peaks denoted by (#) correspond to the tetragonal BTO lattice (JCPDS No. 05-0626), while peaks denoted by (*) correspond to the orthorhombic BaSO<sub>4</sub> impurities.</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1190-g004\"/></fig><p>XRD pattern of core–shell BTO-PTh nanoparticles shows the characteristic diffractions of tetragonal BTO along with the additional peaks corresponding to certain impurities or by-products formed during the oxidative polymerization of Th. Albeit the crystallinity of core–shell BTO-PTh nanoparticles is slightly affected by the amorphousness of the PTh coating, the tetragonal perovskite structure (indicated by #) is still dominant. The additional diffraction peaks (indicated by *) are attributed to orthorhombic BaSO<sub>4</sub> [<xref rid=\"R23\" ref-type=\"bibr\">23</xref>]. It is a consequence of the leaching of Ba<sup>2+</sup> ions from BTO nanoparticles during the polymerization reaction [<xref rid=\"R24\" ref-type=\"bibr\">24</xref>], which can react with SO<sub>4</sub><sup>2−</sup> ions in the solution to form insoluble BaSO<sub>4</sub>. Nevertheless, XRD patterns of BTO and BTO-PTh nanoparticles confirm the dominant tetragonal perovskite structure of the BTO lattice.</p><p><xref ref-type=\"fig\" rid=\"F4\">Figure 4a</xref> shows a SEM image of as-prepared BTO nanoparticles. Clusters or agglomerates of nanoparticles can be seen with easily identifiable individual BTO nanoparticles. The size of BTO nanoparticles is in the range of 70–150 nm. In contrast, the core–shell BTO-PTh nanoparticles exhibit uniform surface morphology, as shown in <xref ref-type=\"fig\" rid=\"F4\">Figure 4b</xref>. The size of BTO-PTh nanoparticles is in the range of 300–500 nm. The core–shell structure of oval-shaped BTO-PTh nanoparticles is demonstrated in <xref ref-type=\"fig\" rid=\"F4\">Figure 4c</xref>,d. A thick shell of PTh (thickness: 90–170 nm) is formed around BTO nanoparticles, which may comprise more than one layer of PTh. It is assumed that PTh multilayers are held together through π–π stacking interactions between the polymeric chains, which prevent PTh from irreversible slithering out-of-place [<xref rid=\"R25\" ref-type=\"bibr\">25</xref>]. Therefore, core–shell BTO-PTh nanoparticles possess a distinct morphology.</p><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>SEM images of the as-prepared BTO nanoparticles (a) and the core–shell BTO-PTh nanoparticles (b). The core–shell structure of BTO-PTh nanoparticles is demonstrated in panels (c, d).</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1190-g005\"/></fig><p>The surface morphology of all samples is further studied using atomic force microscopy, after depositing the samples on quartz wafers. <xref ref-type=\"fig\" rid=\"F5\">Figure 5</xref> shows the 2D- and 3D-AFM images of BTO, BTO-PTh, and PTh samples along with their surface profiles. The micrographs of BTO nanoparticles show the presence of clusters on the surface. This is in agreement with the SEM image shown in <xref ref-type=\"fig\" rid=\"F4\">Figure 4a</xref>. PTh, on the other hand, exhibits an inhomogeneous surface morphology with large flakes of polymer randomly distributed on the surface. In case of core–shell BTO-PTh nanoparticles (<xref ref-type=\"fig\" rid=\"F5\">Figure 5b</xref>), the surface topography is very consistent with uniformly distributed sub-micrometer particles or agglomerate on the surface. The same is observed in the surface profile of core–shell BTO-PTh nanoparticles, which is monotonous on the height scale. Pristine PTh shows a variable surface profile because of the inconsistent presence of large polymer flakes. BTO nanoparticles also exhibit irregular surface profile, which confirms the occurrence of sub-micrometer clusters and nanoscale particles on the surface.</p><fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p>AFM images of the as-prepared BTO nanoparticles (a), BTO-PTh nanoparticles (b), and pristine PTh (c). 3D images showing the surface topography, and 2D images along with surface profiles showing the surface morphology of all samples.</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1190-g006\"/></fig><p>The permittivity or dielectric constant (ε′), loss tangent (tan δ), dielectric loss (ε″), and ac conductivity (σ<sub>ac</sub>) of the synthesized materials are measured as a function of the ac frequency at room temperature. The variation of the frequency-dependent complex dielectric permittivity is shown in <xref ref-type=\"fig\" rid=\"F6\">Figure 6a</xref>. The real part of the complex dielectric permittivity of all three samples exhibits a relatively low frequency dependence in the 100–1000 kHz range. The rate of decreasing permittivity as a function of frequency is in the order of PTh (13.8%) > BTO (7.9%) > BTO-PTh (6.3%). A lower rate of decreasing permittivity can be ascribed to good interfacial compatibility in core–shell BTO-PTh nanoparticles [<xref rid=\"R26\" ref-type=\"bibr\">26</xref>]. The permittivity values of BTO, BTO-PTh, and PTh nanoparticles at maximum frequency are 30.2, 25.2, and 5.6, respectively.</p><fig id=\"F6\" position=\"float\"><label>Figure 6</label><caption><p>Dielectric properties of the as-prepared BTO nanoparticles, pristine PTh, and BTO-PTh nanoparticles. Permittivity or dielectric constant (a), loss tangent (b), dielectric loss (c), and ac conductivity (d) are plotted as a function of the frequency.</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1190-g007\"/></fig><p>The frequency dependence of the dielectric loss tangent is shown in <xref ref-type=\"fig\" rid=\"F6\">Figure 6b</xref>. The dielectric loss tangent represents the energy loss within the dielectric medium. Contrary to the dielectric permittivity, the loss tangent exhibits considerable frequency dependence. The dielectric loss tangent is observed to decrease rapidly in the low-frequency region, while the rate of decrease slows down as the frequency rises. Such a behavior of dielectric materials is widely accepted. In the low-frequency region that corresponds to the high resistivity of grain boundaries, more energy is required for electron hopping, thus, increasing the loss [<xref rid=\"R27\" ref-type=\"bibr\">27</xref>–<xref rid=\"R28\" ref-type=\"bibr\">28</xref>]. In the high frequency region that corresponds to the higher conductivity, energy required for the hopping of electrons is less and therefore, the loss decreases [<xref rid=\"R27\" ref-type=\"bibr\">27</xref>–<xref rid=\"R28\" ref-type=\"bibr\">28</xref>].</p><p>Dielectric loss is an important part of the total core loss in a dielectric material. <xref ref-type=\"fig\" rid=\"F6\">Figure 6c</xref> shows the dielectric loss of all samples at different frequencies. The dielectric loss of BTO and BTO-PTh nanoparticles also exhibits a substantially high frequency dependence, i.e., ε″ decreases by 50–65% as the frequency increases to 1 MHz. The dielectric loss of BTO nanoparticles is reduced to half by PTh coating in BTO-PTh nanoparticles, which is attributed to conduction loss and smaller interfacial polarization due to good compatibility between the two phases [<xref rid=\"R26\" ref-type=\"bibr\">26</xref>]. These results agree well with the calculated ac conductivity of BTO and core–shell BTO-PTh nanoparticles. The ac conductivity as a function of the frequency is shown in <xref ref-type=\"fig\" rid=\"F6\">Figure 6d</xref>. It is observed that BTO-PTh nanoparticles have a lower ac conductivity than BTO, and the ac conductivity increases linearly with the frequency of the applied field. At lower frequencies, the greater resistive influence of grain boundaries results in lower ac conductivity [<xref rid=\"R28\" ref-type=\"bibr\">28</xref>–<xref rid=\"R29\" ref-type=\"bibr\">29</xref>]. It is important to note that pristine PTh inherently exhibits the lowest ac conductivity and the highest loss tangent. Therefore, core–shell BTO-PTh nanoparticles offer an excellent combination of electrical properties with high permittivity (ε′ = 25.2), very low loss tangent (tan δ = 0.04) and dielectric loss (ε″ = 0.93), and 41% reduced ac conductivity compared to the as-prepared BTO nanoparticles.</p><p><xref ref-type=\"fig\" rid=\"F7\">Figure 7</xref> shows the breakdown strength (<italic>E</italic><sub>b</sub>) measured at the room temperature and the calculated energy storage density (<italic>J</italic>) of all samples. The energy density is calculated using <xref ref-type=\"disp-formula\" rid=\"FD1\">Equation 1</xref> [<xref rid=\"R30\" ref-type=\"bibr\">30</xref>–<xref rid=\"R31\" ref-type=\"bibr\">31</xref>]:</p><disp-formula id=\"FD1\"><label>[1]</label><alternatives><mml:math id=\"M1\"><mml:mrow><mml:mi>J</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac><mml:msub><mml:mi>ε</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:msup><mml:mi>ε</mml:mi><mml:mo>′</mml:mo></mml:msup><mml:msub><mml:mi>E</mml:mi><mml:mtext>b</mml:mtext></mml:msub><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup><mml:mo>.</mml:mo></mml:mrow></mml:math><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1190-e001.jpg\" position=\"anchor\"/></alternatives></disp-formula><p>Pristine BTO and PTh nanoparticles exhibit a breakdown strength of 47.0 ± 2.0 and 204.3 ± 15.2 kV/mm, respectively. The PTh coating on BTO nanoparticles results in a 3-fold increase in the breakdown strength (i.e., 144.2 ± 4.9 kV/mm) of BTO-PTh nanoparticles compared to pristine BTO. In turn, the energy storage density of BTO-PTh nanoparticles is calculated as 2.48 J/cm<sup>3</sup>, which is extremely high compared to pristine BTO (0.32 J/cm<sup>3</sup>) and PTh (1.20 J/cm<sup>3</sup>) nanoparticles. It has been investigated that the breakdown strength of BTO-polymer composites is considerably reduced after increasing the BTO content to 30–40 wt % because of the free-charge accumulation at the interface of BTO and polymer [<xref rid=\"R10\" ref-type=\"bibr\">10</xref>]. We believe that core–shell structure of BTO-PTh nanoparticles and good interfacial compatibility between the two phases prevent the free-charge accumulation at the interface and, therefore, improve the breakdown strength.</p><fig id=\"F7\" position=\"float\"><label>Figure 7</label><caption><p>A plot of energy storage density as a function of the electric field strength (a), and the calculated maximum energy storage density and electrical breakdown strength (b) of the different dielectric materials.</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1190-g008\"/></fig><p>Furthermore, in situ oxidative polymerization of PTh on BTO surfaces allows for the inclusion of 90 wt % BTO, which results in a high dielectric constant. This means that the tremendous increase in the energy storage density of core–shell BTO-PTh nanoparticles is due to the combined effect of high dielectric constant (ε′) and improved breakdown strength (<italic>E</italic><sub>b</sub>). These results are noteworthy considering the previous examples of BTO-polymer composite dielectric materials. <xref rid=\"T1\" ref-type=\"table\">Table 1</xref> offers a comparison of dielectric properties, breakdown strength, and energy storage density between other BTO-polymer composite-based dielectric materials and those described in this work. It shows that core–shell BTO-PTh nanoparticles can be used alone or as reinforcement in various polymer matrices for prospective microelectronic and energy storage applications.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>A comparison of the electrical properties of core–shell BTO-PTh nanoparticles with other BTO-polymer composite dielectric materials reported in literature.</p></caption><table frame=\"hsides\" rules=\"groups\"><tr><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">material</td><td align=\"center\" colspan=\"3\" valign=\"top\" rowspan=\"1\">dielectric properties</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">breakdown strength<break/>(kV/mm)</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">energy storage density<break/>(J/cm<sup>3</sup>)</td><td align=\"left\" rowspan=\"3\" valign=\"top\" colspan=\"1\">ref.</td></tr><tr><td align=\"left\" colspan=\"3\" valign=\"top\" rowspan=\"1\"><hr/></td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">frequency<break/>(Hz)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">permittivity<break/>(ε′)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">loss tangent<break/>(tan δ)</td></tr><tr><td align=\"left\" colspan=\"7\" valign=\"middle\" rowspan=\"1\"><hr/></td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell BaTiO<sub>3</sub>-polythiophene nanoparticles [BTO/PTh = 9:1 w/w]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>6</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">25.23</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.04</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">144</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.48</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">this work</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell BaTiO<sub>3</sub>@polyaniline/polyarylene ether nitrile nanocomposites [40 wt % BTO@PANI]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>3</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.0</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.025</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">169.8</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.8</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"R10\" ref-type=\"bibr\">10</xref>]</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell BaTiO<sub>3</sub>@SiO<sub>2</sub>/polyamic acid [5 vol % BTO@SiO<sub>2</sub>]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>6</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.1</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.009</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">345</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"R32\" ref-type=\"bibr\">32</xref>]</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell BaTiO<sub>3</sub>@Al<sub>2</sub>O<sub>3</sub>/polyvinylidene fluoride [10 vol % BTO@Al<sub>2</sub>O<sub>3</sub>]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>3</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16.27</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">250</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.32</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"R33\" ref-type=\"bibr\">33</xref>]</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell polylactic acid@polydopamine@BaTiO<sub>3</sub> [20 vol % BTO]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>6</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.01</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">95</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.52</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"R34\" ref-type=\"bibr\">34</xref>]</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell Ag@polydopamine@BaTiO<sub>3</sub>/poly(vinylidene fluoride-<italic>co</italic>-hexafluoropropylene) composites [20 wt % BTO; 2 wt % Ag]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>6</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">≈18</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.16</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">248</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.15</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"R35\" ref-type=\"bibr\">35</xref>]</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell BaTiO<sub>3</sub>@polyamide@poly(methyl methacrylate) [56.7 wt % BTO]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>6</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">39.4</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"R36\" ref-type=\"bibr\">36</xref>]</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell polystyrene/BaTiO<sub>3</sub> nanocomposites [47.7 vol % BTO]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>6</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">24.27</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.013</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.4</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.027</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[<xref rid=\"R37\" ref-type=\"bibr\">37</xref>]</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell BaTiO<sub>3</sub>-poly(styrene-<italic>co</italic>-vinylbenzyl chloride)/polystyrene composite [75 wt % BTO@polymer]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>4</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22.3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.079</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">95</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.9</td><td align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\">[<xref rid=\"R38\" ref-type=\"bibr\">38</xref>]</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">core–shell BaTiO<sub>3</sub>-polystyrene-block-poly(styrene-<italic>co</italic>-vinylbenzyl chloride)/polystyrene composite [75 wt % BTO@polymer]</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>4</sup></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">44.7</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.060</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">222</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.7</td></tr></table></table-wrap></sec><sec><title>Conclusion</title><p>A simple Cu(II)-catalyzed chemical oxidative polymerization reaction in the presence of BaTiO<sub>3</sub> nanoparticles is reported for the synthesis of polythiophene-encapsulated BaTiO<sub>3</sub> nanoparticles as a novel dielectric material. The procedure allows the formation of BTO-rich core–shell-type BTO-PTh hybrid nanoparticles with a BTO/PTh mass ratio of 9:1. We achieved excellent dielectric properties with high permittivity, low dielectric loss, and excellent energy storage density. By PTh encapsulation, it was possible to simultaneously have a high dielectric constant and excellent breakdown strength of BTO nanoparticles, thereby increasing the energy storage density. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"letter\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Beilstein J Org Chem</journal-id><journal-id journal-id-type=\"iso-abbrev\">Beilstein J Org Chem</journal-id><journal-title-group><journal-title>Beilstein Journal of Organic Chemistry</journal-title></journal-title-group><issn pub-type=\"epub\">1860-5397</issn><publisher><publisher-name>Beilstein-Institut</publisher-name><publisher-loc>Trakehner Str. 7-9, 60487 Frankfurt am Main, Germany</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32831951</article-id><article-id pub-id-type=\"pmc\">PMC7431766</article-id><article-id pub-id-type=\"doi\">10.3762/bjoc.16.161</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Letter</subject></subj-group><subj-group subj-group-type=\"topic\"><subject>Chemistry</subject><subj-group subj-group-type=\"topic\"><subject>Organic Chemistry</subject></subj-group></subj-group></article-categories><title-group><article-title>Synthesis of 3(2)-phosphonylated thiazolo[3,2-<italic>a</italic>]oxopyrimidines</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Kaskevich</surname><given-names>Ksenia I</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-3881-3616</contrib-id><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Babushkina</surname><given-names>Anastasia A</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-6454-6465</contrib-id><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gurzhiy</surname><given-names>Vladislav V</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-2730-6264</contrib-id><xref ref-type=\"aff\" rid=\"A2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Egorov</surname><given-names>Dmitrij M</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-3744-9306</contrib-id><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Svintsitskaya</surname><given-names>Nataly I</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-3715-767X</contrib-id><email>nsvincickaya@mail.ru</email><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Dogadina</surname><given-names>Albina V</given-names></name><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Aubé</surname><given-names>Jeffrey</given-names></name><role>Associate Editor</role></contrib></contrib-group><aff id=\"A1\"><label>1</label>Department of Organic Chemistry, St. Petersburg State Institute of Technology, Moskovskii pr. 26, St. Petersburg, 190013, Russia</aff><aff id=\"A2\"><label>2</label>Department of Crystallography, Institute of Earth Sciences, St. Petersburg State University, Universitetskaya emb. 7/9, St. Petersburg, 199034 Russia</aff><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>10</day><month>8</month><year>2020</year></pub-date><volume>16</volume><fpage>1947</fpage><lpage>1954</lpage><ext-link ext-link-type=\"doi\" xlink:href=\"10.3762/bjoc.16.161\">10.3762/bjoc.16.161</ext-link><history><date date-type=\"received\"><day>17</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>30</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, Kaskevich et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Kaskevich et al.</copyright-holder><ali:free_to_read xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\"/><license license-type=\"Beilstein\"><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">https://creativecommons.org/licenses/by/4.0</ali:license_ref><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">https://www.beilstein-journals.org/bjoc/terms</ali:license_ref><license-p>This is an Open Access article under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0\">https://creativecommons.org/licenses/by/4.0</ext-link>). Please note that the reuse, redistribution and reproduction in particular requires that the authors and source are credited.</license-p><license-p>The license is subject to the Beilstein Journal of Organic Chemistry terms and conditions: (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.beilstein-journals.org/bjoc/terms\">https://www.beilstein-journals.org/bjoc/terms</ext-link>)</license-p></license></permissions><abstract><p>A series of 3(2)-phosphonylated thiazolo[3,2-<italic>a</italic>]oxopyrimidines was synthesized for the first time by the reactions of chloroethynylphosphonates with unsubstituted and 5(6)-substituted 2-thiouracils. The reaction of chloroethynylphosphonates with 6-substituted 2-thiouracils bearing electron-donor groups (CH<sub>3</sub>, Ph) proceeded with high regioselectivity involving the cyclization through the N<sup>3</sup>-nitrogen atom to form new 3-phosphonylated thiazolo[3,2-<italic>a</italic>]-5-oxopyrimidines with good yield. In the case of unsubstituted and 5-methyl-2-thiouracils, cyclization occurred predominantly through the N<sup>1</sup> atom and partially via the N<sup>3</sup>-nitrogen atom to form a mixture of the corresponding thiazolo[3,2-<italic>a</italic>]-7- and 5-oxopyrimidines. A dramatic change in the reaction regioselectivity was observed in the case of 6-trifluoromethyl-2-thiouracil that afforded 2- and 3-phosphonylated 5-oxothiazolopyrimidines in a 1:1 ratio.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>chloroethynylphosphonate</kwd><kwd>heterocyclization</kwd><kwd>phosphonylated thiazolopyrimidines</kwd><kwd>phosphonylation</kwd><kwd>thiazolopyrimidine</kwd><kwd>2-thiouracil</kwd></kwd-group><funding-group><funding-statement>This work was financially supported by the Russian Foundation for Basic Research (project no. 18-33-00430).</funding-statement></funding-group></article-meta></front><body><sec><title>Introduction</title><p>Thiazolopyrimidines, whose molecules includes both thiazole and pyrimidine rings, have a structural analogy with the antipsychotic drugs ritanserin and setoperone (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>) [<xref rid=\"R1\" ref-type=\"bibr\">1</xref>–<xref rid=\"R3\" ref-type=\"bibr\">3</xref>]. To date, a wide spectrum of biological activity of thiazolopyrimidines has been determined: anticancer [<xref rid=\"R4\" ref-type=\"bibr\">4</xref>–<xref rid=\"R5\" ref-type=\"bibr\">5</xref>], antimicrobial [<xref rid=\"R6\" ref-type=\"bibr\">6</xref>–<xref rid=\"R7\" ref-type=\"bibr\">7</xref>], anti-inflammatory [<xref rid=\"R8\" ref-type=\"bibr\">8</xref>–<xref rid=\"R9\" ref-type=\"bibr\">9</xref>], and antiviral [<xref rid=\"R10\" ref-type=\"bibr\">10</xref>–<xref rid=\"R11\" ref-type=\"bibr\">11</xref>].</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Structure of ritanserin and setoperone drugs.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g002\"/></fig><p>The best known methods for the preparation of thiazolopyrimidines are based on condensation reactions. The most commonly used synthesis is the three-component condensation of 2-aminothiazoline, aromatic aldehyde, and ethyl cyanoacetate, which leads to the formation of 5- and 7-oxothiazolopyrimidine-6-carbonitriles (<xref ref-type=\"fig\" rid=\"C1\">Scheme 1</xref>) [<xref rid=\"R12\" ref-type=\"bibr\">12</xref>–<xref rid=\"R13\" ref-type=\"bibr\">13</xref>].</p><fig id=\"C1\" position=\"float\"><label>Scheme 1</label><caption><p>One-pot synthesis of 5(7)-oxothiazolopyrimidine-6-carbonitriles.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g003\"/></fig><p>The synthesis of thiazolopyrimidines through the reaction of 2-aminothiazoles with 1,3-ketoesters in the presence of acids, bases or condensing agents (<xref ref-type=\"fig\" rid=\"C2\">Scheme 2</xref>) has been studied fairly well [<xref rid=\"R6\" ref-type=\"bibr\">6</xref>,<xref rid=\"R8\" ref-type=\"bibr\">8</xref>,<xref rid=\"R14\" ref-type=\"bibr\">14</xref>–<xref rid=\"R16\" ref-type=\"bibr\">16</xref>].</p><fig id=\"C2\" position=\"float\"><label>Scheme 2</label><caption><p>Synthesis of thiazolopyrimidine-5-ones through the reaction of 2-aminothiazoles with ethyl acetoacetate.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g004\"/></fig><p>The most accessible approach to the synthesis of 5<italic>H</italic>-thiazolo[3,2-<italic>a</italic>]pyrimidine-5(7)-ones is the reaction of 2-thiouracil derivatives with α-halo ketones and α-halo acids, involving successive alkylation and condensation steps (<xref ref-type=\"fig\" rid=\"C3\">Scheme 3</xref>) [<xref rid=\"R17\" ref-type=\"bibr\">17</xref>–<xref rid=\"R21\" ref-type=\"bibr\">21</xref>].</p><fig id=\"C3\" position=\"float\"><label>Scheme 3</label><caption><p>Synthesis of 2-(benzo[<italic>d</italic>]thiazol-2-yl)-2-(7-R-5-oxo-5<italic>H</italic>-thiazolo[3,2-<italic>a</italic>]pyrimidin-3-yl)acetonitriles.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g005\"/></fig><p>A convenient one-step synthesis of thiazolopyrimidine-5-ones by reacting 6-methyl-2-thiouracils with bromoethynylketones has been reported by Shishkin and co-workers (<xref ref-type=\"fig\" rid=\"C4\">Scheme 4</xref>) [<xref rid=\"R20\" ref-type=\"bibr\">20</xref>]. The authors for the first time proved the structure of the 5-oxo isomer by single crystal X-ray diffraction analysis.</p><fig id=\"C4\" position=\"float\"><label>Scheme 4</label><caption><p>Synthesis of 3-acyl-7-methyl-5<italic>H</italic>-thiazolo[3,2-<italic>a</italic>]pyrimidin-5-ones.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g006\"/></fig><p>The Pd-catalyzed Sonogashira coupling reaction between 2-thiouracil and propargyl bromide yielded 5<italic>H</italic>-thiazolo[3,2-<italic>a</italic>]pyrimidine-5-one (<xref ref-type=\"fig\" rid=\"C5\">Scheme 5</xref>) [<xref rid=\"R20\" ref-type=\"bibr\">20</xref>–<xref rid=\"R24\" ref-type=\"bibr\">24</xref>].</p><fig id=\"C5\" position=\"float\"><label>Scheme 5</label><caption><p>Sonogashira coupling reaction of 6-amino-2-thiouracil with propargyl bromide.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g007\"/></fig><p>Despite the wide variety of thiazolopyrimidines reported to date, phosphonylated analogues of compounds of this series are unknown. Of special interest is the design of molecules containing practically significant heteroaromatic rings and a biologically active and hydrolysis-resistant phosphonate group, as it has been reported that the combination of several pharmacophore fragments in one molecule can lead to a synergistic increase in biological activity or an additional variety of the latter [<xref rid=\"R22\" ref-type=\"bibr\">22</xref>–<xref rid=\"R28\" ref-type=\"bibr\">28</xref>].</p><p>Herein, we report the synthesis of a new series of phosphonylated thiazolopyrimidines. In our studies, chloroethynylphosphonate was used as the phosphonylating agent, which allowed the formation of a thiazole ring with simultaneous phosphonylation of the latter. As the second reaction component, available 2-thiouracils were chosen as the most studied objects used for creating thiazolopyrimidine systems. The main objective of the study was to determine the regioselectivity of the reaction. Literature data mainly report the formation of 5-oxopyrimidines by cyclization through the N<sup>3</sup> atom of the starting 2-thiouracil [<xref rid=\"R14\" ref-type=\"bibr\">14</xref>–<xref rid=\"R24\" ref-type=\"bibr\">24</xref>]. The formation of 7-oxopyrimidines by cyclization through the N<sup>1</sup> atom has been noted only in a few reports [<xref rid=\"R29\" ref-type=\"bibr\">29</xref>–<xref rid=\"R32\" ref-type=\"bibr\">32</xref>]. However, reliable data for the identification of the 5- and 7-oxo isomers are not available to date and the determination of the structures of 5- and 7-oxo isomers were mainly based on <sup>1</sup>H NMR spectroscopy. The most comprehensive and convincing evidence for the formation of a 5-oxothiazolopyrimidine was provided by Shishkin and co-workers [<xref rid=\"R20\" ref-type=\"bibr\">20</xref>], who performed single crystal X-ray diffraction analysis along with <sup>1</sup>H and <sup>13</sup>C NMR spectral studies. Unfortunately, the majority of reports on the synthesis of thiazolopyrimidines relied on <sup>1</sup>H NMR data to prove the structure of the obtained compounds [<xref rid=\"R18\" ref-type=\"bibr\">18</xref>,<xref rid=\"R33\" ref-type=\"bibr\">33</xref>–<xref rid=\"R35\" ref-type=\"bibr\">35</xref>]. There are no systematic data on <sup>13</sup>C NMR spectroscopy of thiazolopyrimidines, which could be used as an additional approach to estimate the regioselectivity of the reaction. A single example of the use of <sup>13</sup>C NMR spectroscopy for unambiguous establishing the structure of thiazolo[3,2-<italic>a</italic>]pyrimidines obtained was given by Iranian researchers [<xref rid=\"R36\" ref-type=\"bibr\">36</xref>], but only for the 5-oxo isomers. In our opinion, the presence of a phosphorus fragment in a thiazolo[3,2-<italic>a</italic>]pyrimidine molecule significantly facilitates the determination of the structure by means of <sup>13</sup>C, <sup>1</sup>H, and <sup>31</sup>P NMR spectroscopy methods.</p></sec><sec><title>Results and Discussion</title><p>Aiming to synthesize a new series of phosphonylated thiazolopyrimidines, we performed reactions of unsubstituted and substituted 2-thiouracils <bold>1a</bold>–<bold>e</bold> with chloroethynylphosphonates <bold>2a</bold>–<bold>c</bold>. We found that the reaction with 6-substituted 2-thiouracils bearing either methyl or phenyl groups occurred regioselectively with a N<sup>3</sup> atom ring-closure to afford the 3-phosphonylated thiazolo[3,2-<italic>a</italic>]-5-oxopyrimidines <bold>3a</bold>–<bold>f</bold> in good yields (<xref ref-type=\"fig\" rid=\"C6\">Scheme 6</xref>). Likely, in this case the attack by the N<sup>3</sup> atom was more favorable than by the N<sup>1</sup> atom due to the steric effect of the substituent in position 6. The reactions proceeded under mild conditions within 3–5 hours. Anhydrous K<sub>2</sub>CO<sub>3</sub> was used as a base to neutralize HCl formed during the reaction. The need for the use of anhydrous solvents and reagents is caused by the possibility of the formation of byproducts, if any, due to hydrolysis as we have noted earlier in the case of the reactions of chloroethynylphosphonates with nitrogen-containing nucleophiles [<xref rid=\"R37\" ref-type=\"bibr\">37</xref>–<xref rid=\"R39\" ref-type=\"bibr\">39</xref>].</p><fig id=\"C6\" position=\"float\"><label>Scheme 6</label><caption><p>Reactions of 6-substituted 2-thiouracils <bold>1a</bold>,<bold>b</bold> with chloroethynylphosphonates <bold>2a</bold>–<bold>c</bold>.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g008\"/></fig><p>The assignment of the reaction product to the 5-oxo isomer was made based on <sup>13</sup>C NMR spectral analysis: the carbon atoms of the CH=СR fragment (R=CH<sub>3</sub>, Ph) are represented by a strong signal at δ<sub>C</sub> 101–105 ppm (СН=) and a weak signal at δ<sub>C</sub> 157–158 ppm (=CR). These data coincide with those for 3-phenylthiazolo[3,2-<italic>a</italic>]-5-oxopyrimidine [<xref rid=\"R40\" ref-type=\"bibr\">40</xref>]. In addition, the structures of the phosphonylated thiazolopyrimidines <bold>3a</bold> and <bold>3d</bold> were unambiguously confirmed by single crystal X-ray diffraction data.</p><p>The presence of the CH<sub>3</sub> group at the position 5 of the thiouracil ring changes the reaction regioselectivity, as the main direction is cyclization through the N<sup>1</sup> nitrogen atom with the formation of 3-phosphonylated thiazolo[3,2-<italic>a</italic>]-7-oxopyrimidines <bold>4a</bold>–<bold>c</bold> and 5-oxo regioisomers <bold>5a</bold>–<bold>c</bold> in a ≈1:0.1–0.3 ratio with yields of 87–91% (<xref ref-type=\"fig\" rid=\"C7\">Scheme 7</xref>).</p><fig id=\"C7\" position=\"float\"><label>Scheme 7</label><caption><p>Reaction of 5-methyl-2-thiouracil (<bold>1c</bold>) with chloroethynylphosphonates <bold>2a</bold>–<bold>c</bold>.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g009\"/></fig><p>The structure of thiazolopyrimidines <bold>4a</bold>–<bold>c</bold> and <bold>5a</bold>–<bold>с</bold> is difficult to establish from the <sup>1</sup>H and <sup>13</sup>C NMR spectral data. The signals of the vinyl proton of the uracil moiety are represented by quartets (<sup>4</sup><italic>J</italic><sub>HH</sub> = 1.4 Hz) in the δ<sub>H</sub> 8.1 ppm region (quartet at δ<sub>H</sub> 6 ppm for 6-methyl-5-oxo isomer). In the <sup>13</sup>C NMR spectra, the signals of ethylene carbons of the uracil ring are presented at δ<sub>C</sub> 131 and 122 ppm (δ<sub>C</sub> 101 and 158 ppm for 5-oxopyrimidines <bold>5a</bold>–<bold>c</bold>). These data are in accordance with values of the chemical shifts of carbon atoms and protons of the pyrimidine ring in 3-methylthiazolo[3,2-<italic>a</italic>]pyrimidine-7-one [<xref rid=\"R29\" ref-type=\"bibr\">29</xref>], 3-phenylthiazolo[3,2-<italic>a</italic>]pyrimidine-7-one [<xref rid=\"R40\" ref-type=\"bibr\">40</xref>], and 5-phenylthiazolo[3,2-<italic>a</italic>]pyrimidine-7-one [<xref rid=\"R16\" ref-type=\"bibr\">16</xref>].</p><p>A similar reaction outcome was observed when chloroethynylphosphonates <bold>2a</bold>–<bold>c</bold> were reacted with unsubstituted 2-thiouracil (<bold>1d</bold>). The cyclization reaction also proceeded predominantly through the N<sup>1</sup> atom to form the corresponding 3-phosphonylated thiazolo[3,2-<italic>a</italic>]-7-oxopyrimidines <bold>6a</bold>–<bold>c</bold> together with a small amount of 5-oxo isomers <bold>7a</bold>–<bold>c</bold> (<xref ref-type=\"fig\" rid=\"C8\">Scheme 8</xref>). In addition, it should be noted that the regioselectivity of the reaction was higher when using diisopropyl 2-chloroetynylphosphonate (<bold>2c</bold>).</p><fig id=\"C8\" position=\"float\"><label>Scheme 8</label><caption><p>Reaction of 2-thiouracil (<bold>1d</bold>) with chloroethynylphosphonates <bold>2a</bold>–<bold>c</bold>.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g010\"/></fig><p>The assignment of the major reaction product to the 7-oxo isomers was made by help of <sup>13</sup>C NMR spectroscopy. In the <sup>13</sup>C NMR spectra of the thiazolopyrimidines <bold>6a</bold>–<bold>c</bold>, the O=C–<italic>CH=CH</italic> fragment is observed by signals of equal intensity at δ<sub>C</sub> 112–113 and 135–136 ppm. However, the unambiguous proof of the structure of thiazolopyrimidine-7-one <bold>6b</bold> was obtained by single crystal X-ray diffraction analysis.</p><p>It is important to note, that the reaction of dimethyl 2-chloroethynylphosphonate (<bold>2a</bold>) with 2-thiouracil had some features. The reaction proceeded with the formation of a mixture of 7-oxo and 5-oxo isomers in a ≈1: 0.1–0.3 ratio. However, a decrease in the signal of the 7-oxo isomer was observed in the <sup>31</sup>P NMR spectrum upon standing at room temperature for 24 hours. The analysis of the formed precipitate identified product <bold>6aa</bold>, representing the monodealkylation product of the dimethylphosphonate group. A similar phenomenon has been reported earlier [<xref rid=\"R41\" ref-type=\"bibr\">41</xref>].</p><p>In the case of 6-trifluoromethyl-2-thiouracil (<bold>1e</bold>), a dramatic change in the reaction regioselectivity was observed. The cyclization took place with the formation of a mixture of the corresponding 2- and 3-phosphonylated thiazolopyrimidine-5-ones <bold>8a</bold>–<bold>c</bold> and <bold>9a</bold>–<bold>c</bold> (<xref ref-type=\"fig\" rid=\"C9\">Scheme 9</xref>). As in the case of the 6-substituted 2-thiouracils <bold>1a</bold> and <bold>1b</bold>, the presence of a trifluoromethyl group in position 6 favored an attack by the N<sup>3</sup>-nitrogen atom, resulting in the formation of the 5-oxo isomer. The formation of the 2-phosphonylated thiazolopyrimidine-5-one could be explained by a primary attack of the electrophilic carbon atom bonded to the chlorine atom by the N<sup>3</sup> nitrogen atom of the uracil fragment followed by cyclization.</p><fig id=\"C9\" position=\"float\"><label>Scheme 9</label><caption><p>Reaction of 6-trifluoromethyl-2-thiouracil (<bold>1e</bold>) with chloroethynylphosphonates <bold>2a</bold>–<bold>c</bold>.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g011\"/></fig><p>The mixture of phosphonylated 5-oxothiazolopyrimidines <bold>8c</bold> and <bold>9c</bold> was separated by column chromatography and the <sup>1</sup>Н, <sup>13</sup>С, and <sup>31</sup>Р NMR spectral data of the individual thiazolopyrimidine isomers did not allow convincing assignment. In the <sup>1</sup>H NMR spectra, the proton of the thiazole ring of both isomers is represented by a doublet signal in the low field region at δ<sub>H</sub> 7.91 (<sup>3</sup><italic>J</italic><sub>HP</sub> = 3.2 Hz) and 8.41 ppm (<sup>3</sup><italic>J</italic><sub>HP</sub> = 7.5 Hz), whereas the corresponding proton of the uracil ring resonated as a singlet at δ<sub>H</sub> 6.61 and 6.71 ppm, respectively. Note that in the case of the reported 3-(4-bromophenyl)-7-trifluoromethyl-5<italic>H</italic>-[1,3]thiazolo[3,2-<italic>a</italic>]pyrimidine-5-one [<xref rid=\"R18\" ref-type=\"bibr\">18</xref>] the signal of the uracil proton H<sup>6</sup> appeared at δ<sub>H</sub> 6.67 ppm.</p><p>The <sup>13</sup>C NMR spectra of the isomers <bold>8c</bold> and <bold>9c</bold> differed only in the values of the carbon signals at the phosphorus atom, i.e., doublets at 119.78 (<sup>1</sup><italic>J</italic><sub>HP</sub> = 209.5 Hz) and 130.24 ppm (<sup>1</sup><italic>J</italic><sub>HP</sub> = 217.5 Hz). The remaining signals were completely identical. Finally, the X-ray diffraction analysis of the 2-phosphonylated thiazolopyrimidine <bold>9c</bold> allowed us to uniquely determine its structure.</p><p>In our opinion, the unusual formation of the 2-phosphonylated thiazolopyrimidine can be explained by the electron-withdrawing effect of the trifluoromethyl group in the starting 2-thiouracil. In contrast to 6-methyl- or 6-phenyl-2-thiouracil, where the nucleophilicity is localized on the sulfur atom, the presence of the electron-withdrawing CF<sub>3</sub> group in 6-trifluoromethyl-2-thiouracil (<bold>1e</bold>) enhances the acidity of the N<sup>3</sup>H hydrogen by direct conjugation to the carbonyl moiety. As a result, 2-thiouracil <bold>1e</bold> acts as an ambident nucleophile. Thus, the attack of the carbon atom attached to the chlorine by the N<sup>3</sup> nitrogen atom is accompanied by the elimination of hydrogen chloride (<xref ref-type=\"fig\" rid=\"C10\">Scheme 10</xref>). A further 5-<italic>endo-dig</italic>-type cyclization results in the formation of the 2-phosphonylated 5-oxopyrimidines <bold>9a</bold>–<bold>c</bold>. The formation of the 3-phosphonylated 5-oxopyrimidines <bold>8a</bold>–<bold>c</bold> is due to the implementation of the usual favorable direction with the attack of chloroethynylphosphonate by the sulfur atom [<xref rid=\"R33\" ref-type=\"bibr\">33</xref>,<xref rid=\"R42\" ref-type=\"bibr\">42</xref>–<xref rid=\"R46\" ref-type=\"bibr\">46</xref>].</p><fig id=\"C10\" position=\"float\"><label>Scheme 10</label><caption><p>A plausible mechanism of the reaction between 6-trifluoromethyl-2-thiouracil (<bold>1e</bold>) and chloroethynylphosphonates.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1947-g012\"/></fig></sec><sec><title>Conclusion</title><p>In conclusion, a series of phosphonylated thiazolo[3,2-<italic>a</italic>]oxopyrimidines was first synthesized by reacting unsubstituted and substituted 2-thiouracils with chloroethynylphosphonates. The main regularities of this reaction were revealed. In the case of 6-substituted 2-thiouracil the primary attack by the most favorable nucleophilic site C=S takes place with further cyclization through the N<sup>3</sup> atom of 2-thiouracil to form 5-oxopyrimidines. When using unsubstituted and 5-substituted 2-thiouracils, cyclization occurs predominantly through the N<sup>1</sup> atom of the uracil ring, leading to the formation of 7-oxopyrimidines.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting Information</title><supplementary-material content-type=\"local-data\" id=\"SD1\"><label>File 1</label><caption><p>General experimental procedure, characterization data, and copies of NMR spectra.</p></caption><media mime-subtype=\"pdf\" mimetype=\"application\" xlink:href=\"Beilstein_J_Org_Chem-16-1947-s001.pdf\" xlink:type=\"simple\" id=\"d39e978\" position=\"anchor\"/></supplementary-material></sec></body><back><ref-list><ref id=\"R1\"><label>1</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Awad</surname><given-names>S M</given-names></name><name><surname>Youns</surname><given-names>M M</given-names></name><name><surname>Ahmed</surname><given-names>N 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"letter\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Beilstein J Org Chem</journal-id><journal-id journal-id-type=\"iso-abbrev\">Beilstein J Org Chem</journal-id><journal-title-group><journal-title>Beilstein Journal of Organic Chemistry</journal-title></journal-title-group><issn pub-type=\"epub\">1860-5397</issn><publisher><publisher-name>Beilstein-Institut</publisher-name><publisher-loc>Trakehner Str. 7-9, 60487 Frankfurt am Main, Germany</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32831952</article-id><article-id pub-id-type=\"pmc\">PMC7431767</article-id><article-id pub-id-type=\"doi\">10.3762/bjoc.16.162</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Letter</subject></subj-group><subj-group subj-group-type=\"topic\"><subject>Chemistry</subject><subj-group subj-group-type=\"topic\"><subject>Organic Chemistry</subject></subj-group></subj-group></article-categories><title-group><article-title>Synthesis of monophosphorylated lipid A precursors using 2-naphthylmethyl ether as a protecting group</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Xue</surname><given-names>Jundi</given-names></name><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Han</surname><given-names>Ziyi</given-names></name><xref ref-type=\"aff\" rid=\"A2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Gen</given-names></name><xref ref-type=\"aff\" rid=\"A2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Emmanuel</surname><given-names>Khalisha A</given-names></name><xref ref-type=\"aff\" rid=\"A3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>McManus</surname><given-names>Cynthia L</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-1774-2807</contrib-id><xref ref-type=\"aff\" rid=\"A3\">3</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Sui</surname><given-names>Qiang</given-names></name><email>Chem_sq@163.com</email><xref ref-type=\"aff\" rid=\"A1\">1</xref><xref ref-type=\"aff\" rid=\"A2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Ge</surname><given-names>Dongmian</given-names></name><email>gedm001@sina.com</email><xref ref-type=\"aff\" rid=\"A4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gao</surname><given-names>Qi</given-names></name><xref ref-type=\"aff\" rid=\"A2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Cai</surname><given-names>Li</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-6098-1168</contrib-id><email>caili@mailbox.sc.edu</email><xref ref-type=\"aff\" rid=\"A3\">3</xref></contrib></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Bräse</surname><given-names>Stefan</given-names></name><role>Associate Editor</role></contrib></contrib-group><aff id=\"A1\"><label>1</label>Shanghai University of Engineering Science, 333 Long Teng Road, Shanghai 201620, China</aff><aff id=\"A2\"><label>2</label>China State Institute of Pharmaceutical Industry, 285 Gebaini Rd, Shanghai 201203, China</aff><aff id=\"A3\"><label>3</label>Department of Chemistry, University of South Carolina Lancaster, 476 Hubbard Drive, Lancaster, South Carolina 29720, USA,</aff><aff id=\"A4\"><label>4</label>Suzhou Jingye Medicine & Chemical Co., Ltd, 88 Sanlian Street, Suzhou, Jiangsu Province, 215129, China</aff><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>10</day><month>8</month><year>2020</year></pub-date><volume>16</volume><fpage>1955</fpage><lpage>1962</lpage><ext-link ext-link-type=\"doi\" xlink:href=\"10.3762/bjoc.16.162\">10.3762/bjoc.16.162</ext-link><history><date date-type=\"received\"><day>2</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, Xue et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Xue et al.</copyright-holder><ali:free_to_read xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\"/><license license-type=\"Beilstein\"><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">https://creativecommons.org/licenses/by/4.0</ali:license_ref><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">https://www.beilstein-journals.org/bjoc/terms</ali:license_ref><license-p>This is an Open Access article under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0\">https://creativecommons.org/licenses/by/4.0</ext-link>). Please note that the reuse, redistribution and reproduction in particular requires that the authors and source are credited.</license-p><license-p>The license is subject to the Beilstein Journal of Organic Chemistry terms and conditions: (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.beilstein-journals.org/bjoc/terms\">https://www.beilstein-journals.org/bjoc/terms</ext-link>)</license-p></license></permissions><abstract><p>Lipid A, the hydrophobic domain of lipopolysaccharide (LPS), is a strong immunostimulator and therefore a valuable target for the development of novel immunomodulators. Various lipid A derivatives have been chemically synthesized in order to reduce toxicity while retaining the immunostimulatory activity. In this work, we describe a novel approach to the frequently problematic synthesis of monophosphorylated mono- and disaccharide lipid X using a combination of established chemistry and a novel 2-naphthylmethyl ether (Nap) protecting group for “permanent” protection of hydroxy groups. Of particular note is the fact that the key Nap protecting group is able to remain in the molecule until the final global deprotection step. Our synthetic strategy is not only efficient in regards to the yield of the various chemical transformations, but also robust in regards to the potential application of this route to the production of other lipid A analogs.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>lipid A</kwd><kwd>lipid X</kwd><kwd>lipopolysaccharide</kwd><kwd>2-naphthylmethyl ether</kwd><kwd>synthesis</kwd></kwd-group><funding-group><funding-statement>This work was partially supported by the National Natural Science Foundation of China (21602134) and the Shanghai Science and Technology Innovation – International Collaboration Fund of Shanghai Science and Technology Committee (18430721800). LC would like to acknowledge the partial financial support through an RISE (Research Initiative for Summer Engagement) grant from the Office of the Vice President for Research at the University of South Carolina (2020).</funding-statement></funding-group></article-meta></front><body><sec><title>Introduction</title><p>Bacterial cell surfaces are decorated with various types of glycoconjugates (in the form of glycoproteins and glycolipids) that are known to participate in many biological processes, especially in the interactions between bacteria and the environment [<xref rid=\"R1\" ref-type=\"bibr\">1</xref>]. For example, lipopolysaccharide (LPS) comprises the Gram-negative bacterial cell wall and is crucial in bacterial pathogenicity [<xref rid=\"R2\" ref-type=\"bibr\">2</xref>]. LPS is a complex molecule that is composed of three structural regions: lipid A (endotoxin), a non-repeating core oligosaccharide, and <italic>O</italic>-antigen [<xref rid=\"R2\" ref-type=\"bibr\">2</xref>]. While <italic>O</italic>-antigen and the core oligosaccharide are exposed to the external environment, lipid A, the hydrophobic domain of LPS, is embedded in the cell wall. The lipid A substructure is relatively conserved that consists of a β-1,6-linked diglucosamine with 1,4′-di-<italic>O</italic>-phosphorylation and 2,2′-<italic>N</italic>- and 3,3′-<italic>O</italic>-acylation (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). The associated fatty acid acyl chains may be conserved within a species but can vary significantly in terms of the chain number and length for lipid A of different bacterial origins [<xref rid=\"R3\" ref-type=\"bibr\">3</xref>–<xref rid=\"R4\" ref-type=\"bibr\">4</xref>]. Lipid A represents a particularly important subject to research given the continued rise of problematic bacterial infections. Notably, the LPS pathogenicity is almost entirely due to lipid A because it leads to immunostimulatory effects when LPS dissociates from bacterial membranes within a host [<xref rid=\"R5\" ref-type=\"bibr\">5</xref>]. While these immunostimulatory effects can be beneficial in the setting of localized infections, the occurrence of severe sepsis causes systemic release of inflammation mediators and stimulatory molecules, thus leading to various pathophysiological effects [<xref rid=\"R6\" ref-type=\"bibr\">6</xref>]. Accordingly, structure–activity relationship studies of lipid A which examine or facilitate the examination of how one might harness these immunostimulatory effects are particularly valuable as they can provide basis for the development of vaccines and adjuvants. For example, recent studies have disclosed that both the fatty acid structure and the phosphorylation degree can affect the activity and endotoxic effects [<xref rid=\"R7\" ref-type=\"bibr\">7</xref>–<xref rid=\"R9\" ref-type=\"bibr\">9</xref>].</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Chemical structures of hexa-acylated <italic>Escherichia coli</italic> lipid A, monophosphorylated lipid X (the reducing monosaccharide lipid A precursor), and disaccharide lipid A precursor (“disaccharide lipid X”).</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1955-g002\"/></fig><p>Various lipid A derivatives have since been synthesized to dissociate endotoxic effects from beneficial immunomodulatory activities. Lipid X, 2-<italic>N</italic>;3-<italic>O</italic>-di[(<italic>R</italic>)-3-hydroxytetradecanoyl]-ᴅ-glucosamine-1-phosphate, is the naturally occurring early monosaccharide precursor of lipid A biosynthesis (structure <bold>1</bold>, <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). It was found that lipid X retained some immunomodulatory activity while having drastically reduced toxicity [<xref rid=\"R10\" ref-type=\"bibr\">10</xref>–<xref rid=\"R11\" ref-type=\"bibr\">11</xref>]. Lipid X was also found to give partial protection against a 100% lethal dose of endotoxin in mice [<xref rid=\"R11\" ref-type=\"bibr\">11</xref>]. However, there were also studies with conflicting results that showed that synthetic lipid X could be contaminated with small amounts of disaccharide-1-phosphate containing four (<italic>R</italic>)-3-hydroxytetradecanoic acids at the 2,2’ and 3,3’ positions (structure <bold>2</bold>, <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). This disaccharide precursor <bold>2</bold> was identified as the main immunostimulatory side product [<xref rid=\"R12\" ref-type=\"bibr\">12</xref>–<xref rid=\"R13\" ref-type=\"bibr\">13</xref>]. While the research suggested chemically pure lipid X had no immunostimulatory properties of lipid A, it did behave as a competitive inhibitor of LPS [<xref rid=\"R13\" ref-type=\"bibr\">13</xref>].</p><p>In this paper we describe a synthesis of lipid X (<bold>1</bold>) and the disaccharide lipid A precursor <bold>2</bold> (2,2′-<italic>N</italic>;3,3′-<italic>O-</italic>tetra[(<italic>R</italic>)-3-hydroxytetradecanoyl]-β(1→6)-ᴅ-glucosamine disaccharide 1-phosphate) (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). The synthesis of such precursors is particularly important as it will facilitate the aforementioned goal of harnessing the immunostimulatory effects of lipid A through development of a clear understanding of the structure–activity relationship. More importantly, we employed the 2-naphthylmethyl ether (Nap) group for protection of various hydroxy groups on the carbohydrate and acyl moieties, aiming to provide an advantage over previous methods that mainly used the benzyl group [<xref rid=\"R4\" ref-type=\"bibr\">4</xref>,<xref rid=\"R14\" ref-type=\"bibr\">14</xref>–<xref rid=\"R16\" ref-type=\"bibr\">16</xref>] in synthesizing lipid A derivatives. We also aim at developing a robust strategy in regards to the potential application of our route to the production of other lipid A analogs.</p></sec><sec><title>Results and Discussion</title><p>The acyl chain (<italic>R</italic>)-3-(2-naphthylmethoxy)tetradecanoic acid <bold>7</bold> was prepared via an enantioselective route as previously reported (<xref ref-type=\"fig\" rid=\"C1\">Scheme 1</xref>) [<xref rid=\"R16\" ref-type=\"bibr\">16</xref>]. Lauroyl chloride (<bold>3</bold>) was treated with Meldrum’s acid (2,2-dimethyl-1,3-dioxane-4,6-dione) followed by decarboxylation in methanol to give methyl 3-oxotetradecanoate (<bold>4</bold>) in 77% yield. The enantioselective hydrogenation of the β-carbonyl group using (<italic>R</italic>)-Ru(OAc)<sub>2</sub>(BINAP) at 65 °C and under 1.5 MPa H<sub>2</sub> afforded methyl (<italic>R</italic>)-3-hydroxytetradecanoate (<bold>5</bold>) in 98% yield. The same hydrogenation reaction was carried out using the (<italic>S</italic>)-Ru(OAc)<sub>2</sub>(BINAP) catalyst. Then both the <italic>R</italic> and <italic>S</italic> products were compared using chiral HPLC to confirm the absolute configuration and enantiomeric purity (Figure S1, <xref ref-type=\"supplementary-material\" rid=\"SD1\">Supporting Information File 1</xref>). The 3-hydroxy group in <bold>5</bold> was then protected as a Nap ether through a TMSOTf-catalyzed one-pot reductive naphthylmethylation process [<xref rid=\"R17\" ref-type=\"bibr\">17</xref>–<xref rid=\"R18\" ref-type=\"bibr\">18</xref>], by which free hydroxy groups were first trimethylsilylated in situ with hexamethyldisiloxane ((TMS)<sub>2</sub>O) before being naphthylmethylated by treatment with 2-naphthaldehyde, trimethylsilyl trifluoromethanesulfonate (TMSOTf), and triethylsilane (Et<sub>3</sub>SiH) [<xref rid=\"R17\" ref-type=\"bibr\">17</xref>,<xref rid=\"R19\" ref-type=\"bibr\">19</xref>]. On a 10 g scale, the protected methyl ester <bold>6</bold> could be purified by recrystallization followed by filtration to remove the major byproduct 2-methylnaphthalene. Subsequent saponification of the methyl ester <bold>6</bold> with LiOH gave (<italic>R</italic>)-3-(2-naphthylmethoxy)tetradecanoic acid (<bold>7</bold>) in 78% yield over two steps.</p><fig id=\"C1\" position=\"float\"><label>Scheme 1</label><caption><p>Enantioselective synthesis of Nap-protected (<italic>R</italic>)-3-hydroxytetradecanoic acid (<bold>7</bold>). Conditions: (a) Meldrum's acid, pyridine, CH<sub>2</sub>Cl<sub>2</sub>, 0 °C; (b) CH<sub>3</sub>OH, reflux, 77% over two steps; (c) (<italic>R</italic>)-Ru(OAc)<sub>2</sub>(BINAP), H<sub>2</sub>, CH<sub>3</sub>OH, 65 °C, 98%; (d) NapCHO, TMSOTf, (TMS)<sub>2</sub>O, Et<sub>3</sub>SiH, THF, 0 °C; (e) LiOH, THF, H<sub>2</sub>O, 65 °C, 78% (over two steps).</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1955-g003\"/></fig><p>The glucosamine building block <bold>14</bold> was synthesized using the procedures described in previous literature [<xref rid=\"R20\" ref-type=\"bibr\">20</xref>] (<xref ref-type=\"fig\" rid=\"C2\">Scheme 2</xref>). The protection of the free amine of glucosamine with a 2,2,2-trichloroethoxycarbonyl (Troc) group under basic conditions followed by peracetylation afforded compound <bold>10</bold> on a ≈150 g scale. The regioselective anomeric deacetylation with hydrazine and reprotection of the anomeric hydroxy group as <italic>tert</italic>-butyldimethylsilyl ether (TBS) led to compound <bold>12</bold>. Compound <bold>12</bold> was then treated with sodium methoxide in guanidine hydrochloride buffer solution (pH ≈ 9) to remove the <italic>O</italic>-3,4,6-acetyl groups [<xref rid=\"R14\" ref-type=\"bibr\">14</xref>]. Because the deacetylation reaction was later neutralized with cation exchange resin, extra washing with saturated NaHCO<sub>3</sub> during reaction work-up seemed necessary to avoid cleavage of the TBS ether in compound <bold>13</bold>. Then, (2-naphthyl)methylene acetal [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>] was used to protect the C-4,6-hydroxy groups using 2-naphthaldehyde dimethyl acetal and 0.2 equiv of camphorsulfonic acid (CSA). These protecting group manipulations resulted in the exposure of the C-3 hydroxy group in compound <bold>14</bold> for further acylation [<xref rid=\"R4\" ref-type=\"bibr\">4</xref>]. They are also essential for orthogonal protection of glucosamine, allowing the specific deprotection in subsequent steps (for example, the arylidene acetals at O4 and O6 could be regioselectively opened and transformed into Nap ethers) [<xref rid=\"R19\" ref-type=\"bibr\">19</xref>]. The C-3 hydroxy group in compound <bold>14</bold> was then acylated with (<italic>R</italic>)-3-(2-naphthylmethoxy)tetradecanoic acid (<bold>7</bold>) using 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) and 4-dimethylaminopyridine (DMAP) as the activation reagents [<xref rid=\"R14\" ref-type=\"bibr\">14</xref>] to give the key/common building block <bold>15</bold> in good yield (<xref ref-type=\"fig\" rid=\"C2\">Scheme 2</xref>).</p><fig id=\"C2\" position=\"float\"><label>Scheme 2</label><caption><p>Synthesis of monoacylated glucosamine building blocks. Conditions: (a) NaHCO<sub>3</sub>, TrocCl, H<sub>2</sub>O, 0 °C, 94% ; (b) Ac<sub>2</sub>O, pyridine, rt, 96%; (c) N<sub>2</sub>H<sub>4</sub>, AcOH, DMF, rt, 89%; (d) TBSCl, imidazole, DMF, rt, 93%; (e) guanidine hydrochloride buffer, rt; (f) NapC(OMe)<sub>2</sub>, camphorsulfonic acid, CH<sub>3</sub>CN, rt, 68% (2 steps); (g) acid <bold>7</bold>, EDC·HCl, DMAP, CH<sub>2</sub>Cl<sub>2</sub>, rt, 85%; (h) Zn/AcOH, CH<sub>2</sub>Cl<sub>2</sub>, rt; (i) DIPEA, FmocCl, CH<sub>2</sub>Cl<sub>2</sub>, rt, 80% (2 steps); (j) PhBCl<sub>2</sub>, Et<sub>3</sub>SiH, CH<sub>2</sub>Cl<sub>2</sub>, MS 4 Å, −78 °C, 80%; (k) HF/pyridine, THF,−40 °C to rt, 93%; (l) DBU, ClCN(Ph)CF<sub>3</sub>, CH<sub>2</sub>Cl<sub>2</sub>, 95%.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1955-g004\"/></fig><p>Glycosyl acceptor <bold>18</bold> and donor <bold>20</bold> were thus conveniently prepared from the common building block <bold>15</bold> through multiple protecting group manipulations (<xref ref-type=\"fig\" rid=\"C2\">Scheme 2</xref>). The <italic>N</italic>-Troc group in <bold>15</bold> was removed by treatment with zinc in a mixture of acetic acid and CH<sub>2</sub>Cl<sub>2</sub>. The resulting amine <bold>16</bold> was protected immediately as fluorenylmethylenoxy (Fmoc) carbamate by reaction with FmocCl in the presence of diisopropylethylamine (DIPEA) to give the fully protected compound <bold>17</bold>. The regioselective opening of the arylidene acetal at O6 with Et<sub>3</sub>SiH and PhBCl<sub>2</sub> in the presence of molecular sieves at −78 °C [<xref rid=\"R22\" ref-type=\"bibr\">22</xref>] gave compound <bold>18</bold> in good yield (80%) having a free C-6 hydroxy group. Compound <bold>18</bold> is the glycosyl acceptor for the synthesis of the disaccharide lipid A precursor (<xref ref-type=\"fig\" rid=\"C4\">Scheme 4</xref>). For the synthesis of donor <bold>20</bold>, first removal of the anomeric TBS in building block <bold>15</bold> was achieved by treatment with HF-pyridine followed by conversion of the resulting lactol into the desired <italic>N</italic>-phenyltrifluoroacetimidate glycosyl donor <bold>20</bold> by reaction with 2,2,2-trifluoro-<italic>N</italic>-phenylacetimidoyl chloride in the presence of base DBU [<xref rid=\"R14\" ref-type=\"bibr\">14</xref>].</p><p>The monoacylated derivative <bold>15</bold> is also the key building block for the synthesis of lipid X monosaccharide <bold>1</bold> (<xref ref-type=\"fig\" rid=\"C3\">Scheme 3</xref>). After the <italic>N</italic>-Troc protecting group was removed as described above, the free amine was immediately acylated with (<italic>R</italic>)-3-(2-naphthylmethoxy)tetradecanoic acid (<bold>7</bold>) using EDC and DMAP as the activation reagents to give the diacylated compound <bold>21</bold> in good yield. The anomeric TBS ether of <bold>21</bold> was then cleaved with HF, and the resulting anomeric hydroxy group was phosphorylated using tetrabenzyl diphosphate in the presence of lithium bis(trimethyl)silylamide (LHMDS) in THF at −78 °C [<xref rid=\"R23\" ref-type=\"bibr\">23</xref>] to afford the anomeric phosphate <bold>23</bold> exclusively as the α-anomer. Finally, global deprotection of <bold>23</bold> (benzyl phosphate, Nap ethers, and naphthylidene acetal) were accomplished by catalytic hydrogenolysis over Pd/C under 15 kg/cm<sup>2</sup> of H<sub>2</sub> to give the target lipid X monosaccharide <bold>1</bold> (as triethylammonium salt) in good yield.</p><fig id=\"C3\" position=\"float\"><label>Scheme 3</label><caption><p>Synthesis of lipid X monosaccharide <bold>1</bold>. Conditions: (a) Zn, AcOH, CH<sub>2</sub>Cl<sub>2</sub>, rt; (b) acid <bold>7</bold>, EDC·HCl, DMAP, CH<sub>2</sub>Cl<sub>2</sub>, rt, 67.5% (2 steps); (c) HF/Py, THF, −40 °C to rt, 78%; (d) tetrabenzyl pyrophosphate, LHMDS, THF, −78 °C, 91%; (e) H<sub>2</sub> (15 kg/cm<sup>2</sup>), Pd/C, THF/H<sub>2</sub>O, 38 °C, 86%.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1955-g005\"/></fig><p>Having the glycosyl donor <bold>20</bold> and acceptor <bold>18</bold> at hand (<xref ref-type=\"fig\" rid=\"C2\">Scheme 2</xref>), in order to prepare the disaccharide precursor, the glycosylation reaction was performed first, followed by deprotection, acylation, and phosphorylation reactions (<xref ref-type=\"fig\" rid=\"C4\">Scheme 4</xref>). The triflic acid (TfOH)-mediated glycosylation of donor <bold>20</bold> and acceptor <bold>18</bold> in the presence of molecular sieves in CH<sub>2</sub>Cl<sub>2</sub> at −20 °C gave disaccharide <bold>24</bold> [<xref rid=\"R14\" ref-type=\"bibr\">14</xref>] in excellent yield (β-anomer only). The <italic>N’</italic>-Troc protecting group (non-reducing end) was first removed using Zn dust in acetic acid, and the resulting free amine was immediately acylated with (<italic>R</italic>)-3-(2-naphthylmethoxy)tetradecanoic acid (<bold>7</bold>) using EDC and DMAP as the coupling reagents to afford triacylated disaccharide <bold>26</bold>. Then, the <italic>N</italic>-Fmoc group (reducing end) in <bold>26</bold> was removed by treatment with triethylamine, and the resulting amine again was immediately acylated with (<italic>R</italic>)-3-(2-naphthylmethoxy)tetradecanoic acid (<bold>7</bold>) to afford disaccharide <bold>28</bold> with four fatty acid chains. After cleavage of the anomeric TBS moiety employing HF in pyridine, the resulting anomeric hydroxy group of <bold>29</bold> was phosphorylated using tetrabenzyl diphosphate in the presence of LHMDS in THF at −78 °C. Then finally global deprotection (hydrogenation over Pd-black) was carried out to remove the naphthylidene acetal, Nap ethers, and the benzyl phosphate groups in compound <bold>30</bold>. By this route the target disaccharide lipid A precursor <bold>2</bold> (as triethylammonium salt) was obtained in 88% yield.</p><fig id=\"C4\" position=\"float\"><label>Scheme 4</label><caption><p>Synthesis of the disaccharide lipid A precursor <bold>2</bold>. Conditions: (a) TfOH, 4 Å MS, dry CH<sub>2</sub>Cl<sub>2</sub>, 94%; (b) Zn, AcOH, CH<sub>2</sub>Cl<sub>2</sub>; (c) acid <bold>7</bold>, EDC·HCl, DMAP, CH<sub>2</sub>Cl<sub>2</sub>, 88% (2 steps); (d) Et<sub>3</sub>N, DMF; (e) acid <bold>7</bold>, EDC·HCl, CH<sub>2</sub>Cl<sub>2</sub>, 82% (2 steps); (f) HF/pyridine, pyridine, THF, −40 °C to rt, 92%; (g) tetrabenzyl pyrophosphate, LHMDS, dry THF, −78 °C, 82%; (h) H<sub>2</sub>, Pd-black, THF, 38 °C, 88%.</p></caption><graphic xlink:href=\"Beilstein_J_Org_Chem-16-1955-g006\"/></fig></sec><sec><title>Conclusion</title><p>As described, we have developed an efficient approach for the chemical synthesis of two monophosphorylated lipid A precursors. Lipid X (<bold>1</bold>) could be prepared from the common building block <bold>15</bold> via deprotection, acylation, phosphorylation, and global deprotection. The glycosyl acceptor and donor for the synthesis of the disaccharide precursor could also be readily obtained starting from the same key building block. After glycosylation, the disaccharide lipid A precursor <bold>2</bold> was synthesized following a similar reaction sequence of deprotection, acylation, phosphorylation, and global deprotection.</p><p>The Nap protecting group has emerged as a particularly valuable addition to carbohydrate chemistry [<xref rid=\"R24\" ref-type=\"bibr\">24</xref>–<xref rid=\"R25\" ref-type=\"bibr\">25</xref>]. Not only does it not significantly alter carbohydrate reactivity, it also can be readily cleaved under hydrogenolytic conditions as well as a variety of oxidative [<xref rid=\"R26\" ref-type=\"bibr\">26</xref>] and acid-mediated conditions [<xref rid=\"R25\" ref-type=\"bibr\">25</xref>,<xref rid=\"R27\" ref-type=\"bibr\">27</xref>] that are orthogonal to benzyl ethers. Therefore, we employed the Nap ether as a “permanent” protecting group for the carbohydrate and the 3-hydroxy group of the acyl chain, aiming to provide an advantage over literature reported methods that mainly used the benzyl group in synthesizing lipid A derivatives. Of particular note is the fact that the key Nap protecting group is able to remain in the molecule until the final global deprotection step. The presence of this protecting group until such a late stage likely helps avoiding the problematic acyl migration often observed with similar molecules [<xref rid=\"R28\" ref-type=\"bibr\">28</xref>]. In addition, the 4,6-<italic>O</italic>-naphthylidene acetal (e.g., in compounds <bold>23</bold> and <bold>30</bold>) can be regioselectively opened at O6 or O4 under different conditions [<xref rid=\"R19\" ref-type=\"bibr\">19</xref>,<xref rid=\"R29\" ref-type=\"bibr\">29</xref>]. This could potentially allow the incorporation of other functionalities in target molecules for the synthesis of glycoconjugates. Based on the synthetic strategy described in this work (a common building block and Nap ether protection), we have already designed a route to MPLA, a clinically safe [<xref rid=\"R30\" ref-type=\"bibr\">30</xref>] monophosphoryl lipid A derivative with one phosphate group linked to the 4′-OH group. This work is currently underway in our lab.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting Information</title><supplementary-material content-type=\"local-data\" id=\"SD1\"><label>File 1</label><caption><p>Experimental details and copies of NMR spectra.</p></caption><media mime-subtype=\"pdf\" mimetype=\"application\" xlink:href=\"Beilstein_J_Org_Chem-16-1955-s001.pdf\" xlink:type=\"simple\" id=\"d39e973\" position=\"anchor\"/></supplementary-material></sec></body><back><ref-list><ref id=\"R1\"><label>1</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Whitfield</surname><given-names>C</given-names></name></person-group><source>Annu Rev Biochem</source><year>2006</year><volume>75</volume><fpage>39</fpage><lpage>68</lpage><pub-id pub-id-type=\"doi\">10.1146/annurev.biochem.75.103004.142545</pub-id><pub-id pub-id-type=\"pmid\">16756484</pub-id></element-citation></ref><ref 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Beilstein J Nanotechnol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Beilstein J Nanotechnol</journal-id><journal-title-group><journal-title>Beilstein Journal of Nanotechnology</journal-title></journal-title-group><issn pub-type=\"epub\">2190-4286</issn><publisher><publisher-name>Beilstein-Institut</publisher-name><publisher-loc>Trakehner Str. 7-9, 60487 Frankfurt am Main, Germany</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32832316</article-id><article-id pub-id-type=\"pmc\">PMC7431768</article-id><article-id pub-id-type=\"doi\">10.3762/bjnano.11.105</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Full Research Paper</subject></subj-group><subj-group subj-group-type=\"topic\"><subject>Nanoscience</subject></subj-group><subj-group subj-group-type=\"topic\"><subject>Nanotechnology</subject></subj-group></article-categories><title-group><article-title>Influence of the magnetic nanoparticle coating on the magnetic relaxation time</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Osaci</surname><given-names>Mihaela</given-names></name><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0003-4062-7556</contrib-id><email>mihaela.osaci@fih.upt.ro</email><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Cacciola</surname><given-names>Matteo</given-names></name><xref ref-type=\"aff\" rid=\"A2\">2</xref></contrib></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Leiderer</surname><given-names>Paul</given-names></name><role>Associate Editor</role></contrib></contrib-group><aff id=\"A1\"><label>1</label>“Politehnica” University of Timisoara, Department of Electrical Engineering and Industrial Informatics, 2 Victoriei Square, 300006 Timisoara, Timis County, Romania</aff><aff id=\"A2\"><label>2</label>Cooperativa TEC, Via Nazionale, n. 439, 89134 Pellaro di Reggio Calabria, Italy</aff><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>12</day><month>8</month><year>2020</year></pub-date><volume>11</volume><fpage>1207</fpage><lpage>1216</lpage><ext-link ext-link-type=\"doi\" xlink:href=\"10.3762/bjnano.11.105\">10.3762/bjnano.11.105</ext-link><history><date date-type=\"received\"><day>6</day><month>12</month><year>2019</year></date><date date-type=\"accepted\"><day>16</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020, Osaci and Cacciola</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Osaci and Cacciola</copyright-holder><ali:free_to_read xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\"/><license license-type=\"Beilstein\"><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">https://creativecommons.org/licenses/by/4.0</ali:license_ref><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">https://www.beilstein-journals.org/bjnano/terms</ali:license_ref><license-p>This is an Open Access article under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0\">https://creativecommons.org/licenses/by/4.0</ext-link>). Please note that the reuse, redistribution and reproduction in particular requires that the authors and source are credited.</license-p><license-p>The license is subject to the Beilstein Journal of Nanotechnology terms and conditions: (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.beilstein-journals.org/bjnano/terms\">https://www.beilstein-journals.org/bjnano/terms</ext-link>)</license-p></license></permissions><abstract><p>Colloidal systems consisting of monodomain superparamagnetic nanoparticles have been used in biomedical applications, such as the hyperthermia treatment for cancer. In this type of colloid, called a nanofluid, the nanoparticles tend to agglomeration. It has been shown experimentally that the nanoparticle coating plays an important role in the nanoparticle dispersion stability and biocompatibility. However, theoretical studies in this field are lacking. In addition, the ways in which the nanoparticle coating influences the magnetic properties of the nanoparticles are not yet understood. In order to fill in this gap, this study presents a numerical simulation model that elucidates how the nanoparticle coating affects the nanoparticle agglomeration tendency as well as the effective magnetic relaxation time of the system. To simulate the self-organization of the colloidal nanoparticles, a stochastic Langevin dynamics method was applied based on the effective Verlet-type algorithm. The Néel magnetic relaxation time was obtained via the Coffey method in an oblique magnetic field, adapted to the local magnetic field on a nanoparticle.</p></abstract><kwd-group kwd-group-type=\"author\"><kwd>colloidal system</kwd><kwd>effective Verlet-type algorithm</kwd><kwd>magnetic relaxation time</kwd><kwd>nanoparticle coating</kwd><kwd>numerical simulation</kwd><kwd>stochastic Langevin dynamics method</kwd><kwd>superparamagnetic nanoparticles</kwd></kwd-group></article-meta></front><body><sec><title>Introduction</title><p>One of the most important biomedical applications of colloidal magnetic nanoparticle systems is magnetic hyperthermia applied as an alternative for cancer treatment. Upon reaching the tumour, the magnetic nanoparticles are locally subjected to an alternating magnetic field, generating heat that kills the cancer cells [<xref rid=\"R1\" ref-type=\"bibr\">1</xref>]. The heat is generated due to two phenomena: Néel relaxation (an internal phenomenon driven by the rotation of the particle magnetic moment inside the particle) and Brown relaxation (an external phenomenon driven by the rotation of the nanoparticle along the magnetic moment). Both internal and external sources of friction lead to a delay in the orientation of the particle magnetic moment in the direction of the applied magnetic field, thus generating heat. This heat increases the tumour cell temperature which leads to cell death [<xref rid=\"R1\" ref-type=\"bibr\">1</xref>–<xref rid=\"R4\" ref-type=\"bibr\">4</xref>].</p><p>Iron-oxide magnetic nanoparticles, in particular magnetite (Fe<sub>3</sub>O<sub>4</sub>) and maghemite (γ-Fe<sub>2</sub>O<sub>3</sub>), have been intensely studied in the context of magnetic hyperthermia applications. These nanoparticles can be synthesized in small dimensions, which ensures low toxicity and the possibility for easy surface functionalization.</p><p>A common method for synthesising iron-oxide nanoparticles includes chemical co-precipitation, which involves the simultaneous precipitation of magnetic nanoparticles and a solid matrix through a sol–gel process, yielding metal-oxide nanoparticles dispersed in a mesoporous matrix. [<xref rid=\"R5\" ref-type=\"bibr\">5</xref>]. Other methods used for synthesising these nanoparticles include modifications of the sol–gel method. These methods involve supercritical conditions, such as ethyl alcohol and alkaline co-precipitation, and an additional step in which the hydrothermal method or thermal decomposition technique are used. The method used to obtain nanoparticles by thermal decomposition of an iron precursor in the presence of NaBH<sub>4</sub> in a polyol was found to be suitable for size control in both chemical approaches [<xref rid=\"R1\" ref-type=\"bibr\">1</xref>–<xref rid=\"R4\" ref-type=\"bibr\">4</xref><xref rid=\"R6\" ref-type=\"bibr\">6</xref>].</p><p>Since the methods used to synthesise nanoparticles can affect their size, chemical composition and crystalline structure, special attention has been given to improving nanoparticle production quality. For example, the Plackett–Burman technique is a filtration method used for investigating the initial steps that influence the characteristics of the final material [<xref rid=\"R7\" ref-type=\"bibr\">7</xref>].</p><p>Uncoated superparamagnetic nanoparticles are difficult to synthesise since they are not stable in colloidal suspensions. Therefore, it is challenging to use these nanoparticles in magnetic hyperthermia therapy [<xref rid=\"R8\" ref-type=\"bibr\">8</xref>]. By exposing these nanoparticles to the acidic environment of living organisms, certain structural degradation processes occur due to the corrosion of nanoparticle surfaces. This biodegradation in acidic media leads to significant changes in the nanoparticle magnetic properties over time [<xref rid=\"R9\" ref-type=\"bibr\">9</xref>]. Since the nanoparticle surfaces are in direct contact with blood and other tissues, a biocompatible and nontoxic coating needs to be placed around the nanoparticles to prevent biodegradation processes. The coating thickness can significantly affect the magnetic properties and the hyperthermia of the nanoparticles. The coating is performed to reduce the sensitivity of nanoparticles to air, humidity and acidity. In addition, it allows for the functionalization and absorption of proteins and creation of hydrophilic molecules at the surface of the nanoparticles to prevent agglomeration, reducing capillary obstruction risk. Coating can also improve nanoparticle circulation in the blood and proper transport to the targeted areas, while preserving their physical–chemical properties. Additionally, coating prevents nanoparticle opsonisation by the reticuloendothelial system, which is pivotal for determining how fast nanoparticles can flow on the bloodstream before reaching their target.</p><p>The materials used as coating agents for magnetic nanoparticles can be organic or inorganic. The inorganic coating enables the surface of the nanoparticles to bind to their biological ligands, while maintaining the nanoparticle stability. On the other hand, organic coating (particularly polymers) has a number of advantages over inorganic coating, such as better particle dispersion, good colloidal stability, biocompatibility, good nanoparticle circulation in the blood, reduced toxicity and low risk of blood capillary obstruction.</p><p>In the last years, a new class of stable and biocompatible nanofluids have been developed by using a combination of electrostatic and steric stabilisation methods [<xref rid=\"R10\" ref-type=\"bibr\">10</xref>]. Despite these stabilization methods, a number of recent studies have experimentally shown a tendency for nanoparticle agglomeration, even in the absence of an external magnetic field [<xref rid=\"R11\" ref-type=\"bibr\">11</xref>–<xref rid=\"R12\" ref-type=\"bibr\">12</xref>]. This can be a potential problem when ferrofluids are used in medical applications, since nanoparticle agglomeration and sedimentation can create thrombi inside the blood vessels [<xref rid=\"R13\" ref-type=\"bibr\">13</xref>].</p><p>Controlling nanoparticle agglomeration is essential to improve the applicability of the magnetic nanoparticles. In this regard, the optimized microemulsion method can be used to obtain a homogenous silica coating on Fe<sub>3−</sub><italic><sub>x</sub></italic>O<sub>4</sub> nanoparticles [<xref rid=\"R14\" ref-type=\"bibr\">14</xref>]. This method controls the thickness of the coating layer, enabling a higher average separation among particles when compared to the oleic acid coating method used on pristine nanoparticles [<xref rid=\"R14\" ref-type=\"bibr\">14</xref>].</p><p>Homogeneous, polymer-coated spherical magnetite nanoparticles with superparamagnetic properties have been successfully synthesised. The polymer coating provides extra stability to the magnetic nanoparticles in aqueous media [<xref rid=\"R15\" ref-type=\"bibr\">15</xref>]. To increase biocompatibility or to enable specific hydrophilic properties, nanoparticles were coated with poly(ethylene glycol) (PEG) [<xref rid=\"R16\" ref-type=\"bibr\">16</xref>].</p><p>Experimental data concerning how different coatings influence nanoparticle magnetic properties are quite controversial. A few studies indicate that a thin polymer coating layer enhances the hyperthermia efficiency [<xref rid=\"R17\" ref-type=\"bibr\">17</xref>], while others do not suggest a correlation between the coating layer thickness and the magnetic hyperthermia properties (i.e., the absorption rate) [<xref rid=\"R18\" ref-type=\"bibr\">18</xref>].</p><p>These issues demonstrate the importance of investigating the ways in which the coating influences magnetic nanoparticle properties [<xref rid=\"R8\" ref-type=\"bibr\">8</xref>]. In order to solve these issues, the current study aims to use simulation models to study the influence of nanoparticle coating on nanoparticle agglomeration tendency and on the Néel magnetic relaxation time, as well as on the effective magnetic relaxation time.</p></sec><sec><title>Results and Discussion</title><sec><title>Simulation methods used in the study</title><p>The agglomeration of magnetic nanoparticles evolves depending on the initial configuration of the system and on the specific parameters related to the nanoparticle coating. For each agglomeration state, the relaxation time is calculated with respect to the corresponding magnetic configuration of the system.</p><p>For the numerical simulation, two widely known models have been used [<xref rid=\"R19\" ref-type=\"bibr\">19</xref>–<xref rid=\"R21\" ref-type=\"bibr\">21</xref>]. We started with a system of single-domain magnetic nanoparticles, consisting of spherical iron-oxide nanoparticles with uniaxial magnetic anisotropy, which have a lognormal distribution of the grain size. Each nanoparticle is composed of a magnetic core and a nonmagnetic surface layer of stabilizing surfactant. The system temperature is considered to be constant.</p><p>To simulate the self-organization of the colloidal magnetic nanoparticles we used the Langevin dynamics stochastic method, based on an effective Verlet-type algorithm [<xref rid=\"R19\" ref-type=\"bibr\">19</xref>].</p><p>The Néel magnetic relaxation time <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i001.jpg\"/> is obtained through the Coffey method in an oblique magnetic field, adapted to the local magnetic field of a nanoparticle [<xref rid=\"R22\" ref-type=\"bibr\">22</xref>–<xref rid=\"R23\" ref-type=\"bibr\">23</xref>].</p><p>For each nanoparticle, the effective magnetic relaxation time can be described as follows [<xref rid=\"R24\" ref-type=\"bibr\">24</xref>]:</p><disp-formula id=\"FD1\"><label>[1]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e001.jpg\" position=\"anchor\"/></disp-formula><p>where <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i002.jpg\"/> is the Brownian relaxation time. The Brownian process represents the nanoparticle rotation in the fluid environment. For spherical particles, the Brownian relaxation time is usually described as [<xref rid=\"R24\" ref-type=\"bibr\">24</xref>]:</p><disp-formula id=\"FD2\"><label>[2]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e002.jpg\" position=\"anchor\"/></disp-formula><p>where <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i003.jpg\"/> is the hydrodynamic volume of the <italic>i</italic>-th nanoparticle, η is the coefficient of dynamic viscosity, <italic>k</italic><sub>B</sub> is the Boltzmann constant, and <italic>T</italic> is the temperature.</p><p>After obtaining the effective magnetic relaxation time value of each nanoparticle, we can calculate the average effective magnetic relaxation time. The effective magnetic relaxation time is influenced by the magnetic nanoparticle coating. This influence is either due to the Brownian relaxation time (via the hydrodynamic volume, <xref ref-type=\"disp-formula\" rid=\"FD2\">Equation 2</xref>), or due to the Néel relaxation time, via the nanoparticle configuration in the agglomerates, playing an important role in the calculation of the dipolar magnetic field acting on each particle [<xref rid=\"R25\" ref-type=\"bibr\">25</xref>].</p><p>The internal dipolar magnetic field is given as</p><disp-formula id=\"FD3\"><label>[3]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e003.jpg\" position=\"anchor\"/></disp-formula><p>where <italic>D</italic><italic><sub>ij</sub></italic> is the distance between the centres of those two nanoparticles, <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i004.jpg\"/> is the versor of the direction connecting the <italic>i</italic>-th and <italic>j</italic>-th nanoparticles, μ<italic><sub>j</sub></italic> is the magnetic moment of the <italic>j</italic>-th nanoparticle (<inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i005.jpg\"/> where <italic>V</italic><italic><sub>j</sub></italic> is the magnetic core volume of the <italic>j</italic>-th nanoparticle, <italic>M</italic><sub>s</sub> is the spontaneous magnetisation and <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i006.jpg\"/> and <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i007.jpg\"/> are the unit vectors of the magnetic moments of the <italic>i</italic>-th and <italic>j</italic>-th nanoparticles, respectively).</p><p>The local magnetic field acting on a nanoparticle is the vectorial sum of the applied external magnetic field (<inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i008.jpg\"/>) and the internal dipolar magnetic field (<inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i009.jpg\"/>) determined by the magnetic dipolar interactions among the nanoparticles [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>],</p><disp-formula id=\"FD4\"><label>[4]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e004.jpg\" position=\"anchor\"/></disp-formula></sec><sec><title>Method for simulating the self-organization of colloidal magnetic nanoparticles</title><p>This method starts by obtaining the numerical solutions of the Langevin equations for the translational and rotational motions of a nanoparticle <italic>i</italic> in the fluid environment [<xref rid=\"R19\" ref-type=\"bibr\">19</xref>] given as</p><disp-formula id=\"FD5\"><label>[5]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e005.jpg\" position=\"anchor\"/></disp-formula><disp-formula id=\"FD6\"><label>[6]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e006.jpg\" position=\"anchor\"/></disp-formula><disp-formula id=\"FD7\"><label>[7]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e007.jpg\" position=\"anchor\"/></disp-formula><disp-formula id=\"FD8\"><label>[8]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e008.jpg\" position=\"anchor\"/></disp-formula><p>where <italic>m</italic><italic><sub>i</sub></italic> is the mass of the <italic>i</italic>-th nanoparticle, <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i010.jpg\"/> is the linear speed of the <italic>i-</italic>th nanoparticle, <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i011.jpg\"/> is the resultant of the conservative forces acting on the <italic>i-</italic>th nanoparticle, α<italic><sub>i</sub></italic><sub>,tr</sub> and α<italic><sub>i</sub></italic><sub>,rot</sub> are the translational and rotational friction coefficients, respectively, η is the dynamic viscosity coefficient, <italic>r</italic><sub>i</sub> is the radius of the <italic>i-</italic>th nanoparticle, β<italic><sub>i</sub></italic><sub>,tr</sub>(<italic>t</italic>) and β<italic><sub>i</sub></italic><sub>,rot</sub>(<italic>t</italic>) are the random Brownian force and torque, respectively, <italic>I</italic><italic><sub>i</sub></italic> is the moment of inertia of the <italic>i-</italic>th nanoparticle, <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i012.jpg\"/> is the angular speed of the <italic>i-</italic>th nanoparticle, <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i013.jpg\"/> is the resultant of the conservative torques acting on the <italic>i-</italic>th nanoparticle.</p><p>The forces acting on the nanoparticles of the system are the van der Waals forces, electrostatic repulsive forces, magnetic dipolar forces, steric repulsion forces and the random Brownian force [<xref rid=\"R19\" ref-type=\"bibr\">19</xref>,<xref rid=\"R26\" ref-type=\"bibr\">26</xref>–<xref rid=\"R29\" ref-type=\"bibr\">29</xref>]. The stabilisation of magnetic particles can be achieved by the equilibrium between the electrostatic and steric repulsive forces [<xref rid=\"R19\" ref-type=\"bibr\">19</xref>,<xref rid=\"R26\" ref-type=\"bibr\">26</xref>,<xref rid=\"R28\" ref-type=\"bibr\">28</xref>].</p><p>The influence of nanoparticle coating on the nanoparticle interaction forces depends on the hydrodynamic dimension of the nanoparticles, on the distances between the centres of the nanoparticles (i.e., surface-to-surface separation between nanoparticles), and on the surface density of the polymer coating layer. Thus, the model uses the van der Waals interaction force equation, as follows [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>]:</p><disp-formula id=\"FD9\"><label>[9]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e009.jpg\" position=\"anchor\"/></disp-formula><p>where <italic>r</italic><italic><sub>i</sub></italic> and <italic>r</italic><italic><sub>j</sub></italic> are spherical particle radii of the <italic>i</italic>-th and <italic>j</italic>-th nanoparticles, <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i014.jpg\"/> is the versor of the direction connecting the <italic>i</italic>-th and <italic>j</italic>-th particles, <italic>D</italic><italic><sub>ij</sub></italic> is the distance between the centres of the <italic>i</italic>-th and <italic>j</italic>-th nanoparticles, <italic>s</italic><italic><sub>ij</sub></italic> = <italic>D</italic><italic><sub>ij</sub></italic> – (<italic>r</italic><italic><sub>i</sub></italic> + <italic>r</italic><italic><sub>j</sub></italic>) is the surface-to-surface separation between the <italic>i</italic>-th and <italic>j</italic>-th nanoparticles, and <italic>A</italic><sub>eff</sub> is the Hamaker effective constant for iron-oxide nanoparticles in water. When the surface-to-surface separation between two particles, <italic>s</italic><italic><sub>ij</sub></italic>, is less than 1 nm, <italic>s</italic><italic><sub>ij</sub></italic> is fixed at 1 nm to avoid a singularity in <xref ref-type=\"disp-formula\" rid=\"FD9\">Equation 9</xref>.</p><p>When the normalized distances are <italic>k</italic>·<italic>s</italic><italic><sub>ij</sub></italic> ≥ 4, the double layer electrostatic force is [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>]</p><disp-formula id=\"FD10\"><label>[10]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e010.jpg\" position=\"anchor\"/></disp-formula><p>When the normalized distances are <italic>k</italic>·<italic>s</italic><italic><sub>ij</sub></italic> < 4 [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>],</p><disp-formula id=\"FD11\"><label>[11]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e011.jpg\" position=\"anchor\"/></disp-formula><p>where <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i015.jpg\"/>\n<inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i016.jpg\"/> Φ<sub>0</sub><italic><sub>i</sub></italic> is the surface potential of the <italic>i</italic>-th nanoparticle at infinite separation, <italic>z</italic> is the ion valence, <italic>e</italic> = 1.6 × 10<sup>−19</sup> C and <italic>k</italic> is the thickness of the screening ionic layer “κ”, estimated by the inverse of Debye constant.</p><p>Polymers and surfactants are usually used for steric stabilization. The model uses the following expression for the steric stabilization force [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>]:</p><disp-formula id=\"FD12\"><label>[12]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e012.jpg\" position=\"anchor\"/></disp-formula><p>where <italic>d</italic><italic><sub>i</sub></italic> = 2<italic>r</italic><italic><sub>i</sub></italic>, <italic>l</italic> = 2<italic>s</italic><italic><sub>ij</sub></italic>/<italic>d</italic><italic><sub>i</sub></italic>, <italic>t</italic> = 2δ/<italic>d</italic><italic><sub>i</sub></italic> (δ is the thickness of the surfactant layer) and ξ is the polymer surface density.</p><p>The dipolar magnetic force exerted between the magnetic moments of the <italic>i</italic>-th and <italic>j</italic>-th nanoparticles is given by [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>]:</p><disp-formula id=\"FD13\"><label>[13]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e013.jpg\" position=\"anchor\"/></disp-formula><p>where µ<sub>0</sub> is the vacuum magnetic permeability.</p><p>The random Brownian force and torque are usually modelled using the Gaussian noise [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>–<xref rid=\"R22\" ref-type=\"bibr\">22</xref>]. Besides the random Brownian torque, the conservative torque acting on the nanoparticle is the magnetic torque:</p><disp-formula id=\"FD14\"><label>[14]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e014.jpg\" position=\"anchor\"/></disp-formula><p>where <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i017.jpg\"/> is the local magnetic field on each nanoparticle, given by <xref ref-type=\"disp-formula\" rid=\"FD4\">Equation 4</xref>.</p><p>To solve the equations of motion numerically we use the effective Verlet-type algorithm [<xref rid=\"R20\" ref-type=\"bibr\">20</xref>–<xref rid=\"R21\" ref-type=\"bibr\">21</xref>].</p></sec><sec><title>The Coffey method in an oblique magnetic field adapted to the local magnetic field on a nanoparticle</title><p>According to the literature, as a general rule, the Néel–Brown model is used to obtain the Néel relaxation time [<xref rid=\"R28\" ref-type=\"bibr\">28</xref>]. This approximation is valid only when the nanoparticles do not interact magnetostatically with one another. The external magnetic field and the dipolar magnetic field acting on the nanoparticle generate a resultant internal magnetic field <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i018.jpg\"/> on the nanoparticle. This internal magnetic field does not generally act along the direction of the easy magnetisation axis of the nanoparticle, known as the oblique magnetic field [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>–<xref rid=\"R22\" ref-type=\"bibr\">22</xref>]. This field is calculated based on <xref ref-type=\"disp-formula\" rid=\"FD4\">Equation 4</xref>, in which the internal dipolar magnetic field is calculated by a direct sum based on <xref ref-type=\"disp-formula\" rid=\"FD3\">Equation 3</xref>.</p><p>The nanoparticle Néel relaxation time in oblique magnetic fields is given by [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>–<xref rid=\"R22\" ref-type=\"bibr\">22</xref>]</p><disp-formula id=\"FD15\"><label>[15]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e015.jpg\" position=\"anchor\"/></disp-formula><p>where Δ<italic>E</italic><italic><sub>i</sub></italic><sub>12</sub> and Δ<italic>E</italic><italic><sub>i</sub></italic><sub>21</sub> are the normalized energy barriers for the magnetic moment reorientations. The magnetisation-free diffusion time (τ<italic><sup>i</sup></italic><sub>0N</sub>) for low damping is [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>–<xref rid=\"R22\" ref-type=\"bibr\">22</xref>]</p><disp-formula id=\"FD16\"><label>[16]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e016.jpg\" position=\"anchor\"/></disp-formula><p>where <italic>v</italic><italic><sub>i</sub></italic> is the volume of the <italic>i</italic>-th nanoparticle, <italic>M</italic><sub>s</sub> is the spontaneous magnetisation, <italic>k</italic><sub>B</sub> is the Boltzmann constant, <italic>T</italic> is the temperature, α is the damping constant, and γ is the gyromagnetic ratio.</p><p>In <xref ref-type=\"disp-formula\" rid=\"FD1\">Equation 1</xref>,</p><disp-formula id=\"FD17\"><label>[17]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e017.jpg\" position=\"anchor\"/></disp-formula><disp-formula id=\"FD18\"><label>[18]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e018.jpg\" position=\"anchor\"/></disp-formula><p>where Ψ<italic><sub>i</sub></italic> is the angle between <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i019.jpg\"/> and the easy anisotropy axis of the <italic>i</italic>-th nanoparticle.</p><p>θ<italic><sub>ip</sub></italic> are the solutions of the following transcendental equation:</p><disp-formula id=\"FD19\"><label>[19]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e019.jpg\" position=\"anchor\"/></disp-formula><p>In <xref ref-type=\"disp-formula\" rid=\"FD9\">Equation 9</xref>, θ<italic><sub>i</sub></italic> is the angle between the easy magnetisation and anisotropy axes of the <italic>i</italic>-th nanoparticle; therefore:</p><disp-formula id=\"FD20\"><label>[20]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e020.jpg\" position=\"anchor\"/></disp-formula><p>In <xref ref-type=\"disp-formula\" rid=\"FD18\">Equation 18</xref> and <xref ref-type=\"disp-formula\" rid=\"FD20\">Equation 20</xref>, <inline-graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-i020.jpg\"/> is the effective magnetic anisotropy constant of the <italic>i</italic>-th nanoparticle. If <italic>h</italic><italic><sub>i</sub></italic> < <italic>h</italic><italic><sub>ic</sub></italic>(Ψ<sub>i</sub>) < 1 [<xref rid=\"R21\" ref-type=\"bibr\">21</xref>–<xref rid=\"R22\" ref-type=\"bibr\">22</xref>], then</p><disp-formula id=\"FD21\"><label>[21]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e021.jpg\" position=\"anchor\"/></disp-formula><disp-formula id=\"FD22\"><label>[22]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e022.jpg\" position=\"anchor\"/></disp-formula><disp-formula id=\"FD23\"><label>[23]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e023.jpg\" position=\"anchor\"/></disp-formula><disp-formula id=\"FD24\"><label>[24]</label><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-e024.jpg\" position=\"anchor\"/></disp-formula></sec><sec><title>Simulation conditions and results</title><p>For this study, we considered the case in which a colloid is electrostatically stabilised. The system is composed of water-dispersed spherical magnetite nanoparticles whose sizes follow a lognormal distribution. The Hamaker constant for magnetite in water is given as a reference value [<xref rid=\"R20\" ref-type=\"bibr\">20</xref>]. The system parameter values are given in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. The external magnetic field intensity was set along the <italic>z</italic>-axis.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>The values of the parameters involved in the simulation.</p></caption><table frame=\"hsides\" rules=\"groups\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Parameter name</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Parameter value</td></tr><tr><td align=\"left\" colspan=\"2\" valign=\"top\" rowspan=\"1\"><hr/></td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">number of spherical magnetite nanoparticles</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">50</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">average diameter of the nanoparticles</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><italic>d</italic><sub>m</sub> = 10 nm</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">standard deviation of nanoparticle diameter</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.1·<italic>d</italic><sub>m</sub></td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">volume fraction of the nanoparticles</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.05</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">spontaneous magnetisation</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.46 × 10<sup>5</sup> A/m</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">dynamic viscosity</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.9 × 10<sup>−4</sup> Pa·s</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">relative electrical permittivity</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">78.5</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hamaker constant for magnetite in water</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">39 × 10<sup>−20</sup> J</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">temperature</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">298 K</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">thickness range of the coating layer</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1–3 nm</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ion concentration in solution</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>26</sup> ions/m<sup>3</sup></td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">ion valence</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">surface density range of the polymers (ξ)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10<sup>16</sup> m<sup>−2</sup>–4.5 × 10<sup>17</sup> m<sup>−2</sup></td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">surface charge</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.6 × 10<sup>−15</sup> C</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">external magnetic field intensity</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15 kA/m</td></tr></table></table-wrap><p>A random nanoparticle arrangement in a face-centred-cubic grid was initially considered. By using the Langevin dynamics stochastic method, an aggregate structure was obtained. After obtaining the aggregate structures, the effective magnetic relaxation times were calculated for the nanoparticles in the system. Then, the average effective relaxation time value was obtained as the arithmetic mean of the relaxation times. For example, <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> and <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> show the nanoparticle positions inside the test cube a) in the initial moment and b) after 0.1 ms (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>: coating layer thickness = 1 nm, polymer surface density ξ = 10<sup>16</sup> m<sup>−2</sup>; <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>: coating layer thickness = 1 nm, polymer surface density ξ = 4.5 × 10<sup>17</sup> m<sup>−2</sup>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Positions of nanoparticles inside the test cube: a) initial moment; b) after 0.1 ms (thickness of the coating layer = 1 nm, polymer surface density ξ = 10<sup>16</sup> m<sup>−2</sup>).</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-g002\"/></fig><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Positions of nanoparticles inside the test cube: a) initial moment; b) after 0.1 ms (thickness of the coating layer = 1 nm, polymer surface density ξ = 4.5•10<sup>17</sup> m<sup>−2</sup>).</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-g003\"/></fig><p>We can see in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> and <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> that the polymer concentration in the nanoparticle coating influences how the nanoparticles aggregate. To study how the thickness of the nanoparticle coating layer and the polymer surface density influence the magnetic behaviour of the nanoparticles, for different values of the polymer surface density, the thickness of the nanoparticle coating layer was varied from 1 nm to 3 nm. Then, for each thickness of the coating layer, the polymer surface density was varied from 10<sup>16</sup> m<sup>−2</sup> to 4.5 × 10<sup>17</sup> m<sup>−2</sup>. The results are depicted in Figures 3–5. As shown, the average effective magnetic relaxation time is affected either by the thickness of the nanoparticle coating layer or by the density of the polymer surface layer.</p><p><xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref> and <xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref> show the average value of the effective magnetic relaxation time versus the thickness of the nanoparticle coating layer for low and high values of the polymer surface density, respectively. For low values of the polymer surface density in the nanoparticle coating layer (10<sup>16</sup> m<sup>−2</sup> and 5 × 10<sup>16</sup> m<sup>−2</sup>), the average value of the effective magnetic relaxation time decreases with an increase in layer thickness. For high polymer surface density values, the average value of the effective magnetic relaxation time increases with the increase in coating layer thickness, then reaches a maximum value and then slightly decreases. The obtained results can be explained by the competition between the attraction forces, especially the magnetic dipolar interaction forces (<xref ref-type=\"disp-formula\" rid=\"FD13\">Equation 13</xref>). The magnetic dipolar interaction forces are directly proportional to the magnetic moments of the particles and inversely proportional to the 5th power of the interparticle distances and the forces of repulsion, especially the steric forces (<xref ref-type=\"disp-formula\" rid=\"FD12\">Equation 12</xref>). In addition, the magnetic dipolar interaction forces are directly proportional to the thickness of the surfactant coating layer. At low polymer surface density values, the repulsion forces, in particular the steric forces (<xref ref-type=\"disp-formula\" rid=\"FD12\">Equation 12</xref>), are weaker. Therefore, the attraction forces predominate, especially the magnetic dipolar interaction forces which act on the nanoparticles (<xref ref-type=\"disp-formula\" rid=\"FD13\">Equation 13</xref>). As such, the nanoparticles tend to agglomerate, resulting in a large local volumetric concentration of large nanoparticles. At high polymer surface density values, the repulsion forces, in particular the steric forces (<xref ref-type=\"disp-formula\" rid=\"FD12\">Equation 12</xref>), are stronger in comparison to the attraction forces (e.g., the magnetic dipolar interaction forces acting on the nanoparticles, <xref ref-type=\"disp-formula\" rid=\"FD13\">Equation 13</xref>), resulting in a smaller local nanoparticle concentration. In the extreme points (minimum, maximum), an unstable equilibrium is established between the repulsion and attraction forces. Regarding the magnetic behaviour of the superparamagnetic nanoparticle system, published studies are controversial. While some studies show that for diluted systems there is a decrease in the relaxation time when the interparticle interaction increases [<xref rid=\"R30\" ref-type=\"bibr\">30</xref>–<xref rid=\"R33\" ref-type=\"bibr\">33</xref>], others claim that the relaxation time increases when the particle concentration increases [<xref rid=\"R34\" ref-type=\"bibr\">34</xref>–<xref rid=\"R35\" ref-type=\"bibr\">35</xref>].</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Average values of the effective magnetic relaxation time versus thickness of the nanoparticle coating layer, at low polymer surface density values.</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-g004\"/></fig><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>Average values of the effective magnetic relaxation time versus thickness of the nanoparticle coating layer, at high polymer surface density values.</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-g005\"/></fig><p><xref ref-type=\"fig\" rid=\"F5\">Figure 5</xref> shows the effective magnetic relaxation time versus the polymer surface density for different thicknesses of the nanoparticle coating layer. Regardless of the coating layer thickness, for low polymer surface density values, the average values of both the Néel relaxation time and effective magnetic relaxation time decrease with an increase in polymer surface density until they reach a minimum value. For high values of the polymer surface density, there is an increase in the average values of both the Néel relaxation time and the effective magnetic relaxation time. The average of the minimum values of the Néel relaxation time and of the effective magnetic relaxation time increases as the thickness of the nanoparticle surfactant coating layer increases. For relatively small thickness values of the nanoparticle coating layer, the average of the minimum values of the Néel relaxation time and effective magnetic relaxation time shifts to small values of the polymer surface density, as the thickness of the surfactant coating layer of the magnetic nanoparticles increases. For larger thickness values this shift is almost unnoticeable.</p><fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p>Average values of the effective magnetic relaxation time versus polymer surface density at different thickness values of the nanoparticle coating layer.</p></caption><graphic xlink:href=\"Beilstein_J_Nanotechnol-11-1207-g006\"/></fig><p>This complicated dependence can also be explained by the competition between the repulsion forces (in particular the steric repulsion forces (<xref ref-type=\"disp-formula\" rid=\"FD12\">Equation 12</xref>)) and the attraction forces (in particular the magnetic dipolar interaction forces acting on the nanoparticles (<xref ref-type=\"disp-formula\" rid=\"FD13\">Equation 13</xref>)).</p></sec></sec><sec><title>Conclusion</title><p>Two simulation models are used in this study to investigate how the thickness of the surfactant coating layer and the density of the polymer surface layer influence both the Néel relaxation time and the effective magnetic relaxation time in a system consisting of magnetic nanoparticles suspended in a liquid matrix. To simulate the self-organization of the colloidal nanoparticles we used a stochastic method called the Langevin dynamics, which is based on an effective Verlet-type algorithm. To simulate the Néel relaxation time we used the Coffey solution in an oblique magnetic field, adapted to the local magnetic field on a nanoparticle (<xref ref-type=\"disp-formula\" rid=\"FD1\">Equation 1</xref>). The effective magnetic relaxation time was calculated based on the <xref ref-type=\"disp-formula\" rid=\"FD11\">Equation 11</xref>.</p><p>The numerical simulation results showed that the average values of the Néel relaxation time and the effective magnetic relaxation time are affected either by the thickness of the surfactant coating layer or by the density of the polymer surface layer.</p><p>More specifically, for small values of the polymer surface layer density, the average values of both the Néel relaxation time and the effective magnetic relaxation time decrease with an increase in coating thickness. At intermediate values of the polymer surface layer density, the average values of both the Néel relaxation time and the effective magnetic relaxation time decrease with an increase in coating thickness. Then, these relaxation time values reach a minimum, after which a slight increase occurs. At high values of the polymer surface layer density, the average values of both the Néel relaxation time and the effective magnetic relaxation time increase with an increase in coating thickness. Then, these relaxation times reach a maximum value, after which a slight decrease occurs.</p><p>It was also shown that, regardless of the coating thickness, for small values of the polymer surface layer density, the average values of both the Néel relaxation time and the effective magnetic relaxation time decrease with an increase in the polymer surface layer density. Then, these relaxation time values reach a minimum, after which, at high values of the polymer surface layer density, these relaxation time values increase again. The average of the minimum values of the Néel relaxation time and the effective magnetic relaxation time increases when the thickness of the nanoparticle surfactant coating increases. For relatively small thickness values of the nanoparticle coating layer, the average of the minimum values of the Néel relaxation time and the effective magnetic relaxation time shifts to low polymer surface density values when the surfactant coating thickness of the magnetic nanoparticles increases. For large thickness values, this shift is almost unnoticeable.</p><p>All of these behaviours related to average Néel and effective magnetic relaxation times can be explained by the competition between the repulsion and attraction forces acting on the nanoparticles.</p><p>The results presented here have the potential to be applied in several fields that use colloidal magnetic nanoparticle systems, in particular the biomedical field [<xref rid=\"R36\" ref-type=\"bibr\">36</xref>–<xref rid=\"R41\" ref-type=\"bibr\">41</xref>]. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">BMJ Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">BMJ Open</journal-id><journal-id journal-id-type=\"hwp\">bmjopen</journal-id><journal-id journal-id-type=\"publisher-id\">bmjopen</journal-id><journal-title-group><journal-title>BMJ Open</journal-title></journal-title-group><issn pub-type=\"epub\">2044-6055</issn><publisher><publisher-name>BMJ Publishing Group</publisher-name><publisher-loc>BMA House, Tavistock Square, London, WC1H 9JR</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32727740</article-id><article-id pub-id-type=\"pmc\">PMC7431772</article-id><article-id pub-id-type=\"publisher-id\">bmjopen-2020-039369</article-id><article-id pub-id-type=\"doi\">10.1136/bmjopen-2020-039369</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Epidemiology</subject></subj-group><subj-group subj-group-type=\"hwp-journal-coll\"><subject>1506</subject><subject>2474</subject><subject>1692</subject></subj-group><series-title>Original research</series-title></article-categories><title-group><article-title>Excess cases of influenza and the coronavirus epidemic in Catalonia: a time-series analysis of primary-care electronic medical records covering over 6 million people</article-title></title-group><contrib-group><contrib id=\"author-22338816\" contrib-type=\"author\"><name><surname>Coma Redon</surname><given-names>Ermengol</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">1</xref><xref ref-type=\"aff\" rid=\"aff2\">2</xref></contrib><contrib id=\"author-77686782\" contrib-type=\"author\"><name><surname>Mora</surname><given-names>Nuria</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">1</xref><xref ref-type=\"aff\" rid=\"aff2\">2</xref></contrib><contrib id=\"author-74467234\" contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">http://orcid.org/0000-0003-1202-9153</contrib-id><name><surname>Prats-Uribe</surname><given-names>Albert</given-names></name><xref ref-type=\"aff\" rid=\"aff3\">3</xref></contrib><contrib id=\"author-31260954\" contrib-type=\"author\"><name><surname>Fina Avilés</surname><given-names>Francesc</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">1</xref><xref ref-type=\"aff\" rid=\"aff2\">2</xref></contrib><contrib id=\"author-42453710\" contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">http://orcid.org/0000-0002-3950-6346</contrib-id><name><surname>Prieto-Alhambra</surname><given-names>Daniel</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">2</xref><xref ref-type=\"aff\" rid=\"aff3\">3</xref></contrib><contrib id=\"author-37987273\" contrib-type=\"author\"><name><surname>Medina</surname><given-names>Manuel</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">1</xref><xref ref-type=\"aff\" rid=\"aff2\">2</xref></contrib></contrib-group><aff id=\"aff1\">\n<label>1</label>\n<institution content-type=\"department\">Sistemes d’Informació dels Serveis d’Atenció Primària (SISAP)</institution>, <institution>ICS</institution>, <addr-line content-type=\"city\">Barcelona</addr-line>, <addr-line content-type=\"state\">Catalunya</addr-line>, <country>Spain</country>\n</aff><aff id=\"aff2\">\n<label>2</label>\n<institution>IDIAP Jordi Gol, Universitat Autònoma de Barcelona</institution>, <addr-line content-type=\"city\">Barcelona</addr-line>, <addr-line content-type=\"state\">Catalunya</addr-line>, <country>Spain</country>\n</aff><aff id=\"aff3\">\n<label>3</label>\n<institution content-type=\"department\">Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Science</institution>, <institution>University of Oxford</institution>, <addr-line content-type=\"city\">Oxford</addr-line>, <addr-line content-type=\"state\">Oxfordshire</addr-line>, <country>UK</country>\n</aff><author-notes><corresp><label>Correspondence to</label> Dr Daniel Prieto-Alhambra; <email>daniel.prietoalhambra@ndorms.ox.ac.uk</email></corresp><fn fn-type=\"other\"><p>ECR, NM and AP-U are joint first authors.</p></fn></author-notes><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><volume>10</volume><issue>7</issue><elocation-id>e039369</elocation-id><history><date date-type=\"received\"><day>13</day><month>4</month><year>2020</year></date><date date-type=\"rev-recd\"><day>01</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>09</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY. Published by BMJ.</copyright-statement><copyright-year>2020</copyright-year><license license-type=\"open-access\"><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\" start_date=\"2020-07-29\">https://creativecommons.org/licenses/by/4.0/</ali:license_ref><license-p>This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">https://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"bmjopen-2020-039369.pdf\"/><self-uri content-type=\"reviewers-comments-pdf\" xlink:href=\"bmjopen-2020-039369.reviewer_comments.pdf\"/><self-uri content-type=\"draft-revisions-pdf\" xlink:href=\"bmjopen-2020-039369.draft_revisions.pdf\"/><abstract><sec><title>Objectives</title><p>There is uncertainty about when the first cases of COVID-19 appeared in Spain. We aimed to determine whether influenza diagnoses masked early COVID-19 cases and estimate numbers of undetected COVID-19 cases.</p></sec><sec><title>Design</title><p>Time-series study of influenza and COVID-19 cases, 2010–2020.</p></sec><sec><title>Setting</title><p>Primary care, Catalonia, Spain.</p></sec><sec><title>Participants</title><p>People registered in primary-care practices, covering >6 million people and >85% of the population.</p></sec><sec><title>Main outcome measures</title><p>Weekly new cases of influenza and COVID-19 clinically diagnosed in primary care.</p></sec><sec><title>Analyses</title><p>Daily counts of both cases were computed using the total cases recorded over the previous 7 days to avoid weekly effects. Epidemic curves were characterised for the 2010–2011 to 2019–2020 influenza seasons. Influenza seasons with a similar epidemic curve and peak case number as the 2019–2020 season were used to model expected case numbers with Auto Regressive Integrated Moving Average models, overall and stratified by age. Daily excess influenza cases were defined as the number of observed minus expected cases.</p></sec><sec><title>Results</title><p>Four influenza season curves (2011–2012, 2012–2013, 2013–2014 and 2016–2017) were used to estimate the number of expected cases of influenza in 2019–2020. Between 4 February 2020 and 20 March 2020, 8017 (95% CI: 1841 to 14 718) excess influenza cases were identified. This excess was highest in the 15–64 age group.</p></sec><sec><title>Conclusions</title><p>COVID-19 cases may have been present in the Catalan population when the first imported case was reported on 25 February 2020. COVID-19 carriers may have been misclassified as influenza diagnoses in primary care, boosting community transmission before public health measures were taken. The use of clinical codes could misrepresent the true occurrence of the disease. Serological or PCR testing should be used to confirm these findings. In future, this surveillance of excess influenza could help detect new outbreaks of COVID-19 or other influenza-like pathogens, to initiate early public health responses.</p></sec></abstract><kwd-group><kwd>epidemiology</kwd><kwd>primary care</kwd><kwd>public health</kwd></kwd-group><funding-group><award-group id=\"funding-1\"><funding-source><institution-wrap><institution>National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC)</institution></institution-wrap></funding-source></award-group><award-group id=\"funding-2\"><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">http://dx.doi.org/10.13039/501100000265</institution-id><institution>Medical Research Council</institution></institution-wrap></funding-source><award-id>MR/K501256/1, MR/N013468/1</award-id></award-group><award-group id=\"funding-3\"><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">http://dx.doi.org/10.13039/501100000659</institution-id><institution>Research Trainees Coordinating Centre</institution></institution-wrap></funding-source><award-id>SRF-2018-11-ST2-004</award-id></award-group></funding-group><custom-meta-group><custom-meta><meta-name>special-feature</meta-name><meta-value>unlocked</meta-value></custom-meta></custom-meta-group></article-meta></front><body><boxed-text id=\"BX1\" position=\"float\" orientation=\"portrait\"><caption><title>Strengths and limitations of this study</title></caption><list list-type=\"bullet\"><list-item><p>We used good quality data covering >6 million people and >85% of the Catalan population, obtained directly from primary record records.</p></list-item><list-item><p>Data had previously been validated against gold-standard influenza sentinel systems.</p></list-item><list-item><p>We used ecological data and modelled it using data from previous seasons, therefore assuming a direct link between excess influenza cases and the COVID-19 pandemic.</p></list-item><list-item><p>Excess influenza cases could also have been due to a panic effect, where current coronavirus epidemic had encouraged people to consult healthcare professionals more frequently and for milder symptoms than usual.</p></list-item><list-item><p>We lack confirmatory tests or antigenic data for the estimated excess influenza cases, but our results agree with the proportion of influenza samples that tested positive for SARS-CoV-2 in a recent study.</p></list-item></list></boxed-text><sec id=\"s1\"><title>Background</title><p>A new infectious disease, now named COVID-19, was identified by Chinese authorities on 7 January 2020 as the cause of an outbreak of pneumonia in Wuhan.<xref rid=\"R1\" ref-type=\"bibr\">1</xref> Caused by SARS-CoV-2, COVID-19 is asymptomatic or presymptomatic in a high proportion of patients, with estimates around 15%–30%.<xref rid=\"R2\" ref-type=\"bibr\">2–4</xref> Most patients present mild influenza-like symptoms, including fever, dry cough, fatigue, sore throat, dyspnoea, headache and myalgia.<xref rid=\"R5\" ref-type=\"bibr\">5 6</xref> Around 15%–20% of symptomatic cases present severe forms of disease that require hospital admission.<xref rid=\"R1\" ref-type=\"bibr\">1 7</xref> Older people, men and those with multiple comorbidities appear more likely to suffer more serious types of COVID-19.<xref rid=\"R5\" ref-type=\"bibr\">5 6 8–10</xref> Conversely, children seem to have a similar probability of infection, but milder and often asymptomatic forms of the disease.<xref rid=\"R11\" ref-type=\"bibr\">11</xref>\n</p><p>Cases of COVID-19 have grown exponentially and have been reported all over the world. The first three cases in Europe were reported in France on 24 January 2020.<xref rid=\"R12\" ref-type=\"bibr\">12</xref> The first imported COVID-19 case in Spain was dated 31 January 2020 in La Gomera, and the first in Catalonia reported a month after, on 25 February 2020. The total number of confirmed cases in Catalonia then increased exponentially, with 715 cumulative cases reported by 14 March 2020 and a striking 4203 on 20 March 2020. Despite these official figures, it is uncertain whether SARS-CoV-2 was circulating in the community before the first official cases. It is difficult to believe, for example, that this airborne infection did not cross the uncontrolled borders between Catalonia and France for a whole month. Some have thus speculated that undetected COVID-19 cases may have been categorised as influenza before the first official case was reported in Spain.<xref rid=\"R13\" ref-type=\"bibr\">13</xref>\n</p><p>Catalonia is fortunate to have a reliable system for influenza surveillance in place. A network of 60 sentinel general practitioners (GPs) covering 1% of the total population report daily cases of influenza-like illness (ILI) and take samples for differential diagnosis and confirmation of influenza infections in the region.<xref rid=\"R14\" ref-type=\"bibr\">14</xref> A specialised hospital-based system takes samples from severe hospitalised influenza cases.<xref rid=\"R14\" ref-type=\"bibr\">14</xref> A community-based surveillance system called Diagnosticat also extracts counts of ILI diagnoses from a network of GP health records in real-time, covering 85% of the population.<xref rid=\"R15\" ref-type=\"bibr\">15</xref> This last approach allows us to examine trends with granularity and to stratify analyses by age and other factors.</p><p>As the first cases of SARS-CoV-2 appeared in Catalonia during the influenza epidemic season and the disease shares some symptomatology with influenza, we hypothesised that SARS-CoV-2 could have been circulating in the community before the first confirmed case, resulting in an excess of influenza diagnoses. We aimed to estimate the number of excess influenza cases in Catalonia, globally and by age, and to examine its relationship with the number of clinically diagnosed COVID-19 cases.</p></sec><sec sec-type=\"methods\" id=\"s2\"><title>Methods</title><p>We used a time-series study of influenza and COVID-19 cases. We extracted data from primary-care electronic medical records covering about 85% of the population of Catalonia, around 6 million people. The study period included all influenza seasons from autumn–winter 2010–2011 to autumn–winter 2019–2020.</p><p>The key study outcomes were diagnoses of influenza and COVID-19. Daily frequency of influenza cases recorded in primary-care records were obtained from electronic medical records, as is routinely done for the Diagnosticat database.<xref rid=\"R16\" ref-type=\"bibr\">16</xref>\n</p><p>Diagnosticat is a website that reports in real-time all influenza diagnoses recorded by all GPs working at any of the primary-care centres run by the Institut Català de la Salut (ICS). ICS is the main primary-care health service provider in Catalonia and covers about 85% of practices in the region, who all use the same electronic medical record software, ECAP.<xref rid=\"R17\" ref-type=\"bibr\">17</xref> Diagnosticat includes all clinical influenza diagnosis codes (International Classification of Diseases-10 codes in <xref ref-type=\"supplementary-material\" rid=\"SP1\">online supplementary table 1</xref>) and is updated from ECAP daily (since 2010). It presents the frequency of daily influenza cases and the weekly incidence rates per 10<sup>5</sup> population, a unit that allows diagnoses to be compared between territories independently of the number of inhabitants. Influenza data on Diagnosticat has been shown to accurately represent that in a gold-standard source, the sentinel network of influenza infection reports data set.<xref rid=\"R15\" ref-type=\"bibr\">15</xref>\n</p><supplementary-material content-type=\"local-data\" id=\"SP1\"><object-id pub-id-type=\"doi\">10.1136/bmjopen-2020-039369.supp1</object-id><label>Supplementary data</label><p>\n<inline-supplementary-material id=\"SS1\" xlink:href=\"bmjopen-2020-039369supp001.pdf\" mime-subtype=\"pdf\" mimetype=\"application\" content-type=\"local-data\"/>\n</p></supplementary-material><p>The number of COVID-19 clinical diagnoses were extracted and aggregated using the same data source and methods as for influenza diagnoses. Clinical diagnoses of COVID-19 have been recorded in ECAP since 27 February 2020, when bespoke codes were introduced (<xref ref-type=\"supplementary-material\" rid=\"SP1\">online supplementary table 1</xref>). Since 15 March 2020, Catalan policies have advocated for cases to be defined based on symptoms alone, with serological or PCR confirmation only required when patients are admitted to hospital or are healthcare staff.<xref rid=\"R18\" ref-type=\"bibr\">18</xref>\n</p><sec id=\"s2-1\"><title>Statistical analysis</title><p>Daily counts of influenza and COVID-19 cases were computed based on the frequency of cases recorded in the previous 7-day period to avoid weekly effects on recording practice. All influenza seasons in the study period (2010–2011 to 2019–2020) were analysed separately to characterise annual epidemic curves for seasonal influenza.</p><p>Influenza seasons with a visually similar epidemic curve and similar peak case number to that of the 2019–2020 season were selected to model predictions for 2019–2020. We selected these specific seasons after an assessment of the number of cases at the peak to maximise comparability with the current influenza season before the COVID-19 outbreak. Auto Regressive Integrated Moving Average (ARIMA) models<xref rid=\"R19\" ref-type=\"bibr\">19</xref> were fitted to the seasons included in the analysis for the whole population and for three age groups, paediatric patients (under 15), adults (15–64) and elderly (over 64 years old).</p><p>From the fitted time series, the expected speed of decrease in the number of weekly influenza cases for the 2019–2020 influenza season was calculated for each day after the peak. The expected speed of decrease was defined as the difference between the number of influenza diagnoses predicted between the current day t and the previous day t−1, divided by the number of diagnoses predicted for the previous day t−1 ((cases<sub>t</sub>(cases<sub>t−1</sub>)−1). Expected influenza cases were calculated using the sequence G<sub>t</sub> = G<sub>0</sub> *∏<sup>t</sup>\n<sub>k=1</sub> V<sub>k</sub>, where G<sub>t</sub> was the expected influenza cases in the period t, G<sub>0</sub> the number of cases at the peak and V<sub>k</sub> the speed of decrease at day k.</p><p>The expected influenza cases for each day on the 2019–2020 season were calculated from the day of the season peak to 20 March 2020, the day of the data extraction. Excess influenza cases were defined as the number of observed minus expected cases, estimated daily as above. We calculated 95% CIs for each estimate. All analyses were performed in R V.3.5.1.<xref rid=\"R20\" ref-type=\"bibr\">20</xref>\n</p><p>We further tested our method, as a sensitivity analysis, with data from the most recent (2018–2019) season as a negative control. We checked whether the method was able to identify the season as a ‘regular’ influenza season not detecting excess influenza cases.</p></sec><sec id=\"s2-2\"><title>Patient and public involvement</title><p>This research was done without patient involvement. Patients were not invited to comment on the study design and were not consulted to develop patient relevant outcomes or interpret the results. Patients were not invited to contribute to the writing or editing of this document for readability or accuracy.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec id=\"s3-1\"><title>Previous influenza epidemic curves</title><p>Four of the previous nine influenza season curves (2011–2012, 2012–2013, 2013–2014 and 2016–2017) had an epidemic curve and number of influenza cases during the peak similar to the 2019–2020 season, as shown in <xref ref-type=\"fig\" rid=\"F1\">figure 1</xref>. These four curves were used to estimate the number of expected cases of influenza in 2019–2020. The mean peak number of cases in the included and excluded seasons was 12 762 and 14 680, respectively. The peak number of cases in 2019–2020 was 12 066.</p><fig id=\"F1\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Epidemic curves showing the weekly number of new influenza cases during the influenza seasons from autumn–winter 2010–2011 to autumn–winter 2019–2020 in Catalonia, Spain. Curves in solid lines were similar to the 2019–2020 season and included in further modelling. Curves in dashed lines were not similar to the 2019–2020 season and were excluded from further modelling.</p></caption><graphic xlink:href=\"bmjopen-2020-039369f01\"/></fig><p>ARIMA models were fitted using the included seasons. <xref ref-type=\"supplementary-material\" rid=\"SP1\">Online supplementary table 2</xref> shows the full modelling process and the fitted parameters.</p></sec><sec id=\"s3-2\"><title>2019–2020 influenza epidemic description</title><p>In Catalonia, the 2019–2020 influenza epidemic reached its peak on 4 February 2020, with 12 066 cases in the previous 7 days. <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> shows the evolution of the season compared with past seasons, centred on the day of the peak. By eye, the downwards trend after the peak initially looks very similar to the previous seasons. However, 20 days after the peak, the curve starts to flatten, and the slope slows down. This abnormal pattern in the descending part of the curve differs from the pattern in the previous seasons.</p><fig id=\"F2\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Epidemic curves for the 2019–2020 Catalonia influenza season (solid line) and the four seasons in the past decade with a similar peak number of cases (dotted lines: 2011–2012, 2012–2013, 2013–2014 and 2016–2017), centred on the day of the peak number of cases in each curve.</p></caption><graphic xlink:href=\"bmjopen-2020-039369f02\"/></fig></sec><sec id=\"s3-3\"><title>Expected versus observed cases</title><p>\n<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref> shows the observed and estimated numbers of weekly new influenza cases (with 95% CI) after the peak of the 2019–2020 influenza season. The estimated expected number of cases were predicted using the selected previous influenza seasons.</p><fig id=\"F3\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Observed and expected (with 95% CI) weekly new influenza cases each day after the peak of the 2019–2020 Catalonia influenza season, in the full population and in each age group.</p></caption><graphic xlink:href=\"bmjopen-2020-039369f03\"/></fig><p>In the whole population, observed cases were greater than expected after the seasonal influenza peak, but not always significant during the whole study period. The difference was statistically significant for 23 days between 4 February 2020 and 20 March 2020. Most of these days fell after 8 March 2020, when the difference between observed and expected increased significantly and observed cases remained above the 95% CI band for expected cases for 2 weeks.</p><p>There was a greater difference between observed and expected cases among people aged 15–64 years than in both the total population and other age groups, with 25 total days of significant difference. The observed and expected cases diverged earlier than for the total population, separating around 26 February 2020 and remaining significantly different for the rest of the study period.</p><p>Observed and expected cases were generally similar in those older than 64 years, until 6 March 2020. Observed cases then quickly rose above expected cases, with the difference becoming significant on 11 March 2020 and remaining so for 9 days, until 19 March 2020.</p><p>The shape of the observed cases curve for people younger than 15 years was similar to that for people aged 15–64 years. However, the difference between observed and expected cases was only significantly different for 11 days, between 6 March 2020 and 16 March 2020.</p><p>We estimated 8017 excess influenza cases (95% CI: 1841 to 14 718) between 4 February 2020 and 20 March 2020. This excess is presented stratified by age in <xref rid=\"T1\" ref-type=\"table\">table 1</xref>.</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Number of excess influenza cases in Catalonia from 4 February 2020 to 20 March 2020, after the peak of the seasonal influenza epidemic, and the percentage of all influenza cases in that period that they make up, overall and by age group</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Age group</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Estimated number of excess influenza cases<break/>(95% CI)</td><td align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Percentage of all influenza cases in this age group made up by the estimated excess cases<break/>(95% CI)</td></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Younger than 15</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2078 (160 to 4078)</td><td align=\"char\" char=\".\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13.6 (1.0 to 26.7)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Between 15 and 64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4670 (2387 to 7124)</td><td align=\"char\" char=\".\" valign=\"top\" rowspan=\"1\" colspan=\"1\">20.9 (10.7 to 31.8)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Older than 64</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">142 (33 to 260)</td><td align=\"char\" char=\".\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.9 (2.1 to 16.3)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Total</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8017 (1841 to 14 718)</td><td align=\"char\" char=\".\" valign=\"top\" rowspan=\"1\" colspan=\"1\">20.4 (4.7 to 37.5)</td></tr></tbody></table></table-wrap><p>Results for our negative control influenza season are shown in <xref ref-type=\"supplementary-material\" rid=\"SP1\">online supplementary figure 1</xref>. We found no excess influenza in the previous (2018–2019) influenza season using the same method.</p></sec><sec id=\"s3-4\"><title>Excess influenza cases compared with COVID-19 diagnoses</title><p>\n<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref> depicts the number of excess influenza cases and COVID-19 diagnoses each day after the peak of the 2019–2020 seasonal influenza epidemic. Excess influenza cases increased rapidly from 24 February 2020, peaking on 7 March 2020. They steeply declined from 15 March 2020, coinciding with an increase in the number of COVID-19 diagnoses.</p><fig id=\"F4\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Excess influenza cases and clinically diagnosed COVID-19 cases in Catalonia, Spain, as number of cases in the previous 7-day period, from the peak of the 2019–2020 seasonal influenza epidemic (4 February 2020).</p></caption><graphic xlink:href=\"bmjopen-2020-039369f04\"/></fig><p>There were 4347 excess influenza cases and 1497 clinical diagnoses of COVID-19 on 14 March 2020, comparing with just 2575 excess influenza cases (40% less) and a striking 16 547 (539% increase) clinical diagnoses of COVID-19 on 20 March 2020.</p></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>In mid-February 2020, we observed an unusually high, larger than expected number of influenza cases in the daily published data. In Catalonia, the 2019–2020 seasonal influenza epidemic reached its peak on 4 February 2020. Based on previous years’ data, influenza diagnoses were expected to decrease rapidly over the following weeks. However, the number of influenza diagnoses instead remained stable, which was counterintuitive and inconsistent with data from past influenza seasons. This increase in observed influenza diagnoses over those expected, here named ‘excess influenza’, correlates over time with the observed number of COVID-19 cases. Excess influenza cases could be used in future for the early detection of competing outbreaks.</p><p>Using four of the previous nine influenza seasons as a benchmark, we detected 8017 excess influenza cases between 4 February 2020 and 20 March 2020. This excess was higher in people aged 15–64 years, with over 20% more cases than expected. The excess started to decrease after 15 March 2020. Worryingly, these results suggest that SARS-CoV-2 could have already been circulating in the Catalan population when the first imported case was reported on 25 February 2020. People infected with COVID-19 may have been masked under ILI diagnoses in primary care, allowing continuing community transmission of COVID-19 before public health measures were taken.</p><p>To our knowledge, this is the first study attempting to quantify the start of the COVID-19 epidemic in Spain by comparing the number of reported ILI cases with the expected figures based on previous influenza seasons. The excess influenza cases metric could be useful for monitoring future outbreaks of COVID-19 and other competing viral epidemics.</p><p>Our study has several limitations. We used ecological data and modelled it using data from previous seasons, therefore assuming a direct causal link between excess influenza cases and the COVID-19 pandemic. As our method is based on crude count of influenza cases, major changes in denominator and population structure could limit the use of the proposed method, but population age and gender has remained relatively stable in the study period.<xref rid=\"R21\" ref-type=\"bibr\">21</xref> Our main limitation is the possible misclassification of disease status due to limitations related to the use of clinical codes. We lack serological tests or antigenic data for confirmation, and this should be investigated to confirm our findings. Our results agree with a study that tested all influenza samples in Los Angeles for SARS-CoV-2, finding 2.2%–10.7% of the tested samples positive for the pathogen, and a centre for disease control report that times the start of limited community transmission round mid-January to February.<xref rid=\"R22\" ref-type=\"bibr\">22 23</xref>\n</p><p>The observed excess influenza cases could have been due to a panic effect, in which the current coronavirus infodemic, a rapid spread of misinformation, has encouraged people to consult healthcare professionals more frequently and for milder symptoms than usual. However, our data showed that the number of influenza diagnoses dropped drastically and COVID-19 diagnoses increased after 15 March 2020. New COVID-19 guidelines were released on 15 March 2020 in Spain that recommended only testing hospital-admitted patients and healthcare staff and encouraging GPs to diagnose COVID-19 clinically without PCR confirmation.<xref rid=\"R18\" ref-type=\"bibr\">18</xref> At least some of the excess ILI cases were thus likely to have actually been COVID-19 cases.</p><p>Our study also has strengths. The data used were good quality, as demonstrated in many previous publications,<xref rid=\"R24\" ref-type=\"bibr\">24–30</xref> were obtained directly from primary-care records, and have been validated against gold-standard sentinel systems. This existing database covers over 85% of the population of Catalonia, which allowed us to rapidly detect excess influenza cases across the whole population and in different age groups.</p><p>In conclusion, the full extent of the COVID-19 pandemic is still unknown. The confirmed number of cases may be just the tip of the iceberg, due to the lack of testing of patients presenting mild COVID-19 symptoms. We need comprehensive, well-designed, seroprevalence studies to know how many people have been infected. This novel analysis approach could offer a quantitative approach to population surveillance that may be useful for other institutions/regions/countries and could be easily integrated into current information systems. This surveillance of excess influenza cases using widely available primary-care electronic medical records could help detect new outbreaks of COVID-19 and other ILI-causing pathogens, supporting early testing and public health responses.</p></sec><sec sec-type=\"supplementary-material\"><title>Supplementary Material</title><supplementary-material content-type=\"local-data\" id=\"d38e229\"><caption><title>Reviewer comments</title></caption><media mimetype=\"application\" mime-subtype=\"pdf\" xlink:href=\"bmjopen-2020-039369.reviewer_comments.pdf\"/></supplementary-material><supplementary-material content-type=\"local-data\" id=\"d38e230\"><caption><title>Author's manuscript</title></caption><media mimetype=\"application\" mime-subtype=\"pdf\" xlink:href=\"bmjopen-2020-039369.draft_revisions.pdf\"/></supplementary-material></sec></body><back><ack><p>The authors acknowledge English language editing by Dr Jennifer A de Beyer of the Centre for Statistics in Medicine, University of Oxford.</p></ack><fn-group><fn fn-type=\"other\"><p><bold>Contributors:</bold> ECR, NM, AP-U, FFA, MM and DP-A contributed to the design of the study, the interpretation of the results, and reviewed the manuscript. EC and NM had access to the data, performed the statistical analysis, and acted as guarantors. ECR, NM, AP-U, and DP-A are joint first authors and wrote the first draft of the manuscript. MM and DP-A are joint senior authors.</p></fn><fn fn-type=\"other\"><p><bold>Funding:</bold> The research was partially supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). DP-A is funded through an NIHR Senior Research Fellowship (Grant number SRF-2018-11-ST2-004). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. AP-U is supported by Fundacion Alfonso Martin Escudero and the Medical Research Council (grant numbers MR/K501256/1, MR/N013468/1).</p></fn><fn fn-type=\"COI-statement\"><p><bold>Competing interests:</bold> DP-A reports grants and other from AMGEN; grants, non-financial support and other from UCB Biopharma; grants from Les Laboratoires Servier, outside the submitted work; and Janssen, on behalf of IMI-funded EHDEN and EMIF consortiums, and Synapse Management Partners have supported training programmes organised by DP-A's department and open for external participants. AP-U reports grants from Fundacion Alfonso Martin Escudero and the Medical Research Council. No other relationships or activities that could appear to have influenced the submitted work.</p></fn><fn fn-type=\"other\"><p><bold>Patient and public involvement:</bold> Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.</p></fn><fn fn-type=\"other\"><p><bold>Patient consent for publication:</bold> Not required.</p></fn><fn fn-type=\"other\"><p><bold>Provenance and peer review:</bold> Not commissioned; externally peer reviewed.</p></fn><fn fn-type=\"other\"><p><bold>Data availability statement:</bold> Data are available upon reasonable request. Data on flu cases are publicly available. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849193</article-id><article-id pub-id-type=\"pmc\">PMC7431773</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00710</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>The Virtual Morris Water Task in 64 Patients With Bilateral Vestibulopathy and the Impact of Hearing Status</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Dobbels</surname><given-names>Bieke</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/644955/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Mertens</surname><given-names>Griet</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/389361/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Gilles</surname><given-names>Annick</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/313330/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Moyaert</surname><given-names>Julie</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>van de Berg</surname><given-names>Raymond</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/30372/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fransen</surname><given-names>Erik</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/194294/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Van de Heyning</surname><given-names>Paul</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/339074/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Van Rompaey</surname><given-names>Vincent</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/257549/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Faculty of Medicine and Health Sciences, University of Antwerp</institution>, <addr-line>Antwerp</addr-line>, <country>Belgium</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Otorhinolaryngology and Head and Neck Surgery, Antwerp University Hospital</institution>, <addr-line>Edegem</addr-line>, <country>Belgium</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Otorhinolaryngology and Head and Neck Surgery, Zuyderland Medical Center</institution>, <addr-line>Heerlen</addr-line>, <country>Netherlands</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Division of Balance Disorders, Department of Otorhinolaryngology and Head and Neck Surgery, Maastricht University Medical Center</institution>, <addr-line>Maastricht</addr-line>, <country>Netherlands</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Faculty of Physics, Tomsk State University</institution>, <addr-line>Tomsk</addr-line>, <country>Russia</country></aff><aff id=\"aff6\"><sup>6</sup><institution>StatUa Center for Statistics, University of Antwerp</institution>, <addr-line>Antwerp</addr-line>, <country>Belgium</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Michael Strupp, Ludwig Maximilian University of Munich, Germany</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Shinichi Iwasaki, The University of Tokyo, Japan; Derek Alexander Hamilton, University of New Mexico, United States; Klaus Jahn, Schön Klinik, Germany</p></fn><corresp id=\"c001\">*Correspondence: Bieke Dobbels <email>bieke.dobbels@uza.be</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Neuro-Otology, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>710</elocation-id><history><date date-type=\"received\"><day>06</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>10</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Dobbels, Mertens, Gilles, Moyaert, van de Berg, Fransen, Van de Heyning and Van Rompaey.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Dobbels, Mertens, Gilles, Moyaert, van de Berg, Fransen, Van de Heyning and Van Rompaey</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Background:</bold> Previous studies have demonstrated spatial cognitive deficits in patients with bilateral vestibulopathy (BVP). However, BVP patients frequently present with a concomitant sensorineural hearing loss, which is a well-established risk factor of cognitive impairment and incident dementia. Nonetheless, previous research on spatial cognitive deficits in BVP patients have not taken hearing status into account.</p><p><bold>Objective:</bold> This study aims to compare spatial cognition of BVP patients with healthy controls, with analyses adjusting for hearing status.</p><p><bold>Methods:</bold> Spatial cognition was assessed in 64 BVP patients and 46 healthy controls (HC) by use of the Virtual Morris Water Task (VMWT). All statistical analyses were adjusted for hearing (dys)function, sex, age, education, and computer use.</p><p><bold>Results:</bold> Overall, patients with BVP performed worse on all outcome measures of the VMWT. However, these differences between BVP patients and healthy controls were not statistically significant. Nonetheless, a statistically significant link between sensorineural hearing loss and spatial cognition was observed. The worse the hearing, the longer subjects took to reach the hidden platform in the VMWT. Furthermore, the worse the hearing, the less time was spent by the subjects in the correct platform quadrant during the probe trial of the VMWT.</p><p><bold>Conclusion:</bold> In this study, no difference was found regarding spatial cognition between BVP patients and healthy controls. However, a statistically significant link was observed between sensorineural hearing loss and spatial cognition.</p></abstract><kwd-group><kwd>spatial cognition</kwd><kwd>vestibular loss</kwd><kwd>hearing loss</kwd><kwd>hippocampus</kwd><kwd>Morris Water Maze</kwd></kwd-group><counts><fig-count count=\"9\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"45\"/><page-count count=\"12\"/><word-count count=\"7639\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>A growing body of literature recognizes that the function of the vestibular system goes far beyond balance and gaze stability. Both animal and human research suggests that the vestibular system plays a critical role in cognition (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>–<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), cognitive functioning can be subdivided into six domains: language, learning and memory, social cognition, attention, executive function, and visuospatial abilities (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Of these, it seems that visuospatial abilities, which compromises spatial memory and navigation, is by far the most studied cognitive domain in animals and humans with loss of peripheral vestibular input (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>). For example, spatial cognition has been studied in patients with vestibular loss using the Virtual Morris Water Task (VMWT) (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>). This is a virtual version of the Morris Water Maze, considered the golden standard for assessing spatial cognition in rodents (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Impaired spatial cognition has repeatedly been observed in patients with bilateral vestibulopathy (BVP) (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Patients with BVP suffer from a bilateral partial or complete loss of function of the vestibular structures of the inner ear, vestibular nerves, or a combination of both. BVP patients often present with oscillopsia and gait imbalance as primary complaints (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p><p>The link between spatial cognition and the vestibular system is of clinical importance for several reasons. First, cognitive training might yield therapeutic opportunities for BVP. Conventional treatment for patients with BVP is limited to counseling and intensive daily vestibular physical therapy to improve gaze and postural stabilization (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). However, these therapeutic strategies often remain insufficient (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Although the utility of cognitive training has been demonstrated to enhance balance in the elderly and in patients with mild cognitive impairment and dementia, cognitive training is not included in the current treatment of BVP (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). According to a recent computational model, cognitive training facilitates the central compensation process in BVP patients by increasing the knowledge about self-motion (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>).</p><p>Second, interest has been directed toward the link between cognitive impairment and the vestibular system because of the rising prevalence of dementia. As in BVP patients, impaired spatial cognition is among the most frequently observed cognitive deficits in patients with dementia. One of the hallmark symptoms of Alzheimer's disease is wandering behavior and loss of topographic memory (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). The vestibular system, more than any other sensory system, makes widespread cortical projections, including to the hippocampus. The hippocampus is thought to play a key role in the neuronal substrate underlying spatial cognitive deficits in BVP patients (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). For instance, in a leading study by Brandt et al., BVP patients showed bilateral hippocampal atrophy and spatial cognitive deficits (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Interestingly, in Alzheimer's disease, damage to the hippocampus is the most important anatomopathological feature (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>).</p><p>Furthermore, several studies have found significantly poorer vestibular function in patients with dementia compared with their healthy peers (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>–<xref rid=\"B20\" ref-type=\"bibr\">20</xref>).</p><p>These observations have led to the hypothesis that vestibular loss might cause cognitive decline and thus may contribute to the development of dementia. Given the rising prevalence of dementia and the lack of curative treatment, the identification of potentially modifiable risk factors is crucial (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>).</p><p>In the previous literature, however, little attention has been paid to the hearing status of vestibular patients when drawing conclusions about the link between cognitive decline and the vestibular system. A systematic review pointed out that none of the studies investigating cognition in BVP patients have adjusted their analysis for the hearing status of the enrolled subjects (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). However, because of the close anatomical relationship between the vestibular system and the cochlea, hearing loss is observed in up to half of BVP patients (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Hearing loss is a well-established risk factor for dementia (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>–<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Therefore, it is uncertain whether the cognitive deficits observed in BVP patients can be solely attributed to their vestibular loss as previously assumed. The frequently associated hearing loss in BVP patients might also play an essential role in their cognitive impairment (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>).</p><p>The goal of this study is to compare spatial cognitive performance, assessed using the VMWT, of BVP patients with healthy controls. In contrast to previous studies, the analyses in this study were specifically designed to take the hearing loss of BVP patients into account.</p></sec><sec sec-type=\"methods\" id=\"s2\"><title>Methods</title><sec><title>Study Design</title><p>The current study was a single-center, prospective, cross-sectional study, recruiting from October 2017 until August 2018 at the Antwerp University Hospital. The study was approved by the local ethics committee of the Antwerp University Hospital/University of Antwerp (protocol number 16/42/426) and informed consent was obtained in all study participants before the start of the study. The study was registered on <ext-link ext-link-type=\"uri\" xlink:href=\"https://ClinicalTrials.gov\">ClinicalTrials.gov</ext-link> (NCT03690817). The majority of the enrolled participants received general cognitive assessment at another scheduled appointment on a different day. Results have been published earlier (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>).</p></sec><sec><title>Study Participants</title><p>BVP patients were recruited from the Otorhinolaryngology, Head and Neck Surgery Department at Antwerp University Hospital, Belgium. Inclusion criteria for the BVP group were (1) BVP disease duration of more than 6 months and (2) definite diagnosis of BVP as defined by the diagnostic criteria of the Bárány Society (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>):</p><list list-type=\"alpha-lower\"><list-item><p>Horizontal angular vestibulo-ocular reflex (VOR) gain <0.6 measured by the video head impulse test (vHIT), and/or</p></list-item><list-item><p>Reduced caloric response (sum of bithermal, 30 and 44°, maximum peak slow phase velocity (SPV) on each side <6°/s), and/or</p></list-item><list-item><p>Reduced horizontal angular VOR gain <0.1 upon sinusoidal stimulation on a rotatory chair.</p></list-item></list><p>Control participants were recruited by means of the population registries at the local city councils in southern Antwerp (Belgium), by advertisements in the hospital, and by approaching friends, family, and colleagues. Only control subjects with no history of vertigo, scores <5 on the Dizziness Handicap Inventory, and normal hearing thresholds at 0.25–8 kHz, based on age and sex (defined by the BS 6951:1988, EN 27029:1991, and ISO 7029-1984 standards), were enrolled in the study.</p><p>The following additional inclusion criteria were applied for both BVP patients and healthy controls: (1) age ≥18 years, (2) fluency in Dutch, (3) no history of neurological diseases (e.g., dementia, Parkinson's disease, cerebrovascular accident, etc.), (4) absence of clinical signs indicating dementia or mild cognitive impairment, and (5) normal or appropriate corrected vision.</p><p>Regarding the necessity of computer use in the VMWT, all participants were asked about their frequency of computer use (daily vs. 2–5 days/week vs. seldom/never). Education of all participants was categorized as primary school, lower secondary school, upper secondary school, and college/university.</p></sec><sec><title>Vestibular Testing</title><p>By enrollment in the study, all BVP patients received new neuro-otological testing on site. The evaluation of the lower and mid frequencies function of the lateral semi-circular canals was performed by electronystagmography with bithermal caloric tests and rotatory chair test (Nystagliner Toennies, Germany). At our clinic, rotatory chair tests are performed using sinusoidal rotation (0.05 Hz) with a peak velocity of 60°/s (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). More detailed methodology and normative data were previously described (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). High-frequency function of all six semi-circular canals was measured by the vHIT. In the standard procedure used at our clinic, 10 valid head impulses are required for each canal. Angular head velocity was determined by three mini-gyroscopes, eye velocity by means of an infrared camera recording the right eye, all incorporated in commercially available vHIT goggles (Otometrics, Taastrup, Denmark). VOR gain was defined as the ratio of the area under the eye velocity curve to the head velocity curve from the impulse onset until the head velocity was again 0 (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>).</p></sec><sec><title>The Virtual Morris Water Task (VMWT)</title><p>To assess spatial learning and spatial memory retrieval, the VMWT was used. This task was designed by Derek Hamilton and was inspired by the original animal research tool, which is considered the gold standard for testing spatial cognition in rodents (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>). A 15.6-in. PC laptop monitor was used to display the virtual environment generated by the VMWT software version 1.10 (Neuro Investigations). In this task, participants had to navigate toward a hidden platform as fast as possible. The virtual environment consisted of a round pool, located in the middle of a square room. Each wall of the room contained a different visual cue on which a participant could rely to find his way to the hidden platform. The cues were positioned in such a way that the platform could not be encountered by simply moving toward a single cue (see <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). On the computer screen, a first-person view of the virtual environment was shown. Participants could move in the pool by using the arrow keys on the keyboard. Backward movement or up–down movement was not possible.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>The environment of the Virtual Morris Water Task. Overview of the virtual environment used in the Virtual Morris Water Task, with on each wall a different visual cue. The platform is indicated by the black square in the northeast quadrant of the pool.</p></caption><graphic xlink:href=\"fneur-11-00710-g0001\"/></fig><p>Before testing, all participants were given the same written instructions. Afterwards, time to ask questions to the examiner was foreseen. In both groups, four phase trials were performed:</p><sec><title>Phase I—Exploration Trial</title><p>The first part consisted of one block of four trials with a hidden platform. Participants were familiarized with the concept of the game and the use of the key arrows. By observing the participant, the examiner checked for good understanding of the task. When necessary, supplemental explanations were given.</p></sec><sec><title>Phase II—Hidden Trial</title><p>The test was started with 20 hidden platform trials. A virtual environment with different visual cues was used than in the exploration trial (see <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). The hidden platform was located, in all trials, at the same spot in the northeastern quadrant of the pool. As it was submerged underneath the pool's surface, it was not visible to the participants. Starting locations during each trial were sampled pseudo-randomly from the four cardinal direction points of the pool. If participants were unable to find the hidden platform after 60 s, the platform was made visible and a message appeared prompting the participant to swim to the platform. During each of these trials, three measures were computed:</p><list list-type=\"simple\"><list-item><p>- The latency, i.e., time to reach the platform.</p></list-item><list-item><p>- The covered path length, i.e., total distance traveled, divided by the pool diameter.</p></list-item><list-item><p>- The heading error, when the participant has traveled a distance >25% of the pool diameter from the start position; the angular deviation is computed between the straight trajectory to the center of the platform and the starting position (see <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>).</p></list-item></list><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>The user's view during the Virtual Morris Water Task. Spatial cognition was assessed by the Virtual Morris Water Task. This figure shows the first-person view of the virtual environment, presented on a computer screen.</p></caption><graphic xlink:href=\"fneur-11-00710-g0002\"/></fig><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Heading error in the Virtual Morris Water Task. During the hidden trials of the Virtual Morris Water Task, the heading error (α) is computed. When the participant first reaches a distance >25% of the pool diameter from the starting point, the angular deviation from a straight trajectory to the platform is measured. The smaller the heading error, the better the spatial orientation of the participant.</p></caption><graphic xlink:href=\"fneur-11-00710-g0003\"/></fig><p>Performance during these hidden platform trials represent a measure for spatial learning performance.</p></sec><sec><title>Phase III—Probe Trial</title><p>Subsequently, the platform was removed from the pool, unbeknownst to the participants. In this one trial, we measured the percentage of time a participant spent in the platform quadrant. A higher percentage was considered to be related to a better spatial memory retrieval of the participant.</p></sec><sec><title>Phase IV—Visible Trials</title><p>The last part of the test represents a control task for motor condition. Participants had to perform eight visible trials in which the platform was visible and participants had to swim to the platform as soon as possible. Again, latency and path length were recorded for each trial.</p></sec></sec><sec><title>Hearing Assessment</title><p>To correct cognitive outcome measures for the hearing status of the enrolled participants, a pure tone audiometry was performed. The unaided hearing thresholds were measured in a sound-isolated booth. For air conduction, hearing thresholds were determined at 125, 250, 500, 1,000, 2,000, 3,000, 4,000, 6,000, and 8,000 Hz using a two-channel Interacoustics AC-40 audiometer and insert earphones. Bone conduction thresholds were tested at 250, 500, 1,000, 2,000, 3,000, and 4,000 Hz. The high Fletcher index, which is the mean of air conduction hearing thresholds at 1, 2, and 4 kHz, was calculated for both ears. The hearing status of a participant was defined by the high Fletcher index of the better-hearing ear.</p></sec><sec><title>Data Collection and Statistical Analysis</title><p>Data were stored in OpenClinica LLC (Waltham, MA, USA), a secured online database for electronic data registration and data management developed for clinical research. For statistical analyses, IBMS SPSS Statistics (IBM Corp. Released 2016. Version 24.0. Armonk, NY) and “R” was used (R: A language and environment for statistical computing. Released 2013. R Foundation for Statistical Computing, Vienna, Austria).</p><p>Depending on distribution, demographic data were analyzed with either <italic>t</italic>-test and χ<sup>2</sup> test, or Mann–Whitney test and Fisher's exact test. Analogous to previous work, the 20 hidden platform trials were divided into three blocks: block 1 with trials 1–4, block 2 with trials 5–12, and block 3 with trials 13–20. First, for each performance variable of the hidden trial (latency, path length, and heading error), a linear mixed model was fitted. A random effect of individual was added to account for the non-independence between observations from the same individual. Fixed effects included group (BVP vs. healthy controls), time (repeated measurements during the three blocks of trials), and their interaction. The latter interaction term evaluates whether there is a difference in learning between BVP patients and healthy controls during the VMWT. In other words, a statistically significant interaction term points out that during all 20 hidden trials, one group progressively found the platform faster compared with the other group, indicating a better learning curve in this group throughout the test.</p><p>In the absence of a significant interaction, linear mixed models were fitted for all performance variables of the hidden trials, with the main factors group (BVP vs. healthy controls) and time (repeated measurements during the three blocks of trials), without interaction term and hearing status as indicated by the high Fletcher index. Using these models, we evaluated whether there was a statistically significant different performance between the two participant's groups, BVP and healthy controls, across all trial blocks. Moreover, using this model, we assessed whether there was a statistically significant main effect of the hearing status on VMWT performance. In all of these models, the following covariates were added: age, sex, computer use, and education.</p><p>To compare the spatial memory retrieval during the probe trial, a multiple linear regression model was fitted with the main factors group (BVP vs. healthy controls) and hearing status (high Fletcher index of the better-hearing ear). Again, age, sex, computer use, and education were entered as covariates.</p><p>Finally, mean path lengths and latencies during the 10 visible trials were computed and used as dependent variables in a similar multiple linear regression model.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>Participant Characteristics</title><p>Sixty-four BVP patients with a mean age of 59 ± 14 years met the study inclusion criteria; 60% of them were male. Forty-six healthy controls with a mean age of 48 ± 17 were enrolled in the study; 44% of them were male (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). The BVP group was gender matched to the control group. BVP patients were on average older, less educated, and had less computer experience than healthy controls (<italic>p</italic> < 0.05). Hearing loss was more frequent in BVP patients (High Fletcher index 58 ± 42 dB in BVP patients vs. 11 ± 12 dB in healthy controls, p < 0.05). To diagnose BVP, the Bárány Society criteria needed to be fulfilled (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Forty percent of BVP patients met all Bárány Society criteria: a bilateral reduced response on caloric testing, rotatory chair test, and vHIT. In 30% of BVP patients, two out of three Bárány Society criteria were fulfilled, and in the remaining 30% of the BVP patients there was only found a vestibular hypofunction in one of the three vestibular tests (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>). The mean gain of the left and right vHIT was, respectively, 4.2 ± 0.3 and 0.47 ± 0.3. The mean gain on the rotatory swing was 0.08 ± 0.08.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Demographic data.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>BVP patients</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Healthy controls</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>P</italic>-value</bold></th></tr><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>n</italic> = 64</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>n</italic> = 46</bold></th><th rowspan=\"1\" colspan=\"1\"/></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age (mean, SD)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59 (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48 (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sex (<italic>n</italic>, %)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Male</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38 (60)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Female</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26 (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26 (57)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Years of education (mean, SD)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Computer use (<italic>n</italic>, %)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Seldom/never</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15 (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> 2–5 days/week</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Daily</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31 (55)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (83)</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"4\" rowspan=\"1\">Hearing performance: pure tone audiometry</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"> Fletcher index better-hearing ear (mean, SD in dB)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58 (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11 (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.05</td></tr></tbody></table><table-wrap-foot><p>BVP, bilateral vestibulopathy; dB, decibel.</p></table-wrap-foot></table-wrap><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>Vestibular test results of BVP patients. According to the Bárány criteria, only one of the vestibular tests have to be bilaterally impaired to establish a BVP diagnosis.</p></caption><graphic xlink:href=\"fneur-11-00710-g0004\"/></fig><p>An underlying cause of vestibular loss could not be identified in 33.9% of BVP patients. With a prevalence of nearly 20%, a mutation in the <italic>COCH</italic> gene causing DFNA9 was the most frequent underlying non-idiopathic etiology in our BVP cohort (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). In 16% of BVP patients, an infectious cause was found (e.g., meningitis, neuritis, Lyme disease). Menière's disease and head trauma accounted for, respectively, 6 and 11% of BVP causes. In four BVP patients, an ototoxic cause was suspected (three aminoglycosides antibiotics and one chemotherapy, not further specified).</p></sec><sec><title>Results of the VMWT</title><sec><title>Hidden Platform Trials: Spatial Learning</title><p>First, during the hidden platform trials, a significant main effect of time was found for all outcome measurements, indicating faster determination of the hidden platform location over time (see <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). No statistically significant interaction between group × time was found in any of the outcome measures. This indicates that, regardless of the absolute outcome measurements in both groups, the learning curve in BVP patients was not significantly slower than in healthy controls. This is also illustrated by a similar slope of the curves in BVP patients and controls showed in <xref ref-type=\"fig\" rid=\"F5\">Figures 5</xref>–<xref ref-type=\"fig\" rid=\"F7\">7</xref>.</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Results of the linear mixed models used for the hidden platform trials of the Virtual Morris Water Maze.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold><italic>P</italic>-value (effect size if p < 0.05)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Group (BVP vs. healthy controls)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Hearing status (high Fletcher index of better-hearing ear)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Age</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Latency</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.006 (0.11)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001 (0.86)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Path length</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04 (0.01)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Heading error</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.56</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><0.001 (0.3)</td></tr></tbody></table><table-wrap-foot><p>Outcome measures for spatial learning were (1) latency, (2) path length, and (3) heading error. All analyses adjusted for age, sex, computer use, education, and hearing loss. A <italic>p</italic> < 0.05 indicates a statistically significant main effect of the examined factor.</p><p>BVP, bilateral vestibulopathy.</p></table-wrap-foot></table-wrap><fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p>Latencies from patients with bilateral vestibulopathy and healthy controls. This figure shows performance of patients with bilateral vestibulopathy (BVP, red) and healthy controls (HC, blue) during the 20 hidden platform trials and 10 visible platform trials of the Virtual Morris Water Maze. The latency, defined by the time in seconds needed to find the platform, is a measure for spatial learning. During all hidden platform trials, HC outperform BVP patients.</p></caption><graphic xlink:href=\"fneur-11-00710-g0005\"/></fig><fig id=\"F6\" position=\"float\"><label>Figure 6</label><caption><p>Path length from patients with bilateral vestibulopathy and healthy controls. This figure shows performance of patients with bilateral vestibulopathy (BVP, red) and healthy controls (HC, blue) during the 20 hidden platform trials and 10 visible platform trials of the Virtual Morris Water Maze. The path length, defined by the relative distance to the pool diameter covered to reach the platform, is a measure for spatial learning. During all hidden platform trials, performance of BVP patients is worse than HC.</p></caption><graphic xlink:href=\"fneur-11-00710-g0006\"/></fig><fig id=\"F7\" position=\"float\"><label>Figure 7</label><caption><p>Heading errors from patients with bilateral vestibulopathy and healthy controls. This figure shows the performance of patients with bilateral vestibulopathy (BVP, red) and healthy controls (HC, blue) during the 20 hidden platform trials and 10 visible platform trials of the Virtual Morris Water Maze. The higher the heading error, the worse the performance and thus spatial learning. In all hidden trials, the heading errors of BVP patients are higher than those of HC.</p></caption><graphic xlink:href=\"fneur-11-00710-g0007\"/></fig><p>Second, as shown in <xref ref-type=\"fig\" rid=\"F5\">Figures 5</xref>, <xref ref-type=\"fig\" rid=\"F6\">6</xref>, BVP patients took both more time and longer paths, compared with healthy controls, to reach the hidden platform during all three trial blocks (1–4, 5–12, and 13–20). Likewise, the heading error of BVP patients was larger during all three trial blocks (see <xref ref-type=\"fig\" rid=\"F7\">Figure 7</xref>). Importantly, in linear mixed models no significant group effect for latency, path length, or heading error was found. In other words, the worse performance of BVP patients compared with healthy controls was not statistically significant. All these analyses were adjusted for hearing status, age, sex, computer use, and education. Third, a statistically significant association between hearing loss and spatial learning was seen. The higher the Fletcher index, the longer the latencies were during the hidden trials (<italic>p</italic> = 0.006, effect size 0.11). As the Fletcher index increased by 1 dB, the latency was 0.11 second longer. There was no significant effect of hearing loss on path length or heading error.</p></sec><sec><title>Probe Trial: Spatial Memory Retrieval</title><p>During the probe trial, BVP patients searched 38% (±23.3) of their time in the correct quadrant, whereas healthy controls spent 52.1% (±22.7) in the correct quadrant. However, this difference was not statistically significant between the two groups (<italic>p</italic>-value in multiple linear regression model of groups = 0.9).</p><p>Nonetheless, the analysis revealed a significant main effect of hearing loss on relative amount of time spent in the correct quadrant (<italic>p</italic> = 0.05, β −0.1). This indicates that the worse the hearing, the poorer the memory retrieval. The results of the probe trial are demonstrated in <xref ref-type=\"fig\" rid=\"F8\">Figures 8</xref>, <xref ref-type=\"fig\" rid=\"F9\">9</xref>.</p><fig id=\"F8\" position=\"float\"><label>Figure 8</label><caption><p>Results of the probe trial of the Virtual Morris Water Task. This boxplot shows the performance of BVP patients (red) and healthy controls (blue) during the probe trial. Spatial memory retrieval was defined by the relative amount of search time (%) spent in the correct quadrant of the pool.</p></caption><graphic xlink:href=\"fneur-11-00710-g0008\"/></fig><fig id=\"F9\" position=\"float\"><label>Figure 9</label><caption><p>Hearing thresholds and performance in the probe trial. This figure indicates the worse the hearing (higher hearing thresholds or Fletcher indexes), the worse the performance on the VMWT probe trial (less time spent in the correct quadrant).</p></caption><graphic xlink:href=\"fneur-11-00710-g0009\"/></fig></sec><sec><title>Visible Trials: Motor Control Condition</title><p>No significant effects of group or hearing status were found during the visible platform trials regarding latency, path length, and heading error. This indicates that BVP patients showed no difference in their motor control condition, compared with healthy controls. Results of the visible trials are shown in <xref ref-type=\"fig\" rid=\"F5\">Figures 5</xref>–<xref ref-type=\"fig\" rid=\"F7\">7</xref>.</p></sec></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>The present study was designed to evaluate whether BVP patients suffer from spatial cognitive deficits compared with healthy controls. Furthermore, the analyses in this study were especially set out with the aim of evaluating the importance of concomitant hearing loss of BVP patients regarding the suspected relationship between cognition and the vestibular system.</p><p>In one of the largest BVP patient group so far, this study found a worse performance on all outcome measures of the VMWT in BVP patients compared with healthy controls. However, this difference was never statistically significant between the BPV group and the healthy control group. In contrast to earlier studies, all statistical analyses of this study included correction for hearing (dys)function. Although no significant group difference was observed, it seemed that, on the other hand, hearing loss was found to be statistically significantly associated with worse spatial cognition. The worse the hearing of BVP patients, the worse the spatial learning indicated by longer latencies in the hidden trial of the VMWT. Likewise, in the probe trial, hearing loss resulted in less time spent in the platform quadrant, which suggests worse spatial memory retrieval.</p><sec><title>Vestibular Loss and Spatial Cognition</title><p>Previous studies have not dealt with the hearing status of the enrolled BVP patients when drawing conclusions about the relationship between vestibular loss and cognitive decline (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Given the observed statistically significant effect of hearing loss on spatial cognition, this study highlights the need to correct for hearing loss when evaluating cognition in vestibular patients. Furthermore, our findings raise intriguing questions regarding the assumed link between cognition and the vestibular system. According to our results, it could be questioned whether the spatial cognitive deficits of BVP patients might be solely attributed to their hearing loss and not to their vestibular loss. However, it is important to bear in mind that the control group in this study included subjects with normal age-appropriate hearing. Hence, some of the control subjects suffered from presbyacusis, but overall the prevalence of hearing loss in the control group is low. Therefore, results should be interpreted with caution and it cannot be concluded that hearing loss is the only factor resulting in the spatial cognitive deficits of BVP patients. Vestibular loss might play an additional role. Moreover, previous studies observing spatial deficits in BVP patients included patients with complete vestibular loss. In this study, patients with BVP, as defined by the Bárány criteria, were included. This implicates that also patients with partial vestibular loss were included, for example, preserved function on vHIT in the absence of caloric function (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Future work is required to further unravel the link between cognition, vestibular loss, and hearing loss. An interesting study protocol would be to compare spatial cognition between four groups: healthy controls, patients with hearing dysfunction and normal vestibular function, patients with normal hearing and vestibular dysfunction and finally, patients with both vestibular and hearing dysfunction.</p><p>Nonetheless, for all future studies investigating cognition in BVP patients, our results implicate that it is obligatory to take the hearing status of BVP patients into account.</p></sec><sec><title>Hearing Loss, Spatial Cognition, and the Hippocampus</title><p>In accordance with the present results, previous studies have demonstrated a link between spatial cognition and hearing loss. A recent meta-analysis showed a significant impairment of visuospatial abilities in patients with hearing loss across cross-sectional studies, using a wide variety of spatial cognitive tasks (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>).</p><p>Two recent animal studies investigated spatial cognition using the Morris Water Maze (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>). Mice with presbyacusis were found to have worse spatial learning and spatial memory retrieval compared with mice with normal hearing (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Likewise, mice with noise-induced hearing loss showed poorer performance during the Morris Water Maze (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). This was pointed out by longer latencies during the hidden platform trials and less time spent in the platform quadrant during a probe trial in mice with noise-induced hearing loss compared with mice with normal hearing. It is important to note that mice typically do not perform well in the Morris Water Maze and authors suggest that they might not use spatial strategy. Hence, caution should be taken to extrapolate these findings to humans (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>).</p><p>The hippocampus is the area of the brain that has long been implicated in spatial memory. Animal and human studies have shown altered functioning and even atrophy of the hippocampus in subjects with vestibular loss [for review see (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>)]. Interestingly, the poorer spatial performance of mice with hearing loss was also accompanied by a decrease of hippocampal neurogenesis (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). As the Morris Water Maze does not rely upon auditory function, authors hypothesize that the auditory input plays a maintenance role for hippocampal function and neurogenesis (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). However, it should be noted that exposing mice to noise trauma does not only result in hearing loss but might also induce peripheral vestibular damage (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>). This has not been taken into account in the study. Hence, it is possible that the decreased hippocampal neurogenesis observed in mice with noise-induced hearing loss is (partially) related to a loss of peripheral vestibular input. Vice versa, despite the extensive previous research, many questions remain about the neuroanatomical substrate underlying the association between the vestibular system, spatial cognition, and the hippocampus. Little research has been carried out to investigate if subjects with hearing loss have altered hippocampal function and volume. Hence, it could conceivably be hypothesized that hearing loss plays a role in the assumed neuroanatomical pathways between the peripheral vestibular input and the hippocampal and cortical areas involved in spatial cognition.</p></sec><sec><title>The VMWT to Assess Spatial Cognition in BVP Patients</title><p>In previous literature, the VMWT seemed to be one of the most used tools to assess spatial cognition in vestibular patients (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>). In patients with complete loss of vestibular input after a bilateral vestibular neurectomy, distinct poorer performance was observed on the hidden and probe trials of the VMWT (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). In a more recent study of the same group, patients with severe but incomplete BVP showed more subtle spatial cognitive deficits (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Likewise, in patients with a unilateral loss of vestibular input, only one of the outcome measures was impaired in patients with right unilateral vestibulopathy. In the patients with left unilateral vestibulopathy, none of the outcome measures differed significantly from controls (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). These results suggest that with increasing loss of peripheral vestibular input, the spatial cognition decreases. In our study, most BVP patients suffered from a deep but incomplete loss of vestibular function. It is possible, therefore, that the worse VMWT performance in our BVP patients did not yield statistically significance level.</p><p>Furthermore, the purely stationary set-up of the VMWT might underestimate the real-life spatial cognitive deficits of BVP patients as a result of loss of vestibular input. Previous research has established that, while navigating, an “inner neural map” is created, based on peripheral vestibular input. This neural representation of the external environment is computed in the hippocampus and entorhinal cortex and consists of several cooperative cell types: angular head velocity cells; head direction cells; place and grid cells (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Rodent studies have demonstrated that vestibular input modulates the activity of the head direction cells and the place cells (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>). As the VMWT is static, the task does not rely on any vestibular input from real locomotion. Hence, it is likely that real-life navigation tasks will be more sensitive to reveal spatial cognitive deficits in BVP patients.</p><p>Moreover, attentional deficits are demonstrated in BVP patients (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>). According to Kahneman's Capacity Model of Attention in which an individual has a limited total amount of cognitive resources available to divide among mental tasks, dual tasking might be more demanding in BVP patients because of the increased attentional need for keeping balance (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). As subjects stay seated during the VMWT, attentional resources can be fully directed toward the spatial memory task. This might be a second reason why the VMWT underestimates the real-life spatial cognitive deficits of BVP patients.</p><p>To sum up, the VMWT is a widely used method to assess spatial cognition in humans (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Several studies have demonstrated spatial cognitive deficits in BVP patients, using the VMWT (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>). However, previous studies have not dealt with the concomitant hearing loss of BVP patients. As hearing loss is a risk factor for dementia, this might be an important forgotten factor. This is the first study investigating spatial cognition by use of the VMWT in a BVP group as large as 64 patients, and with correction for the hearing (dys)function in all analyses. All outcome measures of the VMWT were worse in BVP patients compared with healthy controls; however, these differences were not statistically significant. Contrarily, hearing loss was statistically significantly associated with worse spatial learning and spatial memory retrieval. Regarding our study protocol with healthy controls without severe hearing loss, it is not excluded that vestibular loss has an additional effect on spatial cognition. Nonetheless, our findings confirm the negative repercussion of hearing loss on spatial cognition (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>), and highlight the need to correct for hearing loss when investigating cognition in a vestibular population group.</p><p>Both vestibular and hearing dysfunction are prevalent in the elderly (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>). Given the rising prevalence of dementia, and the current lack of therapy, future studies are needed to identify modifiable risk factors (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Therefore, the link between cognitive decline and the hippocampus on the one hand and hearing loss and vestibular loss on the other hand needs to be further unraveled. To develop a full picture, a study protocol that would additionally include patients with normal vestibular function but different levels of sensorineural hearing loss would be interesting. Furthermore, considering the static and single task paradigm involved in the VMWT, real navigation tasks might give more insights in the potential spatial cognitive deficits related to the loss of vestibular input.</p></sec><sec><title>Limitations</title><p>The subjects in our HC group were not perfectly matched to BVP patients regarding age, education, and computer experience. As HC were on average younger, more educated, and more computer experienced, VMWT performance could be relatively overestimated in the HC group. Regarding the observed negative effect of hearing loss on spatial cognition (longer latencies and less time spent in the correct quadrant), it is important to bear in mind that the majority of patients with hearing loss were in the BVP group. Therefore, it is not excluded that vestibular loss plays an additional role in spatial cognition, which could not be observed in this study protocol using healthy controls with normal hearing. Second, there was a correlation with hearing loss and age. Although all models were corrected for age, it is not excluded that age might play an important role in the observed link between spatial cognition and hearing loss.</p></sec></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusion</title><p>The present study assesses spatial cognitive performance in one of the largest BVP cohorts so far. The study was especially designed to determine the relative importance of hearing loss in spatial cognition of BVP patients, as this has been frequently overlooked. We found worse spatial cognitive performance on all outcome measures of BVP patients. However, these differences were not statistically significant between the BVP patients and healthy controls, when corrected for age, gender, education, level of computer use, and hearing loss. Interestingly, only hearing loss was found to be statistically significantly associated with worse spatial cognition. These findings highlight the need to correct for hearing loss in future studies investigating cognition in BVP patients. As the control group did not include subjects with severe hearing loss, an additional effect of vestibular loss on spatial cognitive performance cannot be excluded.</p></sec><sec id=\"s6\"><title>Additional Comments</title><p>These data have partially been presented on the 30th Bárány conference, Uppsala, June 2018.</p></sec><sec sec-type=\"data-availability\" id=\"s7\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"s8\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by local ethics committee of the Antwerp University Hospital/University of Antwerp (protocol number 16/42/426). The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"s9\"><title>Author Contributions</title><p>BD: data collection, statistical analyses, study concept, and writing manuscript. GM, RB, and PV: study concept and supervision. JM: data collection. EF: study concept and statistical analyses. VV: study concept, writing manuscript, and supervision. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s10\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> The Antwerp University Hospital and Maastricht University Medical Center have received research and travel grants from MED-EL. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Plant Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Plant Sci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Plant Sci.</journal-id><journal-title-group><journal-title>Frontiers in Plant Science</journal-title></journal-title-group><issn pub-type=\"epub\">1664-462X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849757</article-id><article-id pub-id-type=\"pmc\">PMC7431774</article-id><article-id pub-id-type=\"doi\">10.3389/fpls.2020.01223</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Plant Science</subject><subj-group><subject>Mini Review</subject></subj-group></subj-group></article-categories><title-group><article-title>The Resistant Soybean-<italic>Aphis glycines</italic> Interaction: Current Knowledge and Prospects</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Natukunda</surname><given-names>Martha I.</given-names></name><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/939454\"/><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"fn001\">\n<sup>*</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>MacIntosh</surname><given-names>Gustavo C.</given-names></name><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/58160\"/><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref></contrib></contrib-group><aff id=\"aff1\">\n<sup>1</sup>\n<institution>MacIntosh Laboratory, Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University</institution>, <addr-line>Ames, IA</addr-line>, <country>United States</country>\n</aff><aff id=\"aff2\">\n<sup>2</sup>\n<institution>Department of Agronomy, Iowa State University</institution>, <addr-line>Ames, IA</addr-line>, <country>United States</country>\n</aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Monica Fernandez-Aparicio, Spanish National Research Council, Spain</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Surendra Neupane, University of Florida, United States; Dechun Wang, Michigan State University, United States</p></fn><corresp id=\"fn001\">*Correspondence: Martha I. Natukunda, <email xlink:href=\"mailto:mibore@iastate.edu\" xlink:type=\"simple\">mibore@iastate.edu</email>\n</corresp><fn fn-type=\"other\" id=\"fn002\"><p>This article was submitted to Plant Breeding, a section of the journal Frontiers in Plant Science</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1223</elocation-id><history><date date-type=\"received\"><day>28</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Natukunda and MacIntosh</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Natukunda and MacIntosh</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Soybean aphids (<italic>Aphis glycines</italic> Matsumura) are invasive insect pests of soybean, and they cause significant yield losses. Resistance to soybean aphids is conferred by Resistance to <italic>Aphis glycines</italic> (<italic>Rag</italic>) genes. Since the first discovery of aphid-resistant soybean genotypes in 2004, several studies have attempted to characterize <italic>Rag</italic> genes from aphid-resistant soybean genotypes. To date, 12 <italic>Rag</italic> genes and four quantitative trait loci for aphid resistance have been reported on soybean chromosomes 07, 08, 13, 16, and 17. Although candidate genes have been proposed for several discovered <italic>Rag</italic> loci, additional studies are needed to pinpoint, validate, and further explain the potential mechanisms of <italic>Rag</italic> gene action. A major challenge to utilizing host plant resistance is the discovery of virulent aphid biotypes that can colonize aphid-resistant soybean. This occurrence suggests the need for additional studies to devise strategies to enhance the effectiveness of aphid-resistant soybean. In this mini review, we discuss current knowledge on the resistant soybean-<italic>Aphis glycines</italic> interaction, potential mechanisms of <italic>Rag</italic> gene action, opportunities to discover new <italic>Rag</italic> genes, and prospects for utilization of host plant resistance to manage soybean aphids. A clearer understanding of host plant resistance to soybean aphids will guide researchers on strategies for developing soybean varieties with more durable aphid resistance, reducing the present challenge of virulent aphid biotypes.</p></abstract><kwd-group><kwd>soybean</kwd><kwd>soybean aphids</kwd><kwd>biotypes</kwd><kwd>host plant resistance</kwd><kwd><italic>Rag</italic> genes</kwd><kwd>gene pyramiding</kwd></kwd-group><counts><fig-count count=\"0\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"76\"/><page-count count=\"8\"/><word-count count=\"3831\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Soybean aphids (<italic>Aphis glycines</italic> Matsumura) are important insect pests that can cause yield loss of up to 50% in soybean (<italic>Glycine max</italic> [L.] Merr.) by sucking plant assimilates using their piercing and sucking mouthparts (stylets) (<xref rid=\"B58\" ref-type=\"bibr\">Ragsdale et al., 2007</xref>; <xref rid=\"B5\" ref-type=\"bibr\">Beckendorf et al., 2008</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Ragsdale et al., 2011</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Tilmon et al., 2011</xref>; <xref rid=\"B7\" ref-type=\"bibr\">Bhusal et al., 2013</xref>). Cultivation of aphid-resistant soybean varieties is a preferred strategy for controlling soybean aphids (<xref rid=\"B59\" ref-type=\"bibr\">Ragsdale et al., 2011</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Hodgson et al., 2012</xref>). Resistance to soybean aphids is conferred by Resistance to <italic>Aphis glycines</italic> (<italic>Rag</italic>) genes (<xref rid=\"B29\" ref-type=\"bibr\">Hill et al., 2004</xref>). To date, 12 <italic>Rag</italic> genes have been reported on chromosomes Gm07(<italic>Rag1</italic>, <italic>rag1b</italic>, and <italic>rag1c</italic>)(<xref rid=\"B45\" ref-type=\"bibr\">Li et al., 2007</xref>; <xref rid=\"B69\" ref-type=\"bibr\">Zhang et al., 2009</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Kim et al., 2010b</xref>; <xref rid=\"B56\" ref-type=\"bibr\">Nurden et al., 2010</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Bales et al., 2013</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Hill et al., 2017</xref>), Gm08 (<italic>Rag6</italic>)(<xref rid=\"B73\" ref-type=\"bibr\">Zhang et al., 2017b</xref>), Gm13(<italic>Rag2</italic>, <italic>Rag4</italic>, <italic>rag4</italic>, and <italic>Rag5</italic>-proposed)(<xref rid=\"B52\" ref-type=\"bibr\">Mian et al., 2008</xref>; <xref rid=\"B69\" ref-type=\"bibr\">Zhang et al., 2009</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Kim et al., 2010a</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Jun et al., 2012</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Bales et al., 2013</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Wang et al., 2015</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Hill et al., 2017</xref>), and Gm16(<italic>Rag3</italic>, <italic>rag3</italic>, <italic>rag3b</italic>, and <italic>Rag3c</italic>)(<xref rid=\"B70\" ref-type=\"bibr\">Zhang et al., 2010</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Bales et al., 2013</xref>; <xref rid=\"B71\" ref-type=\"bibr\">Zhang et al., 2013</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Hill et al., 2017</xref>; <xref rid=\"B72\" ref-type=\"bibr\">Zhang et al., 2017a</xref>)(reviewed by <xref rid=\"B54\" ref-type=\"bibr\">Neupane et al. (2019a)</xref>. Additionally, four quantitative trait loci (QTL) have been reported on chromosomes Gm07(<italic>qChrom.07.1</italic>), Gm13(<italic>qChrom.13.1</italic>), Gm16(<italic>qChrom.16.1</italic>), and Gm17(<italic>qChrom.17.1</italic>)(<xref rid=\"B9\" ref-type=\"bibr\">Bhusal et al., 2017</xref>)(reviewed by <xref rid=\"B54\" ref-type=\"bibr\">Neupane et al. (2019a)</xref>. The type of soybean aphid resistance can be antibiosis (adverse effect on insect biology) or antixenosis (feeding deterrence), and plants exhibiting tolerance (similar yield in the presence or absence of soybean aphids) have also been identified (<xref rid=\"B57\" ref-type=\"bibr\">Painter, 1951</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Kogan and Ortman, 1978</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Hill et al., 2004</xref>; <xref rid=\"B60\" ref-type=\"bibr\">Smith, 2005</xref>). Application of insecticides such as pyrethroids and organophosphates, has been used to manage soybean aphids to prevent aphid populations from reaching economic injury levels (<xref rid=\"B39\" ref-type=\"bibr\">Koch et al., 2016</xref>). However, since 2015, pyrethroid-resistant soybean aphids have been reported in Midwestern states (Iowa, Minnesota, North Dakota and South Dakota) (<xref rid=\"B39\" ref-type=\"bibr\">Koch et al., 2016</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Koch et al., 2018</xref>), which indicates an urgent need for incorporation of host-based resistance in management strategies. Additionally, host plant resistance is an environmentally-friendly alternative strategy for the management of soybean aphids compared to application of insecticides.</p><p>A major limitation of utilizing host plant resistance is the discovery of virulent soybean aphid biotypes that successfully colonize aphid-resistant soybeans. In the United States, soybean aphid biotypes are classified based on their response to <italic>Rag1</italic> and/or <italic>Rag2</italic> genes. Biotype 1, the Illinois isolate, is unable to colonize soybean plants containing any <italic>Rag</italic> genes (<xref rid=\"B29\" ref-type=\"bibr\">Hill et al., 2004</xref>), and is “avirulent”; biotype 2, the Ohio isolate, is virulent on <italic>Rag1</italic> soybean, but not <italic>Rag2</italic> (<xref rid=\"B36\" ref-type=\"bibr\">Kim et al., 2008</xref>); biotype 3, the Indiana isolate, is virulent on <italic>Rag2</italic> but not <italic>Rag1</italic> soybean (<xref rid=\"B30\" ref-type=\"bibr\">Hill et al., 2010</xref>); and biotype 4, the Wisconsin isolate, is virulent on <italic>Rag1</italic>, <italic>Rag2</italic>, the <italic>Rag1</italic>+<italic>Rag2</italic> pyramid line (<xref rid=\"B2\" ref-type=\"bibr\">Alt and Ryan-Mahmutagic, 2013</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Crossley and Hogg, 2015</xref>). Recent and future studies to genetically characterize soybean aphid biotypes will unravel mechanisms of aphid virulence on resistant soybean (<xref rid=\"B14\" ref-type=\"bibr\">Coates et al., 2020</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Giordano et al., 2020</xref>).</p><p>The focus of this mini review is to discuss potential mechanisms of <italic>Rag</italic> gene action, opportunities to discover new <italic>Rag</italic> genes, and prospects for future research on host plant resistance to soybean aphids. In some instances, knowledge of the susceptible soybean-<italic>Aphis glycines</italic> interaction is used to explain phenomena related to the resistant soybean-<italic>Aphis glycines</italic> interaction. This review will not discuss the genomic locations of known <italic>Rag</italic> loci or QTLs since these aspects have been extensively reviewed by <xref rid=\"B54\" ref-type=\"bibr\">Neupane et al. (2019a)</xref>.</p></sec><sec id=\"s2\"><title>Potential Mechanisms of <italic>Rag</italic> Gene Action</title><p>At the phenotypic level, <italic>Rag</italic> genes reduce aphid populations on soybean plants by negatively affecting aphid biology or through feeding deterrence. Both single-Resistance (<italic>R</italic>) gene and multiple-<italic>R</italic> gene soybean genotypes significantly reduce soybean aphid populations (<xref rid=\"B49\" ref-type=\"bibr\">McCarville and O’Neal, 2012</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Hesler et al., 2013</xref>; <xref rid=\"B50\" ref-type=\"bibr\">McCarville et al., 2014</xref>; <xref rid=\"B12\" ref-type=\"bibr\">Chandrasena et al., 2015</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Ajayi-Oyetunde et al., 2016</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Varenhorst et al., 2017</xref>; <xref rid=\"B74\" ref-type=\"bibr\">Zhang et al., 2018</xref>). Interestingly, multiple-<italic>R</italic> gene soybean genotypes had significantly lower aphid populations compared to those carrying a single-<italic>R</italic> gene (<xref rid=\"B68\" ref-type=\"bibr\">Wiarda et al., 2012</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Ajayi-Oyetunde et al., 2016</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Varenhorst et al., 2017</xref>; <xref rid=\"B74\" ref-type=\"bibr\">Zhang et al., 2018</xref>). This increased aphid resistance due to the presence of multiple <italic>Rag</italic> genes highlights the great potential of gene pyramiding as an aphid management strategy. Furthermore, using gene-pyramid varieties could extend their durability (<xref rid=\"B64\" ref-type=\"bibr\">Varenhorst et al., 2015b</xref>).</p><p>While <italic>Rag</italic> loci have been mapped and their approximate chromosomal location is known, none of the genes has yet been cloned. However, previous studies have identified nucleotide-binding/leucine-rich-repeat (NLR) genes, members of the most common <italic>R</italic> gene family <xref rid=\"B19\" ref-type=\"bibr\">Cui et al. (2015)</xref>, as candidates for <italic>Rag1</italic> (<italic>Glyma07g06890</italic> and <italic>Glyma07g06920</italic>) (<xref rid=\"B38\" ref-type=\"bibr\">Kim et al., 2010b</xref>) and <italic>Rag2</italic> (<italic>Glyma13g26000</italic> and <italic>Glyma13g25970</italic>) (<xref rid=\"B37\" ref-type=\"bibr\">Kim et al., 2010a</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Brechenmacher et al., 2015</xref>). In addition to candidate NLR genes, other genes have also been proposed in respective genomic regions, but all candidate genes and their mechanism of action are yet to be tested. Functional studies will be critical for the successful identification and future utilization of <italic>Rag</italic> genes in soybean breeding programs.</p><p>Electrical penetration graph (EPG) studies reported differences in feeding behavior of soybean aphids colonizing resistant and susceptible plants. During a 9-h period of feeding, the average time for the stylet to reach the first sieve element was shorter (~3.5 h) in the aphid-susceptible soybean genotype but longer (~7.5 h) in the aphid-resistant soybean genotype (<xref rid=\"B20\" ref-type=\"bibr\">Diaz-Montano et al., 2007</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Crompton and Ode, 2010</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Chandran et al., 2013</xref>). Additionally, the total duration of stylets in the sieve tube elements and phloem was longer (>1 h) in the susceptible genotype but only 2–7 min in resistant soybean genotypes and fewer aphids reached the sieve tube elements in resistant plants (<xref rid=\"B20\" ref-type=\"bibr\">Diaz-Montano et al., 2007</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Chandran et al., 2013</xref>).</p><p>Insect colonization on plants triggers gene expression changes that mount defense responses consisting of morphological changes and biochemical defenses (<xref rid=\"B22\" ref-type=\"bibr\">Fernandes, 1994</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Farha et al., 2010</xref>; <xref rid=\"B67\" ref-type=\"bibr\">War et al., 2012</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Furstenberg-Hagg et al., 2013</xref>). Transcriptome analysis studies have been conducted for soybean genotypes carrying <italic>Rag1</italic>, <italic>Rag2</italic> (both providing antibiosis-type resistance), and <italic>Rag5</italic> (antixenosis). In response to aphid feeding, a rapid and strong response in resistant plants (between 4 and 48 h) was observed, while a resistance response was not observed at the transcriptome level at later time points (7 or 21 days of aphid feeding), although only <italic>Rag1</italic> was analyzed for these prolonged infestations (<xref rid=\"B46\" ref-type=\"bibr\">Li et al., 2008</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Studham and MacIntosh, 2013</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Brechenmacher et al., 2015</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Lee et al., 2017</xref>; <xref rid=\"B33\" ref-type=\"bibr\">Hohenstein et al., 2019</xref>). An interesting biphasic response, with a maximum number of proteins or transcripts differentially expressed (DE) at 8 h post aphid feeding, a weak 24 h response, and another peak at 48 h was observed for <italic>Rag2</italic> (<xref rid=\"B10\" ref-type=\"bibr\">Brechenmacher et al., 2015</xref>). It is currently not known if <italic>Rag1</italic> and <italic>Rag5</italic> induce similar biphasic responses, as <italic>Rag1</italic> has not been analyzed 48 h post infestation, and <italic>Rag5</italic> has not been analyzed at 24 h post aphid feeding. Stated collectively, the transcriptional resistant response to soybean aphid feeding involves upregulation of transcripts involved in cell wall modification, plant defense, hormone metabolism, stress signaling, secondary metabolism, and downregulation of transcripts involved in photosynthesis and carbon metabolism (<xref rid=\"B46\" ref-type=\"bibr\">Li et al., 2008</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Studham and MacIntosh, 2013</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Brechenmacher et al., 2015</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Lee et al., 2017</xref>). Changes in gene expression for phytohormone biosynthesis and signaling transcripts mainly jasmonic acid (JA), salicylic acid (SA), and ethylene (ET) to feeding by soybean aphids were evident, and gene expression patterns indicated cooperative action of JA and SA.</p><p>Analysis of the <italic>Rag2</italic> response was conducted using transcriptome and proteome analyses (<xref rid=\"B10\" ref-type=\"bibr\">Brechenmacher et al., 2015</xref>). The <italic>Rag2</italic> response included suppression of photosynthesis, an increase in primary and secondary cell wall metabolism, and the activation of secondary metabolism, including a large number of transcripts associated with the phenylpropanoid pathway. No clear phytohormone signature was observed, although a large number of ethylene-related transcripts were DE (both up and downregulated). Lack of correlation between DE protein and transcript abundance suggested an important role for transcriptional regulation in the <italic>Rag2</italic> response, an observation supported by the large number of DE transcripts related to RNA metabolism upregulated in the resistant line in response to aphid feeding (<xref rid=\"B10\" ref-type=\"bibr\">Brechenmacher et al., 2015</xref>). The <italic>Rag5</italic> response activated jasmonate and reactive oxygen species signaling and showed upregulation of the phenylpropanoid pathway including secondary cell wall synthesis (<xref rid=\"B44\" ref-type=\"bibr\">Lee et al., 2017</xref>). The <italic>Rag1</italic> response resembles a hypersensitive response and is, at least in part, mediated by salicylate signaling, and also affects cell wall, and increases the activity of the phenylpropanoid pathway (<xref rid=\"B46\" ref-type=\"bibr\">Li et al., 2008</xref>; <xref rid=\"B61\" ref-type=\"bibr\">Studham and MacIntosh, 2013</xref>).</p><p>Transcriptome comparisons between near-isogenic lines with or without the individual <italic>Rag</italic> genes in the absence of aphids detected DE genes (<xref rid=\"B61\" ref-type=\"bibr\">Studham and MacIntosh, 2013</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Brechenmacher et al., 2015</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Lee et al., 2017</xref>), leading to the suggestion that the presence of <italic>Rag1</italic>, <italic>Rag2</italic>, or <italic>Rag5</italic> causes constitutive expression of some defense responses (<xref rid=\"B61\" ref-type=\"bibr\">Studham and MacIntosh, 2013</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Lee et al., 2017</xref>). Moreover, it is apparent that the salicylate response is primed in <italic>Rag1</italic> plants (<xref rid=\"B61\" ref-type=\"bibr\">Studham and MacIntosh, 2013</xref>).</p><p>A common theme among these <italic>Rag</italic> responses is the induction of genes related to secondary metabolite production, and it is well-documented that chemical defenses are a key plant response against aphids (<xref rid=\"B76\" ref-type=\"bibr\">Zust and Agrawal, 2016</xref>). In susceptible (non-<italic>Rag</italic>) soybean, long-term colonization led to upregulation of genes in the phenylpropanoid pathway, and the isoflavone daidzein has a deterrent effect on soybean aphids (<xref rid=\"B33\" ref-type=\"bibr\">Hohenstein et al., 2019</xref>). A correlation between QTL associated with soybean aphid resistance and loci associated with high isoflavone content was reported (<xref rid=\"B51\" ref-type=\"bibr\">Meng et al., 2011</xref>). Moreover, aphids feeding on <italic>Rag1</italic> plants induce a set of genes associated with detoxification, indicating that aphids colonizing these resistant plants are under xenobiotic stress (<xref rid=\"B4\" ref-type=\"bibr\">Bansal et al., 2014</xref>). Thus, it is possible that isoflavones or other chemical defenses are employed in the resistant response. Future studies should examine the role of chemical defenses for aphid-resistant soybean genotypes by quantifying defense-related metabolite levels such as isoflavonoids, phenolics, and others. Correlating specific metabolites and changes in aphid feeding behavior in resistant plants will advance knowledge on host plant resistance.</p><p>The effect of pyramiding <italic>Rag</italic> genes has also been studied at the transcriptome level (<xref rid=\"B34\" ref-type=\"bibr\">Ibore, 2017</xref>). Compared to soybean genotypes with <italic>Rag1</italic> or <italic>Rag2</italic> genes alone, the <italic>Rag1</italic>+<italic>Rag2</italic> pyramid line had a greater number of DE genes, distinctive gene sets, and activation of unique biological processes (<xref rid=\"B34\" ref-type=\"bibr\">Ibore, 2017</xref>). In the distinctive <italic>Rag1</italic>+<italic>Rag2</italic> response, there was a significant increase in defense transcripts involved in phytohormone (JA and SA) biosynthesis and signaling, secondary cell wall biogenesis, regulation of plant-type hypersensitive response, regulation of hydrogen peroxide metabolism, incompatible interaction, systemic acquired resistance, and MAPK cascade, and DE genes were mainly upregulated. A concomitant repression of all photosynthesis-related transcripts (chlorophyll biosynthesis), was observed in the <italic>Rag1</italic>+<italic>Rag2</italic> response, 6 h after aphid feeding. Differential expression of transcripts involved in secondary cell wall biogenesis can enforce stronger physical barriers to prevent further aphid colonization. The cell wall is a physical barrier that must be overcome by insects or pathogens (<xref rid=\"B6\" ref-type=\"bibr\">Bellincampi et al., 2014</xref>; <xref rid=\"B47\" ref-type=\"bibr\">Malinovsky et al., 2014</xref>), and increased cell wall thickness and lignification can prevent successful insect colonization (<xref rid=\"B67\" ref-type=\"bibr\">War et al., 2012</xref>). Reinforcement of cell walls with various macromolecules such as lignin, cellulose, and callose, occurs during insect feeding (<xref rid=\"B23\" ref-type=\"bibr\">Furstenberg-Hagg et al., 2013</xref>). Mechanisms of cell wall modification in the defense against soybean aphids are yet to be functionally characterized. However, differences in the feeding behavior of soybean aphids colonizing resistant and susceptible plants reported by EPG studies suggest that phloem-based defenses could be related to physical barriers to feeding (<xref rid=\"B20\" ref-type=\"bibr\">Diaz-Montano et al., 2007</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Crompton and Ode, 2010</xref>; <xref rid=\"B75\" ref-type=\"bibr\">Zhu et al., 2011</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Chandran et al., 2013</xref>).</p><p>Genes that were DE early only in the pyramid line could explain the increased aphid resistance for the <italic>Rag1</italic>+<italic>Rag2</italic> pyramid line observed at the phenotypic level. The increased aphid resistance in the <italic>Rag1</italic>+<italic>Rag2</italic> pyramid line could be caused by activation of different subsets of genetic pathways by the <italic>Rag1</italic> and <italic>Rag2</italic> genes that act synergistically to induce unique and more effective defenses against soybean aphids. While initial reports have examined effects of pyramiding <italic>Rag</italic> genes at the transcriptome level, additional studies are needed to fully characterize specific gene sets that contribute to increased aphid resistance, the endpoint chemical and physical responses triggered by <italic>Rag</italic> genes, and the functional basis of improved crop performance by <italic>R</italic> gene pyramiding to further improve host plant resistance. It will be important to also characterize molecular mechanisms of resistance for soybean genotypes carrying other <italic>Rag</italic> genes.</p><p>Another phenomenon that needs more research is induced susceptibility, in which prior colonization by virulent aphid biotypes facilitates later colonization by other aphid biotypes (<xref rid=\"B63\" ref-type=\"bibr\">Varenhorst et al., 2015a</xref>). While the mechanisms by which aphids suppress resistance are still unknown, promising induced susceptibility studies with <italic>Rag1</italic> soybean have been conducted by <xref rid=\"B55\" ref-type=\"bibr\">Neupane et al. (2019b)</xref>, and future analyses will unravel induced susceptibility mechanisms at the transcriptome level.</p><p>A clearer understanding of potential mechanisms of <italic>Rag</italic> gene action will enhance development of more durable aphid-resistant soybean genotypes that can effectively control virulent aphid biotypes. When developing soybean genotypes with multiple <italic>Rag</italic> genes, parent genotypes must be carefully chosen to take advantage of their unique individual advantages and potential synergy in gene combinations. For instance, combining <italic>Rag</italic> genes that primarily elicit chemical defenses and other genes that utilize physical barriers such as cell wall modification may provide durable resistance to virulent soybean aphid biotypes.</p></sec><sec id=\"s3\"><title>Opportunities to Discover New Aphid Resistance Genes</title><p>In addition to known <italic>Rag</italic> genes, screening studies have discovered other aphid-resistant soybean genotypes, providing opportunities to discover new <italic>Rag</italic> genes (<xref rid=\"B7\" ref-type=\"bibr\">Bhusal et al., 2013</xref>; <xref rid=\"B8\" ref-type=\"bibr\">Bhusal et al., 2014</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Hanson et al., 2016</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Conzemius et al., 2019a</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Conzemius et al., 2019b</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Natukunda et al., 2019</xref>). New sources of aphid resistance are particularly important because certain aphid biotypes are able to colonize resistant soybean. Identification of additional sources of aphid resistance was followed by studies that aimed to explain the genetic basis of aphid resistance and discover new <italic>Rag</italic> genes. Three candidate gene identification studies conducted genome-wide association studies (GWAS) (<xref rid=\"B13\" ref-type=\"bibr\">Chang and Hartman, 2017</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Hanson et al., 2018</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Natukunda et al., 2019</xref>). <xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref> lists 69 aphid-resistant soybean plant introductions (PIs) included in GWAS studies, carrying resistance to biotypes 1, 2, and 3 that are prospective sources of new <italic>Rag</italic> genes. Additionally, across the 20 soybean chromosomes, a total of 49 significant SNPs associated with aphid resistance were reported, some of which were located on chromosomes with no reported <italic>Rag</italic> genes (<xref rid=\"T2\" ref-type=\"table\">\n<bold>Table 2</bold>\n</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Aphid-resistant soybean genotypes (N=69) tested in genome-wide association studies (GWAS).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Soybean accession</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">PI is resistant to aphid biotype</th></tr></thead><tbody><tr><td valign=\"top\" colspan=\"2\" align=\"left\" rowspan=\"1\">\n<bold><italic>Glycine max</italic></bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 437658</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 605765 B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 157492</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 606394</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 606390 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 606398</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 250844</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 437282</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 592389</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 437353</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 438118</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 358317 B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 561285 B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 639534 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 639537</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 578388 B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507713</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 340034</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 274207</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 86116</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 248514</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 612759 B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 171506</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 430491</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 603426 D</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 646911</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 603432 B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 200595</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 603587 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 567250 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 603326</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 603339 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 153214</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 189946</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 437075</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 567597 C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 and 3<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 603712</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 and 3<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 378663</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 and 3<sup>AB</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 612759 C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>AB</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 054854</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 438031</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 603337 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 578374</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 540739</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 603546 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 612711 B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 417513 B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 437950</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 096162</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>B</sup>\n</td></tr><tr><td valign=\"top\" colspan=\"2\" align=\"left\" rowspan=\"1\">\n<bold><italic>Glycine soja</italic></bold>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 549046</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 483464 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 468397 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 468399 C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 479749</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 479747</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507786</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 522232</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 522219 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 522228</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507749</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507844 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507828</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507767</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507838 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507756</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507741 A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507826</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507840</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PI 507825</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1<sup>A</sup>\n</td></tr></tbody></table><table-wrap-foot><p>Letters next to aphid biotypes denote reference articles that reported soybean accessions. <sup>A</sup>\n<xref rid=\"B26\" ref-type=\"bibr\">Hanson et al. (2018)</xref>; <sup>B</sup>\n<xref rid=\"B53\" ref-type=\"bibr\">Natukunda et al. (2019)</xref>.\n</p></table-wrap-foot></table-wrap><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>SNPs reported by genome-wide association studies (GWAS).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chromosome</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SNP</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Minor allele</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SNP position</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Reference article </th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715578827</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2,637,003</td><td valign=\"top\" rowspan=\"13\" align=\"left\" colspan=\"1\">\n<xref rid=\"B26\" ref-type=\"bibr\">Hanson et al., 2018</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715580619</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55,775,590</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715583602</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5,475,047</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715589122</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6,142,596</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715590206</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24,133,841</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715590836</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33,212,449</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715590997</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34,337,698</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715594602</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46,884,182</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715594619</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46,950,450</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715596585</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,671,208</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715596894</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2,530,979</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715598285</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5,062,637</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715597329</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35,436,934</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715596142</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11,259,155</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<xref rid=\"B13\" ref-type=\"bibr\">Chang and Hartman, 2017</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715599482</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13,783,090</td><td valign=\"top\" rowspan=\"9\" align=\"left\" colspan=\"1\">\n<xref rid=\"B26\" ref-type=\"bibr\">Hanson et al., 2018</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715599561</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14,338,011</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715600535</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20,464,889</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715600829</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22,052,131</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715601800</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41,031,762</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715603059</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,431,512</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715606645</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38,676,101</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715607270</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43,371,238</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715607701</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47,716,772</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715608208</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51,462,329</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<xref rid=\"B26\" ref-type=\"bibr\">Hanson et al., 2018</xref> and <xref rid=\"B53\" ref-type=\"bibr\">Natukunda et al., 2019</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715605620</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,421,982</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<xref rid=\"B53\" ref-type=\"bibr\">Natukunda et al., 2019</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715609271</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25,347,421</td><td valign=\"top\" rowspan=\"2\" align=\"left\" colspan=\"1\">\n<xref rid=\"B26\" ref-type=\"bibr\">Hanson et al., 2018</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715612718</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36,995,143</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715613201</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5,693,819</td><td valign=\"top\" rowspan=\"2\" align=\"left\" colspan=\"1\">\n<xref rid=\"B53\" ref-type=\"bibr\">Natukunda et al., 2019</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715613209</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5,808,606</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715614449</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27,392,456</td><td valign=\"top\" rowspan=\"9\" align=\"left\" colspan=\"1\">\n<xref rid=\"B26\" ref-type=\"bibr\">Hanson et al., 2018</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715614803</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29,459,954</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715614932</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30,186,161</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715615008</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30,654,291</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715615024</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30,724,301</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715615352</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32,859,112</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715615402</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33,280,297</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715616460</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43,544,806</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715616609</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45,558,151</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715617401</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10,274,971</td><td valign=\"top\" rowspan=\"6\" align=\"left\" colspan=\"1\">\n<xref rid=\"B26\" ref-type=\"bibr\">Hanson et al., 2018</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715618940</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43,805,410</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715625258</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6,093,779</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715624134</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29,528,105</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715628067</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5,888,944</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715631460</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">49,223,187</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715634601</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">228,660</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">\n<xref rid=\"B26\" ref-type=\"bibr\">Hanson et al., 2018</xref> and <xref rid=\"B53\" ref-type=\"bibr\">Natukunda et al., 2019</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715635565</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">T</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46,220,139</td><td valign=\"top\" rowspan=\"4\" align=\"left\" colspan=\"1\">\n<xref rid=\"B26\" ref-type=\"bibr\">Hanson et al., 2018</xref>\n</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715635663</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47,348,833</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715635693</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">G</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47,552,973</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ss715637718</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36,626,029</td></tr></tbody></table></table-wrap><p>Chromosome 13, on which the <italic>Rag2</italic>, <italic>Rag4</italic>, <italic>rag4</italic>, and <italic>Rag5</italic> genes have been reported, had the highest number of SNPs (N=9) (<xref rid=\"T2\" ref-type=\"table\">\n<bold>Table 2</bold>\n</xref>). <xref rid=\"B26\" ref-type=\"bibr\">Hanson et al. (2018)</xref> and <xref rid=\"B53\" ref-type=\"bibr\">Natukunda et al. (2019)</xref> detected the same two significant SNPs (ss715608208 and ss715605620) associated with aphid resistance (<xref rid=\"T2\" ref-type=\"table\">\n<bold>Table 2</bold>\n</xref>), confirming the usefulness of genome-wide markers for detecting candidate genes. Due to the low number of aphid-resistant soybean genotypes included in the <xref rid=\"B13\" ref-type=\"bibr\">Chang and Hartman (2017)</xref> GWAS study, only one significant SNP (ss715596142) was detected. This SNP was located on Gm07, was not close to <italic>Rag1</italic>, and the genomic region contained three LRR-containing genes, and one MYB transcription factor (<xref rid=\"B13\" ref-type=\"bibr\">Chang and Hartman, 2017</xref>).</p><p>Although GWAS studies have detected genomic regions associated with aphid resistance, no additional studies have validated candidate <italic>Rag</italic> genes for each identified soybean genotype, yet this knowledge is critical prior to utilization of new resistant soybean genotypes to develop aphid-resistant varieties. To address this knowledge gap and accelerate utilization of identified resistant soybean genotypes in plant breeding programs, future studies should identify and validate aphid-resistance genes for each resistant soybean genotype based on the location of reported significant SNPs. Wild soybeans (<italic>Glycine soja</italic>) are another great genetic reservoir for resistance genes (<xref rid=\"B41\" ref-type=\"bibr\">Kofsky et al., 2018</xref>), and can be utilized in plant breeding programs to develop aphid-resistant soybean varieties. For instance, PI 65549, PI 101404A, and “85-32” conferred resistance against soybean aphids (<xref rid=\"B28\" ref-type=\"bibr\">Hesler, 2013</xref>; <xref rid=\"B72\" ref-type=\"bibr\">Zhang et al., 2017a</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Conzemius et al., 2019b</xref>). <xref rid=\"T1\" ref-type=\"table\">\n<bold>Table 1</bold>\n</xref> lists 20 additional wild soybean accessions that are resistant to soybean aphid biotype 1.</p></sec><sec id=\"s4\"><title>Prospects</title><p>Since their discovery, soybean aphids have become a major challenge for soybean production worldwide. A clear understanding of phenotypic, transcriptional, and molecular mechanisms by which <italic>Rag</italic> genes confer aphid resistance is critical for development of soybean genotypes with stronger and more durable resistance. However, the mechanisms of <italic>Rag</italic> gene action are not fully understood, and additional studies are required to further understand the resistant soybean-<italic>Aphis glycines</italic> interaction. Cloning and functional validation of <italic>Rag</italic> genes is still the priority. Since only <italic>Rag1</italic>, <italic>Rag2</italic>, <italic>Rag5</italic>, and the <italic>Rag1</italic>+<italic>Rag2</italic> pyramid line have been studied at the transcriptome level, soybean genotypes carrying other known <italic>Rag</italic> genes, and pyramid lines with more than two genes also need to be studied. Knowledge of the mechanistic variability for the different <italic>Rag</italic> genes will be useful for guiding future gene pyramiding efforts, as combining different modes of killing action can lead to enhanced durability. Additionally, genes and gene networks reported by transcriptome analysis studies should be utilized to identify and validate candidate genes and genetic markers for aphid resistance traits in marker-assisted plant breeding. Soybean genotypes confirmed to be resistant to biotype 4 aphids (<italic>Glycine max</italic>: PI 437696 and PI 606390A; <italic>Glycine soja</italic>: PI 65549 and PI 101404A) (<xref rid=\"B15\" ref-type=\"bibr\">Conzemius et al., 2019a</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Conzemius et al., 2019b</xref>; <xref rid=\"B43\" ref-type=\"bibr\">LaMantia et al., 2019</xref>) potentially carry new <italic>Rag</italic> genes and should be utilized in breeding programs to develop aphid-resistant soybean varieties. Although this mini review does not discuss the biology of the soybean aphid itself, ongoing studies to genetically distinguish the four biotypes are critical to determine distinguishing factors that make certain aphid biotypes virulent on resistant soybean. Publication of the updated version of the <italic>Aphis glycines</italic> genome provides a reliable reference and enhances the ability to detect biotype variability, advancing biotype-specific genetic studies (<xref rid=\"B24\" ref-type=\"bibr\">Giordano et al., 2020</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Mathers, 2020</xref>). Future studies should also examine virulence of aphid biotypes on resistant soybean and utilization of additional sources of resistance to develop soybean cultivars that confer resistance to the most virulent aphid biotype 4. Association studies have laid a foundation for characterizing the genetic architecture of resistance for understudied aphid-resistant soybean genotypes, providing opportunities to use SNP markers for marker-assisted selection in breeding programs.</p></sec><sec id=\"s5\"><title>Author Contributions</title><p>MIN conceptualized and prepared this manuscript. GCM intellectually contributed to and edited the manuscript.</p></sec><sec sec-type=\"funding-information\" id=\"s6\"><title>Funding</title><p>Iowa State University paid for publication of this mini review article through the Library Open Access Agreements with the Frontiers Journals (<uri xlink:type=\"simple\" xlink:href=\"https://open.lib.iastate.edu/open-access/agreements\">https://open.lib.iastate.edu/open-access/agreements</uri>).</p></sec><sec id=\"s7\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors are grateful to Dr. Jessica D. Hohenstein, Dr. Chantal E. McCabe, and Dr. Edna K. Mageto for their meaningful suggestions to this article. Funding from the U.S. National Institute of Food and Agriculture-Agriculture and Food Research Initiative grant no. 2019-67013-29352 awarded to GM is also gratefully acknowledged. The authors are also grateful to Iowa State University for covering publication costs for this mini review article.</p></ack><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Ajayi-Oyetunde</surname><given-names>O. O.</given-names></name><name><surname>Diers</surname><given-names>B. W.</given-names></name><name><surname>Lagos-Kutz</surname><given-names>D.</given-names></name><name><surname>Hill</surname><given-names>C. B.</given-names></name><name><surname>Hartman</surname><given-names>G. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849195</article-id><article-id pub-id-type=\"pmc\">PMC7431775</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00716</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Botulinum Neurotoxin for the Treatment of Neuropathic Pain</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Egeo</surname><given-names>Gabriella</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1045198/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fofi</surname><given-names>Luisa</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/967114/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Barbanti</surname><given-names>Piero</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/727775/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Headache and Pain Unit, Department of Neurological, Motor and Sensorial Sciences, IRCCS San Raffaele Pisana</institution>, <addr-line>Rome</addr-line>, <country>Italy</country></aff><aff id=\"aff2\"><sup>2</sup><institution>San Raffaele University</institution>, <addr-line>Rome</addr-line>, <country>Italy</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Massimiliano Valeriani, Bambino Gesù Children Hospital (IRCCS), Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Andrea Truini, Sapienza University of Rome, Italy; Grazia Devigili, Fondazione IRCCS Istituto Neurologico Carlo Besta, Italy</p></fn><corresp id=\"c001\">*Correspondence: Piero Barbanti <email>piero.barbanti@sanraffaele.it</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Headache Medicine and Facial Pain, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>716</elocation-id><history><date date-type=\"received\"><day>15</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>11</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Egeo, Fofi and Barbanti.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Egeo, Fofi and Barbanti</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Botulinum neurotoxin is widely used for the treatment of central and peripherical neurological conditions. Initially used to treat strabismus, over the years its use has been expanded also to spasticity and other neurological disorders. This review summarizes the evidence from the published literature regarding its effect on neuropathic pain. Almost all investigations were performed using onabotulinum toxin type A (BoNT/A). Most studies provided positive results, even though toxin formulation, dose, dilution, injection techniques, and sites are heterogeneous across studies. Future larger, high-quality, specifically designed clinical trials are warranted to confirm botulinum neurotoxin efficacy in neuropathic pain.</p></abstract><kwd-group><kwd>botulinum toxin</kwd><kwd>neuropathic pain</kwd><kwd>pain treatments</kwd><kwd>visual analog scale</kwd><kwd>disability</kwd></kwd-group><counts><fig-count count=\"2\"/><table-count count=\"13\"/><equation-count count=\"0\"/><ref-count count=\"106\"/><page-count count=\"15\"/><word-count count=\"10564\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Neuropathic pain (NP) is a pain caused by a lesion or a disease affecting the somatosensory nervous system and encompasses common neurological pain syndromes such as trigeminal neuralgia (TN), postherpetic neuralgia (PHN), diabetic neuropathic pain (DN), and postsurgical neuralgia.</p><p>NP is caused by pathological changes involving the peripheral (nerves, plexus, roots, and sensitive ganglia) and the central nervous system (CNS). The pathologies responsible for tissue specific symptoms of NP comprise viral infections (e.g., herpes simplex, varicella zoster, and human immunodeficiency virus), metabolic disorders with mitochondrial dysfunctions (e.g., diabetes), stroke, mechanical injuries to the CNS or peripheral nerves (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>), and toxic effects, above all anti-neoplastic compounds (e.g., oxaliplatin, vincristine) (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>).</p><p>Nociceptor activation is one of the most relevant NP peripheral mechanism, causing abnormal neuronal hyperexcitability, hyperalgesia, and allodynia (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>–<xref rid=\"B6\" ref-type=\"bibr\">6</xref>).</p><p>Nociceptors consist of free nerve endings related to unmyelinated C-fibers and small-myelinated Aδ-fibers; they are activated by different mechanical, thermal, and chemical stimuli and a variety of endogenous substances [e.g., substance P (SP), bradykinin, serotonin, calcitonin gene-related peptide (CGRP), prostaglandins, excitatory amino acids histamine, growth factors, and proinflammatory cytokines] (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>–<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Increased nociceptor excitability due to nerve injury causes glutamate-mediated pronociceptive activation and a decrease in inhibitory influences. Primary nociceptive Aδ- and C-fibers terminate at two distinct types of spinal second-order neurons, i.e., spinal neurons projecting to higher neuronal structures, and spinal interneurons modulating synaptic transmission in the dorsal horn where resident microglial activation plays also a key role (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Glutamate participates in the transmission of nociceptive inputs from the periphery to the brain by binding to α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), N-Methyl-D-Aspartate (NMDA), and metabotrobic (mGluR) receptors. The glutamate pathway mediates basic responses to nociceptive stimuli and contributes to the spinal dorsal horn hyperexcitability, manifesting with synaptic plasticity, and long-term potentiation (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>).</p><p>The activity of second-order neurons is modulated by the descending brainstem inhibitory noradrenergic, serotonergic, and opiatergic pathways, spinal GABAergic and glycine inhibitory inputs, as well as by the cannabinoid system (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>–<xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p><p>Current pharmacological and non-pharmacological treatment of NP is still unsatisfactory (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Besides lidocaine, capsaicin, antidepressants, anticonvulsants, and opioids, botulinum toxin (BoNT) has more recently emerged as a promising NP therapeutic strategy (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). The first evidence of BoNT efficacy in NP in humans dates back to 2001 when Freund and Schwartz (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>) described seven patients with postherpetic neuralgia (PHN) treated for >6 months with subcutaneous BoNT injections at 38th Interagency Botulism Research Coordinating Committee Meeting (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Currently, the use of BoNT is considered for NP whenever common pharmacological agents have been ineffective (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>).</p><p>BoNT is a potent neurotoxin produced by Clostridium botulinum, which blocks acetylcholine release at neuromuscular junctions causing muscle relaxation. The mechanism of action of BoNT in NP is related to the inhibition of the release of neurotransmitters and neuropeptides involved in pain mechanisms and inflammation (substance P, CGRP, glutamate) (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>). Moreover, BoNT reduces the activity of the transient receptor potential vanilloid 1 ion channels (TRPV1), involved in the transduction of noxious stimuli (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B19\" ref-type=\"bibr\">19</xref>). Two different BoNT serotypes are used: Botulinumtoxin A (BoNT/A)—encompassing onabotulinumtoxinA (A/Ona, BOTOX® Allergan), abobotulinumtoxinA (A/Abo, Dysport® Ipsen), and incobotulinumtoxinA (A/Inco, Xeomin® Merz)—and Botulinumtoxin B (BoNT/B) i.e., rimabotulinumtoxinB (B/Rima, Myobloc®/Neurobloc® ElanPharmaceuticals). These toxins differ for complexity, purity, potency, dosing, and immunogenicity. Most studies in NP have been conducted using BoNT/A.</p><p>The aim of the present paper is to systematically review the evidence on BoNT usefulness in the management of NP, to highlight scientific certainties and doubts, addressing research progresses, and suggesting directions for future investigations.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>Search Strategy and Criteria for Selecting Articles</title><p>We searched the electronic database MEDLINE, PubMed, and the Cochrane Database for published papers and extracted data for (1) pain, (2) neuropathic pain, (3) botox, (4) botulinum toxin, (5) neuralgia, and (6) neuropathy. We considered randomized controlled trials (RCT), open label (OL) studies, retrospective/prospective case-control (CC) studies, case reports (CR), and case-series (CS) on adult patients with neuropathic pain. Our search also included meta-analyses with no language restrictions and all the titles and abstracts identified by the search were evaluated for eligibility (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Studies assessing the efficacy of botulinum toxin in different types of neuropathic pain. TN: trigeminal neuralgia; PHN: post-herpetic neuralgia; DN: diabetic neuropathy; PSP: post-stroke pain; CTS: carpal tunnel syndrome; PTPS: post-thoracotomy pain syndrome; CPSP: chronic post-surgical pain; CPRS: complex regional pain syndrome; PLP: phantom limb pain; SCI: spinal cord injury; NTOS: neurogenic thoracic outlet syndrome; CPPS: Chronic pelvic pain syndrome.</p></caption><graphic xlink:href=\"fneur-11-00716-g0001\"/></fig><p>We considered all the articles on human studies providing abstract and full-text published in English language, regardless the year of publication.</p><p>We also searched clinical trials on BoNT and NP on <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.clinicaltrials.gov\">www.clinicaltrials.gov</ext-link>.</p><p>The search and selection of the articles were made independently by two evaluators (GE, LF) and then discussed with the third author (PB).</p></sec></sec><sec id=\"s3\"><title>Results: BoNT in Neuropathic Pain</title><p>Among the numerous pharmacological studies on BoNT for NP treatment in adults, we identified 22 RCTs, 20 CR, and 10 OL studies including a total of 1,543 patients. Eighteen studies focused on the effect of botulinum toxin in TN, nine in traumatic, compressive and post-surgical causes of NP, six in PHN, five in complex regional pain syndrome (CRPS), four in post-stroke pain (PSP), four in spinal cord injury (SCI), three in painful diabetic neuropathy (PDN), two in occipital neuralgia, two in phantom limb pain (PLP), two in neurogenic thoracic outlet syndrome (NTOS), and four in chronic pelvic pain syndrome (CPPS) (see <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). The search on <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.clinicaltrials.gov\">www.clinicaltrials.gov</ext-link> documented 17 experimental trials: nine completed, four terminated, two ongoing, and one withdrawn. One study actually focused on migraine and was therefore exclude. Complete results are available only for one of the above trials included in our review.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Studies assessing the efficacy of botulinum toxin in different types of neuropathic pain. TN: trigeminal neuralgia; PHN: post-herpetic neuralgia; DN: diabetic neuropathy; PSP: post-stroke pain; occipital neuralgia; CTS: carpal tunnel syndrome; PTPS: post-thoracotomy pain syndrome; post-surgical pain; CRPS: complex regional pain syndrome; PLP: phantom limb pain; SCI: spinal cord injury; NTOS: neurogenic thoracic outlet syndrome; CPPS: Chronic pelvic pain syndrome.</p></caption><graphic xlink:href=\"fneur-11-00716-g0002\"/></fig><sec><title>Trigeminal Neuralgia</title><p>TN is the most common and disabling cranial neuralgia in adults and is characterized by a unilateral, abrupt, brief electric shock-like pain, limited to the distribution of one or more divisions of the trigeminal nerve, typically triggered by innocuous stimuli (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). TN is etiologically classified as <italic>idiopathic</italic> (without any reliable organic substrate), <italic>classic</italic> (due to a neurovascular conflict between an anomalous vessel and the trigeminal root close to its entry into the pons), and <italic>secondary</italic> (due to major neurologic diseases, such as multiple sclerosis or tumors at the cerebellopontine angle) (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>).</p><p>Indisputably, carbamazepine (400–1,200 mg/day) and oxcarbazepine (900–1,800 mg/ day) represent the first-choice TN medical treatment (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). However, even though they are effective in 80% of patients, their clinical benefit may decrease over time and their use is frequently associated to significant side effects (drowsiness, nausea, dizziness, ataxia, hyponatremia, and liver enzymes elevation) (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Neurosurgical procedures (such as microvascular decompression and radio-surgical treatment)—considered for refractory cases—induce clinical benefit in almost 60–90% of cases but may be followed by complications or pain recurrence (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>).</p><p>Diverse investigations provided encouraging data on the efficacy of onabotulinum toxin type A (BoNT/A) in reducing pain severity and attack frequency in TN (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>). It's worth mentioning, however, that these studies are quite heterogeneous in terms of BoNT/A dose, dilution, route of administration, number/sites of injection and needle type used and include CR (<italic>n</italic> = 7) (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>–<xref rid=\"B33\" ref-type=\"bibr\">33</xref>), OL studies (<italic>n</italic> = 8) (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>–<xref rid=\"B41\" ref-type=\"bibr\">41</xref>), and RCTs (<italic>n</italic> = 4) (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>–<xref rid=\"B45\" ref-type=\"bibr\">45</xref>) (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Studies on the use of botulinum toxin in TN.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n<sup><bold>°</bold></sup></bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B27\" ref-type=\"bibr\">27</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 × 8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS reduction score from 82 (baseline) to 45</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B28\" ref-type=\"bibr\">28</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type-A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.5 × 2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">More than 90% relief over 2 months with consequent analgesic overuse cessation.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B29\" ref-type=\"bibr\">29</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Complete pain relief at the right external nasal region on the second day after the injection. Partial relief at the mental region. Recurrence after 5 months</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Facial paralysis</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B31\" ref-type=\"bibr\">31</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID, SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinumtoxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Improvement after 15 days followed by total pain relief. Pain disappearance at month 28</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Local injection site swelling and slight distal eye- brow ptosis</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B32\" ref-type=\"bibr\">32</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>Case 1:</italic> after 3 months, VAS score reduction from 5 to 2<italic>; Case 2</italic>: after 3 months, VAS score reduction from 10 to 3.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dryness in the injection area, facial asymmetry</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B33\" ref-type=\"bibr\">33</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">First treatment: SM, IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A- HengLi® Botox, Lanzhou, Gansu, China</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS score reduction from 5–8 to 3–5 after 1 week; pain free after 2 weeks</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B34\" ref-type=\"bibr\">34</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">OL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS score reduction</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B35\" ref-type=\"bibr\">35</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">OL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum-A neurotoxin</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">NS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant pain reduction at day 10, symptom free at day 20, > 50% reduction in preventive medication, multiple medications reverted to monotherapy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">None</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B36\" ref-type=\"bibr\">36</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">OL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20–50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant reduction in VAS score (8.83 to 4.08) and in n° of paroxysms (from 23.42 to 8.67).</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B37\" ref-type=\"bibr\">37</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">OL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BoNT/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant reduction in VAS score and attack frequency at 1 week, 1 month, and 6 months after injection (<italic>P</italic> = 0.001). 7 patients became pain free</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Transient paresis of the buccal branch of the facial nerve in 3 patients</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B38\" ref-type=\"bibr\">38</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">OL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A- HengLi® Botox, Lanzhou, Gansu, China</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25–170</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Effective <1st month in 81 pts and at 2nd month in all subjects. The therapeutic effect decreased after the 3rd month</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Local swelling at injection in 3 pts, facial paralysis in 10 pts</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B39\" ref-type=\"bibr\">39</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">OL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID, SM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A- HengLi® Botox, Lanzhou, Gansu, China</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70–140</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS score reduction; comparable efficacy and side effects between single and repeated doses.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">“Mild, moderate side effects”</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B40\" ref-type=\"bibr\">40</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">OL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Maxillary and mandibular root</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">At month 6 significant reduction in VAS score (from 9.7 to 1.6) and attack frequency (from 217.7 to 55.15); 44.4% of the patients were pain-free</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">“Mild, moderate side effects</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B41\" ref-type=\"bibr\">41</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">OL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID, SM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A- HengLi® Botox, Lanzhou, Gansu, China</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30–200</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS score reduction: in older patients from 8.5 to 4.5 after 1 month, in younger patients from 8.0 to 5.0</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Whole-body mild discomfort, left eye ptosis + slight oral deviation/drooling (2 pts); facial paralysis (2 pts)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B42\" ref-type=\"bibr\">42</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID, SM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BoNT/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant reduction in VAS score and attack frequency after 2 weeks</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Facial asymmetry, facial oedema</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B44\" ref-type=\"bibr\">44</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SM, IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS score reduction after 2 months (<italic>p</italic> = 0.07) and after 3 months (<italic>p</italic> = 0.01)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hematoma, slight facial asymmetry</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B43\" ref-type=\"bibr\">43</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SM, IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40–60</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant reduction in VAS score and number of weekly acute medications and increase in QoL functioning scale</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Transient facial asymmetry, hematoma, itching and pain at the site of injection</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B45\" ref-type=\"bibr\">45</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID, SM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BoNT/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25 or 75</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No difference in short term efficacy with low or high dose</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Transient facial asymmetry, oedema at the site of injection</td></tr></tbody></table><table-wrap-foot><p><italic>ITN, idiopathic trigeminal neuralgia; TN, trigeminal neuralgia; s, single blind arm; d, double blind; na, not applicable; <sup>*</sup>term as originally reported in the study; U, units; IM, intramuscular; SC, subcutaneous; ID, intradermal; SM, submucosal; RCT, randomized controlled trials; OL, open label; BoTN/A, onabotulinum toxin type A; VAS, visual analog scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap><p>In one RCT, 42 patients affected by classical TN were randomized to multiple intradermal and/or submucosal injections of BoNT/A 75U (22 pts) or saline (20 pts) in the skin and/or mucosa of affected pain areas. BoNT/A significantly reduced pain intensity at week 2 and pain attack frequency at week 1 compared to the placebo (68.2 vs. 15.0%; <italic>p</italic> < 0.01), showing sustained efficacy and good tolerability (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>).</p><p>BoNT/A (100 U) has been demonstrated to be effective also in intractable TN in a randomized, single-blinded, placebo-controlled study on 20 patients, significantly reducing pain intensity (6.5 vs. 0.3; <italic>p</italic> < 0.0001) and acute medication intake and increasing quality of life (QoL) functioning scale at week 12. Each point injection, detected using a “follow the pain” method, received BoNT/A (5 U) or 0.1 ml placebo subcutaneously. In patients with mandibular root involvement, a larger toxin dose was injected posteriorly in the masseter to avoid undesired cosmetic effects (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>).</p><p>Similarly, BoNT/A (50 U) outperformed placebo in pain severity reduction at 3 months post-dosing (VAS 4.75 vs. 6.94; <italic>p</italic> = 0.01) in a RCT on 36 patients (20 randomized to active and 16 to placebo) affected by idiopathic TN. Injections were delivered subcutaneously in the affected area and also in the masseter muscle in patients with the involvement of the third branch of the trigeminal nerve (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>).</p><p>The efficacy of BoNT/A in the treatment of classical TN is not dose-dependent. Zang et al. (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>) randomized 84 patients to placebo (<italic>n</italic> = 28), BoNT/A 25U (<italic>n</italic> = 27), or BoNT/A 75U (<italic>n</italic> = 29) and found that both toxin doses were equally significantly more effective than placebo in reducing VAS scores as early as week 1, showing higher response rates at week 8 (70.4 and 86.2%, respectively, vs. 32.1%; <italic>p</italic> < 0.017). The proportion of patients reporting their pain symptoms as “much improved” or “very much improved” at Patient Global Impression of Change was comparable at both 25U (66.7%) and 75U (75.9%) doses and clearly superior to placebo (32.1%).</p><p>A recent meta-analysis of 4 RCTs (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>–<xref rid=\"B45\" ref-type=\"bibr\">45</xref>) including 178 patients (BoNT/A: 99; placebo: 79) revealed a significant superiority of BoNT/A in reducing pain intensity as measured by VAS total score (RR 2.87, <italic>p</italic> < 0.0001) and frequency of attacks (<italic>p</italic> < 0.0001), documenting a benefit duration up to 3 months and mild–moderate and self-limiting adverse events (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). BoNT/A proved effective also in TN persisting after microvascular decompression surgery (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>), in symptomatic TN due to exostosis in Meckel's cave (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>) and in refractory ITN (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>).</p><p>To sum up, BoNT/A may represent a useful therapeutic tool in the clinical management of TN. However, the low quality of evidence has led the recent European Academy of Neurology guidelines to limit the use of BoNT/A to a medium-term treatment “in some selected cases” (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>).</p></sec><sec><title>Post-herpetic Neuralgia</title><p>Herpes zoster results from reactivation of varicella zoster virus which lies dormant in sensory dorsal roots, cranial nerves, and autonomic ganglia. It presents as a painful maculopapular or vesicular rash in a dermatomal distribution, most commonly in thoracic and cranial distributions. The PHN is its most common complication, occurring in 10–50% of patients (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>), and it can persist for weeks or years after regression of the rash, impairing patients' quality of life (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>).</p><p>BoNT/A may represent an effective therapeutic option for PHN. Some case-reports documented the efficacy of the toxin in patients with PHN refractory to conventional treatments in term of VAS score reduction at a mean dose of 100 U, with an analgesic effect duration ranging from 52 to 64 days and a good tolerability (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>–<xref rid=\"B52\" ref-type=\"bibr\">52</xref>).</p><p>The encouraging results of this small series have been confirmed by three RCTs and a recent meta-analysis. In one RCT with active comparator, 60 patients affected by PHN in different cutaneous areas were randomized to receive BoNT/A (20 pts), lidocaine (20 pts), or placebo (20 pts). The volumes of administration varied according to the area of tactile allodynia, but fewer than 40 mL volumes (200 units for the maximum BoNT/A dose) were used. BoNT/A reduced pain more effectively than both lidocaine and placebo at day 7 and after 3 months compared to baseline (<italic>p</italic> < 0.01). The improvement of sleep time in the BoNT/A group was also significantly greater compared with the other groups (<italic>P</italic> < 0.01) (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>).</p><p>A long-lasting BoNT/A therapeutic effect has been confirmed in a RCT on 30 adults with PHN in which only the 13 subjects randomized to BoNT/A achieved a >50% reduction in VAS score (NNT=1.2, 95% CI, 2–1; ARR=0.87, 95% CI, 055–096; <italic>P</italic> < 0.001). Notably, BoNT/A improvement in pain and sleep scores persisted for 16 weeks (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>).</p><p>In a RCT on 68 patients affected by a miscellany of diverse peripheral neuropathic pain syndromes (post-traumatic or postsurgical pain, polyneuropathy, postherpetic neuralgia), 34 subjects were randomized to receive two subcutaneous administrations of BoNT/A (up to 300 U) or placebo, 12 weeks apart. Two successive BoNT/A administrations significantly decreased (<italic>p</italic> < 0.0001) the mean pain intensity over 24 weeks after the first treatment administration compared with placebo. The study, which included six subjects with PHN, confirmed the role of BoNT/A in reducing pain severity, evidencing that it was particularly efficacious in participants with preserved nociceptive input. Moreover, the authors suggested that at least two administrations of BoNT/A might be necessary in non-responders before deciding to withdrawing the treatment (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>).</p><p>Despite BoNT/A efficacy in both chronic TN and PHN (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>), herpes zoster has been described as a complication of BoNT/A administration in a 72-year-old woman affected by chronic migraine who developed ophthalmicus herpes zoster 5 days after treatment, probably due to local stress reaction following tissue injury inducing VZV reactivation (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>).</p><p>In conclusion, the efficacy and safety of BoNT/A in the treatment of PHN is supported by scientific evidence (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>), but studies on larger populations are needed.</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Studies on the use of botulinum toxin in PHN.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B50\" ref-type=\"bibr\">50</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A- HengLi® Botox, Lanzhou, Gansu, China</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS score reduction from 10 to 1 after 2 days, lasting 52 days</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B51\" ref-type=\"bibr\">51</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BoNT/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">-</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS score reduction lasting 2 months</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B52\" ref-type=\"bibr\">52</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS score reduction from 8.3 to 2 after 2 weeks</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Temporary erythema</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B53\" ref-type=\"bibr\">53</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60 (20 treated)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BoNT/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">200</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant reduction of overall symptoms severity (pain, opioid use, sleep interference); marked improvement in quality of life.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B54\" ref-type=\"bibr\">54</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 (15 treated)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BoNT/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">200</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant reduction in VAS and sleep scores at week 2; the effect lasted 16 weeks.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pain during injections</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B55\" ref-type=\"bibr\">55</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">up to 60 × 5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS score reduction</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>PHN, post-herpetic neuralga; RCT, randomized controlled trials; s, single blind arm; d, double blind; na, not applicable; IM, intramuscular; SC, subcutaneous; ID, intradermal; SM, submucosal; *term as originally reported in the study; BoTN/A, onabotulinum toxin type A; U, units; VAS, visual analog scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Diabetic Neuropathy</title><p>DN is a common debilitating complication of diabetes. About a third of patients develop painful diabetic neuropathy (PDN) (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>, <xref rid=\"B59\" ref-type=\"bibr\">59</xref>). As current pharmacological treatments are not always effective, BoNT/A has been investigated for pain control in PDN in some RTCs.</p><p>Intradermal BoNT/A administration in 18 patients (50 U in the dorsum of the foot for a total of 12 sites at the dose of 4 U for each injection point) induced a significant reduction in VAS score at weeks 1, 4, 8, and 12 compared to placebo with a reduction of VAS ≥3 within 3 months after injection in 44% of patients receiving the active drug. A transient sleep quality improvement was also described (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>). Using the same study protocol, Chen et al. (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>) reported that BoNT/A may also be beneficial in reducing tactile and mechanical pain threshold in PDN (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Intradermal BoNT/A at the dose of 8–10 U per injection site (total dose = 100 U) induced a significant (<italic>p</italic> = 0.05) reduction in neuropathic pain scale (NPS) scores for all items—except cold sensation—in VAS (<italic>p</italic> = 0.01) and DN4 scores (<italic>p</italic> < 0.05) compared to placebo in a study performed on 40 patients affected by PDN aged <70 years. One third of patients in the treatment group showed bilateral pain reduction 3 weeks after injection (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>).</p><p>The use of BoNT/A in DN is promising although the studies are scarce and carried out on small populations (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>–<xref rid=\"B63\" ref-type=\"bibr\">63</xref>). Larger RTCs are needed.</p></sec><sec><title>Post-stroke Pain (PSP)</title><p>PSP is a heterogeneous clinical entity caused by neuropathic and nociceptive mechanisms, which affects from 10 to 70% of patients with stroke and includes central PSP, pain related to spasticity, muscle-skeletal pain, complex regional pain syndrome, and post-stroke headache (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>, <xref rid=\"B65\" ref-type=\"bibr\">65</xref>).</p><p>PSP is one of the factors contributing to patients' disability and interferes with daily activities, sleep, walking, physiotherapy, greatly affecting their quality of life.</p><p>The efficacy of BoNT/A in central PSP control has been investigated (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>) (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>–<xref rid=\"B69\" ref-type=\"bibr\">69</xref>). A prospective RCT performed on 37 patients failed to demonstrate any BoNT/A efficacy on PSP (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>). Conversely, a larger RCT on post-stroke spasticity on 273 patients—mostly complaining of PSP (74.3%)—randomized to BoNT/A plus standard care showed a significantly greater reduction in pain and in pain interference with work (<italic>p</italic> < 0.05) compared to patients treated with placebo plus standard care (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>) (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>).</p><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Studies on the use of botulinum toxin in DN.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B60\" ref-type=\"bibr\">60</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS reduction (<italic>P</italic> < 0.05)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Local skin infection</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B61\" ref-type=\"bibr\">61</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant decrease in tactile threshold and pain threshold (<italic>P</italic> < 0.05)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B62\" ref-type=\"bibr\">62</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A-Dysport®-Ipsen, UK</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reduction in NPS (<italic>P</italic> = 0.05) and DN4 scores (<italic>P</italic> < 0.05). Pain freedom in 30% of patients (<italic>p</italic> = 0.01)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>DN, diabetics neuropathy; RCT, randomized controlled trials; d, double; ID, intradermal; *term as originally reported in the study; U, units; VAS, visual analog scale; NPS, neuropathy pain scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap><table-wrap id=\"T4\" position=\"float\"><label>Table 4</label><caption><p>Studies on the use of botulinum toxin in PSP.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B67\" ref-type=\"bibr\">67</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25, 75, and 100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pain reduction after 2 days; spasticity improvement after 1 week</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B66\" ref-type=\"bibr\">66</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">200</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NRS reduction for more than 3 months</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B69\" ref-type=\"bibr\">69</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Placebo+SC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">273 (139 BoNT/A + SC vs. 134 Placebo + SC)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">As needed</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reduction in VAS and pain interference with work (P < 0.05)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B69\" ref-type=\"bibr\">69</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37 (21 BoNT/A vs. 16 Placebo)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">140, 200</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No significant differences between the groups found for any of the daily pain ratings</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>PSP, Post-stroke pain; RCT, randomized controlled trials; d, double; na, not applicable; SC, Standard care; IM, intramuscular; *term as originally reported in the study; U, units; VAS, visual analog scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap><p>The usefulness of BoNT/A in the management of PSP deserves to be investigated in further <italic>ad hoc</italic> designed RTCs.</p></sec><sec><title>Occipital Neuralgia</title><p>Occipital neuralgia is a unilateral or bilateral radiating pain in the posterior part of the scalp in the distribution of the greater, lesser, and/or third occipital nerves (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). The causes of occipital neuralgia include irritation or injury to the divisions of the occipital nerve, its focal entrapment and myofascial spasm. Persistent occipital neuralgia can produce severe headaches that are difficult to control by conservative or surgical approaches. The occipital nerve blocks using BoNT/A at the dose of 50 U for each block provided a meaningful reduction in pain intensity and disability in five out of six patients in patients who had failed prior oral therapies or traditional nerve blocks in a case series study (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>). BoNT/A, at the same dose, improved the sharp/shooting type of pain associated with occipital neuralgia in a pilot study on six patients, inducing also a significant improvement of headache-specific quality of life (<italic>p</italic> = 0.0315) (<xref rid=\"T5\" ref-type=\"table\">Table 5</xref>) (<xref rid=\"B71\" ref-type=\"bibr\">71</xref>).</p><table-wrap id=\"T5\" position=\"float\"><label>Table 5</label><caption><p>Studies on the use of botulinum toxin in Occipital Neuralgia.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B70\" ref-type=\"bibr\">70</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cases series</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Occipital nerve block</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100 (50 for each block)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant reduction in pain VAS scores and improvement in PDI</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B71\" ref-type=\"bibr\">71</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Occipital nerve block</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100 for each block</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Improvement in the sharp/shooting type of pain most commonly associated with occipital neuralgia</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>PS, prospective study; s, single; na, not applicable; ID, intradermal; *term as originally reported in the study; U, units; VAS, visual analog scale; PDI, pain disability index; -, not reported</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Carpal Tunnel Syndrome (CTS)</title><p>ARCT with botulinum toxin type B (BoNT/B) in 20 outpatients affected by CTS did not confirm the positive findings reported in an open label trial on five women (<xref rid=\"T6\" ref-type=\"table\">Table 6</xref>) (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>, <xref rid=\"B73\" ref-type=\"bibr\">73</xref>).</p><table-wrap id=\"T6\" position=\"float\"><label>Table 6</label><caption><p>Studies on the use of botulinum toxin in CTS.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B72\" ref-type=\"bibr\">72</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intracarpal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A- Dysport®, Beaufour Ipsen, UK</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not superior to placebo (<italic>p</italic> = 0.2)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Local weakness and discomfort</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B73\" ref-type=\"bibr\">73</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Intracarpal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30 for sides</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not superior to placebo</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>CTS, carpal tunnel syndrome; PS, prospective study; RCT, randomized controlled trials; s, single; d, double; na, not applicable; *term as originally reported in the study; U, units; -, not reported</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Post-thoracotomy Pain Syndrome (PTPS) and Chronic Post-surgical Pain</title><p>PTPS is a traumatic neuropathy that can affect as many as 50% of patients undergoing thoracotomy, is often refractory to conservative management and may require multiple analgesics for adequate pain control. BoNT/A may represent an alternative or adjunct treatment to improve symptom management in patients with PTPS (<xref rid=\"T7\" ref-type=\"table\">Table 7</xref>) (<xref rid=\"B79\" ref-type=\"bibr\">79</xref>). Two case reports documented the efficacy of BoNT/A (total dose: 50–100 U, along the scar) in inducing a significant and prolonged pain reduction in patients affected by PTPS with multiple prior therapeutic failures (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>, <xref rid=\"B75\" ref-type=\"bibr\">75</xref>).</p><table-wrap id=\"T7\" position=\"float\"><label>Table 7</label><caption><p>Studies on the use of botulinum toxin in post-surgical syndrome.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cause of pain</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B74\" ref-type=\"bibr\">74</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Post-thoracotomy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.5 for site</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">50% VAS score improvement, sustained up to week 12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B75\" ref-type=\"bibr\">75</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Post-thoracotomy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant reduction of pain at day 4 sustained up to month 4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B76\" ref-type=\"bibr\">76</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Post-surgical and post radiation therapy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">s</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM, SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Incobotulinum toxin A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant improvement in VAS score and patients satisfaction</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2 skin reactions</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B77\" ref-type=\"bibr\">77</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Post-mastectomy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40 for each pectoralis</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant pain reduction in VAS score (p < .05)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B78\" ref-type=\"bibr\">78</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Post-mastectomy</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Controls</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant reduction pain in postoperative (<italic>p</italic> < 0.0001), during initial (<italic>P</italic> = 1.6 × 10(6)) and final expansion (<italic>p</italic> = 0.009)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>PS, prospective study; RCT, randomized controlled trials; RS, retrospective study; s, single; d, double; na, not applicable; SC, subcutaneous; IM, intramuscular; *term as originally reported in the study; U, units; VAS, visual analog scale; NRS, numeric rating scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap><p>The prevalence of chronic post-surgical pain in cancer patients ranges from 20 to 70% according to different studies. Chronic post-surgical pain pathophysiology is likely to include both peripheral and sensitization mechanisms (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>, <xref rid=\"B81\" ref-type=\"bibr\">81</xref>).</p><p>A prospective study on 48 post-mastectomy patients demonstrated that BoNT/A (100 U) administration in the pectoralis major, serratus anterior, and rectus abdominis muscle followed by immediate insertion of tissue expander leads to a significant reduction of post-operative pain (<italic>p</italic> < 0.0001) and pain during both initial and final expansion (<italic>p</italic> = 0.009), greater volume of expansion per session (<italic>p</italic> = 0.010), reduced number of expansion sessions (<italic>p</italic> = 0.025), and lower narcotic use compared to standard procedures (<italic>p</italic>= 0.012) (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>).</p><p>Similarly, a prospective RCT evaluating BoNT/A in expander-based breast reconstruction (40 U into the pectoralis major muscle), demonstrated a reduction in the use of oxycodone (<italic>p</italic> < 0.0001) and diazepam (<italic>p</italic> < 0.0001) and an increase in the expansion volume per visit in the active group compared to placebo (<italic>p</italic> < 0.05) (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>).</p><p>BoNT/A (doses up to 100 U, intramuscularly or subcutaneously) proved effective in reducing pain and improving quality of life in eight out of 12 female cancer patients who had surgery or radiation for local cancer and failed >2 analgesic treatments (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>) (<xref rid=\"T7\" ref-type=\"table\">Table 7</xref>).</p></sec><sec><title>Complex Regional Pain Syndrome (CRPS)</title><p>CRPS is characterized by disabling chronic-relapsing burning pain, vasomotor changes, and occasionally trophic or motor function changes (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>).</p><p>BoNT/A administration in muscular trigger points was reported to be effective in CRPS (<xref rid=\"B83\" ref-type=\"bibr\">83</xref>) but this finding was not confirmed in a larger prospective RCT on 14 individuals delivering BoNT/A into the allodynic skin areas (<xref rid=\"B84\" ref-type=\"bibr\">84</xref>). Caroll et al. randomly treated nine patients with refractory CRPS using standard lumbar sympathetic block (LSB) with bupivacaine (0.5%) or LSB with bupivacaine (0.5%) + BoNT/A (75 U) and found a significantly lower rate of pain return (<italic>p</italic> < 0.02) and greater reduction in pain intensity (<italic>p</italic> < 0.0001) in those receiving BoNT/A compared with local anesthetic alone (<xref rid=\"B85\" ref-type=\"bibr\">85</xref>).</p><p>In a retrospective, uncontrolled study, the EMG-guided administration of 100 U of BoNT/A (10–20 U per pain site) to 37 patients with severe local pain at baseline induced a significant pain reduction (mean pain score from 8.2 to 4.5; <italic>p</italic> < 0.001) in almost all individuals (97%) (<xref rid=\"B86\" ref-type=\"bibr\">86</xref>). Lumbar sympathetic block with levobupivacaine 0.25% 5 mL plus botulinum toxin type B 5,000 IU under fluoroscopic guidance was associated, 2 months later, to a meaningful reduction of pain intensity, allodynia, Leeds assessment of neuropathic symptoms, skin coldness and discoloration, and tissue swelling (<xref rid=\"B87\" ref-type=\"bibr\">87</xref>). The studies are summarized in <xref rid=\"T8\" ref-type=\"table\">Table 8</xref>.</p><table-wrap id=\"T8\" position=\"float\"><label>Table 8</label><caption><p>Studies on the use of botulinum toxin in CRPS.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B87\" ref-type=\"bibr\">87</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Lumbar sympathetic block</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type B-Myobloc®, Solstice Neurosciences, USA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5,000</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reduction in VAS score and CRPS symptoms</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nausea and vomiting (1 pt)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B83\" ref-type=\"bibr\">83</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cases series</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 for site (total 200)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reduced pain and distal allodynia</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B86\" ref-type=\"bibr\">86</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pain reduction in 97% of patients</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Transient neck drop (1 pt)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B84\" ref-type=\"bibr\">84</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT + OL</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8+6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">ID, SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40–200</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ineffective</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Poorly tolerated</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B85\" ref-type=\"bibr\">85</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.5% bupivacaine</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sympathetic block</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Longer duration of pain reduction (71 vs. 10 days; <italic>p</italic> < 0.02). Significant VAS core reduction (<italic>p</italic> < 0.0001)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nausea and vomiting (1 pt</td></tr></tbody></table><table-wrap-foot><p><italic>CRPS, Complex regional pain syndrome; RCT, randomized controlled trials; RS, retrospective study; OL, Open label; d, double; na, not applicable; SC, subcutaneous; IM, intramuscular; ID, intradermal; *term as originally reported in the study; U, units; VAS, visual analog scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Piriformis Syndrome</title><p>Piriformis syndrome is caused by the entrapment of the sciatic nerve by the piriformis muscle and accounts to up 8% of sciatic pain. The diagnosis of piriformis syndrome is sometimes challenging due to clinical overlap with low back and buttock pain. A single RCT on 56 patients treated with physical therapy and allocated to BoNT/A A (300 U) or placebo revealed a more marked reduction in VAS score, compared with placebo, at 2, 4, 6, 8, 10, and 12 weeks post-injection (<italic>P</italic> < 0.0001) (<xref rid=\"T9\" ref-type=\"table\">Table 9</xref>) (<xref rid=\"B88\" ref-type=\"bibr\">88</xref>).</p><table-wrap id=\"T9\" position=\"float\"><label>Table 9</label><caption><p>Studies on the use of botulinum toxin in Piriformis syndrome.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B88\" ref-type=\"bibr\">88</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Incobotulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">300</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS score reduction at weeks 2,4,6,8, and 12 post-injection (<italic>p</italic> < 0.0001)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Injection site pain, stiff neck, wobbly neck, flu-like symptoms</td></tr></tbody></table><table-wrap-foot><p><italic>RCT, randomized controlled trials; d, double; *term as originally reported in the study; U, units; VAS, visual analog scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Phantom Limb Pain (PLP)</title><p>The long-term treatment of PLP using BoNT/A administration (4 × 25 U, quarterly) in the stump muscles of a lower limb amputee led to almost complete pain freedom (<xref rid=\"B89\" ref-type=\"bibr\">89</xref>).</p><p>A prospective RCT on 14 patients randomized to receive BoNT/A (250–300 U) or lidocaine plus depomedrol at the focal tender point demonstrated that both treatments were equally effective in PLP relief (<xref rid=\"T10\" ref-type=\"table\">Table 10</xref>) (<xref rid=\"B90\" ref-type=\"bibr\">90</xref>).</p><table-wrap id=\"T10\" position=\"float\"><label>Table 10</label><caption><p>Studies on the use of botulinum toxin in PLP.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B90\" ref-type=\"bibr\">90</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100 (4 injections performed every 3 months)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Almost complete pain-freedom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B89\" ref-type=\"bibr\">89</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Lidocaine/Depomedrol</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM/cutaneous/SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50 for sides</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not superior to placebo</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>PLP, Phantom Limb Pain; RCT, randomized controlled trials; d, double; na, not applicable; *term as originally reported in the study; IM, intramuscular; SC, subcutaneous; U, units; -, not reported</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Spinal Cord Injury (SCI)</title><p>Pain often complicates SCI. Neuropathic pain after SCI is generally severe, refractory to treatment and persistent over time, reducing quality of life and interfering with cognitive, emotional, and physical functioning. Its prevalence rate ranges between 75 and 81% (<xref rid=\"B91\" ref-type=\"bibr\">91</xref>, <xref rid=\"B92\" ref-type=\"bibr\">92</xref>).</p><p>A few case reports described a notable VAS score improvement of SCI-related neuropathic pain and allodynia using BoNT/A at a dose ranging from 80 to 200 U, documenting also a quite rapid onset of the clinical benefit and a long-lasting effect (>3 months) (<xref rid=\"B93\" ref-type=\"bibr\">93</xref>, <xref rid=\"B94\" ref-type=\"bibr\">94</xref>). These promising findings were confirmed by two RCTs. BoNT/A (200 U) subcutaneous administration into the painful area proved effective in a trial on 40 patients affected by SCI-associated neuropathic pain, exhibiting a statistically significant decrease in VAS at weeks 4 and 8 compared to the placebo group (<xref rid=\"B95\" ref-type=\"bibr\">95</xref>). Similar results were reported by another study including 44 patients which documented a greater efficacy of BoNT/A over placebo in decreasing the VAS score after weeks 4 and 8 post treatments (<italic>p</italic> < 0.01) and in improving quality of life (<xref rid=\"T11\" ref-type=\"table\">Table 11</xref>) (<xref rid=\"B96\" ref-type=\"bibr\">96</xref>).</p><table-wrap id=\"T11\" position=\"float\"><label>Table 11</label><caption><p>Studies on the use of botulinum toxin in SCI.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B94\" ref-type=\"bibr\">94</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS decreased from 96 to 23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B93\" ref-type=\"bibr\">93</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A-Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS score reduction for > 3 months</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B95\" ref-type=\"bibr\">95</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A- BTX-A Meditoxin Medytox, Seoul, South Korea</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">200</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS score reduction at week 4 (<italic>p</italic> < 0.0001) and at week 8 (<italic>p</italic> = 0.0012).</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B96\" ref-type=\"bibr\">96</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">SC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A- HengLi Botox, Lanzhou, Gansu, China</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">200</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS score reduction at week 4 and week 8 of treatment (<italic>p</italic> < 0.01)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>SCI, spinal cord injury; RCT, randomized controlled trials; d, double; na, not applicable; SC, subcutaneous; *term as originally reported in the study; U, units; VAS, visual analog scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Neurogenic Thoracic Outlet Syndrome (NTOS)</title><p>NTOS is a complex entity characterized by different neurovascular signs and symptoms involving the upper limb due to a compression of the brachial plexus trunks or cords, including nerves which comes from the C5-T1 spinal levels. According to some studies, BoNT may be useful to reduce NTOS symptoms in those patients who did not benefit from physical therapy (<xref rid=\"B97\" ref-type=\"bibr\">97</xref>–<xref rid=\"B99\" ref-type=\"bibr\">99</xref>) (<xref rid=\"T12\" ref-type=\"table\">Table 12</xref>).</p><table-wrap id=\"T12\" position=\"float\"><label>Table 12</label><caption><p>Studies on the use of botulinum toxin in NTOS.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B98\" ref-type=\"bibr\">98</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case report</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Symptomatic relief</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B99\" ref-type=\"bibr\">99</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS scores did not result in clinically or statistically significant improvements in pain (<italic>P</italic> = 0.36).</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>NTOS, neurogenic thoracic outlet syndrome; RCT, randomized controlled trials; d, double; na, not applicable; IM, intramuscular; *term as originally reported in the study; U, units; VAS, visual analog scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Chronic Pelvic Pain Syndrome (CPPS)</title><p>CPPS is defined as “a chronic pain and inflammation in the pelvic organs lasting >6 months” (<xref rid=\"B100\" ref-type=\"bibr\">100</xref>). Its treatment includes behavioral interventions, physical therapy, medications, nerve blocks, neurostimulation techniques, surgical interventions, and alternative therapies. BoNT/A has also been considered in a multimodal treatment plan in selected cases, being able to act on the pelvic peripheral nerves through different mechanisms. Some studies have reported encouraging results as regards VAS score reduction (<xref rid=\"B101\" ref-type=\"bibr\">101</xref>–<xref rid=\"B104\" ref-type=\"bibr\">104</xref>) (<xref rid=\"T13\" ref-type=\"table\">Table 13</xref>).</p><table-wrap id=\"T13\" position=\"float\"><label>Table 13</label><caption><p>Studies on the use of botulinum toxin in CPPS.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study type</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Blinding</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Comparator</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Pts n°</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Injection route/site</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Toxin type*</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Dose (U)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Major findings</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AEs</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B101\" ref-type=\"bibr\">101</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pilot study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A Botox®</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS and Quality of life scores significantly improved (p = 0.01)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Influenza-like symptoms (2 pts)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B102\" ref-type=\"bibr\">102</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pilot study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">IM</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum toxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Partial response rate on the GRA (<italic>p</italic> = 0.0002). Significant reduction in CPSI pain subdomain score (<italic>p</italic> = 0.05)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B103\" ref-type=\"bibr\">103</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RCT</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">d</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Placebo</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Transurethral intraprostatic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Botulinum neurotoxin type A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Significant VAS score decrease and significant improvement in QoL scores (<italic>p</italic> < 0.05)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(<xref rid=\"B104\" ref-type=\"bibr\">104</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">na</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Transurethral intraprostatic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Onabotulinum toxin type A</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS −79 and −27.4% at 3 and 12 months (<italic>p</italic> < 0.0001)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">-</td></tr></tbody></table><table-wrap-foot><p><italic>CPPS, chronic pelvic pain syndrome; RCT, randomized controlled trials; PS, prospective study; d, double; na, not applicable; IM, intramuscular; <sup>*</sup>term as originally reported in the study; U, units; GRA, Global Response Assessment; CPSI, Chronic Prostatitis Symptom Index; VAS, visual analog scale; -, not reported</italic>.</p></table-wrap-foot></table-wrap></sec></sec><sec sec-type=\"conclusions\" id=\"s4\"><title>Conclusions</title><p>The majority of the studies were performed using BoNT/A, two studies using BoNT/B. VAS score reduction was the primary endpoint in all the studies. A positive effect of BoNT/A on NP was documented in 19 out of 21 RCT studies. The only RCT performed with BoNT/B provided negative results. Negative results emerged in two RCTs, (one in PSP and one in CTS) (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>, <xref rid=\"B73\" ref-type=\"bibr\">73</xref>) while one RCT in CRPS was stopped due to low tolerability (<xref rid=\"B84\" ref-type=\"bibr\">84</xref>). The positive effects of BoNT/A on NP started after 4–8 weeks [after 1 week in TN (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>)] and persisted up to 6 months after treatment (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>–<xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>, <xref rid=\"B60\" ref-type=\"bibr\">60</xref>, <xref rid=\"B62\" ref-type=\"bibr\">62</xref>, <xref rid=\"B68\" ref-type=\"bibr\">68</xref>, <xref rid=\"B70\" ref-type=\"bibr\">70</xref>, <xref rid=\"B71\" ref-type=\"bibr\">71</xref>, <xref rid=\"B89\" ref-type=\"bibr\">89</xref>, <xref rid=\"B90\" ref-type=\"bibr\">90</xref>, <xref rid=\"B94\" ref-type=\"bibr\">94</xref>). The duration of BoNT/A benefit was dependent on toxin dose, injection site, number and depth of injections in NP (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>) but not in TN (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>–<xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>). The effect of BoNT/B, associated with levobupivacaine, was positive only in two case reports (<xref rid=\"B87\" ref-type=\"bibr\">87</xref>); negative effect of BoNT/B was observed in a pilot studies on 20 patients (<xref rid=\"B73\" ref-type=\"bibr\">73</xref>).</p><p>In all the studies, BoNT/A had been used as a second line treatment in patients who had had previous pharmacological therapeutic failures. Prior unresponsiveness to standard of care did not affect patient's responsivity to BoNT/A. The overall tolerability of BoNT/A in the different clinical setting was good, and adverse events were usually transient and mild. No safety concerns emerged. The treatment of the face for trigeminal neuralgia or post-herpetic neuralgia was burdened by a greater number of side effects compared to limb or thorax districts due to potential facial asymmetry induced by the muscle relaxant effect of BoNT/A.</p><p>NP is a chronic, highly disabling condition caused by a lesion or disease of the somatosensory nervous system which affects from 1.5 to 6.9% of individuals aged 50–64 years (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>).</p><p>There is a stringent need for innovative and alternative NP treatments because the current standard of care—including antidepressants, anticonvulsants, opioids, topical capsaicin, and lidocaine as well as non-pharmacological approaches—is still unsatisfactory due to the low responder rate (<30% of patients), the frequency and severity of adverse events (encompassing dizziness, ataxia, nausea, vomiting, somnolence, and cutaneous rash), and the relevant proportion of patients with treatment discontinuation (30–50%) (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>).</p><p>Botulinum toxin could represent a promising therapeutic tool for NP for its documented efficacy and tolerability in a wide range of NP conditions. BoNT/A is the toxin most extensively studied, having being investigated in 21 RCTs. BoNT/A seems helpful in particular in TN, PHN, PDN, occipital neuralgia, post-surgical pain and in SCI-related pain. However, the quality of evidence is low overall due to the paucity of RCT in some NP types, the small number of patients studied and methodological heterogeneities. One major limitation is the use of different toxin serotypes and preparations which hampers the comparison of studies' results. Most studies specified the use of the common BoNT/A brand (Botox®) (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>, <xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B52\" ref-type=\"bibr\">52</xref>, <xref rid=\"B55\" ref-type=\"bibr\">55</xref>, <xref rid=\"B67\" ref-type=\"bibr\">67</xref>–<xref rid=\"B71\" ref-type=\"bibr\">71</xref>, <xref rid=\"B74\" ref-type=\"bibr\">74</xref>, <xref rid=\"B77\" ref-type=\"bibr\">77</xref>, <xref rid=\"B84\" ref-type=\"bibr\">84</xref>, <xref rid=\"B89\" ref-type=\"bibr\">89</xref>, <xref rid=\"B90\" ref-type=\"bibr\">90</xref>, <xref rid=\"B93\" ref-type=\"bibr\">93</xref>, <xref rid=\"B101\" ref-type=\"bibr\">101</xref>) or other BoNT/A compounds (e.g., HengLi®, Meditoxin®, Disport®) (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>, <xref rid=\"B62\" ref-type=\"bibr\">62</xref>, <xref rid=\"B72\" ref-type=\"bibr\">72</xref>, <xref rid=\"B94\" ref-type=\"bibr\">94</xref>, <xref rid=\"B96\" ref-type=\"bibr\">96</xref>), but, in many cases, no specification of the BoNT/A serotype was provided (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B47\" ref-type=\"bibr\">47</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>, <xref rid=\"B54\" ref-type=\"bibr\">54</xref>, <xref rid=\"B60\" ref-type=\"bibr\">60</xref>, <xref rid=\"B61\" ref-type=\"bibr\">61</xref>, <xref rid=\"B66\" ref-type=\"bibr\">66</xref>, <xref rid=\"B75\" ref-type=\"bibr\">75</xref>, <xref rid=\"B78\" ref-type=\"bibr\">78</xref>, <xref rid=\"B83\" ref-type=\"bibr\">83</xref>, <xref rid=\"B85\" ref-type=\"bibr\">85</xref>, <xref rid=\"B86\" ref-type=\"bibr\">86</xref>, <xref rid=\"B94\" ref-type=\"bibr\">94</xref>, <xref rid=\"B102\" ref-type=\"bibr\">102</xref>–<xref rid=\"B104\" ref-type=\"bibr\">104</xref>). Furthermore, two studies were performed with botulinum toxin type B (<xref rid=\"B73\" ref-type=\"bibr\">73</xref>, <xref rid=\"B87\" ref-type=\"bibr\">87</xref>) and two with incobotulinum toxin type A (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>, <xref rid=\"B88\" ref-type=\"bibr\">88</xref>).</p><p>No major safety issue emerged in the studies reported in the present review. Adverse events were rated as mild or moderate and included local skin reaction (swelling), pain at the injection site, muscles weakness, flu symptoms, nausea, and vomiting. However, there is a need for a specific evaluation of this aspect in human trials as, at present, only data on experimental animal models have been provided (<xref rid=\"B105\" ref-type=\"bibr\">105</xref>, <xref rid=\"B106\" ref-type=\"bibr\">106</xref>).</p><p>The cost of the toxin and the need of specific injection expertise may represent a restriction for its widespread use.</p><p>Bearing in mind these limitations, we deem that the use of botulinum toxin should be carefully considered in patients with NP not responsive to current standard of care and to avoid undesired adverse events and safety concerns. BoNT could also reduce the use of surgical or invasive procedures, often applied in patients refractory to common therapeutic strategies.</p><p>Larger and specifically designed RCTs are awaited to confirm efficacy and tolerability of BoNT and also to provide standardized treatment models for the different types of NP, systematically specifying serotypes, doses, treatment sites, and the depth and number of injections. Future researches are also expected to ascertain the proportion of patients developing anti-toxin antibodies during prolonged treatment, evaluate the risk of systemic effects after local delivery and appraise the safety of the botulinum toxin in the elderly and in fragile individuals.</p></sec><sec id=\"s5\"><title>Author Contributions</title><p>GE and LF equally contributed to the review of the literature and to the draft of the article. PB contributed to the draft and revision of the article. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s6\"><title>Conflict of Interest</title><p>GE has received travel grants and honoraria from Eli-Lilly, Novartis, and New Penta. LF has received travel grants and honoraria from TEVA, Eli-Lilly, and Novartis. PB has received travel grants or research support and honoraria or both for lecturing and being a consultant and scientific advisor for Merck, Lusofarmaco, Bayer, TEVA, Novartis, Eli-Lilly, Visufarma, and Assosalute.</p></sec></body><back><ack><p>The English language in the manuscript was edited by Silvia Riondino.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was partially supported by the Italian Ministry of Health (institutional funding ricerca corrente).</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Jensen</surname><given-names>TS</given-names></name><name><surname>Finnerup</surname><given-names>NB</given-names></name></person-group>. <article-title>Allodynia and hyperalgesia in neuropathic pain: clinical manifestations and mechanisms</article-title>. <source>Lancet Neurol</source>. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurosci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurosci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Neuroscience</journal-title></journal-title-group><issn pub-type=\"ppub\">1662-4548</issn><issn pub-type=\"epub\">1662-453X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848560</article-id><article-id pub-id-type=\"pmc\">PMC7431776</article-id><article-id pub-id-type=\"doi\">10.3389/fnins.2020.00787</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neuroscience</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Combined Brain-Perfusion SPECT and EEG Measurements Suggest Distinct Strategies for Speech Comprehension in CI Users With Higher and Lower Performance</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Kessler</surname><given-names>Mariella</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/947915/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Schierholz</surname><given-names>Irina</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1005448/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Mamach</surname><given-names>Martin</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Wilke</surname><given-names>Florian</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/358462/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Hahne</surname><given-names>Anja</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/76132/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Büchner</surname><given-names>Andreas</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Geworski</surname><given-names>Lilli</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/358464/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Bengel</surname><given-names>Frank M.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Sandmann</surname><given-names>Pascale</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/105095/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Berding</surname><given-names>Georg</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/304398/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Nuclear Medicine, Hannover Medical School</institution>, <addr-line>Hanover</addr-line>, <country>Germany</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Cluster of Excellence Hearing4all, Hannover Medical School, University of Oldenburg</institution>, <addr-line>Oldenburg</addr-line>, <country>Germany</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Otorhinolaryngology, Hannover Medical School</institution>, <addr-line>Hanover</addr-line>, <country>Germany</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Otorhinolaryngology, University of Cologne</institution>, <addr-line>Cologne</addr-line>, <country>Germany</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Department of Medical Physics and Radiation Protection, Hannover Medical School</institution>, <addr-line>Hanover</addr-line>, <country>Germany</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Department of Otorhinolaryngology, Faculty of Medicine Carl Gustav Carus, Saxonian Cochlear Implant Center, Technical University Dresden</institution>, <addr-line>Dresden</addr-line>, <country>Germany</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Claude Alain, Rotman Research Institute (RRI), Canada</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Andrew Dimitrijevic, Sunnybrook Health Sciences Centre, Canada; Iiro P. Jääskeläinen, Aalto University, Finland</p></fn><corresp id=\"c001\">*Correspondence: Georg Berding, <email>berding.georg@mh-hannover.de</email></corresp><fn fn-type=\"other\" id=\"fn002\"><p><sup>†</sup>These authors have contributed equally to this work</p></fn><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Auditory Cognitive Neuroscience, a section of the journal Frontiers in Neuroscience</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>14</volume><elocation-id>787</elocation-id><history><date date-type=\"received\"><day>10</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>06</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Kessler, Schierholz, Mamach, Wilke, Hahne, Büchner, Geworski, Bengel, Sandmann and Berding.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Kessler, Schierholz, Mamach, Wilke, Hahne, Büchner, Geworski, Bengel, Sandmann and Berding</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Cochlear implantation constitutes a successful therapy of inner ear deafness, with the majority of patients showing good outcomes. There is, however, still some unexplained variability in outcomes with a number of cochlear-implant (CI) users, showing major limitations in speech comprehension. The current study used a multimodal diagnostic approach combining single-photon emission computed tomography (SPECT) and electroencephalography (EEG) to examine the mechanisms underlying speech processing in postlingually deafened CI users (<italic>N</italic> = 21). In one session, the participants performed a speech discrimination task, during which a 96-channel EEG was recorded and the perfusions marker <sup>99m</sup>Tc-HMPAO was injected intravenously. The SPECT scan was acquired 1.5 h after injection to measure the cortical activity during the speech task. The second session included a SPECT scan after injection without stimulation at rest. Analysis of EEG and SPECT data showed N400 and P600 event-related potentials (ERPs) particularly evoked by semantic violations in the sentences, and enhanced perfusion in a temporo-frontal network during task compared to rest, involving the auditory cortex bilaterally and Broca’s area. Moreover, higher performance in testing for word recognition and verbal intelligence strongly correlated to the activation in this network during the speech task. However, comparing CI users with lower and higher speech intelligibility [median split with cutoff + 7.6 dB signal-to-noise ratio (SNR) in the Göttinger sentence test] revealed for CI users with higher performance additional activations of parietal and occipital regions and for those with lower performance stronger activation of superior frontal areas. Furthermore, SPECT activity was tightly coupled with EEG and cognitive abilities, as indicated by correlations between (1) cortical activation and the amplitudes in EEG, N400 (temporal and occipital areas)/P600 (parietal and occipital areas) and (2) between cortical activation in left-sided temporal and bilateral occipital/parietal areas and working memory capacity. These results suggest the recruitment of a temporo-frontal network in CI users during speech processing and a close connection between ERP effects and cortical activation in CI users. The observed differences in speech-evoked cortical activation patterns for CI users with higher and lower speech intelligibility suggest distinct processing strategies during speech rehabilitation with CI.</p></abstract><kwd-group><kwd>single-photon emission computed tomography</kwd><kwd>electroencephalography</kwd><kwd>cochlear-implant</kwd><kwd>memory</kwd><kwd>N400</kwd><kwd>speech processing</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Deutsche Forschungsgemeinschaft<named-content content-type=\"fundref-id\">10.13039/501100001659</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"7\"/><table-count count=\"7\"/><equation-count count=\"0\"/><ref-count count=\"126\"/><page-count count=\"24\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Cochlear implantation is an established and effective method of treating sensorineural hearing loss (<xref rid=\"B122\" ref-type=\"bibr\">Wilson and Dorman, 2008a</xref>, <xref rid=\"B123\" ref-type=\"bibr\">b</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Gaylor et al., 2013</xref>). Cochlear implants (CIs) bypass the damaged structures of the inner ear by electrical stimulation of the auditory nerve (<xref rid=\"B122\" ref-type=\"bibr\">Wilson and Dorman, 2008a</xref>, <xref rid=\"B123\" ref-type=\"bibr\">b</xref>). Although cochlear implantation allows open-set speech perception in most of the cases, there is a high variability in CI outcomes (<xref rid=\"B55\" ref-type=\"bibr\">Heydebrand et al., 2007</xref>). This variability cannot be completely explained so far (<xref rid=\"B77\" ref-type=\"bibr\">Lazard et al., 2012</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Blamey et al., 2013</xref>) but seems to be at least partially related to individual differences in the auditory nerve, the position of the implant electrodes, cognitive abilities, and neuronal plasticity (<xref rid=\"B86\" ref-type=\"bibr\">Nadol, 1997</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Drennan and Rubinstein, 2008</xref>; <xref rid=\"B77\" ref-type=\"bibr\">Lazard et al., 2012</xref>; <xref rid=\"B90\" ref-type=\"bibr\">Rönnberg et al., 2013</xref>; <xref rid=\"B94\" ref-type=\"bibr\">Sandmann et al., 2015</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Finke et al., 2016a</xref>). Neuroimaging can help improve the understanding of the individual differences in speech comprehension by providing important insights into the sensory and cognitive processes underlying speech perception in CI users.</p><p>Previous studies using electroencephalography (EEG) and event-related potentials (ERPs) in particular have shown differences in cortical speech processing between CI users and normal-hearing (NH) listeners, both at initial sensory and at later higher-level cognitive processing stages (e.g., <xref rid=\"B52\" ref-type=\"bibr\">Hahne et al., 2012</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Finke et al., 2016a</xref>). In particular, CI users have shown smaller amplitudes of N1 ERPs to speech sounds, indicating smaller assembly or reduced synchronization of activated neurons in the auditory cortex of CI users when compared with NH listeners (<xref rid=\"B47\" ref-type=\"bibr\">Groenen et al., 2001</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Aggarwal and Green, 2012</xref>). Regarding the later cognitive processing stages, ERPs in response to semantic anomalies (N400) and syntactic violations (P600) have been rarely examined in CI users (<xref rid=\"B52\" ref-type=\"bibr\">Hahne et al., 2012</xref>; <xref rid=\"B54\" ref-type=\"bibr\">Henkin et al., 2014</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Kallioinen et al., 2016</xref>; <xref rid=\"B119\" ref-type=\"bibr\">Vavatzanidis et al., 2018</xref>). The N400, reflecting semantic memory use during language comprehension (<xref rid=\"B69\" ref-type=\"bibr\">Kutas and Federmeier, 2000</xref>), has been shown to be prolonged in adult CI users when compared with NH listeners (<xref rid=\"B52\" ref-type=\"bibr\">Hahne et al., 2012</xref>; <xref rid=\"B54\" ref-type=\"bibr\">Henkin et al., 2014</xref>), suggesting a delayed and a more effortful speech processing with the limited CI input (<xref rid=\"B27\" ref-type=\"bibr\">Finke et al., 2016a</xref>). However, it is currently unknown whether the N400 can distinguish between CI users who have good versus poor speech recognition, although such a distinctiveness has been previously shown for other auditory ERPs (<xref rid=\"B105\" ref-type=\"bibr\">Soshi et al., 2014</xref>; <xref rid=\"B113\" ref-type=\"bibr\">Turgeon et al., 2014</xref>).</p><p>Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) enable the precise spatial assignment of neuronal activity that underlies speech perception in CI users (<xref rid=\"B40\" ref-type=\"bibr\">Giraud et al., 2001a</xref>,<xref rid=\"B41\" ref-type=\"bibr\">b</xref>,<xref rid=\"B42\" ref-type=\"bibr\">c</xref>; <xref rid=\"B124\" ref-type=\"bibr\">Wong et al., 2002</xref>). In general, the spatial resolution of both PET and SPECT enables investigations of neuronal activity (changes) in the auditory cortex and associated brain regions (<xref rid=\"B1\" ref-type=\"bibr\">Abraham and Feng, 2011</xref>). Regarding CI users, SPECT has been shown previously to be a suitable tool to objectively evaluate speech comprehension performance (<xref rid=\"B3\" ref-type=\"bibr\">Allen et al., 2004</xref>). In particular, different cortical activations were observed for higher and lower CI performers during speech comprehension (<xref rid=\"B112\" ref-type=\"bibr\">Tobey et al., 2004</xref>). SPECT has also been suggested to be considered for the presurgical evaluation of prelingually deaf adults being candidates for cochlear implantation (<xref rid=\"B25\" ref-type=\"bibr\">Di Nardo et al., 2013</xref>). Furthermore, previous PET studies with CI users have revealed a positive correlation between speech recognition ability and activation in the primary and association auditory cortices (<xref rid=\"B45\" ref-type=\"bibr\">Green et al., 2005</xref>). They have also shown a different network recruited for speech processing in proficient and non-proficient CI users, suggesting that activation in both the temporal cortices and the left inferior prefrontal cortex are a prerequisite for successful speech comprehension (<xref rid=\"B84\" ref-type=\"bibr\">Mortensen et al., 2006</xref>). On the other hand, in NH listeners, a stronger activation of inferior frontal regions has been related to enhanced listening effort (<xref rid=\"B22\" ref-type=\"bibr\">Davis and Johnsrude, 2003</xref>) and to poorer cognitive abilities, in particular lower working memory capacity (<xref rid=\"B125\" ref-type=\"bibr\">Zekveld et al., 2012</xref>). Although previous results point to a remarkable influence of cognitive abilities on speech recognition with a CI (<xref rid=\"B55\" ref-type=\"bibr\">Heydebrand et al., 2007</xref>), no study so far has examined how individual differences in cognitive abilities and listening effort relate to the different cortical response patterns in proficient and non-proficient CI users.</p><p>The combination of SPECT/PET and EEG measurements allows a synergistic examination of speech processing, as it provides the excellent temporal resolution of the EEG and the good spatial resolution of the emission tomography. A few studies so far have used combined sequential SPECT or PET and EEG measurements to study auditory processing in different groups of patients, in particular in patients with mild and moderate Alzheimer’s disease (<xref rid=\"B87\" ref-type=\"bibr\">O’Mahony et al., 1996</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Gungor et al., 2005</xref>), Schizophrenia (<xref rid=\"B12\" ref-type=\"bibr\">Blackwood et al., 1994</xref>; <xref rid=\"B101\" ref-type=\"bibr\">Shajahan et al., 1997</xref>; <xref rid=\"B82\" ref-type=\"bibr\">Medved et al., 2001</xref>) or obsessive–compulsive disorders (<xref rid=\"B83\" ref-type=\"bibr\">Molina et al., 1995</xref>). The results have revealed correlations between ERPs and regional cerebral perfusion, suggesting a connection between disease-related alterations in auditory ERPs and cortical activation (<xref rid=\"B87\" ref-type=\"bibr\">O’Mahony et al., 1996</xref>; <xref rid=\"B48\" ref-type=\"bibr\">Gungor et al., 2005</xref>).</p><p>The principal aim of this study was to contribute to the better understanding of the high variability in CI outcomes. We used, for the first time, a synchronized multimodal SPECT-ERP approach in CI users to thoroughly examine the neuronal activation patterns underlying speech comprehension and their relation to cognitive abilities. The study also aimed to prove the suitability of a typical EEG paradigm for SPECT imaging. CI users with higher and lower speech performance were tested with a semantic-anomaly paradigm to study the N400 ERP in response to sentences with semantic violations (<xref rid=\"B71\" ref-type=\"bibr\">Kutas and Hillyard, 1980a</xref>; <xref rid=\"B75\" ref-type=\"bibr\">Lau et al., 2008</xref>). It has been previously shown for NH listeners that the left posterior middle temporal gyrus (MTG) is critically involved in N400 generation (for a review, see <xref rid=\"B75\" ref-type=\"bibr\">Lau et al., 2008</xref>). Given this finding and previous PET results about speech processing in CI users (<xref rid=\"B45\" ref-type=\"bibr\">Green et al., 2005</xref>; <xref rid=\"B84\" ref-type=\"bibr\">Mortensen et al., 2006</xref>), we predicted positive correlations between the N400 response and activation in the MTG. We also expected differences in cortical activation in the (pre)frontal, the superior temporal, and the posterior middle temporal regions, i.e., in temporo-frontal networks, between CI users with higher and lower speech recognition ability (<xref rid=\"B33\" ref-type=\"bibr\">Friederici, 2012</xref>). In addition, we expect that CI users with higher speech comprehension have stronger activation in regions and networks representing cognitive functions, such as the temporal cortex (memory) and parietal cortex (attention) (<xref rid=\"B21\" ref-type=\"bibr\">Coez et al., 2014</xref>). Finally, we tested the hypothesis that cross-modal (i.e., auditory) activation of the occipital (i.e., visual) cortex is beneficial in terms of speech understanding with a CI (<xref rid=\"B41\" ref-type=\"bibr\">Giraud et al., 2001b</xref>). Indeed, our results suggest a close connection between ERP effects and cortical activation in CI users and different activation patterns during speech processing between higher and lower performers, pointing to different neural resource allocation and strategies used for speech processing.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><p>In this section, the following methodological issues are described in subchapters: the patient characteristics (<italic>Patients</italic>), the sequence of procedures (<italic>Sequence of Procedures</italic>), the audiometric and cognitive tests (<italic>Audiometric and Neurocognitive Testing</italic>), the applied EEG/SPECT paradigm (<italic>Speech Condition Stimuli for Combined EEG-SPECT Measurement</italic>), and details on EEG (<italic>EEG Recording</italic>) and SPECT (<italic>SPECT – Acquisition and Reconstruction</italic>) acquisition. For the latter two methods, <italic>Data Analysis</italic> gives details on data analysis.</p><sec id=\"S2.SS1\"><title>Patients</title><p>Twenty-one postlingually deafened CI users [mean age, 62.1 years; standard deviation (SD), 11.5 years; range, 30–80 years; 10 female) participated in the present study, with 18 CI users being consistent right-handers and three being consistent left-handers (<xref rid=\"B5\" ref-type=\"bibr\">Annett, 1970</xref>). Eight CI users were implanted unilaterally (5 left), and 13 were implanted bilaterally. In case of bilateral implantation, the “better” ear, according to the performance in the Freiburg monosyllabic word test (<xref rid=\"B51\" ref-type=\"bibr\">Hahlbrock, 1970</xref>), was used for stimulation (5 left). All CI users were native German speakers, had at least 11 months of CI experience (mean, 99.4 months; SD, 68.5; range, 11.0–346.0 months) and achieved a word recognition score of at least 20% in the Hochmair–Schulz–Moser (HSM) sentence test in quiet (<xref rid=\"B57\" ref-type=\"bibr\">Hochmair-Desoyer et al., 1997</xref>) with the tested CI. None of the CI users reported using sign language for communication. Details concerning the subject’s implant system and demographics can be obtained in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. None of the CI users reported neurological or psychiatric disorders or used medications affecting the central nervous system.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Patient characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Sex</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Age</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Handedness</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Etiology</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Side of stim.</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Implant</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Contralateral to stim. side</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Age at onset of profound deafness (years)</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Age at implantation (years)</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Duration of deafness (months)</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Implant use (months)</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Hearing threshold of contralateral ear</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"12\" rowspan=\"1\"><bold>Lower performers</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI512</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus<break/>CI24RE (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">27</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">27</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">64</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Otitis media</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI422</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus<break/>CI24RE<break/>Hybrid-L (H)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">55</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">59</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">53</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AB HiRes90K Helix</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AB HiRes90K Helix</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">31</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">133</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">124</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">73</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI512</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">87</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">52</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI512</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI24R (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">346</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">70</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Acute hearing loss</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI24RE Hybrid-L (no ACO)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">deaf</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">61</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">61</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">112</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">75</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI24RE (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI24M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">62</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">492</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">151</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">77</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Noise trauma</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MED-EL<break/>Sonata Flex<break/>EAS 20 (Hann.)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MED-EL<break/>Sonata Flex<break/>EAS 20 (Hann.)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">69</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">69</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">80 dB HL<break/>0.25 kHz</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">70</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Morbus Menière</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI532</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">68</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">41</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">75–85 dB HL<break/>0.25–2 kHz</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI24RE (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI24R (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">41</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">54</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">157</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">129</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">62.9 ± 13.6</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45.9 ± 18.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">53.0 ± 15.8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">88.0 ± 145.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">115.2 ± 87.7</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" colspan=\"11\" rowspan=\"1\"><bold>Higher performers</bold></td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">64</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Otosclerosis</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI24R (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus<break/>CI24RE (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">49</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">160</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus<break/>CI24RE<break/>Hybrid-L (no ACO)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI422</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">39</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">39</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">79</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">48</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI24RE (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">43</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">43</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">65 dB HL<break/>0.25 kHz</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Morbus Menière</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MED-EL<break/>Sonata ti100</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">57</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">57</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">80</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">59</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AB HiRes90K Helix</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AB Clarion CII</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">49</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">274</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">116</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MED-EL<break/>Concerto Flex<break/>EAS 28 (no ACO)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">53</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">53</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45–90 dB HL<break/>0.25–2 kHz</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">68</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Acute hearing loss</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AB HiRes 90K Advantage HiFokus Mid-Scala</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">61</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">63</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">40–90 dB HL<break/>0.25–8 kHz</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">60</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AB HiRes90K Helix</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AB HiRes90K Helix</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">109</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">67</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Genetic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AB HiRes90K Helix</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus<break/>CI24RE (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">56</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">56</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">129</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">80</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Acute hearing loss</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI512 Profile (CA)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">73</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">79</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">66</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">60–80 dB HL<break/>0.25–8 kHz</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">62</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Right</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Left</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI522</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleus CI422</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">59</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">59</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">78</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">≥ 90 dB</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">61.5 ± 9.1</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51.5 ± 11.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">54.5 ± 10.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">35.5 ± 77.7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">85.1 ± 39.2</td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Overall mean ± SD</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">62.1 ± 11.5</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><attrib><italic><italic>F, female; M, male; HA, hearing aid; stim, stimulation; ACO, acoustic component of the hybrid/electric acoustic stimulation (EAS) device. Age at onset of profound deafness refers to the age at which the amount of hearing loss was too severe to be successfully treated by a conventional hearing aid. Duration of deafness is defined as the time between the age at onset of profound deafness and the CI implantation. Comparing age at onset of profound deafness (years), age at implantation (years), duration of deafness (months), and implant use (months), respectively, between groups of lower and higher performing CI users did not reveal any significant differences between groups (unpaired t-test).</italic></italic></attrib></table-wrap-foot></table-wrap><p>All participants gave informed written consent before the experiment. The study was approved by the Ethics Committee of the Hannover Medical School (vote number 6678) and the German Federal Office for Radiation Protection (reference number Z5-22461/2-2014-012) and was carried out in accordance with the Declaration of Helsinki.</p></sec><sec id=\"S2.SS2\"><title>Sequence of Procedures</title><p>In this subchapter, the sequence of procedures is described. Details on the procedures are given in the following subchapters. The participants underwent two individual sessions, separated by 13.0 ± 6.5 days. In one session, CI users completed the neuropsychological testing, except for the size-comparison span test (SICSPAN) (<xref rid=\"B104\" ref-type=\"bibr\">Sorqvist et al., 2010</xref>). Subsequently, a SPECT scan was performed after application of 729.1 ± 8.1 MBq Technetium-99m (<sup>99m</sup>Tc) labeled HMPAO without stimulation (“rest condition”). Injection of the substance took place in a quiet room with dimmed light, where participants stayed for 15 min before and 5 min after application for uptake. The SPECT scan itself was performed ∼1.5 h postinjection (p.i.). In the other session, participants underwent the audiometric testing and the SICSPAN (<xref rid=\"B104\" ref-type=\"bibr\">Sorqvist et al., 2010</xref>), which was followed by combined sequential EEG and SPECT measurements (“speech condition”). The participants were seated comfortably in a dimly lit, as well as electrically and acoustically shielded cabin, 100 cm in front of a computer screen. Before the actual start of the EEG experiment, participants performed a training block with seven sentences. Subsequently, the participants listened to 80 different sentences (40 semantically correct/40 semantically incorrect), which were presented within the course of two experimental blocks, with 55 sentences being presented in block 1 (∼7 min) and 25 sentences presented in block 2 (∼3 min). The length of the respective blocks was adapted so that – without interrupting the paradigm – application (2 min after the start of block 1) and subsequent radiopharmaceutical uptake phase (for another 5 min) was possible. The order of the sentences was pseudo-randomized between participants. The CI users were instructed in written form to listen to each sentence while focusing on a black screen. A white fixation point, appearing 1,000 ms after the offset of each sentence, signaled the participants to provide a response via a button press on whether the sentence was semantically correct or not. Assignment of the buttons to the two answer possibilities was counterbalanced across participants. The fixation point remained on the screen for 3,000 ms, which constituted the response window. The delayed response window ensured decoupling the N400 and P600 ERPs from the motor response. After the task, subjects were asked to evaluate the <italic>subjective listening effort</italic> during the task with a 5-point rating scale (1.0 = not demanding, 5.0 = too demanding; the words could not be understood). During the first block, 2 min after the start of the task, 731.5 ± 6.8 MBq <sup>99m</sup>Tc-labeled HMPAO was applied intravenously via medical tubing from outside the shielded cabin. Approximately 1.5 h after injection, a SPECT scan was acquired, reflecting cortical activity during the sentence discrimination task. In general, the “rest condition” was performed first, and the “speech condition” (combined sequential SPECT and EEG measurements) took place in the second session. Due to organizational issues, however, the “speech condition”’ was carried out first in a few cases (<italic>n</italic> = 4).</p></sec><sec id=\"S2.SS3\"><title>Audiometric and Neurocognitive Testing</title><p>Speech recognition abilities obtained with the CI used in the experimental session were assessed using three frequently applied German speech tests: (1) the Freiburg monosyllabic word test in quiet (<xref rid=\"B51\" ref-type=\"bibr\">Hahlbrock, 1970</xref>), (2) the HSM sentence test (<xref rid=\"B57\" ref-type=\"bibr\">Hochmair-Desoyer et al., 1997</xref>) in quiet and in noise [10 dB signal-to-noise ratio (SNR)], and (3) the Göttinger sentence test (GÖSA; adaptive noise; <xref rid=\"B68\" ref-type=\"bibr\">Kollmeier and Wesselkamp, 1997</xref>). The GÖSA is a widely used audiometric test, which contains complete German sentences that reflect the everyday speech situation. It uses an adaptive procedure to measure the signal-to-noise ratio at which 50% of the speech signal is correctly understood. In the current study, the speech material of all of the three speech tests was presented at a sound intensity of 65 dB SPL. Participants were instructed to report all words perceived.</p><p>Our study aimed to compare brain activation patterns between CI users with different performance levels. However, the present study did not enable to compare markedly poor and good performers, since all CI users had to at least be able to perform the sentence discrimination task to a certain extent, allowing for an adequate number of correct EEG trials for analysis. Groups, therefore, rather represent CI users with higher or lower performance levels. Accordingly, the group assignment was based on a median split procedure, for which we relied on the GÖSA, resulting in a cutoff of +7.6 dB SNR. This procedure was not based on previous studies, but rather exploratory, with the aim of obtaining groups of CI users with different performance levels. In the following, the groups are referred to as “lower” (50% speech reception threshold at > 7.6 dB SNR; note that more positive values indicate worse performance) and “higher” CI performers (50% speech reception threshold at < 7.6 dB SNR). In the present study, the GÖSA was chosen to be the most appropriate one for the group selection based on the median split procedure, for the following reasons: (1) It contains meaningful sentences from everyday life (ecological validity of stimulus material), (2) it allows the measurement of an individual speech reception threshold by means of an adaptive procedure (speech test with high accuracy), and (3) it is highly demanding and provides performance scores with fair variability in performance scores (no problem of floor and ceiling effects). Thus, with regards to group assignment, the GÖSA test is preferable to the Freiburg monosyllabic word test and the HSM sentence test, given that the GÖSA test uses a more appropriate stimulus material (some of the words in the Freiburg monosyllabic word test are outdated), and the test results are not confounded by floor and ceiling effects (non-adaptive speech tests, for instance the HSM sentence test with a fixed SNR, provide a risk for these boundary effects).</p><p>To control for residual hearing, the contralateral device was detached at the time of testing and the ear was closed by means of an earplug. Beforehand, to assess residual hearing in the non-tested contralateral ear, a pure-tone audiometry (unaided; range, 0.25–8 kHz) was performed.</p><p>Beside the audiometric tests, participants completed four different cognitive tests, assessing working memory capacity and verbal abilities: (1) The “Mehrfachwahl-Wortschatz-Intelligenz-Test” (MWT-B, <xref rid=\"B79\" ref-type=\"bibr\">Lehrl, 1977</xref>) was applied to measure <italic>verbal intelligence</italic>. Here, participants had to identify a real word among four pseudowords. According to the official guidelines provided with the test material, individual percentiles (in relation to a normative sample) were used for the statistical analyses. (2) The lexical verbal fluency subtest of the “Regensburger Wortflüssigkeits-Test” (RWT, <xref rid=\"B8\" ref-type=\"bibr\">Aschenbrenner et al., 2001</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Harth et al., 2004</xref>) was used to test <italic>verbal fluency</italic>. During this test, subjects were asked to report as many words as possible with the initial letter “s” within 2 min. Here, likewise, individual percentiles in relation to a normative sample provided with the test manual were used for the statistical analyses. (3) A German version of the SICSPAN (<xref rid=\"B104\" ref-type=\"bibr\">Sorqvist et al., 2010</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Finke et al., 2016a</xref>) was used to assess the verbal <italic>working-memory</italic> capacity. The SICSPAN was analyzed using the total percentage of correctly remembered words. (4) We used two subtests (<italic>verbal learning</italic>, <italic>verbal recall</italic>) of the CERAD-Plus test battery (Memory Clinic Basel)<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup> to assess verbal abilities. <italic>Z</italic>-scores in relation to age-specific normative data were used for statistical analysis.</p></sec><sec id=\"S2.SS4\"><title>Speech Condition Stimuli for Combined EEG-SPECT Measurement</title><p>The stimulus material consisted of 87 sentences in German language, constructed out of 6 words each (determinative, subject, the auxiliary “hat/haben”/“has/have,” determinative, object, past participle). The sentences were clearly pronounced by a female speaker, spoken at a moderate pace (213 ± 92 ms between the words). The sentences’ final word was either semantically correct (e.g., “Die Mutter hat den Kuchen gebacken”/“The mother has baked the cake”) or incorrect (e.g., “Der Junge hat das Radio gebadet”/“The boy has bathed the radio”) with regards to the previous sentential context. All critical sentence final words appeared in a correct as well as in an a semantically incorrect sentence, thereby guaranteeing that the integration of the word in the semantic context is responsible for different ERP effects rather than the word itself. All sentences were spoken by a trained female native German speaker. Audio files had a sampling frequency of 44 kHz with a 32-bit resolution. Sentence duration ranged from 4.01 to 5.85 s. The onset of each final word was carefully identified by auditory and visual inspection to ensure an accurate time locking of the N400 and P600 ERPs in response to the final word. Stimuli were delivered using the Presentation software (version 16.5; Neurobehavioral Systems, Inc., Berkeley, CA, United States) running on a personal computer. Sentences were presented via two loudspeakers (HECO victa 301) located at 50° azimuth. In the case of a second CI or a conventional hearing aid at the contralateral side, the device was detached for the duration of the experiment, and the respective ear was closed with a wax earplug. In total, 10 CI users were stimulated on the left and 11 on the right side. Similar to previous studies (e.g., <xref rid=\"B94\" ref-type=\"bibr\">Sandmann et al., 2015</xref>; <xref rid=\"B97\" ref-type=\"bibr\">Schierholz et al., 2017</xref>), the participants used a 7-point loudness-rating scale, which allowed adjusting the perceived loudness of the sentences to a comfortable level, equivalent to 60–70 dB (<xref rid=\"B4\" ref-type=\"bibr\">Allen et al., 1990</xref>; <xref rid=\"B126\" ref-type=\"bibr\">Zeng, 1994</xref>).</p></sec><sec id=\"S2.SS5\"><title>EEG Recording</title><p>EEG data were recorded using 94 Ag/AgCl electrodes, integrated in an infracerebral electrode cap with an equidistant electrode layout (Easycap, Herrsching, Germany). To record an electrooculogram, two additional electrodes were placed below the two eyes. The reference electrode was positioned on the nose tip. A midline electrode, placed anterior to the frontocentral scalp region (AFz), served as ground. Data were recorded by means of three linked 32-channel BrainAmp amplifiers (BrainProducts, Gilching, Germany), with a sampling rate of 1,000 Hz and an online analog filter from 0.02 to 250 Hz. For data acquisition, electrode impedances were kept below 10 kΩ.</p></sec><sec id=\"S2.SS6\"><title>SPECT – Acquisition and Reconstruction</title><p>For the scan, participants were positioned as comfortably as possible on the patient bed, and their head was carefully fixed with a special headband with Velcro straps. The participants were instructed to avoid head movements during the scan. Acquisition was performed using a dual-head SPECT camera (Discovery 670 NM/CT, GE Healthcare, Haifa, Israel) equipped with low-energy high-resolution (LEHR) parallel-hole collimators. In total, 180 projections, that is, 90 projections for each of the two detectors, were acquired using a step and shot mode with circular orbit (rotation around the head of the patient with the smallest possible distance, normally 15 cm). With this setup, typically, a total number of counts in the order of 8–9 million could be achieved per acquisition. The required projection time was individually determined before starting the scan on the basis of the count rate detected with the patients’ head in the camera field of view. Typically, the count rate was between 1.4 and 1.6 kCts, and the according total recording time was about 55 min. Projections were acquired with a 128 × 128 matrix size and a zoom factor of 2.0 (pixel size, 2.23 × 2.23 mm<sup>2</sup>). The quality of unprocessed projection data was assessed visually in cine mode and in the form of sinograms, e.g., with respect to motion artifacts, immediately after the recording. Data were reconstructed iteratively, using an ordered-subset expectation maximization (OSEM) algorithm with 5 iterations, 10 subsets, and a Butterworth filter with a cutoff frequency of 0.55 cycles/cm, power of 10 (<xref rid=\"B60\" ref-type=\"bibr\">Hudson and Larkin, 1994</xref>), and a dual window scatter correction (scaling 1.1) (<xref rid=\"B62\" ref-type=\"bibr\">Jaszczak et al., 1984</xref>) including attenuation correction according to Chang (threshold for boundary detection of 5% and attenuation coefficient of 0.11/cm) (<xref rid=\"B17\" ref-type=\"bibr\">Chang, 1978</xref>).</p></sec><sec id=\"S2.SS7\"><title>Data Analysis</title><sec id=\"S2.SS7.SSS1\"><title>EEG Preprocessing</title><p>EEG data were preprocessed using custom scripts in MATLAB 9.2.0.556344 (R2017a; Mathworks, Natick, MA) and EEGLAB (version 13.6.5b, <xref rid=\"B24\" ref-type=\"bibr\">Delorme and Makeig, 2004</xref>). Raw data were imported, down-sampled to 500 Hz, and low-pass filtered (40 Hz) using a Hann-windowed zero-phase finite impulse response (FIR) filter implemented in EEGLAB (pop firws.m; <xref rid=\"B121\" ref-type=\"bibr\">Widmann and Schröger, 2012</xref>). Electrodes covering the CI speech processor as well as the transmitter coil were omitted for recording and accordingly removed for the analysis. Subsequently, the continuous data were segmented into 2-s segments and pruned for unique, non-stereotype artifacts. The remaining data were high-pass filtered (1 Hz; Hann-windowed FIR filter; pop_firws.m) and subjected to an extended infomax-independent component analysis (ICA, <xref rid=\"B9\" ref-type=\"bibr\">Bell and Sejnowski, 1995</xref>). The resulting ICA weights were applied to the raw data that were filtered (0.1–30 Hz; Hann-windowed FIR filter; pop_firws.m) and epoched (-250–7,950 ms) relative to sentence onset. The prestimulus interval (-250–0 ms) was used for baseline correction. ICA components representing eye blinks, horizontal eye movements, heartbeat activity, and CI artifacts were identified and removed (mean, 36.7%; <italic>SD</italic> = 14.5%; <xref rid=\"B63\" ref-type=\"bibr\">Jung et al., 2000a</xref>, <xref rid=\"B64\" ref-type=\"bibr\">b</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Debener et al., 2008</xref>). Regarding the latter, we identified the components representing the CI artifact by the centroid on the side of the implanted device and by the pedestal artifact in the time course of the respective component (<xref rid=\"B92\" ref-type=\"bibr\">Sandmann et al., 2009</xref>, <xref rid=\"B93\" ref-type=\"bibr\">2010</xref>, <xref rid=\"B94\" ref-type=\"bibr\">2015</xref>). Missing channels were interpolated using a spherical spline (mean, 5.8; SD, 1.6; range, 2–10 electrodes). Additional triggers were set, marking the onset of the final word (correct, semantic violation) in each sentence. Based on these triggers, additional epochs, time locked to the onset of the final words (−250–950 ms) were created. Data were corrected using the time interval of −250–0 ms relative to the onset of the critical word.</p></sec><sec id=\"S2.SS7.SSS2\"><title>EEG Data Analysis</title><p>Single-subject ERPs were computed to the <italic>onset of the sentences</italic> by averaging over all correctly categorized trials, irrespective of the condition (correct, semantic violation) of the sentence (ERP<sub>onset</sub>). Additionally, ERPs to the <italic>onset of the final word</italic> were computed for each participant, separately for semantically correct sentences (ERP<sub>critCorr</sub>) and sentences with a semantic violation (ERP<sub>critViol</sub>), including only the correctly identified trials (critCon: mean, 87.3%; SD, 6.6%; critViol: mean, 87.5%; SD, 7.3%). Furthermore, a difference wave was computed relative to the onset of the final word (ERP<sub>critDiff</sub> = ERP<sub>critViol</sub> - ERP<sub>critCorr</sub>). The single-subject ERPs to sentence onset were analyzed using a fronto-central region-of-interest (ROI), including seven electrodes around FCz (see <xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>), and a time window of the auditory N1 and P2 (N1, 80–200 ms; P2, 160–280 ms), determined by visual inspection of the grand average ERP and based on previous studies with CI users (see, e.g., <xref rid=\"B29\" ref-type=\"bibr\">Finke et al., 2015</xref>, <xref rid=\"B28\" ref-type=\"bibr\">2016b</xref>). Regarding the final word onset, the N400 and the P600 ERPs were analyzed by considering a centroparietal ROI around CPz for both the N400 (time window, 300–900 ms; seven electrodes) and the P600 (time window, 750–940 ms; seven electrodes, see <xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>). Both time windows were defined by visual inspection of the grand average ERPs and based on previous studies with CI users (<xref rid=\"B52\" ref-type=\"bibr\">Hahne et al., 2012</xref>). For the quantification of the evoked responses, we determined the local minimum (N1, N400) or the local maximum (P2, P600), respectively, of the ERP amplitudes in the respective time window and the respective ROI (peakdet.m)<sup><xref ref-type=\"fn\" rid=\"footnote2\">2</xref></sup>. The mean amplitude was computed for ± 10 ms around the local minimum/maximum. The latency of the respective peaks was determined by detecting the time of the local peak minimum (N1, N400) or maximum (P2, P600). Amplitude and latency measures were subjected to correlation analyses with the SPECT data. Moreover, ERPs were compared between a group of higher and a group of lower performing CI users by means of independent <italic>t</italic>-tests, separately for each ERP components.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Grand averages of the event-related potentials (ERPs), once for all subjects (<italic>N</italic> = 21; solid lines) and once separately for the groups of higher (<italic>N</italic> = 11; dotted lines) and lower (<italic>N</italic> = 10; dashed lines) cochlear-implant (CI) performers. <bold>(A)</bold> Shows the grand average ERPs in response to the sentence onset for a centrofrontal electrode region of interest (ROI). A clear negative (∼150 ms) and a clear positive (∼240 ms) deflection, referred to as the N1 and P2, respectively, can be observed for all three ERPs. <bold>(B)</bold> Shows the average difference waves (ERPcritViol - ERPcritCorr), time locked to the onset of the critical words, using a centroparietal electrode ROI. A negative (N400) and positive deflection (P600) can be observed for all three ERP waves around 650 ms. At the bottom of each panel, the topographical voltage maps are displayed for the time of the respective peak latencies of the different components [N1, P2 <bold>(A)</bold>, N400 and P600 <bold>(B)</bold>, separately for the different average ERPs (all, higher performers, lower performers).</p></caption><graphic xlink:href=\"fnins-14-00787-g001\"/></fig></sec><sec id=\"S2.SS7.SSS3\"><title>SPECT Data Analysis</title><p>SPECT images were analyzed using the statistical parametric mapping software (SPM8, Wellcome Trust Center for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom), running within MATLAB 9.2.0.556344 (2017a; Mathworks, Natick, MA, United States). First baseline (“rest condition”) and stimulation (“speech condition”) images of each patient were realigned and transformed into a standard stereotaxic anatomical space according to the Montreal Neurological Institute (MNI) employing the default brain perfusion SPECT template provided in SPM8. Further preprocessing included scaling of the images before statistical testing. In order to compare the speech and the rest condition, images were scaled to the 75th percentile (<xref rid=\"B16\" ref-type=\"bibr\">Buchert et al., 2006</xref>). Then, effects of stimulus presentation were assessed using a paired <italic>t-</italic>test. For further group comparisons and correlations to speech audiometry and EEG, difference images were generated based on procedures included in subtraction ictal SPECT coregistered to MRI (SISCOM) analysis (<xref rid=\"B59\" ref-type=\"bibr\">Huberfeld et al., 2006</xref>; <xref rid=\"B6\" ref-type=\"bibr\">Apostolova et al., 2008</xref>), in particular a two-step scaling procedure. First, images were scaled to the global average, using a gray matter mask excluding the cerebellar voxels. Then, preliminary difference images (speech condition minus rest condition) were created. Proceeding from these, a mean value of voxels with a low difference between speech condition and rest condition (i.e., <2 times the standard deviation of the mean) was calculated. This mean value was used for rescaling the speech condition study, which avoids an impact of voxels from activated areas on scaling. Thereafter, final difference images were calculated by subtracting rest condition images scaled to global average from rescaled speech condition images. These final <italic>difference images</italic> (speech condition minus rest condition) were used for further group comparisons and correlations. Moreover, for the assessment of group difference and correlations based on <italic>baseline images</italic> (rest condition), these images were scaled to the 75th percentile.</p><p>For image-based statistics, SPM8 was used as well. First of all, results of statistical tests presented here were generated using a significance level of <italic>p</italic> < 0.001 (uncorrected for multiple comparisons) for inferences. The threshold was chosen with respect to previous studies, in particular studies of central auditory processing via auditory implants, where this threshold has been successfully employed (<xref rid=\"B42\" ref-type=\"bibr\">Giraud et al., 2001c</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Coez et al., 2009</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Berding et al., 2015</xref>; <xref rid=\"B81\" ref-type=\"bibr\">Mamach et al., 2018</xref>). Furthermore, it has been proposed as a good compromise compensating for the limited sensitivity of brain perfusion SPECT, whereby it is still protecting from false positive results (<xref rid=\"B103\" ref-type=\"bibr\">Signorini et al., 1999</xref>). Results are listed without extent voxel threshold (<italic>k</italic> = 0) in the tables. For displaying results, however, two different voxel thresholds were employed. This was done in order to account for different magnitudes in cluster sizes observed across test results.</p><p>For test results with a relatively small size of the largest cluster, an extent voxel threshold of <italic>k</italic> = 19 was used. Corresponding results are displayed using the so-called glass brain visualization. For all other test results including relatively large cluster sizes, an operational extent voxel threshold of <italic>k</italic> = 50 was used. Corresponding results are presented using surface rendered MRI image in MNI space. In general, the “modern design” option provided by SPM was employed to include some information on the depth of localized activations. Moreover, locations of significant differences were spatially assigned by automated anatomical labeling, specifically by overlaying the statistical parametric map with a Brodmann volume of interest (VOI) atlas (<xref rid=\"B91\" ref-type=\"bibr\">Rorden and Brett, 2000</xref>; <xref rid=\"B114\" ref-type=\"bibr\">Tzourio-Mazoyer et al., 2002</xref>). Additionally, statistical analyses were performed including correction for multiple comparisons based on the family-wise error (FWE) rate procedure, together with a cutoff of <italic>p</italic> < 0.05 and an extent threshold of <italic>k</italic> = 0 for statistical inferences (<xref rid=\"B30\" ref-type=\"bibr\">Flandin and Friston, 2019</xref>). However, this is a quit conservative approach for the correction for multiple comparison, and nevertheless, analyses without correction for multiple comparison as described above can be regarded as justified particularly in the context of preexisting according <italic>a priori</italic> hypotheses.</p><p>To assess potential effects of handedness, paired <italic>t</italic>-tests comparing speech and rest condition were performed twice, once including and once excluding the three left-handed participants. As no differences were obvious in the resulting statistical parametric maps, all left-handers were included in the further analyses. Another potentially influencing factor is the side of stimulation (right = 11; left = 10). Therefore, an additional comparison of speech and rest condition was performed, with images from patients with left-ear stimulation being flipped in the mid-sagittal plane. Only minor differences for flipped vs. non-flipped images were observed in primary and secondary auditory cortices. Therefore, the original, non-flipped images were used for all other analyses in order to avoid confusion with primarily unilateral components of brain networks related to speech processing (like, e.g., Broca’s area).</p><p>Further analyses included the comparison of the cortical baseline activity (baseline images) and stimulated activation (difference images) between CI users with higher and lower performance in speech comprehension. Similar to the EEG data analysis, the subgroups of higher and lower CI performance were compared by means of two-sample <italic>t</italic>-tests in consideration of two different contrasts [(1) lower performer > higher performers and (2) higher performers > lower performers]. Additionally, correlation analyses were performed using SPM to explore for the relationship between difference images on the one hand and EEG, audiometric, as well as neuropsychological data on the other hand.</p></sec></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Speech Comprehension Ability and Cognitive Functions</title><p>Results for speech audiometry, cognitive tests, and speech task performance are listed in <xref rid=\"T3\" ref-type=\"table\">Table 2</xref> with separate means for patients with higher and lower speech comprehension as well as the results from group comparisons across the different tests. In the context of speech audiometry, the CI users showed overall high performance for speech recognition in quiet, with average scores of 81.7 ± 12.1% (Freiburg monosyllabic word test) and 93.0 ± 11.0% (HSM sentence test without background noise). As expected, speech recognition in noise was remarkably lower, as revealed by the average recognition score of 48.5 ± 19.1% in the HSM sentence test (10 dB SNR) and the average 50% speech reception threshold of + 8.6 ± 4.2 dB SNR in the GÖSA.</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 2</label><caption><p>Results of speech audiometry, cognitive tests, and sentence-discrimination task during EEG recording.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><bold>Speech audiometry</bold><hr/></td><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><bold>Cognitive testing</bold><hr/></td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" colspan=\"3\" rowspan=\"1\"><bold>Sentence-discrimination taskance</bold><hr/></td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Freiburg monosyllabic word test (%)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>HSM sentence test in quiet (%)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>HSM sentence test in noise (10 dB SNR) (%)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Göttinger sentence test (dB SNR for SRT50%)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Verbal intelligence (percentile rank)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Verbal fluency (percentile rank)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Working memory (% total score)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Verbal learning (<italic>Z</italic>-score)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Verbal recall (<italic>Z</italic>-score)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Hits (%)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Correct rejections (%)</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Subjective listening effort</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" colspan=\"4\" rowspan=\"1\"><bold>Lower performers</bold></td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">54.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">88.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">88.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">65.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">59.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">94.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">86.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">88.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">65.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−2.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">78.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">94.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">66.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">97.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">94.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">97.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">88.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">99.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">97.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">85.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.0</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81.0 ± 10.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">89.4 ± 14.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42.0 ± 23.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.2 ± 3.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">71.1 ± 25.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.9 ± 25.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51.1 ± 10.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.4 ± 1.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.1 ± 1.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">88.7 ± 6.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">92.4 ± 7.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8 ± 0.60</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" colspan=\"4\" rowspan=\"1\"><bold>Higher performers</bold></td><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">97.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">49.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">94.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">79.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">68.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">87.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">83.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">97.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">71.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">76.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">66.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">99.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">94.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">86.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">92.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−2.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">85.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">65.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">85.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">98.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">93.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">85.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">91.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">80.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">85.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Mean ± SD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">82.3 ± 13.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">96.3 ± 4.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">54.5 ± 10.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.3 ± 1.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">66.2 ± 22.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">53.7 ± 31.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">49.4 ± 11.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−0.1 ± 1.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1 ± 1.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">96.2 ± 5.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95.0 ± 5.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.5 ± 0.5</td></tr><tr><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Sign.</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">< 0.0001</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.64</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.19</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic>Significant differences between groups of lower and higher performers have been detected for scores for the Göttinger sentence test (p < 0.0001, unpaired t-test) and the hit rates (%) in the sentence-discrimination task during EEG recording (p = 0.01). dB, decibel; SNR, signal to noise; SRT, speech reception threshold, subjective listening effort with a 5-point rating scale (1 = not demanding, 5 = too demanding; the words could not be understood).</italic></italic></attrib></table-wrap-foot></table-wrap><p>Performance scores in the cognitive tests showed for the MWT-B (verbal intelligence) an average percentile rank of 68.5 ± 24.5; for the SICSPAN (working memory capacity), an average of 50.2 ± 11.3 (total sum); and for the RWT (verbal fluency), an average percentile rank of 54.8 ± 28.9. <italic>Z</italic>-scores for verbal learning were on average of −0.2 ± 1.2 and for verbal recall of 0.0 ± 1.0.</p><p>The performance in the semantic-anomaly paradigm was generally high, with mean hit rates of 92.6 ± 6.9% (correct identification of semantically correct sentences) and mean correct rejection rates of 93.8 ± 6.6% (correct identification of sentences with semantic violations). Comparing the performance in the semantic-anomaly paradigm between CI users with lower and higher speech reception thresholds (median split with cutoff + 7.6 dB SNR in the Göttinger sentence test) revealed reduced hit rates in speech comprehension ability for the lower as compared to the higher CI performers [88.7 ± 6.3% vs. 96.2 ± 5.3%; <italic>t</italic>(19) = 2.8, <italic>p</italic> < 0.05, <italic>r</italic> = 0.5]. The number of <italic>correct rejections</italic> in contrast was not different between groups [92.4 ± 7.2% vs. 95.0 ± 5.6%; <italic>t</italic>(19) = -0.9, <italic>p</italic> = 0.39, <italic>r</italic> = 0.2].</p><p>The subjective rating of the listening effort during the EEG task was relatively low, as indicated by the ratings of 1.6 ± 0.6 on average. Comparison of scores for cognitive tests (tested in an unpaired <italic>t</italic>-test) and listening effort between groups of lower and higher performers (using the Mann–Whitney <italic>U</italic>-test) did not reveal any statistical significant differences [SICSPAN: <italic>t</italic>(19) = 0.3, <italic>p</italic> = 0.74, <italic>r</italic> = 0.1; MWT-B: <italic>t</italic>(19) = 0.4, <italic>p</italic> = 0.67, <italic>r</italic> = 0.1; RWT: <italic>t</italic>(19) = 0.2, <italic>p</italic> = 0.87, <italic>r</italic> = 0.1; CERAD verbal learning: <italic>t</italic>(19) = -0.5, <italic>p</italic> = 0.64, <italic>r</italic> = 0.1; CERAD verbal recall: <italic>t</italic>(19) = -0.5, <italic>p</italic> = 0.64, <italic>r</italic> = 0.1 and listening effort, <italic>U</italic> = 39, <italic>p</italic> = 0.28, <italic>r</italic> = 0.24].</p></sec><sec id=\"S3.SS2\"><title>EEG Components</title><p><xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> shows the group average ERPs separately for the onset of the sentence (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>) and the onset of the final word of the sentence (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>).</p><p>A clear negative peak can be observed around 150 ms (N1 peak), followed by a positive deflection around 240 ms (P2 peak). On a group level, the N1 showed a mean amplitude of -4.6 ± 1.6 μV with a mean peak latency of 154.1 ± 14.7 ms. Average values for the P2 mean amplitude and peak latency were 4.1 ± 2.0 μV and of 244.7 ± 19.3 ms, respectively. Independent <italic>t</italic>-tests revealed no significant differences between groups with lower and higher CI performance for N1/P2 amplitudes [N1: <italic>t</italic>(19) = 0.8, <italic>p</italic> = 0.45, <italic>r</italic> = 0.2; P2: <italic>t</italic>(19) = -1.1, <italic>p</italic> = 0.27, <italic>r</italic> = 0.3] and latencies [N1: <italic>t</italic>(19) = 0.8, <italic>p</italic> = 0.45, <italic>r</italic> = 0.2; P2: <italic>t</italic>(19) = 0.8, <italic>p</italic> = 0.45, <italic>r</italic> = 0.2]. Sequential two-tailed <italic>t</italic>-tests, using a sliding window of 2 ms at α = 0.5% were used to compare ERPs on sentence onset between groups of lower and higher CI performers, whereby an interval was considered as significantly different between groups if at least 10 consecutive data points reached a <italic>p</italic> < 0.05 (<xref rid=\"B49\" ref-type=\"bibr\">Guthrie and Buchwald, 1991</xref>). To control for multiple comparisons, <italic>p</italic>-values were adjusted using the false discovery rate (FDR; <xref rid=\"B10\" ref-type=\"bibr\">Benjamini and Hochberg, 1995</xref>). Results showed no statistical difference for time windows of the N1 and the P2, although on the descriptive level, the N1 and P2 peaks of lower performers were reduced compared with the higher performers (lower vs. higher performer; N1: 80–200 ms, <italic>p</italic> ≥ 0.99, corrected; P2: 160–280 ms, <italic>p</italic> ≥ 0.99, corrected).</p><p>Difference waves on the onset of the final word (ERP<sub>critViol</sub> - ERP<sub>critCorr</sub>) are shown in <xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>, using a centroparietal ROI, respectively. A slow negative deflection can be observed between 500 and 800 ms, referred to as the N400. The average mean amplitude on a group level was -3.6 ± 1.6 μV with mean peak latencies of 593.5 ± 132.7 ms. Around 900 ms, a positive deflection (P600) with an average mean amplitude of 3.0 ± 1.5 μV and a mean peak latency of 867.8 ± 41.7 ms can be detected. Independent <italic>t</italic>-tests revealed no significant differences between groups with lower and higher CI performance for N400 amplitudes [<italic>t</italic>(19) = 0.9, <italic>p</italic> = 0.36, <italic>r</italic> = 0.2] and latencies [<italic>t</italic>(19) = 0.2, <italic>p</italic> = 0.85, <italic>r</italic> = 0.1]. For the P600, results, however, revealed a significant difference for P600 amplitudes [<italic>t</italic>(19) = 2.1, <italic>p</italic> = 0.05, <italic>r</italic> = 0.4], but not for latencies [<italic>t</italic>(19) = -0.1, <italic>p</italic> = 0.89, <italic>r</italic> = 0.02]. Sequential two-tailed <italic>t</italic>-tests, using a sliding window of 2 ms at α = 0.5% were used to compare ERPs on the onset of the critical word between groups of lower and higher CI performers, whereby an interval was considered as significantly different between groups if at least 10 consecutive data points reached a <italic>p</italic> < 0.05 (<xref rid=\"B49\" ref-type=\"bibr\">Guthrie and Buchwald, 1991</xref>). To control for multiple comparisons, <italic>p</italic>-values were adjusted using the FDR (<xref rid=\"B10\" ref-type=\"bibr\">Benjamini and Hochberg, 1995</xref>). Results showed no statistical difference for the time window of the N400, although on the descriptive level, the N400 of lower performers was reduced compared with the higher performers (300–900 ms, <italic>p</italic> ≥ 0.64, corrected). For the time window of the P600, lower performers compared to higher ones, descriptively, revealed elevated P600 peaks, which however could not be verified by sequential two-tailed <italic>t</italic>-tests, when correcting for multiple comparisons (750–940 ms, <italic>p</italic> ≥ 0.64, corrected).</p></sec><sec id=\"S3.SS3\"><title>Comparison of Brain Perfusion: Rest Condition vs. Speech Condition</title><p>The activation pattern induced by the sentence discrimination task (speech condition > rest condition) is displayed in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> (employing a threshold of <italic>p</italic> < 0.001 uncorrected for multiple comparisons). In <xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>, the results of a voxel-wise paired <italic>t</italic>-test are shown using the original, non-flipped data, whereas <xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref> shows the results of the paired <italic>t</italic>-test including flipped images (left to right reversed) for patients stimulated on the left side.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Statistical parametric maps (SPMs) reflecting relative increases of perfusion due to performing the speech condition in comparison to the rest condition. SPMs are overlaid to a surface rendered MRI data set in the Montreal Neurological Institute (MNI) space. There are some areas displayed more transparent than others, which refers to their distance to the projection surface. <bold>(A)</bold> The results obtained using the original data without left/right flipping are displayed. <bold>(B)</bold> To explore for potential effects of the side of stimulation, each data set with left-sided stimulation was flipped in the median sagittal plane, while data sets with right-sided presentations remained unflipped. This resulted uniformly in data sets with stimulation from the “right” side in relation to the images for analysis. For both comparisons, the contrast speech condition > rest condition is displayed. Strong significant perfusion increases due to the task are similarly visible in <bold>(A,B)</bold>, showing bilateral activation in the superior and middle temporal cortices and the inferior prefrontal cortex. Note the minor difference between unflipped and flipped images, with a lack of perfusion increase in the ipsilateral (right) primary auditory cortex (BA 41; yellow arrow) for the flipped in contrast to the unflipped image (see also <xref ref-type=\"supplementary-material\" rid=\"TS3\">Supplementary Table A.2</xref>).</p></caption><graphic xlink:href=\"fnins-14-00787-g002\"/></fig><p>Regarding the <italic>original, non-flipped data</italic> (<xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>), the paired <italic>t</italic>-test between the speech and rest condition revealed a strong bilateral activation in the temporal lobe, including the superior [STG; Brodmann areas (BAs) 22, 41, 42], the middle (MTG; BA 21), and the inferior temporal gyrus (ITG; BA 20), as well as the temporo-polar (BA 38) area.</p><p>Furthermore, the paired <italic>t</italic>-test showed significant activations in Broca’s area (BA 45 left), bilaterally in the pars orbitalis (BA 47), and the orbitofrontal cortex (BA 11) (<xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>) as well as in smaller areas of the left premotor cortex (BA 6) (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table A.1.1</xref>).</p><p>Regarding the paired <italic>t</italic>-test for the <italic>flipped data</italic>, we observed significant activations in the bilateral STG (BA 22, 42), left BA 41, bilateral temporo-polar (BA 38), and left frontal areas (BAs 10, 11, 46, 47), as well as in left BA 45 (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>), which is bilaterally activated without using an extent voxel threshold (<italic>p</italic> < 0.001, <italic>k</italic> = 0, <xref ref-type=\"supplementary-material\" rid=\"TS3\">Supplementary Table A.2.1</xref>). Here, also small area activations for the left motor cortex (BA 6) have been detected. However, no activation could be detected at that level of significance in the right primary auditory cortex (BA 41) with stimulation always from the (ipsilateral) right side. The analyses of either data (non-flipped and flipped) including correction for multiple comparisons (FWE) revealed significant activations induced by stimulation only in temporal regions at a level of <italic>p</italic> < 0.05, specifically, on the right (BAs 22, 21) and left side (BA 42) for non-flipped data and the left (BAs 42, 48, 22) and the right side (BAs 22, 21) for the flipped data (<xref ref-type=\"supplementary-material\" rid=\"TS2\">Supplementary Tables A.1.2</xref>, <xref ref-type=\"supplementary-material\" rid=\"TS4\">A.2.2</xref>).</p></sec><sec id=\"S3.SS4\"><title>Contrasting Groups of Higher and Lower CI Performance in Speech Comprehension</title><sec id=\"S3.SS4.SSS1\"><title>Difference Image</title><p>Group comparisons were performed based on the difference images (speech condition - rest condition) (<xref rid=\"T4\" ref-type=\"table\">Table 3</xref> and <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>) and the rest condition images (<xref rid=\"T5\" ref-type=\"table\">Table 4</xref> and <xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>). The following results were obtained employing a threshold of <italic>p</italic> < 0.001 uncorrected for multiple comparisons. Group comparisons including correction for multiple comparisons described in <italic>Contrasting Groups of Higher and Lower CI Performance in Speech Comprehension</italic> did not reveal any suprathreshold voxel with <italic>p</italic> < 0.05. During the <italic>speech comprehension task</italic>, lower compared to higher performers showed a significantly higher activation in the left frontal BA 9 (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>). Smaller areas of activation were seen in the left ITG (BA 20), as well as in the right frontal BA 8 (<xref rid=\"T4\" ref-type=\"table\">Table 3</xref>). In contrast, higher compared to lower performers showed significantly higher activation in the left occipital area (BA17), as well as in the right parietal (BA 3) and temporal (BA 20) areas (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref> and <xref rid=\"T4\" ref-type=\"table\">Table 3</xref>).</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Statistical parametric maps (SPMs) reflecting group differences between higher and lower cochlear-implant (CI) performers with regards to speech-related activation in the context of a semantic-anomaly paradigm. Relative perfusion increases (activations) due to performing the sentence discrimination task are shown. Note a pattern of a <bold>(A)</bold> prefrontal perfusion increase in lower compared to higher performers and <bold>(B)</bold> an increased occipital and parietal perfusion in higher compared to lower performers.</p></caption><graphic xlink:href=\"fnins-14-00787-g003\"/></fig><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Statistical parametric maps (SPMs) reflecting relative differences in baseline perfusion between groups of cochlear-implant (CI) users with higher and lower performance in speech comprehension according to the GÖSA test (median-split procedure). SPMs are overlaid to a surface rendered MRI data set in the Montreal Neurological Institute (MNI) space. There are some areas displayed more transparent than others, which refers to their distance to the projection surface. Note a pattern of higher baseline perfusion in right parietal areas and motor cortex in CI users with lower performance compared to higher performers <bold>(A)</bold>, while a pattern of higher baseline perfusion in hippocampal and inferior frontal areas is seen in higher as compared to lower CI performers <bold>(B)</bold>.</p></caption><graphic xlink:href=\"fnins-14-00787-g004\"/></fig><table-wrap id=\"T4\" position=\"float\"><label>TABLE 3</label><caption><p>Results of unpaired <italic>t</italic>-tests comparing the difference images (speech condition - rest condition) between CI users with lower and higher speech comprehension (significance level used for inferences at a voxel level <italic>p</italic> < 0.001, extent voxel threshold <italic>k</italic> = 0).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Brain region</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Corresponding Brodmann area</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Hemisphere</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>x<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>y<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>z<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>T</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Nb voxel cluster</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>% Cluster</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"9\" rowspan=\"1\"><bold>Lower CI performer > higher CI performer</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19.7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35.7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inferior temporal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"9\" rowspan=\"1\"><bold>Higher CI performer > lower CI performer</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.3</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Parietal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inferior temporal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.0</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic><sup><italic>a</italic></sup>MNI coordinates.</italic></italic></attrib></table-wrap-foot></table-wrap><table-wrap id=\"T5\" position=\"float\"><label>TABLE 4</label><caption><p>Results of unpaired <italic>t</italic>-test comparing the rest condition image between cochlear-implant (CI) users with lower and higher speech comprehension (significance level used for inferences at a voxel level <italic>p</italic> < 0.001, extent voxel threshold <italic>k</italic> = 0).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Brain region</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Corresponding Brodmann area</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Hemisphere</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>x<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>y<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>z<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>T</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Nb voxel cluster</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>% Cluster</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"9\" rowspan=\"1\"><bold>Lower CI performer > higher CI performer</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Parietal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69.4</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Parietal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Motor cortex</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.9</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Motor cortex</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.8</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Parietal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Motor cortex</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81.0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Motor cortex</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.8</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"9\" rowspan=\"1\"><bold>Higher CI performer > lower CI performer</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hippocampal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">242</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.1</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">231</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60.2</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inferior frontal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">231</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26.8</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inferior temporal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">74.4</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Temporal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Temporopolar</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.4</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.6</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inferior frontal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3. 9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.6</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic><sup><italic>a</italic></sup>MNI coordinates.</italic></italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS4.SSS2\"><title>Rest Condition Image</title><p>During the <italic>rest condition</italic>, CI users with lower performance demonstrated significantly higher activity in the right motor and premotor cortex (BAs 4, 6) as well as the right parietal regions (BAs 2, 3, 5) (<xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref> and <xref rid=\"T5\" ref-type=\"table\">Table 4</xref>). However, the group of CI users with higher performance showed significantly higher baseline activity in the left hippocampal area (BA 48) and left inferior frontal areas (BA 11, 25) as shown in <xref ref-type=\"fig\" rid=\"F4\">Figure 4B</xref>. Additionally, a higher resting-state perfusion in higher compared to the lower performers was detected in smaller areas of right temporo-polar area (BA 38), the frontal cortex BA 47 (bilateral), and the left inferior temporal cortex (BA 20) (<xref rid=\"T5\" ref-type=\"table\">Table 4</xref>).</p></sec></sec><sec id=\"S3.SS5\"><title>Correlation Analyses</title><sec id=\"S3.SS5.SSS1\"><title>Brain Activation in SPECT vs. Audiometric and Cognitive Performance</title><p>We observed widespread positive correlations between activation in the difference image (speech condition minus rest condition) and the results of the Freiburg monosyllabic word test (<xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref>; see also <xref ref-type=\"supplementary-material\" rid=\"TS5\">Supplementary Table A.3.1</xref>) and the MWT-B, assessing the verbal intelligence (<xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref>; see also <xref ref-type=\"supplementary-material\" rid=\"TS7\">Supplementary Table A.4.1</xref>). Both correlations showed a similar distribution pattern of significantly activated regions, including broad bilateral temporal (BAs 20, 21, 22, 38, 41, 42), frontal (BAs 9, 10, 11, 44, 45, 46, 47), and parietal areas (BA 1, 2, 3, 40), as well as the bilateral motor cortex (BAs 4, 6) (<xref ref-type=\"fig\" rid=\"F5\">Figures 5A,B</xref>). These results for the Freiburg monosyllable test and the MWT-B test were obtained employing a threshold of <italic>p</italic> < 0.001 uncorrected for multiple comparisons. Including correction for multiple comparisons (FWE) and using a threshold of <italic>p</italic> < 0.05 restrained the observed significances to the temporal cortices. Specifically, significant correlations were detected with the Freiburg test on the right (BA 22, 21) and left side (BA 42, 48, 22) and the MWT-B on the left (BA 42, 48, 22, 21, 20) and the right side (BA 48, 22, 21) (<xref ref-type=\"supplementary-material\" rid=\"TS6\">Supplementary Tables A.3.2</xref>, <xref ref-type=\"supplementary-material\" rid=\"TS8\">A.4.2</xref>).</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>Statistical parametric maps (SPMs) reflecting correlations between activation during the speech-discrimination task (semantic-anomaly paradigm) and the result of the Freiburg monosyllabic word test (speech recognition; <bold>A</bold>) and the MWT-B (verbal intelligence; <bold>B</bold>), respectively. SPMs are overlaid to a surface rendered MRI data set in the Montreal Neurological Institute (MNI) space. There are some areas displaying more transparency than others, which refers to their distance to the projection surface. Note the extended activation of bilateral areas in temporal, frontal, and parietal cortices, showing significant relationships with speech processing (during task) and verbal intelligence.</p></caption><graphic xlink:href=\"fnins-14-00787-g005\"/></fig><p>Furthermore, we found smaller areas of positive correlations [only in testing without correction for multiple comparisons and not in tests including correction (FWE)] between the difference image (speech condition minus rest condition) and the results of the SICSPAN test for working memory (<xref ref-type=\"fig\" rid=\"F6\">Figure 6A</xref>), as well as with verbal learning (<xref ref-type=\"fig\" rid=\"F6\">Figure 6B</xref>) and verbal recall (<xref ref-type=\"fig\" rid=\"F6\">Figure 6C</xref>). Specifically, higher capacity in working memory (SICSPAN test) correlated with enhanced perfusion in the left STG (BA 22), MTG (21), and ITG (BA 20), as well as in left parietal (BAs 2, 3) and right occipital regions (BAs 18, 19) (<xref ref-type=\"fig\" rid=\"F6\">Figure 6A</xref>, see also <xref ref-type=\"supplementary-material\" rid=\"TS9\">Supplementary Table A.5</xref>). With regards to verbal learning, higher <italic>Z</italic>-scores were related to higher perfusion in the left STG (BA 22), MTG (BA 21), and the temporopolar area (BA 38) (<xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref>; see also <xref ref-type=\"supplementary-material\" rid=\"TS10\">Supplementary Table A.6</xref>). Finally, a better performance in the verbal recall was associated with a higher activation in the left STG (BA 22) MTG (BA 21), and ITG (BA 20) (<xref ref-type=\"fig\" rid=\"F6\">Figure 6C</xref>, see also <xref ref-type=\"supplementary-material\" rid=\"TS11\">Supplementary Table A.7</xref>). In the <xref ref-type=\"supplementary-material\" rid=\"TS9\">Supplementary Tables A.5</xref>–<xref ref-type=\"supplementary-material\" rid=\"TS11\">7</xref>, the results are listed without application of an extent voxel threshold (<italic>k</italic> = 0).</p><fig id=\"F6\" position=\"float\"><label>FIGURE 6</label><caption><p>Statistical parametric maps (SPMs) reflecting correlations between activation during the speech-discrimination task (semantic-anomaly paradigm) and cognitive tests, in particular <bold>(A)</bold> working-memory capacity, <bold>(B)</bold> verbal learning and <bold>(C)</bold> verbal recall. Note vastly predominant left temporal correlations for all three tests.</p></caption><graphic xlink:href=\"fnins-14-00787-g006\"/></fig></sec><sec id=\"S3.SS5.SSS2\"><title>Brain Activation in SPECT vs. EEG Components</title><p>Brain activation (speech condition minus rest condition) correlated (based on analyses without correction for multiple comparisons) in specific small brain regions negatively with the mean peak amplitude values of the N400 ERP (<xref ref-type=\"fig\" rid=\"F7\">Figure 7A</xref>). An enhanced N400 ERP was related to a higher regional brain activation during the speech comprehension task in the following brain areas: the right temporal BA 37 and occipital in BA 19 (<xref ref-type=\"fig\" rid=\"F7\">Figure 7A</xref>). Further correlations in smaller areas were seen in the left temporal (BA 37) and occipital areas (BA 17, 19; see <xref rid=\"T6\" ref-type=\"table\">Table 5</xref>). With regard to the P600 ERP, larger amplitudes were correlated (based on analyses without correction for multiple comparisons) with enhanced activation in the left parietal cortex (BA 39; <xref ref-type=\"fig\" rid=\"F7\">Figure 7B</xref>). Additionally, smaller regions of positive correlation were observed in occipital regions (left and right BA 18 and left BA 19; see <xref rid=\"T7\" ref-type=\"table\">Table 6</xref>). None of the correlations performed with correction for multiple comparisons (FWE) revealed any suprathreshold voxels with <italic>p</italic> < 0.05.</p><fig id=\"F7\" position=\"float\"><label>FIGURE 7</label><caption><p>Statistical parametric maps (SPMs) reflecting correlations between relative increases of perfusion during the speech-discrimination task (semantic-anomaly paradigm) and the <bold>(A)</bold> N400 and the <bold>(B)</bold> P600 amplitudes, respectively. SPMs are shown as glass brain images. Note the predominant correlations in the temporal cortex (BA 37, <bold>A</bold>) and the parietal–occipital areas (in <bold>B</bold>).</p></caption><graphic xlink:href=\"fnins-14-00787-g007\"/></fig><table-wrap id=\"T6\" position=\"float\"><label>TABLE 5</label><caption><p>Results of negative correlations: difference image (speech condition - rest condition) vs. N400 EEG component (significance level used for inferences at a voxel level: <italic>p</italic> < 0.001, extent voxel threshold <italic>k</italic> = 0).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Brain region</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Corresponding Brodmann area</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Hemisphere</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>x<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>y<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>z<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>T</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Nb voxel cluster</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>% cluster</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Temporal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−62</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">364</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.6</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−62</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">364</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.3</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Temporal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−62</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16.7</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic><sup><italic>a</italic></sup>MNI coordinates.</italic></italic></attrib></table-wrap-foot></table-wrap><table-wrap id=\"T7\" position=\"float\"><label>TABLE 6</label><caption><p>Results of positive correlations: difference image (speech condition - rest condition) vs. P600 EEG component (significance level used for inferences at a voxel level <italic>p</italic> < 0.001, extent voxel threshold <italic>k</italic> = 0).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Brain region</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Corresponding Brodmann area</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Hemisphere</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>x<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>y<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>z<sup>a</sup></bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>T</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Nb voxel cluster</bold></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>% cluster</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Parietal</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.3</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100.0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.0</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">BA 19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.0</td></tr></tbody></table><table-wrap-foot><attrib><italic><italic><sup><italic>a</italic></sup>MNI coordinates.</italic></italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS5.SSS3\"><title>Audiometric and Cognitive Performance vs. EEG Components</title><p>Individual linear regression analyses between measures of audiometric/cognitive performance (Freiburg monosyllabic word test, HSM sentence test in quiet and in noise, GÖSA, verbal intelligence and fluency, working memory, and verbal learning) and EEG components (N1, P2, N400 and P600) were performed. None of these reached significance (<italic>p</italic> always > 0.05) and squares of the correlation coefficients were always below 0.15 excluding a relevant correlation between the respective data.</p></sec></sec></sec><sec id=\"S4\"><title>Discussion</title><p>The present study aimed to better understand the high variability in CI outcomes. We used an innovative multimodal diagnostic approach, including brain-perfusion SPECT with tracer injection during EEG measurement to examine speech processing in CI users. Three main findings were obtained: First, the CI users activated a temporo-frontal network for speech processing and showed correlations between activation in the temporal gyrus and occipital regions on the one hand and cognitive ERP amplitudes on the other hand. This demonstrates a close connection between ERP effects and cortical activation in CI users. Second, the CI users with lower and higher speech comprehension showed different activation patterns for baseline brain activity (“rest condition”) as well as for activation during speech processing (“speech condition”), pointing to differential allocation of neural resources and strategies used for speech processing. Third, we observed strong correlations between the brain networks activated during speech processing and specific cognitive abilities, in particular working memory capacity and verbal memory functions, implying that these cognitive functions play a crucial role for speech comprehension in CI users.</p><sec id=\"S4.SS1\"><title>Brain Regions Recruited for Speech Processing in NH Listeners and in CI Users</title><p>The speech processing cascade, comprising the primary acoustic analysis, the identification of phonemes and words, and the integration of semantic and syntactic information, includes a complex system of interacting brain areas (<xref rid=\"B56\" ref-type=\"bibr\">Hickok and Poeppel, 2000</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Friederici, 2002</xref>; <xref rid=\"B99\" ref-type=\"bibr\">Scott and Johnsrude, 2003</xref>). The traditional view of speech processing proposed the dominant involvement of the left-hemisphere inferior frontal and temporal cortices (<xref rid=\"B15\" ref-type=\"bibr\">Broca, 1861</xref>; <xref rid=\"B120\" ref-type=\"bibr\">Wernicke, 1874</xref>). However, more recent findings have suggested a bilateral involvement and the existence of two dorsal and two ventral pathways (<xref rid=\"B34\" ref-type=\"bibr\">Friederici et al., 2006</xref>; <xref rid=\"B95\" ref-type=\"bibr\">Saur et al., 2008</xref>; <xref rid=\"B96\" ref-type=\"bibr\">Saur et al., 2010</xref>; <xref rid=\"B33\" ref-type=\"bibr\">Friederici, 2012</xref>), with the dorsal streams connecting the superior temporal gyrus (STG) with the premotor cortex and BA 44, respectively, whereas the ventral streams connect, on the one hand, the STG to BA 45/47 and, on the other hand, the anterior temporal cortex to the frontal operculum. Regarding semantic sentence processing, temporal as well as inferior frontal cortical areas are involved (for a review, see, e.g., <xref rid=\"B31\" ref-type=\"bibr\">Friederici, 2002</xref>), in particular BA 45/47 and the left middle temporal gyrus (MTG). Further MEG and functional MRI (fMRI) findings with NH listeners support the essential role of the left MTG in semantic processing (see, e.g., <xref rid=\"B75\" ref-type=\"bibr\">Lau et al., 2008</xref>).</p><p>CI users have been shown to recruit similar brain regions and circuits for speech processing when compared with NH listeners, although they reveal lower activation in the temporal voice area (<xref rid=\"B44\" ref-type=\"bibr\">Giraud et al., 2000</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Coez et al., 2008</xref>). Furthermore, the CI users show a compensatory increase in activation in the left inferior prefrontal cortex (Broca’s region) (<xref rid=\"B43\" ref-type=\"bibr\">Giraud and Truy, 2002</xref>), the anterior superior temporal phonologic region (<xref rid=\"B44\" ref-type=\"bibr\">Giraud et al., 2000</xref>), temporo-occipital visual areas (<xref rid=\"B41\" ref-type=\"bibr\">Giraud et al., 2001b</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Giraud and Truy, 2002</xref>), parietal attentional regions (<xref rid=\"B44\" ref-type=\"bibr\">Giraud et al., 2000</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Coez et al., 2014</xref>), and parahippocampal memory areas (<xref rid=\"B42\" ref-type=\"bibr\">Giraud et al., 2001c</xref>). Consistent with previous observations with CI users and NH listeners, in our study, the comparison of speech task versus rest revealed large activations in the bilateral temporal cortex (BA 41, 42, 22, 21, and 20) as well as the inferior prefrontal cortex (BA 45, 47) (<xref rid=\"B71\" ref-type=\"bibr\">Kutas and Hillyard, 1980a</xref>, <xref rid=\"B72\" ref-type=\"bibr\">b</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Friederici, 2002</xref>; <xref rid=\"B117\" ref-type=\"bibr\">Van Petten and Luka, 2006</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Hahne et al., 2012</xref>). Thus, our results are in line with previous observations by demonstrating the recruitment of a temporo-frontal network of brain areas during speech processing in CI users. Moreover, they reveal that a sentence comprehension task typically employed to study ERP effects is likewise suitable to achieve synchronously brain activations detectable in emission tomography.</p><p>Interestingly, we did not observe an activation of the inferior parietal cortex and the dorsal part of Broca’s area in the inferior prefrontal cortex (BA 44). This suggests that the dorsal pathway – known to be particularly involved in processing of syntactically complex sentences (<xref rid=\"B33\" ref-type=\"bibr\">Friederici, 2012</xref>) – was not considerably activated in our patients. This is plausible due to the fact that the sentences used in our discrimination paradigm were syntactically simple and their processing may have relied rather on the ventral than the dorsal pathways. Regarding the parietal areas, the lack of activation in these regions might be related to the fact that participants were stimulated <italic>unilaterally</italic>, while previous studies reporting parietal recruitment during speech processing are restricted to CI users with <italic>bilateral</italic> stimulation (<xref rid=\"B21\" ref-type=\"bibr\">Coez et al., 2014</xref>). However, our CI users showed supplementary activation in the hippocampus (BA 48), pointing to memory functions involved in performing the semantic-anomaly paradigm.</p><p>Interestingly, when correcting our data sets for the side of stimulation (left-sided stimulation flipped, right-sided stimulation unflipped), we did not observe activation in the ipsilateral primary auditory cortex (BA 41). Similarly, previous fMRI studies with NH listeners have reported that monaural presentation of speech results in a stronger contralateral activation of the primary auditory cortex (<xref rid=\"B61\" ref-type=\"bibr\">Jäncke et al., 2002</xref>; <xref rid=\"B106\" ref-type=\"bibr\">Stefanatos et al., 2008</xref>). Contralaterally predominant activation has also been observed with unilateral as opposed to bilateral stimulation in CI users (<xref rid=\"B46\" ref-type=\"bibr\">Green et al., 2011</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Coez et al., 2014</xref>).</p></sec><sec id=\"S4.SS2\"><title>Different Patterns of Brain Activity at Rest and Activation Related to Speech Processing in CI Users With Lower and Higher Performance</title><p>There is high interindividual variability in speech comprehension abilities across CI users (<xref rid=\"B77\" ref-type=\"bibr\">Lazard et al., 2012</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Blamey et al., 2013</xref>). Differences between proficient and non-proficient CI users seem to exist already at the time before implantation, as indicated by the finding of distinct preimplantation activation patterns during a (written) word rhyming task between (prospective) lower and higher performance after implantation (<xref rid=\"B76\" ref-type=\"bibr\">Lazard et al., 2010</xref>). After implantation, proficient and non-proficient CI users have been shown to recruit the auditory cortex to a different degree, with reduced recruitment of the temporal voice area (with regard to extent and only unilaterally) in the poor performers when compared with good performers (<xref rid=\"B19\" ref-type=\"bibr\">Coez et al., 2008</xref>). However, in the present study, the activation of temporal regions (BA 20) was comparable between the two groups of CI users. This might be explained by the fact that the current study compared CI users with high and moderate speech comprehension (referred here as higher and lower CI performers), while the two aforementioned studies compared CI users with clearly different good and bad performance, leaving out a broad spectrum of patients with intermediate performance.</p><p>Despite the lack of a group difference in the temporal regions, we observed for the higher performing CI users additional activations of parietal and occipital regions, whereas for the group of lower performing subjects, we found a stronger activation of superior frontal areas. Similar to our results, previous studies have reported for good CI performers increased activity in temporo-occipital visual areas (<xref rid=\"B41\" ref-type=\"bibr\">Giraud et al., 2001b</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Giraud and Truy, 2002</xref>) and parietal attentional regions (<xref rid=\"B44\" ref-type=\"bibr\">Giraud et al., 2000</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Coez et al., 2014</xref>), suggesting compensatory networks of speech processing in these proficient individuals. The <italic>auditor</italic>y-evoked activation in the <italic>visual</italic> cortex might be related to functional, cross-modal reorganization of the <italic>visua</italic>l cortex that is used to compensate for the degraded auditory input via the CI. Accordingly, it has been shown in previous studies that activation of the <italic>visual</italic> cortex by <italic>auditor</italic>y stimulation is positively related to the CI performance (<xref rid=\"B41\" ref-type=\"bibr\">Giraud et al., 2001b</xref>, <xref rid=\"B42\" ref-type=\"bibr\">c</xref>; <xref rid=\"B110\" ref-type=\"bibr\">Strelnikov et al., 2013</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Chen et al., 2016</xref>). Thus, it can be speculated that, in the present study, the two groups of CI users used different compensatory strategies that may be related to differences in cross-modal reorganization of the visual and auditory cortex. CI speech performance seems to be good as long as the (beneficial) <italic>auditor</italic>y-evoked activation in the <italic>visual</italic> cortex is higher than the (maladaptive) <italic>visual</italic>-evoked activation in the <italic>auditory</italic> cortex (<xref rid=\"B18\" ref-type=\"bibr\">Chen et al., 2016</xref>). This is in line with the current findings, showing that specifically the higher performers showed an enhanced beneficial cross-modal reorganization in the <italic>visual</italic> cortex.</p><p>Regarding the lower performing CI users, we observed increased superior frontal activations (BA 9) compared with those with higher performance. This is in contrast to a previous study reporting that activation in non-proficient CI users is restricted to temporal areas (<xref rid=\"B84\" ref-type=\"bibr\">Mortensen et al., 2006</xref>). The discrepancy of results is likely attributable to variations in methodology, in particular in terms of the experimental task (active vs. passive task) and the speech comprehension of the lower performing group (open-set speech comprehension: ≤ 60 vs. ≥ 65%). Furthermore, our observation of increased superior frontal activation particularly in the poorer performing CI users is meaningful, as it might reflect enhanced neural resource allocation due to limitations in electrical hearing. Indeed, peripheral factors, for instance the distance of the CI electrode arrays to the modiolar wall and the number of surviving spiral ganglion cells have been shown to affect speech comprehension with the CI (<xref rid=\"B86\" ref-type=\"bibr\">Nadol, 1997</xref>; <xref rid=\"B58\" ref-type=\"bibr\">Holden et al., 2013</xref>). In case of suboptimal peripheral conditions, the resulting strong(er) mismatch between the CI input and the attributes stored in the long-term memory may require additional explicit processing and involve cognitive resources, in particular working memory functions (<xref rid=\"B90\" ref-type=\"bibr\">Rönnberg et al., 2013</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Finke et al., 2015</xref>), and may cause enhanced listening effort (<xref rid=\"B11\" ref-type=\"bibr\">Berding et al., 2015</xref>). Indeed, it has been shown previously that NH listeners recruit additional prefrontal regions specifically in difficult listening conditions in which the listening effort is enhanced (<xref rid=\"B22\" ref-type=\"bibr\">Davis and Johnsrude, 2003</xref>; <xref rid=\"B89\" ref-type=\"bibr\">Peelle, 2018</xref>). Thus, it is likely that during speech processing, the lower CI performers rely on a different processing strategy compared with the higher performers by particularly allocating executive cognitive resources located in specific frontal regions. However, this compensatory strategy seems to be limited, as indicated by the fact that the lower CI performers did not reach the speech comprehension performance levels of higher CI performers.</p><p>Additionally, we observed CI-outcome-related distinct brain activity patterns at rest: lower performers showed higher resting state perfusion in motor cortex and parietal areas, whereas higher performers showed higher perfusion in (inferior) frontal (BA47), inferior temporal (BA20), temporopolar (BA38), and hippocampal (memory) regions. The prognostic relevance of resting activity has been demonstrated before (<xref rid=\"B78\" ref-type=\"bibr\">Lee et al., 2007</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Giraud et al., 2011</xref>; <xref rid=\"B108\" ref-type=\"bibr\">Strelnikov et al., 2015a</xref>; <xref rid=\"B111\" ref-type=\"bibr\">Suh et al., 2015</xref>). It has been shown, for example, that a low (resting-state) activity in the primary auditory/superior temporal cortex – indicating that no maladaptive cross-modal visual take-over has taken place – is related to a better CI outcome (<xref rid=\"B78\" ref-type=\"bibr\">Lee et al., 2007</xref>; <xref rid=\"B108\" ref-type=\"bibr\">Strelnikov et al., 2015a</xref>; <xref rid=\"B111\" ref-type=\"bibr\">Suh et al., 2015</xref>). The same has been observed for an increased activity in the prefrontal cortex, in particular Broca’s area (<xref rid=\"B78\" ref-type=\"bibr\">Lee et al., 2007</xref>; <xref rid=\"B111\" ref-type=\"bibr\">Suh et al., 2015</xref>). Our results are consistent with these previous findings by showing that CI users with higher performance have increased resting-state perfusion particularly in left-sided (inferior) frontal (BA 11, 47, 25) areas.</p><p>Overall, our results show that CI users with lower and higher speech comprehension recruit distinct brain networks not only during speech processing but also during rest. This points to different compensation strategies for the processing of the degraded CI speech signal and suggests different adaptation of the brain in response to the individual auditory experience.</p></sec><sec id=\"S4.SS3\"><title>Cognitive Abilities and Their Relationship With Brain Activation During Speech Processing</title><p>The performance of CI users has been shown to be influenced by cognitive factors, like verbal fluency and working memory capacity (<xref rid=\"B90\" ref-type=\"bibr\">Rönnberg et al., 2013</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Finke et al., 2016a</xref>). However, it is currently widely unknown how individual differences in cognitive abilities relate to cortical response patterns during speech processing. The present study suggests that enhanced activation in (predominantly) temporo-frontal areas during speech processing is positively associated with higher word recognition scores and higher verbal intelligence. Temporo-frontal regions encompass areas related to auditory, executive, and memory functions that are used for speech comprehension in the context of the semantic-anomaly paradigm (<xref rid=\"B75\" ref-type=\"bibr\">Lau et al., 2008</xref>; <xref rid=\"B70\" ref-type=\"bibr\">Kutas and Federmeier, 2011</xref>). Consistent with our observations, a coupling between speech recognition scores and activation of auditory areas, in particular the Heschl’s gyrus (BA 41), the superior temporal, and the angular gyrus, has been observed previously (<xref rid=\"B80\" ref-type=\"bibr\">Łukaszewicz-Moszyńska et al., 2014</xref>). In sum, these results underpin that cognitive abilities and speech comprehension ability in CI users relate to specific speech-evoked cortical activation in temporo-frontal regions. Thus, enhanced activation in these regions seems to allow better verbal abilities and higher word comprehension ability with a CI.</p><p>Additionally, we observed better verbal abilities and enhanced working memory capacity being associated with increased activations in the superior and middle temporal gyrus. This indicates that better cognition results in enriched activations of typical auditory areas, which might be related to better speech comprehension with CI as well. As all cognitive tests recall phonetic, vocabulary, and memory abilities simultaneously, this might indicate the use of similar resources during the performance of these cognitive tests and the semantic speech task.</p></sec><sec id=\"S4.SS4\"><title>ERPs and Their Relationship With Brain Activation Detected With SPECT</title><p>In the current study, the CI users showed an N1-P2 ERP in response to the onset of the sentence, indicating processing of speech at the level of the auditory cortex in CI users (<xref rid=\"B85\" ref-type=\"bibr\">Naatanen and Picton, 1987</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Friesen and Picton, 2010</xref>). Furthermore, the difference waves (ERP<sub>critViol</sub> - ERP<sub>critCorr</sub>), time locked to the onset of the final word of the sentence, revealed a more negative amplitude to sentences with a semantic violation compared to correct sentences, referred to as an N400 effect (<xref rid=\"B73\" ref-type=\"bibr\">Kutas and Hillyard, 1980c</xref>; <xref rid=\"B74\" ref-type=\"bibr\">Kutas and Hillyard, 1984</xref>). The N400 is considered as an index of neural effort of automatic word-into-context integration (<xref rid=\"B107\" ref-type=\"bibr\">Strauss et al., 2013</xref>). In other words, it is assumed to reflect the level of difficulty with which a word is integrated in the respective context (<xref rid=\"B116\" ref-type=\"bibr\">Van Petten et al., 1999</xref>). Interestingly, the observed latency of the N400 effect in CI users (∼700–800 ms) was delayed when compared with the N400 latency of NH listeners reported in the literature (∼400 ms; e.g., <xref rid=\"B75\" ref-type=\"bibr\">Lau et al., 2008</xref>). Nevertheless, it was comparable to a previous study, reporting a delayed N400 response in CI users when compared with NH listeners (<xref rid=\"B52\" ref-type=\"bibr\">Hahne et al., 2012</xref>). These results suggest that adverse listening conditions, as experienced by CI users with the degraded auditory input from the implant, lead to more effortful and thus delayed semantic integration processes (<xref rid=\"B27\" ref-type=\"bibr\">Finke et al., 2016a</xref>).</p><p>The present study showed negative correlations between the N400 response and the temporal (in particular BA 37), as well as activation in the visual cortex (SPECT difference image: speech condition - rest condition). Specifically, more negative N400 amplitudes in the present study were found to be associated with higher perfusion in a broad network, including temporal and occipital regions. The N400 has been suggested to be primarily generated in the left middle temporal gyrus (<xref rid=\"B75\" ref-type=\"bibr\">Lau et al., 2008</xref>; <xref rid=\"B33\" ref-type=\"bibr\">Friederici, 2012</xref>). BA 37 has also been suggested previously to be part of the semantic processing network (<xref rid=\"B75\" ref-type=\"bibr\">Lau et al., 2008</xref>; <xref rid=\"B7\" ref-type=\"bibr\">Ardila et al., 2015</xref>). The observed correlation with the temporal region suggests this region to be as well involved in the generation of the N400 in CI users. However, results of the present study also showed a strong correlation with visual areas, strongly suggesting an additional cross-modal recruitment of visual areas during semantic processing in CI users. The engagement of occipital areas might be related to the fact that although sentences are presented purely auditorily, they might be internally visualized. Furthermore, it has been previously reported that CI users show an enhanced audiovisual coupling (<xref rid=\"B98\" ref-type=\"bibr\">Schierholz et al., 2015</xref>, <xref rid=\"B97\" ref-type=\"bibr\">2017</xref>; <xref rid=\"B109\" ref-type=\"bibr\">Strelnikov et al., 2015b</xref>) and that activation of the <italic>visual</italic> cortex by <italic>auditor</italic>y stimulation is positively related to the CI performance (<xref rid=\"B41\" ref-type=\"bibr\">Giraud et al., 2001b</xref>, <xref rid=\"B42\" ref-type=\"bibr\">c</xref>; <xref rid=\"B110\" ref-type=\"bibr\">Strelnikov et al., 2013</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Chen et al., 2016</xref>), indicating that cross-modal reorganization in the visual cortex and enhanced audiovisual coupling support speech processing in CI users.</p><p>The difference waves revealed that the N400 was followed by a positive deflection at around 900 ms after the final word onset. This late component has been referred to the P600 (<xref rid=\"B88\" ref-type=\"bibr\">Osterhout and Holcomb, 1992</xref>), which typically peaks between 300 and 800 ms (<xref rid=\"B36\" ref-type=\"bibr\">Friederici et al., 2000</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Friederici, 2006</xref>). Similar to the N400 response, our results suggest a delayed P600 response in our CI users, which can be attributed to the degraded input from the implant, leading to delayed higher-level speech processing. Traditionally, the P600 has been related to syntactic processing effort in general and it is, for example, observed in the context of syntactical repair, syntactical complexity, and difficulties with syntactic integration (<xref rid=\"B88\" ref-type=\"bibr\">Osterhout and Holcomb, 1992</xref>; <xref rid=\"B50\" ref-type=\"bibr\">Hagoort et al., 1993</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Kaan et al., 2000</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Friederici et al., 2002</xref>). Nevertheless, the P600 has been recently discussed in a semantic context as well (see, e.g., <xref rid=\"B14\" ref-type=\"bibr\">Bornkessel-Schlesewsky and Schlesewsky, 2008</xref>). The studies by <xref rid=\"B67\" ref-type=\"bibr\">Kolk et al. (2003)</xref> and <xref rid=\"B115\" ref-type=\"bibr\">van Herten et al. (2005)</xref>, for example, observed a P600 effect elicited by semantic anomalies, challenging the merely syntactic account of the P600. This view has been supported by the study by <xref rid=\"B102\" ref-type=\"bibr\">Shen et al. (2016)</xref>, suggesting that the P600 reflects a general mechanism of semantic reinterpretation and conflict monitoring that leads to the retrieval of word knowledge from long-term memory. A systematic review on the effects of semantic incongruency by <xref rid=\"B118\" ref-type=\"bibr\">Van Petten and Luka (2012)</xref> has identified 21 out of 64 studies that exhibited a biphasic N400/P600 effect to incongruent sentences, confirming that indeed the P600 is elicited by semantic anomalies in sentences. Regarding the generators of the P600, the bilateral medial/posterior temporal cortex has been identified in NH listeners (<xref rid=\"B100\" ref-type=\"bibr\">Service et al., 2007</xref>). The current results showed that stronger P600 amplitudes were associated with higher perfusion in parietal and occipital areas. The parietal correlation involved BA 39 (angular gyrus), which has been shown to be a part of the semantic processing network (<xref rid=\"B75\" ref-type=\"bibr\">Lau et al., 2008</xref>). Additionally, stronger P600 responses were associated with higher perfusion in occipital areas, suggesting once again that CI users recruit additional visual regions during semantic processing.</p><p>Our results revealed that both the N400 and the P600 ERPs correlate with the activation of a broad but distinct network. Importantly, both components correlate with activation in occipital areas (N400: BAs 17, 19; P600: BAs 18, 19). This extends previous research by showing that CI users strongly rely on visual cortex activation during semantic speech processing. All in all, our findings show a close connection between ERP effects and cortical activation in CI users, demonstrating that the combination of SPECT and EEG measurements provides unique and valuable insights into the cognitive processes underlying speech comprehension in CI users.</p><p>Our results also extend previous studies by indicating that not only sensory but also cognitive ERPs, in particular the N400 and the P600 response, can distinguish – although in the present study not with statistical significance, but at least on the descriptive level – between CI users who have higher versus lower speech comprehension. Thus, our results might point to potential different abilities of lower and higher performing CI users in detecting and integrating semantic violations in sentences. We speculate that increasing the sample size would have resulted in less variance in the data and statistical group differences for the N400 and the P600 amplitudes, respectively. Interestingly, on the descriptive level, our results showed that the amplitudes of the N1, P2, and N400 are <italic>reduced</italic> in the lower compared to the higher CI performers, while we observed on a descriptive level the opposite pattern, that is an <italic>enhanced</italic> amplitude, for the P600 in the lower compared to the higher performers. These descriptive observations support the significant group differences revealed in the SPECT data and point together to different strategies for speech comprehension in lower and higher performers. While lower performers invest less neural resources in automatic word-into-context integration (reflected by the reduced N400 response, here observed on a descriptive level), they use additional explicit processing resources for semantic reinterpretation and retrieval of word knowledge from the long-term memory (reflected by the enhanced P600 response, here observed on a descriptive level). Furthermore, this is in line with our observation of increased frontal activation in the lower compared to the higher CI performers and the Ease of Language Understanding (ELU) model (<xref rid=\"B90\" ref-type=\"bibr\">Rönnberg et al., 2013</xref>), according to which additional cognitive resources are required for speech comprehension in demanding listening situations, which particularly applies to CI users with lower speech comprehension.</p></sec></sec><sec id=\"S5\"><title>Conclusion</title><p>The present study showed that higher and lower CI outcome is associated with different brain activation patterns. Furthermore, our results revealed the meaningful applicability of a combined EEG and SPECT multimodal diagnostic approach for examination of speech processing in CI users. Our results revealed that based on a sentence discrimination task, activation of a temporo-frontal network can be detected in both diagnostic modalities correspondingly to previous observations with PET in CI users. Furthermore, the present results revealed significantly different activation patterns between lower and higher CI performers. The results point to the use of different compensational strategies for the degraded auditory input and different adaptations of the brain in response to the individual auditory experience for groups of CI users with higher and lower performance. Moreover, differences between these groups of CI users, at least on the descriptive level, were observed for the EEG data. Here, the lower performers showed reduced amplitudes for the sensory ERPs (auditory N1 and P2) and the later cognitive N400 ERP, whereas the opposite pattern, that is enhanced amplitudes, were observed for the P600 ERP in the lower compared to the higher performers. These findings point to more pronounced deficits/limitations in CI users with lower performance due to a particularly degraded auditory input and compensatory strategies to overcome these limitations by a stronger recruitment of higher-cognitive resources, involving frontal regions.</p></sec><sec sec-type=\"data-availability\" id=\"S6\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p></sec><sec id=\"S7\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by the local ethics committee at Hannover Medical School (vote no. 6678) and the German Federal Office for Radiation Protection (reference number Z 5-22461/2-2014-012). The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>MK designed the study, recruited the study sample, collected and analyzed EEG and SPECT data, wrote the manuscript, and prepared the tables and figures. IS designed the study, recruited the study sample, collected and analyzed EEG data, contributed to the writing of the manuscript, and prepared the figures. MM contributed to the design of the study, assisted during the data collection, provided technical support, and revised the manuscript. FW provided technical support for the SPECT measurement, contributed to the SPECT data analysis, and revised the manuscript. AH contributed to the experimental setup, advised on the EEG data analysis, and revised the manuscript. AB contributed to the setup of the EEG facility. LG provided expertise for the SPECT acquisition protocol. FB allocated personnel resources enabling SPECT acquisitions. PS conceived and designed the study, supervised the EEG data collection, contributed to the EEG data analysis, and contributed to the writing of the manuscript. GB conceived and designed the study, supervised the SPECT data acquisition, contributed to the SPECT data analysis, and contributed to the writing of the manuscript. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2177/1 – Project ID 390895286. This work was supported by the DFG Cluster of Excellence EXC 1077/1 “Hearing4all.”</p></fn></fn-group><ack><p>We would like to thank Prof. Dr. Stefan Debener for his methodological support and Prof. Dr. Bruno Kopp for his expertise with regard to the neurocognitive testing. We furthermore would like to thank Dr. Angelika Illg for support in patient recruitment and Dr. Mareike Finke for her contribution to the initial study design. Moreover, we thank Maria Nierada and Claudia Diekmann for their support during the SPECT scans. Last but not the least, we would like to thank all CI patients who participated in the present study.</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.memoryclinic.ch/de/main-navigation/neuropsychologen/cerad-plus/\">https://www.memoryclinic.ch/de/main-navigation/neuropsychologen/cerad-plus/</ext-link></p></fn><fn id=\"footnote2\"><label>2</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.billauer.co.il/peakdet.html\">http://www.billauer.co.il/peakdet.html</ext-link></p></fn></fn-group><sec id=\"S11\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fnins.2020.00787/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fnins.2020.00787/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"TS1\"><media xlink:href=\"Table_1.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS2\"><media xlink:href=\"Table_2.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS3\"><media xlink:href=\"Table_3.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS4\"><media xlink:href=\"Table_4.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS5\"><media xlink:href=\"Table_5.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS6\"><media xlink:href=\"Table_6.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS7\"><media xlink:href=\"Table_7.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS8\"><media xlink:href=\"Table_8.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS9\"><media xlink:href=\"Table_9.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS10\"><media xlink:href=\"Table_10.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS11\"><media xlink:href=\"Table_11.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Abraham</surname><given-names>T.</given-names></name><name><surname>Feng</surname><given-names>J.</given-names></name></person-group> (<year>2011</year>). <article-title>Evolution of brain imaging instrumentation.</article-title>\n<source><italic>Semin. 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Virol</journal-id><journal-id journal-id-type=\"hwp\">jvi</journal-id><journal-id journal-id-type=\"pmc\">jvi</journal-id><journal-id journal-id-type=\"publisher-id\">JVI</journal-id><journal-title-group><journal-title>Journal of Virology</journal-title></journal-title-group><issn pub-type=\"ppub\">0022-538X</issn><issn pub-type=\"epub\">1098-5514</issn><publisher><publisher-name>American Society for Microbiology</publisher-name><publisher-loc>1752 N St., N.W., Washington, DC</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32581099</article-id><article-id pub-id-type=\"pmc\">PMC7431780</article-id><article-id pub-id-type=\"publisher-id\">00837-20</article-id><article-id pub-id-type=\"doi\">10.1128/JVI.00837-20</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Virus-Cell Interactions</subject></subj-group></article-categories><title-group><article-title>CD300LF Polymorphisms of Inbred Mouse Strains Confer Resistance to Murine Norovirus Infection in a Cell Type-Dependent Manner</article-title><alt-title alt-title-type=\"running-head\">CD300LF Polymorphism for Cell Type-Dependent MNV Entry</alt-title><alt-title alt-title-type=\"short-authors\">Furlong et al.</alt-title></title-group><contrib-group><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Furlong</surname><given-names>Kevin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Biering</surname><given-names>Scott B.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref><xref ref-type=\"author-notes\" rid=\"fn1\">*</xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Choi</surname><given-names>Jayoung</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Wilen</surname><given-names>Craig B.</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>c</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>d</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-8216-4946</contrib-id><name><surname>Orchard</surname><given-names>Robert C.</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>e</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-5286-0924</contrib-id><name><surname>Wobus</surname><given-names>Christiane E.</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>f</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Nelson</surname><given-names>Christopher A.</given-names></name><xref ref-type=\"aff\" rid=\"aff7\"><sup>g</sup></xref><xref ref-type=\"aff\" rid=\"aff8\"><sup>h</sup></xref><xref ref-type=\"aff\" rid=\"aff9\"><sup>i</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Fremont</surname><given-names>Daved H.</given-names></name><xref ref-type=\"aff\" rid=\"aff7\"><sup>g</sup></xref><xref ref-type=\"aff\" rid=\"aff8\"><sup>h</sup></xref><xref ref-type=\"aff\" rid=\"aff9\"><sup>i</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Baldridge</surname><given-names>Megan T.</given-names></name><xref ref-type=\"aff\" rid=\"aff9\"><sup>i</sup></xref><xref ref-type=\"aff\" rid=\"aff10\"><sup>j</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Randall</surname><given-names>Glenn</given-names></name><xref ref-type=\"aff\" rid=\"aff11\"><sup>k</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\" equal-contrib=\"no\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-0846-5462</contrib-id><name><surname>Hwang</surname><given-names>Seungmin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref><xref ref-type=\"author-notes\" rid=\"fn1\">*</xref></contrib><aff id=\"aff1\"><label>a</label><addr-line>Committee on Microbiology, The University of Chicago, Chicago, Illinois, USA</addr-line></aff><aff id=\"aff2\"><label>b</label><addr-line>Department of Pathology, The University of Chicago, Chicago, Illinois, USA</addr-line></aff><aff id=\"aff3\"><label>c</label><addr-line>Department of Laboratory Medicine, Yale University School of Medicine, New Haven, Connecticut, USA</addr-line></aff><aff id=\"aff4\"><label>d</label><addr-line>Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut, USA</addr-line></aff><aff id=\"aff5\"><label>e</label><addr-line>Department of Immunology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA</addr-line></aff><aff id=\"aff6\"><label>f</label><addr-line>Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA</addr-line></aff><aff id=\"aff7\"><label>g</label><addr-line>Department of Pathology & Immunology, Washington University, St. Louis, Missouri, USA</addr-line></aff><aff id=\"aff8\"><label>h</label><addr-line>Department of Biochemistry & Molecular Biophysics, Washington University, St. Louis, Missouri, USA</addr-line></aff><aff id=\"aff9\"><label>i</label><addr-line>Department of Molecular Microbiology, Washington University, St. Louis, Missouri, USA</addr-line></aff><aff id=\"aff10\"><label>j</label><addr-line>Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA</addr-line></aff><aff id=\"aff11\"><label>k</label><addr-line>Department of Microbiology, The University of Chicago, Chicago, Illinois, USA</addr-line></aff></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Williams</surname><given-names>Bryan R. G.</given-names></name><role>Editor</role><aff>Hudson Institute of Medical Research</aff></contrib></contrib-group><author-notes><corresp id=\"cor1\">Address correspondence to Seungmin Hwang, <email>shwang@vir.bio</email>.</corresp><fn id=\"fn1\" fn-type=\"present-address\"><label>*</label><p>Present address: Scott B. Biering, Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, California, USA; Seungmin Hwang, VIR Biotechnology, San Francisco, California, USA.</p></fn><fn fn-type=\"equal\"><p>Kevin Furlong and Scott B. Biering contributed equally to this work. Author order was determined by their contribution to the manuscript.</p></fn><fn fn-type=\"other\"><p><bold>Citation</bold> Furlong K, Biering SB, Choi J, Wilen CB, Orchard RC, Wobus CE, Nelson CA, Fremont DH, Baldridge MT, Randall G, Hwang S. 2020. CD300LF polymorphisms of inbred mouse strains confer resistance to murine norovirus infection in a cell type-dependent manner. J Virol 94:e00837-20. <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.1128/JVI.00837-20\">https://doi.org/10.1128/JVI.00837-20</ext-link>.</p></fn></author-notes><pub-date pub-type=\"epreprint\"><day>24</day><month>6</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>9</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>94</volume><issue>17</issue><elocation-id>e00837-20</elocation-id><history><date date-type=\"received\"><day>4</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Furlong et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Furlong et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International license</ext-link>.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"JVI.00837-20.pdf\"/><abstract abstract-type=\"precis\"><p>MNV is a prevalent model system for studying human norovirus, which is the leading cause of gastroenteritis worldwide and thus a sizeable public health burden. Elucidating mechanisms underlying susceptibility of host cells to MNV infection can lead to insights on the roles that specific cell types play during norovirus pathogenesis. Here, we show that different alleles of the proteinaceous receptor for MNV, CD300LF, function in a cell type-dependent manner. In contrast to the C57BL/6J allele, which functions as an MNV entry factor in all tested cell types, including human cells, I/LnJ CD300LF does not function as an MNV entry factor in macrophage-like cells but does allow MNV entry in other cell types. Together, these observations indicate the existence of cell type-specific modifiers of CD300LF-dependent MNV entry.</p></abstract><abstract><title>ABSTRACT</title><p>Human norovirus is the leading cause of gastroenteritis worldwide, yet basic questions about its life cycle remain unanswered due to an historical lack of robust experimental systems. Recent studies on the closely related murine norovirus (MNV) have identified CD300LF as an indispensable entry factor for MNV. We compared the MNV susceptibilities of cells from different mouse strains and identified polymorphisms in murine CD300LF which are critical for its function as an MNV receptor. Bone marrow-derived macrophages (BMDMs) from I/LnJ mice were resistant to infection from multiple MNV strains which readily infect BMDMs from C57BL/6J mice. The resistance of I/LnJ BMDMs was specific to MNV, since the cells supported infection of other viruses comparably to C57BL/6J BMDMs. Transduction of I/LnJ BMDMs with C57BL/6J CD300LF made the cells permissible to MNV infection, suggesting that the cause of resistance lies in the entry step of MNV infection. In fact, we mapped this phenotype to a 4-amino-acid difference at the CC′ loop of CD300LF; swapping of these amino acids between C57BL/6J and I/LnJ CD300LF proteins made the mutant C57BL/6J CD300LF functionally impaired and the corresponding mutant of I/LnJ CD300LF functional as an MNV entry factor. Surprisingly, expression of the I/LnJ CD300LF in other cell types made the cells infectible by MNV, even though the I/LnJ allele did not function as an MNV receptor in macrophage-like cells. Correspondingly, I/LnJ CD300LF bound MNV virions in permissive cells but not in nonpermissive cells. Collectively, our data suggest the existence of a cell type-specific modifier of MNV entry.</p><p><bold>IMPORTANCE</bold> MNV is a prevalent model system for studying human norovirus, which is the leading cause of gastroenteritis worldwide and thus a sizeable public health burden. Elucidating mechanisms underlying susceptibility of host cells to MNV infection can lead to insights on the roles that specific cell types play during norovirus pathogenesis. Here, we show that different alleles of the proteinaceous receptor for MNV, CD300LF, function in a cell type-dependent manner. In contrast to the C57BL/6J allele, which functions as an MNV entry factor in all tested cell types, including human cells, I/LnJ CD300LF does not function as an MNV entry factor in macrophage-like cells but does allow MNV entry in other cell types. Together, these observations indicate the existence of cell type-specific modifiers of CD300LF-dependent MNV entry.</p></abstract><kwd-group><title>KEYWORDS</title><kwd>CD300LF</kwd><kwd>I/LnJ</kwd><kwd>entry</kwd><kwd>norovirus</kwd><kwd>polymorphism</kwd><kwd>receptors</kwd></kwd-group><funding-group><award-group id=\"award1\"><funding-source><institution-wrap><institution>HHS | National Institutes of Health (NIH)</institution><institution-id>https://doi.org/10.13039/100000002</institution-id></institution-wrap></funding-source><award-id>T32GM007183</award-id><principal-award-recipient><name><surname>Furlong</surname><given-names>Kevin</given-names></name></principal-award-recipient><principal-award-recipient><name><surname>Biering</surname><given-names>Scott</given-names></name></principal-award-recipient></award-group><award-group id=\"award2\"><funding-source><institution-wrap><institution>HHS | National Institutes of Health (NIH)</institution><institution-id>https://doi.org/10.13039/100000002</institution-id></institution-wrap></funding-source><award-id>R01AI127518</award-id><principal-award-recipient><name><surname>Randall</surname><given-names>Glenn</given-names></name></principal-award-recipient></award-group><award-group id=\"award3\"><funding-source><institution-wrap><institution>HHS | National Institutes of Health (NIH)</institution><institution-id>https://doi.org/10.13039/100000002</institution-id></institution-wrap></funding-source><award-id>R01AI127552</award-id><principal-award-recipient><name><surname>Fremont</surname><given-names>Daved H.</given-names></name></principal-award-recipient><principal-award-recipient><name><surname>Baldridge</surname><given-names>Megan Tierney</given-names></name></principal-award-recipient></award-group></funding-group><counts><fig-count count=\"8\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"50\"/><page-count count=\"15\"/><word-count count=\"8873\"/></counts><custom-meta-group><custom-meta><meta-name>cover-date</meta-name><meta-value>September 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>INTRODUCTION</title><p>Human norovirus (HNV) is a nonenveloped, positive-sense RNA virus of the <italic>Caliciviridae</italic> family and is the leading cause of acute gastroenteritis worldwide (<xref rid=\"B1\" ref-type=\"bibr\">1</xref><xref ref-type=\"bibr\" rid=\"B2\">–</xref><xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Despite its significant public health burden, a complete understanding of the host factors controlling the life cycle of HNV is still lacking. Currently, there are few <italic>in vitro</italic> models that support replication and detection of HNV, making it a difficult pathogen to study directly, though these systems are rapidly improving (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref><xref ref-type=\"bibr\" rid=\"B5\">–</xref><xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Murine norovirus (MNV) is a genetically similar virus discovered in 2003 as a lethal agent in <italic>Rag2</italic><sup>−/−</sup>\n<italic>Stat1</italic><sup>−/−</sup> mice and has since been used as a model virus to study HNV biology (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Unlike HNV, MNV replicates robustly in several macrophage-like cell lines, including BV2 and RAW264.7 (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p><p>Several studies have identified a wide range of host factors that modulate norovirus attachment and entry, including histo-blood group antigens (HBGAs), bile acids, sialic acid, and divalent cations (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref><xref ref-type=\"bibr\" rid=\"B12\">–</xref><xref rid=\"B14\" ref-type=\"bibr\">14</xref>). While these attachment factors have been shown to enhance attachment for several different noroviruses, none of them are required for MNV infection. In contrast, recent clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 screens uncovered CD300LF, a type I integral membrane protein containing a single immunoglobulin-like domain, as an indispensable host factor for MNV attachment and entry (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>). In fact, expression of murine CD300LF alone was sufficient to confer MNV susceptibility to otherwise resistant host cells, including those from other species, such as human 293T and HeLa cells (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Additional receptor molecules and attachment factors have been identified for closely related members of the <italic>Caliciviridae</italic> family, including feline junctional adhesion molecule A (fJAM-A) as the receptor for feline calicivirus, which has been used historically as a surrogate for HNV (<xref rid=\"B16\" ref-type=\"bibr\">16</xref><xref ref-type=\"bibr\" rid=\"B17\">–</xref><xref rid=\"B23\" ref-type=\"bibr\">23</xref>). As understanding the mechanisms by which viruses enter susceptible host cells is integral to understanding the viral life cycle, recent studies on MNV entry have significantly advanced our understanding of norovirus biology (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref><xref ref-type=\"bibr\" rid=\"B25\">–</xref><xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Nevertheless, the modulation of norovirus entry factors and their mode of interaction with the viruses are still unclear, and it remains to be determined how these factors underlie norovirus host cell tropism.</p><p>The study of how genetically divergent hosts respond to viral infections can reveal the importance of host genetic factors, which may not be evident when using a single strain (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). With many cellular factors influencing norovirus infection, we asked if hosts from different genetic backgrounds might have different susceptibilities to MNV. Variation exists in the protein sequences of different mouse strains, and these polymorphisms can help elucidate the functions of certain proteins. Here, we show that bone marrow-derived macrophages (BMDMs) from two different mouse strains have dramatically different susceptibilities to MNV infection. We found that these different susceptibilities are primarily due to divergence in the CC′ loop domain of CD300LF, which is essential for its function as an MNV receptor (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Surprisingly, the CD300LF variant that cannot function as an MNV receptor in macrophage-like cells is able to bind MNV virions and is functional as an MNV receptor in different cell types. These data suggest the existence of cell type-specific modifiers of CD300LF-MNV interactions during viral entry.</p></sec><sec sec-type=\"results\" id=\"s2\"><title>RESULTS</title><sec id=\"s2.1\"><title>I/LnJ BMDMs resist MNV infection.</title><p>Inbred mouse strains show differences in innate susceptibility to viral infections (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). While examining the susceptibility of BMDMs from different mouse strains to MNV inoculation, we found that BMDMs derived from I/LnJ mice, which are resistant to mouse retroviruses (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>), were completely resistant to MNV infection. Such resistance is in strong contrast to the case for C57BL/6J BMDMs, which support robust MNV infection (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1A</xref>). To investigate the specificity of the resistance of I/LnJ BMDMs to MNV infection, we examined the replication of encephalomyocarditis virus (EMCV), as another virus with a positive-sense RNA genome, and of murine gammaherpesvirus 68 (MHV-68), as a virus with a DNA genome. In contrast to that of MNV, the replication of both EMCV and MHV-68 was supported in C57BL/6J and I/LnJ BMDMs with similar growth kinetics (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1B</xref> and <xref ref-type=\"fig\" rid=\"F1\">C</xref>). We also performed infection with a high multiplicity of infection (MOI) (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>) and did not detect any sign of MNV replication in I/LnJ BMDMs other than remaining input virus (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1D</xref>). These results suggest that the inability of I/LnJ BMDMs to support viral replication is specific to MNV.</p><fig id=\"F1\" orientation=\"portrait\" position=\"float\"><label>FIG 1</label><caption><p>I/LnJ BMDMs resist MNV infection. (A to C) Analysis of replication kinetics of MNV (A), EMCV (B), and MHV-68 (C) in C57BL/6J and I/LnJ BMDMs. Cells were inoculated at a multiplicity of infection (MOI) of 0.05 TCID<sub>50</sub>/cell and harvested at the indicated time points to determine the titers infectious viruses via TCID<sub>50</sub> assay in BV2 cells. All experiments were done in triplicates, and data are presented as mean ± standard error of the mean (SEM). (D) Analysis of MNV replication at 24 h postinfection in C57BL/6J and I/LnJ BMDMs after inoculation at MOIs of 0.05 and 5 TCID50/cell. The experiment was done twice, and data are presented as dots with mean as a bar. N.D., not detected.</p></caption><graphic xlink:href=\"JVI.00837-20-f0001\"/></fig><p>Different strains of MNV are known to bind glycoproteins differentially, which can lead to different levels of infection <italic>in vivo</italic> (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). To examine whether I/LnJ BMDMs are resistant to a broad range of MNV strains, we inoculated C57BL/6J and I/LnJ BMDMs with several strains of MNV and measured viral replication over 48 h. Specifically, in addition to the CW3 strain, we chose CW1, RVSS, and CR3. CW1 is a plaque isolate of MNV-1, RVSS is a mutant strain of CW1 with an altered glycoprotein binding profile, and CR3 is a field isolate with over 95% sequence identity with the capsid regions of CW1 and CW3 (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>). Similar to the result with strain CW3, I/LnJ BMDMs were resistant to infection with all MNV strains tested (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2A</xref> and <xref ref-type=\"fig\" rid=\"F2\">B</xref>). These data demonstrated that I/LnJ BMDMs are resistant to multiple MNV isolates, suggesting a general resistance of I/LnJ BMDMs to MNV infection.</p><fig id=\"F2\" orientation=\"portrait\" position=\"float\"><label>FIG 2</label><caption><p>I/LnJ BMDMs are resistant to different strains of MNV. Analysis of replication kinetics of MNV strains CR3, RVSS, CW1, and CW3 in C57BL/6J (A) or I/LnJ (B) BMDMs is shown. Cells were inoculated at an MOI of 0.05 TCID<sub>50</sub>/cell and harvested at the indicated time points to determine the titer of infectious viruses via TCID<sub>50</sub> assay in BV2 cells. All experiments were done in triplicates, and data are presented as mean ± SEM.</p></caption><graphic xlink:href=\"JVI.00837-20-f0002\"/></fig></sec><sec id=\"s2.2\"><title>I/LnJ BMDMs expressing C57BL/6J CD300LF are susceptible to MNV infection.</title><p>Recently, unbiased CRISPR/Cas9 screenings of host factors for MNV infection identified CD300LF as a host factor to mediate MNV entry into cells (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Given the requirement of CD300LF for MNV entry coupled with the complete resistance of I/LnJ BMDMs to MNV infection, we speculated that I/LnJ BMDMs might fail to express a functional CD300LF receptor required for MNV to infect cells. Therefore, we transduced BMDMs from C57BL/6J and I/LnJ mice with lentiviruses expressing either the C57BL/6J allele of CD300LF (B6/CD300LF) that was shown to mediate MNV entry (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>) or enhanced green fluorescent protein (EGFP) as a control. Expression of B6/CD300LF in C57BL/6J BMDMs did not significantly affect susceptibility of the cells to MNV infection. In contrast, I/LnJ BMDMs became susceptible to MNV infection upon expression of B6/CD300LF (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3A</xref> and <xref ref-type=\"fig\" rid=\"F3\">B</xref>). These data clearly demonstrated that the resistance of I/LnJ BMDMs to MNV infection was largely, if not completely, at the step of viral entry, suggesting a defect of I/LnJ BMDMs in expressing functional MNV receptor CD300LF.</p><fig id=\"F3\" orientation=\"portrait\" position=\"float\"><label>FIG 3</label><caption><p>I/LnJ BMDMs expressing C57BL/6J CD300LF are susceptible to MNV infection. (A) Comparison of MNV replication in C57BL/6J and I/LnJ BMDMs transduced with lentivirus expressing EGFP (control) or the C57BL/6J allele of CD300LF. Transduced cells were inoculated with MNV at an MOI of 0.05 TCID<sub>50</sub>/cell and harvested at 24 h postinfection to determine the titer of infectious viruses via TCID<sub>50</sub> assay in BV2 cells. All experiments were done in triplicates, and data are presented as mean ± SEM. N.D., not detected. (B) A representative Western blot of C57BL/6J and I/LnJ BMDMs transduced with lentivirus expressing EGFP (control) or the C57BL/6J allele of CD300LF as detected with goat anti-mouse CD300LF (R&D systems, AF2788). *, nonspecific signal in the blot. Actin is included as a loading control; <italic>n</italic> = 3 replicates.</p></caption><graphic xlink:href=\"JVI.00837-20-f0003\"/></fig></sec><sec id=\"s2.3\"><title>A cluster of four consecutive amino acids determines the functionality of CD300LF to support MNV infection.</title><p>The difference in susceptibility to MNV infection between I/LnJ and C57BL/6J BMDMs could reflect a simple difference in the cell surface expression of CD300LF or genetic variations within the protein itself. Unfortunately, none of the currently available antibodies for CD300LF could detect I/LnJ CD300LF, and we have been unable to detect surface expression of endogenous CD300LF in I/LnJ BMDMs. Thus, we examined publicly available sequences of murine CD300LF proteins to determine how the different alleles of CD300LF vary from each other and whether these differences could contribute to the functionality of the proteins. When we compared the sequences of CD300LF proteins from the C57BL/6J and I/LnJ mouse strains, we found that the C57BL/6J and I/LnJ variants of CD300LF differ by 14 amino acids, 9 of them in the regions important for function as a receptor for MNV (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). We hypothesized that the sequence difference in CD300LF proteins could underlie the inability of I/LnJ CD300LF to function as an MNV receptor in BMDMs.</p><p>To determine the amino acids that could potentially be responsible for this MNV-resistant phenotype of I/LnJ CD300LF, we sought a mouse strain that could serve as a “genetic intermediate” between C57BL/6J and I/LnJ. The CD300LF sequences of the CAST/EiJ and I/LnJ mouse strains differ by 11 amino acids; 7 of these 11 sites are identical between the CAST/EiJ and C57BL/6J CD300LF proteins, but the other 4 amino acids are not identical between the two strains (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4A</xref>). To test whether the CD300LF allele of CAST/EiJ mice could serve as a receptor for MNV, we assessed whether BMDMs from the CAST/EiJ mouse are susceptible to MNV infection. In contrast to those from the I/LnJ strain, BMDMs from the CAST/EiJ strain supported MNV replication (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4B</xref>). These data suggest that the seven amino acids that are identical in CAST/EiJ and C57BL/6J CD300LF but different in I/LnJ CD300LF might be responsible for the MNV-resistant phenotype of I/LnJ BMDMs.</p><fig id=\"F4\" orientation=\"portrait\" position=\"float\"><label>FIG 4</label><caption><p>Identification of seven amino acids determining the functionality of CD300LF as an MNV receptor. (A) Amino acid sequences for the CD300LF proteins of the indicated mouse strains at the indicated ranges. Boxed sequences denote the amino acids of CAST/EiJ CD300LF that are different from those of I/LnJ CD300LF but identical to those of C57BL6J CD300LF. (B) Comparison of MNV replication in C57BL/6J, I/LnJ, or Cast/EiJ BMDMS. Cells were inoculated with MNV at an MOI of 0.05 TCID<sub>50</sub>/cell and harvested at 24 h postinfection to determine the titer of infectious viruses via TCID<sub>50</sub> assay in BV2 cells. The experiment was done twice, and data are presented as dots with mean as a bar. N.D., not detected. (C) Comparison of MNV replication in BV2 cells. BV2 cells, BV2 cells with endogenous <italic>Cd300lf</italic> knocked out (BV2-KO), or BV2-KO cells transduced with lentiviruses expressing either C57BL/6J or I/LnJ CD300LF were inoculated with MNV at an MOI of 5 TCID<sub>50</sub>/cell and harvested at 24 h postinfection to determine the titer of infectious viruses via TCID<sub>50</sub> assay in BV2 cells. All experiments were done in triplicates, and data are presented as mean ± SEM. (D) Comparison of MNV replication in BV2-KO cells transduced with WT or 7aa swapped constructs. Cells were inoculated with MNV at an MOI of 5 TCID<sub>50</sub>/cell and harvested at 24 h postinfection to determine the titer of infectious viruses via TCID<sub>50</sub> assay in BV2 cells. All experiments were done in triplicates, and data are presented as mean ± SEM. N.D., not detected. (E) Analysis of surface expression of transduced CD300LF of the indicated background in BV2-KO cells. A representative image from three independent experiments is shown.</p></caption><graphic xlink:href=\"JVI.00837-20-f0004\"/></fig><p>To expedite the mapping process, we also examined whether the highly tractable macrophage-like BV2 cell line could mimic the phenotypic difference between C57BL/6J and I/LnJ BMDMs (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). We transduced <italic>Cd300lf</italic><sup>−/−</sup> BV2 (BV2-KO) cells with lentiviruses expressing the hemagglutinin (HA)-tagged CD300LF protein of either C57BL/6J or I/LnJ. While C57BL/6J CD300LF (B6/CD300LF) expression confers MNV infection capacity to BV2-KO cells, expression of I/LnJ CD300LF (IL/CD300LF) is insufficient to permit infection of BV-2KO cells (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4C</xref>), similar to the results obtained using BMDMs (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1</xref> and <xref ref-type=\"fig\" rid=\"F2\">2</xref>). To determine the role of the CAST/EiJ-identified seven amino acids in MNV infection, we expressed chimeric alleles of CD300LF: an otherwise wild-type (WT) C57BL/6J CD300LF with the seven amino acids of I/LnJ CD300LF at the CAST/EiJ-identified loci (B6/CD300LF-7aa) and the converse (IL/CD300LF-7aa). BV2-KO cells expressing B6/CD300LF-7aa had a significant reduction in their ability to support MNV replication (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4D</xref>). In contrast, BV2-KO cells expressing IL/CD300LF-7aa supported MNV replication like the BV2-KO cells with B6/CD300LF. All CD300LF proteins were expressed similarly on the cell surface (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4E</xref>). Collectively, these data suggested that the seven-amino-acid difference of the I/LnJ CD300LF from that of CD57BL/6J is responsible for the majority of the resistance of I/LnJ BMDMs to MNV infection.</p><p>We next set out to determine which combination of these seven amino acids is responsible for the resistance phenotype; we chose to prioritize six of the seven, as they are in the extracellular domain of CD300LF. According to software predictive of O-glycosylation, NetOGlyc (DTU Bioinformatics), two of the seven amino acid variations in B6/CD300LF had a high probability to be O-glycosylated. Since differential glycosylation status has been linked to the susceptibility of hosts to norovirus infection (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>), we examined these loci first. Expression of CD300LF mutants swapped at these potential glycosylation sites did not switch the phenotype of B6/CD300LF and IL/CD300LF in BV2-KO (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5A</xref>); that is, B6/CD300LF mutants with the I/LnJ amino acid sequence at the potential glycosylation sites (B6/CD300LF S174G and T131A) still made BV2-KO cells infectible by MNV, and expression of the corresponding I/LnJ CD300LF mutants (IL/CD300LF G174S and A131T) did not, despite similar levels of cell surface expression (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5B</xref>). The remaining difference in the extracellular domain of the CD300LF alleles was four consecutive amino acids at the CC′ loop, which flanks a phospholipid binding pocket along with the CDR3 domain and is critical for MNV infection (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>). When we swapped these four amino acids between C57BL/6J and I/LnJ CD300LF, the respective mutants, B6/CD300LF-4aa and IL/CD300LF-4aa, recapitulated the phenotype of the seven-amino-acid-swapped mutants in BV2-KO cells (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5C</xref>; phenotypes summarized in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Again, a similar level of cell surface expression was observed for all constructs (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5D</xref>). Taken together, these data demonstrated that the four-amino-acid polymorphisms at the CC′ loop of I/LnJ CD300LF is primarily responsible for its nonfunctionality as an MNV receptor in macrophage-like cells (<xref ref-type=\"fig\" rid=\"F6\">Fig. 6</xref>).</p><fig id=\"F5\" orientation=\"portrait\" position=\"float\"><label>FIG 5</label><caption><p>A cluster of four amino acids determines the functionality of CD300LF as an MNV receptor. (A and C) Analysis of MNV replication in BV2-KO cells transduced with the indicated mutant alleles of CD300LF. Cells were inoculated with MNV at an MOI of 5 TCID<sub>50</sub>/cell and harvested at 24 h postinfection to determine the titer of infectious viruses via TCID<sub>50</sub> assay in BV2 cells. All experiments were done in triplicates, and data are presented as mean ± SEM. N.D., not detected. (B and D) Analysis of surface expression of HA-tagged CD300LF mutants in the indicated cells. Data shown are representative plots from three independent experiments. Asterisks indicate significance compared to B6/CD300LF. ns, not significant.</p></caption><graphic xlink:href=\"JVI.00837-20-f0005\"/></fig><table-wrap id=\"T1\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>BV2 variant infection phenotypes<xref ref-type=\"table-fn\" rid=\"T1F1\"><sup><italic>a</italic></sup></xref></p></caption><graphic xlink:href=\"JVI.00837-20-t0001\"/><table-wrap-foot><fn fn-type=\"other\" id=\"T1F1\"><label>a</label><p>Resistant, no growth of MNV, as in I/LnJ BMDMs; attenuated, reduced growth of MNV compared to that in C57BL/6J BMDMs; susceptible: growth of MNV, as in C57BL/6J BMDMs.</p></fn></table-wrap-foot></table-wrap><fig id=\"F6\" orientation=\"portrait\" position=\"float\"><label>FIG 6</label><caption><p>Residues critical for MNV infection occur in the CC′ loop flanking the phospholipid binding pocket of CD300LF. (A) Model showing the orientation of the MNV major capsid protein (VP1) P2 subdomain relative to CD300LF in complex with bound calcium ion. Positions that differ in sequence between B6/CD300LF and IL/CD300LF are colored white or cyan on the B6/CD300LF surface. (B) Close-up of the CD300LF-P domain complex showing the side chain of Asn364 from the P domain DE loop binding to CD300LF in a pocket made by the CC′ loop. The carbonyl oxygen of Asn364 coordinates with a calcium ion at the binding interface. A second calcium ion supports the P domain DE loop, holding it against the binding pocket, by bridging Asp410 and Asp366. The four residues in the CC' loop that differ between B6/CD300LF and IL/CD300LF are labeled with residue numbers in black boxes, and their side chain carbon atoms are colored in white. The area contributed by these residues is in white on the solvent-accessible surface. Most of the hydrophobic pocket shown to bind PC choline is contributed by the CD300LF residues Q68 and R69. Binding of the viral P2 domain to CD300LF is metal ion dependent and requires the DE loop of P2 to associate with the FG and CC' loops of CD300LF through a network of metal ion coordination and hydrogen bonding centered on Asn364. Substitution of the four critical residues in the CC' loop (VPQR to AYWK) almost certainly perturbs conformation of the CC' loop, disrupting the receptor binding interface. The four consecutive amino acids in the CC' loop correspond to positions 39 to 42 in the B6/CD300LF crystal structure (PDB accession no. 6E48).</p></caption><graphic xlink:href=\"JVI.00837-20-f0006\"/></fig></sec><sec id=\"s2.4\"><title>I/LnJ CD300LF can function as an MNV receptor in a cell type-dependent manner.</title><p>Expression of C57BL/6J CD300LF in cells noninfectible by MNV makes the cells permissive to MNV infection, demonstrating the entry step as a major barrier for MNV infection (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). We examined whether I/LnJ CD300LF functions similarly in cell lines other than macrophage-like cells. Thus, we stably expressed C57BL/6J or I/LnJ CD300LF via lentiviral transduction in a variety of cell types that do not express murine CD300LF and thus are not susceptible to MNV infection, including mouse embryonic fibroblasts (MEFs) and human cell lines 293T, HeLa, BJAB, and Jurkat. CD300LF was expressed on the cell surface of all transduced cells (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7</xref>). Strikingly, while I/LnJ CD300LF did not function as an entry factor for MNV infection in BMDMs and BV2 cells, its expression in MEFs and in 293T, HeLa, and BJAB cells made the cells permissive for MNV replication (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7A</xref> to <xref ref-type=\"fig\" rid=\"F7\">D</xref>). In human Jurkat T cells, however, only the expression of C57BL/6J CD300LF, and not that of I/LnJ CD300LF, made the cells susceptible to MNV infection, reminiscent of the macrophage-like phenotype (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7E</xref>). Taking the results together, while C57BL/6J CD300LF expression is necessary and sufficient for MNV infection in all contexts tested, the cell type-dependent functionality of I/LnJ CD300LF suggests the necessity of additional cell type-specific modifiers mediating MNV entry.</p><fig id=\"F7\" orientation=\"portrait\" position=\"float\"><label>FIG 7</label><caption><p>I/LnJ CD300LF can function as an MNV receptor in a cell type-dependent manner. (Left) Analysis of MNV replication in MEF (A), 293T (B), HeLa (C), BJAB (D), and Jurkat (E) cell lines transduced with the indicated allele of CD300LF. Cells were inoculated with MNV at an MOI of 5 (A, B, and C) or 0.1 (D and E) TCID<sub>50</sub>/cell and harvested at 24 h postinfection to determine the titer of infectious viruses via TCID<sub>50</sub> assay in BV2 cells. Untransduced cells were included as a control. All experiments were done in triplicates, and data are presented as mean ± SEM. N.D., not detected. (Right) Analysis of surface expression of HA-tagged CD300LF alleles in the indicated cells. A representative plot from three independent experiments is shown.</p></caption><graphic xlink:href=\"JVI.00837-20-f0007\"/></fig><p>Binding of CD300LF to MNV capsid proteins and its role in MNV attachment to host cells have been established (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). To investigate the cell type-dependent functionality of I/LnJ CD300LF, we performed binding assays to determine how different CD300LF expression affects the attachment of MNV to cells, as described previously (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). BV2 and BJAB cells were chosen for this assay, as they produced the highest viral titer in infection experiments yet showed varied ability to support replication when expressing IL/CD300LF (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7</xref>). MNV bound to BV2-KO cells expressing B6/CD300LF significantly more than to the BV2-KO cells; in contrast, MNV binding to BV2-KO cells expressing IL/CD300LF was comparable to its binding to BV2-KO cells (<xref ref-type=\"fig\" rid=\"F8\">Fig. 8A</xref>). These data suggest that B6/CD300LF mediated the binding between MNV and the BV2-KO cells that but IL/CD300LF could not, which is consistent with the nonfunctionality of IL/CD300LF as an MNV receptor in BV2-KO cells (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4</xref>). On the contrary, MNV binding to BJAB cells expressing IL/CD300LF was significantly greater than its binding to WT BJABs, although less than its binding to BJAB cells expressing B6/CD300LF (<xref ref-type=\"fig\" rid=\"F8\">Fig. 8B</xref>). The result was in line with the MNV replication of the BJAB cells expressing CD300LF proteins (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7D</xref>). Collectively, these data suggest that different binding of IL/CD300LF to MNV virions in different cell types might be responsible for the cell type-dependent functionality of I/LnJ CD300LF.</p><fig id=\"F8\" orientation=\"portrait\" position=\"float\"><label>FIG 8</label><caption><p>I/LnJ CD300LF supports MNV attachment in a cell type-dependent manner. Analysis of MNV binding to BV2 cells (A) or BJAB cells (B) expressing the indicated allele of CD300LF is shown. Cells were inoculated at 4°C at an MOI of 2 TCID<sub>50</sub>/cell. At 1 h postinfection, cells were washed 3 times with ice-cold PBS to remove unbound virus. Cells were lysed with 1 ml TRI-Reagent (Sigma), and genome equivalents were determined via qPCR. All experiments were done in triplicate, and results are presented as mean ± SEM. Bars represent the ratio of MNV genomes to actin compared to mean KO (BV2) or WT (BJAB) results.</p></caption><graphic xlink:href=\"JVI.00837-20-f0008\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s3\"><title>DISCUSSION</title><p>Previous studies have demonstrated that expression of murine CD300LF is necessary and sufficient for MNV infection; indeed, deletion of <italic>Cd300lf</italic> in C57BL/6J mice or in BV2 cells, a microglial cell line derived from C57BL/6 mice, makes them resistant to MNV infection, and expression of CD300LF in HeLa cells confers MNV susceptibility to human cells (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Here, we show that BMDMs from the I/LnJ mouse strain are resistant to MNV infection while susceptible to a range of other viruses and that this resistance is due to polymorphisms in the extracellular domain of CD300LF in the I/LnJ mouse strain. Published amino acid sequences of CD300LF indicate significant variation among different mouse strains, including in regions important for MNV attachment and entry (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>). We identified a series of four amino acids at the CC′ loop which swapped the MNV susceptibility phenotype when exchanged between C57BL/6J and I/LnJ CD300LF. Surprisingly, I/LnJ CD300LF could function as an MNV entry factor when expressed in MEFs and in 293T, HeLa, and BJAB cells, although it could not do so when expressed in BV2 CD300lf-KO or Jurkat T-cell lines. Collectively, our data suggest that cell type-specific factors can modulate utilization of I/LnJ CD300LF in MNV entry.</p><p>Public sequence data for various mouse strains indicate that CD300LF proteins broadly segregate into two distinct groups: C57BL/6J-like, such as BALB/cJ and FVB/NJ, and I/LnJ-like, such as C3H/HeJ and CBA/J (aligned in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). Comparatively fewer strains express an intermediate CD300LF like CAST/EiJ. Since the origin and lineage history of inbred mouse strains are not well documented (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>), it is difficult to speculate on whether this clustering traces back to an evolutionary pressure in nature or a single divergence point. Evasion of MNV infection might be a selective pressure to drive the divergence of CD300LF proteins. However, as MNV infection in immunocompetent mice is not lethal (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>), it is possible that MNV infection would not have exerted a selective pressure strong enough to cause this kind of divergence. Furthermore, considering that there are many cell types known to be infected by MNV <italic>in vivo</italic> (e.g., macrophages, dendritic cells, tuft cells, B cells, and T cells) (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>) and that I/LnJ CD300LF could function as an MNV receptor in a cell type-specific manner, it is likely that even the mouse strains containing an I/LnJ-like <italic>Cd300lf</italic> allele are susceptible to MNV infection <italic>in vivo</italic>. Indeed, the C3H/HeJ mouse strain has a CD300LF identical to that of the I/LnJ strain, and it was previously shown to be infected with MNV (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Our preliminary data also suggest that I/LnJ mice are infectible with MNV, even though I/LnJ BMDMs are not infectible with MNV (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1</xref> and <xref ref-type=\"fig\" rid=\"F2\">2</xref>), potentially corresponding with a change in tissue tropism (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Based on the data shown in <xref ref-type=\"fig\" rid=\"F7\">Fig. 7</xref>, we would predict that I/LnJ B cells are susceptible to MNV infection. Therefore, substantive discussion on the natural variation of CD300LF, including its potential causes and consequences, would require a robust understanding of its function outside the context of infection.</p><table-wrap id=\"T2\" orientation=\"portrait\" position=\"float\"><label>TABLE 2</label><caption><p>Alignment of CD300LF coding sequences<xref ref-type=\"table-fn\" rid=\"T2F1\"><sup><italic>a</italic></sup></xref></p></caption><graphic xlink:href=\"JVI.00837-20-t0002\"/><table-wrap-foot><fn fn-type=\"other\" id=\"T2F1\"><label>a</label><p>Available protein sequences for CD300LF in the indicated mouse line, grouped based on their similarity to the sequence of C57BL/6J or I/LnJ CD300LF. Asterisks indicate an amino acid that is common among all sequences.</p></fn></table-wrap-foot></table-wrap><p>Although a significant amount of work has been done tracing the evolutionary lineage of the CD300 family, the exact ligands, specific function, and redundancy of function between members is not well understood (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). It is also unclear whether the 4-amino-acid difference in the CC′ loop between mouse strains impacts the cellular functions of CD300LF. It is useful to note that in each of our experiments in which the “4aa” exchange was made (i.e., 7aa and 4aa experiments), the C57BL/6J background-containing allele showed a severe attenuation in ability to confer infectivity, rather than a complete loss of function. This suggests that while the CC′ loop contributes substantially to conferment of MNV susceptibility, it is likely that other sections of the CD300LF protein that differ between the C57BL/6J and I/LnJ alleles also contribute. Further study on the structure and function of CD300LF and CD300 family members in general is warranted.</p><p>The most surprising finding in our study is the functionality of I/LnJ CD300LF as an MNV entry factor in a cell type-dependent manner. We consider the following three possibilities for this cell type-specific functionality. First, there may be differential binding of ligands. Other groups have posited that the binding pocket flanked by the CC′ and CDR3 loops may function to augment MNV attachment to CD300LF through the binding of a soluble factor(s), including ceramide (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>). Given the proximity of the 4 amino acids to the lipid binding CC′ loop, the polymorphism could impact the ligand binding capacity of CD300LF, such that the CD300LF-localized concentration of that ligand or local cell surface density could differ between cell types and consequently affect functionality for MNV entry. Second, there may be cell type-specific modulators of MNV entry. Positive modifiers of MNV entry would likely be cell type-specific coreceptors. A classic example is HIV entry, in which the virus requires both its receptor, CD4, and a coreceptor in order to successfully enter a host cell (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). These coreceptors, CXCR4 and CCR5, contribute substantially to the host cell tropism of different strains of HIV (<xref rid=\"B39\" ref-type=\"bibr\">39</xref><xref ref-type=\"bibr\" rid=\"B40\">–</xref><xref rid=\"B45\" ref-type=\"bibr\">45</xref>). For MNV, the I/LnJ CD300LF might be unable to interact with the macrophage-/T-cell-specific cofactor, while maintaining its interaction with alternative coreceptors expressed in other cell types. Lastly, there may be cell type-specific inhibitors of MNV entry which can interact with I/LnJ CD300LF but not with C57BL/6J CD300LF. A recent study into critical factors for entry of MNV into macrophages and dendritic cells identified several proteins, including CD98 and CD36, that influence attachment and could potentially fulfill such a role (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). All these three possibilities are plausible, and future studies will investigate these possibilities and attempt to identify this modulatory factor(s).</p></sec><sec sec-type=\"materials|methods\" id=\"s4\"><title>MATERIALS AND METHODS</title><sec id=\"s4.1\"><title>Cloning.</title><p>CD300LF cDNAs of mouse strains C57BL/6J and I/LnJ were synthesized based on public sequence databases (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ensembl.org\">http://www.ensembl.org</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.informatics.jax.org\">http://www.informatics.jax.org</ext-link>) and cloned into pCDH-MCS-T2A-copGFP-MSCV (System Biosciences, CD523A-1) and pLenti-CMV-Puro-DEST (w118-1; Addgene plasmid number 17452). To add an HA-epitope at the N terminus of the CD300LF protein after cleavage of the signal peptide, each <italic>Cd300lf</italic> sequence was modified as follows: C57BL/6J, …ACGGCTtacccatacgatgttccagattacgctGAGGAT… (…TAypydvpdyaED…); I/LnJ, …ACGGCTtacccatacgatgttccagattacgctCAGGAT… (…TAypydvpdyaQD…). Lowercase letters indicate the nucleotide and amino acid sequences of inserted HA tag. Specific amino acid swapping mutants (B6/CD300LF-7aa, IL/CD300LF-7aa, B6/CD300LF-4aa, and IL/CD300LF-4aa) were also synthesized and cloned into pLenti-CMV-Puro-DEST (w118-1). The swapping mutants of potential glycosylation sites (B6/CD300LF S174G, B6/CD300LF T131A, IL/CD300LF G174S, and IL/CD300LF A131T) were generated using the QuikChange XL site-directed mutagenesis kit (Agilent) according to the manufacturer’s instructions. The mutated sequences are as follows: C57BL/6J CD300LF-7aa mutant, …CAAGGAGTTCCTCAGAGATCATGT… → …CAAGGAGcTtaTtgGAaATCATGT… (…QGVPQRSC… > …QGaywkSC…), …AAAGTTACTGTGAAC… → …AAAGTTgCTGTGAAC… (…KVTVN… → …KVaVN…), …CTGACTAGCTACTAC… → …CTGACTgGCTACTAC… (…LTSYY… → …LTgYY…), and …GCCATGCCT → …GCCATGcaT (…AMP → …AMh); I/LnJ C300LF-7aa mutant, …CGAGGAGCTTATTGGAAATCATGT… → …CGAGGAGtTccTcaGAgATCATGT… (…RGAYWKSC… → …RGvpqrSC…), …AAAGTTGCTGTGAAC… → …AAAGTTaCTGTGAAC… (…KVAVN… → …KVtVN…), …CTGACCGGCTACTAC… → …CTGACCtcCTACTAC… (…LTGYY… → …LTsYY…), and …GCCATGCAT → …GCCATGCcT (…AMH → …AMp). Lowercase letters indicate the nucleotide and amino acid sequences of the mutants. The sequences of all constructs were checked and confirmed upon cloning via sequencing.</p></sec><sec id=\"s4.2\"><title>Cells.</title><p>BMDMs were prepared as described previously (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>) from mice provided by Tatyana Golovkina at The University of Chicago. Briefly, bone marrow was isolated from femurs and tibias of 6- to 8-week-old mice and plated in non-tissue culture-treated dishes in 10 ml of BMDM medium. Cells were supplemented with fresh medium on day 4 and seeded for experiments on day 7. Wild-type (WT) and <italic>Cd300lf</italic><sup>−/−</sup> BV2 cells, mouse embryonic fibroblasts (MEFs), and 293T cells were provided by Herbert “Skip” Virgin (Washington University, St. Louis, MO). HeLa cells were purchased from ATCC. We obtained Jurkat (JRT3.5) cells from Erin Adams (The University of Chicago) and BJAB cells from Stephanie Karst (University of Florida).</p></sec><sec id=\"s4.3\"><title>Viruses.</title><p>MNV-1.CW3 (herein referred to as CW3) was produced by transfection of 1 × 10<sup>6</sup> 293T cells with 2.5 μg of a cDNA clone containing the genome of CW3 (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). Cells were then incubated at 37°C for 48 h, frozen at –80°C, thawed, and passed through a 0.45-μm filter. Five hundred microliters of the resulting filtered virus was used to infect 1 × 10<sup>6</sup> WT BV2 cells. Inoculated cells were incubated until ∼90% cytopathic effect (CPE) was observed. Two 15-cm tissue culture-treated plates were then seeded with 1.5 × 10<sup>7</sup> BV2 cells, then inoculated with 50 μl of virus, and incubated at 37°C until ∼90% CPE was observed. These cells were frozen at –80°C and thawed. Cell lysates were pooled and centrifuged at 3,000 rpm for 20 min to remove cell debris. The supernatants were ultracentrifuged at 123,918.9 × <italic>g</italic> for 3 h in order to concentrate virus. The resulting pellet was resuspended in Dulbecco modified Eagle medium (DMEM) supplemented to contain 10 mM HEPES (Mediatech, 25-060-CI), 1× MEM nonessential amino acids (Mediatech, 25-025-CI), 100 U/ml each of penicillin and streptomycin (Mediatech, 30-002-CI), and 10% fetal bovine serum (Biowest, US1520) and frozen at –80°C until use. Passage 2 stocks of MNV strains MNV-1.RVSS (RVSS) and MNV-1.CW1 (CW1) and passage 5 stocks of MNV strain GV/CR3/2005/USA (CR3) were used for infection as described for <xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref> (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>). For each passage, inoculated cells were incubated at 37°C for near-complete cell death and frozen at –80°C. Thawed cell lysates were clarified by centrifugation, and supernatant was used to inoculate new cells for amplification. Encephalomyocarditis virus (EMCV) and murine gammaherpesvirus 68 (MHV-68) were provided by Marco Colonna and Herbert “Skip” Virgin at Washington University (St. Louis, MO), respectively. EMCV and MHV-68 viral stocks were further passaged and amplified in L929 and MEF cells, respectively.</p></sec><sec id=\"s4.4\"><title>Viral infection.</title><p>All infections were performed as described previously (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Briefly, 1 × 10<sup>5</sup> cells were seeded per well in 24-well tissue culture-treated plates. At 24 h after seeding, either the medium was replaced with virus-containing inoculum (adherent cell lines) or a concentrated sample of virus was added to the medium (nonadherent cells) at the indicated MOI. Adherent cells were incubated in virus-containing inoculum for 30 min, washed twice with phosphate-buffered saline (PBS), and replenished with fresh medium. Nonadherent cells were inoculated with 50 μl of concentrated viral stock and mixed by gentle pipetting to achieve the indicated MOI. Cells were then washed and pelleted twice to remove inoculum, and infected cells were harvested at the indicated time points by freezing at –80°C for median tissue culture infectious dose (TCID<sub>50</sub>) analysis.</p></sec><sec id=\"s4.5\"><title>TCID<sub>50</sub> assay.</title><p>TCID<sub>50</sub> assays were performed as reported previously (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Briefly, inoculated cells were frozen at –80°C to lyse the cells via a cycle of freeze and thaw. Lysates were serially diluted 10-fold in cell growth medium. The samples were then added to BV2 cells seeded in a 96-well plate. Eight wells of cells were inoculated per dilution and incubated for 5 days. The TCID<sub>50</sub> was calculated by determining the dilution required to show cytopathic effect in 4 out of 8 wells, according to a standard protocol (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Briefly, the following formula was used to calculate proportionate distance (−PD): [(% positive at or above 50%) – 50%]/[(% positive at or above 50%) – (% positive below 50%)]. The PD was then used to calculate log TCID<sub>50</sub> using the formula (log dilution at or above 50%) + (−PD). This log value was then used to express the virus titer as TCID<sub>50</sub>/unit volume, e.g., TCID<sub>50</sub>/ml.</p></sec><sec id=\"s4.6\"><title>Western blotting.</title><p>Western blotting was performed as described previously (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). In short, total cell lysates were harvested in protein sample buffer (0.1 M Tris [pH 6.8], 4% SDS, 4 mM EDTA, 286 mM 2-mercaptoethanol, 3.2 M glycerol, 0.05% bromophenol blue), and proteins were resolved by SDS-PAGE. Proteins were then transferred onto polyvinylidene difluoride (PVDF) membranes, probed with primary antibody diluted in PBS–0.1% Tween 20 (PBST) containing 5% skim milk overnight at 4°C, and then stained with secondary antibody diluted in PBST with 5% skim milk for 1 h at room temperature (RT). Probed proteins were detected using ECL reagents on a ChemiDoc system with Image Lab software (Bio-Rad). The following antibodies were used: mouse anti-HA (Frank W. Fitch Monoclonal Antibody Facility, The University of Chicago, clone 12CA5), goat anti-mouse CD300LF (R&D systems, AF2788), horseradish peroxidase (HRP)-conjugated mouse anti-beta-actin (Santa Cruz Biotechnology, sc-47778), and HRP-conjugated goat anti-mouse (BioLegend, number 405306).</p></sec><sec id=\"s4.7\"><title>Flow cytometry.</title><p>To examine the cell surface expression of HA-tagged CD300LF mutants, cells (1 × 10<sup>6</sup> cells/well in 6-well plates) were gently detached by a 5-min incubation with 600 μl of 0.5 mM EDTA and gentle scraping. At this point, cells were centrifuged for 5 min at 1,000 rpm. Cells were then washed in PBS containing 2% fetal bovine serum (FBS) and 1 mM EDTA. Cells were resuspended in wash buffer containing 1% FcR blocker, stained with mouse anti-HA antibody on ice for 30 min, washed 5 times, stained with donkey anti-mouse antibody conjugated with Alexa Fluor 647 for 30 min at RT, and washed again 5 times. Cells were then immediately analyzed with a BD LSRFortessa cell analyzer. Untransduced, unstained samples stained only with primary antibody, samples stained only with secondary antibody, and samples incubated in primary isotype control (mouse IgG2b k isotype; BioLegend) antibodies were used as controls.</p></sec><sec id=\"s4.8\"><title>Lentiviral transduction.</title><p>A modified version of lentiviral vector pCDH-MCS-T2A-copGFP-MSCV was used to express CD300LF alleles in C57BL/6J and I/LnJ BMDMs, as described previously (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Lentivirus was generated by transfecting lentiviral vectors with a packaging vector (psPAX2) and a pseudotyping vector (pCMV-MD2.G) into 293T cells via a standard calcium phosphate method. A third-generation lentiviral vector, pLenti-CMV-Puro-DEST (w118-1), was used to express HA-tagged CD300LF alleles in all immortalized cell lines used. Lentivirus was generated by transfecting lentiviral vectors with HIV gag/pol (pMDLg/pRRE), rev (pRSV-rev), and pseudotyping vector (pCMV-MD2.G). Supernatants were collected at 24 and 48 h posttransfection, filtered through a 0.45-μm filter (Millipore), and added to the indicated cells. After 48 h, cells were selected with puromycin (2 μg/ml for HeLa cells and 3 μg/ml for all non-HeLa cells).</p></sec><sec id=\"s4.9\"><title>Binding assays.</title><p>Binding experiments were performed in accordance with previously published assays (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Briefly, 1 × 10<sup>5</sup> cells were seeded in 12-well plates, and 24 h later, the cells were inoculated with MNV (CW3) at an MOI of 2 TCID<sub>50</sub>/cell and incubated at 4°C for 1 h with gentle rocking. Cells were then washed three times with ice-cold PBS to remove unbound virus. BJAB cells were pelleted between washes via centrifugation. Cells were then lysed with 1 ml TRI-Reagent (Sigma), and RNA was extracted according to the manufacturer’s instructions. Genome equivalents were determined via quantitative PCR (qPCR) as described previously (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>).</p></sec><sec id=\"s4.10\"><title>Statistical Analysis.</title><p>All statistical analyses were performed in GraphPad Prism using two-tailed, unpaired <italic>t</italic> tests. All differences not specifically indicated as significant were not significant (<italic>P</italic> > 0.05). Significant values are indicated as follows *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001; ****, <italic>P</italic> < 0.0001.</p></sec></sec></body><back><ack><title>ACKNOWLEDGMENTS</title><p>We thank Tatyana Golovkina and Kelly O’Grady for providing mice for BMDM analysis, providing key reagents (including antibody), and cloning and analyzing <italic>Cd300lf</italic> transcripts from C57BL/6J and I/LnJ mice.</p><p>K.F. and S.B.B. were supported (in part) by NIH grant T32 GM007183. G.R. and S.H. are supported by grant R01AI127518. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">J Virol</journal-id><journal-id journal-id-type=\"iso-abbrev\">J. Virol</journal-id><journal-id journal-id-type=\"hwp\">jvi</journal-id><journal-id journal-id-type=\"pmc\">jvi</journal-id><journal-id journal-id-type=\"publisher-id\">JVI</journal-id><journal-title-group><journal-title>Journal of Virology</journal-title></journal-title-group><issn pub-type=\"ppub\">0022-538X</issn><issn pub-type=\"epub\">1098-5514</issn><publisher><publisher-name>American Society for Microbiology</publisher-name><publisher-loc>1752 N St., N.W., Washington, DC</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32591466</article-id><article-id pub-id-type=\"pmc\">PMC7431783</article-id><article-id pub-id-type=\"publisher-id\">01083-20</article-id><article-id pub-id-type=\"doi\">10.1128/JVI.01083-20</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Minireview</subject></subj-group></article-categories><title-group><article-title>COVID-19 Vaccines: “Warp Speed” Needs Mind Melds, Not Warped Minds</article-title><alt-title alt-title-type=\"running-head\">Minireview</alt-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-9902-6096</contrib-id><name><surname>Moore</surname><given-names>John P.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-8222-278X</contrib-id><name><surname>Klasse</surname><given-names>P. J.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><aff id=\"aff1\"><label>a</label><addr-line>Department of Microbiology and Immunology, Weill Cornell Medical College, New York, New York, USA</addr-line></aff></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Silvestri</surname><given-names>Guido</given-names></name><role>Editor</role><aff>Emory University</aff></contrib></contrib-group><author-notes><corresp id=\"cor1\">Address correspondence to John P. Moore, <email>jpm2003@med.cornell.edu</email>, or P. J. Klasse, <email>pek2003@med.cornell.edu</email>.</corresp><fn fn-type=\"other\"><p><bold>Citation</bold> Moore JP, Klasse PJ. 2020. COVID-19 vaccines: “Warp Speed” needs mind melds, not warped minds. J Virol 94:e01083-20. <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.1128/JVI.01083-20\">https://doi.org/10.1128/JVI.01083-20</ext-link>.</p></fn></author-notes><pub-date pub-type=\"epreprint\"><day>26</day><month>6</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>9</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>94</volume><issue>17</issue><elocation-id>e01083-20</elocation-id><permissions><copyright-statement>Copyright © 2020 Moore and Klasse.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Moore and Klasse</copyright-holder><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International license</ext-link>.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"JVI.01083-20.pdf\"/><abstract abstract-type=\"precis\"><p>In this review, we address issues that relate to the rapid “Warp Speed” development of vaccines to counter the COVID-19 pandemic. We review the antibody response that is triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of humans and how it may inform vaccine research. The isolation and properties of neutralizing monoclonal antibodies from COVID-19 patients provide additional information on what vaccines should try to elicit. The nature and longevity of the antibody response to coronaviruses are relevant to the potency and duration of vaccine-induced immunity.</p></abstract><abstract><title>ABSTRACT</title><p>In this review, we address issues that relate to the rapid “Warp Speed” development of vaccines to counter the COVID-19 pandemic. We review the antibody response that is triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection of humans and how it may inform vaccine research. The isolation and properties of neutralizing monoclonal antibodies from COVID-19 patients provide additional information on what vaccines should try to elicit. The nature and longevity of the antibody response to coronaviruses are relevant to the potency and duration of vaccine-induced immunity. We summarize the immunogenicity of leading vaccine candidates tested to date in animals and humans and discuss the outcome and interpretation of virus challenge experiments in animals. By far the most immunogenic vaccine candidates for antibody responses are recombinant proteins, which were not included in the initial wave of Warp Speed immunogens. A substantial concern for SARS-CoV-2 vaccines is adverse events, which we review by considering what was seen in studies of SARS-CoV-1 and Middle East respiratory syndrome coronavirus (MERS-CoV) vaccines. We conclude by outlining the possible outcomes of the Warp Speed vaccine program, which range from the hoped-for rapid success to a catastrophic adverse influence on vaccine uptake generally.</p></abstract><kwd-group><title>KEYWORDS</title><kwd>SARS-CoV-2</kwd><kwd>S-protein</kwd><kwd>RBD</kwd><kwd>COVID-19</kwd><kwd>neutralizing antibodies</kwd><kwd>serology</kwd><kwd>vaccines</kwd><kwd>animal models</kwd><kwd>Warp Speed</kwd></kwd-group><funding-group><award-group id=\"award1\"><funding-source><institution-wrap><institution>HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID)</institution><institution-id>https://doi.org/10.13039/100000060</institution-id></institution-wrap></funding-source><award-id>P01 AI110657</award-id><award-id>R01 AI36082</award-id><principal-award-recipient><name><surname>Klasse</surname><given-names>P. J.</given-names></name></principal-award-recipient><principal-award-recipient><name><surname>Moore</surname><given-names>John P.</given-names></name></principal-award-recipient></award-group><award-group id=\"award2\"><funding-source><institution-wrap><institution>Bill and Melinda Gates Foundation (BMGF)</institution><institution-id>https://doi.org/10.13039/100000865</institution-id></institution-wrap></funding-source><award-id>OPP1132237</award-id><award-id>INV-002022</award-id><principal-award-recipient><name><surname>Klasse</surname><given-names>P. J.</given-names></name></principal-award-recipient><principal-award-recipient><name><surname>Moore</surname><given-names>John P.</given-names></name></principal-award-recipient></award-group></funding-group><counts><fig-count count=\"3\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"175\"/><page-count count=\"32\"/><word-count count=\"26027\"/></counts><custom-meta-group><custom-meta><meta-name>cover-date</meta-name><meta-value>September 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>INTRODUCTION</title><p>An effective vaccine is the best long-term solution to the COVID-19 pandemic. Worldwide, governments are responding by investing in the research, testing, production, and distribution programs required to make a vaccine and are doing so with highly aggressive timelines (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/who-documents-detail/draft-landscape-of-covid-19-candidate-vaccines\">https://www.who.int/who-documents-detail/draft-landscape-of-covid-19-candidate-vaccines</ext-link>). In the United States, the words “Warp Speed” were adopted by politicians to indicate the urgency of the need for a vaccine. If a vaccine is proven effective even as early as the first few months of 2021, then the accomplishment will have sliced many years off the usual timeline for vaccine development. The need for speed is understandable, but it comes with risks: specifically, the vaccine candidates that are most capable of rapid production on the required massive scale may not be the most effective, and there are concerns that immune responses to COVID-19 vaccines could enhance infection or exacerbate disease in individuals who become infected despite vaccination. These topics have been raised in multiple perspectives and reviews in recent weeks (<xref rid=\"B1\" ref-type=\"bibr\">1</xref><xref ref-type=\"bibr\" rid=\"B2\">–</xref><xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Here, we attempt to address key subjects in greater detail, with an emphasis on quantitative aspects of antibody-based immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The literature on COVID-19 expands daily, so a review like this is out-of-date the moment both fingers cease tapping the keyboard. We have relied not just on peer-reviewed publications but also on manuscripts deposited on preprint servers, in the full knowledge that some information in some of those reports may be inaccurate. Accordingly, we urge readers to inspect key papers themselves, particularly in their final peer-reviewed forms. We also recommend using an additional resource for judging some preprints (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). We regret that we have, no doubt, overlooked key papers among the daily torrent.</p><p>Most of the COVID-19 vaccines in development are intended to induce antibody responses that neutralize SARS-CoV-2, thereby preventing it from entering target cells and infecting the host. In some cases, the vaccines may also induce antibody and/or cellular immune responses that can kill and eliminate already infected cells, thereby limiting the replication of the virus within a transiently infected host. Nonetheless, most emphasis is being placed on the induction of virus-neutralizing antibodies (NAbs) directed against the SARS-CoV-2 spike (S) protein (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1</xref>). The immunogens used to elicit NAbs are various forms of the S-protein, including the isolated receptor-binding domain (RBD) (<xref rid=\"B14\" ref-type=\"bibr\">14</xref><xref ref-type=\"bibr\" rid=\"B15\">–</xref><xref rid=\"B18\" ref-type=\"bibr\">18</xref>). The S-proteins can be expressed <italic>in vivo</italic> from DNA or mRNA constructs or by recombinant virus vectors such as adenovirus or vaccinia. Alternatively, they can be directly delivered as recombinant proteins with or without an adjuvant or as a constituent of a killed virus vaccine (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). All of these methods, and more, are included in the hundreds of vaccine programs now at the preclinical and animal model stages, and they are represented among the now very-high-profile programs being ramped up in different countries (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/who-documents-detail/draft-landscape-of-covid-19-candidate-vaccines\">https://www.who.int/who-documents-detail/draft-landscape-of-covid-19-candidate-vaccines</ext-link>). Some nations are working together in consortia, although the United States appears to be adopting a go-it-alone policy (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). The American “Warp Speed” program was reported to have been narrowed down to five front-line candidates that are based on mRNA (Moderna, Pfizer) or adenovirus and other viral vectors (Oxford University/AstraZeneca, Janssen, and Merck) (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). However, decisions in this area change rapidly, and a U.S. government website should be consulted for updated information (<ext-link ext-link-type=\"uri\" xlink:href=\"https://medicalcountermeasures.gov/app/barda/coronavirus/COVID19.aspx\">https://medicalcountermeasures.gov/app/barda/coronavirus/COVID19.aspx</ext-link>). One S-protein-based vaccine, made in insect cells by Protein Sciences/Sanofi, is listed.</p><fig id=\"F1\" orientation=\"portrait\" position=\"float\"><label>FIG 1</label><caption><p>The SARS-CoV-2 S-protein. (A) Schematic of the S-protein showing the S1 and S2 domains and the RBD. The soluble S-protein ends at the engineered truncation point. The areas colored in gray indicate the transmembrane and intracytoplasmic domains that are present in the full-length S-protein on virions. The most commonly used immunogens are the soluble S-protein, the S1 domain, and the RBD, although some nucleic acid and viral vector constructs are based on the full-length S-protein. (B) Structure-based representation of the S-protein trimer viewed from above and the side, as indicated. The protein surface is in gray, with the ACE2 binding site on the RBD highlighted in aquamarine. On one protomer, the RBD is shown in the “up” position, while on the other two it is in the “down” position, as indicated. Glycans are colored according to the scale, based on their oligomannose content. Adapted from reference <xref rid=\"B77\" ref-type=\"bibr\">77</xref> under a CC BY 4.0 license.</p></caption><graphic xlink:href=\"JVI.01083-20-f0001\"/></fig><table-wrap id=\"T1\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>Categories of vaccines for protection against SARS-CoV-2 infection and/or disease<xref ref-type=\"table-fn\" rid=\"T1F1\"><sup><italic>a</italic></sup></xref></p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Vaccine category</th><th rowspan=\"1\" colspan=\"1\">Safety<xref ref-type=\"table-fn\" rid=\"T1F2\"><sup><italic>b</italic></sup>\n</xref></th><th rowspan=\"1\" colspan=\"1\">Speed and ease of production</th><th rowspan=\"1\" colspan=\"1\">Logistics of global distribution</th><th rowspan=\"1\" colspan=\"1\">Potential for NAb induction</th><th rowspan=\"1\" colspan=\"1\">Potential for cell-mediated immunity<xref ref-type=\"table-fn\" rid=\"T1F3\"><sup><italic>c</italic></sup>\n</xref></th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">Live attenuated virus</td><td rowspan=\"1\" colspan=\"1\">Substantial concerns</td><td rowspan=\"1\" colspan=\"1\">NA<xref ref-type=\"table-fn\" rid=\"T1F4\"><sup><italic>d</italic></sup>\n</xref></td><td rowspan=\"1\" colspan=\"1\">NA</td><td rowspan=\"1\" colspan=\"1\">Probably high</td><td rowspan=\"1\" colspan=\"1\">Probably good</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Inactivated virus</td><td rowspan=\"1\" colspan=\"1\">Some concerns<xref ref-type=\"table-fn\" rid=\"T1F5\"><sup><italic>e</italic></sup>\n</xref></td><td rowspan=\"1\" colspan=\"1\">Intermediate</td><td rowspan=\"1\" colspan=\"1\">Feasible</td><td rowspan=\"1\" colspan=\"1\">Moderate</td><td rowspan=\"1\" colspan=\"1\">Poor</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Nonreplicating virus vector (recombinant DNA virus)</td><td rowspan=\"1\" colspan=\"1\">High</td><td rowspan=\"1\" colspan=\"1\">High</td><td rowspan=\"1\" colspan=\"1\">Feasible</td><td rowspan=\"1\" colspan=\"1\">Weak</td><td rowspan=\"1\" colspan=\"1\">Probably good</td></tr><tr><td rowspan=\"1\" colspan=\"1\">DNA plasmid given by electroporation</td><td rowspan=\"1\" colspan=\"1\">High</td><td rowspan=\"1\" colspan=\"1\">High</td><td rowspan=\"1\" colspan=\"1\">Some concerns<xref ref-type=\"table-fn\" rid=\"T1F6\"><sup><italic>f</italic></sup>\n</xref></td><td rowspan=\"1\" colspan=\"1\">Very weak</td><td rowspan=\"1\" colspan=\"1\">Probably good</td></tr><tr><td rowspan=\"1\" colspan=\"1\">mRNA</td><td rowspan=\"1\" colspan=\"1\">High</td><td rowspan=\"1\" colspan=\"1\">High</td><td rowspan=\"1\" colspan=\"1\">May be difficult<xref ref-type=\"table-fn\" rid=\"T1F7\"><sup><italic>g</italic></sup>\n</xref></td><td rowspan=\"1\" colspan=\"1\">Weak</td><td rowspan=\"1\" colspan=\"1\">Probably good</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Soluble or nanoparticle S- or RBD-protein, with adjuvant</td><td rowspan=\"1\" colspan=\"1\">High</td><td rowspan=\"1\" colspan=\"1\">Low<xref ref-type=\"table-fn\" rid=\"T1F8\"><sup><italic>h</italic></sup>\n</xref></td><td rowspan=\"1\" colspan=\"1\">Feasible</td><td rowspan=\"1\" colspan=\"1\">High</td><td rowspan=\"1\" colspan=\"1\">Poor</td></tr></tbody></table><graphic xlink:href=\"JVI.01083-20-t0001\"/></alternatives><table-wrap-foot><fn fn-type=\"other\" id=\"T1F1\"><label>a</label><p>For a complete list of vaccine candidates in preclinical and phase 1/2/2b/3 clinical trials, see <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines\">https://www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines</ext-link>. All the categories listed in the table are represented except live attenuated virus, which is a traditional and widely used method that is not being tested for SARS-CoV-2. How the various categories are summarized in this table is based on the small amount of available data, combined with general experience of how similarly designed vaccines have performed against other viral pathogens. Nonetheless, there are considerable uncertainties behind some of the assessments in the table. Emerging clinical trial data will determine whether they are accurate.</p></fn><fn fn-type=\"other\" id=\"T1F2\"><label>b</label><p>Safety indicates the likelihood the vaccine will be tolerated without serious adverse effects in the absence of infection. For all categories, there are substantial uncertainties about the risk of exacerbated pathogenesis postinfection, by ADE and VAERD mechanisms (see the text). These risks may be the greatest for vaccines that induce only low NAb titers and/or a high non-NAb/NAb ratio.</p></fn><fn fn-type=\"other\" id=\"T1F3\"><label>c</label><p>Most emphasis has been placed on the induction of NAbs, although some data on cellular immune responses are emerging from animal studies and more will be obtained in human trials. Attempts to induce cytotoxic T cells might include immunization with viral proteins other than S, including nonsurface exposed internal ones (e.g., the N-protein). Extrapolation from other vaccines leads to the assessments listed.</p></fn><fn fn-type=\"abbr\" id=\"T1F4\"><label>d</label><p>NA, not applicable. There are no known plans to produce this type of vaccine.</p></fn><fn fn-type=\"other\" id=\"T1F5\"><label>e</label><p>For a killed virus vaccine to be safe, the pathogen must be fully inactivated. Historically, inactivation has sometimes been incomplete (e.g., with polio vaccines).</p></fn><fn fn-type=\"other\" id=\"T1F6\"><label>f</label><p>Delivering DNA vaccines into muscles via electroporation is a relatively complex procedure compared to direct injection via needles or oral delivery.</p></fn><fn fn-type=\"other\" id=\"T1F7\"><label>g</label><p>The ease with which mRNA vaccines can be formulated and distributed has not been widely discussed. However, if these vaccines turn out to be unstable at ambient temperatures, it will be challenging to distribute frozen or chilled stocks.</p></fn><fn fn-type=\"other\" id=\"T1F8\"><label>h</label><p>General experience suggests that producing a stable cell line and using it to make large stocks of recombinant proteins under good manufacturing process conditions can take 1 to 2 years.</p></fn></table-wrap-foot></table-wrap><p>Both binding antibody (enzyme-linked immunosorbent assay [ELISA]) and NAb responses are quantified and presented in different ways in different studies of SARS-CoV-2 infection or vaccination. The most useful method involves deriving endpoint or midpoint titers from titration curves, but that is not always done and alternative measurements are quite common. Moreover, it is often not specified whether a titer is an endpoint or a midpoint, which matters greatly when trying to judge the relative immunogenicity of different vaccine candidates (see below) (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). Endpoint titers can be orders of magnitude higher than midpoint (half-maximal inhibitory dilution-factor [ID<sub>50</sub>]) values, depending on which cutoff is chosen and the slope of the curve. NAb responses to vaccines are presented in some papers as endpoint titers (higher numbers), which should be born in mind in comparisons with other vaccines for which NAb data are reported as midpoint titers (much lower numbers). Variation in how laboratories generate ELISA and NAb data further complicates cross-study comparisons. When possible, we specify whether a titer value is an endpoint or a midpoint or make an educated guess. Titers are not always recorded in the text of papers; in those cases, we have estimated key values by visual inspection of plotted data. It is to be hoped that standardized methods of data generation and presentation will be used in the Warp Speed and other national vaccine development programs.</p><fig id=\"F2\" orientation=\"portrait\" position=\"float\"><label>FIG 2</label><caption><p>Magnitudes of S-protein binding antibody (ELISA) and NAb responses in COVID-19 cases and vaccinated humans and animals. (A to C) Anti-S protein (open symbols) and anti-RBD (closed symbols) endpoint titers. (D to F) NAb midpoint titers (ID<sub>50</sub>) from PV assays (open symbols) and RV assays (closed symbols). In each plot, the titers for individual study subjects, the median values for a test group, or the range recorded in a study cohort are presented. The data in panels A and D are derived from virus-infected humans and nonhuman primates (NHPs) and show titers obtained in the first several weeks post symptoms. (B, C, E and F) Peak responses to S-protein- or RBD-based vaccines in humans and animals. (B and E) Studies in humans and NHPs; (C and F) studies in small animals (mice, guinea pigs, and rabbits), as indicated by the labels on the <italic>x</italic> axes. In the small-animal experiments, the immunogens used are grouped together from left to right as follows: DNA, RNA, adenovirus vectors, killed virus, recombinant S-protein, or RBD-protein. Data relating to SARS-CoV-2 are in red, SARS-CoV-1 in blue, and MERS-CoV in green. For experimental details, the cited papers listed on the <italic>x</italic> axes should be consulted. Assay methodologies vary between studies, which reduces the comparability of the resulting data sets. However, we judge that broad trends can still be seen. We have only included binding antibody endpoint titers and NAb midpoint (ID<sub>50</sub>) titers on the plots, excluding other methods of data representation. Multiple other papers cited in the text report on antibody responses to the S-protein (or other antigens) in infected humans but do so using other formats; in those papers, the responses usually span a >1,000-fold range. We note that NAb endpoint titers were presented in the following papers and the unrecorded midpoint titer values would probably be >100-fold lower: endpoint titer range <10 to ∼300 for MERS-CoV DNA vaccine-immunized humans (<xref rid=\"B103\" ref-type=\"bibr\">103</xref>); median endpoint titers of 34 and 46 in RV and PV assays, respectively, for Ad5 vaccine-immunized humans (<xref rid=\"B94\" ref-type=\"bibr\">94</xref>); endpoint titer range of 5 to 60 for SARS-CoV-2-infected rhesus macaques (<xref rid=\"B128\" ref-type=\"bibr\">128</xref>); median endpoint titer of ∼40 for ChAdOx1-immunized rhesus macaques (<xref rid=\"B131\" ref-type=\"bibr\">131</xref>).</p></caption><graphic xlink:href=\"JVI.01083-20-f0002\"/></fig></sec><sec id=\"s2\"><title>ANTIBODY RESPONSES IN COVID-19 CASES VARY GREATLY DURING THE CLINICAL COURSE</title><p>Nearly all SARS-CoV-2-infected people develop IgM, IgG, and IgA antibodies against the viral nucleocapsid (N)- and S-proteins between 1 and 2 weeks post symptoms; the titers of antibodies, including sometimes NAbs, then remain elevated for at least several weeks after the virus is no longer detectable and the patient recovers (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref><xref ref-type=\"bibr\" rid=\"B22\">–</xref><xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Titer decay rates over long periods have yet to be reported (see below). In one study, all 20 convalescent patients had virus-specific CD4<sup>+</sup> T cells and, in 70%, a measurable CD8<sup>+</sup> T-cell response. The magnitude of the S-protein-specific CD4<sup>+</sup> T-cell response in that cohort correlated with IgG and IgA titers against the RBD, suggesting that the antibody response to SARS-CoV-2 is, as expected, T-help dependent (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Endpoint titers of anti-S protein IgG Abs are highly variable in acute and convalescent COVID-19 cases, ranging from undetectable to >100,000 (<xref rid=\"B25\" ref-type=\"bibr\">25</xref><xref ref-type=\"bibr\" rid=\"B26\">–</xref><xref rid=\"B31\" ref-type=\"bibr\">31</xref>).</p><p>Both Env-pseudotype virus (PV) and, less often, replicating virus (RV) assays are used for NAb quantitation. Studies that compare these two formats generally show concordance with respect to rank orders for test antibodies, with the PV assays usually but not always being a fewfold more sensitive (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref><xref ref-type=\"bibr\" rid=\"B33\">–</xref><xref rid=\"B35\" ref-type=\"bibr\">35</xref>). NAb titers are best reported as ID<sub>50</sub> values and also vary greatly for COVID-19 sera. Midpoint (ID<sub>50</sub>) titers in COVID-19 sera span the range from undetectable to >10,000, although titers of >5,000 are uncommon (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>). In one study of 22 convalescent COVID-19 patients, NAb midpoint titers ranged from below detection (<30) to 1,900 (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). The median NAb titer was ∼1,000 in a larger cohort of 175 patients who had recovered from mildly symptomatic COVID-19; in only 10 cases were NAbs undetectable, while 25 had midpoint titers of >2,500 (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). The highest recorded titers in three studies were ∼1,000 (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>), 21,000 (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>), and ∼3,000 (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Similar variation was found in another cohort study in which the extent of neutralization was measured in a PV assay (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). In general, measurements of anti-S and anti-RBD IgG antibodies, but sometimes also antibodies to the neutralization-irrelevant N-protein, correlate quite well with the output of neutralization assays, although sometimes the analyses are not titer-to-titer comparisons (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Thus, neutralization-relevant epitopes on the RBD are highly antigenic and immunogenic, but so are other epitopes elsewhere on the S-protein that are less associated with neutralization (<xref rid=\"B25\" ref-type=\"bibr\">25</xref><xref ref-type=\"bibr\" rid=\"B26\">–</xref><xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>). One reason is that some epitopes may be antigenic on recombinant S-proteins but not on the functional virion-associated spike. Taken together, the anti-S protein antibody and NAb measurements in COVID-19 cohorts are a useful frame of reference for interpreting vaccine trials in animals and humans (see below). Quantitative aspects of the infection- and vaccine-induced antibody responses to S-proteins are summarized in <xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>.</p><p>The extreme variation in antibody titers seen in COVID-19 cases may reflect the pathological consequences of infection, which could limit the development of the antibody response. In some early studies, the samples may sometimes have been collected too early in the disease course, before the titers had reached their peak. Nonetheless, the titer variation holds true across multiple cohort studies of ever-increasing size and sophistication. What is also seen consistently is the lack of correlation between strong antibody responses and the amelioration of disease; indeed, the converse is true in that the highest antibody titers are seen in the patients who later develop the most severe disease and also in the oldest ones. In contrast, people with mildly symptomatic infection that did not require hospitalization generally have far weaker antibody responses (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B44\" ref-type=\"bibr\">44</xref>). The same was seen in the SARS epidemic, where cases with the earliest and strongest NAb responses also had the poorest prognosis (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). A particularly striking example is a COVID-19 cohort study in which serum IgM, IgG, and IgA responses were stratified by disease status (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Median anti-RBD antibodies, estimated as concentrations at their peaks, were IgA, 8.8 μg/ml on days 16 to 20; IgM, 7.2 μg/ml on days 16 to 20; and IgG, 16 μg/ml on days 21 to 25. Of note is that serum IgA concentrations were very strongly correlated with severe disease (<italic>P</italic> < 0.0001), much more so than for IgG (<italic>P</italic> < 0.001 for moderate disease but not significant for severe) and IgM (not significant). High serum IgA levels and their correlation with severe disease were also seen in three more COVID-19 cohorts (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>, <xref rid=\"B46\" ref-type=\"bibr\">46</xref>). In one study, virus-specific IgA antibodies to the S-, N-, and RBD-proteins were detected a few days sooner than either IgM and IgG and initially dominated over these isotypes at the B-cell and serum antibody levels as infection progressed. Eventually, however, IgG titers rose to match IgA and then became the strongest response over the longer term (1 to 2 months). Unexpectedly, serum antibody fractionation experiments showed that both IgG and IgA isotypes had neutralization activity, with IgA being significantly stronger during peak infection. Bronchoalveolar lavage (BAL) samples also contained NAbs as well as IgA and IgG anti-S antibodies (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). These observations of high-titer serum IgA responses may be yet another novel aspect of SARS-CoV-2 infection. In contrast, infection by influenza viruses is not associated with unusually strong serum IgA responses (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>).</p><p>NAb titers in the early stages of infection are inversely correlated with subsequent viral loads, measured as RNA copies in sputum, throat swabs, and stool, but directly correlated with more severe subsequent disease (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>). The relationships between the amount of infectious virus in key body compartments and disease severity and the influence of NAbs remain to be understood. It is worth noting, however, that infectious SARS-CoV-2 is very rarely found in blood, which is the body fluid used in most assessments of the antibody response to this virus. Conversely, antibody responses are only rarely measured in mucosal fluids, where infectious virus titers are far higher (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>). The same dichotomy generally applies in animal and human vaccine studies, where there is very little information on the induction of mucosal antibodies.</p><p>It is not known why earlier and stronger serum antibody responses correlate with disease severity (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Do the higher viral loads and hence antigen supply associated with more severe disease drive antibody production, or do stronger antibody responses help drive the disease process (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>)? Although it is not clear what role antibodies play during SARS-CoV-2 infection, it is certainly not possible to identify what, if any, titers protect against disease in COVID-19 cohorts. This point is relevant because S-protein immunization studies in animals and humans often compare the magnitude of the anti-S binding antibody and NAb responses with what is seen in SARS-CoV-2-infected humans of sometimes unspecified disease status. Given the wide range of titers seen in infected people and how the strongest antibody titers are seen in the sickest patients, this kind of comparison is meaningless and not helpful (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). Furthermore, the concept of “immunity passports” for infected people is often discussed. Only a minority of infected people develop severe symptoms; most recover fully at home. But, as noted above, minimally or moderately symptomatic individuals have far weaker antibody responses than hospitalized COVID-19 cases. It is very far from certain that these weak responses would be sufficient to protect against a second exposure, and for long (see below). Whether plasma from moderately symptomatic people could be useful for passive immunotherapy also needs to be considered, in view of the generally low NAb titers generated by such individuals.</p></sec><sec id=\"s3\"><title>LONGEVITY AND PROTECTIVE CAPACITY OF ANTIBODY RESPONSES TO CORONAVIRUSES</title><p>A key question that has societal implications beyond vaccine development is whether the antibody response to SARS-CoV-2 will confer immunity against reinfection and, if so, for how long? Will humans who recover from this infection be protected against a future exposure to the same virus months or years later? Knowing the duration of the antibody response to SARS-CoV-2 vaccines will also help to determine whether, and how often, boosting immunizations will be needed if the initial response exceeds the protection threshold (see <xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>).</p><fig id=\"F3\" orientation=\"portrait\" position=\"float\"><label>FIG 3</label><caption><p>SARS-CoV-2 vaccine responses and their relationships to protective immunity. (A) The rate of decay of SARS-CoV-2 NAbs from an initial vaccine-induced peak ID<sub>50</sub> titer of 3,000 during the following year. The titer intersects the protective titer value of 300 (dotted line) after 6 months. The values chosen are hypothetical, although a titer decay to below protective levels over a 6-month period would be broadly consistent with the decline after infection with common-cold coronaviruses (<xref rid=\"B65\" ref-type=\"bibr\">65</xref><xref ref-type=\"bibr\" rid=\"B66\">–</xref><xref rid=\"B67\" ref-type=\"bibr\">67</xref>). (B) Variation in SARS-CoV-2 NAb titers among a cohort of vaccinated individuals and the relationship to the protective titer value of 300 (dotted line). The value of 300 is hypothetical but is consistent with values discussed in the text (e.g., see reference <xref rid=\"B62\" ref-type=\"bibr\">62</xref>). The assay has a titer quantitation limit of 10. (Left) Peak titers immediately after the immunization schedule is completed; (right) titers 6 months later. Scenario 1, the vaccine induces a strong enough peak response for most recipients to be protected and vaccine efficacy is high; scenario 2, for a weaker vaccine, the protective threshold is initially exceeded in only half of the recipients; scenario 3, titers in only a minority of the recipients of a poorly immunogenic vaccine exceed the protective threshold. In each scenario, the 50-fold titer decay over 6 months causes far fewer of the vaccine recipients to be protected at this time. A booster immunization for the two stronger vaccines could restore immunity to protective levels in some people. As for panel A, the titer values and decay rates are hypothetical. However, the range of titers seen in an immunization cohort is consistent with published data (<xref rid=\"B103\" ref-type=\"bibr\">103</xref>, <xref rid=\"B133\" ref-type=\"bibr\">133</xref>, <xref rid=\"B134\" ref-type=\"bibr\">134</xref>); an ∼50-fold decrease in the SARS-CoV-1 NAb titer during a 6-month period was measured in RBD-immunized mice (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>), and NAb titers induced by a MERS-CoV DNA vaccine in humans had declined by ∼50-fold within a year (<xref rid=\"B103\" ref-type=\"bibr\">103</xref>).</p></caption><graphic xlink:href=\"JVI.01083-20-f0003\"/></fig><p>Antibody responses to many viral infections wane so slowly that lifelong immunity is maintained, with plasma cells and B-memory cells playing central roles in resistance to reinfection (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B52\" ref-type=\"bibr\">52</xref>). RBD-specific memory B cells that have switched to IgG were found in the blood of COVID-19 patients (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>). SARS CoV-2-specific plasma cells were identified in both severely ill patients and recent convalescent cases (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B54\" ref-type=\"bibr\">54</xref>). The SARS CoV-2 antibody responses in 47 patients were unchanged 2 weeks after their discharge from hospital (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). IgG antibodies to the S-protein were detected in all 31 COVID-19 patients soon after infection, rose during the first 3 weeks after symptom onset, and then declined but remained detectable at 8 weeks (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). Of necessity, the early studies on COVID-19 cohorts were conducted only over fairly short time periods. Longer-term assessments of antibody decay kinetics are now starting to emerge. In one cohort, antibody and NAb responses peaked, on average, at approximately days 30 to 40 postinfection and then began to decline over the next few weeks. In one individual, the highest NAb titer was ∼1,600 on day 20 but had fallen to ∼50 by day 45, which is a worrying rate of decay if seen more generally (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>). In another report, IgA titers to the RBD-, S-, and N-proteins rose strongly for about 3 weeks postinfection and then rapidly declined to the extent that they were undetectable by 1 month postrecovery. In contrast, IgG titers were much more persistent in this time frame (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). Antibody responses (virus-specific IgG and NAbs in a PV assay) declined markedly in both symptomatic and asymptomatic individuals within 8 weeks after discharge from the hospital, to the extent that 13% of the former group and 40% of the latter become seronegative for virus-specific IgG in the assay used (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>).</p><p>Until data on the persistence of antibody and NAb responses to SARS-CoV-2 over a multimonth period become available, we can only extrapolate from studies of SARS-CoV-1, Middle East respiratory syndrome coronavirus (MERS-CoV) and the common-cold coronaviruses (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). Antibody responses to SARS-CoV-1 dropped continuously during the first few years after infection (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref><xref ref-type=\"bibr\" rid=\"B58\">–</xref><xref rid=\"B60\" ref-type=\"bibr\">60</xref>). In one study, IgG levels started to decline ∼6 months post symptoms and then fell steadily over the next 3 years (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>). Virus-specific IgG remained detectable at a low serum dilution of one-tenth throughout a 13-year study of 34 SARS-CoV-1-infected health care workers but dropped to very low levels over this period and eventually approached the assay detection limit (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). S-protein-specific IgG memory cells were found at 2, 4, 6, and 8 months after SARS-CoV-1 infection, but their abundance fell by 90% between the first and last time points (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Immunization of mice with the SARS-CoV-1 RBD protein induced very strong peak anti-S protein endpoint titers of ∼150,000 and NAb ID<sub>50</sub> titers of 4,000. However, these titers declined to ∼10 and <40 within 9 months (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). Thus, well within a year, the antibody response to the RBD had dropped by as much as 15,000-fold.</p><p>Animal model experiments have started to address the nature of protective immunity to SARS-CoV-2. Two rhesus macaques experimentally infected on day 0 were protected from a second challenge on day 28, soon after they had recovered from their initial mild disease (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>). A follow-up study on a larger scale (9 animals in 3 groups of 3) drew similar but more detailed conclusions (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). The macaques become viremic soon after SARS-CoV-2 challenge with 3 different virus doses, with viral loads in BAL fluid in the range of 5.3 to 9.0 (median 6.6) log RNA copies/g. There was a moderate relationship between challenge dose and viral loads. In contrast to humans, the macaques largely cleared the infection over days 10 to 28. Anti-S protein endpoint titers were ∼1,000 on day 35, with NAb ID<sub>50</sub> values of ∼100 in both PV and RV assays. On day 35, the 9 animals were rechallenged with same doses they received in the primary challenge and remained minimally infected or uninfected as judged by viremia and other assessments. However, anti-S protein and NAb anamnestic responses were triggered rapidly, which is a sign that the animals did become reinfected. No correlates of protection could be identified. Hence, at least in the short term (5 weeks) and in an animal model where disease is minimal (see below), SARS-CoV-2 infection is associated with the development of nonsterilizing immunity that reduces viral loads. It is difficult to extrapolate from small-scale animal studies to human SARS-CoV-2 exposure, particularly when the time-dependent decay of immune responses is considered. What would happen if the second challenge were delayed for several months?</p><p>Useful information can be derived from studies of common-cold coronavirus infections in humans. Serum IgG and IgA antibodies and NAbs increased by ∼10-fold within 3 weeks of experimental infection with coronavirus 229E, declined markedly over the next 9 weeks, and were at or near baseline levels after one year (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>, <xref rid=\"B66\" ref-type=\"bibr\">66</xref>). In a more recent report, 10 subjects were monitored over a 35-year period (1985 to 2020) for their antibody responses (measured using an N-protein fragment) to four different seasonal common-cold coronaviruses (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>). Protective immunity waned over time, with substantial reductions in anti-N antibody titers by 6 months postinfection. By 12 months after an initial infection, reinfections were frequent, implying that immunity was not sustained. The probability of infection by month of the year was also assessed, showing that there is a steady decline from May until September (the summer months in this Dutch Northern Hemisphere study) before a steady increase during the winter months (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>).</p><p>Two experiments were conducted in which humans infected with common-cold coronaviruses were rechallenged several months later (reviewed in reference <xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Six individuals exposed to one strain of the 229E coronavirus became infected, but none was reinfected when challenged with the same strain one year later. However, in a similar experiment, 5 of 8 initially infected volunteers were susceptible to a heterologous strain when rechallenged 8 to 14 months later (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). In a later study, when 15 volunteers were initially exposed to the 229E coronavirus, 10 of them became infected and 8 developed colds. Serum IgG levels were ∼3-fold higher in the uninfected group on the day of challenge. Antibody levels had returned to near baseline values when the same volunteers were rechallenged one year later. All 5 of the originally uninfected volunteers became infected after the second exposure, which was also the case for 6 of the 9 people who had been infected one year earlier (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>). The small scale of these studies precludes drawing any strong conclusions about protective immunity other than that it can persist for at least one year, does not occur in all subjects, and may be antibody mediated. These long-ago experiments did not identify antibody titers that protect against common-cold coronaviruses. It was not possible to infer anything about protective titers in a more recent study of coronavirus immunity, as only antibodies to the N-protein were measured (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>). In any case, extrapolating from common-cold coronaviruses to SARS-CoV-2 would be difficult, at best, because of the influence of various differences such as neutralization sensitivity and transmission efficiency.</p><p>Human antibodies induced in response to infection by common-cold coronaviruses bind only minimally to the SARS-CoV-2 S-protein (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B68\" ref-type=\"bibr\">68</xref>). It seems unlikely that low-level cross-reactivity would affect susceptibility to infection by SARS-CoV-2 or the subsequent COVID-19 disease course, either beneficially or adversely. However, ∼40% to 60% of a cohort of unexposed people had SARS-CoV-2 cross-reactive CD4<sup>+</sup> T-cells, suggesting that there is the potential for T-cell cross-recognition between common-cold coronaviruses and their more pathogenic cousin (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). In another cohort, 34% of healthy seronegative donors had CD4<sup>+</sup> T-cell responses to SARS-CoV-2 (compared to 83% of COVID-19 cases), which was attributed to cross-reactivity from responses to common-cold coronavirus infections. The cross-reactive epitopes are most likely in the relatively conserved S2 domain of the S-protein (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>). Whether such cross-reactivity might be associated with protection from infection needs to be investigated in larger-scale studies.</p><p>Taken together, long-term studies indicate that antibody responses to common-cold and pathogenic coronaviruses are not very long lasting (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B52\" ref-type=\"bibr\">52</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref>, <xref rid=\"B60\" ref-type=\"bibr\">60</xref>, <xref rid=\"B62\" ref-type=\"bibr\">62</xref>, <xref rid=\"B67\" ref-type=\"bibr\">67</xref>, <xref rid=\"B70\" ref-type=\"bibr\">70</xref>). If SARS-CoV-2 behaves similarly, there are significant implications for how long infected individuals resist reinfection, the maintenance of herd immunity in a population, and the frequency with which vaccine booster immunizations may need to be given (see <xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>).</p></sec><sec id=\"s4\"><title>NEUTRALIZING MONOCLONAL ANTIBODIES TO THE SARS-COV-2 S-PROTEIN</title><p>Both neutralization-relevant and -irrelevant epitopes are present on S-proteins. As noted above, antibodies to the RBD were detected in the majority of COVID-19 patients and are sometimes strongly neutralizing (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). Intensive efforts to isolate and characterize neutralizing monoclonal antibodies (nMAbs) from COVID-19 cases or experimentally immunized animals are now ongoing, both to better understand the nature of the antibody response in the context of vaccine development and to produce reagents for passive immunotherapy or prevention. As with vaccines (see below), the use of different assays in different laboratories affects, but probably does not preclude, direct comparisons of reported nMAb potencies (50% inhibitory concentration [IC<sub>50</sub>] values).</p><p>In an early study on a Chinese cohort, 206 RBD-specific IgG memory B cells were isolated from eight patients (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). The resulting MAbs represented heavy- and light-chain families apparently at random. One patient had coexisting germ line and matured S-reactive clones, with both categories having neutralization activity. Overall, the degree of somatic mutation (SM) was low and did not correlate with affinity. The nMAbs that most strongly competed with angiotensin-converting enzyme 2 (ACE2) binding neutralized most potently (in one case, with an IC<sub>50</sub> of 30 ng/ml), but the overall correlation was weak, as was the relationship between affinity (dissociation constant [<italic>K<sub>d</sub></italic>] from the nanomolar range upwards) and competitive capacity. Moreover, some of the nMAbs with the highest affinity for the RBD did not block ACE2 binding (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>).</p><p>Much more potent nMAbs have now been isolated from a Dutch COVID-19 cohort (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Blood was drawn from three infected people at about 4 weeks post symptoms, two (COSCA-1 and -2) being mild “at-home” cases, while the third (COSCA-3) had severe disease requiring intensive care. Anti-S protein endpoint titers in the three patients were 13,600, 6,100, and 48,100, respectively, with ID<sub>50</sub> NAb titers in a PV assay of 383, 626, and 7,645, respectively. Of note is that the highest anti-S protein and NAb titers were in the patient with the most severe disease (see above). S-proteins were used to isolate B cells, leading to 409 paired heavy-chain (HC) and light-chain (LC) clones (137, 165, and 107, respectively, from the above three individuals). SM levels were very low at 1% to 2%, implying that these antibodies have sequences very close to those of the human germ line. All of the HC/LC pairs were expressed in 293F cells, leading to 84 MAbs that were derived mostly from the COSCA 1- and -2 patients. Among them, 32 bound to the RBD, 33 recognized epitopes elsewhere on the soluble S-protein, and several others reacted strongly with S-protein expressed on the cell surface but not with the soluble version. Only 19 of the 84 S-protein MAbs had neutralizing activity in a PV assay, of which 14 were against the RBD. Nine MAbs neutralized at <100 ng/ml, the rest with lower potencies. The RBD-targeting nMAbs COVA1-18 and COVA2-15 were the most potent, with IC<sub>50</sub>s of 8 ng/ml. The best nMAbs had similar potencies in an RV assay. In general, but with exceptions, the RBD-directed nMAbs blocked ACE2 binding strongly. A small number of MAbs modestly (<2-fold) increased infectivity in a concentration-dependent manner, although the sera from the three donors lacked any antibody-dependent enhancement (ADE) activity. The epitopes of the most potent nMAbs were studied in substantial detail using a variety of techniques, with multiple subclusters identified (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>).</p><p>Comparably potent nMAbs were isolated from a San Diego-based COVID-19 cohort of 22 patients with a range of disease profiles from moderate to severe (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Anti-S protein titers ranged from very low to >10,000 (note that these are midpoint titers, whereas Brouwer et al. [<xref rid=\"B25\" ref-type=\"bibr\">25</xref>] reported endpoints). NAb titers varied from undetectable to >1,000, with excellent concordance between PV and RV assays and a strong correlation between NAb titers and anti-RBD and anti-S protein ELISA titers. Three donors, CC6, CC12, and CC25, were selected for B-cell sorting, which yielded 2,045 IgG antibodies. Only a small proportion of antibodies to the S-protein can neutralize, as many nonneutralizing MAbs were identified. In total, 19 different NAb lineages were identified, and SM was again very low at 1% to 2%. The epitopes for the best 27 MAbs were studied in detail, leading to the identification of three different epitope clusters on the RBD and three more elsewhere on the S-protein. Neutralization titers were highly variable. The most potent nMAbs recognized an epitope designated RBD-A, with the three best having IC<sub>50</sub>s of ∼10 ng/ml in the PV neutralization assay and similar activities against RV. Many of the less-potent nMAbs were also less effective, with maximum neutralization well below 100%, even for some to the RBD-A site. This observation needs to be better understood. The Syrian hamster challenge model was then used to test two of the nMAbs: CC12.1 to the RBD-A cluster and CC12.23 against the S-B site, with NAb IC<sub>50</sub> titers of 19 ng/ml and 22 μg/ml, respectively (see below).</p><p>A New York-area cohort involving 68 convalescing COVID-19 patients also yielded highly potent nMAbs (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Samples were collected on average 30 days after the onset of symptoms. Plasma anti-S protein and anti-RBD IgG and IgM measurements varied widely, with a strong correlation between the magnitude of the antibody responses and the duration of symptoms. Midpoint NAb titers in a PV assay ranged from 5 to >5,000 (only 2 serum samples reached that high level), with a geometric mean titer of 212 for the cohort. Most plasma samples from patients who recovered from modest disease had only low to modest neutralization activity, with anti-S protein and anti-RBD titers again strongly correlating with NAb titer. An RBD bait was used to isolate B cells from 6 donors, including the two with the highest plasma neutralization titers, leading to 534 HC/LC pairs, of which 34 were then expressed. The very strong sequence similarities among antibodies from different donors has useful implications for the response to S-protein vaccines on a population basis. Of the 34 MAbs, 32 bound to two different subclusters on the RBD, with an average 50% effective concentration (EC<sub>50</sub>) of 6.6 ng/ml, while 20 of them neutralized SARS-CoV-2, with IC<sub>50</sub>s in the range 4.4 to 709 ng/ml. Of note is that some potent nMAbs emerged from donors whose plasma was only weakly or modestly neutralizing. Thus, the serology assays were unable to predict the presence of rare antibody clones with significant neutralization activity (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>).</p><p>Sixty Chinese convalescent COVID-19 patients were screened to isolate >8,500 B-cell clonotypes that in turn yielded 14 potent nMAbs, all of them against the RBD (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>). None of 72 MAbs against other S1 and S2 epitopes was neutralizing. The best nMAb, BD-368-2, had an IC<sub>50</sub> of 1.2 ng/ml in a PV assay and 15 ng/ml when tested against an infectious virus. At 9.3%, it was more highly mutated from the germ line sequence than the others described above. BD-368-2 inhibits ACE2 binding, as expected, and its cryo-electron microscopy (cryo-EM) structure has been solved as an S-protein complex. Its performance in a passive transfer experiment in transgenic mice is summarized below.</p><p>A large set of nMAbs against the RBD emerged from DNA plus S-protein-immunized mice and SARS-CoV-2-infected humans. The most potent among them neutralized a SARS-CoV-2 vesicular stomatitis virus (VSV)-PV with IC<sub>50</sub> values in the range 7.2 to 99 pM (∼1.1 to 15 ng/ml). Although the nMAbs did not act synergistically in neutralization assays, the best among them will be tested clinically in combinations so as to reduce the potential for the emergence of escape mutants (<xref rid=\"B75\" ref-type=\"bibr\">75</xref>).</p><p>All five of the above-described studies led to the identification of nMAbs that neutralize SARS-CoV-2 with IC<sub>50</sub> values in the 1 to 10 ng/ml range via interactions with the RBD (<xref rid=\"B25\" ref-type=\"bibr\">25</xref><xref ref-type=\"bibr\" rid=\"B26\">–</xref><xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B74\" ref-type=\"bibr\">74</xref>, <xref rid=\"B75\" ref-type=\"bibr\">75</xref>). Apparently less-potent nMAbs emerged from three other efforts. One involved a single patient in the Seattle area from whom 44 MAbs were isolated using an S-protein-based bait at 21 days postinfection (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). The anti-S protein endpoint titer in the donor plasma was ∼10,000 and the NAb ID<sub>50</sub> in a PV assay was ∼3,000. The MAb sequences were, again, close to those of the germ line. Only 3 of the 44 MAbs bound the RBD, the remainder recognizing epitopes elsewhere on the S-protein. Two of the MAbs had neutralization activity: CV30 had an IC<sub>50</sub> of 30 ng/ml, bound the RBD, and blocked its interaction with ACE2, while CV1 recognized an epitope outside the RBD but neutralized only partially and with an IC<sub>50</sub> of 15 μg/ml (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Another MAb isolation program was based on samples taken at 35 to 50 days post symptoms from four individuals who were infected in China very early in the COVID-19 pandemic and then traveled to the United States (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). NAbs were detected in sera from patients 3 and 4 using an RV assay, with ID<sub>50</sub> values of ∼100. B cells from these two patients yielded 386 recombinant MAbs that were grouped into 5 categories based on antigen recognition patterns. Most of those with neutralization activity mapped to the RBD, the most potent (MAb 30) having an IC<sub>50</sub> of ∼300 ng/ml (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). An RBD bait was used to isolate 17 paired B cell clones from a single COVID-19 patient in China (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Those clones yielded four MAbs that were RBD-reactive in biolayer interferometry (BLI) assays. Their neutralization IC<sub>50</sub> values in an RV assay ranged from 0.177 to 1.375 μg/ml. Two of these nMAbs, B38 and H4 against different epitopes, were tested in a virus challenge experiment using transgenic mice (see below).</p><p>Taken together, the nMAb isolation projects show that highly potent antibodies against the RBD are induced in multiple COVID-19 patients at different stages of disease, including those with only mild symptoms. The dominance of the RBD as a neutralization epitope(s) is consistent with its relatively limited shielding by glycans that substantially occlude the rest of the S-protein’s surface (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>, <xref rid=\"B77\" ref-type=\"bibr\">77</xref>). The similarity of the RBD epitopes for these nMAbs and their very limited maturation from germ line sequences are encouraging indicators that potent polyclonal NAbs will be triggered by S-protein or RBD vaccines (<xref rid=\"B25\" ref-type=\"bibr\">25</xref><xref ref-type=\"bibr\" rid=\"B26\">–</xref><xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>). While the most potent nMAbs to the RBD block interactions with the ACE2 receptor, some do not (<xref rid=\"B25\" ref-type=\"bibr\">25</xref><xref ref-type=\"bibr\" rid=\"B26\">–</xref><xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B75\" ref-type=\"bibr\">75</xref>). One early example is nMAb S309 that was isolated from memory B cells of a patient who had recovered from SARS-CoV-1 infection in 2003, which neutralizes SARS-CoV-1 and -2 with similar potencies (IC<sub>50</sub> ∼100 ng/ml) by ligating the RBD but does not interfere with ACE2 binding and was ineffective as a Fab (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>). Of note is that the conserved S309 epitope involves glycans. Thus, an S-protein vaccine candidate may need to have an appropriate glycan profile to be able to induce this particularly broad NAb specificity. The 47D11 nMAb binds the S1-B domain of the S-protein and neutralizes by an unidentified mechanism that also does not involve competition with ACE2 binding. It was isolated from transgenic mice immunized with a series of CoV S-proteins and neutralizes both SARS-CoV-1 and -2 with EC<sub>50</sub>s in the 0.1 to 1 μg/ml range (<xref rid=\"B79\" ref-type=\"bibr\">79</xref>). Other SARS-CoV-1 and -2 cross-reactive nMAbs are also known, but they are not very potent (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>, <xref rid=\"B81\" ref-type=\"bibr\">81</xref>).</p><p>Other immunogenic epitopes on the S-protein are targeted by a substantial proportion of nonneutralizing MAbs (<xref rid=\"B25\" ref-type=\"bibr\">25</xref><xref ref-type=\"bibr\" rid=\"B26\">–</xref><xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>). The presence of both NAbs and non-NAbs in plasma shows that anti-S protein ELISAs (which detect both categories) are not a perfect surrogate for virus neutralization assays (which detect only NAbs), despite the frequently seen correlations between these two variables. Whether non-NAbs might contribute to ADE and related adverse events is discussed further below.</p></sec><sec id=\"s5\"><title>IMMUNOGENICITY OF SARS-COV-2 VACCINE CANDIDATES</title><p>How good are the leading vaccine candidates at inducing antibodies to S-proteins? What binding antibody and NAb titers can be expected? Here, we review how well various SARS-CoV-1, MERS-CoV, and SARS-CoV-2 S-protein-based vaccines elicit antibodies, including NAbs, in animals and humans (for reviews, see references <xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>, and <xref rid=\"B17\" ref-type=\"bibr\">17</xref>). As noted above, the outcomes of immunogenicity studies are generally evaluated in different ways. Not only do the assays themselves vary (e.g., whether NAbs are quantified using PVs or RVs), but the resulting data are also presented in a range of formats (e.g., endpoint, midpoint, or unspecified titers or as the extent of neutralization at a fixed serum dilution). Similarly, ELISA data are reported in different ways, including midpoint or endpoint titers, area under the curve (AUC) plots, or signals at a fixed dilution. The lack of standardization can make it very difficult to cross-compare the outcomes of different experiments. However, by using only ELISA endpoint and NAb midpoint (ID<sub>50</sub>) titers, we can compare the magnitudes of antibody responses induced by different vaccines and by SARS-CoV-2 infection (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). Inspecting other papers where antibody responses are recorded in other formats reinforces what is shown (see below and the legend to <xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). The generally stronger responses to recombinant protein and killed virus vaccines, compared to those for other concepts, are clearly visible.</p><p>A major obstacle to the development of an effective HIV-1 vaccine or a broadly active influenza virus vaccine is sequence diversity in their spike glycoproteins that affects NAb epitopes (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>). Variation in the SARS-CoV-2 S-protein sequence has been reported but on a much smaller scale and without, to date, significant implications for the development of an effective NAb response (<xref rid=\"B83\" ref-type=\"bibr\">83</xref><xref ref-type=\"bibr\" rid=\"B84\">–</xref><xref rid=\"B85\" ref-type=\"bibr\">85</xref>). Clearly, this is an area for intensive monitoring, on a global scale, but the relatively static nature of the antibody targets on coronaviruses is a welcome aspect of this particular vaccine challenge. Conversely, the SARS-CoV-2 S-protein is highly glycosylated (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1B</xref>). Its glycan content approaches the exceptional density seen on its HIV-1 counterpart and far exceeds what is seen on the influenza virus hemagglutinin (HA) protein (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>, <xref rid=\"B77\" ref-type=\"bibr\">77</xref>, <xref rid=\"B82\" ref-type=\"bibr\">82</xref>, <xref rid=\"B86\" ref-type=\"bibr\">86</xref>). The ability of glycans to both shield NAb epitopes and shift their positions under selection pressures should not be ignored. Nonetheless, the hope is that eliciting NAbs against the SARS-CoV-2 S-protein may be fairly straightforward compared with that for, e.g., HIV-1 Env, whose key epitopes are more complex, often more shielded, and much harder to present properly to the immune system (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>, <xref rid=\"B86\" ref-type=\"bibr\">86</xref>). The use of the smaller and less glycosylated RBD as a NAb-inducing immunogen could be particularly beneficial in this regard (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>, <xref rid=\"B87\" ref-type=\"bibr\">87</xref>).</p><p>Awareness of how the same vaccine technologies perform in other viral settings can also provide some insights. Most of the larger industry-based vaccine programs announced to date fall into one of three general categories: nucleic acid (mRNA or DNA) plasmids, replicating virus vectors (adenovirus or vaccinia virus), and recombinant S-proteins or the RBD (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref><xref ref-type=\"bibr\" rid=\"B15\">–</xref><xref rid=\"B18\" ref-type=\"bibr\">18</xref>). There are also well-advanced killed virus vaccine programs (<xref rid=\"B88\" ref-type=\"bibr\">88</xref>, <xref rid=\"B89\" ref-type=\"bibr\">89</xref>). Multiple other technologies are being evaluated by individual research groups, but at a slower pace and with far fewer resources applied (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/who-documents-detail/draft-landscape-of-covid-19-candidate-vaccines\">https://www.who.int/who-documents-detail/draft-landscape-of-covid-19-candidate-vaccines</ext-link>). Passive immunization with nMAbs is an alternative approach to protection that is beyond the scope of this article (<xref rid=\"B14\" ref-type=\"bibr\">14</xref><xref ref-type=\"bibr\" rid=\"B15\">–</xref><xref rid=\"B18\" ref-type=\"bibr\">18</xref>). However, as for early passive transfer experiments in animals (see below), human studies with COVID-19 plasma or nMAbs may reveal useful information on protective antibody titers.</p><p>A major driving force behind the more-prominent programs appears to be the speed at which a vaccine product can be manufactured <italic>en masse</italic>, using existing production facilities (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). Thus, political pressures and societal needs are creating unusually aggressive timelines for the delivery of a product that can be used widely in humans. There are even indications of competition among nation states rather than the more productive cooperative process (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). The relative simplicity with which nucleic acid vaccines can be designed and manufactured has given this technology a substantial head-start in the race to the clinic and beyond. Similarly, viral vector vaccines, notably the adenovirus candidates, were relatively straightforward to repurpose from existing candidates (e.g., for MERS-CoV or HIV-1); these vaccines can be produced in very large amounts in already available facilities. Conversely, producing and purifying recombinant proteins in the doses needed to immunize large populations is likely to be slower and more challenging, and few facilities outside China produce killed virus vaccines in bulk nowadays.</p><p>General experience, combined with emerging data, suggests that the most rapidly produced vaccines (i.e., nucleic acids and virus vectors) may also be the least capable of eliciting high titers of antibodies and NAbs to the S-protein (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). That is not to say that these vaccine designs will necessarily fail, as it is possible that they will be sufficiently immunogenic to meet the goal of protecting against SARS-CoV-2. Their prospects will be critically dependent on the NAb titer (efficiency) and maximum extent of neutralization (efficacy) needed for protection, how close the immune responses they elicit meet those marks on a population basis, and how long the initial titers are maintained over time (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>).</p><p>In the HIV-1 vaccine arena, adenovirus vaccines induce only weak antibody responses in animals and humans compared to those from recombinant proteins, even when the endpoint is only nonneutralizing antibodies (non-NAbs) (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>, <xref rid=\"B90\" ref-type=\"bibr\">90</xref>). Indeed, an adenovirus-based vaccine from Janssen that is now in phase 2b trials for HIV-1 prevention includes a recombinant spike-derived protein boost component that is specifically intended to increase non-NAb titers (it is unable to elicit NAbs in a meaningful way) (<xref rid=\"B90\" ref-type=\"bibr\">90</xref>, <xref rid=\"B91\" ref-type=\"bibr\">91</xref>). This adenovirus vector seems conceptually similar to the one that is the basis of the same company’s SARS-CoV-2 vaccine program. A different adenovirus vector (ChAdOx1) that is also part of the Warp Speed programs was only weakly immunogenic in humans when its counterpart was used to deliver the MERS-CoV S-protein. Thus, anti-S protein endpoint titers in the highest dose group were only ∼1,500, while NAb titers varied from undetectable to ∼20 in an intravenous (i.v.) IV assay (<xref rid=\"B92\" ref-type=\"bibr\">92</xref>). In a study of a MERS-CoV modified vaccinia virus Ankara vector, the highest geometric mean NAb titer in an RV assay was ∼100 (<xref rid=\"B93\" ref-type=\"bibr\">93</xref>). As summarized above, anti-S protein antibody and NAb titers in COVID-19 patients can be orders of magnitude greater than has been achieved in these virus vector immunization studies.</p><p>A different adenovirus vector expressing the full-length SARS-CoV-2 S-protein, CanSino’s Ad5 vaccine candidate, has been tested in a phase I human trial (<xref rid=\"B94\" ref-type=\"bibr\">94</xref>). Immunogenicity was vaccine dose dependent. In the highest dose group, the geometric mean endpoint anti-S protein titer on day 28 was ∼600, while the anti-RBD titer was 1,400. It was not explained why anti-RBD titers were greater than anti-S protein titers, which is not usually seen. Geometric mean endpoint NAb titers were 34 and 46 in RV and PV assays, respectively, and were strongly correlated with anti-RBD titers. Considering the uncertainties involved in cross-study comparisons, this vaccine candidate does not seem to be very efficient for inducing antibodies to the S-protein in humans. One factor may have been some interference by preexisting immunity to the Ad5 vector itself (<xref rid=\"B94\" ref-type=\"bibr\">94</xref>, <xref rid=\"B95\" ref-type=\"bibr\">95</xref>). This problem affected an Ad5 vector-based vaccine that conferred no protection against HIV-1 acquisition in a large-scale trial (<xref rid=\"B96\" ref-type=\"bibr\">96</xref>, <xref rid=\"B97\" ref-type=\"bibr\">97</xref>). A substantial number of mild-to-moderate adverse events were reported in the CanSino SARS-CoV-2 trial (<xref rid=\"B94\" ref-type=\"bibr\">94</xref>). To the extent that comparisons are possible, the frequency and nature of these adverse events seem to be worse than was found in the HIV-1 Ad5 vaccine trials (<xref rid=\"B96\" ref-type=\"bibr\">96</xref>, <xref rid=\"B97\" ref-type=\"bibr\">97</xref>). If there is such a difference and it reflects a property of the SARS-CoV-2 S-protein, concerns could arise about other vaccines based on this protein.</p><p>A substantial unknown is the magnitude and nature of the antibody responses that will be elicited by the mRNA vaccines, as this technology is very new and there is only limited information on the designs, safety, and immunogenicity of the major candidates (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B98\" ref-type=\"bibr\">98</xref>, <xref rid=\"B99\" ref-type=\"bibr\">99</xref>). A press release asserts that the Moderna mRNA vaccine induced antibodies to the SARS-CoV-2 S-protein in humans but contains no data that can placed into an appropriate context (Moderna Inc., Cambridge, MA). An animal study is summarized below (<xref rid=\"B98\" ref-type=\"bibr\">98</xref>). Like the viral vector vaccines, DNA vaccines against HIV-1 are often used in the prime-boost mode, in which a recombinant protein is administered to increase the generally weak response to several earlier immunizations with its DNA plasmid-delivered counterpart (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>, <xref rid=\"B90\" ref-type=\"bibr\">90</xref>). Inovio’s INO-4800 SARS-CoV-2 DNA vaccine has now been tested in mice and guinea pigs (<xref rid=\"B71\" ref-type=\"bibr\">71</xref>). This product, like its MERS-CoV predecessor, is based on the S-protein and was delivered by <italic>in vivo</italic> electroporation, a method that involves applying electric fields to muscle and skin tissue, opening membrane channels to allow uptake of the plasmid. Anti-RBD endpoint titers in the mice were ∼2,000 14 days after a single immunization. After 2 doses on days 0 and 14, the midpoint NAb titers on day 21 in an RV assay were in the range of 50 to 150 in one experiment and 240 to 640 in a PV assay in a second study. In guinea pigs, the endpoint anti-S protein titer 14 days after a single immunization was ∼10,000. After 3 doses on days 0, 14, and 28, the median midpoint NAb titers 1 to 2 weeks later were 570 and >320 in PV and RV assays, respectively. IgG capable of inhibiting the binding of the S-protein to ACE2 was purified from the guinea pig sera. Anti-S protein IgG antibodies were also present in mouse and guinea pig BAL samples at endpoint titers of ∼75 and ∼200, respectively. T-cell response assays on splenocytes were also performed (<xref rid=\"B71\" ref-type=\"bibr\">71</xref>).</p><p>The Moderna SARS-CoV-2 vaccine mRNA-1273 and an earlier MERS-CoV mRNA vaccine have been evaluated in normal and transgenic mice (<xref rid=\"B98\" ref-type=\"bibr\">98</xref>). In the MERS-CoV study, the mRNA was formulated as lipid nanoparticles and given at different doses to transgenic mice. It encoded a stabilized (S2P) S-protein, which was more immunogenic when expressed as a full-length membrane-associated protein than as a soluble S2P-foldon protein. The S2P form was also superior to unmodified S-protein. Geometric-mean NAb titers (ID<sub>50</sub>) in a PV assay were ∼15,000, ∼1,000, and ∼300 in the high-, intermediate-, and low-dose groups. When the mice were challenged with MERS, the two higher dose groups were fully protected (judged by weight and viral load [VL] assays), while the lowest-dose group was partially protected. A protective serum NAb titer in the 300 to 1,000 range can be inferred. The equivalent SARS-CoV-2 S2P mRNA was then given at weeks 0 and 3 to 3 different species of mice, again at 3 different doses. After the second dose, the anti-S protein endpoint titers in the highest-dose group were ∼250,000, 30,000, and 1,000,000 in BALB/cJ, C57BL6/J, and B6C3F1/J mice, respectively, with corresponding geometric-mean NAb ID<sub>50</sub> titers of 820, 89, and 1,100. Two doses of an adjuvanted S-2P protein gave endpoint anti-S protein titers of ∼1,000,000 and NAb titers in the 160 to 890 range in BALB/cJ mice. A single high dose of mRNA in BALB/cJ mice yielded NAb titers with a geometric mean of 320. Various IgG isotype, cytokine, and cellular immunity studies drew the conclusion that the overall immune response was balanced between Th1 and Th2, which was interpreted as beneficial for avoiding adverse events postinfection. Young adult BALB/cJ mice were then immunized and challenged with mouse-adapted SARS-CoV-2. The higher mRNA doses were fully protective against infection, as judged by VL endpoints in different tissues. The NAb titers in the various protected versus nonprotected groups were not listed, which precluded an assessment of the protective titer. A cross-comparison with earlier dosing experiments in the same mice suggests that a NAb titer of ∼800 is protective, but one of ∼80 is not (<xref rid=\"B98\" ref-type=\"bibr\">98</xref>).</p><p>The immunogenicity of various doses of a SARS-CoV-2 S-protein-based self-amplifying RNA (saRNA) vaccine, encapsulated in lipid nanoparticles, has been assessed in mice (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Although RNA based, the principle behind this vaccine is different from that of Moderna’s. After 2 immunizations at weeks 0 and 4, serum anti-S protein antibodies were induced in a dose-dependent manner that exceeded 1 mg/ml in the highest-dose group. Information on titers, the more traditional method of data presentation, was not included, but in the same assay, sera from COVID-19 patients were reported to contain anti-S antibodies that ranged from 10 ng/ml to 100 μg/ml, with a median value of 1 μg/ml. If the quantitation method used is accurate, the implication is that ∼10% of the IgG antibodies in the sera of the highest-dose mice were specific to the S-protein. NAb midpoint titers in a PV assay were also immunogen dose dependent, ranged from 5,000 to 100,000, and were correlated with the anti-S protein antibody responses in the same mice (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>).</p><p>Another variant of the mRNA vaccine concept involves an RNA replicon expressing the full-length S-protein and formulated as a lipid nanoparticle (<xref rid=\"B100\" ref-type=\"bibr\">100</xref>). The immunogen was given once to mice at 3 different doses. The two higher doses induced NAbs (PV assay) at ID<sub>50</sub> titers of 640 and 230 2 weeks later. Weaker binding antibody responses to the S-protein were induced in older mice rather than younger, which may have a bearing on age-dependent immunogenicity. Pigtailed macaques were then immunized either once (high dose) or twice (lower dose at weeks 0 and 4). The single high dose gave NAb ID<sub>50</sub> titers in the range of 20 to 50 after 4 weeks, which increased to the 100 to 450 range at week 6. In the two-dose regimen, NAb titers in the two immunized animals were 220 and 360 at week 6, and NAbs were also detected at similar ID<sub>80</sub> (note, not ID<sub>50</sub>) titers in an RV assay (<xref rid=\"B100\" ref-type=\"bibr\">100</xref>).</p><p>For all of the above-described RNA-based vaccines, it is unknown whether the results obtained in small animals will be matched in humans. The immunogenicity of mRNA and DNA vaccines is generally far stronger in small animals than in macaques, and more so in humans (<xref rid=\"B101\" ref-type=\"bibr\">101</xref>, <xref rid=\"B102\" ref-type=\"bibr\">102</xref>). In humans, 3 doses of an S-protein-expressing MERS-CoV DNA vaccine induced peak anti-S protein endpoint titers ranging from undetectable (<10) to 300,000, with peak endpoint NAb titers of <10 to ∼300 that were mostly undetectable 6 months later (<xref rid=\"B103\" ref-type=\"bibr\">103</xref>). An Inovio DNA vaccine, also delivered by <italic>in vivo</italic> electroporation, induced only very weak antibody responses to HIV-1 Env proteins in a recent human trial (<xref rid=\"B104\" ref-type=\"bibr\">104</xref>).</p><p>Recombinant proteins, delivered with an adjuvant, generally trigger stronger antibody responses than viral or nucleic acid vectors. Antibody endpoint titers induced by SARS-CoV-1 and -2 S-protein vaccines can reach ∼100,000 (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>, <xref rid=\"B105\" ref-type=\"bibr\">105</xref>). Multiple doses of adjuvanted proteins are generally needed to elicit high antibody titers in animals and humans. The standard human immunization schedule involves 3 doses at 0, 8, and 24 weeks, with similar protocols as used in animals (<xref rid=\"B106\" ref-type=\"bibr\">106</xref><xref ref-type=\"bibr\" rid=\"B107\">–</xref><xref rid=\"B108\" ref-type=\"bibr\">108</xref>). While the third dose could be given sooner, this type of schedule would be problematic for a vaccine aimed at a rapid rollout. However, strong antibody responses were elicited in rabbits after only 2 S-protein doses at weeks 0 and 2, which is encouraging. In this experiment, the animals were immunized twice at 14-day intervals with 50 μg of the complete SARS-CoV-2 S-protein ectodomain (i.e., S1+S2), the S1 or S2 fragments, or the RBD with Emulsigen adjuvant (<xref rid=\"B109\" ref-type=\"bibr\">109</xref>). The S1+S2 and S2 proteins were produced in insect cells, the other two in HEK 293 mammalian cells. Endpoint ELISA titers to the various S-proteins (except S2) after the second dose were around 100,000. NAb titers in a PV assay were not presented, but a 1:40 serum dilution conferred 80% to 100% neutralization for all the immunization groups except S2. The highest affinity Abs were directed against the RBD.</p><p>A much more complex regimen was used to assess a SARS-CoV-2 RBD-Fc protein. The construct was expressed in mammalian cells, conjugated to keyhole limpet hemocyanin (KLH), and mixed with AS01 adjuvant for an immunogenicity study in rats (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). The eight animals were dosed 7 times, daily over a 1-week period, with ever-increasing amounts of the protein; they received a total dose of 500 μg. After a 30-day period, the immunization regimen was repeated using another 500 μg. NAbs were measured in a PV assay, with the data were presented in a nontraditional format. By comparing the extent of PV infection inhibition with that conferred by an ACE2-Ig construct, the authors concluded that the pooled rat sera contained NAbs that were equivalent to a 100 μg/ml (1 μM) concentration of an inhibitor with an IC<sub>50</sub> of 1 nM. It is not simple to translate this estimate to studies of other immunogens. Of note is that the anti-RBD sera did not mediate ADE under <italic>in vitro</italic> conditions in which this outcome was seen with Zika virus and rat sera raised against it (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>).</p><p>Mice were given a different RBD-Fc (mouse) fusion construct on days 0, 8, and 13 (100 μg of protein in Alum/CpG adjuvant and then 50 μg in complete Freund’s adjuvant and finally 50 μg in Titermax). By day 26, serum anti-RBD Abs blocked ACE2 binding at dilutions in the 100 to 10,000 range, while NAb ID<sub>50</sub> titers in a PV assay were ∼10,000. In a follow-up experiment using a simpler protocol, the mice received 5 μg of an Alum-adjuvanted RBD-His protein on days 1, 10, and 25. Anti-RBD endpoint titers by day 40 were 3,000,000, ACE2-blocking titers were also high, and NAb ID<sub>50</sub> titers in a PV assay were 13,000. In an assay to monitor ADE, the sera did not enhance virus entry into FcR-expressing cells (<xref rid=\"B110\" ref-type=\"bibr\">110</xref>).</p><p>In another study, mice were immunized on days 0 and 21 with 25-μg doses of the SARS-CoV-2 RBD in “QuickAntibody adjuvant.” The autologous endpoint anti-RBD titer was ∼75,000 on day 35, while midpoint autologous NAb titers in a PV assay were ∼15,000. Titers this high exceed by orders of magnitude what is seen with, for example, nucleic acid-based vaccines in small animals, to the extent that cross-study comparisons are possible (<xref rid=\"B111\" ref-type=\"bibr\">111</xref>).</p><p>Presentation of proteins as particulate antigens usually benefits antibody responses (<xref rid=\"B112\" ref-type=\"bibr\">112</xref>). MERS-CoV RBD-based nanoparticles (NPs) of the SpyTag/SpyCatcher design were ∼10-fold more immunogenic than their soluble protein counterparts when rabbits were immunized in Adjuplex adjuvant on days 0 and 28. Endpoint titers to S-proteins in the RBD-NP group exceeded 100,000 by day 46, while 90% (ID<sub>90</sub>) NAb titers in a RV assay of ∼5,000 compared favorably to ∼500 for the soluble RBD protein group. After challenge with MERS-CoV, nasal swab viremia was reduced by ∼1,000-fold in the RBD-NP recipients but not in the soluble RBD group, which implies titer-dependent NAb-mediated protection (<xref rid=\"B113\" ref-type=\"bibr\">113</xref>).</p><p>Recombinant protein vaccines are usually given with an adjuvant to boost their immunogenicity, and although details are scarce, it seems likely this would be the case when SARS-CoV-2 proteins are used in humans. Adjuvants vary in potency, with the one most commonly used in humans (Alum) being considerably less effective than newer but less-well-studied alternatives (<xref rid=\"B114\" ref-type=\"bibr\">114</xref>). Attention clearly needs to be placed on this area, so that the best possible adjuvant is used, whichever company produces it. It is encouraging, however, that multiple different adjuvants have supported very strong NAb responses to SARS-CoV-2 (and, earlier, to SARS-CoV-1) RBD immunogens in various animals (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B87\" ref-type=\"bibr\">87</xref>, <xref rid=\"B110\" ref-type=\"bibr\">110</xref>, <xref rid=\"B111\" ref-type=\"bibr\">111</xref>). As adjuvants such as complete Freund’s are known to be quite damaging to the structural integrity of proteins, the key NAb epitopes on RBD proteins may be quite robustly presented. Countering this optimism somewhat is the rapid decay of antibody and NAb responses to the SARS-CoV-1 RBD protein in immunized mice, as noted above (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>).</p></sec><sec id=\"s6\"><title>PRODUCTION OF RECOMBINANT S-PROTEIN AND RBD VACCINE CANDIDATES</title><p>The most immunogenic vaccine candidates tested to date are recombinant S- and RBD-proteins (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). How easily can such proteins be produced in bulk? General experience suggests that constructing stable lines and producing high-quality recombinant proteins will take well more than one year. The SARS-CoV-2 S-protein is highly glycosylated (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>, <xref rid=\"B77\" ref-type=\"bibr\">77</xref>) (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1B</xref>). Experience from the HIV-1 field suggests that making large amounts of glycan-rich proteins can be extremely challenging. When properly folded HIV-1 Env trimers are produced by transient transfection of mammalian cells under academic laboratory conditions, the yields are in the range 1 to 5 mg/liter (<xref rid=\"B108\" ref-type=\"bibr\">108</xref>, <xref rid=\"B115\" ref-type=\"bibr\">115</xref>). Soluble SARS-CoV-2 S-proteins can be expressed and purified under similar conditions at generally similar levels. The smaller and less-glycosylated RBD is easier to make than the full-length S-protein or the S1 fragment. Thus, the RBD was produced at 25 to 50 mg per liter in Expi293F cells, a 5- to 10-fold higher yield than the S-protein (<xref rid=\"B116\" ref-type=\"bibr\">116</xref>). A much greater yield, ∼200 mg/liter, was achieved for the SARS-CoV-1 RBD in yeast cells (<xref rid=\"B117\" ref-type=\"bibr\">117</xref>). Fully purified SARS-CoV-2 RBD and S proteins were produced at 30 and 1 mg/liter, respectively, in insect cells, which, like yeasts, express different glycoforms than mammalian cells (<xref rid=\"B118\" ref-type=\"bibr\">118</xref>). Whether the characteristics of the glycans matter for immunogenicity can only be determined in comparative studies. The application of structure-guided design principles improved the yield of the S-protein variant by ∼10-fold compared to that of the wild type, with a 5°C increase in thermal stability; the stabilized HexaPro variant was produced at 32 mg/liter in transiently transfected ExpiCHO cells (<xref rid=\"B119\" ref-type=\"bibr\">119</xref>). An additional S-protein polymorphism, D614G, increases protein stability and may also benefit production (<xref rid=\"B85\" ref-type=\"bibr\">85</xref>).</p><p>Mammalian cell lines are likely to be the substrates for large-scale vaccine production, but even if highly producing lines are cultured in industrial facilities, the amounts of immunogens needed will still be daunting. Mice have been immunized with 5-μg doses of SARS-CoV-2 S-proteins or 10 μg of a SARS-CoV-1 RBD-Fc (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>), while rabbits were given 50 μg of SARS-CoV-2 S-protein-based immunogens (<xref rid=\"B109\" ref-type=\"bibr\">109</xref>) and macaques and rats received 500- and 1,000-μg doses (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B105\" ref-type=\"bibr\">105</xref>). The protein dose used in humans varies, but HIV-1 envelope glycoproteins are generally given in the 100 to 500 μg range. Even if SARS-CoV-2 S-proteins are given to humans at a relatively low dose of 100 μg, a full course of 3 immunizations would require 300 μg of protein. In other words, around 1 g of S-protein would be needed to immunize 3,000 people and, hence, 1 kg for 3 million. It will be no simple matter to produce these amounts of recombinant proteins rapidly. Gram quantities of properly folded HIV-1 trimers were made for phase I trials (<xref rid=\"B115\" ref-type=\"bibr\">115</xref>). Larger amounts of earlier generation HIV-1 gp120 subunits were produced for efficacy trials in a few thousand people, but the process was not simple (<xref rid=\"B120\" ref-type=\"bibr\">120</xref>). A MERS-CoV RBD construct has been produced in a stable CHO cell line at a final yield, post purification, of 89 mg/liter (<xref rid=\"B121\" ref-type=\"bibr\">121</xref>). A stable CHO cell line can express 50 mg/liter of the S1-Fc protein; it has been estimated that a 3,000-liter bioreactor could produce 3 million doses of a human COVID-19 vaccine of this design every 10 days (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). RBD proteins might be produced more efficiently (<xref rid=\"B87\" ref-type=\"bibr\">87</xref>).</p></sec><sec id=\"s7\"><title>VACCINE CHALLENGE EXPERIMENTS IN MONKEY MODELS</title><p>Animal model studies involving vaccine immunization followed by virus challenge can provide useful information on the requirements for human protection but are often difficult to interpret unequivocally. Several such experiments in small animals are summarized above. Our experience with the HIV-1 vaccine field over 30 years tells us that the outcomes of animal experiments tend to be emphasized when they support the development of a particular vaccine candidate but dismissed as of minimal relevance when they do not. We see few grounds to believe that this aspect of human nature will be any different for SARS-CoV-2 vaccines. The extensive SARS-CoV-1 and MERS-CoV animal model literature has been thoroughly reviewed (<xref rid=\"B122\" ref-type=\"bibr\">122</xref><xref ref-type=\"bibr\" rid=\"B123\">–</xref><xref rid=\"B124\" ref-type=\"bibr\">124</xref>). What it teaches us about vaccine-mediated adverse events is a topic that we address separately below. A comprehensive review of SARS-CoV-2 infection and pathogenesis models is now available (<xref rid=\"B125\" ref-type=\"bibr\">125</xref>).</p><p>A common finding in HIV-1 animal model research is that it is easier to protect against a virus that replicates inefficiently in the host and that does not cause severe disease than against a more lethal challenge. This scenario may apply also to SARS-CoV-2 animal models (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B125\" ref-type=\"bibr\">125</xref><xref ref-type=\"bibr\" rid=\"B126\">–</xref><xref rid=\"B127\" ref-type=\"bibr\">127</xref>). Thus, the more limited replication of this virus in monkeys may make these animals easier to protect than humans. When 8 rhesus macaques were infected with SARS-CoV-2, they all became sick with signs of lung pathology. Three were killed on day 3 for postmortem analyses, the other 4 postrecovery. However, all 4 living animals recovered between 9 and 17 days postinfection and none died (<xref rid=\"B128\" ref-type=\"bibr\">128</xref>). Anti-S-protein endpoint titers were in the range of 1,500 to 3,000, which is at the lowest end of what is seen in human COVID-19 cases and is associated with mild disease (see above). NAb endpoint titers varied from 5 to 60 and were also low compared to that in humans (<xref rid=\"B128\" ref-type=\"bibr\">128</xref>). Although the study was too small for a definitive comparison with human COVID-19 disease cohorts, one interpretation is that the rhesus macaque model may best reflect what happens in humans with mild-to-moderate disease and who do not require hospitalization. The African Green Monkey could be a superior model, as SARS-CoV-2 replicates to quite high titers in this species and causes substantial disease as measured by various criteria, including lung pathology (<xref rid=\"B129\" ref-type=\"bibr\">129</xref>). The animals did seroconvert rapidly, although as the antibody assays were based on whole virus lysates, titer comparisons to other species are problematic. Earlier studies on SARS-CoV-1 infection also showed that African Green Monkeys were more susceptible to disease than their macaque counterparts (<xref rid=\"B130\" ref-type=\"bibr\">130</xref>). In contrast, cynomolgus macaques are less affected than rhesus by SARS-CoV-2 (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>).</p><p>Four high-profile studies of candidate SARS-CoV-2 vaccine candidates in the macaque challenge model have now been published (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B88\" ref-type=\"bibr\">88</xref>, <xref rid=\"B89\" ref-type=\"bibr\">89</xref>, <xref rid=\"B131\" ref-type=\"bibr\">131</xref>). Superficially, the outcomes were similar; the animals were reportedly protected from disease, although not from infection. There are, however, substantial differences among them. Data from these studies are included in <xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>.</p><p>The first paper to appear was based on a killed virus vaccine (Sinovac) in an Alum adjuvant (<xref rid=\"B88\" ref-type=\"bibr\">88</xref>). When tested initially in mice and rats, anti-S protein endpoint titers exceeded 100,000 and approached 1,000,000 by the end of the immunization schedule. These titers are at the high end of the range measured in COVID-19 cases; the authors recorded a titer of ∼30,000 for one such human serum sample in the same assay. The peak midpoint NAb titers, measured in a RV assay, in these rodents were ∼1,000, far higher than the value of 30 for a human COVID-19 serum sample under the same conditions. The 4 macaques given different doses of the same vaccine responded with peak anti-S protein endpoint titers of 13,000 and NAb titers of ∼50. There was an immunodominant response to the RBD over that for other components of the killed virus vaccine, implying that it might behave in a broadly similar way to an S-protein or RBD subunit vaccine. All 4 macaques became infected after SARS-CoV-2 challenge, but the severity of the (normally mild) disease was reduced compared to that in control animals. Viral loads in throat swabs were also lower in the vaccinated animals than in controls, particularly in the highest-dose group, and continued to decline during the period 3 to 7 days postchallenge when they remained constant in the control animals. No adverse events were reported, either before challenge or after infection (<xref rid=\"B88\" ref-type=\"bibr\">88</xref>).</p><p>A later report on another killed virus vaccine involved the Sinopharm BBIBP-CorV product (<xref rid=\"B89\" ref-type=\"bibr\">89</xref>). Three different doses (2, 4, and 8 μg) in Alum adjuvant were given, once, to BALB/c mice. The peak NAb IC<sub>50</sub> titer (RV assay) in the higher-dose groups on day 21 was 1,024. Similar titers were induced by a 2-dose regimen, given on days 0 and 7, while 3 immunizations on days 0, 7, and 14 led to NAb titers of ∼4,000. Various 1- and 3-dose regimens were then tested in other species. The higher-dose groups gave the following NAb titers in the 3-dose regimen: cynomolgus monkeys, ∼250; rabbits, ∼400; guinea pigs, ∼400; rats, ∼500; mice, ∼3,000. Safety studies in rats, guinea pigs, and cynomolgus macaques, assessed in various ways at different doses and times, found nothing notable. In the macaque challenge study, the animals (4 per group) were given either 2 μg or 8 μg of the vaccine on days 0 and 14, with median NAb titers of 215 and 256, respectively, at the time of SARS-CoV-2 challenge on day 24. There were no changes in body temperature in the vaccine or placebo groups over the next 7 days, which is indicative of the mild disease course in these animals. Viral loads in throat and anal swabs and postmortem lung tissues were lower by several orders of magnitude (depending on the time point and sample location) in the two vaccine groups than for the placebo, particularly in the higher-dose group. Lung pathology was also reduced/eliminated in the vaccine groups. It is possible but not unequivocally demonstrated that the higher-dosed animals were completely protected from infection (<xref rid=\"B89\" ref-type=\"bibr\">89</xref>).</p><p>Another macaque study involved the “Oxford vaccine,” a chimpanzee adenovirus construct (ChAdOx1 nCoV-19) that has attracted considerable media attention worldwide. The recombinant virus vector expresses the SARS-CoV-2 S-protein (<xref rid=\"B131\" ref-type=\"bibr\">131</xref>). This vaccine induced anti-S endpoint titers of 100 to 1,000 in BALB/c mice and around 1,000 in CD1 mice, which are very low responses at the bottom end of the range seen in COVID-19 human cases. NAb endpoint titers (i.e., not the more usually reported and much lower midpoints) in an RV assay were ∼40 for the BALB/c mice but were undetectable for 2 of the 5 animals; in the CD1 mice, the median titer was 80. In the macaque study, the peak endpoint anti-S protein titers were ∼1,000, which is similar to the peak titer of ∼1,500 seen when the same group’s MERS-CoV adenovirus vector vaccine was tested at its highest dose in humans (<xref rid=\"B92\" ref-type=\"bibr\">92</xref>, <xref rid=\"B131\" ref-type=\"bibr\">131</xref>). The median NAb endpoint titer measured in the macaques was ∼40. Taken together, in both mice and macaques, the antibody responses to this live recombinant virus vector seem very weak, which is consistent with how adenovirus-based HIV-1 vaccines perform in macaques and humans unless a protein boost is given (<xref rid=\"B90\" ref-type=\"bibr\">90</xref>, <xref rid=\"B91\" ref-type=\"bibr\">91</xref>). All 6 of the ChAdOx1-vaccinated macaques became infected after SARS-CoV-2 challenge, although with fewer symptoms, including reduced lung damage compared to that of the control group. Significant viral load reductions in various tissues were also reported. No adverse events were found, before or after infection, that could be vaccine attributed (<xref rid=\"B131\" ref-type=\"bibr\">131</xref>).</p><p>DNA vaccines expressing 6 different SARS-CoV-2 S-protein variants, including the full-length S-protein and the RBD, were tested in rhesus macaques (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). The DNA plasmids, without adjuvant, were given intramuscularly at weeks 0 and 3, and the animals were challenged with SARS-CoV-2 at week 6. Median endpoint anti-S protein titers at week 5 varied moderately with the immunogen but were ∼100 for the S-protein and RBD immunogen groups. These titers are ∼10-fold and ∼150-fold lower than recorded in the ChAdOx1 and killed virus studies in the same species, respectively. Midpoint NAb titers induced by the DNA vaccines at week 5 also varied by immunogen, with median values of ∼100 to 200 in a PV assay and ∼20 to 30 when infectious virus was used. The PV NAb assay titers for sera from human COVID-19 cases ranged from ∼20 to 200 in the same assay. The NAb titers in the RV assay seem comparable to those induced by the ChAdOx1 and killed virus immunogens, assuming the different tests have similar sensitivities. When the animals were challenged, all of them became infected as judged by anamnestic antibody responses, although 8 of the 25 DNA vaccine recipients were RNA negative in lung and nasal samples. Viral loads in the other 17 animals were 3 to 4 logs lower than in the 10 control animals. NAb titers were significantly higher in the 8 nonviremic macaques than in the 17 in which viremia was quantified, suggesting that NAbs were a correlate of protection. As in the other three experiments, no adverse events were identified (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>).</p><p>Few if any of the animals in the above-described macaque experiments were completely protected from infection, although in each case, there was a reduction in the severity of the already mild disease this virus causes in macaques. Viral loads in nasal swabs were, however, comparable between the vaccine and control groups. This observation caused questions to be raised about the efficacy of the ChAdOx1 vaccine (<xref rid=\"B132\" ref-type=\"bibr\">132</xref>). However, interpretation of viral load data is complicated by the likely sustained presence of challenge virus RNA in some sites, particularly those accessible by nasal swabs (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). What is particularly surprising is that the similar outcomes were associated with substantial (in some cases >100-fold) differences in antibody titers to the S-protein, with the killed virus vaccines being the strongest immunogens (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). Are the antibody responses induced by the ChAdOx1 and DNA vaccines solely responsible for any protection that was conferred? Perhaps cellular immune responses or some other unmeasured factor, such as mucosal IgA, were contributory (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). On a more technical level, it is not clear why very low anti-S protein titers are associated with significant NAb titers in the DNA vaccine experiment but not in the ChAdOx1 study (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B131\" ref-type=\"bibr\">131</xref>). Nonetheless, it can reasonably be concluded that the ChAdOx1 vaccine, whether for MERS-CoV or SARS-CoV-2, is not a strong inducer of antibody responses to the S-protein in macaques, which also seems true of the DNA plasmids (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). modified vaccinia Ankara (MVA) vector systems are likely to behave similarly to the ChAdOx1 and DNA immunogens, based on the weak anti-S protein response to their encoded MERS-CoV S-protein in humans (<xref rid=\"B93\" ref-type=\"bibr\">93</xref>). These inferences are similar to what has been seen in studies of other vaccines, such as HIV-1 Env, where only protein-based immunogens induce very strong antibody titers (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>).</p></sec><sec id=\"s8\"><title>WHAT IS A PROTECTIVE ANTIBODY TITER FOR A SARS-COV-2 VACCINE AND HOW LONG MIGHT IT PERSIST?</title><p>Poorly understood genetic variables affect how different people respond to the same immunogen, which is a key point when vaccinating large populations. A vaccine is useful if the majority of the recipients develop an antibody response that exceeds the protection threshold and, preferably, for a period measured in years not weeks (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>). Typically, antibody titers vary by well more than 100-fold among people given HIV-1 or influenza virus protein vaccines (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>, <xref rid=\"B133\" ref-type=\"bibr\">133</xref>, <xref rid=\"B134\" ref-type=\"bibr\">134</xref>). Antibody responses, neutralizing or not, to SARS-CoV-1 and -2 and MERS-CoV S-protein-based vaccines are similarly variable in animals and humans (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B62\" ref-type=\"bibr\">62</xref>, <xref rid=\"B88\" ref-type=\"bibr\">88</xref>, <xref rid=\"B92\" ref-type=\"bibr\">92</xref>, <xref rid=\"B93\" ref-type=\"bibr\">93</xref>, <xref rid=\"B103\" ref-type=\"bibr\">103</xref>). As an extreme example of how antibody responses can vary across a human study cohort, peak anti-S protein antibody titers induced by a MERS-CoV DNA vaccine ranged from 3 to 300,000, and in many volunteers, no antibodies were detectable at most time points (<xref rid=\"B103\" ref-type=\"bibr\">103</xref>). Thus, a key parameter is where a protective titer lies compared to the range of responses induced by the various vaccine candidates (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>). Do only the strongest responders or most of them exceed a protective threshold? For how long?</p><p>We do not know what magnitude of a vaccine-elicited antibody response could protect humans from SARS-CoV-2 infection and/or severe disease. In the small-scale rhesus macaque SARS-CoV-2 rechallenge experiment referred to above, the two apparently protected macaques had NAb midpoint titers of 8 and 16 in a RV assay on the day of their second challenge. Although anti-S antibodies were measured, no titer data were reported (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>). No correlate of protection could be identified in either this study or the somewhat larger one of a similar design (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>, <xref rid=\"B64\" ref-type=\"bibr\">64</xref>). Information on possibly protective NAb titers may emerge in the coming months from passive immunotherapy studies in which plasma samples from recovered COVID-19 patients are infused into those with active infection (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B135\" ref-type=\"bibr\">135</xref><xref ref-type=\"bibr\" rid=\"B136\">–</xref><xref rid=\"B137\" ref-type=\"bibr\">137</xref>). The NAb and binding antibody titers infused could be compared with the observed clinical outcomes, although any relationship to vaccine-mediated protection will be imprecise.</p><p>The ACE2 proteins of multiple animals have been sequenced and their abilities to bind the SARS-CoV-2 S-protein modeled (<xref rid=\"B138\" ref-type=\"bibr\">138</xref>). The modeling includes species possibly relevant to cross-species transmission (bats, pangolins, civets, and raccoons), domestic pets (cats, dogs, and tigers), farm animals (cows and sheep), and possible infection models (hamsters, mice, guinea pigs, and ferrets). Future model systems may emerge from this kind of analysis. In addition to the macaque experiments reviewed above, various small animal models have already been used in SARS-CoV-2 challenge experiments. When Syrian golden hamsters were exposed nasally to SARS-CoV-2, virus was detected in the lungs by day 2, but the animals cleared the infection by day 7 and fully recovered (<xref rid=\"B139\" ref-type=\"bibr\">139</xref>). NAb titers of 1:640 were measured on day 7 using an RV assay. The infection can be transmitted, via shared air, to other hamsters in adjacent cages, which is a useful feature that could also be exploited for vaccine efficacy studies (<xref rid=\"B139\" ref-type=\"bibr\">139</xref>). Transgenic human ACE 2 (hACE2) mice can be infected by SARS-CoV-2, leading to modest disease that includes lung damage associated with infiltration of macrophages and lymphocytes (<xref rid=\"B140\" ref-type=\"bibr\">140</xref>). Both of these small animal models have been used in nMAb passive transfer and challenge studies summarized below (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>).</p><p>Several human nMAbs have now been evaluated for passive protection of small animals or rhesus macaques (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B74\" ref-type=\"bibr\">74</xref>, <xref rid=\"B141\" ref-type=\"bibr\">141</xref>). The caveats expressed above about vaccine protection in animal models applies also to passive immunization experiments, which complicates quantitative extrapolations to human protection. Two nMAbs were tested in the Syrian hamster challenge model (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). MAb CC12.1 is to the RBD-A site, with a NAb IC<sub>50</sub> titer of 19 ng/ml, while CC12.23 recognizes the S-B epitope and is ∼1,000-fold less potent, with an IC<sub>50</sub> of 22 μg/ml. Five different doses were delivered intraperitoneally to the animals, which then received an intranasal SARS-CoV-2 challenge 12 h later. Animal weight was used as an endpoint to measure disease as were viral load assays on lung tissue postmortem (day 5). The potent CC12.1 nMAb conferred dose-dependent protection from disease. There was a trend toward greater weight loss than with a control MAb in the animals given the lowest doses of CC12.1, which is a potential concern because of the possibility of ADE (or similar) at a subthreshold NAb dose (see below). Pharmacokinetic measurements show that a serum antibody concentration of 22 μg/ml was required for full protection, which corresponds to 1,200 times the neutralization IC<sub>50</sub> in the PV assay (for 50% protection from disease, the values were 12 μg/ml and 630 times the IC<sub>50</sub>). The much-less-potent CC12.23 nMAb was not protective at any dose, further indicating that protection correlates with dose-dependent neutralization (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>).</p><p>Single doses of the B38 and H4 nMAbs were administered to hACE2 transgenic mice followed by SARS-CoV-2 challenge 12 h later, with body weight and viral load serving as endpoints (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). B38 (IC<sub>50</sub>, 180 ng/ml) was modestly effective at reducing the weight loss, but the viral loads were significantly suppressed ∼1,000-fold.</p><p>The BD-368-2 MAb (IC<sub>50</sub> of 1.2 ng/ml in a PV assay) was also tested in hACE2 transgenic mice (<italic>n</italic> = 3 per group) both for therapy and prevention (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>). Given at 20 mg/kg 24 h prior to SARS-CoV-2 challenge, the MAb blocked infection completely as judged by viral load measurements in the lung, although there was a modest weight loss that might indicate low-level infection. When BD-368-2 was instead given 2 h after challenge, there was a similarly modest weight loss, but the mice did become infected albeit with a 3 to 4 log reduction in lung viral load compared to that in the control. Protective antibody doses could not be inferred from this study, but it does suggest that the window for complete protection may be quite short if nMAbs are used for postexposure prophylaxis.</p><p>Anti-RBD MAb CB6, with an IC<sub>50</sub> value in the range of 20 to 50 ng/ml that depends on the assay used, was evaluated in rhesus macaques after modification of its Fc region to reduce the risk of ADE (<xref rid=\"B141\" ref-type=\"bibr\">141</xref>). A single dose of 50 mg/kg intraperitoneally (i.p.) given 1 day prior to challenge conferred substantial protection from infection, as judged by VLs in throat swabs. When the same dose was administered 1 and 3 days postchallenge, the rate of decrease of VL was significantly greater than in the control animals (which naturally clear the virus within 7 days). Lung damage was also lower in the MAb recipients (<xref rid=\"B141\" ref-type=\"bibr\">141</xref>).</p><p>We noted above that complete protection of Syrian hamsters against SARS-CoV-2 challenge required a serum nMAb concentration equivalent to 1,200 times the neutralization IC<sub>50</sub> (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). For comparison, a comprehensive meta-analysis of nMAb passive immunization experiments showed that 95% protection of macaques from mucosal simian-human immunodeficiency virus (SHIV) challenge required serum nMAb levels 680-fold greater than the ID<sub>50</sub> (<xref rid=\"B142\" ref-type=\"bibr\">142</xref>). Although there are obvious differences between intranasal SARS-CoV-2 infection of hamsters and rectal SHIV infection of monkeys, the quantitative aspects of passive nMAb protection seem quite similar. When 5 mice were immunized with the SARS-CoV-1 RBD protein and then virus challenged, 4 were apparently completely protected and the fifth partially. The serum ID<sub>50</sub> NAb titer in the infected mouse at the time of challenge was 57, while the titers in the protected animals ranged from 189 to 505 with a mean value of 390 (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). A broadly similar serum ID<sub>50</sub> NAb titer of 500 was associated with 90% protection against rectal challenge of macaques after active vaccination with an HIV-1 envelope glycoprotein trimer (<xref rid=\"B107\" ref-type=\"bibr\">107</xref>). In a conceptually similar experiment, the same envelope glycoprotein trimers induced protective serum NAb titers of ∼300. However, when the animals were immunized with a viral vector vaccine before boosting with the trimer, durable protection was achieved at substantially low NAb titers (<xref rid=\"B143\" ref-type=\"bibr\">143</xref>). A combination prime-boost vaccine incorporating components that induce both cellular immune responses and NAbs (e.g., a viral vector or a nucleic acid plasmid plus a recombinant RBD or S-protein) might be worth exploring.</p><p>Almost all attention has been placed on measuring antibody responses to vaccines in serum. However, SARS-CoV-2 levels in blood are very low, both in absolute terms and compared to that in other body fluids such as nasal secretions (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). This virus is, of course, usually transmitted via mucosal surfaces, where IgA antibodies are a substantial source of immunity. Very little is known about the mucosal IgA response in COVID-19 cases or after experimental vaccination, which are gaps that warrant filling. In one report, IgA antibodies to the S-protein were found in nasal swabs, tears, and saliva from a few health care workers who were exposed to SARS-CoV-2 but remained uninfected; in general, the mucosal IgA responses were stronger in younger people than in older ones (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). The possibility exists, therefore, that some virus-exposed people may develop mucosal immunity without becoming systemically infected or seroconverting. In this context, a proposal to focus SARS-CoV-2 vaccine development more on mucosal immune responses is worth considering (<xref rid=\"B144\" ref-type=\"bibr\">144</xref>). Passive transfer experiments with mucosally administered IgA antibodies seem worth pursuing. An engineered IgA version of an nMAb, with moderate potency, has been described (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>).</p><p>Antibody responses to coronavirus infection are not particularly long lasting (see above). Hence, another key unknown is how long any protective response to a SARS-CoV-2 vaccine might last. Active or passive immunization experiments in animals almost always involve virus challenges when the antibody titers are at or near their peak values (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref> and <xref ref-type=\"fig\" rid=\"F3\">3</xref>). This scenario would rarely apply to vaccinated humans. The few human studies of MERS-CoV and SARS-CoV vaccines show that anti-S antibody titers decline fairly rapidly (within months) from the peak, although detailed information on the decay rates is not available (<xref rid=\"B92\" ref-type=\"bibr\">92</xref>, <xref rid=\"B93\" ref-type=\"bibr\">93</xref>, <xref rid=\"B103\" ref-type=\"bibr\">103</xref>, <xref rid=\"B131\" ref-type=\"bibr\">131</xref>). As noted above, the binding antibody titers to the SARS-CoV-1 RBD protein in mice declined by ∼15,000 over a 9-month period (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). Obtaining data on the medium- and long-term antibody decay rates in SARS-CoV-2 vaccinated humans will be essential. It is possible that exposure to SARS-CoV-2 will trigger rapidly protective recall responses even months to years after the course of vaccination. Alternatively, frequent boosting regimens may need to be used.</p><p>In summary, it is not known what benchmark serum antibody and NAb titers must be reached for a SARS-CoV-2 S-protein vaccine to protect humans. The animal challenge experiments reviewed above suggest that a serum NAb ID<sub>50</sub> titer in the approximate range of 100 to 500 is required for sterilizing immunity (i.e., complete protection from acquisition). If so, this magnitude of response in a human population may be best achieved by a recombinant S-protein or, arguably better, an RBD immunogen (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref> and <xref ref-type=\"fig\" rid=\"F3\">3</xref>). It is, conceivably, more feasible to induce B-cell memory responses that might protect from disease but not from acquisition. If the early observations in macaques hold true for humans, protection from disease might be the best that the Warp Speed vaccines can accomplish anyway. It also remains to be seen how long protective immunity might persist, but regular booster immunizations may be necessary (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>).</p></sec><sec id=\"s9\"><title>VACCINE-MEDIATED ADVERSE EVENTS</title><p>If a vaccine confers protection to almost all of its recipients and has no deleterious effect in the minor proportion of people it fails to protect, there are few grounds for concern. No vaccine is fully protective for a large population, but creating herd immunity against SARS-CoV-2 may require a vaccine efficacy rate of only ∼70% if the basic reproductive number for the infection in a naive population is ∼3 (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>, <xref rid=\"B145\" ref-type=\"bibr\">145</xref>). It would take ∼1 million deaths for this degree of herd immunity to be achieved in the United States without a vaccine, and manyfold more for a protective outcome worldwide (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>). Such estimates, of course, assume that SARS-CoV-2 infection does confer long-lasting immunity (see above).</p><p>Whereas a lack of efficacy is clearly undesirable, a vaccine used on a large scale that increases the risk of acquiring an infection or that exacerbates disease postinfection would be disastrous. A poorly protective vaccine will lead to the infection of many individuals who have already mounted antiviral immune responses. In particular, vaccinating during a pandemic could involve a scenario in which weak and potentially deleterious priming responses are induced in people who then encounter the virus before they receive their boosting immunizations (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). In a recent review of what was observed with several SARS-CoV-1 and MERS-CoV vaccines in virus-challenged animals, 36 research papers were identified that reported adverse outcomes, including but not limited to lung pathologies (<xref rid=\"B123\" ref-type=\"bibr\">123</xref>). Other reviews have also listed multiple examples of adverse events in coronavirus vaccine experiments (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref><xref ref-type=\"bibr\" rid=\"B5\">–</xref><xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B122\" ref-type=\"bibr\">122</xref>, <xref rid=\"B146\" ref-type=\"bibr\">146</xref><xref ref-type=\"bibr\" rid=\"B147\">–</xref><xref rid=\"B148\" ref-type=\"bibr\">148</xref>). Severe disease caused by SARS-CoV-1 tends to occur around week 3 after infection, when the viral load in the respiratory tract diminishes as NAb titers rise (<xref rid=\"B149\" ref-type=\"bibr\">149</xref>). As summarized above, the inverse correlations for the magnitude of the antibody response are seen in both SARS and COVID-19 cases. Taken together, there are concerns that the antibody responses to SARS-CoV-1 and -2 may not protect against disease but could even contribute to pathogenesis (see above).</p><p>One widely discussed area of concern is ADE. For some viruses, such as dengue and West Nile viruses, antibodies can enhance the degree of infection of the standard target cells by ligating proteins on the viral surface while also interacting with Fc receptors (or indirectly with complement receptors) on the cell surface. The outcome is to increase the uptake of viruses into the endosomal compartment, where receptor-mediated membrane fusion leads to productive infection of the cell. ADE can be mediated by non-NAbs or by ineffective NAbs when their occupancy of viral spikes is too low for neutralization. Only antibodies that bind to epitopes exposed on the virion surface can mediate ADE, and it is not clear how they could do so without interfering with infection; one possibility is via binding to nonfunctional S-proteins (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>, <xref rid=\"B150\" ref-type=\"bibr\">150</xref>). Whatever the mechanism, a previous infection with an antigenically related virus or a vaccine that induces non-NAbs, or inadequately effective or poorly persistent NAbs, could cause ADE (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B150\" ref-type=\"bibr\">150</xref>). ADE was responsible for the adverse outcomes of some dengue virus vaccine trials (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>).</p><p>The risk of ADE for SARS-CoV-2 is a topic for serious discussion (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref><xref ref-type=\"bibr\" rid=\"B5\">–</xref><xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B73\" ref-type=\"bibr\">73</xref>, <xref rid=\"B122\" ref-type=\"bibr\">122</xref>, <xref rid=\"B123\" ref-type=\"bibr\">123</xref>, <xref rid=\"B146\" ref-type=\"bibr\">146</xref><xref ref-type=\"bibr\" rid=\"B147\">–</xref><xref rid=\"B148\" ref-type=\"bibr\">148</xref>). However, the evidence for ADE arising in SARS-CoV-1 and -2 and MERS-CoV experimental infections and vaccination studies is ambiguous (reviewed in references <xref rid=\"B4\" ref-type=\"bibr\">4</xref> and <xref rid=\"B17\" ref-type=\"bibr\">17</xref>). It should be noted that, for these coronaviruses, the mechanism for ADE may differ from what applies to viruses whose standard target cells are of the myeloid lineage and express Fc receptors. In contrast, SARS-CoV-2 primarily infects pulmonary, endothelial, renal, and intestinal parenchymal cells that express ACE2. In these circumstances, Fc receptor-mediated ADE would not only enhance infection of already susceptible cells but also could expand tropism to, e.g., monocytes and macrophages, thereby changing the already complex disease course. There are various examples of this scenario. Strong ADE was observed in studies of feline infectious peritonitis virus (FIPV), a macrophage-tropic coronavirus that triggers systemic vasculitis (<xref rid=\"B151\" ref-type=\"bibr\">151</xref>). Immunization of cats with a vaccinia vector expressing the cognate S-protein increased death rates after FIPV challenge (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B152\" ref-type=\"bibr\">152</xref>). Antibodies elicited when rodents were immunized with the SARS-CoV-1 S-protein enabled the virus to now enter human B-cell lymphoma cells <italic>in vitro</italic> in an ACE2-independent FcR-dependent manner, although this did not lead to productive infection (<xref rid=\"B153\" ref-type=\"bibr\">153</xref>). A nMAb to the MERS-CoV S-protein neutralized ACE2-mediated entry but could also enhance FcR-dependent entry in model cell lines (<xref rid=\"B154\" ref-type=\"bibr\">154</xref>). Serum from SARS-CoV-1-infected patients with S protein-specific antibodies facilitated virus infection of macrophages <italic>in vitro</italic> (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>).</p><p>In contrast to the above-described examples, ADE was not seen after animals were immunized with the SARS-CoV-1 RBD (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>) or with inactivated SARS-CoV-2, vector-expressed S- protein, or recombinant RBD (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B88\" ref-type=\"bibr\">88</xref>, <xref rid=\"B110\" ref-type=\"bibr\">110</xref>). Even with flaviviruses, ADE detected <italic>in vitro</italic> does not always translate into enhanced disease <italic>in vivo</italic> (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Likewise, multiple passive immunization studies in mice and nonhuman primates have failed to show signs of ADE <italic>in vivo</italic> upon challenge with SARS or MERS coronaviruses (<xref rid=\"B123\" ref-type=\"bibr\">123</xref>), although there was a trend toward greater weight loss when a poorly neutralizing MAb was tested in a Syrian hamster SARS-CoV-2 challenge model (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>).</p><p>A rational approach to avoiding ADE is to minimize the induction of poorly or nonneutralizing antibodies by using the RBD to focus the antibody response on its key NAb epitopes (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B87\" ref-type=\"bibr\">87</xref>, <xref rid=\"B123\" ref-type=\"bibr\">123</xref>, <xref rid=\"B147\" ref-type=\"bibr\">147</xref>). However, all of the leading Warp Speed vaccine candidates involve the full-length S-protein, which expresses both NAb and non-NAb epitopes.</p><p>A concept related to ADE has been termed antibody-mediated enhanced respiratory disease (ERD) (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>) or vaccine-associated enhanced respiratory disease (VAERD) (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). While ADE may be relevant to this scenario, so might other immunopathological aspects of vaccine-induced immunity. One clinical manifestation of COVID-19 is a dramatic decline in respiratory function, which occurs in some patients around 7 to 14 days after symptoms appear. That timeline mirrors the onset of seroconversion, and there are data suggesting that the formation of immune complexes between antibodies and virions might activate monocytes and macrophages to trigger a cytokine storm (reviewed in references <xref rid=\"B44\" ref-type=\"bibr\">44</xref> and <xref rid=\"B17\" ref-type=\"bibr\">17</xref>). In principle, vaccine-induced antibodies could have similar pathogenic effects, in some cases via an FcR-dependent mechanism (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Complement activation by all three pathways has also been implicated in lung pathogenesis (<xref rid=\"B155\" ref-type=\"bibr\">155</xref>, <xref rid=\"B156\" ref-type=\"bibr\">156</xref>). Drivers of the complement pathways are mannose-binding lectin (MBL) and related innate factors that recognize carbohydrate structures on viral spike glycoproteins, including SARS-CoV-1 and -2, HIV-1, and Ebola virus (<xref rid=\"B157\" ref-type=\"bibr\">157</xref><xref ref-type=\"bibr\" rid=\"B158\">–</xref><xref rid=\"B159\" ref-type=\"bibr\">159</xref>). This pathway has been implicated in the pathogenesis of Ebola virus infection (<xref rid=\"B157\" ref-type=\"bibr\">157</xref>).</p><p>Vaccination of humans against the pneumovirus respiratory syncytial virus (RSV) and the paramyxovirus morbillivirus that causes measles provides additional concerns about the potential for VAERD in the SARS-CoV-2 context. The exacerbated pathogenesis observed in the 1960s principally involved killed virus vaccines (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B160\" ref-type=\"bibr\">160</xref><xref ref-type=\"bibr\" rid=\"B161\">–</xref><xref rid=\"B163\" ref-type=\"bibr\">163</xref>). Among children receiving an inactivated RSV vaccine, 80% were hospitalized after infection compared to only 5% of the placebo controls (<xref rid=\"B161\" ref-type=\"bibr\">161</xref>). What mechanisms were responsible? First, it has been argued that a high ratio of binding antibodies (i.e., non-NAbs) to NAbs yields immune complexes and detrimental complement activation. This mechanism was shown to be relevant in infants vaccinated with formalin-inactivated RSV who then became RSV infected; complement activation was associated with inflammation and airway obstruction (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B162\" ref-type=\"bibr\">162</xref>). A similar pathology was seen in macaques immunized with an inactivated measles virus (<xref rid=\"B163\" ref-type=\"bibr\">163</xref>). Second, vaccination can prime for allergic reactions that are triggered after infection with the corresponding virus. The ensuing pathogenesis comprises increased production of interleukin 4 (IL-4), IL-5, and -13, eosinophil recruitment, and impeded cytotoxic T lymphocyte (CTL) responses (i.e., Th2 polarization), leading to pulmonary dysfunction (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B164\" ref-type=\"bibr\">164</xref>).</p><p>What might these observations mean for COVID-19 vaccines? Vaccination with inactivated SARS CoV-1 and MERS-CoV and with the SARS-CoV-1 S-protein has also yielded histopathological pulmonary and hepatic manifestations in various animal models (reviewed in references <xref rid=\"B148\" ref-type=\"bibr\">148</xref> and <xref rid=\"B123\" ref-type=\"bibr\">123</xref>). One study in particular highlights the risks of VAERD. Here, SARS-CoV-1 S-protein-specific antibodies were elicited by immunization of rhesus macaques with a vaccinia vector followed by autologous viral challenge. The outcome was severe acute lung injury, extreme pulmonary accumulation of monocytes and macrophages, and elevated cytokine secretion. The underlying mechanisms were difficult to determine but could involve ADE augmented by additional immunopathology and may be at least partly FcR dependent and involve immune complex formation (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Based on histopathological analyses of pulmonary parenchyma, however, it is uncertain whether the observations made in macaques apply to SARS-CoV-2-infected humans. Thus, the lung tissues of both postmortem COVID-19 cases and studies of asymptomatic infections showed prominent infiltration by lymphoid cells but not by macrophages or monocytes (reviewed in reference <xref rid=\"B122\" ref-type=\"bibr\">122</xref>). To date, no adverse events of the above-described nature have been reported in SARS-CoV-2 vaccine challenge studies in macaques (see above).</p><p>Some pathogenic effects seen in SARS-CoV-1 and MERS-CoV animal vaccinations have been linked to strong Th2 in relation to Th1 responses (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>); the former, promoted by adjuvants such as Alum, have been associated with eosinophil accumulation in lungs (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B147\" ref-type=\"bibr\">147</xref>). The eosinophilic histopathology notwithstanding, Th2-polarized responses to SARS-CoV-1 virus-like particle, inactivated virus, and DNA-delivered S-protein vaccines in mice can be partially protective by reducing viral loads postinfection (<xref rid=\"B165\" ref-type=\"bibr\">165</xref>). Some adjuvants, such as Toll-like receptor agonists and inulin, have been suggested to shift Th2 responses to Th1 and reduce VAERD (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B123\" ref-type=\"bibr\">123</xref>, <xref rid=\"B166\" ref-type=\"bibr\">166</xref>). Furthermore, the murine IgG subclass profile is linked to Th polarization, which therefore could affect the FcR interactions of the elicited IgG antibodies (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). The main human IgG subclass, IgG1, is not associated with Th polarization, and there are many other species differences that influence viral tropism and virus-immune system interactions. The murine and other small animal models may, therefore, be problematic for understanding SARS-CoV-2 infection of humans and how vaccines perform. The weaker and less sustained replication of SARS-CoV-2 in macaques, compared to that in humans, could limit the development of ERD/VAERD in this species. Despite these limitations, when animal models are used to derive vaccine efficacy data, as much safety data as possible should also be obtained both before and after experimental infection (<xref rid=\"B148\" ref-type=\"bibr\">148</xref>).</p><p>In normal circumstances, there would be an extensive assessment of the kinds of adverse events noted above to better understand the interplay between SARS-CoV-2 and the human immune system and to minimize the risks to the vaccinated population. The exceptional circumstances of the COVID-19 pandemic are reducing the time that would normally be taken to analyze critical aspects of vaccine development. Will Institutional Review Boards have all the information required to judge the safety of novel vaccines with limited safety data (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>)? Will vaccinated humans be placed at serious risk of harm when they encounter SARS-CoV-2? It is unlikely that ERD/VAERD events will be assessed until a sufficient number of infections occur in vaccinated people during efficacy trials, as too few infections may occur at the phase I/II stages. There are now specific recommendations for how immunogenicity trials in animals and safety trials in humans should be conducted and what information should be sought (<xref rid=\"B148\" ref-type=\"bibr\">148</xref>).</p><p>Altruistic volunteers are willing to be vaccinated and then challenged with SARS-CoV-2, a scenario that raises difficult ethical questions that are being debated now at some length (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B167\" ref-type=\"bibr\">167</xref><xref ref-type=\"bibr\" rid=\"B168\">–</xref><xref rid=\"B171\" ref-type=\"bibr\">171</xref>). The major beneficiaries of a SARS-CoV-2 vaccine will be older people who are at the most serious risk of death from COVID-19. However, most proposals for human challenge studies involve young healthy people. Would their experience after vaccination appropriately mimic what might happen in an older population with preexisting conditions that render them particularly vulnerable to severe COVID-19? These quite complex scenarios will need to be analyzed from multiple perspective by decision makers with qualifications in the relevant areas of science and public health.</p></sec><sec id=\"s10\"><title>SCENARIOS FOR FAVORABLE AND UNFAVORABLE OUTCOMES</title><p>The most favorable outcome, and the one that all vaccine researchers would like to see, is that the first large-scale efficacy trials show that SARS-CoV-2 vaccines confer robust protection that will bring a speedy end to the pandemic. In favor of that scenario is the presence of what seem to be immunodominant neutralization epitopes on the S-protein’s RBD that are well represented in the human germ line. NAbs to these sites may, therefore, be induced quite efficiently by S-protein-based immunogens. The relative lack of S-protein sequence variation is another favorable factor for vaccine success. Protection could arise either by the induction of a serum antibody titer that exceeds the protective threshold (presently unknown, but see <xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>) for a meaningful period or if an antibody response is primed that can be rapidly recalled on systemic exposure to SARS-CoV-2. Cellular and/or mucosal immune responses to some vaccine components may also contribute to protection. It is feasible, but by no means certain, that vaccines that can be relatively quickly manufactured in bulk (e.g., mRNA, DNA, and adenovirus vectors) will be sufficiently immunogenic to elicit protective NAb responses in a high proportion of the population.</p><p>An undesirable outcome will be if the first vaccines tested are not immunogenic enough to be protective but are not associated with significant adverse events before or after SARS-CoV-2 infection. Recombinant S- or RBD-proteins are markedly more immunogenic than the current Warp Speed mRNA or adenovirus-based vaccines (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). Follow-on trials of these proteins, used alone or in combination with the earlier candidates (i.e., prime-boost strategies), could provide the answer. The most substantive concern here is the time lost in the face of a spreading pandemic.</p><p>There is, however, a foreseeable outcome that could set back the wider vaccine field for decades. If the first-tested vaccines fail to protect most recipients but prime or trigger an antibody or other immune response that exacerbates COVID-19 disease in people who become infected, there will be a ferocious public backlash against vaccines in general. The Warp Speed COVID-19 vaccine trials are of enormous interest to our society and are receiving constant attention from the press and public. A small but vocal faction that opposes vaccination for irrational reasons would become even more energized by adverse events and, in the politically polarized America of 2020, could receive high-level support. The consequences could be serious harm not just to the prospects for a successful COVID-19 vaccine but also for the uptake of the commonly used vaccines that are essential to the health and wellbeing of our children. The stakes are high. A powerful Opinion piece in the New York Times argues strongly for the need to obtain the most comprehensive data set possible on the potential risks of SARS-CoV-2 vaccines and urges the FDA to not issue an emergency-use approval based solely on immunogenicity data (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.nytimes.com/2020/06/08/opinion/trump-coronavirus-vaccine.html\">https://www.nytimes.com/2020/06/08/opinion/trump-coronavirus-vaccine.html</ext-link>). We wholeheartedly agree.</p></sec><sec sec-type=\"conclusions\" id=\"s11\"><title>CONCLUSIONS</title><p>A protective vaccine against SARS-CoV-2 is a goal that is achievable but by no means certain. Although SARS-CoV-1 vaccine development gradually petered out once that virus stopped spreading in humans, considerable efforts are thought to have been made in Saudi Arabia over the past 8 years to develop a MERS vaccine to protect commercially valuable camels and horses. No such vaccine has ever emerged. The various SARS-CoV-2 vaccine designs are associated with perceived advantages and drawbacks (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><p>For the aggressive timelines of the Warp Speed program to be met, very little can go wrong at any stage of the research and development processes. Few if any large-scale projects proceed smoothly, particularly when there are major and quite fundamental gaps in the underlying science. Moreover, obtaining a rapid endpoint in an efficacy trial requires a high incidence of infection in the area of the trial sites, but infection rates are now declining in many areas of the United States and Europe where leading research institutions are located. Conducting trials in areas of the world where infection rates are still high, or even increasing, would overcome such concerns. Recent media reports suggest that efficacy trials of vaccines from both American and Chinese programs will involve sites in Brazil, a currently high-incidence country. The primary endpoint in the Moderna mRNA vaccine phase 3 trial is prevention of symptomatic COVID-19 disease, while secondary endpoints include prevention of severe disease (hospitalization) and prevention of infection. Quantifying a disease reduction endpoint rather than sterilizing protection from infection could be an additional complication, which is, perhaps, portended by the performance of early vaccine candidates in animal models (see above). That complexity would be exacerbated if effective antiviral drug combinations, including nMAbs, become the immediate standard of care for people with SARS-CoV-2 infection. As noted above, a COVID-19 vaccine is most needed for the more vulnerable populations, which include people who are older (particularly those >70 years) and/or those with preexisting health conditions. Age and perhaps some health concerns may adversely affect the development of immune responses to vaccines (<xref rid=\"B172\" ref-type=\"bibr\">172</xref>). Testing a vaccine in predominantly young and healthy volunteers may not predict what happens in their older and sicker counterparts.</p><p>If protection against SARS-CoV-2 requires only fairly modest serum antibody titers, then the most easily produced vaccine designs could succeed. But if much higher titers are needed, those vaccines may need to be replaced, or supplemented, by other components that are perhaps produced by another company or in a different country. For example, an American mRNA vaccine may work better if boosted by a Chinese killed virus preparation or a British adenovirus vector when followed by a recombinant protein made within the European Community. Even if an effective vaccine is identified, it may be challenging to manufacture and distribute on the scale needed to immunize a significant fraction of the world’s population (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B173\" ref-type=\"bibr\">173</xref>) (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). An effective vaccine that is too complex to make in bulk, is difficult to formulate, is highly unstable without refrigeration or freezing, is difficult to administer, or that requires too many doses over a prolonged period may represent a Pyrrhic victory for science but not the answer to the problems faced by the societies that science serves. The complexities of developing a vaccine at ultrashort notice are best tackled by the melding of minds irrespective of wherever the bodies are geographically located (<xref rid=\"B173\" ref-type=\"bibr\">173</xref>, <xref rid=\"B174\" ref-type=\"bibr\">174</xref>). Will this happen? We hope so, but fear it may not (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>).</p></sec></body><back><ack><title>ACKNOWLEDGMENTS</title><p>We thank Kathrina Guemo for administrative support, Victor Cruz Portillo, Erik Francomano, Yasa Watanabe, and Max Crispin for assistance with figures, and Ian Wilson, Rogier Sanders, and Andrew Ward for helpful comments.</p><p>This work was supported by the National Institute of Allergy and Infectious Diseases of the NIH (HIVRAD P01 AI110657 and R01 AI36082) and the Bill and Melinda Gates Foundation (OPP1132237 and INV-002022).</p></ack><notes><sec id=\"s12\"><title>ADDENDUM IN PROOF</title><p>We recommend consulting reference <xref rid=\"B175\" ref-type=\"bibr\">175</xref> with respect to the immunogenicity of MERS-CoV full-length S DNA and S1 protein in mice and macaques. 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Virol</journal-id><journal-id journal-id-type=\"hwp\">jvi</journal-id><journal-id journal-id-type=\"pmc\">jvi</journal-id><journal-id journal-id-type=\"publisher-id\">JVI</journal-id><journal-title-group><journal-title>Journal of Virology</journal-title></journal-title-group><issn pub-type=\"ppub\">0022-538X</issn><issn pub-type=\"epub\">1098-5514</issn><publisher><publisher-name>American Society for Microbiology</publisher-name><publisher-loc>1752 N St., N.W., Washington, DC</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32581103</article-id><article-id pub-id-type=\"pmc\">PMC7431787</article-id><article-id pub-id-type=\"publisher-id\">00766-20</article-id><article-id pub-id-type=\"doi\">10.1128/JVI.00766-20</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Virus-Cell Interactions</subject></subj-group><subj-group subj-group-type=\"editorial-class\"><subject>Spotlight</subject></subj-group></article-categories><title-group><article-title>Hazara Nairovirus Requires COPI Components in both Arf1-Dependent and Arf1-Independent Stages of Its Replication Cycle</article-title><alt-title alt-title-type=\"running-head\">COPI Is Required for HAZV Infection</alt-title><alt-title alt-title-type=\"short-authors\">Álvarez-Rodríguez et al.</alt-title></title-group><contrib-group><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Fuller</surname><given-names>J.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Álvarez-Rodríguez</surname><given-names>B.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Todd</surname><given-names>E. J. A. A.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Mankouri</surname><given-names>J.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Hewson</surname><given-names>R.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\" equal-contrib=\"no\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-9035-2039</contrib-id><name><surname>Barr</surname><given-names>J. N.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><aff id=\"aff1\"><label>a</label><addr-line>School of Molecular and Cellular Biology, University of Leeds, Leeds, United Kingdom</addr-line></aff><aff id=\"aff2\"><label>b</label><addr-line>National Infection Service, Public Health England, Salisbury, United Kingdom</addr-line></aff></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Dutch</surname><given-names>Rebecca Ellis</given-names></name><role>Editor</role><aff>University of Kentucky College of Medicine</aff></contrib></contrib-group><author-notes><corresp id=\"cor1\">Address correspondence to J. N. Barr, <email>j.n.barr@leeds.ac.uk</email>.</corresp><fn fn-type=\"equal\"><p>J. Fuller and B. Álvarez-Rodríguez contributed equally to this work. Author order was determined by drawing straws.</p></fn><fn fn-type=\"other\"><p><bold>Citation</bold> Fuller J, Álvarez-Rodríguez B, Todd EJAA, Mankouri J, Hewson R, Barr JN. 2020. Hazara nairovirus requires COPI components in both Arf1-dependent and Arf1-independent stages of its replication cycle. J Virol 94:e00766-20. <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.1128/JVI.00766-20\">https://doi.org/10.1128/JVI.00766-20</ext-link>.</p></fn></author-notes><pub-date pub-type=\"epreprint\"><day>24</day><month>6</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>9</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>94</volume><issue>17</issue><elocation-id>e00766-20</elocation-id><history><date date-type=\"received\"><day>23</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>10</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Fuller et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Fuller et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International license</ext-link>.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"JVI.00766-20.pdf\"/><abstract abstract-type=\"precis\"><p>Nairoviruses are tick-borne enveloped RNA viruses that include several pathogens responsible for fatal disease in humans and animals. Here, we analyzed host genes involved in trafficking networks to examine their involvement in nairovirus replication. We revealed important roles for genes that express multiple components of the COPI complex, which regulates transport of Golgi apparatus-resident cargos. COPI components influenced at least two stages of the nairovirus replication cycle: an early stage prior to and including gene expression and also a later stage during assembly of infectious virus, with COPI knockdown reducing titers by approximately 1,000-fold. Importantly, while the late stage was Arf1 dependent, as expected for canonical COPI vesicle formation, the early stage was found to be Arf1 independent, suggestive of a previously unreported function of COPI unrelated to vesicle formation. Collectively, these data improve our understanding of nairovirus host-pathogen interactions and suggest a new Arf1-independent role for components of the COPI coatomer complex.</p></abstract><abstract><title>ABSTRACT</title><p>Hazara nairovirus (HAZV) is an enveloped trisegmented negative-strand RNA virus classified within the <italic>Nairoviridae</italic> family of the <italic>Bunyavirales</italic> order and a member of the same subtype as Crimean-Congo hemorrhagic fever virus, responsible for fatal human disease. Nairoviral subversion of cellular trafficking pathways to permit viral entry, gene expression, assembly, and egress is poorly understood. Here, we generated a recombinant HAZV expressing enhanced green fluorescent protein and used live-cell fluorescent imaging to screen an siRNA library targeting genes involved in cellular trafficking networks, the first such screen for a nairovirus. The screen revealed prominent roles for subunits of the coat protein 1 (COPI)-vesicle coatomer, which regulates retrograde trafficking of cargo between the Golgi apparatus and the endoplasmic reticulum, as well as intra-Golgi transport. We show the requirement of COPI-coatomer subunits impacted at least two stages of the HAZV replication cycle: an early stage prior to and including gene expression and also a later stage during assembly and egress of infectious virus, with COPI-knockdown reducing titers by approximately 1,000-fold. Treatment of HAZV-infected cells with brefeldin A (BFA), an inhibitor of Arf1 activation required for COPI coatomer formation, revealed that this late COPI-dependent stage was Arf1 dependent, consistent with the established role of Arf1 in COPI vesicle formation. In contrast, the early COPI-dependent stage was Arf1 independent, with neither BFA treatment nor siRNA-mediated ARF1 knockdown affecting HAZV gene expression. HAZV exploitation of COPI components in a noncanonical Arf1-independent process suggests that COPI coatomer components may perform roles unrelated to vesicle formation, adding further complexity to our understanding of cargo-mediated transport.</p><p><bold>IMPORTANCE</bold> Nairoviruses are tick-borne enveloped RNA viruses that include several pathogens responsible for fatal disease in humans and animals. Here, we analyzed host genes involved in trafficking networks to examine their involvement in nairovirus replication. We revealed important roles for genes that express multiple components of the COPI complex, which regulates transport of Golgi apparatus-resident cargos. COPI components influenced at least two stages of the nairovirus replication cycle: an early stage prior to and including gene expression and also a later stage during assembly of infectious virus, with COPI knockdown reducing titers by approximately 1,000-fold. Importantly, while the late stage was Arf1 dependent, as expected for canonical COPI vesicle formation, the early stage was found to be Arf1 independent, suggestive of a previously unreported function of COPI unrelated to vesicle formation. Collectively, these data improve our understanding of nairovirus host-pathogen interactions and suggest a new Arf1-independent role for components of the COPI coatomer complex.</p></abstract><kwd-group><title>KEYWORDS</title><kwd>COPI</kwd><kwd>cell biology</kwd><kwd>host factors</kwd><kwd>virology</kwd></kwd-group><funding-group><award-group id=\"award1\"><funding-source><institution-wrap><institution>Public Health England (PHE)</institution><institution-id>https://doi.org/10.13039/501100002141</institution-id></institution-wrap></funding-source><award-id>JF1</award-id><principal-award-recipient><name><surname>Fuller</surname><given-names>Jack</given-names></name></principal-award-recipient></award-group><award-group id=\"award2\"><funding-source><institution-wrap><institution>Wellcome Trust (Wellcome)</institution><institution-id>https://doi.org/10.13039/100004440</institution-id></institution-wrap></funding-source><award-id>102174/B/13/Z</award-id><principal-award-recipient><string-name>Eleanor J. A. A. Todd</string-name></principal-award-recipient></award-group><award-group id=\"award3\"><funding-source><institution-wrap><institution>EU | Horizon 2020 Framework Programme (H2020)</institution><institution-id>https://doi.org/10.13039/100010661</institution-id></institution-wrap></funding-source><award-id>721367</award-id><principal-award-recipient><name><surname>Alvarez-Rodriguez</surname><given-names>Beatriz</given-names></name></principal-award-recipient></award-group></funding-group><counts><count count=\"1\" count-type=\"supplementary-material\"/><fig-count count=\"7\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"61\"/><page-count count=\"16\"/><word-count count=\"9682\"/></counts><custom-meta-group><custom-meta><meta-name>cover-date</meta-name><meta-value>September 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>INTRODUCTION</title><p>The <italic>Bunyavirales</italic> order of enveloped, segmented negative-sense RNA viruses comprises a diverse collection of over 500 viruses that are classified into 12 families, with five of these containing the causative agents of human disease, namely, the <italic>Hantaviridae</italic>, <italic>Nairoviridae</italic>, <italic>Arenaviridae</italic>, <italic>Peribunyaviridae</italic>, and <italic>Phenuiviridae</italic> families (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). The nairoviruses currently comprise 12 named species, all of which are arboviruses, associated with hard ticks of the <italic>Ixodidae</italic> family, for which transmission to mammalian and avian hosts occurs through acquisition of a blood meal. The family was named after Nairobi sheep disease virus, which causes fever, hemorrhagic gastroenteritis, and abortion in goats and sheep and carries a case-fatality rate of around 80% resulting in considerable economic impact (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>). Notable nairovirus member, priority pathogen Crimean-Congo hemorrhagic fever virus (CCHFV) is the responsible agent for Crimean-Congo hemorrhagic fever (CCHF), a devastating human disease with fatality rates averaging approximately 30% (<xref rid=\"B3\" ref-type=\"bibr\">3</xref><xref ref-type=\"bibr\" rid=\"B4\">–</xref><xref rid=\"B5\" ref-type=\"bibr\">5</xref>) for which preventative or therapeutic measures are not available. Concern surrounding the spread of CCHFV to new geographic locations is rising, due in major part to the altered habitat of the tick host, possibly in response to climate change, exemplified by recent fatal human infections in northern Spain (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Due to these factors, CCHFV is classified as an emerging hazard group 4 pathogen, and therefore research on this virus requires highly specialized laboratory facilities. CCHFV shares the same CCHFV serogroup with Hazara virus (HAZV), also possessing extensive similarities in the sequence, structure, and function of its constituent proteins (<xref rid=\"B8\" ref-type=\"bibr\">8</xref><xref ref-type=\"bibr\" rid=\"B9\">–</xref><xref rid=\"B10\" ref-type=\"bibr\">10</xref>). However, HAZV is not recognized as a serious human pathogen and is categorized as a hazard level 2 virus, facilitating its use as a model system for CCHFV in a containment level 2 laboratory infrastructure.</p><p>All nairoviruses possess a negative-stranded RNA genome composed of three segments named small (S), medium (M), and large (L) based on their relative sizes. Each genome template is transcribed to yield a single mRNA; the S mRNA encodes the RNA-binding nucleocapsid protein (N); the M segment mRNA encodes a glycoprotein precursor that is cleaved into envelope spikes Gn and Gc, as well as nonstructural NSm; and the L segment mRNA encodes the large RNA-dependent RNA polymerase responsible for all viral RNA synthesis activities. An additional nonstructural protein NSs has been described expressed by ambisense transcription from the CCHFV S segment antigenome, with possible roles in apoptotic signaling (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). Whether HAZV expresses an analogous product is unknown.</p><p>Despite the importance of nairoviruses due to their impact on both human and animal health, knowledge of the nairovirus replication cycle at the molecular level is still lacking. Nairovirus particles infect cells through a clathrin- and Rab5-dependent process (<xref rid=\"B12\" ref-type=\"bibr\">12</xref><xref ref-type=\"bibr\" rid=\"B13\">–</xref><xref rid=\"B14\" ref-type=\"bibr\">14</xref>), enhanced by DC-SIGN (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>), and the RNA segments escape the endocytic system from multivesicular bodies (MVBs) (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>), mediated by the Gn/Gc spikes that orchestrate fusion of the MVB and viral envelopes in response to the resident biochemical milieu (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Following release, the viral segments likely traffic in a microtubule-dependent manner (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>) to a currently elusive cellular compartment to establish replication factories, within which gene expression, RNA replication, protein synthesis, and subsequent virus assembly occurs. By analogy with Bunyamwera virus (BUNV), the prototypical <italic>Bunyavirales</italic> order member, one candidate location for these factories is the Golgi apparatus; BUNV establishes its replication factories surrounding the Golgi stacks (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>), inducing the formation of specialized viral tubes that accumulate viral components, with subsequent assembly and maturation of virions involving the passage through Golgi subcompartments prior to virus budding from secretory vesicles (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>). This utilization of cellular endomembranes to support various viral processes is a common strategy adopted by enveloped viruses but is poorly understood for nairoviruses.</p><p>A detailed picture of how nairoviruses exploit cellular trafficking networks during all stages of the virus replication cycle have been limited by the paucity of virus-associated tools that permit large screening approaches. In this study, we used our recently developed reverse genetics system for HAZV (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>) to rescue a recombinant HAZV expressing enhanced green fluorescent protein (rHAZV-eGFP) as a nonfused reporter protein, by inserting a P2A ribosome-skipping sequence from porcine teschovirus (<xref rid=\"B22\" ref-type=\"bibr\">22</xref><xref ref-type=\"bibr\" rid=\"B23\">–</xref><xref rid=\"B24\" ref-type=\"bibr\">24</xref>) within the HAZV S segment mRNA, as has recently been reported for CCHFV (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). We assessed the trafficking components required for rHAZV-eGFP infection using a targeted library of small interfering RNAs (siRNAs) specific for cellular components involved in vesicular transport and measured their influence on virus growth by live cell fluorescence microscopy. We revealed a role for coat protein 1 (COPI)-vesicle coatomer subunits, which are primarily involved in retrograde trafficking of cargo between the Golgi apparatus and the endoplasmic reticulum (ER) and intra-Golgi transport, but with additional reported roles in maintaining functionality of the endocytic network (<xref rid=\"B26\" ref-type=\"bibr\">26</xref><xref ref-type=\"bibr\" rid=\"B27\">–</xref><xref rid=\"B28\" ref-type=\"bibr\">28</xref>), breakdown of the nuclear envelope (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>), and lipid homeostasis (<xref rid=\"B30\" ref-type=\"bibr\">30</xref><xref ref-type=\"bibr\" rid=\"B31\">–</xref><xref rid=\"B32\" ref-type=\"bibr\">32</xref>). The requirement of COPI-coatomer subunits for HAZV growth impacted an early stage of the virus replication cycle, prior to translation, as well as a later stage, involving infectious virus assembly. While the late stage was Arf1 dependent, as expected for canonical COPI vesicle formation, the early stage was found to be Arf1 independent, suggestive of a previously unreported function of COPI unrelated to vesicle formation. These data provide new insight in our understanding of how nairoviruses usurp important cellular trafficking components to establish a productive infection.</p></sec><sec sec-type=\"results\" id=\"s2\"><title>RESULTS</title><sec id=\"s2.1\"><title>Generation of a recombinant HAZV expressing enhanced green fluorescent protein.</title><p>To facilitate high-throughput siRNA screens to determine the involvement of cellular trafficking factors in HAZV replication, we generated a recombinant HAZV (rHAZV) expressing eGFP. The expression strategy adopted involved the porcine teschovirus-1 2A peptide linker (P2A) sequence that induces ribosome skipping (<xref rid=\"B22\" ref-type=\"bibr\">22</xref><xref ref-type=\"bibr\" rid=\"B23\">–</xref><xref rid=\"B24\" ref-type=\"bibr\">24</xref>), to allow two open reading frames (ORFs) to be expressed from the HAZV S segment mRNA. The P2A linker was inserted between eGFP and HAZV N ORFs within previously described S segment expression plasmid (pMK-RQ-S) (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>) to generate pMK-RQ-S(eGFP) (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1A</xref>).</p><fig id=\"F1\" orientation=\"portrait\" position=\"float\"><label>FIG 1</label><caption><p>Rescue of rHAZV expressing eGFP. (A) Schematic representation of pMK-RQ-S(eGFP), encoding a modified HAZV S segment, designed to express eGFP and N using a porcine teschovirus-1 2A peptide linker (P2A). The sequence of the P2A region is shown, intervening the ORFs for eGFP, HAZV nucleoprotein (HAZV N), and flanked by nontranslated regions (NTR). The hepatitis delta virus ribozyme (HDR) and T7 RNA polymerase promoter and terminator signals (T7P/T7T) are also shown for reference. (B) BSR-T7 cells were transfected with either WT S, M, and L rescue plasmids (WT SML) or with plasmids required for rescue of rHAZV-eGFP (eGFP SML). At 120 hpt, supernatants were transferred to naive SW13 cells. Lysates from BSR-T7 at 120 hpt and SW13 cells at 72, 96, and 120 hpi were analyzed for expression of eGFP and HAZV N by Western blot analysis using the corresponding antisera. Lysates were also collected from a recovery experiment in which the L segment expression plasmid was omitted [(–)L control]. (C) Green cell count at hourly intervals during a 48-h time course infection of SW13 cells with either rHAZV-eGFP or WT rHAZV. (D) Representative live cell images taken under UV illumination at 0 and 48 hpi of SW13 cells.</p></caption><graphic xlink:href=\"JVI.00766-20-f0001\"/></fig><p>Transfection of modified plasmid pMK-RQ-S(eGFP), along with wild-type (WT) M and L segment expression plasmids, was performed alongside a transfection comprising all three WT plasmids for comparison. An additional plasmid, pCAGGS-T7pol, which expresses bacteriophage T7 RNA polymerase was also cotransfected into cells, due to its previously described beneficial effects on the recovery of rHAZV (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). At 120 h posttransfection (hpt), supernatants were harvested and used to reinfect fresh monolayers of SW13s. Western blot analysis of corresponding lysates at 72, 96, and 120 h postinfection (hpi) revealed abundant HAZV N expression confirming successful rescue of both rHAZV and rHAZV-eGFP viruses (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1B</xref>). As expected, no HAZV N was detected in fresh cells following transfer of supernatants from control rescues in which the L segment expressing plasmid was omitted. As further confirmation of rHAZV-eGFP rescue, the eGFP signal was exclusively detected in rHAZV-eGFP-infected SW13 cell lysates following Western blot analysis (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1B</xref>).</p><p>The purpose of generating rHAZV-eGFP was to allow the rapid and quantitative measurement of HAZV gene expression through the detection of eGFP fluorescence using live cell imaging. To test the utility of this assay, SW13 cells were infected with supernatants containing either rHAZV or rHAZV-eGFP collected at 120 hpt, with infected cells analyzed using a live cell imaging IncuCyte system at hourly intervals. Quantification of the green cell count showed increasing numbers of eGFP-expressing infected cells over the 48-h duration confirming detectable levels of rHAZV-eGFP infection (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1C</xref> and <xref ref-type=\"fig\" rid=\"F1\">D</xref>).</p><p>We next compared the growth properties of rHAZV and rHAZV-eGFP to determine the impact of the addition of the eGFP ORF and P2A linker on overall virus fitness. Titers of stocks of both viruses were determined by plaque assay and used to infect fresh SW13 cells at a multiplicity of infection (MOI) of 0.01. Cell supernatants were collected at 24-h intervals across a 5-day period, and the titers of released viruses were further assessed by plaque assays. This analysis revealed that titers for WT rHAZV and rHAZV-eGFP were similar across the entire 5-day time period, with both viruses exhibiting peak titers at 72 hpi for which rHAZV titers were approximately 2-fold higher (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2A</xref>). The plaque size and morphology for rHAZV and rHAZV-eGFP remained indistinguishable (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2B</xref>). Taken together, these findings suggest that rHAZV and rHAZV-eGFP possess broadly similar growth parameters, demonstrating that rHAZV-eGFP represents a useful tool for studying nairovirus growth and infectivity.</p><fig id=\"F2\" orientation=\"portrait\" position=\"float\"><label>FIG 2</label><caption><p>Growth comparison of rHAZV and rHAZV-eGFP. (A) Titers of infectious supernatant harvested at 24, 48, 72, and 96 hpi from SW13 cells infected with either recombinant Hazara virus (rHAZV) or rHAZV expressing eGFP (rHAZV-eGFP) at an MOI of 0.01. Error bars show the variance over three independent experiments. (B) Representative plaque assays from mock-, rHAZV-, and rHAZV-eGFP-infected SW13 cells at 6 days postinfection, showing indistinguishable plaque size and morphology.</p></caption><graphic xlink:href=\"JVI.00766-20-f0002\"/></fig></sec><sec id=\"s2.2\"><title>Identification of the trafficking components required during HAZV replication.</title><p>We next used rHAZV-eGFP to identify key host cell trafficking components that play a role during HAZV growth, achieved using a library comprising three unique siRNAs for each of 142 gene targets. Following transfection of each unique siRNA, SW13 cells were infected with rHAZV-eGFP at an MOI of 0.25 for 24 h, and the total integrated intensity of the eGFP (TIIE) signal was quantified as a marker of rHAZV-eGFP growth. At this 24-h time point, no cells newly infected and fluorescing cells are detected; thus, quantification of the eGFP signal recorded at 24 hpi would mostly reflect the influence of siRNA knockdown on virus replication cycle stages up to and including eGFP expression, within the first round of infected cells. The influence of knockdown on later events in the growth cycle, including virus assembly, budding, and infection of new cells, would not be represented in the TIIE signal. The effect of each unique siRNA was tested four times (see Data Set S1 in the supplemental material), and the 25 gene identities associated with greatest overall reduction in TIIE signal, over the mean of all three unique siRNA knockdowns against each gene target, are summarized in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>.</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>Listing of top 25 siRNA targets based on percentage knockdown of total integrated intensity of eGFP signal across three unique siRNAs<xref ref-type=\"table-fn\" rid=\"T1F1\"><sup><italic>a</italic></sup></xref></p></caption><graphic xlink:href=\"JVI.00766-20-t0001\"/><table-wrap-foot><fn fn-type=\"other\" id=\"T1F1\"><label>a</label><p>Each percentage value represents the mean of four experimental repeats, and colors indicate the levels of knockdown from green (most knockdown) to orange (least knockdown). Genes highlighted in boldface represent those associated with COPI vesicles.</p></fn></table-wrap-foot></table-wrap></sec><sec id=\"s2.3\"><title>COPI components are important for HAZV intracellular growth.</title><p>The cell factors for which siRNA knockdown resulted in greatest TIIE reduction (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) include seven genes expressing COPI subunits, including components of both the cage-like (α, β′, and ε) and adapter-like (β, γ, δ, and ζ) subcomplexes (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>). The three most impactful knockdowns corresponded to COPA, COPB1, and COPB2 genes, encoding α, β, and β′ subunits, respectively (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>); siRNA knockdown of COPA had the largest effect on eGFP signal with all three unique siRNAs displaying 70 to 80% reduction compared to untreated controls (siRNA 1 = 0.311 [<italic>P</italic> = 0.0003]; siRNA 2 = 0.241 [<italic>P</italic> < 0.0001]; siRNA 3 = 0.228 [<italic>P</italic> = 0.0001]). COPB1 knockdown also caused a significant reduction of the eGFP signal with all three siRNAs, but to a lesser degree, displaying between 20 and 40% reduction versus untreated controls (siRNA 1 = 0.682 [<italic>P</italic> = 0.0127]; siRNA 2 = 0.803 [<italic>P</italic> = 0.0078]; siRNA 3 = 0.634 [<italic>P</italic> = 0.0008]). COPB2 knockdown reduced the eGFP signal significantly in two of three siRNAs (siRNA 1 = 0.430 [<italic>P</italic> = 0.0289]; siRNA 2 = 0.237 [<italic>P</italic> = 0.0009]; siRNA 3 = 1.033 [<italic>P</italic> = 0.6497]), as did COPE knockdown (siRNA 1 = 0.709 [<italic>P</italic> = 0.0043]; siRNA 2 = 0.908 [<italic>P</italic> = 0.2701]; siRNA 3 = 0.828 [<italic>P</italic> = 0.0146]). siRNA knockdown of other complex subunits COPZ2, COPG1, and COPG2 resulted in mean TIIE reductions of around 20%, but with lower overall significance. In contrast, COPZ1 knockdown had no influence on TIIE (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>).</p><fig id=\"F3\" orientation=\"portrait\" position=\"float\"><label>FIG 3</label><caption><p>HAZV gene expression in first-round infected cells requires several components of the COPI complex. Histogram of the relative fold change in total integrated intensity of eGFP signal in SW13 cells infected with rHAZV-eGFP at an MOI of 0.25, following independent reverse transfection of triplicate siRNAs (siRNA 1, orange bars; siRNA 2, green bars; siRNA 3, blue bars) against the indicated cellular genes that express components of COPI complexes. Transfection reagent only and scrambled siRNA transfections were included as controls.</p></caption><graphic xlink:href=\"JVI.00766-20-f0003\"/></fig></sec><sec id=\"s2.4\"><title>Validation of successful COPA, COPB2, and COPB1 silencing.</title><p>For the three cellular genes with the greatest impact on rHAZV-eGFP expression, namely, COPI coat-like complex components COPA and COPB2 and the adapter-like component COPB1, further validation of their observed impact was performed using infections WT rHAZV. First, confirmation of the knockdown of the corresponding genes was achieved using quantitative reverse transcriptase PCR (qRT-PCR) targeting COPA, COPB1, and COPB2, for which mRNA copy numbers in knockdown cells were reduced in all three cases by ≥50% compared to the untreated infection controls (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4A</xref>). Next, we measured the effect of COPA, COPB1, and COPB2 knockdown on rHAZV RNA synthesis through quantifying the abundance of intracellular HAZV S segment copy numbers by qRT-PCR analysis. The abundance of S segment-specific RNAs were reduced by ≥50% in all three cases (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4B</xref>), and while the reduction in S segment RNA abundance following COPB2 knockdown is greater than that for COPA or COPB1 knockdowns, none of the observed differences are statistically significant. Next, we examined HAZV N protein abundance by Western blot analysis using N protein antisera, which confirmed reduced N protein levels following the silencing of all three COPI components (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4C</xref> and <xref ref-type=\"fig\" rid=\"F4\">D</xref>), a finding in close agreement with the reduction in TIIE (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> and <xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>). Finally, the expression of eGFP from rHAZV-eGFP infected cells following independent COPA, COPB2, or COPB1 knockdowns were measured at hourly intervals over a 24-h time course, using live cell fluorescence microscopy (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4E</xref>). The resulting time course shows that eGFP expression in COPA, COPB2, and COPB1 knockdown cells mirrors the findings at 24 hpi, with all three knockdowns causing a consistent reduction in eGFP signal throughout the 24-h time period.</p><fig id=\"F4\" orientation=\"portrait\" position=\"float\"><label>FIG 4</label><caption><p>Validation of the effects of COPI complex knockdown on COPI expression, HAZV N protein expression, viral replication, and green cell count. (A) qRT-PCR analysis of COPI subunit gene expression in SW13 cells transfected with siRNAs targeting COPA, COPB1, and COPB2, with error bars representing data from two experimental repeats. (B) qRT-PCR analysis of HAZV S segment-specific RNAs in rHAZV-infected SW13 cell lysates after treatment with siRNAs targeting COPA, COPB1, and COPB2 at 24 hpi. Mock-infected (Mock) and infected (Infected) samples were added. (C) Western blot analysis using HAZV N antisera of rHAZV-infected SW13 cell lysates after treatment with siRNAs targeting COPA, COPB1, and COPB2. A scrambled control (Scrambled) was also included alongside a mock-infected (Mock) and infected (Infected) sample in which transfection reagent but no siRNA was added, with GAPDH detected by corresponding antisera, as a loading control. The resulting band intensities on blots from three independent experiments were quantified by densitometry, represented graphically in panel D. (E) Hourly measurement of eGFP expression in SW13 cells infected with rHAZV-eGFP following treatment with siRNAs targeting COPA, COPB1, and COPB2 over a 24-h time course. The statistical significance between conditions is indicated (ns, not significant; *, <italic>P</italic> < 0.1; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001).</p></caption><graphic xlink:href=\"JVI.00766-20-f0004\"/></fig><p>Taken together, these findings suggest that COPA, COPB2, and COPB1 knockdown influences a stage of the HAZV replication cycle up to, and including, the translation of viral proteins.</p></sec><sec id=\"s2.5\"><title>Localization of COPA in relation to HAZV N protein in infected cells.</title><p>To examine whether the influence of COPI complex components was due to direct interaction or sequestration with either a viral protein or virus induced structure, we next examined the spatial location of COPA and the major HAZV structural protein, N. Confocal immunofluorescence microscopy of HAZV-infected SW13 cultures using COPA and HAZV N antisera revealed that COPA staining was predominantly localized within a single large perinuclear structure that exhibited a classical Golgi morphology. In contrast, HAZV N staining appeared in more densely stained perinuclear structures within infected cells, with a secondary diffuse intensity throughout the cytosol. Although regions of HAZV N and COPA staining did overlap in areas, their corresponding peak intensities did not precisely coincide (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5</xref>). Of interest, the distribution of COPA in HAZV-infected cells had noticeably more diffuse cytosolic staining than in uninfected cells within the same field of view (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5</xref>, top left panel), indicating that the distribution of COPA location was influenced by HAZV infection.</p><fig id=\"F5\" orientation=\"portrait\" position=\"float\"><label>FIG 5</label><caption><p>Cellular localization of HAZV N and COPA in SW13 cells. SW13 cells were mock infected or infected at an MOI of 0.25 and imaged at 18 hpi. Scale bar, 10 μm.</p></caption><graphic xlink:href=\"JVI.00766-20-f0005\"/></fig></sec><sec id=\"s2.6\"><title>Inhibition of retrograde transport confirms a dependency of HAZV growth on COPI vesicles in an Arf1-independent mechanism.</title><p>The formation of COPI vesicles requires the small GTPase ADP-ribosylation factor 1 (Arf1) (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>) which, upon activation by guanine exchange factors (GEFs) such as GBF1, becomes GTP bound and anchored within membranes. This GTP-bound form of Arf1 promotes recruitment of the intact heptameric COPI coatomer to its resident membrane (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>), three copies of which associate with six copies of Arf1 to form a triad structure, with multiple triads interacting to form COPI vesicles of various sizes. Interestingly, our membrane trafficking siRNA screen suggested no role for either Arf1 or GBF1 in HAZV infection (<xref ref-type=\"fig\" rid=\"F6\">Fig. 6</xref>; see Data Set S1 in the supplemental material), raising the possibility that HAZV requires COPI components for its replication cycle, but in an Arf1- and GBF1-independent manner.</p><fig id=\"F6\" orientation=\"portrait\" position=\"float\"><label>FIG 6</label><caption><p>HAZV gene expression in first-round infected cells requires COPA but is Arf1 and GBF1 independent. (A) Histogram of the relative fold change in total integrated intensity of eGFP signal in SW13 cells infected with rHAZV-eGFP at an MOI of 0.25 after independent reverse transfection of triplicate siRNAs (siRNA 1, orange bars; siRNA 2, green bars; siRNA 3, blue bars) against Arf1 and GBF1. Transfection reagent only and scrambled siRNA transfections were included as controls. (B) Western blot analysis of lysates collected from A549 cells pretreated with the indicated concentrations of brefeldin A (BFA) for 45 min prior to infection with rHAZV or rIAV at an MOI of 0.1. Samples were probed with antisera for their respective nucleoproteins (HAZV N or IAV N) and GAPDH as a loading control. Nucleoprotein expression was quantified by densitometric analysis of Western blot data from three independent experimental repeats, with error bars to show variance, represented graphically in panel C.</p></caption><graphic xlink:href=\"JVI.00766-20-f0006\"/></fig><p>To confirm the Arf1 independence of HAZV intracellular replication, we examined the growth ability of HAZV in the presence of brefeldin A (BFA), a noncompetitive inhibitor of GEF-mediated Arf1 activation with an established role in blocking COPI vesicle formation and retrograde Golgi-ER trafficking (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). After BFA pretreatment, the cells were infected with rHAZV at an MOI of 0.1, and intracellular virus growth was examined at 24 hpi by Western blotting of cell lysates using N protein antisera. Recombinant H1N1 influenza A virus (rIAV) was included in these assays as a positive control due its known dependence on Arf1 for replication, as well as its sensitivity to BFA (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). As expected, Western blot analysis of IAV-infected cell lysates using IAV NP antisera at 24 hpi revealed a dose-dependent reduction in NP expression in response to increasing BFA concentrations. In contrast, quantification of the levels of HAZV N remained unchanged at all BFA concentrations (<xref ref-type=\"fig\" rid=\"F6\">Fig. 6B</xref> and <xref ref-type=\"fig\" rid=\"F4\">C</xref>), confirming that the dependence of HAZV gene expression on COPI components is Arf1 independent.</p></sec><sec id=\"s2.7\"><title>COPI components regulate the assembly of infectious HAZV.</title><p>The quantification of TIIE in cells infected with rHAZV-eGFP at 24 hpi identified an influence of both COPI complex silencing, but not BFA treatment, on the stages of the virus replication cycle up to, and including, protein synthesis. To measure effects of the COPI components on later stages of the HAZV replication cycle, we next analyzed the effects of COPA, COPB2, and COPB1 knockdown on the production of infectious virus. siRNA knockdowns were performed in SW13 cells prior to infection with WT rHAZV at an MOI 0.1; supernatants were then collected at 48 hpi, and titers were calculated by plaque assay. All three siRNA knockdowns resulted in a decrease in virus titers, with the knockdown of COPI cage components COPA and COPB1 in particular, reducing infectious virus production by ∼1,000-fold compared to scrambled siRNA controls (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7A</xref>).</p><fig id=\"F7\" orientation=\"portrait\" position=\"float\"><label>FIG 7</label><caption><p>Production of infectious HAZV is reduced by COPA knockdown and brefeldin A treatment. (A) SW13 cell cultures were pretreated with siRNAs targeting COPA, COPB1, and COPB2 and then infected with rHAZV at an MOI of 0.1, alongside cells transfected with scrambled siRNA (Scrambled), cells treated using transfection reagent alone (Infected), and mock-infected cells (Mock) as controls. Supernatants were harvested at 48 hpi, with released virus measured by a plaque assay. (B) Cells were pretreated with brefeldin A at the various stated concentrations and then infected with HAZV at an MOI of 0.1. Released infectious virus in supernatants at 24 hpi was quantified by a plaque assay, measured in duplicate. The statistical significance between conditions is indicated (ns, not significant; *, <italic>P</italic> < 0.1; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001).</p></caption><graphic xlink:href=\"JVI.00766-20-f0007\"/></fig><p>This approximately 1,000-fold reduction in infectious virus production contrasts with the previously measured 2-fold reduction in N production (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4C</xref> and <xref ref-type=\"fig\" rid=\"F4\">D</xref>), or 2- to 5-fold reduction in eGFP expression (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4E</xref>). One possible explanation to account for this discrepancy is that knockdown of COPI complex components exerts an influence at two or more stages of the HAZV replication cycle, with a minor effect at an early stage, prior to, or including virus protein translation, and also with a major effect at a later stage that is required for infectious virus production.</p></sec><sec id=\"s2.8\"><title>Brefeldin A-mediated inhibition of retrograde transport reveals that the production of infectious HAZV is Arf1 dependent.</title><p>The results described above showed that siRNA knockdown of COPI components influenced HAZV gene expression in an Arf1-independent manner and, in addition, reduced the production of infectious HAZV particles. To extend these studies and to establish whether this COPI dependence was also Arf1-independent, we next investigated whether the production of infectious HAZV was sensitive to BFA treatment.</p><p>Following BFA pretreatment at a range of concentrations, SW13 cells were infected with rHAZV at an MOI of 0.1, and at 48 hpi released infectious virus in supernatants was quantified by using a plaque assay (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7B</xref>). BFA treatment markedly reduced the production of infectious HAZV, with a BFA concentration of 50 ng/ml reducing released titers by >100-fold.</p><p>Taken together, these results show that the HAZV replication cycle can be divided into at least two distinct phases based on the relative influence of COPI and dependence on Arf1 functionality: an early phase prior to and including protein production that exhibits a minor COPI dependence and is Arf1 independent, followed by a second phase involved in infectious virus production that is highly COPI dependent and dependent on Arf1 function.</p></sec></sec><sec sec-type=\"discussion\" id=\"s3\"><title>DISCUSSION</title><p>Here, we describe the development of rHAZV-eGFP, a modified recombinant HAZV able to express eGFP and its use in the identification of host cell factors involved in nairovirus replication. The multistep growth kinetics of rHAZV-eGFP and WT rHAZV were similar, with an ∼2-fold reduction in rHAZV-eGFP titer at most; this is not surprising given the increased size of the S segment and a possible reduction in expression of N protein downstream of a P2A linker. This virus has great utility, allowing the rapid and simple real-time analysis of nairovirus growth and infectivity, with the added benefit that it requires only low BSL-2 containment, in contrast to other nairoviruses that require a higher containment infrastructure.</p><p>The measurement of eGFP expression in rHAZV-eGFP-infected cells provided a convenient and rapid assessment of virus activities using live cell imaging, and these properties were exploited in an siRNA-based screen to identify membrane trafficking factors required during the nairovirus replication cycle. Analysis of viral-eGFP expression in cells treated with a total of 426 siRNAs, specific for 142 target genes, revealed an important role for several components of the COPI coatomer complex, in particular COPA, COPB1, and COPB2. Knockdown of these targets affected eGFP expression and N protein production by a factor of around 2-fold, implying the corresponding COPI targets play a role in virus activities up to and including protein expression. In contrast, knockdown of COPI component expression reduced infectious virus production by a factor of around 1,000.</p><p>Since canonical COPI function in vesicle trafficking requires involvement and activation of Arf1 by GEFs (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>), we next examined whether treatment of HAZV-infected cells with BFA, a noncompetitive inhibitor of GEF function (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>), also influenced HAZV gene expression or infectious HAZV release. We showed BFA treatment had no detectable effect on HAZV gene expression in first-round infected cells, whereas BFA treatment reduced infectious virus production by 100-fold at higher concentrations. Taken together, our results suggest involvement of COPI components within at least two stages of the HAZV replication cycle: a minor and Arf1-independent role within early stages of the replication cycle and a major and Arf1-dependent role during infectious virus assembly.</p><p>This is the first report of COPI complex and Arf1 involvement in the replication cycle of a bunyavirus. Recently, Uukuneimi virus, a member of the <italic>Phenuiviridae</italic> family within the <italic>Bunyavirales</italic> order was shown to require the activity of GBF1 (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>), a GEF required for Arf1 activation, and thus it is possible that our findings reported here relate to a common functional requirement of COPI vesicle formation for the <italic>Bunyavirales</italic>. Components of the COPI complex have been implicated in the growth of many viruses, with proposed involvement in several distinct life cycle stages, including entry (<xref rid=\"B42\" ref-type=\"bibr\">42</xref><xref ref-type=\"bibr\" rid=\"B43\">–</xref><xref rid=\"B47\" ref-type=\"bibr\">47</xref>), gene expression (<xref rid=\"B48\" ref-type=\"bibr\">48</xref><xref ref-type=\"bibr\" rid=\"B49\">–</xref><xref rid=\"B51\" ref-type=\"bibr\">51</xref>), virus assembly, and egress (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B52\" ref-type=\"bibr\">52</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>).</p><p>In view of this previous work, it is interesting to speculate at what stage, or stages, of the nairovirus replication cycle the observed COPI dependence might act. COPI complexes are one of three archetypical protein coats, along with COPII and clathrin. Together, they regulate the formation and delivery of vesicles, along with their cargo, to cellular endomembranes, with COPI vesicles performing primary roles in both intra-Golgi and retrograde Golgi-to-ER transport. The Golgi complex is critical for bunyaviruses for many reasons; bunyaviruses modify the Golgi apparatus to establish replication factories, where viral components are synthesized and virus assembly and budding takes place. The Golgi apparatus is also critical for the processing of the viral envelope glycoproteins Gn and Gc (<xref rid=\"B54\" ref-type=\"bibr\">54</xref><xref ref-type=\"bibr\" rid=\"B55\">–</xref><xref rid=\"B58\" ref-type=\"bibr\">58</xref>). Thus, the dependence of infectious HAZV production on COPI components is entirely consistent with these activities, and we hypothesize that COPI vesicles are likely required for the transport of Gn and Gc either between Golgi subcompartments or from the Golgi compartment to specialized virus assembly sites. The mechanism of cargo recognition by the COPI coatomer involves interaction between WD-repeat domains on cage components COP-α (COPA) and COP-β′ (COPB2) that directly bind dilysine motifs (KKxx and KxKxx) on cargo (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>). Consistent with a critical role of COPI complex components in HAZV replication and egress, there are five such dilysine motifs in the C terminus of Gc.</p><p>The dependence of Arf1 for the production of infectious virus is also consistent with its known role; GTP-bound Arf1 becomes membrane associated and initiates vesicle formation by recruiting the COPI coatomer. Thus, the involvement of both COPI and Arf1 in vesicle transport is intrinsically linked, and blockade of Arf1 function by BFA would be expected to influence delivery of Gn and Gc glycoproteins to Golgi body-derived sites of virus assembly, consistent with our observations. It is also possible that the influence of either COPI knockdown or BFA treatment on virus production is less direct; BFA treatment is known to disrupt cellular glycosylation (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>), and blocking COPI activity has been shown to disrupt the recycling of cellular glycotransferases (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>), which are responsible for glycan processing of newly synthesized glycoproteins within the various Golgi subcompartments. It is reasonable to propose that during a HAZV infection, COPI complex knockdown or BFA treatment and the subsequent impairment of glycotransferase activity would similarly affect HAZV Gn and Gc glycoproteins processing, resulting in reduced infectious virus production.</p><p>Our observations that COPI knockdown reduces HAZV-specific gene expression (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4B</xref> to <xref ref-type=\"fig\" rid=\"F4\">E</xref>) implies an involvement at an early stage of the virus replication cycle, prior to or including translation, and what this stage might be is unclear. While COPI vesicles have been associated with entry of several viruses through the endocytic pathway (<xref rid=\"B42\" ref-type=\"bibr\">42</xref><xref ref-type=\"bibr\" rid=\"B43\">–</xref><xref rid=\"B47\" ref-type=\"bibr\">47</xref>), there is considerable evidence to suggest this role is likely to be indirect (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>), and we cannot yet rule out this possibility. However, one possibility is that COPI knockdown reduces or prevents production of critical cellular factors involved in early entry stages, such as a cell surface receptor, thus slowing or reducing virus entry. Another possibility is that COPI vesicles are involved in the initial recruitment of components required for virus factory formation, where viral gene expression takes place. If factory formation was interrupted, expression of the viral proteins would be expected to be reduced, consistent with our observations.</p><p>Our results using both siRNA knockdown of Arf1 and GBF1, as well as Arf1 pharmacological inhibition, revealed the dependence of COPI was Arf1 independent, and this independence is perplexing. To the best of our knowledge, all COPI complex activities thus far described rely on Arf1, on account of its critical role in recruitment of the entire preassembled heptameric COPI coatomer, prior to vesicle formation. Our findings that COPI components are required in an Arf1-independent manner suggests that COPI coatomer components may play additional roles that do not rely on Arf1, and perhaps such roles are unrelated to vesicle formation. It is intriguing to speculate that this noncanonical method of COPI trafficking may represent a newly recognized feature of the mammalian cellular transport system, adding further complexity to our understanding of cargo-mediated transport.</p></sec><sec sec-type=\"materials|methods\" id=\"s4\"><title>MATERIALS AND METHODS</title><sec id=\"s4.1\"><title>Plasmid design.</title><p>Generation of a plasmid expressing HAZV N and eGFP separated by a P2A linker region was achieved via restriction digest and ligation of pMK-RQ-S with pUC57-Kan-eGFP-P2A (Custom purchase; Genewiz). Restriction digests were performed on 1 μg of pMK-RQ-S and pUC57-Kan-eGFP-P2A with EcoRI and NotI (New England Biolabs) for 1 h at 37°C. Digested products were resolved on 1% agarose gels via electrophoresis at 100 V for 45 min in 1× Tris-acetate-EDTA running buffer. DNA bands corresponding to insert and vector were excised from agarose and purified by using a Monarch DNA gel extraction kit (New England Biolabs) according to the manufacturer’s instructions. Ligations were performed using T4 DNA ligase (New England Biolabs) according to the manufacturer’s instructions with successful colonies identified via colony PCR and DNA sequencing (Genewiz) generating pMK-RQ-S-eGFP.</p></sec><sec id=\"s4.2\"><title>Recovery of rHAZV-eGFP.</title><p>Six-well plates were seeded with 2 × 10<sup>5</sup> BSR-T7 cells/well, 1 day prior to transfection, in 2 ml of Dulbecco modified Eagle medium (DMEM) supplemented with 2.5% fetal bovine serum (FBS), 100 U/ml penicillin, and 100 μg/ml streptomycin (2.5% DMEM). After 16 to 24 h, the cells were transfected with 1.2 μg of pMK-RQ-S, pMK-RQ-M, and pMK-RQ-L and 0.6 μg of pCAG-T7pol for WT rHAZV recovery, combined with 2.5 μl of Mirus TransIT-LT1 transfection reagent (Mirus Bio) per μg of DNA, in 200 μl of Opti-MEM. For mutant recovery, the WT plasmid was replaced with pMK-RQ-S-eGFP. A control sample, in which transfection of pMK-RQ-L was omitted, was set up alongside each experiment. At 24 hpt, media containing the transfection mix were removed and replaced with fresh 2.5% DMEM. Reinfection of fresh monolayers was carried out in six-well plates seeded with 2 × 10<sup>5</sup> SW13 cells/well, 1 day prior to infection, in 2 ml of DMEM supplemented with 10% FBS, 100 U/ml penicillin, and 100 μg/ml streptomycin. Cell supernatants from transfected BSR-T7 cells were collected at 120 hpt, and 300 μl was used to infect fresh SW13 cells for 72, 96, or 120 h in a 6-well plate in DMEM with 2.5% FBS. The IncuCyte live cell imaging system (Sartorius) was used to detect total eGFP-positive cells as a marker for virus infection. Infected SW13 cells were imaged hourly for 48 h to permit analysis of total green cell count over time.</p></sec><sec id=\"s4.3\"><title>Virus infections.</title><p>SW13 monolayers were infected with HAZV at the specified MOI in serum-free DMEM (SFM) at 37°C. After 1 h, the inoculum was removed, and the cells were washed in phosphate-buffered saline (PBS); fresh 2.5% DMEM was then applied for the duration of the infection.</p></sec><sec id=\"s4.4\"><title>Viral titration.</title><p>Determination of virus titers was achieved through plaque assays. Supernatant was collected at the time titration was required and serially diluted to infect fresh monolayers of SW13 cells in a 6-well plate. After infection, medium containing virus was removed, cells were washed in ice-cold 1× PBS, a 1:1 ratio of 2.5% DMEM to 1.6% methylcellulose was reapplied, and the cells were incubated a further 6 days prior to fixing and staining with crystal violet. Plaques were then counted, and virus titers were determined.</p></sec><sec id=\"s4.5\"><title>Virus growth curves.</title><p>To assess viral fitness over time, 2 × 10<sup>6</sup> SW13 cells were seeded into 75-cm<sup>2</sup> flasks 1 day prior to infection in 8 ml of DMEM supplemented with 10% FBS. After 24 h, virus was used to infect flasks at the specified MOI in 5 ml of SFM for 1 h with shaking, and an aliquot of the infection medium was collected to permit back-titration, ensuring that equal MOI comparisons were made. After infection, medium containing virus was removed, the cells were washed with PBS, and 7 ml of 2.5% DMEM was reapplied, followed by incubation at 37°C for the remainder of the experiment. At 24-h intervals, 200 μl of supernatant was removed and stored at –80°C until all samples had been collected and stored in a similar manner. Following collection at all time points, the samples were analyzed for infectious virus titers as previously described.</p></sec><sec id=\"s4.6\"><title>Reverse transfection of siRNA library.</title><p>Trypsinized SW13 cells in 2.5% DMEM were counted using a hemocytometer and used to make a cell suspension containing 1 × 10<sup>5</sup> cells/ml. A master mix was made containing 0.3 μl of Lipofectamine RNAiMAX reagent (Invitrogen) and 16.7 μl of Opti-MEM per well, and 17 μl of this master mix was pipetted into each well of a 96-well plate. A 3- μl volume of working stock siRNA (1 μM) was pipetted into the transfection master mix and mixed, resulting in a final concentration of 3 pmol of siRNA per well. A 100-μl aliquot of the 1 × 10<sup>5</sup> cell suspension was then applied per well. The cells were incubated with the transfection mix for 24 h at 37°C, and then 60 μl of the medium was removed and replaced with 140 μl of fresh 2.5% DMEM to dilute out any potential toxic effects of the siRNAs or transfection reagent. At 6 h postdilution, the medium was removed, and the cells were washed in PBS prior to infection with rHAZV-eGFP at an MOI of 0.25 in 100 μl of 2.5% DMEM. At 24 hpt, the eGFP fluorescence intensity was determined using the IncuCyte live cell imaging software as a measure of virus gene expression. The total integrated intensity of eGFP (TIIE; green count units [GCU] × μm<sup>2</sup>/image) was first normalized to confluence per well and then analyzed as a percentage of the total green integrated intensity in positive-control wells containing virus, but omitting siRNA and transfection reagent. Normalized values between two technical repeats were averaged and then averaged across two biological repeats (<italic>n</italic> = 4).</p></sec><sec id=\"s4.7\"><title>Validation of siRNA knockdown of COPI-specific targets.</title><p>To validate the knockdown observed in the siRNA screen, siRNAs specific to COPA, COPB1, and COPB2 were purchased (Ambion) and reverse transfected into SW13 cells. Briefly, 75 pmol of siRNA was mixed with 4.5 μl of Lipofectamine RNAiMAX in 200 μl of Opti-MEM, followed by incubation at room temperature for 20 min. After incubation, the transfection mix was added dropwise to a 6-well plate and overlaid with 2 ml of cell suspension containing 1 × 10<sup>5</sup> cells/ml in 2.5% DMEM. At 24 hpt, the medium was changed to fresh 2.5% DMEM for 6 h and then infected with rHAZV at an MOI of 0.1. Cell lysates were collected at 24 hpi for analysis via Western blotting or qPCR, and the supernatant was collected for the determination of virus titers.</p></sec><sec id=\"s4.8\"><title>Quantitative PCR.</title><p>RNA was harvested from cells of interest via TRIzol extraction. Briefly, cells were washed in nuclease-free PBS and then resuspended in TRI Reagent (Invitrogen); chloroform was then added, and the samples were mixed vigorously and incubated at room temperature for 2 min. Samples were centrifuged at 12,000 × <italic>g</italic>, and the resulting aqueous phase was collected and added to isopropanol to precipitate RNA for 5 min at room temperature. Samples were spun at 12,000 × <italic>g</italic>, and the resulting pellet was washed in ice-cold 75% ethanol prior to resuspension in nuclease-free H<sub>2</sub>O. qPCR was carried out using a One Step Mesa Green qRT-PCR MasterMix for SYBR assay (Eurogentec) according to the manufacturer’s instructions, with samples normalized to GAPDH expression. The primer sequences used were as follows: HAZV S segment (5′-CAA GGC AAG CAT TGC CAC AC-3′ and 5′-GCT TTC TCT CAC CCC TTT TAG GA-3′), COPA (5′-CCA CTA TCA GAA TGC CCT ATA CC-3′ and 5′-CCA CAA ACC CAT CTT CAT CC-3′), COPB1 (5′-ACA GAGA GAA AGA GGC AGC AGA-3′ and 5′-GCA AGG TATA CAC TGG TTT GGT TC-3′), COPB2 (5′-GTG GGG ACA AGC CAT ACC TC 3′ and 5′-GTG CTC TCA AGC CGG TAG G-3′), and GAPDH (5′-TGT GGT CAT GAG TCC TTC CAC GAT-3′ and 5′-AGG GTC ATC ATC TCT GCC CCC TC-3′).</p></sec><sec id=\"s4.9\"><title>Immunofluorescence.</title><p>Trypsinized SW13 cells were seeded onto 16-mm round glass coverslips (VWR) in a 12-well plate at 5 × 10<sup>4</sup> cells/well, followed by incubation at 37°C. After 16 to 24 h, the cells were infected at an MOI of 0.1 in SFM for 1 h at 37°C. After the infection medium was removed, the cells were washed in PBS, 2.5% DMEM was applied, and the samples were incubated at 37°C for a further 24 h. Medium was then removed, and the cells were washed twice in PBS prior to fixation in 4% (vol/vol) paraformaldehyde in PBS for 10 min at room temperature. After fixation, the cells were washed twice in PBS and then incubated in permeabilization buffer (0.1% [vol/vol] Triton X-100, 1% [wt/vol] bovine serum albumin [BSA] in 1× PBS) for 15 min at room temperature. The cells were then blocked in blocking buffer (1% [wt/vol] BSA in 1× PBS) for 45 min then incubated with HAZV N antisera (1:1,000) or COPA (1:200) primary antibody for 1 h at room temperature. The cells were washed three times in PBS and then incubated with the corresponding secondary Alexa Fluor antibody (Life Technologies; 1:500 in blocking buffer) for 1 h at room temperature in a light protected vessel, followed by three washes with PBS. Coverslips were then mounted onto glass slides with the addition of ProLong Gold Antifade reagent with DAPI (Thermo Fisher Scientific), sealed, and stored at 4°C. Images were then taken on an LSM 700 confocal microscope and processed using Zen (Blue Edition) software.</p></sec><sec id=\"s4.10\"><title>Inhibition of retrograde transport.</title><p>Trypsinized A549 cells were seeded into 12-well plates at 1 × 10<sup>5</sup> cells/well and incubated at 37°C. After 16 to 24 h, the cells were pretreated with BFA at the indicated concentrations for 45 min in SFM prior to infection with rHAZV or rIAV at an MOI of 0.1 for 1 h at 37°C. After the infection period, medium containing virus was removed, and cell monolayers were washed three times with PBS. Fresh 2.5% DMEM was then reapplied containing the indicated concentration of BFA for a further 24 h. At this point, lysates were collected and analyzed via Western blotting, and supernatant was collected for the determination of virus titers.</p></sec><sec id=\"s4.11\"><title>Statistical analyses.</title><p>Statistical analyses were performed using an unpaired <italic>t</italic> test to determine statistically significant differences between treatments (ns, nonsignificant; *, <italic>P</italic> < 0.1; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001).</p></sec></sec>\n<sec sec-type=\"supplementary-material\">\n<title>Supplementary Material</title>\n<supplementary-material id=\"PMC_1\" content-type=\"local-data\">\n<caption>\n<title>Supplemental file 1</title>\n</caption>\n<media mimetype=\"application\" mime-subtype=\"vnd.ms-excel\"\nxlink:href=\"JVI.00766-20-sd001.xlsx\"/>\n</supplementary-material>\n</sec>\n</body><back><fn-group><fn fn-type=\"supplementary-material\"><p>Supplemental material is available online only.</p></fn></fn-group><ack><title>ACKNOWLEDGMENTS</title><p>This study was funded by a Public Health England Ph.D. studentship (to J.F.), a Wellcome trust studentship (102174/B/13/Z; E.J.A.A.T.), and an EU Marie Skłodowska-Curie Actions (MSCA) Innovative Training Network (ITN)—H2020-MSCA-ITN-2016, grant agreement 721367 (HONOURs)—to B.A.-R.</p><p>J.F., R.H., J.M., and J.N.B. conceptualized the study. J.F., B.A.-R., and E.J.A.A.T. performed the experimental investigation. J.F. and J.N.B. wrote the original manuscript draft. All authors reviewed and edited the manuscript. J.M., R.H., and J.N.B. supervised the core team. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">J Virol</journal-id><journal-id journal-id-type=\"iso-abbrev\">J. Virol</journal-id><journal-id journal-id-type=\"hwp\">jvi</journal-id><journal-id journal-id-type=\"pmc\">jvi</journal-id><journal-id journal-id-type=\"publisher-id\">JVI</journal-id><journal-title-group><journal-title>Journal of Virology</journal-title></journal-title-group><issn pub-type=\"ppub\">0022-538X</issn><issn pub-type=\"epub\">1098-5514</issn><publisher><publisher-name>American Society for Microbiology</publisher-name><publisher-loc>1752 N St., N.W., Washington, DC</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32611754</article-id><article-id pub-id-type=\"pmc\">PMC7431792</article-id><article-id pub-id-type=\"publisher-id\">00682-20</article-id><article-id pub-id-type=\"doi\">10.1128/JVI.00682-20</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Genetic Diversity and Evolution</subject></subj-group></article-categories><title-group><article-title>Isolation and Characterization of the First Freshwater Cyanophage Infecting <italic>Pseudanabaena</italic></article-title><alt-title alt-title-type=\"running-head\">First Freshwater Cyanophage Infecting <italic>Pseudanabaena</italic></alt-title><alt-title alt-title-type=\"short-authors\">Zhang et al.</alt-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Dong</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>You</surname><given-names>Fang</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>He</surname><given-names>Yiliang</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>c</sup></xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-7288-6512</contrib-id><name><surname>Te</surname><given-names>Shu Harn</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-1266-9276</contrib-id><name><surname>Gin</surname><given-names>Karina Yew-Hoong</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><aff id=\"aff1\"><label>a</label><addr-line>NUS Environmental Research Institute, National University of Singapore, Singapore</addr-line></aff><aff id=\"aff2\"><label>b</label><addr-line>Department of Civil and Environmental Engineering, National University of Singapore, Singapore</addr-line></aff><aff id=\"aff3\"><label>c</label><addr-line>School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China</addr-line></aff></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Pfeiffer</surname><given-names>Julie K.</given-names></name><role>Editor</role><aff>University of Texas Southwestern Medical Center</aff></contrib></contrib-group><author-notes><corresp id=\"cor1\">Address correspondence to Karina Yew-Hoong Gin, <email>ceeginyh@nus.edu.sg</email>.</corresp><fn fn-type=\"other\"><p><bold>Citation</bold> Zhang D, You F, He Y, Te SH, Gin KY-H. 2020. Isolation and characterization of the first freshwater cyanophage infecting <italic>Pseudanabaena</italic>. J Virol 94:e00682-20. <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.1128/JVI.00682-20\">https://doi.org/10.1128/JVI.00682-20</ext-link>.</p></fn></author-notes><pub-date pub-type=\"epreprint\"><day>1</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>9</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>94</volume><issue>17</issue><elocation-id>e00682-20</elocation-id><history><date date-type=\"received\"><day>14</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>18</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Zhang et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Zhang et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International license</ext-link>.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"JVI.00682-20.pdf\"/><abstract abstract-type=\"precis\"><p>This study presents the isolation of the very first freshwater cyanophage, PA-SR01, that infects <italic>Pseudanabaena</italic>, and fills an important knowledge gap on freshwater cyanophages as well as cyanophages infecting <italic>Pseudanabaena</italic>.</p></abstract><abstract><title>ABSTRACT</title><p>Cyanobacteria are the major primary producers in both freshwater and marine environments. However, the majority of freshwater cyanophages remain unknown due to the limited number of cyanophage isolates. In this study, we present a novel lytic freshwater cyanophage, PA-SR01, which was isolated from the Singapore Serangoon Reservoir. To our knowledge, this is the first isolate of a cyanophage that has been found to infect the cyanobacterium <italic>Pseudanabaena</italic>. PA-SR01 has a narrow host range, a short latent period, and is chloroform sensitive. Distinct from the majority of cyanophage isolates, PA-SR01 has a tailless morphology. It is a double-stranded DNA virus with a 137,012-bp genome. Functional annotation for the predicted open reading frames (ORFs) of the PA-SR01 genome identified genes with putative functions related to DNA metabolism, structural proteins, lysis, host-derived metabolic genes, and DNA packaging. Out of 166 predicted ORFs, only 17 ORFs have homology with genes with known function. Phylogenetic analysis of the major capsid protein and terminase large subunit further suggests that phage PA-SR01 is evolutionary distinct from known cyanophages. Metagenomics sequence recruitment onto the PA-SR01 genome indicates that PA-SR01 represents a new evolutionary lineage of phage which shares considerable genetic similarities with phage sequences in aquatic environments and could play key ecological roles.</p><p><bold>IMPORTANCE</bold> This study presents the isolation of the very first freshwater cyanophage, PA-SR01, that infects <italic>Pseudanabaena</italic>, and fills an important knowledge gap on freshwater cyanophages as well as cyanophages infecting <italic>Pseudanabaena</italic>.</p></abstract><kwd-group><title>KEYWORDS</title><kwd>cyanophages</kwd><kwd>freshwater</kwd><kwd>full genome</kwd><kwd>isolation</kwd><kwd><italic>Pseudanabaena</italic></kwd></kwd-group><funding-group><award-group id=\"award1\"><funding-source><institution-wrap><institution>National Research Foundation Singapore (NRF)</institution><institution-id>https://doi.org/10.13039/501100001381</institution-id></institution-wrap></funding-source><award-id>E2S2-CREATE Phase 2</award-id><principal-award-recipient><name><surname>Gin</surname><given-names>Karina Yew-Hoong</given-names></name></principal-award-recipient></award-group></funding-group><counts><fig-count count=\"10\"/><table-count count=\"9\"/><equation-count count=\"0\"/><ref-count count=\"52\"/><page-count count=\"17\"/><word-count count=\"8324\"/></counts><custom-meta-group><custom-meta><meta-name>cover-date</meta-name><meta-value>September 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>INTRODUCTION</title><p>Cyanobacteria play important roles in primary production and trophic interactions. They are the dominant autotrophs in most aquatic environments, such as freshwater and marine environments (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Viruses infecting cyanobacteria are referred to as cyanophages and can play major roles in the dynamics, genetic diversity, and structure of cyanobacterial communities (<xref rid=\"B2\" ref-type=\"bibr\">2</xref><xref ref-type=\"bibr\" rid=\"B3\">–</xref><xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Compared to marine cyanophages, which have been widely studied (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>), there are very limited studies on freshwater cyanophages (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>).</p><p>To better understand the biological interactions and evolutionary relationships between cyanophages and their host, cyanophage whole-genome sequences could provide a solid platform to elucidate such relationships (<xref rid=\"B8\" ref-type=\"bibr\">8</xref><xref ref-type=\"bibr\" rid=\"B9\">–</xref><xref rid=\"B12\" ref-type=\"bibr\">12</xref>). At the same time, as metagenomics becomes a more prevalent approach to monitoring environmental cyanophage diversity, genomic sequences of cultured cyanophages are needed for more precise annotation of the viral metagenome. Most viral sequences in metagenomic databases cannot be allocated putative functions and there are still many viral contigs of unknown identity in metagenomes (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). This further strengthens the case for the need to acquire more genomic sequences of new cyanophage isolates, especially in the case of freshwater cyanophages.</p><p>Based on their morphological differences, cyanophages are generally categorized within three families, the <italic>Myoviridae</italic>, <italic>Podoviridae</italic>, and <italic>Siphoviridae</italic>, which belong to the order <italic>Caudovirales. Myoviridae</italic> have long contractile tails, <italic>Podoviridae</italic> have short noncontratile tails, while <italic>Siphoviridae</italic> have long flexible tails (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Only one tailless cyanophage has been isolated to date (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>).</p><p>PA-SR01 infects and lyses freshwater <italic>Pseudanabaena</italic> strain KCZY-C8. Despite reports of several cases of <italic>Pseudanabaena</italic> presence in cyanobacterial blooms (<xref rid=\"B17\" ref-type=\"bibr\">17</xref><xref ref-type=\"bibr\" rid=\"B18\">–</xref><xref rid=\"B19\" ref-type=\"bibr\">19</xref>), there have been no <italic>Pseudanabaena</italic>-infecting phages isolated to date to the best of our knowledge. PA-SR01 is the first cyanophage infecting and lysing <italic>Pseudanabaena</italic>. To explore the biological properties and ecological roles of PA-SR01, we first studied the morphology and infection process, followed by sequencing the PA-SR01 genome and performing functional gene annotation. To further understand its environmental presence, recruitment of metagenomics reads onto the PA-SR01 genome was performed and revealed its prevalence in aquatic systems around the globe.</p></sec><sec sec-type=\"results|discussion\" id=\"s2\"><title>RESULTS AND DISCUSSION</title><sec id=\"s2.1\"><title>Physical properties of phage PA-SR01.</title><p>The transmission electron microscopy images of PA-SR01 phage (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1</xref>) showed numerous virus particles with similar size and morphology. Unlike most isolated tailed cyanophages, the cross sections of the viral particles appeared hexagonal, without any tails attached, indicating that the virus had an icosahedral symmetry and was tailless. The average diameter of viral particles ranged from 88 to 95 nm (mean ± SD = 91 ± 3 nm). Similar tailless freshwater cyanophages have also been identified in Lake Donghu, China (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>).</p><fig id=\"F1\" orientation=\"portrait\" position=\"float\"><label>FIG 1</label><caption><p>Transmission electron micrographs showing morphological features of PA-SR01. Micrographs show empty capsid (A) and original phage particles (B).</p></caption><graphic xlink:href=\"JVI.00682-20-f0001\"/></fig></sec><sec id=\"s2.2\"><title>Host specificity.</title><p>PA-SR01 lysed only <italic>Pseudanabaena</italic> strain KCZY-C8 and not the additional 2 <italic>Pseudanabaena</italic> strains and 17 cyanobacterial species (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Based on this result, we concluded that PA-SR01 is strain specific rather than species specific, and has a narrow host range. To understand whether PA-SR01 infectivity is correlated with geographical location, more <italic>Pseudanabaena</italic> strains will need to be isolated from other tropical water bodies.</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>List of cyanobacteria used for host range test</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Genus</th><th rowspan=\"1\" colspan=\"1\">Strain</th><th rowspan=\"1\" colspan=\"1\">Origin</th><th rowspan=\"1\" colspan=\"1\">Susceptibility</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\"><italic>Cylindrospermopsis</italic></td><td rowspan=\"1\" colspan=\"1\">CS505</td><td rowspan=\"1\" colspan=\"1\">Lakes in tropical Queensland</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Cylindrospermopsis</italic></td><td rowspan=\"1\" colspan=\"1\">CS511</td><td rowspan=\"1\" colspan=\"1\">Lakes in tropical Queensland</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Cylindrospermopsis</italic></td><td rowspan=\"1\" colspan=\"1\">Cyl UPR</td><td rowspan=\"1\" colspan=\"1\">Upper Peirce Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Cylindrospermopsis</italic></td><td rowspan=\"1\" colspan=\"1\">CS509</td><td rowspan=\"1\" colspan=\"1\">Lakes in tropical Queensland</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Cylindrospermopsis</italic></td><td rowspan=\"1\" colspan=\"1\">Cy2.2</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Cylindrospermopsis</italic></td><td rowspan=\"1\" colspan=\"1\">Cy3.4</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Limnothrix</italic></td><td rowspan=\"1\" colspan=\"1\">MRS2</td><td rowspan=\"1\" colspan=\"1\">Marina Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic></td><td rowspan=\"1\" colspan=\"1\">I21</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic></td><td rowspan=\"1\" colspan=\"1\">B</td><td rowspan=\"1\" colspan=\"1\">Kranji Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic></td><td rowspan=\"1\" colspan=\"1\">I1</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic></td><td rowspan=\"1\" colspan=\"1\">I31</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic></td><td rowspan=\"1\" colspan=\"1\">M1</td><td rowspan=\"1\" colspan=\"1\">Marina Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic></td><td rowspan=\"1\" colspan=\"1\">K18</td><td rowspan=\"1\" colspan=\"1\">Kranji Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Pseudanabaena</italic></td><td rowspan=\"1\" colspan=\"1\">KCZY-C8</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">+</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Pseudanabaena</italic></td><td rowspan=\"1\" colspan=\"1\">MRS2</td><td rowspan=\"1\" colspan=\"1\">Marina Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Pseudanabaena</italic></td><td rowspan=\"1\" colspan=\"1\">M13A</td><td rowspan=\"1\" colspan=\"1\">Marina Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic></td><td rowspan=\"1\" colspan=\"1\">IA</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic></td><td rowspan=\"1\" colspan=\"1\">Cip1</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic></td><td rowspan=\"1\" colspan=\"1\">Cip6</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic></td><td rowspan=\"1\" colspan=\"1\">R4S1</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic></td><td rowspan=\"1\" colspan=\"1\">Cip21</td><td rowspan=\"1\" colspan=\"1\">Serangoon Reservoir</td><td rowspan=\"1\" colspan=\"1\">−</td></tr></tbody></table><graphic xlink:href=\"JVI.00682-20-t0001\"/></alternatives></table-wrap></sec><sec id=\"s2.3\"><title>Chloroform sensitivity.</title><p>The infectivity of PA-SR01 is chloroform sensitive (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). Chloroform treatment makes phage lose its infectivity and there is no observable effect on host cell growth with or without addition of chloroform-treated MLA medium. Chloroform sensitivity serves as a first indication of a viral lipid component (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Chloroform can dissolve lipids that may be structural components of infection mechanisms in lipid-containing phage (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). However, chloroform sensitivity alone does not prove the presence of lipid in viral particles. Further studies are needed to confirm the presence of lipids in PA-SR01. This figure also shows that the latent period of PA-SR01 is approximately 1 day, which is relatively short compared to other freshwater cyanophages such as S-LBS1 (latent period of 4 days), PaV-LD (latent period of 2 days), and S-EIV1 (latent period of 2 days) (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>).</p><fig id=\"F2\" orientation=\"portrait\" position=\"float\"><label>FIG 2</label><caption><p>Effect of chloroform on the infectivity of PA-SR01. OD at 750 nm is shown for <italic>Pseudanabaena</italic> cultures grown in untreated (open triangle) or chloroform-treated MLA medium (black triangle), or else inoculated with chloroform-treated (black circles) or untreated viruses (open circles).</p></caption><graphic xlink:href=\"JVI.00682-20-f0002\"/></fig></sec><sec id=\"s2.4\"><title>Genomic overview.</title><p>The 137,012-bp genome of PA-SR01 is circularly permutated (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref>) with a GC content of 39.5%. One hundred sixty-six ORFs were predicted, though most ORFs in PA-SR01 did not have homologous genes of known function. In total, 47 ORFs had significant similarity to other sequences, and more than 70% of the ORFs could not be annotated to any homologs. Only 11 ORFs were similar to phage sequences and only 17 were similar to genes of known function (BLASTp; E value cutoff = 10<sup>−5</sup>). Three clustered tRNA genes, tRNA<sup>Met</sup>, tRNA<sup>Asp</sup>, and tRNA<sup>Gly</sup>, were identified (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>).</p><fig id=\"F3\" orientation=\"portrait\" position=\"float\"><label>FIG 3</label><caption><p>Genomic map of PA-SR01. Circles from outermost to innermost correspond to (i) predicted ORFs (BLASTp, nr database, E value of <0.00001) on forward strand and (ii) reverse strand; (iii) GC content plotted with green representing G+C content and purple representing A+T content. Only ORFs of >100 bp are shown and are colored as follows: black, hypothetical protein; gray, no homolog; pink, DNA packaging; blue, structural protein; crimson, host-derived metabolic gene; brown, DNA metabolism; purple, lysis.</p></caption><graphic xlink:href=\"JVI.00682-20-f0003\"/></fig><table-wrap id=\"T2\" orientation=\"portrait\" position=\"float\"><label>TABLE 2</label><caption><p>tRNAs predicted with tRNAscan-SE2</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">tRNA number</th><th rowspan=\"1\" colspan=\"1\">tRNA start</th><th rowspan=\"1\" colspan=\"1\">tRNA end</th><th rowspan=\"1\" colspan=\"1\">tRNA type</th><th rowspan=\"1\" colspan=\"1\">Anticodon</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\">58579</td><td rowspan=\"1\" colspan=\"1\">58502</td><td rowspan=\"1\" colspan=\"1\">Met</td><td rowspan=\"1\" colspan=\"1\">CAT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">2</td><td rowspan=\"1\" colspan=\"1\">58497</td><td rowspan=\"1\" colspan=\"1\">58427</td><td rowspan=\"1\" colspan=\"1\">Asp</td><td rowspan=\"1\" colspan=\"1\">ATC</td></tr><tr><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">58356</td><td rowspan=\"1\" colspan=\"1\">58284</td><td rowspan=\"1\" colspan=\"1\">Gly</td><td rowspan=\"1\" colspan=\"1\">TCC</td></tr></tbody></table><graphic xlink:href=\"JVI.00682-20-t0002\"/></alternatives></table-wrap><p>Genome annotation of PA-SR01 ORFs identified putative genes with functions associated with structural proteins, DNA metabolism, DNA packaging, and lysis (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>). HHpred was used to ascribe function to additional ORFs (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>). This resulted in the identification of genes encoding putative functions associated with DNA-binding domains (ORF3), Mu-like prophage I protein (ORF24), major capsid protein (ORF32), and PD-(D/E)XK endonuclease (ORF139). In total, 21 ORFs showing homology to genes of known function were obtained.</p><table-wrap id=\"T3\" orientation=\"portrait\" position=\"float\"><label>TABLE 3</label><caption><p>Predicted ORFs of cyanophage PA-SR01 with similarity to genes of known function</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">ORF</th><th rowspan=\"1\" colspan=\"1\">GenBank ID</th><th rowspan=\"1\" colspan=\"1\">Strand</th><th rowspan=\"1\" colspan=\"1\">% identity</th><th rowspan=\"1\" colspan=\"1\">E value</th><th rowspan=\"1\" colspan=\"1\">Putative protein encoded</th><th rowspan=\"1\" colspan=\"1\">Organism</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">12</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/WP_097778008.1\" assigning-authority=\"ncbi:protein\">WP_097778008.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">29</td><td rowspan=\"1\" colspan=\"1\">1.00E−55</td><td rowspan=\"1\" colspan=\"1\">Phage terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><named-content content-type=\"genus-species\">Faecalibacterium prausnitzii</named-content></td></tr><tr><td rowspan=\"1\" colspan=\"1\">29</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/RTL07602.1\" assigning-authority=\"ncbi:protein\">RTL07602.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">38</td><td rowspan=\"1\" colspan=\"1\">8.40E−72</td><td rowspan=\"1\" colspan=\"1\">DEAD/DEAH box helicase</td><td rowspan=\"1\" colspan=\"1\">Candidatus Dependentiae bacterium</td></tr><tr><td rowspan=\"1\" colspan=\"1\">41</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ADP97718.1\" assigning-authority=\"ncbi:protein\">ADP97718.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">21</td><td rowspan=\"1\" colspan=\"1\">3.40E−26</td><td rowspan=\"1\" colspan=\"1\">Phage tail tape measure protein, family</td><td rowspan=\"1\" colspan=\"1\"><named-content content-type=\"genus-species\">Marinobacter adhaerens</named-content> HP15</td></tr><tr><td rowspan=\"1\" colspan=\"1\">55</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/QBQ77753.1\" assigning-authority=\"ncbi:protein\">QBQ77753.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">35</td><td rowspan=\"1\" colspan=\"1\">8.80E−26</td><td rowspan=\"1\" colspan=\"1\">Putative group I intron endonuclease</td><td rowspan=\"1\" colspan=\"1\"><italic>Escherichia</italic> phage vB_EcoM_WFbE185</td></tr><tr><td rowspan=\"1\" colspan=\"1\">66</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_009100781.1\" assigning-authority=\"ncbi:protein\">YP_009100781.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">-</td><td rowspan=\"1\" colspan=\"1\">35</td><td rowspan=\"1\" colspan=\"1\">3.70E−23</td><td rowspan=\"1\" colspan=\"1\">Homing endonuclease</td><td rowspan=\"1\" colspan=\"1\"><italic>Shigella</italic> phage Shf125875</td></tr><tr><td rowspan=\"1\" colspan=\"1\">68</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/KKP95496.1\" assigning-authority=\"ncbi:protein\">KKP95496.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">30</td><td rowspan=\"1\" colspan=\"1\">1.00E−11</td><td rowspan=\"1\" colspan=\"1\">Crossover junction endodeoxyribonuclease RuvC</td><td rowspan=\"1\" colspan=\"1\">Candidate division TM6 bacterium</td></tr><tr><td rowspan=\"1\" colspan=\"1\">77</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/WP_138275383.1\" assigning-authority=\"ncbi:protein\">WP_138275383.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">33</td><td rowspan=\"1\" colspan=\"1\">6.00E−09</td><td rowspan=\"1\" colspan=\"1\">Crossover junction endodeoxyribonuclease RuvC</td><td rowspan=\"1\" colspan=\"1\">Candidatus Rhodoluna limnophila</td></tr><tr><td rowspan=\"1\" colspan=\"1\">81</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/WP_050045131.1\" assigning-authority=\"ncbi:protein\">WP_050045131.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">90</td><td rowspan=\"1\" colspan=\"1\">2.00E−218</td><td rowspan=\"1\" colspan=\"1\">IS200/IS605 family element transposase accessory protein TnpB</td><td rowspan=\"1\" colspan=\"1\"><named-content content-type=\"genus-species\">Tolypothrix bouteillei</named-content></td></tr><tr><td rowspan=\"1\" colspan=\"1\">83</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/WP_070058383.1\" assigning-authority=\"ncbi:protein\">WP_070058383.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">51</td><td rowspan=\"1\" colspan=\"1\">7.00E−16</td><td rowspan=\"1\" colspan=\"1\">Septal ring lytic transglycosylase RlpA family protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Marinobacter</italic> sp. X15-166B</td></tr><tr><td rowspan=\"1\" colspan=\"1\">90</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/WP_131120822.1\" assigning-authority=\"ncbi:protein\">WP_131120822.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">48</td><td rowspan=\"1\" colspan=\"1\">7.10E−19</td><td rowspan=\"1\" colspan=\"1\">KilA-N domain-containing protein</td><td rowspan=\"1\" colspan=\"1\"><named-content content-type=\"genus-species\">Westiellopsis prolifica</named-content></td></tr><tr><td rowspan=\"1\" colspan=\"1\">98</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/OBQ26706.1\" assigning-authority=\"ncbi:protein\">OBQ26706.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">39</td><td rowspan=\"1\" colspan=\"1\">1.10E−20</td><td rowspan=\"1\" colspan=\"1\">Appr-1-p processing protein</td><td rowspan=\"1\" colspan=\"1\"><named-content content-type=\"genus-species\">Aphanizomenon flos-aquae</named-content> LD13</td></tr><tr><td rowspan=\"1\" colspan=\"1\">103</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/QBQ73194.1\" assigning-authority=\"ncbi:protein\">QBQ73194.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">68</td><td rowspan=\"1\" colspan=\"1\">5.70E−92</td><td rowspan=\"1\" colspan=\"1\">FAD-dependent thymidylate synthase</td><td rowspan=\"1\" colspan=\"1\"><italic>Nodularia</italic> phage vB_NspS-kac65v151</td></tr><tr><td rowspan=\"1\" colspan=\"1\">107</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/BBI90448.1\" assigning-authority=\"ncbi:protein\">BBI90448.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">34</td><td rowspan=\"1\" colspan=\"1\">6.00E−112</td><td rowspan=\"1\" colspan=\"1\">Ribonucleotide-diphosphate reductase subunit beta</td><td rowspan=\"1\" colspan=\"1\"><italic>Tenacibaculum</italic> phage PTm1</td></tr><tr><td rowspan=\"1\" colspan=\"1\">108</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/WP_018298810.1\" assigning-authority=\"ncbi:protein\">WP_018298810.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">49</td><td rowspan=\"1\" colspan=\"1\">3.20E−198</td><td rowspan=\"1\" colspan=\"1\">Ribonucleoside-diphosphate reductase subunit alpha</td><td rowspan=\"1\" colspan=\"1\"><named-content content-type=\"genus-species\">Fangia hongkongensis</named-content></td></tr><tr><td rowspan=\"1\" colspan=\"1\">114</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/PSB68310.1\" assigning-authority=\"ncbi:protein\">PSB68310.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">35</td><td rowspan=\"1\" colspan=\"1\">6.00E−99</td><td rowspan=\"1\" colspan=\"1\">DNA-directed DNA polymerase</td><td rowspan=\"1\" colspan=\"1\">Filamentous cyanobacterium CCP1</td></tr><tr><td rowspan=\"1\" colspan=\"1\">116</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/WP_119501154.1\" assigning-authority=\"ncbi:protein\">WP_119501154.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">30</td><td rowspan=\"1\" colspan=\"1\">3.00E−08</td><td rowspan=\"1\" colspan=\"1\">Deoxynucleotide monophosphate kinase</td><td rowspan=\"1\" colspan=\"1\"><italic>Alteromonas</italic> sp. RKMC-009</td></tr><tr><td rowspan=\"1\" colspan=\"1\">125</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_009042791.1\" assigning-authority=\"ncbi:protein\">YP_009042791.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">48</td><td rowspan=\"1\" colspan=\"1\">1.50E−21</td><td rowspan=\"1\" colspan=\"1\">Recombination protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Anabaena</italic> phage A-4L</td></tr></tbody></table><graphic xlink:href=\"JVI.00682-20-t0003\"/></alternatives></table-wrap><table-wrap id=\"T4\" orientation=\"portrait\" position=\"float\"><label>TABLE 4</label><caption><p>ORFs with distant homology to PA-SR01 identified using HHpred analysis</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">ORF</th><th rowspan=\"1\" colspan=\"1\">Pfam ID<xref ref-type=\"table-fn\" rid=\"T1F1\"><sup><italic>a</italic></sup></xref></th><th rowspan=\"1\" colspan=\"1\">Strand</th><th rowspan=\"1\" colspan=\"1\">E value</th><th rowspan=\"1\" colspan=\"1\">Putative protein encoded</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">PF10544.9</td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">5.2E−16</td><td rowspan=\"1\" colspan=\"1\">T5orf172 domain</td></tr><tr><td rowspan=\"1\" colspan=\"1\">24</td><td rowspan=\"1\" colspan=\"1\">PF10123.9</td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">4.5E−18</td><td rowspan=\"1\" colspan=\"1\">Mu-like prophage I protein</td></tr><tr><td rowspan=\"1\" colspan=\"1\">32</td><td rowspan=\"1\" colspan=\"1\">PF03864.15</td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">1.5E−30</td><td rowspan=\"1\" colspan=\"1\">Phage major capsid protein E</td></tr><tr><td rowspan=\"1\" colspan=\"1\">139</td><td rowspan=\"1\" colspan=\"1\">PF11645.8</td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">0.0000018</td><td rowspan=\"1\" colspan=\"1\">PD-(D/E)XK endonuclease</td></tr></tbody></table><graphic xlink:href=\"JVI.00682-20-t0004\"/></alternatives><table-wrap-foot><fn fn-type=\"other\" id=\"T1F1\"><label>a</label><p>Pfam ID data can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"https://pfam.xfam.org\">https://pfam.xfam.org</ext-link>.</p></fn></table-wrap-foot></table-wrap><p>Seven rho-independent terminators were predicted by Findterm (<xref rid=\"T5\" ref-type=\"table\">Table 5</xref>); 6 are downstream of ORFs with unknown function (ORF30, ORF33, ORF61, ORF62, ORF73, and ORF102) and one is downstream of a gene predicted to encode a Kila-N domain-containing protein (ORF90).</p><table-wrap id=\"T5\" orientation=\"portrait\" position=\"float\"><label>TABLE 5</label><caption><p>PA-SR01 rho-independent terminators predicted by Findterm</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Terminator</th><th rowspan=\"1\" colspan=\"1\">Start</th><th rowspan=\"1\" colspan=\"1\">End</th><th rowspan=\"1\" colspan=\"1\">Length (bp)</th><th rowspan=\"1\" colspan=\"1\">Strand</th><th rowspan=\"1\" colspan=\"1\">Energy (kCal)</th><th rowspan=\"1\" colspan=\"1\">Upstream ORF</th><th rowspan=\"1\" colspan=\"1\">Distance to ORF</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">Term1</td><td rowspan=\"1\" colspan=\"1\">26973</td><td rowspan=\"1\" colspan=\"1\">26924</td><td rowspan=\"1\" colspan=\"1\">50</td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">−18.8</td><td rowspan=\"1\" colspan=\"1\">30</td><td rowspan=\"1\" colspan=\"1\">21</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Term2</td><td rowspan=\"1\" colspan=\"1\">30131</td><td rowspan=\"1\" colspan=\"1\">30088</td><td rowspan=\"1\" colspan=\"1\">44</td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">−17.3</td><td rowspan=\"1\" colspan=\"1\">33</td><td rowspan=\"1\" colspan=\"1\">139</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Term3</td><td rowspan=\"1\" colspan=\"1\">61015</td><td rowspan=\"1\" colspan=\"1\">60972</td><td rowspan=\"1\" colspan=\"1\">44</td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">−21.5</td><td rowspan=\"1\" colspan=\"1\">61</td><td rowspan=\"1\" colspan=\"1\">2</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Term4</td><td rowspan=\"1\" colspan=\"1\">61988</td><td rowspan=\"1\" colspan=\"1\">61943</td><td rowspan=\"1\" colspan=\"1\">46</td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">−22.7</td><td rowspan=\"1\" colspan=\"1\">62</td><td rowspan=\"1\" colspan=\"1\">104</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Term5</td><td rowspan=\"1\" colspan=\"1\">71819</td><td rowspan=\"1\" colspan=\"1\">71770</td><td rowspan=\"1\" colspan=\"1\">50</td><td rowspan=\"1\" colspan=\"1\">+</td><td rowspan=\"1\" colspan=\"1\">−20.9</td><td rowspan=\"1\" colspan=\"1\">73</td><td rowspan=\"1\" colspan=\"1\">89</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Term6</td><td rowspan=\"1\" colspan=\"1\">86765</td><td rowspan=\"1\" colspan=\"1\">86805</td><td rowspan=\"1\" colspan=\"1\">41</td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">−16.9</td><td rowspan=\"1\" colspan=\"1\">90</td><td rowspan=\"1\" colspan=\"1\">67</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Term7</td><td rowspan=\"1\" colspan=\"1\">95947</td><td rowspan=\"1\" colspan=\"1\">95996</td><td rowspan=\"1\" colspan=\"1\">50</td><td rowspan=\"1\" colspan=\"1\">−</td><td rowspan=\"1\" colspan=\"1\">−18</td><td rowspan=\"1\" colspan=\"1\">102</td><td rowspan=\"1\" colspan=\"1\">87</td></tr></tbody></table><graphic xlink:href=\"JVI.00682-20-t0005\"/></alternatives></table-wrap><p>PA-SR01 is morphologically distinct from known <italic>Caudovirales</italic>, and this is also reflected in the genes shared between them. Out of 166 ORFs predicted, merely 6 ORFs were homologous to genes from known <italic>Caudovirales</italic>. This suggested that PA-SR01 did not belong to the order <italic>Caudovirales</italic>, which was further supported by the observed tailless morphology of PA-SR01.</p></sec><sec id=\"s2.5\"><title>Structural genes.</title><p>ORF41 is the only ORF in PA-SR01 encoding tail tape measure protein (TMP), which is a tail-associated protein. Tail tape measure protein of tailed phages determines the tail length and enables DNA transition into the host cell during infection (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Despite the name suggesting its widespread presence in tail phage genomes, the tail tape measure protein-encoding gene has, nevertheless, also been observed to be present in tailless phages (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). This indicates that tail tape measuring protein is not unique to tailed phages. Very few sequences encoding known phage structural proteins were found in the PA-SR01 genome other than the major capsid protein (ORF32). This further supports the structural distinction of PA-SR01 from other known phages. With SDS-PAGE analysis, 4 structural proteins of about 16, 28, 47, and 99 kDa (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4</xref>) were resolved. ORF32, encoding the major capsid protein, can be matched to the 47-kDa band. ORF46, ORF9, and ORF101, encoding hypothetical proteins, can be matched to the 99-kDa, 28-kDa, and 16-kDa bands, respectively. However, 4 structural proteins do not represent the full picture, as 13 structural proteins have been identified in phage with a similar genomic size (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>). To obtain a more thorough understanding of structural proteins in PA-SR01, a mass spectrometer approach is needed.</p><fig id=\"F4\" orientation=\"portrait\" position=\"float\"><label>FIG 4</label><caption><p>SDS-PAGE for structural proteins of PA-SR01.</p></caption><graphic xlink:href=\"JVI.00682-20-f0004\"/></fig></sec><sec id=\"s2.6\"><title>Host-derived genes.</title><p>Host-derived metabolic genes are commonly present in cyanophages and play important roles in interactions between cyanophage and their host (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). For example, a survey of 33 cyanophages revealed that psbA was found in 88% of the cyanophage genomes and 50% of the cyanophages contained both psbA and psbD genes (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Besides photosynthetic genes, other host-derived genes have also been found that are responsible for phycobilisome degradation, carbon metabolism, phosphate uptake, and nucleotide biosynthesis (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref><xref ref-type=\"bibr\" rid=\"B27\">–</xref><xref rid=\"B28\" ref-type=\"bibr\">28</xref>). The only host-derived metabolic gene identified in PA-SR01 genome is ribonucleotide-diphosphate reductase (RNR) (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). This suggests that PA-SR01 is evolutionary distinct from known cyanophages and could have its own special metabolic genes that require further study.</p><p>In PA-SR01, ORF107 and ORF108 were found homologous to ribonucleotide-diphosphate reductase (RNR) subunit alpha and beta, respectively. The RNR gene product can reduce ribonucleotide diphosphate to deoxy-ribonucleotide diphosphate, which is a precursor of DNA (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Cyanophage can thus make use of RNRs to degrade host DNA to provide building blocks for synthesizing genomes of phage progeny. RNR genes are considered essential for the rapid replication found in lytic phage (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>), and this could be a contributing factor to the short latent period of PA-SR01.</p></sec><sec id=\"s2.7\"><title>Nucleotide metabolism.</title><p>Besides RNR, there were several genes identified that are involved in nucleotide metabolism. The PA-SR01 genome encodes a homolog (ORF103) of FAD-dependent thymidylate synthase (ThyX) that produces thymidylate (dTMP) <italic>de novo</italic> from dUMP (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). The importance of ThyX in phage genome replication has been demonstrated in double-stranded DNA virus (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). In the PA-SR01 genome, another gene possibly involved in nucleotide metabolism is ORF116, encoding a homolog of deoxy-nucleotide monophosphate kinase which may phosphorylate dGMP, dTMP, and 5-hydroxymethyl-dCMP to be used in producing new viral DNA genomes (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Both ThyX and deoxy-nucleotide monophosphate kinase might contribute to phage genome replication in PA-SR01. dTMP produced by ThyX could be phosphorylated to dTDP, which could be further phosphorylated by nucleoside-diphosphate kinase (NDPK) to form dTTP, a monomer that can be utilized by DNA polymerase (ORF114) to generate long-chain DNA molecules. However, no homologs of NDPK were found in the PA-SR01 genome, and thus further studies are needed to better understand the detailed nucleotide biosynthesis strategy of PA-SR01.</p></sec><sec id=\"s2.8\"><title>Insertion element.</title><p>PA-SR01 has one ORF showing extremely high similarity to the ORF from cyanobacterium <named-content content-type=\"genus-species\">Tolypothrix bouteillei</named-content>. ORF81 has 90% amino acid sequence similarity to IS200/IS605 family element transposase accessory protein. Such a high sequence similarity is rare in phage genome and suggests that this ORF originated from recent horizontal gene transfer. Similar insertion sequences (IS) have been observed in other phage genomes (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>) and their functions remain unknown. IS elements are rare in phage genomes and are considered disadvantageous for bacteriophage propagation as they could disrupt the efficiency of phage genome organization (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). This also supports the hypothesis that ORF81 came from recent horizontal gene transfer, as it is less likely for a phage genome with IS elements to propagate and pass on its gene over many generations compared to phage without IS elements.</p></sec><sec id=\"s2.9\"><title>Lysis-associated genes.</title><p>The lysozyme homolog is commonly found in cyanophage and is believed to be the functional gene for cell lysis (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). However, no homologs of lysozyme can be found in the PA-SR01 genome; instead, ORF83 encodes a putative septal ring lytic transglycosylase RlpA family protein. Lytic transglycosylases represent a major class of enzymes capable of lysing bacterial cell walls with the same substrate specificity as lysozyme. Across different families of lytic transglycosylases, family 4 has been shown to be involved with bacteriophage-induced lysis (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Rare lipoprotein A (RlpA) was found to be a new lytic transglycosylase with strong preference for naked glycan strands (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). ORF83, encoding a homolog of the RlpA family, could be the key gene responsible for cell lysis. This suggests that PA-SR01 adopts a different lysis strategy from known cyanophages and that PA-SR01 is likely to be evolutionary distinct.</p></sec><sec id=\"s2.10\"><title>PA-SR01, a new evolutionary lineage of cyanophage.</title><p>PA-SR01 represents a new evolutionary lineage of cyanophage based on its genomic content. There is a lack of structural gene similarity between the PA-SR01 genome and other phage genomes, with the exception of the major capsid protein (ORF32) and tail-tape measuring protein (ORF41). This is further supported by the morphological features of PA-SR01. To our knowledge, PA-SR01 is only the second tailless cyanophage discovered and a vast majority of cultured cyanophages belong to the order <italic>Caudovirales</italic>. Besides structural distinction, PA-SR01 adopts a different lysis strategy from other cyanophages, based on the fact that lytic transglycosylase instead of lysozyme is found in the PA-SR01 genome.</p><p>Phylogenetic analysis of the terminase large subunit (terL) and major capsid protein shows that PA-SR01 is evolutionary distinct from other cyanophage isolates. Although PA-SR01 terL is related to T7-like phages, it does not fall within the group of T7-like phages (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5</xref>). Furthermore, the amino acid sequence percentage identity shared between PA-SR01 terL and S-CBS2 terL is merely 26%. The BLASTP result of PA-SR01 terL showed much greater similarity to noncyanophage terL sequences, indicating an evolutionary divergence of terL in PA-SR01.</p><fig id=\"F5\" orientation=\"portrait\" position=\"float\"><label>FIG 5</label><caption><p>Maximum likelihood amino acid tree of the viral terminase large subunit (terL). Bootstrap values are indicated (100 replicates).</p></caption><graphic xlink:href=\"JVI.00682-20-f0005\"/></fig><p>Maximum likelihood amino acid tree of the major capsid protein provides further evidence that PA-SR01 is evolutionarily distinct from other cyanophage isolates (<xref ref-type=\"fig\" rid=\"F6\">Fig. 6</xref>). A majority of the phages fall within the three main groups, <italic>Myoviridae</italic>, <italic>Siphoviridae</italic>, and <italic>Podoviridae,</italic> respectively. However, PA-SR01 does not fall within any of the clades and represents an independent branch, providing further support of the evolutionary divergence of PA-SR01 from other phages.</p><fig id=\"F6\" orientation=\"portrait\" position=\"float\"><label>FIG 6</label><caption><p>Maximum likelihood amino acid tree of the viral major capsid protein. Bootstrap values are indicated (100 replicates).</p></caption><graphic xlink:href=\"JVI.00682-20-f0006\"/></fig></sec><sec id=\"s2.11\"><title>PA-SR01 sequence similarities in the environment.</title><p>The widespread occurrence of viral sequences similar to PA-SR01 in the environment is shown by the recruitment of metagenomics reads onto the translated PA-SR01 genome. Both marine and freshwater environments were investigated in this analysis (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7A</xref> to <xref ref-type=\"fig\" rid=\"F7\">C</xref>), and 146 ORFs were mapped with at least with one freshwater metagenome and 106 ORFs were mapped with multiple freshwater metagenomes. Twenty-six ORFs were extensively mapped to freshwater metagenomes. Fifty-eight ORFs were mapped to at least one marine metagenome and twenty-two ORFs were mapped across several marine metagenomes. This indicates that PA-SR01-like phages are much more prominent in freshwater. Seven ORFs (ORF12, ORF29, ORF64, ORF103, ORF114, ORF121, and ORF134) were extensively mapped with marine metagenomes and they were all extensively mapped with freshwater metagenomes as well. This suggests that phages adopting similar packaging strategies (e.g., ORF12 encoding a terminase large subunit) and similar DNA metabolism (e.g., ORF29 encoding a DEAD/DEAH box helicase, ORF114 encoding a DNA-directed DNA polymerase, and ORF103 encoding an FAD-dependent thymidylate synthase) are widespread in aquatic environments.</p><fig id=\"F7\" orientation=\"portrait\" position=\"float\"><label>FIG 7</label><caption><p>Prevalence of viral sequences similar to PA-SR01 in environmental viral metagenomic data. Fragment recruitment of reads from environmental viral metagenomic data onto the genome of PA-SR01. Each horizontal line represents a read recruited from one of the following publicly available metagenomics data sets: (A) freshwater viral metagenome: Lake Pavin, Lake Bourget, Lake Neagh, reclaimed water virus, and Singapore urban freshwater; (B) marine viral metagenomes: Baltic Sea, Papua New Guinea, Patagonia, Gulf of Mexico, San Pedro Channel, and Pacific Ocean surface; (C) freshwater viral metagenome: Lake Baikal, Lake Limonopolar, Lake Michigan, Lake Soyang, and Han River.</p></caption><graphic xlink:href=\"JVI.00682-20-f0007\"/></fig><p>The FAD-dependent thymidylate synthase ThyX (ORF103) and a hypothetical protein (ORF134) have the most recruited sequences across both marine and freshwater metagenomes. ThyX is a key gene in double-stranded phage genome replication, suggesting that phages with similar DNA replication strategy are widespread in aquatic environments and ThyX is an important part of phage DNA replication for both marine and freshwater phages. There are also a large number of recruited reads to IS200/IS605 family element transposase accessory protein TnpB (ORF81), located around 80 kbp. In contrast to the marine environment, 5 out of 10 freshwater metagenomes were recruited onto ORF81. As mentioned previously, TnpB was considered disadvantageous for bacteriophage and is not widely present in known cultured phage (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). The recruited reads from multiple metagenomes onto ORF81 suggests that TnpB might not be detrimental for phage propagation or there could be extensive horizontal gene transfer of TnpB gene from host bacteria to phage.</p><p>It is clear in <xref ref-type=\"fig\" rid=\"F7\">Fig. 7</xref> that we observed many more recruited reads from freshwater viral metagenomes than marine. The majority of ORFs across the genome of PA-SR01 have recruited sequences in the metagenome from urban freshwaters in Singapore. This was expected, since PA-SR01 was isolated from a water body in Singapore and it is likely that viral sequences similar to PA-SR01 are widely distributed locally. Surprisingly, metagenomes from Lake Michigan produced a comparable amount of recruited reads on the PA-SR01 genome. This strongly suggests the widespread presence of viral sequences similar to PA-SR01 around the globe.</p><p>Further evidence for the widespread presence of viral sequences similar to PA-SR01 in the environment is provided by the relative abundance of different cyanophages in the Lake Michigan metagenome (<xref ref-type=\"fig\" rid=\"F8\">Fig. 8</xref>). It is clear that viral sequences similar to PA-SR01 are prevalent in the Lake Michigan metagenome. Although there are several other phages having higher normalized recruited reads than PA-SR01, they are of comparable amount. Furthermore, the number of recruited reads of PA-SR01 is apparently much higher than the majority of other known cyanophages examined in this analysis. Admittedly, the occurrence of PA-SR01 relative to other cyanophages is overestimated due to the fact that only the blast hit with highest E value was recruited. For example, in the list of selected cyanophages there are several P-HM1-like phages, e.g., P-RSM4, P-SSM2, P-TIM68 and Syn1. Significant sequence similarity and core genes are shared among those phages, but only one phage genome would recruit each read, causing the dilution of read numbers assigned to each P-HM1-like cyanophage. Nonetheless, the data indicate that viral sequences similar to PA-SR01 phages are relatively abundant in freshwater environments.</p><fig id=\"F8\" orientation=\"portrait\" position=\"float\"><label>FIG 8</label><caption><p>Relative abundance of viral sequences similar to PA-SR01 relative to other cyanophages in Lake Michigan. The number of reads was normalized to the number of genome length of each phage as well as the metagenome database size.</p></caption><graphic xlink:href=\"JVI.00682-20-f0008\"/></fig><p>The relative abundance of viral sequences similar to PA-SR01 in the Pacific Ocean surface water (<xref ref-type=\"fig\" rid=\"F9\">Fig. 9</xref>) is much lower than that in Lake Michigan and is among the least abundant, suggesting that viral sequences similar to PA-SR01 are more prevalent in freshwater environments. Since PA-SR01 was isolated from freshwater, it is more likely to have genes specific to freshwater environments.</p><fig id=\"F9\" orientation=\"portrait\" position=\"float\"><label>FIG 9</label><caption><p>Relative abundance of viral sequences similar to PA-SR01 relative to other cyanophages in the Pacific Ocean surface. The number of reads was normalized to the number of genome length of each phage as well as the metagenome database size.</p></caption><graphic xlink:href=\"JVI.00682-20-f0009\"/></fig><p>In conclusion, this study describes the characteristics and genome of PA-SR01, a rare tailless cyanophage with a uniquely different set of genes from other known cyanophages. PA-SR01 infects a tropical isolate of <italic>Pseudanabaena</italic> sp. and represents a new evolutionary lineage of cyanophage. Comparative metagenomics data indicate the global prevalence of PA-SR-01-like phages in both freshwater and marine environments. PA-SR01 and related viruses are likely to play major roles in controlling and shaping <italic>Pseudanabaena</italic> populations. Given the large number of genes without homologies in PA-SR01, more work is needed to characterize the phage-host interactions and ecological roles of PA-SR01.</p></sec></sec><sec sec-type=\"materials|methods\" id=\"s3\"><title>MATERIALS AND METHODS</title><sec id=\"s3.1\"><title>Host cells.</title><p>The <italic>Pseudanabaena</italic> strain KCZY-C8 was isolated in February 2019 from a tropical eutrophic fresh water body (Singapore Serangoon Reservoir) at 1°23′26.2\"N 103°54'58.7\"E 15 cm below the surface water. The strain was isolated by micropipetting from a surface water sample into sterile MLA medium (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>) at 25°C. Identification of the strain was determined to the level of genus following the morphological characteristics (cell shape, dimension, and organization within trichome) reported in Bergey’s Manual of Systematics of Archaea and Bacteria (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>) and other studies (<xref rid=\"B38\" ref-type=\"bibr\">38</xref><xref ref-type=\"bibr\" rid=\"B39\">–</xref><xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Detailed traits of KCZY-C8 can be found in <xref ref-type=\"fig\" rid=\"F10\">Fig. 10</xref>. We also used partial bacterial 16S rRNA sequence to verify the strain identity (<xref rid=\"T6\" ref-type=\"table\">Table 6</xref>). The culture was then incubated and maintained in batch culture at 25°C under low radiance (20 μmol photons m<sup>−2</sup>s<sup>−1</sup>) with a 12-h/12-h light/dark cycle.</p><fig id=\"F10\" orientation=\"portrait\" position=\"float\"><label>FIG 10</label><caption><p>(A) Light microscope image of host strain KCZY-C8. (B) SEM image of host strain KCZY-C8. Both images show that most of the trichomes of KCZY-C8 are in agreement with the morphological characteristics of <italic>Pseudanabaena</italic> reported by Bergey’s Manual of Systematic Archaea and Bacteria (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>), as well as other studies (<xref rid=\"B38\" ref-type=\"bibr\">38</xref><xref ref-type=\"bibr\" rid=\"B39\">–</xref><xref rid=\"B40\" ref-type=\"bibr\">40</xref>), as follows: cell division in one plane and intercellular breakage of trichome (filament); trichomes are straight; trichomes comprised of 3 barrel-shaped cells (usually 3 to 10 cells per trichome); cell length wider than diameter (diameter = 1μm, length = 3 μm); cell walls are constricted at the junction between adjacent cells.</p></caption><graphic xlink:href=\"JVI.00682-20-f0010\"/></fig><table-wrap id=\"T6\" orientation=\"portrait\" position=\"float\"><label>TABLE 6</label><caption><p>BLASTN result of host 16S Sequence against NCBI RefSeq database</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Strain</th><th rowspan=\"1\" colspan=\"1\">Accession no.</th><th rowspan=\"1\" colspan=\"1\">% identity</th><th rowspan=\"1\" colspan=\"1\">Host 16S sequence</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\"><italic>Pseudanabaena</italic> sp. ABRG5-3</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NZ_AP017560.1\" assigning-authority=\"genbank\">NZ_AP017560.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">93.81</td><td rowspan=\"6\" colspan=\"1\">ATCTGNCGTGTCTCAGTCCAGTGTGACTGGTCATCCTCTCAGACCAGTTACCGATCGTCGCCATGGTGTGCCTTTACCACTCCATCTAGCTAATCGGACGCAAGCTCATCTACAGATGATAAATCTTTCACCCGAAGGCATATCCGGTATTAGCAGTCGTTTCCAACTGTTGTCCCGAGTCTGTAGGTAGATTCTTACGCGTTACTCACCCGTAA</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Pseudanabaena</italic> biceps PCC7429</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NZ_ALWB01000102.1\" assigning-authority=\"genbank\">NZ_ALWB01000102.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">91.827</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Pseudanabaena</italic> sp. UWO310</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NZ_SELU01000117.1\" assigning-authority=\"genbank\">NZ_SELU01000117.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">91.827</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Pseudanabaena</italic> sp. Roaring Creek</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NZ_LIRE01000034.1\" assigning-authority=\"genbank\">NZ_LIRE01000034.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">91.827</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Pseudanabaena</italic> sp. BC1403</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NZ_PDDM01000058.1\" assigning-authority=\"genbank\">NZ_PDDM01000058.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">91.346</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Pseudanabaena</italic> SR411</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NZ_NDHW01000108.1\" assigning-authority=\"genbank\">NZ_NDHW01000108.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">91.346</td></tr></tbody></table><graphic xlink:href=\"JVI.00682-20-t0006\"/></alternatives></table-wrap></sec><sec id=\"s3.2\"><title>Cyanophage isolation.</title><p>Cyanophage PA-SR01 was isolated from viral concentrates collected from surface water as described above. Briefly, 450 ml of water was filtered through 0.2 μm (Nuclepore) pore size filters. The virus-sized particles in the filtrate were concentrated 100- to 200-fold with a 100-kDa molecular weight (MW) cutoff in ultrafiltration centrifugal tubes (Amicon Ultra-15 centrifugal filter units; Millipore). Viral concentrate was stored at 4°C in dark before any further action. Viral concentrate was serially diluted up to 10<sup>7</sup> times. PA-SR01 was isolated by adding the aliquots to an exponentially growing culture of <italic>Pseudanabaena</italic> strain KCZY-C8 in a 24-well microtiter plate and incubating at 25°C under low radiance (20 μmol photons m<sup>−2</sup>s<sup>−1</sup>) with 12-h/12-h light/dark cycle for 14 days. Culture lysis was determined by a substantial decrease in optical density at 750 nm (OD<sub>750</sub>) compared with control cultures (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). A clonal viral isolate was obtained by three rounds of extinction dilution (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>) in 96-well microtiter plates with exponentially growing <italic>Pseudanabaena</italic> strain KCZY-C8.</p></sec><sec id=\"s3.3\"><title>Amplification and purification of PA-SR01.</title><p>The cyanophage was amplified by adding 1% (vol/vol) of the virus isolate to 30-ml cultures of <italic>Pseudanabaena</italic> strain KCZY-C8. Both phage-added and control culture were incubated until lysis took place in the culture flasks with phage. The lysates were centrifuged at 15,000 × <italic>g</italic> for 5 min to remove cellular debris. The supernatant containing the majority of viral particles was filtered through a 0.22-μm syringe filter (Minisart syringe filter, Satorius) to remove cellular debris. These purified viral particles were used for subsequent infection experiments.</p></sec><sec id=\"s3.4\"><title>Transmission electron microscopy.</title><p>Thirty milliliters of PA-SR01 lysate was centrifuged at 15,000 × <italic>g</italic> for 5 min followed by filtering through a 0.22-μm syringe filter to remove the cellular debris. The filtered lysate was centrifuged at 5,000 × <italic>g</italic> with a 100-kDa MW cutoff in ultrafiltration centrifugal tubes (Amicon Ultra-15 centrifugal filter units; Millipore) to increase the phage particle concentration. For staining, 20 μl of gadolinium triacetate (1% wt/wt) was adsorbed to the surface of copper grids at room temperature for 1 min. Excess liquid was blotted from the side of the copper grids with clean filter paper. The grids were viewed and photographed on a JEOL JEM-2100F field emission gun transmission electron microscope at the National University of Singapore Faculty of Chemical and Biomolecular Engineering.</p></sec><sec id=\"s3.5\"><title>Host range.</title><p>PA-SR01 infectivity was tested against local freshwater isolates of cyanobacteria strains, as well as cyanobacteria obtained from the Commonwealth Scientific and Industrial Research Organisation (CSIRO) culture collection. PA-SR01 phage lysate (1 ml) was added to cultures of exponentially growing cyanobacteria as listed in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. Growth of cyanobacteria cultures without PA-SR01 addition was also monitored to serve as a control. Infectivity was determined by a reduction in OD reading compared to control.</p></sec><sec id=\"s3.6\"><title>Chloroform sensitivity.</title><p>Chloroform sensitivity of the cyanophage was tested. Filtered lysate (1 ml) was mixed with 1 ml of chloroform followed by shaking manually for 10 min. Chloroform removal was carried out by centrifugation at 4,100 × <italic>g</italic> for 5 min at room temperature. The aqueous phase was transferred to a 1.5 ml microcentrifuge tube and incubated for 6 h at room temperature to remove any remaining chloroform. One milliliter of chloroform was added to treat 1 ml of MLA medium to serve as the control. Chloroform-treated MLA, nontreated MLA, and treated and nontreated virus particles were added to exponentially growing <italic>Pseudanabaena</italic> strain KCZY-C8 cultures and the OD was measured over 6 days.</p></sec><sec id=\"s3.7\"><title>DNA extraction, purification, and sequencing.</title><p><italic>Pseudanabaena</italic> strain KCZY-C8 was grown in 300 ml of MLA medium at 25°C under low irradiance (20 μmol photons m<sup>−2</sup>s<sup>−1</sup>) with a 12-h/12-h light/dark cycle until lysis. The lysates were centrifuged at 15,000 × <italic>g</italic> for 5 min to remove cellular debris. The supernatant containing the majority of viral particles was filtered through a 0.22-μm syringe filter (Minisart syringe filter, Satorius) to remove cellular debris. In order to remove free nucleic acid, the lysate was treated with DNase I. The treated lysate was concentrated with a 100-kDa MW cutoff ultrafiltration centrifugal tube (Amicon Ultra-15 centrifugal filter units; Millipore) at 5,000 × <italic>g</italic> to a final volume of 1 ml. QIAamp DNA minikit was used to extract viral DNA with 20 μl of RNase A added in the first step to remove RNA. The cyanophage genome was sequenced using an Illumina High throughput sequencer, with a 150-bp paired-end library constructed using a New England BioLab Next Ultra DNA library prep kit.</p></sec><sec id=\"s3.8\"><title>Genome assembly.</title><p>The sequencing data were trimmed using BBDuk (version 35.43) to remove adaptors and Phix reads. Reads were <italic>de novo</italic> assembled into contigs by MetaSPAdes genome assembler (3.12.0) (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>).</p></sec><sec id=\"s3.9\"><title>Genome annotation.</title><p>The open reading frames (ORFs) were predicted using GeneMarkS (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>) and Prodigal (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>); where the prediction differed, the longer of the two was kept. Homology searching was performed with BLASTp against NCBI nonredundant (nr) database (accessed in October 2019). Sequences with E values of <10<sup>−5</sup> were considered to be homologs. HHpred against protein data bank (PDB) and Pfam database were used to predict more distant homologs (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). The genome was analyzed for tRNA genes with tRNAscan-SE 2.0 (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>) and for Rho-independent terminators using Findterm (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>), with the energy threshold set to −16 kCal. A genomic map was generated with CGview (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>).</p></sec><sec id=\"s3.10\"><title>SDS-PAGE analysis for structural protein.</title><p>Purified PA-SR01 was diluted in SDS buffer (5:1, vol/vol) and heated at 95°C for 5 min. The sample was then resolved by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) using a Mini-PROTEAN Tetra Cell (Bio-Rad Laboratories). The Mini-PROTEAN TGX stain-free precast gel was run in an SDS running buffer (pH 8.3) at 120 V for 1.5 h using a PageRuler unstained protein ladder (Thermo Fisher) for size calibration.</p></sec><sec id=\"s3.11\"><title>Phylogenetic analysis.</title><p>The large terminase subunit (terL) and major capsid protein were compared phylogenetically with those from other cyanophages and bacteriophages (<xref rid=\"T7\" ref-type=\"table\">Table 7</xref>) using Mega-X software (version 10.1.6). ClustalX was used to align the inferred amino acid sequences with default parameters. Based on the multiple sequence alignment, the Jones-Taylor-Thornton (JTT) model was selected and a maximum likelihood tree was constructed with 100 bootstrap replicates.</p><table-wrap id=\"T7\" orientation=\"portrait\" position=\"float\"><label>TABLE 7</label><caption><p>Accession numbers of genes used in phylogenetic analysis</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Accession no.</th><th rowspan=\"1\" colspan=\"1\">Gene</th><th rowspan=\"1\" colspan=\"1\">Organism</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ADZ31560.1\" assigning-authority=\"ncbi:protein\">ADZ31560.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Planktothrix</italic> phage PaV-LD</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AHV82220.1\" assigning-authority=\"ncbi:protein\">AHV82220.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-EIVl</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_004508468.1\" assigning-authority=\"ncbi:protein\">YP_004508468.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-CRM01</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_717790.1\" assigning-authority=\"ncbi:protein\">YP_717790.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage syn9</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_214662.1\" assigning-authority=\"ncbi:protein\">YP_214662.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-SSM4</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_214360.1\" assigning-authority=\"ncbi:protein\">YP_214360.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-SSM2</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/CAF34164.1\" assigning-authority=\"ncbi:protein\">CAF34164.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-PM2</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_899601.1\" assigning-authority=\"ncbi:protein\">NP_899601.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Vibrio</italic> phage KVP40</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AAQ17878.1\" assigning-authority=\"ncbi:protein\">AAQ17878.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Aeromonas</italic> virus Aeh1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_891724.1\" assigning-authority=\"ncbi:protein\">NP_891724.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Escherichia</italic> phage RB49</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AND75577.1\" assigning-authority=\"ncbi:protein\">AND75577.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Nostoc</italic> phage A1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/Q9T1W6.1\" assigning-authority=\"ncbi:protein\">Q9T1W6.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Escherichia</italic> virus Mu</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_063734.1\" assigning-authority=\"ncbi:protein\">YP_063734.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Salmonella</italic> virus P22</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_853601\" assigning-authority=\"ncbi:protein\">NP_853601</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Salmonella</italic> virus SP6</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/P03694\" assigning-authority=\"ncbi:protein\">P03694</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Enterobacteria</italic> phage T7</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/P10310\" assigning-authority=\"ncbi:protein\">P10310</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Enterobacteria</italic> phage T3</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_224236\" assigning-authority=\"ncbi:protein\">YP_224236</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Listonella</italic> phage phiHSIC</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_224140\" assigning-authority=\"ncbi:protein\">YP_224140</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Enterobacteria</italic> phage ES18</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_050979\" assigning-authority=\"ncbi:protein\">NP_050979</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Hamiltonella</italic> virus APSE1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/P44184\" assigning-authority=\"ncbi:protein\">P44184</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><named-content content-type=\"genus-species\">Haemophilus influenzae</named-content> phage KW20</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_112076\" assigning-authority=\"ncbi:protein\">NP_112076</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Salmonella</italic> virus HK620</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AAA96534\" assigning-authority=\"ncbi:protein\">AAA96534</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Escherichia</italic> virus lambda</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_046897\" assigning-authority=\"ncbi:protein\">NP_046897</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Escherichia</italic> virus N15</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_758915\" assigning-authority=\"ncbi:protein\">NP_758915</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Vibrio</italic> phage VHML</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/BAA89621\" assigning-authority=\"ncbi:protein\">BAA89621</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Wolbachia</italic> phage WO</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AAQ96470\" assigning-authority=\"ncbi:protein\">AAQ96470</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Vibrio</italic> phage VP16T</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_700375\" assigning-authority=\"ncbi:protein\">NP_700375</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Salmonella</italic> phage ST64B</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_892047\" assigning-authority=\"ncbi:protein\">NP_892047</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Yersinia</italic> phage PY54</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_004323723.1\" assigning-authority=\"ncbi:protein\">YP_004323723.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage Syn33</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/BAF36209.1\" assigning-authority=\"ncbi:protein\">BAF36209.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic> virus Ma-LMM01</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AND75488.1\" assigning-authority=\"ncbi:protein\">AND75488.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Nostoc</italic> phage N1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ADP06606.1\" assigning-authority=\"ncbi:protein\">ADP06606.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-CBS1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ADF42459.1\" assigning-authority=\"ncbi:protein\">ADF42459.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-CBS3</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ADF42432.1\" assigning-authority=\"ncbi:protein\">ADF42432.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-CBS2</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AEX55977.1\" assigning-authority=\"ncbi:protein\">AEX55977.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-CBS4</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ACT65564.1\" assigning-authority=\"ncbi:protein\">ACT65564.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\">Cyanophage PSS2</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AGH57666.1\" assigning-authority=\"ncbi:protein\">AGH57666.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\">Cyanophage KBS-S-2A</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ABP87967.1\" assigning-authority=\"ncbi:protein\">ABP87967.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> virus Syn5</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ATS93174.1\" assigning-authority=\"ncbi:protein\">ATS93174.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-LBS1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_009173820.1\" assigning-authority=\"ncbi:protein\">YP_009173820.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Terminase large subunit</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> virus P60</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ABC46474.1\" assigning-authority=\"ncbi:protein\">ABC46474.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\">Cyanophage AN-15</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ABI33181.1\" assigning-authority=\"ncbi:protein\">ABI33181.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Phormidium</italic> virus WMP4</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_001285797.1\" assigning-authority=\"ncbi:protein\">YP_001285797.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Phormidium</italic> virus WMP3</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_009042803.1\" assigning-authority=\"ncbi:protein\">YP_009042803.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Anabaena</italic> phage A-4L</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AND75579.1\" assigning-authority=\"ncbi:protein\">AND75579.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Nostoc</italic> phage A1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ADZ31580.1\" assigning-authority=\"ncbi:protein\">ADZ31580.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Planktothrix</italic> phage PaV-LD</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_008766991.1\" assigning-authority=\"ncbi:protein\">YP_008766991.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\">Cyanophage PP</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_009217771.1\" assigning-authority=\"ncbi:protein\">YP_009217771.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic> phage MaMV-DC</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AND75491.1\" assigning-authority=\"ncbi:protein\">AND75491.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Nostoc</italic> phage N1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/AOO10060.1\" assigning-authority=\"ncbi:protein\">AOO10060.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-RIM2</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ATS93177.1\" assigning-authority=\"ncbi:protein\">ATS93177.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-LBS1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_680487.1\" assigning-authority=\"ncbi:protein\">NP_680487.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Lactobacillus</italic> phage A2</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ATW59308.1\" assigning-authority=\"ncbi:protein\">ATW59308.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Aphanizomenon</italic> phage vB_AphaS-CL131</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_214206.1\" assigning-authority=\"ncbi:protein\">YP_214206.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> virus PSSP7</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/ABP87946.1\" assigning-authority=\"ncbi:protein\">ABP87946.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> virus Syn5</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_009173806.1\" assigning-authority=\"ncbi:protein\">YP_009173806.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> virus P60</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_041998.1\" assigning-authority=\"ncbi:protein\">NP_041998.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Escherichia</italic> phage T7</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/76563988\" assigning-authority=\"ncbi:protein\">ABA46378</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Bacillus</italic> phage Cherry</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_338188.1\" assigning-authority=\"ncbi:protein\">YP_338188.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Bacillus</italic> phage Gamma</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_003347636.1\" assigning-authority=\"ncbi:protein\">YP_003347636.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Klebsiella</italic> phage KP34</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_717802.1\" assigning-authority=\"ncbi:protein\">YP_717802.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage syn9</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_214367.1\" assigning-authority=\"ncbi:protein\">YP_214367.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-SSM2</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_891732.1\" assigning-authority=\"ncbi:protein\">NP_891732.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Escherichia</italic> phage RB49</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_861877.1\" assigning-authority=\"ncbi:protein\">NP_861877.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Escherichia</italic> phage RB69</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/NP_049787.1\" assigning-authority=\"ncbi:protein\">NP_049787.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Escherichia</italic> virus T4</td></tr><tr><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/protein/YP_003097339.1\" assigning-authority=\"ncbi:protein\">YP_003097339.1</ext-link></td><td rowspan=\"1\" colspan=\"1\">Major capsid protein</td><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-RSM4</td></tr></tbody></table><graphic xlink:href=\"JVI.00682-20-t0007\"/></alternatives></table-wrap></sec><sec id=\"s3.12\"><title>Recruitment of reads to metagenomics.</title><p>The presence of viral sequences similar to PA-SR01 in aquatic environments was investigated by recruiting viral metagenomics data onto the genome of PA-SR01 (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). In total, 88 gigabytes of freshwater metagenome data and 173 gigabytes of marine metagenome data were used (<xref rid=\"T8\" ref-type=\"table\">Table 8</xref>). Briefly, metagenomic data were first made into a BLAST nucleotide database and queried with the predicted protein sequence of PA-SR01 using tBLASTn (E value of ≤10<sup>−5</sup>), which performed a six-frame translation of the subject nucleotide sequence into protein sequence (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Metagenomics nucleotide reads with a blast hit to PA-SR01 were then extracted from each metagenome and used as query to blast (BLASTx, E value of ≤10<sup>−5</sup>, max_target_seqs = 1) against a viral protein database containing predicted proteins of PA-SR01 phage and another 2,536 bacteriophage genomes from the NCBI Reference Sequence Database (RefSeq; accessed on Jan 2020). If the best hit was related to PA-SR01 instead of the other phages, it was recruited as viral sequences similar to PA-SR01 and mapped onto the genome of PA-SR01, based on percentage identity of amino acid sequence using ggplot2 (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>).</p><table-wrap id=\"T8\" orientation=\"portrait\" position=\"float\"><label>TABLE 8</label><caption><p>SRA accession numbers of metagenomics data used for metagenome mapping</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Source</th><th rowspan=\"1\" colspan=\"1\">Database size (Gb)</th><th rowspan=\"1\" colspan=\"1\">Accession no.</th><th rowspan=\"1\" colspan=\"1\">Marine or freshwater</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">Pacific Ocean surface</td><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256930\" assigning-authority=\"ncbi:sra\">ERR3256930</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ebi.ac.uk/ena/data/view/ERR3256932\" assigning-authority=\"embl\">ERR3256932</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256934\" assigning-authority=\"ncbi:sra\">ERR3256934</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256936\" assigning-authority=\"ncbi:sra\">ERR3256936</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256953\" assigning-authority=\"ncbi:sra\">ERR3256953</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256955\" assigning-authority=\"ncbi:sra\">ERR3256955</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256957\" assigning-authority=\"ncbi:sra\">ERR3256957</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256959\" assigning-authority=\"ncbi:sra\">ERR3256959</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256964\" assigning-authority=\"ncbi:sra\">ERR3256964</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256973\" assigning-authority=\"ncbi:sra\">ERR3256973</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256977\" assigning-authority=\"ncbi:sra\">ERR3256977</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">76.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR3256975\" assigning-authority=\"ncbi:sra\">ERR3256975</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Baltic Sea</td><td rowspan=\"1\" colspan=\"1\">51.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR7254009\" assigning-authority=\"ncbi:sra\">SRR7254009</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">51.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR7253988\" assigning-authority=\"ncbi:sra\">SRR7253988</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">51.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR7253990\" assigning-authority=\"ncbi:sra\">SRR7253990</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">51.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR7253989\" assigning-authority=\"ncbi:sra\">SRR7253989</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">51.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR7254008\" assigning-authority=\"ncbi:sra\">SRR7254008</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">51.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR7254007\" assigning-authority=\"ncbi:sra\">SRR7254007</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">51.10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR7254010\" assigning-authority=\"ncbi:sra\">SRR7254010</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Singapore urban water</td><td rowspan=\"1\" colspan=\"1\">40.00</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR5995660\" assigning-authority=\"ncbi:sra\">SRR5995660</ext-link>–<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR5995697\" assigning-authority=\"ncbi:sra\">SRR5995697</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lake Michigan</td><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1915829\" assigning-authority=\"ncbi:sra\">SRR1915829</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1915851\" assigning-authority=\"ncbi:sra\">SRR1915851</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974489\" assigning-authority=\"ncbi:sra\">SRR1974489</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974496\" assigning-authority=\"ncbi:sra\">SRR1974496</ext-link>–<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974508\" assigning-authority=\"ncbi:sra\">SRR1974508</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974510\" assigning-authority=\"ncbi:sra\">SRR1974510</ext-link>–<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974511\" assigning-authority=\"ncbi:sra\">SRR1974511</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974513\" assigning-authority=\"ncbi:sra\">SRR1974513</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974515\" assigning-authority=\"ncbi:sra\">SRR1974515</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1296481\" assigning-authority=\"ncbi:sra\">SRR1296481</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1302020\" assigning-authority=\"ncbi:sra\">SRR1302020</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1301999\" assigning-authority=\"ncbi:sra\">SRR1301999</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974488\" assigning-authority=\"ncbi:sra\">SRR1974488</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974490\" assigning-authority=\"ncbi:sra\">SRR1974490</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974491\" assigning-authority=\"ncbi:sra\">SRR1974491</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974493\" assigning-authority=\"ncbi:sra\">SRR1974493</ext-link>–<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974495\" assigning-authority=\"ncbi:sra\">SRR1974495</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974509\" assigning-authority=\"ncbi:sra\">SRR1974509</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974512\" assigning-authority=\"ncbi:sra\">SRR1974512</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974514\" assigning-authority=\"ncbi:sra\">SRR1974514</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974516\" assigning-authority=\"ncbi:sra\">SRR1974516</ext-link>–<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1974517\" assigning-authority=\"ncbi:sra\">SRR1974517</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">23.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1302010\" assigning-authority=\"ncbi:sra\">SRR1302010</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\">San Pedro channel</td><td rowspan=\"1\" colspan=\"1\">20.60</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR10600460\" assigning-authority=\"ncbi:sra\">SRR10600460</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">20.60</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR10600461\" assigning-authority=\"ncbi:sra\">SRR10600461</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Patagonia</td><td rowspan=\"1\" colspan=\"1\">15.80</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR5145173\" assigning-authority=\"ncbi:sra\">SRR5145173</ext-link>–<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR5145178\" assigning-authority=\"ncbi:sra\">SRR5145178</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lake Soyang</td><td rowspan=\"1\" colspan=\"1\">13.90</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERS2758845\" assigning-authority=\"ncbi:sra\">ERS2758845</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">13.90</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERS2759121\" assigning-authority=\"ncbi:sra\">ERS2759121</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">13.90</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERS2759122\" assigning-authority=\"ncbi:sra\">ERS2759122</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Gulf of Mexico</td><td rowspan=\"1\" colspan=\"1\">9.40</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR11048275\" assigning-authority=\"ncbi:sra\">SRR11048275</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Han River</td><td rowspan=\"1\" colspan=\"1\">7.50</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERS1546404\" assigning-authority=\"ncbi:sra\">ERS1546404</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">7.50</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERS1546406\" assigning-authority=\"ncbi:sra\">ERS1546406</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Papua New Guinea</td><td rowspan=\"1\" colspan=\"1\">5.30</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR5644412\" assigning-authority=\"ncbi:sra\">SRR5644412</ext-link>–<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR5644431\" assigning-authority=\"ncbi:sra\">SRR5644431</ext-link></td><td rowspan=\"1\" colspan=\"1\">Marine</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lake Neagh</td><td rowspan=\"1\" colspan=\"1\">1.30</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR2147000\" assigning-authority=\"ncbi:sra\">SRR2147000</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lake Baikal</td><td rowspan=\"1\" colspan=\"1\">0.87</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR5936590\" assigning-authority=\"ncbi:sra\">SRR5936590</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lake Limnopolar</td><td rowspan=\"1\" colspan=\"1\">0.39</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1658897\" assigning-authority=\"ncbi:sra\">SRR1658897</ext-link>–<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR1658894\" assigning-authority=\"ncbi:sra\">SRR1658894</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Reclaimed water virus</td><td rowspan=\"1\" colspan=\"1\">0.38</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR014585\" assigning-authority=\"ncbi:sra\">SRR014585</ext-link>–<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/SRR014589\" assigning-authority=\"ncbi:sra\">SRR014589</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lake Bourget</td><td rowspan=\"1\" colspan=\"1\">0.33</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR019478\" assigning-authority=\"ncbi:sra\">ERR019478</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Lake Pavin</td><td rowspan=\"1\" colspan=\"1\">0.37</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/ERR019477\" assigning-authority=\"ncbi:sra\">ERR019477</ext-link></td><td rowspan=\"1\" colspan=\"1\">Freshwater</td></tr></tbody></table><graphic xlink:href=\"JVI.00682-20-t0008\"/></alternatives></table-wrap><p>The BLAST hits number to PA-SR01 was normalized by dividing by the total number of predicted ORFs and the size of the metagenome (in gigabytes), which provides a normalized measure to compare recruitments across metagenomes of different size. Similar recruitment analysis was also performed for other phage genomes (<xref rid=\"T9\" ref-type=\"table\">Table 9</xref>).</p><table-wrap id=\"T9\" orientation=\"portrait\" position=\"float\"><label>TABLE 9</label><caption><p>List of cyanophages selected for metagenomics recruitment analysis</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Cyanophage</th><th rowspan=\"1\" colspan=\"1\">Accession no.</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage ACG-2014b</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_027130\" assigning-authority=\"genbank\">NC_027130</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-CAM1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_020837\" assigning-authority=\"genbank\">NC_020837</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-CBP1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_025456\" assigning-authority=\"genbank\">NC_025456</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-CBS1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_016164\" assigning-authority=\"genbank\">NC_016164</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-CRM01</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_015569\" assigning-authority=\"genbank\">NC_015569</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-PM2</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_006820\" assigning-authority=\"genbank\">NC_006820</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-RIP1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_020867\" assigning-authority=\"genbank\">NC_020867</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-SKS1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_020851\" assigning-authority=\"genbank\">NC_020851</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-SM1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_015282\" assigning-authority=\"genbank\">NC_015282</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-WAM1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_031944\" assigning-authority=\"genbank\">NC_031944</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage syn9</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_008296\" assigning-authority=\"genbank\">NC_008296</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> virus P60</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_003390\" assigning-authority=\"genbank\">NC_003390</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage MED4-184</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_020847\" assigning-authority=\"genbank\">NC_020847</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-GSP1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_020878\" assigning-authority=\"genbank\">NC_020878</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-HM1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_015280\" assigning-authority=\"genbank\">NC_015280</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-RSM4</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_015283\" assigning-authority=\"genbank\">NC_015283</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-SSM2</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_006883\" assigning-authority=\"genbank\">NC_006883</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-SSP10</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_020835\" assigning-authority=\"genbank\">NC_020835</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-SSP3</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_020874\" assigning-authority=\"genbank\">NC_020874</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage P-TIM68</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_028955\" assigning-authority=\"genbank\">NC_028955</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> phage Syn1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_015288\" assigning-authority=\"genbank\">NC_015288</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Prochlorococcus</italic> virus PSSP7</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_006882\" assigning-authority=\"genbank\">NC_006882</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic> phage MaMV-DC</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_029002\" assigning-authority=\"genbank\">NC_029002</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Microcystis</italic> virus Ma-LMM01</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_008562\" assigning-authority=\"genbank\">NC_008562</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Planktothrix</italic> phage PaV-LD</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/NC_016564\" assigning-authority=\"genbank\">NC_016564</ext-link></td></tr><tr><td rowspan=\"1\" colspan=\"1\"><italic>Synechococcus</italic> phage S-EIV1</td><td rowspan=\"1\" colspan=\"1\"><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/KJ410740.1\" assigning-authority=\"genbank\">KJ410740.1</ext-link></td></tr></tbody></table><graphic xlink:href=\"JVI.00682-20-t0009\"/></alternatives></table-wrap></sec><sec id=\"s3.13\"><title>Data availability.</title><p>The whole-genome sequence of the phage is available in GenBank under accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/MT234670\" assigning-authority=\"genbank\">MT234670</ext-link>.</p></sec></sec></body><back><ack><title>ACKNOWLEDGMENTS</title><p>This research was supported by the Singapore National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program.</p><p>We are grateful to PUB, Singapore's national water agency, for providing logistical field support in this study. We thank the YUNG Lab from the National University of Singapore Department of Chemical and Biomolecular Engineering for their support on the TEM imaging. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">J Virol</journal-id><journal-id journal-id-type=\"iso-abbrev\">J. Virol</journal-id><journal-id journal-id-type=\"hwp\">jvi</journal-id><journal-id journal-id-type=\"pmc\">jvi</journal-id><journal-id journal-id-type=\"publisher-id\">JVI</journal-id><journal-title-group><journal-title>Journal of Virology</journal-title></journal-title-group><issn pub-type=\"ppub\">0022-538X</issn><issn pub-type=\"epub\">1098-5514</issn><publisher><publisher-name>American Society for Microbiology</publisher-name><publisher-loc>1752 N St., N.W., Washington, DC</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32522856</article-id><article-id pub-id-type=\"pmc\">PMC7431799</article-id><article-id pub-id-type=\"publisher-id\">00709-20</article-id><article-id pub-id-type=\"doi\">10.1128/JVI.00709-20</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Virus-Cell Interactions</subject></subj-group><subj-group subj-group-type=\"editorial-class\"><subject>Spotlight</subject></subj-group></article-categories><title-group><article-title>Porcine Reproductive and Respiratory Syndrome Virus Utilizes Viral Apoptotic Mimicry as an Alternative Pathway To Infect Host Cells</article-title><alt-title alt-title-type=\"running-head\">PRRSV Infection by Viral Apoptotic Mimicry</alt-title><alt-title alt-title-type=\"short-authors\">Wei et al.</alt-title></title-group><contrib-group><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Wei</surname><given-names>Xin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\" equal-contrib=\"yes\"><name><surname>Li</surname><given-names>Rui</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Qiao</surname><given-names>Songlin</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Chen</surname><given-names>Xin-xin</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"no\"><name><surname>Xing</surname><given-names>Guangxu</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\" equal-contrib=\"no\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-3834-9975</contrib-id><name><surname>Zhang</surname><given-names>Gaiping</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><aff id=\"aff1\"><label>a</label><addr-line>College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China</addr-line></aff><aff id=\"aff2\"><label>b</label><addr-line>Key Laboratory of Animal Immunology of the Ministry of Agriculture, Henan Provincial Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, Henan, China</addr-line></aff></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Pfeiffer</surname><given-names>Julie K.</given-names></name><role>Editor</role><aff>University of Texas Southwestern Medical Center</aff></contrib></contrib-group><author-notes><corresp id=\"cor1\">Address correspondence to Rui Li, <email>lirui860620@sina.com</email>, or Gaiping Zhang, <email>zhanggaip@126.com</email>.</corresp><fn fn-type=\"equal\"><p>Xin Wei and Rui Li contributed equally to this article. Author order was determined in order of increasing seniority.</p></fn><fn fn-type=\"other\"><p><bold>Citation</bold> Wei X, Li R, Qiao S, Chen X-X, Xing G, Zhang G. 2020. Porcine reproductive and respiratory syndrome virus utilizes viral apoptotic mimicry as an alternative pathway to infect host cells. J Virol 94:e00709-20. <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.1128/JVI.00709-20\">https://doi.org/10.1128/JVI.00709-20</ext-link>.</p></fn></author-notes><pub-date pub-type=\"epreprint\"><day>10</day><month>6</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>9</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>94</volume><issue>17</issue><elocation-id>e00709-20</elocation-id><history><date date-type=\"received\"><day>18</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>3</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Wei et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Wei et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International license</ext-link>.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"JVI.00709-20.pdf\"/><abstract abstract-type=\"precis\"><p>PRRS has caused huge economic losses to pig farming worldwide. Its causative agent, PRRSV, infects host cells through low pH-dependent clathrin-mediated endocytosis and CD163 is indispensable during the process. Whether there exist alternative infection pathways for PRRSV arouses our interest. Here, we found that PRRSV exposed PS on its envelope and disguised as apoptotic debris. The PS receptor TIM-1/4 recognized PRRSV and induced the downstream signaling pathway to mediate viral infection via CD163-dependent macropinocytosis. The current work deepens our understanding of PRRSV infection and provides clues for the development of drugs and vaccines against the virus.</p></abstract><abstract><title>ABSTRACT</title><p>Porcine reproductive and respiratory syndrome (PRRS), caused by PRRS virus (PRRSV), has led to enormous economic losses in global swine industry. Infection by PRRSV is previously shown to be via low pH-dependent clathrin-mediated endocytosis, and CD163 functions as an essential receptor during viral infection. Despite much research focusing on it, PRRSV infection remains to be fully elucidated. In this study, we demonstrated that PRRSV externalized phosphatidylserine (PS) on the envelope as viral apoptotic mimicry and infected host cells through T-cell immunoglobulin and mucin domain (TIM)-induced and CD163-involved macropinocytosis as an alternative pathway. In detail, we identified that PS receptor TIM-1/4 recognized and interacted with PRRSV as viral apoptotic mimicry and subsequently induced macropinocytosis by the downstream Rho GTPases Rac1, cell division control protein 42 (Cdc42), and p21-activated kinase 1 (Pak1). Altogether, these results expand our knowledge of PRRSV infection, which will support implications for the prevention and control of PRRS.</p><p><bold>IMPORTANCE</bold> PRRS has caused huge economic losses to pig farming worldwide. Its causative agent, PRRSV, infects host cells through low pH-dependent clathrin-mediated endocytosis and CD163 is indispensable during the process. Whether there exist alternative infection pathways for PRRSV arouses our interest. Here, we found that PRRSV exposed PS on its envelope and disguised as apoptotic debris. The PS receptor TIM-1/4 recognized PRRSV and induced the downstream signaling pathway to mediate viral infection via CD163-dependent macropinocytosis. The current work deepens our understanding of PRRSV infection and provides clues for the development of drugs and vaccines against the virus.</p></abstract><kwd-group><title>KEYWORDS</title><kwd>PRRSV</kwd><kwd>TIM</kwd><kwd>macropinocytosis</kwd><kwd>phosphatidylserine</kwd><kwd>viral apoptotic mimicry</kwd></kwd-group><funding-group><award-group id=\"award1\"><funding-source><institution-wrap><institution>Science Technology Foundation for Outstanding Young Scientists of Henan Academy of Agricultural Sciences</institution></institution-wrap></funding-source><award-id>2020YQ01</award-id><principal-award-recipient><name><surname>Li</surname><given-names>Rui</given-names></name></principal-award-recipient></award-group><award-group id=\"award2\"><funding-source><institution-wrap><institution>Special Fund for Henan Agriculture Research System</institution></institution-wrap></funding-source><award-id>S2012-06</award-id><principal-award-recipient><name><surname>Qiao</surname><given-names>Songlin</given-names></name></principal-award-recipient></award-group><award-group id=\"award3\"><funding-source><institution-wrap><institution>National Natural Science Foundation of China (NSFC)</institution><institution-id>https://doi.org/10.13039/501100001809</institution-id></institution-wrap></funding-source><award-id>31972690</award-id><principal-award-recipient><name><surname>Li</surname><given-names>Rui</given-names></name></principal-award-recipient></award-group><award-group id=\"award4\"><funding-source><institution-wrap><institution>Earmarked Fund for Modern Agro-industry Technology Research System</institution><institution-id>https://doi.org/10.13039/501100009997</institution-id></institution-wrap></funding-source><award-id>CARS-35</award-id><principal-award-recipient><name><surname>Zhang</surname><given-names>Gaiping</given-names></name></principal-award-recipient></award-group></funding-group><counts><fig-count count=\"10\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"72\"/><page-count count=\"19\"/><word-count count=\"9328\"/></counts><custom-meta-group><custom-meta><meta-name>cover-date</meta-name><meta-value>September 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>INTRODUCTION</title><p>As intracellular obligate pathogens, both DNA and RNA viruses have evolved diverse strategies to infect host cells for productive replication (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>). A variety of viruses incorporate phosphatidylserine (PS), a marker of apoptosis (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>), on the surfaces of their envelopes and disguise as apoptotic debris. Upon recognition by PS receptors (PSRs) and induction of downstream signaling cascades, these viruses are internalized via clathrin-mediated endocytosis (CME) and/or macropinocytosis by host cells to promote their infections (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>), namely, viral apoptotic mimicry (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>).</p><p>For the viruses utilizing apoptotic mimicry, diverse PSRs have been identified, including T-cell immunoglobulin and mucin domain 1/3/4 (TIM-1/3/4), brain-specific angiogenesis inhibitor 1 (BAI1), Stabilin-1/2, CD300a, TAM receptors (Tyro3, Axl or Mer) and integrins (αvβ3 or αvβ5) (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). For invasion routes, CME is constitutively driven by formation of clathrin-coated vesicles (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>), while macropinocytosis is induced by extracellular stimuli and shows several characteristics, such as cytoskeletal rearrangement, fluid uptake, and dependence on Na<sup>+</sup>/H<sup>+</sup> exchanger activity and Rho GTPases (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p><p>Porcine reproductive and respiratory syndrome (PRRS) has become an economically critical factor in global swine industry since it was first reported in the United States in 1987 (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Currently, loss due to PRRS in the United States is annually estimated $664 million (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Caused by PRRS virus (PRRSV), the syndrome is characterized by reproductive failures in the late-term gestation of sows and respiratory diseases in pigs of all ages (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). PRRSV is an enveloped single-stranded positive-sense RNA virus with a genome of approximately 15 kb (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>). All PRRSV isolates are classified into two genotypes, PRRSV-1 and PRRSV-2 (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>), which belong to the order <italic>Nidovirales</italic>, family <italic>Arteriviridae</italic>, and genus <italic>Porartevirus</italic> (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>).</p><p>PRRSV specially infects swine and the differentiated monocytes, particularly porcine alveolar macrophages (PAMs), are its primary target cells <italic>in vivo</italic> (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). In addition, the African green monkey kidney epithelial cell line MA-104 and its derivative, MARC-145, are susceptible to viral infection <italic>in vitro</italic> (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Previous studies have shown that PRRSV infects host cells via low pH-dependent CME (<xref rid=\"B21\" ref-type=\"bibr\">21</xref><xref ref-type=\"bibr\" rid=\"B22\">–</xref><xref rid=\"B23\" ref-type=\"bibr\">23</xref>) and a scavenger receptor CD163 is indispensable for viral infection (<xref rid=\"B24\" ref-type=\"bibr\">24</xref><xref ref-type=\"bibr\" rid=\"B25\">–</xref><xref rid=\"B27\" ref-type=\"bibr\">27</xref>).</p><p>In the present work, we determined an alternative pathway utilized by PRRSV to infect host cells. First, we found that PRRSV exposed PS on the envelope as viral apoptotic mimicry. Next, we dissected the host cell PSRs recognizing PRRSV as apoptotic mimicry and explored the detailed mechanisms, including the downstream signaling pathways and invasion routes.</p></sec><sec sec-type=\"results\" id=\"s2\"><title>RESULTS</title><sec id=\"s2.1\"><title>PRRSV externalizes PS on the envelope as viral apoptotic mimicry.</title><p>In order to validate whether PRRSV incorporates PS on its envelope and utilizes viral apoptotic mimicry, we first detected PS on the virions using annexin V, a specific PS-binding protein (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>), by flow cytometry (FCM). As shown in <xref ref-type=\"fig\" rid=\"F1\">Fig. 1A</xref>, a typical PRRSV-2 strain, BJ-4, was externalized PS on the envelope. Furthermore, we exploited a commercial antibody against PS (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>), a specific antibody against PRRSV major envelope glycoprotein (GP) 5 (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>), and dot blotting to confirm that PRRSV BJ-4 did expose PS (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1B</xref>). In addition, highly pathogenic PRRSV (HP-PRRSV) strain HN07-1 and PRRSV-1 strain GZ11-G1 also externalized PS on the envelopes (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1C</xref> to <xref ref-type=\"fig\" rid=\"F1\">F</xref>). All of these results demonstrate that PRRSV incorporates PS on the envelope surface as viral apoptotic mimicry. Since PRRSV-2 strains are predominantly prevalent and PRRSV-1 strains are sporadic in China (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>), we only applied PRRSV BJ-4 to the following research.</p><fig id=\"F1\" orientation=\"portrait\" position=\"float\"><label>FIG 1</label><caption><p>PRRSV externalizes PS on the envelope. PRRSV-2 strain BJ-4, HP-PRRSV strain HN07-1, and PRRSV-1 strain GZ11-G1 were shown to expose PS by FCM (A, C, and E) and dot blotting (B, D, and F). MARC-145 cells were inoculated with PRRSV (MOI = 10) at 4°C or PBS as an unbound control. Then, PS was assessed to PRRSV-bound or unbound cells by FCM immediately using annexin V-conjugated Alexa Fluor 488. Each experiment was independently performed three times with similar results, and data from one representative experiment are shown in panels A, C, and E. Dot blot assays were set up with anti-PS 1H6 MAb or anti-PRRSV GP5 MAb as the primary antibody. Twofold dilutions of purified PRRSV in PBS were applied for PS and PRRSV GP5 detection, respectively. PBS was spotted onto samples as a negative control.</p></caption><graphic xlink:href=\"JVI.00709-20-f0001\"/></fig></sec><sec id=\"s2.2\"><title>TIM-1 is identified to recognize PRRSV as apoptotic mimicry in MARC-145 cells.</title><p>PSRs TIM-1, Stabilin-1/2, and Axl are specially expressed in epithelial cells (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Here, we sought to identify which host PSRs recognized PRRSV as apoptotic mimicry. Initially, we monitored the transcription of each PSR in MARC-145 cells. <xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref> shows that TIM-1 and Axl were transcribed in the cells. Expression of TIM-1 and Axl were also demonstrated through immunoblotting (IB) analysis (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2B</xref>). To investigate their specific functions during PRRSV infection, knockdown of TIM-1 and Axl were carried out in MARC-145 cells (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2C</xref>). To measure total PRRSV RNA, a pair of internal primers in the viral open reading frame 7 (ORF7) gene were used to amplify all subgenomic mRNA and genomic RNA by quantitative real-time PCR (RT-qPCR) (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). <italic>Axl</italic> knockdown did not significantly influence the abundance of PRRSV RNA (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2D</xref>). In contrast, PRRSV infection was suppressed, as indicated by decreased viral RNA abundance (3.5-fold) at 12 h postinfection (hpi), infectivity with nucleocapsid (N) protein expression (5-fold) at 24 hpi, and progeny viral titers (2.5-fold) at 48 hpi (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2D</xref> to <xref ref-type=\"fig\" rid=\"F2\">F</xref>) in the <italic>TIM-1</italic> knockdown cells. <xref ref-type=\"fig\" rid=\"F2\">Figure 2G</xref> further shows that knockdown of TIM-1 influenced PRRSV infection during viral binding to MARC-145 cells (4-fold).</p><fig id=\"F2\" orientation=\"portrait\" position=\"float\"><label>FIG 2</label><caption><p>TIM-1 is identified to recognize PRRSV as apoptotic mimicry in MARC-145 cells. (A) Transcription of PSRs in MARC-145 cells. MARC-145 cells were collected, and the reverse transcription cDNAs were prepared and subjected to PCR with the specific primers of TIM-1, Axl, Stabilin-1, and Stabilin-2. The PCR products of each gene fragment were subjected to agarose gel electrophoresis. (B) Expression of TIM-1 and Axl as determined by IB analysis. MARC-145 cells were harvested and lysed. TIM-1 and Axl were detected by IB. Knockdown of TIM-1 (C) significantly influenced PRRSV RNA abundance (D), infectivity (E), progeny viral titers (F), and viral binding (G). MARC-145 cells were transfected with siTIM-1, siAxl, or siRNA-NC for 36 h and infected with PPRSV (MOI = 10). The infected cells were collected for analyses of PRRSV RNA by RT-qPCR at 12 hpi, N protein expression by immunofluorescence at 24 hpi, viral titers by determining the TCID<sub>50</sub> at 48 hpi, or binding and entry by RT-qPCR. Immunofluorescence images were quantified by counting the number of cells expressing viral N protein. Four random fields were counted per each condition, and the total number of cells per field was determined by DAPI staining. Each experiment was performed three times, and similar results were obtained. Differences between groups were assessed by using a Student <italic>t</italic> test, and the statistical significance is indicated (*, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001; ns, not significant). Scale bars, 50 μm.</p></caption><graphic xlink:href=\"JVI.00709-20-f0002\"/></fig><p>Next, we determined whether TIM-1 directly bound to PRRSV. <italic>In vitro</italic> Fc-pulldown assay with recombinant Fc-fused TIM-1 (TIM-1-Fc) and purified PRRSV indicated that TIM-1 bound to the virions (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3A</xref>). Dot blot analyses further confirmed that TIM-1 interacted with PRRSV (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3B</xref>). Incubation with recombinant TIM-1-Fc showed an interference of PRRSV binding to MARC-145 cells with decreased viral RNA (2.5-fold; <xref ref-type=\"fig\" rid=\"F3\">Fig. 3C</xref>). A blocking experiment using the anti-PS antibody also showed a significant decrease in PRRSV binding to the cells (3-fold; <xref ref-type=\"fig\" rid=\"F3\">Fig. 3D</xref>), suggesting the specific interaction.</p><fig id=\"F3\" orientation=\"portrait\" position=\"float\"><label>FIG 3</label><caption><p>TIM-1 directly binds to PRRSV demonstrated by Fc-pulldown assay (A), dot blotting (B), viral binding interference (C), and a blocking experiment (D). For the Fc-pulldown assay, the recombinant TIM-1-Fc was bound to protein A/G-beads, whereas the Fc tag served as control. The beads were then incubated with the PRRSV virions. The eluted proteins were then subjected to IB. For dot blotting, TIM-1-Fc or Fc was spotted onto nitrocellulose membranes and detected by anti-human IgG antibody or anti-PRRSV GP5 MAb. After incubation with PRRSV BJ-4 for 4 h, the membranes were eaxmined for PRRSV GP5 detection. For viral binding interference, PRRSV BJ-4 virions were incubated with TIM-1-Fc (5 μg) or Fc at 4°C for 4 h, followed by inoculation into MARC-145 cells at 4°C for 1 h. For the blocking experiment, PRRSV BJ-4 virions were pretreated with anti-PS antibody or isotype control antibody at 4°C for 4 h and then inoculated into MARC-145 cells at 4°C for 1 h. The PRRSV RNA was determined by RT-qPCR. Each experiment was performed three times independently. Statistical analysis for the RT-qPCR was carried out using the Student <italic>t</italic> test (**, <italic>P</italic> < 0.01).</p></caption><graphic xlink:href=\"JVI.00709-20-f0003\"/></fig><p>Taken together, these data provide evidence that TIM-1 recognizes and interacts with PRRSV as apoptotic mimicry in MARC-145 cells.</p></sec><sec id=\"s2.3\"><title>PRRSV induces macropinocytosis via TIM-1 in MARC-145 cells.</title><p>As apoptotic mimicry, viruses infect host cells via CME and/or macropinocytosis (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>). To distinguish which routes were involved in PRRSV infection mediated by TIM-1, we performed confocal microscopy in combination with transferrin or dextran during early infection (i.e., at 30 min postinfection [mpi]) in MARC-145 cells. Transferrin is a marker for CME (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>), and dextran is a fluid-phase marker which is robustly internalized during macropinocytosis (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). As shown in <xref ref-type=\"fig\" rid=\"F4\">Fig. 4A</xref>, knockdown of TIM-1 greatly influenced macropinocytosis (3.5-fold) but not CME, suggesting that PRRSV induced macropinocytosis via TIM-1. To support this conclusion, we conducted the assay once again with dextran using confocal microscopy for different time periods. In <xref ref-type=\"fig\" rid=\"F4\">Fig. 4B</xref> and <xref ref-type=\"fig\" rid=\"F4\">C</xref>, PRRSV induced macropinocytosis at as early as 15 mpi, while <italic>TIM-1</italic> knockdown inhibited PRRSV-induced macropinocytosis (3- to 7-fold). We further demonstrated this conclusion using confocal microscopy simultaneously with the specific antibody against PRRSV N protein and dextran at 30 mpi, which also indicated their colocalization during the process (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4D</xref>).</p><fig id=\"F4\" orientation=\"portrait\" position=\"float\"><label>FIG 4</label><caption><p>PRRSV induces macropinocytosis via TIM-1 in MARC-145 cells. (A) Knockdown of TIM-1 greatly influenced macropinocytosis. After transfection with siRNA-NC or siTIM-1 for 36 h, PRRSV BJ-4 (MOI = 10) was applied to the serum-starved MARC-145 cells at 4°C. The input virus was replaced with medium containing dextran (final concentration, 250 μg/ml) and transferrin (final concentration, 10 μg/ml) and transferred to 37°C for 30 min. The cells were then fixed, and the nuclei were stained with DAPI. Images were acquired with the same confocal microscope settings. The total fluorescence intensity of transferrin or dextran was calculated using ImageJ software. ***, <italic>P</italic> < 0.001; ns, not significant. (B) PRRSV induced macropinocytosis via TIM-1. MARC-145 cells were transfected with mock treatment, siRNA-NC, or siTIM-1 for 36 h and then serum starved for 2 h. The cells were inoculated with or without PRRSV BJ-4 (MOI = 10) at 4°C. The inoculum was replaced with medium containing FITC-dextran and transferred to 37°C for 15, 30, or 45 min. The cells were fixed and examined by confocal microscopy using the same confocal microscope settings. (C) The total fluorescence intensity of the dextran in panel B was calculated using ImageJ software. **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001. (D) Serum-starved and siRNA-transfected MARC-145 cells were inoculated with PRRSV BJ-4 (MOI = 10) for 30 min, followed by dextran uptake (green). PRRSV infection is indicated by anti-PRRSV N protein antibody (red). White arrows indicate the colocalization of dextran and PRRSV. (E) PRRSV induced membrane protrusions. The serum-starved MARC-145 cells were added with PRRSV BJ-4 (MOI = 10) or PBS for 30 min and fixed with 4% PFA. Actin filaments were labeled with phalloidin (green). Images were captured with a 63× oil immersion objective. A higher magnification of the boxed area shows the formation of actin protrusions on the cell surface (white arrows). Scale bars, 10 μm.</p></caption><graphic xlink:href=\"JVI.00709-20-f0004\"/></fig><p>Macropinocytosis is distinct from other endocytic pathways in extensive actin rearrangements and the formation of protrusions on cellular surfaces (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). To determine whether PRRSV induces membrane protrusions, we performed confocal microscopy with phalloidin and monitored actin restructuring. Phalloidin specially binds to the polymerized form of actin (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). As shown in <xref ref-type=\"fig\" rid=\"F4\">Fig. 4E</xref>, PRRSV infection led to depolymerization and distribution changes of actin. More actin-driven membrane protrusions were observed on cell surfaces of MARC-145 cells than that of mock-infected cells (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4E</xref>, white arrows). However, there were decreased membrane protrusions in <italic>TIM-1</italic> knockdown MARC-145 cells.</p><p>These results illustrate that PRRSV induces macropinocytosis via TIM-1 in MARC-145 cells.</p></sec><sec id=\"s2.4\"><title>PRRSV utilizes macropinocytosis to infect MARC-145 cells.</title><p>We analyzed whether PRRSV utilized macropinocytosis to infect MARC-145 cells. We first observed that internalized PRRSV virions colocalized with sorting nexin 5 (SNX5), a marker of specific endosomes for macropinocytosis (macropinosomes, <xref ref-type=\"fig\" rid=\"F5\">Fig. 5A</xref>) (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). The colocalization coefficient was expressed as Manders’ overlap coefficient, and the value was >0.6, indicating an actual overlap of the signals and representing the true degree of colocalization (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Ethylisopropyl amiloride (EIPA) specifically inhibits Na<sup>+</sup>/H<sup>+</sup> exchanger activity and subsequent macropinocytosis (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). As shown in <xref ref-type=\"fig\" rid=\"F5\">Fig. 5B</xref> to <xref ref-type=\"fig\" rid=\"F5\">E</xref>, treatment with EIPA, compared to treatment with dimethyl sulfoxide (DMSO), led to 2- to 4-fold reductions in PRRSV RNA abundance, a 5-fold decrease in infectivity, and a 5-fold decrease in viral titers, respectively. Interestingly, the EIPA inhibited PRRSV infection during viral entry rather than binding (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5F</xref>). Since PRRSV infection was previously reported to be mediated by CME (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>), we attempted to define the relative contribution of CME and macropinocytosis in PRRSV infection. We pretreated MARC-145 cells with chlorpromazine (CPZ), an inhibitor of clathrin lattice polymerization (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). Treatment with CPZ resulted in a greater decrease in PRRSV RNA abundance than treatment with EIPA (6.3-fold versus 3.5-fold). Simultaneous addition of these two inhibitors almost abolished PRRSV infection (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5G</xref>). All of these results indicate that, in addition to CME, PRRSV infects MARC-145 cells via macropinocytosis as an alternative pathway.</p><fig id=\"F5\" orientation=\"portrait\" position=\"float\"><label>FIG 5</label><caption><p>PRRSV utilizes macropinocytosis to infect MARC-145 cells. (A) Colocalization of PRRSV and SNX5-marked macropinosomes. MARC-145 cells were inoculated with MOI = 10 PRRSV at 37°C for 30 min. Cells were fixed and stained with anti-PRRSV N protein (green) and anti-SNX5 (red) antibody. Nuclei were stained with DAPI. Confocal microscopy was performed to detect the location. The colocalization was assessed by determination of Manders’ overlap coefficient. Scale bars, 10 μm. The addition of EIPA decreased PRRSV RNA abundance (B), N protein expression (C), infectivity (D), progeny viral titers (E), and entry (F). The serum-starved MARC-145 cells were pretreated with 25 μM EIPA, 50 μM EIPA, or DMSO and infected with PRRSV BJ-4 (MOI = 10) for 1 h. The cells were collected for assessment of PRRSV RNA abundance by RT-qPCR at 12 hpi, N protein expression by IB or immunofluorescence at 24 hpi, viral titers using TCID<sub>50</sub> at 48 hpi, or binding and entry by RT-qPCR. Scale bars, 50 μm. (G) Simultaneous addition of EIPA and CPZ almost abolished PRRSV infection. The serum-starved MARC-145 cells were pretreated with 50 μM EIPA and/or 10 μM CPZ and then infected with PRRSV BJ-4 (MOI = 10) for 1 h. The cells were collected for assessment of PRRSV RNA abundance by RT-qPCR at 12 hpi. Each experiment was performed three times, and similar results were obtained. Differences between groups were assessed by using a Student <italic>t</italic> test, and the statistical significance is indicated (*, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001; ns, not significant).</p></caption><graphic xlink:href=\"JVI.00709-20-f0005\"/></fig></sec><sec id=\"s2.5\"><title>Disruption of actin dynamics inhibits PRRSV infection via macropinocytosis.</title><p>Since macropinocytosis requires actin rearrangements (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>), we explored whether the disruption of actin dynamics took effect on PRRSV infection. We preincubated MARC-145 cells with cytochalasin D (Cyto D) and latrunculin A (Lat A), respectively, and then inoculated with PRRSV. Cyto D disrupts actin microfilaments (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>) and Lat A inhibits actin polymerization (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). As shown in <xref ref-type=\"fig\" rid=\"F6\">Fig. 6</xref>, both inhibitors significantly suppressed PRRSV infection during viral entry in a dose-dependent manner.</p><fig id=\"F6\" orientation=\"portrait\" position=\"float\"><label>FIG 6</label><caption><p>Disruption of actin dynamics inhibits PRRSV infection via macropinocytosis. Both Cyto D and Lat A decreased PRRSV RNA abundance (A), N protein expression (B), and entry (C). The serum-starved MARC-145 cells were pretreated with Cyto D (20 or 40 μM), Lat A (0.125 or 0.25 μM), or DMSO and infected with PRRSV BJ-4 (MOI = 10) for 1 h. The cells were collected for assessment of PRRSV RNA abundance by RT-qPCR at 12 hpi, N protein expression by IB at 24 hpi, or binding and entry by RT-qPCR. Each experiment was performed three times, and similar results were obtained. Differences between groups were assessed by using a Student <italic>t</italic> test, and the statistical significance is indicated (*, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001; ns, not significant).</p></caption><graphic xlink:href=\"JVI.00709-20-f0006\"/></fig></sec><sec id=\"s2.6\"><title>Rac1/Cdc42-Pak1 signaling pathway is involved in PRRSV infection via macropinocytosis.</title><p>Another characteristic of macropinocytosis is its dependence on Rho GTPases, including Rac1 and cell division control protein 42 (Cdc42). A prominent downstream effect of these Rho GTPases is the activation of p21-activated kinase 1 (Pak1), which modulates actin cytoskeleton dynamics during macropinocytosis (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Therefore, we explored whether Rac1/Cdc42-Pak1 signaling pathway was involved in PRRSV infection via macropinocytosis. We first utilized specific small interference RNAs (siRNAs) targeting Rac1, Cdc42, and Pak1 (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7A</xref>). We found that knockdown of Rac1, Cdc42, and Pak1 inhibited macropinocytosis, as indicated by decreased dextran uptake (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7B</xref> and <xref ref-type=\"fig\" rid=\"F7\">C</xref>). As shown in <xref ref-type=\"fig\" rid=\"F7\">Fig. 7D</xref> and <xref ref-type=\"fig\" rid=\"F7\">E</xref>, knockdown of Rac1, Cdc42, and Pak1 suppressed PRRSV infection. We further inhibited the activity of Rac1, Cdc42, or Pak1 with the selective chemical inhibitors nsc23766 (Rac1), pirl-1 (Cdc42), and IPA-3 (Pak1) and observed similar results (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7F</xref> and <xref ref-type=\"fig\" rid=\"F7\">G</xref>). Phosphoinositol kinase-3 (PI3K) has been reported to be involved in multiple stages of macropinocytosis (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). However, knockdown of PI3K did not suppress PRRSV-induced macropinocytosis in MARC-145 cells, and PRRSV infection was marginally influenced with treatment of siPI3K or its inhibitor LY294002 (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7</xref>). These results verify that Rac1/Cdc42-Pak1 signaling pathway is involved in PRRSV infection via macropinocytosis.</p><fig id=\"F7\" orientation=\"portrait\" position=\"float\"><label>FIG 7</label><caption><p>Rac1/Cdc42-Pak1 signaling pathway is involved in PRRSV infection via macropinocytosis. Knockdown of Rac1, Cdc42, and Pak1 (A) all decreased dextran uptake (B and C), PRRSV RNA abundance (D), and progeny viral titers (E). MARC-145 cells were transfected with siPak1, siRac1, siCdc42, siPI3K, or siRNA-NC for 36 h and infected with PRRSV BJ-4 (MOI = 10). The dextran uptake was detected at 30 mpi as stated above. Scale bars, 10 μm. The total fluorescence intensity of dextran was calculated using ImageJ software. The infected cells were collected to assess PRRSV RNA abundance by RT-qPCR at 12 hpi, and viral titers were determined from the TCID<sub>50</sub> at 48 hpi. Inhibition of Rac1, Cdc42, and Pak1 all decreased PRRSV RNA abundance (F) and progeny viral titers (G). The serum-starved MARC-145 cells were pretreated with IPA-3 (10 μM), nsc23766 (50 μM), LY294002 (20 μM), pirl-1 (10 μM), or DMSO and infected with PRRSV BJ-4 (MOI = 10) for 1 h. The cells were collected for assessment of PRRSV RNA abundance by RT-qPCR at 12 hpi, and viral titers were determined from the TCID<sub>50</sub> at 48 hpi. Each experiment was performed three times, and similar results were obtained. Differences between groups were assessed by using a Student <italic>t</italic> test, and the statistical significance is indicated (*, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001; ns, not significant).</p></caption><graphic xlink:href=\"JVI.00709-20-f0007\"/></fig></sec><sec id=\"s2.7\"><title>PRRSV utilizes macropinocytosis to infect PAMs.</title><p>Since PAMs are primary <italic>in vivo</italic> target for PRRSV (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>) and undergo constitutive macropinocytosis (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>), we determined whether PRRSV utilizes this alternative pathway to infect the cells. TIM-4 is a homolog of TIM-1 specially expressed in macrophages (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). We determined that TIM-4 directly bound to PRRSV as TIM-1 (<xref ref-type=\"fig\" rid=\"F8\">Fig. 8A</xref>). <italic>TIM-4</italic> knockdown played a significant inhibitory effect on PRRSV infection (2- to 2.5-fold, <xref ref-type=\"fig\" rid=\"F8\">Fig. 8B</xref> and <xref ref-type=\"fig\" rid=\"F8\">C</xref>). The impacts of EIPA on PRRSV infection were also demonstrated (<xref ref-type=\"fig\" rid=\"F8\">Fig. 8D</xref> and <xref ref-type=\"fig\" rid=\"F8\">E</xref>). The Rac1/Cdc42-Pak1 signaling pathway was involved in PRRSV infection in PAMs as well (<xref ref-type=\"fig\" rid=\"F8\">Fig. 8F</xref>). These results show that macropinocytosis is utilized by PRRSV in both PAMs and MARC-145 cells.</p><fig id=\"F8\" orientation=\"portrait\" position=\"float\"><label>FIG 8</label><caption><p>PRRSV utilizes macropinocytosis to infect PAMs. (A) TIM-4 directly binds to PRRSV, as demonstrated by dot blotting. TIM-4-Fc or Fc was spotted onto nitrocellulose membranes, followed by incubation with PRRSV BJ-4 for 4 h. Membranes were applied for PRRSV GP5 detection. <italic>TIM-4</italic> knockdown decreased PRRSV RNA abundance (B) and infectivity (C). PAMs were transfected with siTIM-4 or siRNA-NC for 36 h and infected with PRRSV BJ-4 (MOI = 10) for 1 h. The addition of EIPA decreased PRRSV RNA abundance (D) and infectivity (E). PAMs were pretreated with 25 μM EIPA or DMSO and infected with BJ-4 (MOI = 10) for 1 h. The cells were collected for assessment of PRRSV RNA abundance by RT-qPCR at 12 hpi, and N protein expression was determined by immunofluorescence at 24 hpi. (F) The Rac1/Cdc42-Pak1 signaling pathway was involved in PRRSV infection in PAMs. PAMs were pretreated with IPA-3 (5 μM), nsc23766 (25 μM), LY294002 (10 μM), pirl-1 (25 μM), or DMSO and infected with PRRSV BJ-4 (MOI = 10) for 1 h. The cells were collected for assessment of PRRSV RNA abundance by RT-qPCR at 12 hpi. Each experiment was performed three times, and similar results were obtained. Differences between groups were assessed by using a Student <italic>t</italic> test, and the statistical significance is indicated (*, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001). Scale bars, 50 μm.</p></caption><graphic xlink:href=\"JVI.00709-20-f0008\"/></fig></sec><sec id=\"s2.8\"><title>CD163 is essential for PRRSV infection via TIM-induced macropinocytosis.</title><p>It is well established that CD163 is an indispensable receptor for PRRSV infection (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>). We considered the involvement of CD163 and TIMs in PRRSV infection via macropinocytosis. <xref ref-type=\"fig\" rid=\"F9\">Figure 9A</xref> and <xref ref-type=\"fig\" rid=\"F9\">B</xref> show that expression of TIM-4 alone in baby hamster kidney 21 (BHK-21) cells was not sufficient to support PRRSV infection. In contrast, the expression of CD163 alone conferred susceptibility to PRRSV infection, consistent with a previous study (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Importantly, the coexpression of TIM-4 and CD163 contributed to PRRSV infection more than did the expression of CD163 alone. Furthermore, CD163 colocalized with PRRSV in SNX5-marked macropinosomes in PAMs, whereas most TIM-4s did not (<xref ref-type=\"fig\" rid=\"F9\">Fig. 9C</xref>). Consequently, CD163 is essential during PRRSV infection via TIM-induced macropinocytosis.</p><fig id=\"F9\" orientation=\"portrait\" position=\"float\"><label>FIG 9</label><caption><p>CD163 is essential for PRRSV infection via macropinocytosis. (A and B) CD163 is required for PRRSV infection via macropinocytosis. BHK-21 cells were transfected with TIM-4 and/or CD163 for 36 h and then infected with PRRSV BJ-4 (MOI = 10) for 1 h. RT-qPCR and IB were performed with specific primers and antibodies at 12 and 24 hpi, respectively. (C) Colocalization of PRRSV and CD163 in SNX5-marked macropinosomes. PAMs were inoculated with an MOI of 10 PRRSV BJ-4 at 37°C for 30 min. Cells were fixed and stained with anti-SNX5 antibody and anti-TIM4 or anti-CD163 antibody. Nuclei were stained with DAPI, followed by confocal microscopy. The colocalization was assessed by determination of the Manders’ overlap coefficient. Each experiment was performed three times, and similar results were obtained. Scale bars, 10 μm.</p></caption><graphic xlink:href=\"JVI.00709-20-f0009\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s3\"><title>DISCUSSION</title><p>Viruses usually exploit multiple strategies to infect host cells and establish infection (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Viral apoptotic mimicry has been documented for many enveloped viruses to facilitate viral infections, including alphaviruses, flaviviruses, filoviruses, some arenaviruses, baculoviruses, poxviruses, and rhabdoviruses (<xref rid=\"B46\" ref-type=\"bibr\">46</xref><xref ref-type=\"bibr\" rid=\"B47\">–</xref><xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Whether arteriviruses use viral apoptotic mimicry has not yet been authenticated. In the present study, we demonstrated that PRRSV utilized viral apoptotic mimicry and TIM-induced macropinocytosis to promote infection for the first time.</p><p>Several PSRs, including TIM-1/4, have been identified to enhance virus infection (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). In particular, TIMs serve as entry factors or even receptors for dengue virus (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>) and Ebola virus (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>). It is also worth noting that not all PSRs enhance viral entry. For example, the PSRs Stabilin-1/2 and BAI1 do not appear to enhance viral entry (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>). Here, we determined that TIM-1/4 interacted with PRRSV virions (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3</xref> and <xref ref-type=\"fig\" rid=\"F8\">8</xref>) and induced macropinocytosis (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4</xref>) upon viral infection, whereas Axl did not (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>). However, TIM-4 was not sufficient to support PRRSV infection and internalized into macropinosomes in PAMs as CD163 (<xref ref-type=\"fig\" rid=\"F9\">Fig. 9</xref>). Consequently, we assume that TIM-1/4 might only function as an attachment factor for PRRSV and inducer of downstream signaling of macropinocytosis. The detailed mechanisms involved in TIM-PRRSV interaction and TIM induction need to be investigated.</p><p>Macropinocytosis is usually induced by external stimuli, which may be associated with growth factors-triggered activation of receptor tyrosine kinases (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Among them, epidermal growth factor receptor (EGFR) has been demonstrated to induce macropinocytosis (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). Although the EGFR-PI3K signaling pathway has been recently reported to be required for actin reorganization and efficient PRRSV entry into MARC-145 cells (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>, <xref rid=\"B55\" ref-type=\"bibr\">55</xref>), whether PRRSV induces and utilizes macropinocytosis to infect host cells has not been clarified.</p><p>To exclude the interference of growth factors and EGFR, we utilized purified PRRSV virions and serum-starved cells throughout our research unless stated otherwise. Here, we found that PRRSV strains externalized PS on their envelopes as viral apoptotic mimicry (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1</xref>). The specific mechanisms by which PRRSV incorporates and exposes PS are our next issue to be studied. Subsequently, we identified that PRRSV is recognized as apoptotic mimicry by TIM-1/4 (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2</xref>, <xref ref-type=\"fig\" rid=\"F3\">3</xref>, and <xref ref-type=\"fig\" rid=\"F8\">8</xref>). Moreover, we determined that PRRSV induced macropinocytosis via TIM-1 in MARC-145 cells and utilized the pathway to infect both MARC-145 cells and PAMs (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4</xref>, <xref ref-type=\"fig\" rid=\"F5\">5</xref>, and <xref ref-type=\"fig\" rid=\"F8\">8</xref>). All of these results concluded that PRRSV directly induces macropinocytosis via TIM and infects host cells via the pathway. It would be interesting to investigate whether PRRSV exploits other strategies to induce macropinocytosis. We tried to address the individual contribution of macropinocytosis and CME to PRRSV infection and found that CME might contribute more than macropinocytosis during PRRSV infection (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5</xref>). It would be meaningful to define this contribution under actual <italic>in vivo</italic> conditions in the future.</p><p>The Rac1/Cdc42-Pak1 signaling pathway was determined to mediate PRRSV infection via macropinocytosis, whereas PI3K was shown to be minimally involved (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7</xref>). We speculated that PI3K might be involved in external stimulus (e.g., EGF-EGFR)-induced macropinocytosis instead of PRRSV-induced macropinocytosis via TIMs. The discrepancy with previous studies should be addressed (<xref rid=\"B55\" ref-type=\"bibr\">55</xref><xref ref-type=\"bibr\" rid=\"B56\">–</xref><xref rid=\"B57\" ref-type=\"bibr\">57</xref>).</p><p>Other factors, including protein kinase C (PKC) (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>) and myosin II (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>), are also responsible for macropinocytosis. A recent work has demonstrated that PKC is beneficial to PRRSV replication and infection (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>). In addition, nonmuscle myosin IIA, encoded by <italic>myosin heavy chain 9</italic> (<italic>MYH9</italic>) is an essential factor for PRRSV infection (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Mechanistically, blebbistatin, an inhibitor of myosin II heavy chain activity (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>), impairs the viral entry (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). All of these reports strengthen our conclusion that PRRSV utilizes macropinocytosis to infect host cells.</p><p>Based on the results stated above, we propose a model to depict PRRSV infection (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10</xref>). In addition to CME, TIM-1/4 recognizes PRRSV as apoptotic mimicry and induces macropinocytosis via the Rac1/Cdc42-Pak1 signaling pathway. PRRSV enters SNX5-macropinosomes via macropinocytosis as an alternative pathway, where CD163 functions as an indispensable receptor.</p><fig id=\"F10\" orientation=\"portrait\" position=\"float\"><label>FIG 10</label><caption><p>Model showing how PRRSV utilizes viral apoptotic mimicry and TIM-induced macropinocytosis as an alternative pathway to infect host cells. In addition to CME, TIM-1/4 recognizes PRRSV as apoptotic mimicry and induces macropinocytosis via the Rac1/Cdc42-Pak1 signaling pathway. PRRSV enters SNX5-macropinosomes via macropinocytosis as an alternative pathway, where CD163 functions as an indispensable receptor.</p></caption><graphic xlink:href=\"JVI.00709-20-f0010\"/></fig><p>In conclusion, we demonstrate that PRRSV utilizes viral apoptotic mimicry and TIM-induced macropinocytosis as an alternative pathway to infect host cells. The results we obtained deepen our understanding of PRRSV complicated infection and provide novel opportunities for the development of drugs and vaccines against PRRSV.</p></sec><sec sec-type=\"materials|methods\" id=\"s4\"><title>MATERIALS AND METHODS</title><sec id=\"s4.1\"><title>Cells and viruses.</title><p>PAMs were prepared from lung lavage of 4-week-old specific-pathogen-free pigs. The experimental procedure for the collection of PAMs was authorized and supervised by the Ethical and Animal Welfare Committee of Key Laboratory of Animal Immunology of the Ministry of Agriculture of China (permit 2017008). PAMs were maintained in Roswell Park Memorial Institute 1640 medium (RPMI 1640) supplemented with 10% heat-inactivated fetal bovine serum (FBS; Gibco, Carlsbad, CA), penicillin (100 U/ml; Gibco), and streptomycin (100 mg/ml; Gibco) in a humidified 37°C, 5% CO<sub>2</sub> incubator. MARC-145 and BHK-21 cells were purchased from Cellbio (Shanghai, China) and maintained in Dulbecco modified Eagle medium supplemented with 10% heat-inactivated FBS and penicillin-streptomycin.</p><p>HP-PRRSV strain HN07-1 (GenBank accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/KX766378.1\" assigning-authority=\"genbank\">KX766378.1</ext-link>) was previously isolated by our laboratory (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>). A typical PRRSV-2 strain, BJ-4 (GenBank accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/AF331831\" assigning-authority=\"genbank\">AF331831</ext-link>), and PRRSV-1 strain GZ11-G1 (GenBank accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/KF001144\" assigning-authority=\"genbank\">KF001144</ext-link>) were kindly provided by Hanchun Yang from China Agricultural University (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). PRRSV virions were purified by sucrose density gradient ultracentrifugation as previously described (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>), and their infectivities were comparable to those of naive virions (data not shown). Purified PRRSV virions in phosphate-buffered saline (PBS) were utilized throughout this work.</p></sec><sec id=\"s4.2\"><title>Antibodies, inhibitors, and reagents.</title><p>The antibodies used were anti-TIM-1 rabbit polyclonal antibody (catalog no. ab47635), anti-human IgG antibody (EPR4421; catalog no. ab109489), anti-TIM-4 antibody (catalog no. ab47636), anti-SNX5 antibody (EPR14358; catalog no. ab180520), horseradish peroxidase (HRP)-labeled goat anti-rabbit IgG antibody (catalog no. ab6721), and HRP-labeled goat anti-mouse IgG antibody (catalog no. ab6789), all purchased from Abcam (Cambridge, United Kingdom). β-Actin (8H10D10) mouse monoclonal antibody (MAb; catalog no. 3700), Axl (C89E7) rabbit MAb (catalog no. 8661), PI3 kinase p110α (C73F8) rabbit MAb (catalog no. 4249), PAK1 antibody (catalog no. 2602), Rac1/2/3 antibody (catalog no. 2465), and Cdc42 antibody (catalog no. 2462) were all purchased from Cell Signaling Technology (Danvers, CT). Anti-PS mouse MAb 1H6 (catalog no. 05-719) and isotype control mouse IgG (catalog no. NI03) were purchased from Merck Millipore (Ontario, Canada). CD163 antibody 2A10/11 (catalog no. MCA2311GA) and CD163 antibody EDHu-1 (catalog no. MCA1853) were purchased from Bio-Rad Antibodies (Hercules, CA). TIM-4 antibody (catalog no. sc390805) was purchased from Santa Cruz Biotechnology (Santa Cruz, CA). Mouse MAbs against PRRSV N protein and GP5 were kept in our laboratory (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Alexa Fluor 488-goat anti-mouse antibody (catalog no. A-11029), Alexa Fluor 647-goat anti-mouse antibody (catalog no. A-21235), and Alexa Fluor 647-goat anti-rabbit antibody (catalog no. A-21245) were purchased from Invitrogen (Carlsbad, CA).</p><p>The inhibitors CPZ (catalog no. c0982), Lat A (catalog no. 428021), IPA-3 (catalog no. I2285), nsc23766 (catalog no. SML0952), and LY294002 (catalog no. 19-142) were all purchased from Sigma-Aldrich (St. Louis, MO). EIPA (catalog no. sc-202458) was purchased from Santa Cruz Biotechnology. Cyto D (catalog no. PHZ1063) was purchased from Invitrogen. Pirl-1 (catalog no. 5137877) was purchased from ChemBridge (San Diego, CA).</p><p>The reagents Alexa Fluor 488-phalloidin (catalog no. A12379), Alexa Fluor 647-transferrin (catalog no. T23366), annexin V-conjugated Alexa Fluor 488 (catalog no. A13201), annexin-binding buffer for flow cytometry (catalog no. v13246), ProLong glass antifade mountant (catalog no. P36984), Lipofectamine LTX with Plus reagent (catalog no. 15338030), and Lipofectamine RNAiMAX transfection reagent (catalog no. 13778150) were purchased from Invitrogen. The CellTiter 96 AQueous One Solution cell proliferation assay (MTS; catalog no. G3582) was purchased from Promega (Madison, WI). Recombinant TIM-1-Fc (catalog no. SRP8054), recombinant TIM-4-Fc (catalog no. SRP8057), and fluorescein isothiocyanate (FITC)-dextran (average molecular weight, 70,000; catalog no. 46945) were purchased from Sigma-Aldrich.</p></sec><sec id=\"s4.3\"><title>PS detection.</title><p>For detection of PS on PRRSV envelope, MARC-145 cells were washed with PBS and dissociated by using an enzyme-free cell dissociation solution (catalog no. 13151014; Gibco). The cells were then collected at 4°C for 1 h and inoculated with virions at a multiplicity of infection (MOI) of 10 or with PBS as an unbound control. The cells were washed three times with cold PBS, resuspended in annexin-binding buffer, and incubated with annexin V-conjugated Alexa Fluor 488 at 4°C for 20 min. After incubation, the cells were added with annexin-binding buffer, mixed gently, and kept on ice. PS detection was preceded with FCM immediately.</p><p>Purified virions in PBS were spotted onto nitrocellulose membranes (Pierce, Rockford, IL), and PBS was spotted onto membranes as a negative control. Membranes were dried and blocked with 5% bovine serum albumin (BSA) in PBS at 4°C overnight. Next, the membranes were incubated with the anti-PS MAb or anti-PRRSV GP5 MAb as a viral loading control for 1 h at 37°C. After three washes with PBS plus Tween 20 (PBST), the membranes were incubated with HRP-conjugated goat anti-mouse IgG antibody and detected by enhanced chemiluminescence (ECL) Plus reagent (Solarbio, Beijing, China).</p></sec><sec id=\"s4.4\"><title>Detection of PSR transcription.</title><p>MARC-145 cells were collected, and the total RNAs were extracted using TRIzol reagent (Invitrogen). The reverse transcription cDNAs were prepared by using a PrimeScript RT reagent kit with gDNA Eraser (TaKaRa, Dalian, China) according to the manufacturer’s instructions. The cDNAs were then subjected to PCR with specific primers for TIM-1, Axl, Stabilin-1, and Stabilin-2 (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). The PCR products were subjected to agarose gel electrophoresis.</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>Primers used in this study</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"2\" colspan=\"1\">Target gene</th><th colspan=\"2\" rowspan=\"1\">Sequence (5′–3′)<hr/></th></tr><tr><th rowspan=\"1\" colspan=\"1\">Sense</th><th rowspan=\"1\" colspan=\"1\">Antisense</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">PRRSV-ORF7</td><td rowspan=\"1\" colspan=\"1\">AAACCAGTCCAGAGGCAAGG</td><td rowspan=\"1\" colspan=\"1\">GCAAACTAAACTCCACAGTGTAA</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey TIM-1</td><td rowspan=\"1\" colspan=\"1\">ACCTTTGTTCCTCCAACGCC</td><td rowspan=\"1\" colspan=\"1\">CAGCAGTGTCATAGGGTGGG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Pig TIM-4</td><td rowspan=\"1\" colspan=\"1\">GTCGGTGACTTTGCCCTGTA</td><td rowspan=\"1\" colspan=\"1\">TTGGCTGACTTCCTCGACAC</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Axl</td><td rowspan=\"1\" colspan=\"1\">GGGAGATTGCCACAAGAG</td><td rowspan=\"1\" colspan=\"1\">GTGACATCAAGGCATACA</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Pak1</td><td rowspan=\"1\" colspan=\"1\">TTGACCCGGAATACTGAGA</td><td rowspan=\"1\" colspan=\"1\">TGAAGCACCTTGTCCAATC</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Rac1</td><td rowspan=\"1\" colspan=\"1\">CAGTGTTTGACGAAGCGA</td><td rowspan=\"1\" colspan=\"1\">CAAGGGACAGGACCAAGA</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Cdc42</td><td rowspan=\"1\" colspan=\"1\">CAGATTACGACCGCTGAGT</td><td rowspan=\"1\" colspan=\"1\">AGGCACCCACTTTTCTTTC</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey PI3K</td><td rowspan=\"1\" colspan=\"1\">CTTCCACACAATTAAACAGCA</td><td rowspan=\"1\" colspan=\"1\">ATTCCTATGCAATCGGTCTT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Stabilin-1</td><td rowspan=\"1\" colspan=\"1\">GCGATGGGATAGTGTGT</td><td rowspan=\"1\" colspan=\"1\">CATTGCTGTTGATGCTGAC</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Stabilin-2</td><td rowspan=\"1\" colspan=\"1\">GGACCAGGATGAGAAAAGC</td><td rowspan=\"1\" colspan=\"1\">TGCCAAGTGAAGGAAGTTG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">GAPDH</td><td rowspan=\"1\" colspan=\"1\">CCTTCCGTGTCCCTACTGCCAAC</td><td rowspan=\"1\" colspan=\"1\">GACGCCTGCTTCACCACCTTCT</td></tr></tbody></table><graphic xlink:href=\"JVI.00709-20-t0001\"/></alternatives></table-wrap></sec><sec id=\"s4.5\"><title>RT-qPCR.</title><p>Total RNAs were extracted using TRIzol reagent, and the reverse transcription cDNAs were prepared as described above. Then, RT-qPCR was performed using FastStart Universal SYBR green Master (Rox, catalog no. 4913850001; Roche, Basel, Switzerland) on a 7500 Fast RT-PCR system (Applied Biosystems, Foster City, CA). A plasmid containing PRRSV ORF7 was used as the template to generate a standard curve, and the actual viral RNA copies were calculated based on this curve (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). The relative RNA level was evaluated by the 2<sup>–ΔΔ</sup><italic><sup>CT</sup></italic> method using glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA as an endogenous control (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>). The primers for RT-qPCR are listed in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>.</p></sec><sec id=\"s4.6\"><title>IB.</title><p>Cells were harvested and lysed in radioimmunoprecipitation assay lysis buffer (Beyotime Biotechnology, Shanghai, China) supplemented with a cocktail of protease inhibitors (Roche). The lysates were separated by 10% to 15% gradient sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and electrotransferred onto polyvinylidene fluoride membranes (Merck Millipore). The membranes were blocked in 5% skim milk for 1 h and probed with the indicated primary antibodies. After incubation with HRP-labeled goat anti-mouse or rabbit IgG antibody as a secondary antibody, the indicated proteins were detected by ECL Plus reagent.</p></sec><sec id=\"s4.7\"><title>RNA interference.</title><p>All siRNAs and siRNA negative controls (siRNA-NC) were designed and synthesized by GenePharma (Shanghai, China). In knockdown experiments, PAMs or MARC-145 cells were transfected with the indicated siRNAs at a final concentration of 10 nM using Lipofectamine RNAiMAX according to the manufacturer’s instructions for 36 h. After the cell viability was measured by the CellTiter 96 AQueous One Solution assay kit (data not shown), transfected cells were applied for subsequent experiments. The indicated siRNAs are listed in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>.</p><table-wrap id=\"T2\" orientation=\"portrait\" position=\"float\"><label>TABLE 2</label><caption><p>siRNAs used in this study</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"2\" colspan=\"1\">Target gene</th><th colspan=\"2\" rowspan=\"1\">Sequence (5′–3′)<hr/></th></tr><tr><th rowspan=\"1\" colspan=\"1\">Sense</th><th rowspan=\"1\" colspan=\"1\">Antisense</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">Monkey TIM-1</td><td rowspan=\"1\" colspan=\"1\">GCUCACCAUUGUACUCUUATT</td><td rowspan=\"1\" colspan=\"1\">UAAGAGUACAAUGGUGAGCTT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Axl</td><td rowspan=\"1\" colspan=\"1\">CCUGUGGUCAUCUUACCUUTT</td><td rowspan=\"1\" colspan=\"1\">AAGGUAAGAUGACCACAGGTT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Rac1</td><td rowspan=\"1\" colspan=\"1\">CCUAGUGGGAACUAAACUUTT</td><td rowspan=\"1\" colspan=\"1\">AAGUUUAGUUCCCACUAGGTT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Cdc42</td><td rowspan=\"1\" colspan=\"1\">AGACUCCUUUCUUGCUUGUTT</td><td rowspan=\"1\" colspan=\"1\">ACAAGCAAGAAAGGAGUCUTT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey Pak1</td><td rowspan=\"1\" colspan=\"1\">CCACUCCACCAGAUGCUUUTT</td><td rowspan=\"1\" colspan=\"1\">AAAGCAUCUGGUGGAGUGGTT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Monkey PI3K</td><td rowspan=\"1\" colspan=\"1\">CCACACAAUUAAACAGCAUTT</td><td rowspan=\"1\" colspan=\"1\">AUGCUGUUUAAUUGUGUGGTT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Pig TIM-4</td><td rowspan=\"1\" colspan=\"1\">CCCGUGUCCCAAAUCCAAATT</td><td rowspan=\"1\" colspan=\"1\">UUUGGAUUUGGGACACGGGTT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">siRNA-NC</td><td rowspan=\"1\" colspan=\"1\">UUCUCCGAACGUGUCACGUTT</td><td rowspan=\"1\" colspan=\"1\">ACGUGACACGUUCGGAGAATT</td></tr></tbody></table><graphic xlink:href=\"JVI.00709-20-t0002\"/></alternatives></table-wrap></sec><sec id=\"s4.8\"><title>Virus titration assay.</title><p>The treated cells were inoculated with PRRSV at an MOI of 10 and incubated at 37°C for 3 h. The viruses not entering the cells were then washed away. At 48 hpi, the viral yields were measured by determining a 50% tissue culture infected dose (TCID<sub>50</sub>) assay in MARC-145 cells (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>).</p></sec><sec id=\"s4.9\"><title>Binding and entry assay.</title><p>Cells were serum starved for 1 h at 37°C and inoculated with purified virions at an MOI of 10 for 1 h at 4°C, allowing for viral attachment without internalization. The cells were then washed with cold PBS three times so that unbound viruses were removed. The culture medium was replaced with fresh serum-free medium, and the cells were subsequently shifted to 37°C, allowing virus internalization. After 1 h, the cells were washed with citrate buffer solution (pH 3.0) to remove the noninternalized visions and washed with PBS three times. PRRSV RNA abundance was determined by RT-qPCR.</p></sec><sec id=\"s4.10\"><title>Pulldown assay.</title><p>The recombinant Fc-fused TIM-1/4 was first bound to protein A/G-beads (Pierce) at 4°C for 4 h. PRRSV virions were subsequently incubated with the beads at 4°C overnight. After extensive washing with PBS, the target proteins were eluted and subjected to IB with the indicated antibodies.</p></sec><sec id=\"s4.11\"><title>Dot blot assay.</title><p>TIM-1/4-Fc proteins were spotted onto nitrocellulose membranes. Membranes were dried and blocked with 5% BSA in PBS 4°C overnight. Next, membranes were incubated with the PRRSV virions at 4°C for 4 h. After three washes with PBST, the membranes were incubated with the indicated primary antibodies and detected with HRP-conjugated antibodies.</p></sec><sec id=\"s4.12\"><title>Confocal microscopy.</title><p>Cells were grown in 24-well plates on glass coverslips, fixed with 4% paraformaldehyde (PFA) for 15 min, and permeabilized with 0.1% Triton X-100 in PBS for 5 min at room temperature. Phalloidin (dilution 1:100) or DAPI (4′,6′-diamidino-2-phenylindole) was used to stain actin filaments and nuclei, respectively. Alternatively, cells were stained with the appropriate primary and secondary antibodies. Coverslips were mounted to the glass slides and examined by using a microscope (LSM700; Carl Zeiss AG, Oberkochen, Germany) with the confocal laser scanning setup (20×, 40×, or 63× objective). The numerical aperture (NA) of the 20× objective is 0.8, the NA of the 40× objective is 0.95, and the NA of the 63× objective is 1.4. Images were representative as a single slice of a stack from three independent experiments (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>). Colocalization analyses were carried out according to the method of Zinchuk and Grossenbacher-Zinchuk (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Manders’ overlap coefficient (>0.6) shows an actual overlap of the signals and is considered to represent the true degree of colocalization. Quantitative analyses of single-channel fluorescence were performed using ImageJ software (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>, <xref rid=\"B71\" ref-type=\"bibr\">71</xref>).</p></sec><sec id=\"s4.13\"><title>FITC-dextran uptake.</title><p>MARC-145 cells were grown in 24-well plates on glass coverslips. Prior to FITC-dextran uptake, the cells were serum starved for 2 h. FITC-dextran was incubated with the cells (final concentration, 0.25 mg/ml) in the absence or presence of PRRSV virions at an MOI of 10 for different time periods (15, 30, and 45 min). The cells were then washed three times with cold PBS on ice and once with low-pH buffer (0.1 M sodium acetate, 0.05 M NaCl [pH 5.5]), fixed with 4% PFA in PBS, and subsequently permeabilized with 0.1% Triton X-100 in PBS. After mounting, the slides were examined by confocal microscopy.</p></sec><sec id=\"s4.14\"><title>Cytotoxicity of inhibitors.</title><p>MARC-145 cells or PAMs were seeded onto 96-well plates and pretreated with indicated inhibitors at 37°C for 4 h. The cell viability was then measured using a CellTiter 96 AQueous One Solution cell proliferation assay. Briefly, the assay was performed by adding CellTiter 96 AQueous One Solution reagent to wells, followed by incubation for 4 h. The absorbance at 490 nm was then recorded with a Bio-Rad microplate reader (data not shown).</p></sec><sec id=\"s4.15\"><title>Inhibitor treatments.</title><p>MARC-145 cells and PAMs were serum starved for 1 h and treated with noncytotoxic specific inhibitors or DMSO for 1 h at 37°C in serum-free medium before subsequent experiments.</p></sec><sec id=\"s4.16\"><title>Plasmid transfection.</title><p>The optimized cDNA encoding porcine TIM-4 was cloned into the vector pECMV-MCS-FLAG (kept in our laboratory). A construct with complete porcine CD163 cDNA integrated into the PiggyBac transposon system was kindly provided by Enmin Zhou (Northwest Agriculture and Forestry University, China) (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>). BHK-21 cells were seeded at a density of 4 × 10<sup>5</sup> cells/ml culture medium overnight. The cells were then transfected with TIM-4 and/or CD163 plasmid using Lipofectamine LTX with Plus reagent according to the manufacturer’s instructions. The protein expression was tested by IB as stated above.</p></sec><sec id=\"s4.17\"><title>Statistical analysis.</title><p>Three replicates were included in all experiments, and each experiment was independently performed three times. The experimental data are presented as group means and standard deviations (SD) and were analyzed by the unpaired two-tailed Student <italic>t</italic> test with GraphPad Prism software (v7.0). Statistical significance is indicated in the figures (*, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ***, <italic>P</italic> < 0.001; ns, not significant).</p></sec></sec></body><back><ack><title>ACKNOWLEDGMENTS</title><p>We thank Hanchun Yang from China Agricultural University for providing PRRSV-2 strain BJ-4 and PRRSV-1 strain GZ11-G1 and Enmin Zhou from Northwest Agriculture and Forestry University for providing the PiggyBac transposon system.</p><p>This study was supported by grants from the National Natural Science Foundation of China (31972690), the Science-Technology Foundation for Outstanding Young Scientists of Henan Academy of Agricultural Sciences (2020YQ01), the Earmarked Fund for Modern Agro-industry Technology Research System of China (CARS-35), and the Special Fund for Henan Agriculture Research System (S2012-06). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.</p><p>G.Z. and R.L. jointly supervised the work. G.Z., R.L., and X.W. designed the experiments. X.W. performed the studies. X.W. and R.L. analyzed the data and wrote the manuscript. X.W., R.L., S.Q., and X.-X.C. revised the manuscript. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">J Virol</journal-id><journal-id journal-id-type=\"iso-abbrev\">J. Virol</journal-id><journal-id journal-id-type=\"hwp\">jvi</journal-id><journal-id journal-id-type=\"pmc\">jvi</journal-id><journal-id journal-id-type=\"publisher-id\">JVI</journal-id><journal-title-group><journal-title>Journal of Virology</journal-title></journal-title-group><issn pub-type=\"ppub\">0022-538X</issn><issn pub-type=\"epub\">1098-5514</issn><publisher><publisher-name>American Society for Microbiology</publisher-name><publisher-loc>1752 N St., N.W., Washington, DC</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32581110</article-id><article-id pub-id-type=\"pmc\">PMC7431803</article-id><article-id pub-id-type=\"publisher-id\">01104-20</article-id><article-id pub-id-type=\"doi\">10.1128/JVI.01104-20</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Virus-Cell Interactions</subject></subj-group></article-categories><title-group><article-title>P108 and T109 on E2 Glycoprotein Domain I Are Critical for the Adaptation of Classical Swine Fever Virus to Rabbits but Not for Virulence in Pigs</article-title><alt-title alt-title-type=\"running-head\">Determinants of CSFV Adaptation and Attenuation</alt-title><alt-title alt-title-type=\"short-authors\">Xie et al.</alt-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Xie</surname><given-names>Libao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Han</surname><given-names>Yuying</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Ma</surname><given-names>Yuteng</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Yuan</surname><given-names>Mengqi</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Weike</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Lian-Feng</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Miao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Sun</surname><given-names>Yuan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Luo</surname><given-names>Yuzi</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Su</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Hu</surname><given-names>Shouping</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Li</surname><given-names>Yongfeng</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Qiu</surname><given-names>Hua-Ji</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><aff id=\"aff1\"><label>a</label><addr-line>State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin, China</addr-line></aff><aff id=\"aff2\"><label>b</label><addr-line>State Key Laboratory of Veterinary Etiological Biology, Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Lanzhou, China</addr-line></aff></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>López</surname><given-names>Susana</given-names></name><role>Editor</role><aff>Instituto de Biotecnologia/UNAM</aff></contrib></contrib-group><author-notes><corresp id=\"cor1\">Address correspondence to Yongfeng Li, <email>liyongfeng@caas.cn</email>, or Hua-Ji Qiu, <email>qiuhuaji@caas.cn</email>.</corresp><fn fn-type=\"other\"><p><bold>Citation</bold> Xie L, Han Y, Ma Y, Yuan M, Li W, Li L-F, Li M, Sun Y, Luo Y, Li S, Hu S, Li Y, Qiu H-J. 2020. P108 and T109 on E2 glycoprotein domain I are critical for the adaptation of classical swine fever virus to rabbits but not for virulence in pigs. J Virol 94:e01104-20. <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.1128/JVI.01104-20\">https://doi.org/10.1128/JVI.01104-20</ext-link>.</p></fn></author-notes><pub-date pub-type=\"epreprint\"><day>24</day><month>6</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>9</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>94</volume><issue>17</issue><elocation-id>e01104-20</elocation-id><history><date date-type=\"received\"><day>11</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>15</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Xie et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Xie et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International license</ext-link>.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"JVI.01104-20.pdf\"/><abstract abstract-type=\"precis\"><p>Historically, live attenuated vaccines produced by blind passage usually undergo adaptation in cell cultures or nonsusceptible hosts and attenuation in natural hosts, with a classical example being the classical swine fever virus (CSFV) lapinized vaccine C-strain, which was developed by hundreds of passages in rabbits. However, the mechanism of viral adaptation to nonsusceptible hosts and the molecular basis for viral adaptation and attenuation remain largely unknown. In this study, we demonstrated that P108 and T109 on the E2 glycoprotein together with the E<sup>rns</sup> glycoprotein of the rabbit-adaptive C-strain confer adaptation to rabbits on the highly virulent CSFV Shimen strain by affecting viral entry during infection but do not attenuate the Shimen strain in pigs. Our results provide vital information on the different molecular bases of CSFV adaptation to rabbits and attenuation in pigs.</p></abstract><abstract><title>ABSTRACT</title><p>The classical swine fever virus (CSFV) live attenuated vaccine C-strain is adaptive to rabbits and attenuated in pigs, in contrast with the highly virulent CSFV Shimen strain. Previously, we demonstrated that P108 and T109 on the E2 glycoprotein (E2<sup>P108-T109</sup>) in domain I (E2<sup>DomainI</sup>) rather than R132, S133, and D191 in domain II (E2<sup>DomainII</sup>) determine C-strain’s adaptation to rabbits (ATR) (Y. Li, L. Xie, L. Zhang, X. Wang, C. Li, et al., Virology 519:197–206, 2018). However, it remains elusive whether these critical amino acids affect the ATR of the Shimen strain and virulence in pigs. In this study, three chimeric viruses harboring E2<sup>P108-T109</sup>, E2<sup>DomainI</sup>, or E2<sup>DomainII</sup> of C-strain based on the non-rabbit-adaptive Shimen mutant vSM-HCLVE<sup>rns</sup> carrying the E<sup>rns</sup> glycoprotein of C-strain were generated and evaluated. We found that E2<sup>P108-T109</sup> or E2<sup>DomainI</sup> but not E2<sup>DomainII</sup> of C-strain renders vSM-HCLVE<sup>rns</sup> adaptive to rabbits, suggesting that E2<sup>P108-T109</sup> in combination with the E<sup>rns</sup> glycoprotein (E2<sup>P108-T109</sup>-E<sup>rns</sup>) confers ATR on the Shimen strain, creating new rabbit-adaptive CSFVs. Mechanistically, E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain mediates viral entry during infection in rabbit spleen lymphocytes, which are target cells of C-strain. Notably, pig experiments showed that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain does not affect virulence compared with the Shimen strain. Conversely, the substitution of E2<sup>DomainII</sup> and E<sup>rns</sup> of C-strain attenuates the Shimen strain in pigs, indicating that the molecular basis of the CSFV ATR and that of virulence in pigs do not overlap. Our findings provide new insights into the mechanism of adaptation of CSFV to rabbits and the molecular basis of CSFV adaptation and attenuation.</p><p><bold>IMPORTANCE</bold> Historically, live attenuated vaccines produced by blind passage usually undergo adaptation in cell cultures or nonsusceptible hosts and attenuation in natural hosts, with a classical example being the classical swine fever virus (CSFV) lapinized vaccine C-strain, which was developed by hundreds of passages in rabbits. However, the mechanism of viral adaptation to nonsusceptible hosts and the molecular basis for viral adaptation and attenuation remain largely unknown. In this study, we demonstrated that P108 and T109 on the E2 glycoprotein together with the E<sup>rns</sup> glycoprotein of the rabbit-adaptive C-strain confer adaptation to rabbits on the highly virulent CSFV Shimen strain by affecting viral entry during infection but do not attenuate the Shimen strain in pigs. Our results provide vital information on the different molecular bases of CSFV adaptation to rabbits and attenuation in pigs.</p></abstract><kwd-group><title>KEYWORDS</title><kwd>adaptation</kwd><kwd>classical swine fever virus</kwd><kwd>entry</kwd><kwd>virulence</kwd></kwd-group><funding-group><award-group id=\"award1\"><funding-source><institution-wrap><institution>NSFC | Foundation for Innovative Research Groups of the National Natural Science Foundation of China</institution><institution-id>https://doi.org/10.13039/501100012659</institution-id></institution-wrap></funding-source><award-id>31772774</award-id><principal-award-recipient><name><surname>Li</surname><given-names>Yongfeng</given-names></name></principal-award-recipient></award-group><award-group id=\"award2\"><funding-source><institution-wrap><institution>NSFC | Foundation for Innovative Research Groups of the National Natural Science Foundation of China</institution><institution-id>https://doi.org/10.13039/501100012659</institution-id></institution-wrap></funding-source><award-id>31972673</award-id><principal-award-recipient><name><surname>Li</surname><given-names>Yongfeng</given-names></name></principal-award-recipient></award-group><award-group id=\"award3\"><funding-source><institution-wrap><institution>NSFC | Foundation for Innovative Research Groups of the National Natural Science Foundation of China</institution><institution-id>https://doi.org/10.13039/501100012659</institution-id></institution-wrap></funding-source><award-id>31630080</award-id><principal-award-recipient><name><surname>Qiu</surname><given-names>Hua-Ji</given-names></name></principal-award-recipient></award-group></funding-group><counts><fig-count count=\"5\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"51\"/><page-count count=\"14\"/><word-count count=\"8556\"/></counts><custom-meta-group><custom-meta><meta-name>cover-date</meta-name><meta-value>September 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>INTRODUCTION</title><p>Viruses have a predefined host spectrum (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). However, viruses can breach the interspecies barrier through serial passaging in a nonsusceptible host, leading to viral adaptation to nonsusceptible hosts and attenuation in primary hosts. Well-known examples of this are the Chinese hog cholera lapinized virus (HCLV, also known as C-strain) and the lapinized rinderpest virus (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Remarkably, the viral infection is limited if one stage of the life cycle is blocked in nonsusceptible hosts, especially if the virus is unable to utilize a factor(s) which is necessary for infection or to evade a restriction factor(s). Hence, mechanisms of viral adaptation to nonsusceptible hosts by serial passaging are as follows: (i) improving entry efficiency (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>), (ii) enhancing the ability to antagonize or evade the nonsusceptible host's restriction factor(s) (<xref rid=\"B6\" ref-type=\"bibr\">6</xref><xref ref-type=\"bibr\" rid=\"B7\">–</xref><xref rid=\"B8\" ref-type=\"bibr\">8</xref>), and (iii) promoting the assembly and release of virus particles (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Entry is the first and vital step during viral infection, which is mediated by the interaction between viral envelope proteins and cellular receptors (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p><p>Classical swine fever (CSF) is a devastating infectious disease of pigs caused by classical swine fever virus (CSFV), which often leads to huge economic losses to the pork industry (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). CSFV is a small, enveloped virus with a single-stranded positive RNA molecule encoding four structural proteins (C, E<sup>rns</sup>, E1, and E2) and eight nonstructural proteins (N<sup>pro</sup>, p7, NS2, NS3, NS4A, NS4B, NS5A, and NS5B) (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). The E<sup>rns</sup> glycoprotein mediates viral attachment by interacting with LamR (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). The E2 glycoprotein, which forms homodimers and heterodimers with E1, is essential for viral entry into cells (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>) and also plays a pivotal role in viral tropism (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Meanwhile, E<sup>rns</sup> and E2 glycoproteins of CSFV are closely associated with virulence and pathogenicity in pigs (<xref rid=\"B17\" ref-type=\"bibr\">17</xref><xref ref-type=\"bibr\" rid=\"B18\">–</xref><xref rid=\"B22\" ref-type=\"bibr\">22</xref>).</p><p>In general, CSFV infects only domestic pigs and wild boar (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). However, the species barrier of CSFV between pigs and rabbits was crossed by hundreds of passages of a highly virulent strain in rabbits. C-strain, a live attenuated vaccine against CSF, was developed by passaging a highly virulent CSFV strain in rabbits in the 1950s and is characterized by adaptation to rabbits (ATR) and attenuation in pigs (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Until now, however, the mechanism of the C-strain ATR and the molecular basis for ATR and attenuation in pigs have remained elusive.</p><p>Our previous study found that vSM-HCLVE<sup>rns</sup> carrying the E<sup>rns</sup> of C-strain in the background of the highly virulent CSFV Shimen strain is not adaptive to rabbits, indicating that the E<sup>rns</sup> of C-strain alone could not render the Shimen strain adaptive to rabbits (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). In contrast, the E2 and E<sup>rns</sup> glycoproteins of C-strain together are sufficient for enabling the adaptation of the Shimen strain to rabbits. Furthermore, we observed that the C-strain backbone chimeras harboring E2 domain I (E2<sup>DomainI</sup>) or crucial mutation sites (E2<sup>P108L-T109I</sup>) on E2<sup>DomainI</sup> of the Shimen strain do not adapt to rabbits, while the chimera harboring E2 domain II (E2<sup>DomainII</sup>) of the Shimen strain is adaptive to rabbits (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Therefore, P108 and T109 on E2<sup>DomainI</sup> (E2<sup>P108-T109</sup>) of C-strain are responsible for its ATR, whereas E2<sup>DomainII</sup> containing three different residues (R132, S133, and D191) of C-strain are not determinants of its ATR. Because of the above findings, we further investigated the effects of these critical amino acids of C-strain on the Shimen strain ATR and the virulence in pigs.</p><p>In the present study, we demonstrated that E2<sup>P108-T109</sup> in combination with the E<sup>rns</sup> glycoprotein (E2<sup>P108-T109</sup>-E<sup>rns</sup>) of C-strain confers adaptation to rabbits on the Shimen strain. Remarkably, our data revealed that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain promotes viral entry during infection in the target cells using a series of pseudotyped viruses. Finally, we demonstrated that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain, responsible for the CSFV ATR, does not affect viral virulence in pigs, whereas E2<sup>DomainII</sup>-E<sup>rns</sup>, which is not associated with the adaptation, attenuates the Shimen strain in pigs.</p></sec><sec sec-type=\"results\" id=\"s2\"><title>RESULTS</title><sec id=\"s2.1\"><title><italic>In vitro</italic> rescue and evaluation of chimeric viruses in the background of the Shimen strain.</title><p>Based on previous results (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>), we speculated that E2<sup>P108-T109</sup> or E2<sup>DomainI</sup> but not E2<sup>DomainII</sup> of C-strain is necessary for conferring adaptation to rabbits on the non-rabbit-adaptive Shimen mutant vSM-HCLVE<sup>rns</sup>. Since vSM-HCLVE<sup>rns</sup>E2, harboring the E<sup>rns</sup> and E2 glycoproteins of C-strain, showed better adaptation to rabbits than vSM-HCLVE1E2, containing the E1 and E2 glycoproteins of C-strain, according to the reproductive animal experiments in our previous study (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>), three infectious clones (pSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, and pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>) were constructed based on the infectious clone pSM-HCLVE<sup>rns</sup> containing E<sup>rns</sup> of C-strain in the backbone of the Shimen strain (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1A</xref>). Chimeric viruses were rescued by transfecting individual infectious clones into swine kidney (SK6) cells, and transfected cells were serially passaged. Viral genomic sequencing results demonstrated that the sequences of the rescued chimeric viruses were as expected. The results of indirect immunofluorescence assay (IFA) demonstrated that the rescued viruses were infectious in the cells (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1B</xref>). The growth characteristics of the chimeras were determined in porcine kidney (PK-15) cells. Compared with the parental virus (Shimen strain), vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup> had lower viral titers at different time points, while viral titers of vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> and vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> were not significantly different (<italic>P > </italic>0.05) (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1C</xref>). Collectively, the expected chimeric viruses were generated.</p><fig id=\"F1\" orientation=\"portrait\" position=\"float\"><label>FIG 1</label><caption><p>Generation and characterization of chimeric CSFVs. (A) Schematic diagram of the genomic organization of parental and chimeric viruses. Purple boxes indicate genes from the highly virulent Shimen strain, while red boxes indicate genes derived from the lapinized vaccine C-strain (also known as HCLV). The infectious clones of chimeric viruses (vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>, and vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>) were developed based on the infectious clone of the non-rabbit-adaptive chimeric virus vSM-HCLVE<sup>rns</sup> carrying the E<sup>rns</sup> glycoprotein of C-strain. (B) Indirect immunofluorescence staining of the PK-15 cells infected by chimeric viruses and parental virus. The nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI). Bar, 400 μm. (C) Growth kinetics of parental and chimeric viruses in PK-15 cells. The error bars represent the standard deviations for three replicates.</p></caption><graphic xlink:href=\"JVI.01104-20-f0001\"/></fig></sec><sec id=\"s2.2\"><title>E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain confers adaptation to rabbits on the Shimen strain.</title><p>To investigate whether E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain adapts the Shimen strain to rabbits, five groups of 24 rabbits were inoculated with different chimeric viruses (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Rectal temperature was recorded 24 h before and after inoculation and then every 6 h until 72 h postinoculation (hpi). According to the fever response standard described previously (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>), vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> and vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> did not induce a fever response in rabbits, in contrast to C-strain (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Four rabbits from each group were randomly selected to be euthanized for determining copy numbers of the viral genome in the spleens by real-time reverse transcription-quantitative PCR (RT-qPCR). The viral genome was detected in the rabbits inoculated with vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, or C-strain but not vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). To further determine whether the progeny viruses were present in rabbit spleens, virus isolation was performed in SK6 cells. The IFA results demonstrated that the viruses were isolated from the spleens of rabbits inoculated with vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, or C-strain but not with vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> or Dulbecco’s modified Eagle’s medium (DMEM), and no mutation occurred, as confirmed by sequencing (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2A</xref>). The immunohistochemistry results showed that the E2 glycoprotein was expressed in the spleen lymphocytes of the rabbits inoculated with C-strain, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, or vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> but not vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> or the Shimen strain (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2B</xref>). These results indicated that chimeric viruses carrying E2<sup>P108-T109</sup>-E<sup>rns</sup> or E2<sup>DomainI</sup>-E<sup>rns</sup> of C-strain are adaptive to rabbits. Additionally, at 10 days postinoculation (dpi), anti-E2 antibodies were detected in the remaining two rabbits of each group (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). As expected, we demonstrated that E2<sup>P108-T109</sup> or E2<sup>DomainI</sup> but not E2<sup>DomainII</sup> of C-strain could render vSM-HCLVE<sup>rns</sup> adaptive to rabbits, suggesting that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain could confer adaptation to rabbits on the Shimen strain, generating new rabbit-adaptive CSFVs.</p><table-wrap id=\"T1\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>Viral replication in the spleens of the rabbits inoculated with the Shimen-based chimeric viruses and HCLV</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Inoculum</th><th rowspan=\"1\" colspan=\"1\">Dose (TCID<sub>50</sub>)</th><th rowspan=\"1\" colspan=\"1\">No. with fever/total</th><th rowspan=\"1\" colspan=\"1\">No. with viral replication/total</th><th rowspan=\"1\" colspan=\"1\">Mean viral RNA copies in the spleens (copies/μl)</th><th rowspan=\"1\" colspan=\"1\">No. with seroconversion at 10 dpi/total</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup></td><td rowspan=\"1\" colspan=\"1\">10<sup>4</sup></td><td rowspan=\"1\" colspan=\"1\">0/6</td><td rowspan=\"1\" colspan=\"1\">2/4</td><td rowspan=\"1\" colspan=\"1\">2.52 × 10<sup>2</sup></td><td rowspan=\"1\" colspan=\"1\">2/2</td></tr><tr><td rowspan=\"1\" colspan=\"1\">vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup></td><td rowspan=\"1\" colspan=\"1\">10<sup>4</sup></td><td rowspan=\"1\" colspan=\"1\">5/6</td><td rowspan=\"1\" colspan=\"1\">2/4</td><td rowspan=\"1\" colspan=\"1\">1.08 × 10<sup>2</sup></td><td rowspan=\"1\" colspan=\"1\">2/2</td></tr><tr><td rowspan=\"1\" colspan=\"1\">vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup></td><td rowspan=\"1\" colspan=\"1\">10<sup>4</sup></td><td rowspan=\"1\" colspan=\"1\">0/6</td><td rowspan=\"1\" colspan=\"1\">0/4</td><td rowspan=\"1\" colspan=\"1\">No count</td><td rowspan=\"1\" colspan=\"1\">2/2</td></tr><tr><td rowspan=\"1\" colspan=\"1\">HCLV</td><td rowspan=\"1\" colspan=\"1\">10<sup>4</sup></td><td rowspan=\"1\" colspan=\"1\">4/4</td><td rowspan=\"1\" colspan=\"1\">2/2</td><td rowspan=\"1\" colspan=\"1\">6.30 × 10<sup>3</sup></td><td rowspan=\"1\" colspan=\"1\">2/2</td></tr><tr><td rowspan=\"1\" colspan=\"1\">DMEM</td><td rowspan=\"1\" colspan=\"1\">1 ml</td><td rowspan=\"1\" colspan=\"1\">0/2</td><td rowspan=\"1\" colspan=\"1\">0/1</td><td rowspan=\"1\" colspan=\"1\">No count</td><td rowspan=\"1\" colspan=\"1\">0/1</td></tr></tbody></table><graphic xlink:href=\"JVI.01104-20-t0001\"/></alternatives></table-wrap><fig id=\"F2\" orientation=\"portrait\" position=\"float\"><label>FIG 2</label><caption><p>Rabbit-adaptive CSFV mutants were detected in rabbit spleens. (A) Indirect immunofluorescence staining of the SK6 cells infected with viruses isolated from the rabbit spleens and the nuclei stained with DAPI. Bar, 400 μm. (B) Viral antigens in the spleen samples detected by immunohistochemistry. The E2 glycoprotein was detected by anti-E2 antibody in the spleen lymphocytes from the rabbits inoculated with HCLV, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, or vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> but not the Shimen strain, vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>, or DMEM. Bar, 50 μm.</p></caption><graphic xlink:href=\"JVI.01104-20-f0002\"/></fig></sec><sec id=\"s2.3\"><title>Growth curves of rabbit-adaptive CSFV mutants in primary rabbit spleen lymphocytes and swine macrophages.</title><p>To examine the growth property of rabbit-adaptive CSFV mutants in rabbit cells, spleen lymphocytes were identified as target cells in the rabbits infected with C-strain based on the results of immunohistochemistry assay (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2B</xref>). To determine the exact cell type (T cells or B cells) in the spleens of the rabbits inoculated with C-strain, T cells and B cells were isolated from total spleen cells using the FACSAria cell-sorting system from rabbits inoculated with C-strain at 3 dpi (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3A</xref>). The viral genome copy numbers of C-strain in T cells and B cells were determined by RT-qPCR (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3B</xref>). Furthermore, viral genome copy numbers and NanoLuc activities were determined in spleen lymphocytes isolated from the rabbits inoculated with C-strain or the reporter virus vHCLV-NanoLuc (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3C</xref>). Importantly, luciferase activities were detected in the spleen lymphocytes infected with vHCLV-NanoLuc (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3D</xref>). These results indicated that C-strain infects the spleen lymphocytes (both T cells and B cells) of the rabbits.</p><fig id=\"F3\" orientation=\"portrait\" position=\"float\"><label>FIG 3</label><caption><p>Growth curves of CSFV mutants in primary rabbit spleen lymphocytes and swine macrophages. (A) T cells and B cells isolated by flow cytometry. T cells and B cells from the spleen lymphocytes of rabbits inoculated with HCLV were isolated by flow cytometry. The cells in the Q1 region are T cells, and cells in the Q3 region are B cells. (B) Viral genome copy numbers in T cells and B cells isolated from rabbits inoculated with C-strain. The viral genome copy numbers in both T cells and B cells isolated by flow cytometry were determined using RT-qPCR. Total cells before sorting served as a control. (C) NanoLuc activities or viral genome copy numbers were tested in the spleen lymphocytes isolated from rabbits inoculated with vHCLV-NanoLuc, HCLV, or DMEM. (D) NanoLuc activities or viral genome copy numbers were tested in the spleen lymphocytes infected with vHCLV-NanoLuc or HCLV or treated with DMEM only. (E) Spleen lymphocytes isolated from healthy rabbits were infected with HCLV, the Shimen strain, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>, or vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> at an MOI of 0.01. Viral genome copy numbers were measured at 4, 24, 48, and 72 h postinoculation (hpi). Relative replication rates were analyzed based on the viral genome copy numbers at 4 hpi. (F) Viral genome copy numbers in primary swine macrophages infected with parental or chimeric viruses. (G) Titers of progeny viruses in the supernatants of primary swine macrophage cell cultures. The viral titers were determined in SK6 cells.</p></caption><graphic xlink:href=\"JVI.01104-20-f0003\"/></fig><p>Next, primary rabbit spleen lymphocytes were isolated and infected with the Shimen strain, C-strain, vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, or vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>. The viral genome copy numbers in the cells were analyzed at different time points. The results showed that viral genome copy numbers of rabbit-adaptive CSFV mutants (vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> and vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>) or the parental virus C-strain increased over time. In contrast, the viral genome copy numbers of non-rabbit-adaptive CSFVs (vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> and the Shimen strain) decreased over time (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3E</xref>). Unfortunately, the progeny viruses in the supernatants were undetectable using IFA in SK6 cells due to low-level replication, which is consistent with observations in rabbits. These results further demonstrated that chimeric viruses vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> and vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup> but not vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> are adaptive to primary rabbit spleen lymphocytes.</p><p>Furthermore, primary swine macrophages were inoculated with a series of chimeric viruses. The viral genome copy numbers in macrophages or the titers of progeny viruses in the supernatants were determined at different time points. The viral genome copy numbers of the Shimen strain and three chimeric viruses were indistinguishable, except those of C-strain in primary swine macrophages (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3F</xref>). However, the viral titers in the supernatants of the macrophages infected with the Shimen strain were higher than those in cells infected with chimeric viruses (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3G</xref>).</p></sec><sec id=\"s2.4\"><title>E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain affects viral entry in the target cells.</title><p>The E2 glycoprotein has been reported to be responsible for viral entry and tropism (<xref rid=\"B14\" ref-type=\"bibr\">14</xref><xref ref-type=\"bibr\" rid=\"B15\">–</xref><xref rid=\"B16\" ref-type=\"bibr\">16</xref>), while RNA replication is mediated by nonstructural proteins (NS3, NS4A-4B, and NS5A-5B) (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Therefore, we speculated that the distinct adaptation potential of these chimeric viruses in rabbits may be due to different viral entry efficiency. A series of pseudotyped viruses (pps) bearing the E<sup>rns</sup>, E1, and E2 glycoproteins from the Shimen strain (SMpps) or C-strain (HCLVpps) were generated to investigate whether their differences occur in viral entry. We infected rabbits with SMpps or HCLVpps and isolated the spleen lymphocytes at 48 hpi. To amplify the fluorescence signals, an anti-EGFP (enhanced green fluorescent protein) antibody was used to measure EGFP expression. Meanwhile, mouse IgG was used to exclude nonspecific signals. We detected EGFP in the spleen lymphocytes from the rabbits inoculated with HCLVpps but not SMpps (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4A</xref>), suggesting that the C-strain ATR results from improved entry efficiency.</p><fig id=\"F4\" orientation=\"portrait\" position=\"float\"><label>FIG 4</label><caption><p>Entry was affected by the E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain. (A) HCLVpps but not SMpps could enter spleen lymphocytes of rabbits. Spleen lymphocytes were isolated from rabbits inoculated with 10<sup>7</sup> transducing units (TU) of HCLVpps or SMpps or with 1 ml of DMEM. EGFP expression was detected in the spleen lymphocytes of the rabbits inoculated with HCLVpps but not SMpps or DMEM. (B) E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain affected viral entry into lymphocytes. Spleen lymphocytes were isolated from rabbits inoculated with 10<sup>7</sup> TU of SME1-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>pps, SME1-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>pps, or SME1E2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>pps or with 1 ml of DMEM. EGFP expression was detected in the spleen lymphocytes of the rabbits inoculated with SME1-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>pps or SME1E2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>pps but not those inoculated with SME1-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>pps or DMEM. EGFP expression was detected using anti-EGFP antibody, and an irrelevant mouse IgG served as negative control. (C) The E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain did not affect viral entry into swine SK6 cells. EGFP expression was measured in SK6 cells infected with parental and chimeric pseudotyped viruses at an MOI of 2 at 48 hpi.</p></caption><graphic xlink:href=\"JVI.01104-20-f0004\"/></fig><p>We further investigated the role of the E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain in viral entry using the chimeric pseudotyped viruses SME1-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>pps, SME1-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>pps, and SME1E2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>pps. The results showed that EGFP was detected in the spleen lymphocytes from rabbits inoculated with SME1-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>pps or SME1E2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>pps but not in those from SME1-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>pps-inoculated animals (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4B</xref>), indicating that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain plays decisive roles in viral entry. Meanwhile, these pseudotyped viruses could enter swine SK6 cells with similar efficiency (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4C</xref>).</p></sec><sec id=\"s2.5\"><title>E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain does not attenuate the Shimen strain in pigs.</title><p>To examine whether the key amino acids contributing to viral ATR affect virulence in pigs, four groups of pigs were inoculated intramuscularly (i.m.) with vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>, and the Shimen strain, using 10<sup>5</sup> 50% tissue culture infective doses (TCID<sub>50</sub>). Clinical signs and rectal temperatures were monitored. All the pigs showed typical clinical signs (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>) of CSF starting at 3 to 5 dpi, and the pigs inoculated with vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, or the Shimen strain died at 11 to 16 dpi (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). However, the temperatures of the pigs inoculated with vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> normalized at 11 dpi, and the pigs survived until the end of this experiment. Viremia kinetics in animals inoculated with vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> or vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup> were indistinguishable from those induced by the parental Shimen strain (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5A</xref> and <xref ref-type=\"fig\" rid=\"F5\">B</xref>). In contrast, the pigs inoculated with vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> showed lower viremia than the parental virus (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5A</xref>), while virus was undetectable in the blood samples from the pigs inoculated with vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> using IFA in SK6 cells (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5B</xref>).</p><table-wrap id=\"T2\" orientation=\"portrait\" position=\"float\"><label>TABLE 2</label><caption><p>Swine survival and fever response following inoculation with chimeric viruses and the parental Shimen strain</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"2\" colspan=\"1\">Viruses</th><th rowspan=\"2\" colspan=\"1\">No. of survivors/total</th><th rowspan=\"2\" colspan=\"1\">Mean time to death (days)<xref ref-type=\"table-fn\" rid=\"T2F1\"><sup><italic>a</italic></sup>\n</xref></th><th colspan=\"3\" rowspan=\"1\">Fever response<xref ref-type=\"table-fn\" rid=\"T2F1\"><sup><italic>a</italic></sup>\n</xref><hr/></th></tr><tr><th rowspan=\"1\" colspan=\"1\">No. of days to onset</th><th rowspan=\"1\" colspan=\"1\">Duration (days)</th><th rowspan=\"1\" colspan=\"1\">Maximum avg temp (°C)</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup></td><td rowspan=\"1\" colspan=\"1\">0/3</td><td rowspan=\"1\" colspan=\"1\">13 (1.63)</td><td rowspan=\"1\" colspan=\"1\">3 (0)</td><td rowspan=\"1\" colspan=\"1\">9.3 (0.94)</td><td rowspan=\"1\" colspan=\"1\">41.7 (0.16)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup></td><td rowspan=\"1\" colspan=\"1\">0/3</td><td rowspan=\"1\" colspan=\"1\">13 (2.16)</td><td rowspan=\"1\" colspan=\"1\">3 (0)</td><td rowspan=\"1\" colspan=\"1\">10 (1.41)</td><td rowspan=\"1\" colspan=\"1\">41.8 (0.08)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup></td><td rowspan=\"1\" colspan=\"1\">3/3</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">2.67 (0.47)</td><td rowspan=\"1\" colspan=\"1\">6.7 (0.47)</td><td rowspan=\"1\" colspan=\"1\">41.1 (0.08)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Shimen</td><td rowspan=\"1\" colspan=\"1\">0/2</td><td rowspan=\"1\" colspan=\"1\">12.5 (1.5)</td><td rowspan=\"1\" colspan=\"1\">3 (0)</td><td rowspan=\"1\" colspan=\"1\">9.5 (1.5)</td><td rowspan=\"1\" colspan=\"1\">41.7 (0.05)</td></tr></tbody></table><graphic xlink:href=\"JVI.01104-20-t0002\"/></alternatives><table-wrap-foot><fn fn-type=\"other\" id=\"T2F1\"><label>a</label><p>Values are means (standard deviations).</p></fn></table-wrap-foot></table-wrap><fig id=\"F5\" orientation=\"portrait\" position=\"float\"><label>FIG 5</label><caption><p>Viremia and representative pathological and histopathological changes of the pigs inoculated with chimeric viruses. (A) Viral genome copy numbers in the blood samples of the pigs inoculated with vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>, or the Shimen strain. (B) Viral titers in the blood samples from the pigs inoculated with vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, or the Shimen strain in SK6 cells. (C) Representative pathological changes of various organs from the inoculated pigs. (D) Histopathological changes of various organs from the inoculated pigs. Bar, 50 μm.</p></caption><graphic xlink:href=\"JVI.01104-20-f0005\"/></fig><p>The pigs inoculated with vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, or the Shimen strain displayed CSF-specific pathological changes (including enlargement and hemorrhages in the lymph nodes, petechiae in the kidneys, and infarcts in the spleen) (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). In contrast, no obvious or slight pathological lesions were observed in the pigs inoculated with vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5C</xref>).</p><p>Moreover, histopathological examination of the various organs from the pigs inoculated with vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> showed slight histopathological lesions, including depletion in lymph nodes and reactive hyperplasia in spleens. However, the pigs inoculated with vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup> or vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> showed histopathological lesions similar to those in pigs inoculated with the Shimen strain in tonsils (lymphopenia), kidneys (denaturalization and necrosis in some tubular epithelia), and bladder (epithelial degeneration) (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5D</xref>).</p><p>Taken together, these observations indicate that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain contributes to the ATR but does not alter the virulence of the Shimen strain in pigs, whereas E2<sup>DomainII</sup>-E<sup>rns</sup> of C-strain attenuates the Shimen strain, suggesting that there are different molecular determinants of CSFV ATR and virulence in pigs.</p></sec></sec><sec sec-type=\"discussion\" id=\"s3\"><title>DISCUSSION</title><p>C-strain, which was developed by passaging a highly virulent CSFV strain in rabbits, is adaptive to rabbits and attenuated in pigs. It has been established that for the Shimen strain, acquiring the adaptation to rabbits depends on E2 together with E<sup>rns</sup> or E1 of C-strain. Furthermore, P108 and T109 in E2<sup>DomainI</sup> are crucial amino acids for C-strain to be adaptive to rabbits (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). In the present study, three chimeric viruses containing E2<sup>P108-T109</sup>, E2<sup>DomainI</sup>, or E2<sup>DomainII</sup> of C-strain in the backbone of the non-rabbit-adaptive Shimen mutant vSM-HCLVE<sup>rns</sup> were generated and evaluated for CSFV ATR and virulence in pigs. Our study shows for the first time that E2<sup>P108-T109</sup> or E2<sup>DomainI</sup> but not E2<sup>DomainII</sup> of C-strain could render vSM-HCLVE<sup>rns</sup> adaptive to rabbits, suggesting that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain confers adaptation to rabbits on the Shimen strain. Importantly, we demonstrated that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain mediated the adaptation of CSFV by prompting viral entry during infection in rabbit spleen lymphocytes. However, pathogenicity analysis in pigs showed that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain did not alter the virulence of the Shimen strain in pigs.</p><p>It has been demonstrated that the E2 glycoproteins of pestiviruses are associated with viral tropism. A chimeric pestivirus containing border disease virus or CSFV E2 glycoprotein in the background of bovine viral diarrhea virus (BVDV) alters the tropism to different cells in contrast to the parental BVDV (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Recombinant E2 glycoproteins derived from three different pestiviruses have different abilities to modify BVDV and CSFV to be able to infect permissive cells, suggesting that the E2 glycoprotein is involved in host tropism of pestiviruses at the entry stage (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Our findings demonstrated that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain is responsible for the CSFV ATR, which further indicates the important roles of E2 and E<sup>rns</sup> in the tropism of pestiviruses. However, the amount of replication that takes place in primary rabbit spleen lymphocytes is lower than that in primary swine macrophages. The distinct replication ability of CSFV in rabbits and pigs was observed. The possible explanations are as follows: the rabbit is not the natural host for CSFV, the adaptive ability of CSFV is low in primary rabbit spleen lymphocytes, and there is no cell-cell junction in suspended primary rabbit spleen lymphocytes.</p><p>The mechanism of viral adaptation to a heterogeneous host by passaging in nonsusceptible hosts can be associated with mutations in the viral genome, which can improve entry efficiency and promote viral genome replication and virion assembly and release (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Among these possible mechanisms, entry is the first and most important step for viral infection, and it is determined by the viral surface proteins. It has been reported that the highly conserved residue Q226 is associated with H2N2 and H3N2 viral adaptation to human receptors (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Three mutations in the viral glycoproteins E1 (L216R) and E2 (V388G and M405T) improve the efficiency of hepatitis C virus (HCV) entry into Lunet N mCD81 cells, which are likely to promote exposure of the CD81-binding site (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Mutation of A281 was observed during HIV adaptation in macaques and affected the ability of the HIV-1 Env to use macaque CD4 (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). For pestiviruses, increasing evidence has proved that entry is mediated by several glycoproteins. The E<sup>rns</sup> glycoprotein of pestiviruses attaches to the virion envelope by directly interacting with the E2 glycoprotein to form E<sup>rns</sup>-E2 heterodimers and is indispensable for virus attachment and infection of target cells (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). BVDV and CSFV belong to the genus <italic>Pestivirus</italic>, sharing similar properties (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>), and E1-E2 heterodimers are essential for BVDV entry (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). In our study, E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain was demonstrated to be associated with the adaption of CSFV to rabbits by affecting viral entry during infection, which expands our knowledge of the entry of pestiviruses.</p><p>Typically, live attenuated vaccines developed by blind passage in cell cultures or nonsusceptible hosts are adaptive to the nonsusceptible host while being attenuated in the primary cell or host. For example, the adaptation of African swine fever virus to Vero cells leads to a gradual attenuation of virulence in pigs (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Attenuation of the strain PC22A of porcine epidemic diarrhea virus was achieved by cell culture passage (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Live attenuated vaccines such as C-strain and the lapinized rinderpest virus were developed by passaging a highly virulent strain in rabbits. However, the molecular basis for adaptation and attenuation remains largely unclear. Mutations in the surface glycoprotein E increase the adaptation of tick-borne encephalitis virus to BHK-21 cells and significantly attenuate neuroinvasiveness in adult mice (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). A single mutation in VP2 (A221T) confers the adaptation of infectious pancreatic necrosis virus to CHSE cells and attenuates virulence in Atlantic salmon fry (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). In contrast, D279N and A284T mutations can confer infectious bursal disease virus adaptation in cell culture but do not lead to virus attenuation (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). It was reported that the E2 glycoprotein of the CS vaccine strain derived from the LK VNIIVIM parental vaccine strain (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>) could markedly attenuate the CSFV Brescia strain (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Furthermore, many residues on the E2 were identified as being associated with CSFV virulence, including a discrete epitope (TAVSPTTLR) (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>), W871T, W875D, and V878T in the internal fusion peptide (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>), T830A (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>), and T745I and M979K (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). The N269A/Q substitution, which removed the putative glycosylation site in the E<sup>rns</sup> glycoprotein, decreased virulence of the Brescia strain (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Notably, our results showed that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain, which is associated with CSFV ATR, did not affect CSFV virulence in pigs. The E<sup>rns</sup> glycoprotein of C-strain does not affect the virulence of the chimeric viruses, which may be due to the N269 present on both C-strain and the Shimen strain (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). However, E2 glycoprotein domain II, which is irrelevant to adaptation, alters virulence, further demonstrating that the molecular determinants of the ATR are different from those of attenuation of CSFV in pigs.</p><p>In summary, we demonstrated that E2<sup>P108-T109</sup>-E<sup>rns</sup> of C-strain determines the adaptation of CSFV by facilitating viral entry during infection of rabbits. Furthermore, the molecular determinants of CSFV ATR do not affect virulence in pigs. Our findings contribute to our understanding of the molecular basis for adaptation and attenuation of live attenuated vaccines developed by blind passage in cell cultures or hosts. This study also implies that novel live attenuated vaccines against CSF may be developed by targeting genetic modifications instead of random evolution through blind cell passage.</p></sec><sec sec-type=\"materials|methods\" id=\"s4\"><title>MATERIALS AND METHODS</title><sec id=\"s4.1\"><title>Cells and viruses.</title><p>SK6 and PK-15 cells were cultured with Dulbecco’s modified Eagle’s medium (DMEM) (catalog no. C11995500BT; Gibco) supplemented with 5% heat-inactivated fetal bovine serum (FBS) (catalog no. 10099-141C; Gibco) in a 37°C incubator with 5% CO<sub>2</sub>. HEK293T cells were cultured with DMEM supplemented with 10% FBS. The spleen lymphocytes of rabbits were maintained in Roswell Park Memorial Institute (RPMI) 1640 medium (catalog no. C11875500BT; Gibco) supplemented with 10% FBS, 1% antibiotics-antimycotics (catalog no. 15240-062; Gibco), 1% <sc>l</sc>-glutamine, and 0.20 ng/ml interleukin 2 (IL-2) (catalog no. ab119439; Abcam) (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). Primary swine macrophages were prepared as described previously (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). The CSFV C-strain (GenBank no. <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/AY805221\" assigning-authority=\"genbank\">AY805221</ext-link>) and Shimen strain (GenBank no. <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/AF092448.2\" assigning-authority=\"genbank\">AF092448.2</ext-link>) were used for the construction of infectious cDNA clones.</p></sec><sec id=\"s4.2\"><title>Generation of chimeric viruses.</title><p>Based on the infectious cDNA clone pSM-HCLVE<sup>rns</sup>, which harbors E<sup>rns</sup> of C-strain in the background of the Shimen strain (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>), we took advantage of XhoI and BamHI restriction sites to construct pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>, and pSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> using fusion PCR with the primers listed in <xref rid=\"T3\" ref-type=\"table\">Table 3</xref>. The E2 domains I and II were amplified from pCSFV-HCLV using primers pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>-2F/2R and pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>-2F/2R, respectively. The PCR products were fused with products from pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>-1F/1R and pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>-3F/3R or from pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>-1F/1R and pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>-3F/3R. The PCR products obtained with the pSM-HCLVE<sup>rns</sup> template using pSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>-1F/1R and pSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>-2F/2R primers designed for site-specific mutagenesis were fused with pSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>-1F/2R. Both fusion PCR products and pSM-HCLVE<sup>rns</sup> were digested with XhoI and BamHI and then linked with T4 DNA ligase (catalog no. M0202S; New England BioLabs). All these constructed infectious cDNA clones were identified by PCR, enzyme digestion, and sequencing.</p><table-wrap id=\"T3\" orientation=\"portrait\" position=\"float\"><label>TABLE 3</label><caption><p>Primers used in this study</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Primers</th><th rowspan=\"1\" colspan=\"1\">Sequences (5′–3′)</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>-1F</td><td rowspan=\"1\" colspan=\"1\">CCACCTCGAGATGCTATGTGG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>-1R</td><td rowspan=\"1\" colspan=\"1\">GGAATGCAATGGTTGATGCGCTATTCCAGACCCTGGTTAA</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>-2F</td><td rowspan=\"1\" colspan=\"1\">TTAACCAGGGTCTGGAATAGCGCATCAACCATTGCATTCC</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>-2R</td><td rowspan=\"1\" colspan=\"1\">CTTCGGTTGATGGGTTGGTCCCGTCGAACAGGAGCTCGAATG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>-3F</td><td rowspan=\"1\" colspan=\"1\">CATTCGAGCTCCTGTTCGACGGGACCAACCCATCAACCGAAG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>-3R</td><td rowspan=\"1\" colspan=\"1\">TAGATGGATCCTCTCCACTAT</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>-1F</td><td rowspan=\"1\" colspan=\"1\">CACCTCGAGATGCTATGTGGACG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>-1R</td><td rowspan=\"1\" colspan=\"1\">CCTCAGTTGATGGGTTGGTCCCGTCGAACAGGAGCTCGAATGTCACG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>-2F</td><td rowspan=\"1\" colspan=\"1\">CGTGACATTCGAGCTCCTGTTCGACGGGACCAACCCATCAACTGAGG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>-2R</td><td rowspan=\"1\" colspan=\"1\">CTTCATTTTCCACTGTGGTGGTCACACAATCCATTCTGTGCGG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>-3F</td><td rowspan=\"1\" colspan=\"1\">CCGCACAGAATGGATTGTGTGACCACCACAGTGGAAAATGAAG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>-3R</td><td rowspan=\"1\" colspan=\"1\">GATGGATCCTCTCCACTATAATAG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>-1F</td><td rowspan=\"1\" colspan=\"1\">CCACCTCGAGATGCTATGTGG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSME2 <sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>-1R</td><td rowspan=\"1\" colspan=\"1\">CTCGAATGTCACGGAAGTGGGTAAAGCCCCCTTATGC</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSME2 <sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>-2F</td><td rowspan=\"1\" colspan=\"1\">GCATAAGGGGGCTTTACCCACTTCCGTGACATTCGAG</td></tr><tr><td rowspan=\"1\" colspan=\"1\">pSME2 <sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>-2R</td><td rowspan=\"1\" colspan=\"1\">TAGATGGATCCTCTCCACTAT</td></tr></tbody></table><graphic xlink:href=\"JVI.01104-20-t0003\"/></alternatives></table-wrap><p>Three chimeric viruses were rescued as described previously with a slight modification (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Six micrograms of each plasmid mixed with 6 μl of X-tremeGENE HP DNA transfection reagent (catalog no. 6366546001; Roche) was transfected into SK6 cells cultured in a 6-well plate. The transfected cells were passaged several times, and the supernatants were subjected to detection of the E<sup>rns</sup> glycoprotein by a CSFV antigen test kit (catalog no. 99-40939; IDEXX). The positive samples were identified by RT-PCR, sequencing, and IFA.</p></sec><sec id=\"s4.3\"><title>IFA and virus titration.</title><p>The viral titers in the rabbit spleens or the pig blood samples at different time points were determined as previously described (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). SK6 or PK-15 cells were inoculated with serial 10-fold dilutions of the samples and cultured in 37°C for 48 h. Cold absolute ethanol was used for fixing cells at −20°C for 20 min. The fixed cells were washed three times with phosphate-buffered saline (PBS) and incubated with an anti-E2 polyclonal antibody at 37°C for 2 h (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). After five washes with PBS, cells were incubated with Alexa Fluor 488 goat anti-rabbit IgG (catalog no. A11034; Roche) at 37°C for 1 h and then washed five times with PBS. Then, the cells were stained with 0.5 μg/ml 4′,6-diamidino-2-phenylindole (DAPI; catalog no. C0060; Solarbio) for 15 min and washed three times with PBS. The cells were analyzed for green fluorescence using an inverted fluorescence microscope (EVOS FL; Life Technologies). The viral titers were calculated according to the Reed-Muench method and expressed as median tissue culture infective doses (TCID<sub>50</sub>) per milliliter (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>).</p></sec><sec id=\"s4.4\"><title>Growth curves of the rescued viruses.</title><p>To determine the multistep growth curves of the rescued viruses, PK-15 cells cultured in 24-well plates were infected with these rescued viruses at a multiplicity of infection (MOI) of 0.1. Two hours later, the supernatants were removed, and the cells were washed three times with PBS. Then, fresh DMEM supplemented with 2% FBS was added to each well, and the cells were cultured at 37°C and 5% CO<sub>2</sub>. The cells were harvested at 12-h intervals and used to determine viral titers.</p></sec><sec id=\"s4.5\"><title>RNA extraction and RT-qPCR.</title><p>Total RNA from tissues or cells was extracted using RNAiso Plus (catalog no. 9109; TaKaRa) according to the manufacturer’s protocol. cDNA synthesis was processed in a 20-μl volume with avian myeloblastosis virus (AMV) reverse transcriptase XL (catalog no. 2621; TaKaRa). The copy numbers of the CSFV genome were determined using a previously described RT-qPCR assay (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>).</p></sec><sec id=\"s4.6\"><title>Luciferase assay.</title><p>The reporter virus vHCLV-NanoLuc expressing the NanoLuc protein fused with the N<sup>pro</sup> protein (between the amino acids 13 and 14) based on C-strain was generated. Rabbit spleen lymphocytes infected with vHCLV-NanoLuc or C-strain or treated with DMEM only were lysed with 100 μl of passive lysis buffer (catalog no. N1150; Promega) and incubated on a shaker for 1 h at 4°C. The supernatants were collected by centrifuging at 12,000 × <italic>g</italic> for 10 min at 4°C. NanoLuc activities were measured with EnVision multilabel plate readers (PerkinElmer).</p></sec><sec id=\"s4.7\"><title>Flow cytometry.</title><p>The spleens from the rabbits inoculated with C-strain were collected after rabbits were euthanized at 3 dpi, and spleen cells were obtained by smearing the organs on 200-mesh copper wire mesh with RPMI 1640 medium. The red blood cells were lysed using red blood cell lysis buffer (catalog no. R1010; Solarbio), and the lymphocyte suspensions were washed with PBS. The following antibodies were used for flow cytometry, according to the manufacturers’ instructions: goat anti-rabbit IgM μ chain preadsorbed to secondary antibody (DyLight 488) (catalog no. ab98454; Abcam) for isolating B cells, mouse anti-rabbit T lymphocytes (catalog no. MCA800GA; Bio-Rad) for isolating T cells, and Alexa Fluor 633 goat anti-mouse IgG (heavy plus light chain [H+L]) (catalog no. A21052; Invitrogen) as the secondary antibody. Stained mononuclear cells from the spleens of the rabbits infected with C-strain were added to cytometry tubes and sorted with a high-speed cell sorter (MoFlo XDP; Beckman Coulter).</p></sec><sec id=\"s4.8\"><title>Infection of primary cells from rabbits or pigs with chimeric viruses.</title><p>Primary rabbit spleen lymphocytes or swine macrophages were isolated from the animals and infected with parental or chimeric viruses with an MOI of 0.01. After 2 h of incubation in a CO<sub>2</sub> incubator, the infected cells were washed three times with PBS and further incubated for 4, 24, 48, or 72 h (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). The viral genome copy numbers or the viral titers at different time points were measured.</p></sec><sec id=\"s4.9\"><title>Preparation of pseudotyped viruses.</title><p>The DNA fragments encoding the last 60 amino acids of the C, E<sup>rns</sup>, E1, and E2 proteins from the infectious clones of C-strain, the Shimen strain, pSME1-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, pSME1-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>, and pSME1E2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> were amplified and inserted into the pCAGGS vector (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B48\" ref-type=\"bibr\">48</xref>). Pseudotyped viruses were packaged by cotransfection into HEK293T cells with pNL4.3-GFP-ΔEnv and pCAGGS-HCLVE<sup>rns</sup>E1E2, pCAGGS-SME<sup>rns</sup>E1E2, pCAGGS-SME1-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, pCAGGS-SME1-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>, or pCAGGS-SME1E2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup> (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). At 48 h posttransfection (hpt), the supernatants were collected and centrifuged for concentration using Amicon Ultra centrifugal filters (catalog no. UFC901096; Amicon). HIV p24 antigen content was assessed by enzyme-linked immunoassay (ELISA) (catalog no. BF06203; Biodragon Immunotechnologies).</p></sec><sec id=\"s4.10\"><title>Experimental infection of rabbits with chimeric viruses.</title><p>Twenty-four 14-week-old New Zealand White rabbits were divided into 5 groups and inoculated intravenously (i.v.) via the marginal ear vein with the viruses indicated in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. The rectal temperature of all rabbits was monitored every 6 h from 24 to 72 hpi as described previously (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Four rabbits were selected randomly from each group and euthanized at 3 hpi. The viral genome copy numbers in the spleens of the rabbits were determined and virus isolation was performed as described previously (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). The anti-E2 antibodies of the remainder rabbits were tested at 10 hpi using the classical swine fever virus antibody test kit (catalog no. 99-43220; IDEXX) according to the manufacturer's manuals.</p></sec><sec id=\"s4.11\"><title>Experimental infection of rabbits with pseudotyped viruses.</title><p>The rabbits were inoculated i.v. with different chimeric pseudotyped viruses. Spleen lymphocytes were isolated at 48 hpi. Mouse anti-coral green fluorescent protein (cGFP)-tagged monoclonal antibody (MAb) (catalog no. A00185; GenScript) was used to detect the expression of EGFP in lymphocytes, and irrelevant mouse IgG (catalog no. A7028; Beyotime) was used as a negative control. As the secondary immunoreagent, fluorescein isothiocyanate-labeled goat anti-mouse IgG (catalog no. A11029; Invitrogen) was used. All antibodies mentioned were diluted at 1:200 in PBS. For flow cytometry analysis, 10<sup>6</sup> cells in each sample were permeabilized with 0.15% Triton X-100. The cells were further incubated with primary antibody for 1 h and washed three times for 5 min with PBS. Then the cells were incubated with secondary antibody for 45 min and washed three times for 5 min with PBS. The fluorescence signal was analyzed with an Accuri C6 Plus flow cytometer (BD Biosciences).</p></sec><sec id=\"s4.12\"><title>Experimental infection of pigs with chimeric viruses.</title><p>To assess the virulence of chimeric viruses relative to the Shimen strain, 5-week-old healthy pigs were randomly divided into 4 groups (groups 1 to 3, <italic>n </italic>= 3; group 4, <italic>n </italic>= 2), and each group was housed in an individual room. Group 1 was inoculated i.m. with vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, group 2 was inoculated i.m. with vSM-E2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, group 3 was inoculated i.m. with vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup>, and group 4 was inoculated i.m. with the Shimen strain. The inoculation dose of the virus for each group was 10<sup>5</sup> TCID<sub>50</sub> (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). Clinical signs and rectal temperature were monitored daily, and anticoagulated blood samples of pigs were collected every 3 or 4 days. CSFV RNA was determined in anticoagulated blood samples by RT-qPCR. The chimeric viruses in the blood samples were titrated in SK6 cells using IFA.</p></sec><sec id=\"s4.13\"><title>Pathological examinations.</title><p>Macroscopic and microscopic pathological changes of the pig tissues were examined as described previously (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). The tonsils, lymph nodes, kidneys, bladders, and spleens were fixed with 10% formalin and then embedded in paraffin wax. For histopathological examinations, prepared tissue sections were stained with hematoxylin and eosin (H&E).</p></sec><sec id=\"s4.14\"><title>Immunohistochemistry.</title><p>The spleens of rabbits inoculated with the Shimen strain, C-strain, vSME2<sup>L108P-I109T</sup>-HCLVE<sup>rns</sup>, vSM-HCLVE<sup>rns</sup>E2<sup>DomainI</sup>, or vSM-HCLVE<sup>rns</sup>E2<sup>DomainII</sup> were subjected to immunohistochemistry examinations using an anti-CSFV E2 antibody as described previously (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>).</p></sec><sec id=\"s4.15\"><title>Animal ethics.</title><p>All animal experiments were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of Heilongjiang Province of the People's Republic of China. The protocols were approved by the Committee on the Ethics of Animal Experiments of Harbin Veterinary Research Institute (HVRI) of the Chinese Academy of Agricultural Sciences (CAAS) (approval numbers SY-2018-Ra-002-01, SY-2018-Ra-02, and SY-2019-RA-004 for rabbit experiments and SY-2019-SW-033 for pig experiments).</p></sec><sec id=\"s4.16\"><title>Statistical analysis.</title><p>SPSS 22.0 software was used to analyze all data. An unadjusted <italic>P</italic> value of <0.05 was considered significant.</p></sec></sec></body><back><ack><title>ACKNOWLEDGMENTS</title><p>We thank Yonghui Zheng (Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences) for providing the plasmid pNL4.3-GFP-ΔEnv. We thank Ryan H. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">J Virol</journal-id><journal-id journal-id-type=\"iso-abbrev\">J. Virol</journal-id><journal-id journal-id-type=\"hwp\">jvi</journal-id><journal-id journal-id-type=\"pmc\">jvi</journal-id><journal-id journal-id-type=\"publisher-id\">JVI</journal-id><journal-title-group><journal-title>Journal of Virology</journal-title></journal-title-group><issn pub-type=\"ppub\">0022-538X</issn><issn pub-type=\"epub\">1098-5514</issn><publisher><publisher-name>American Society for Microbiology</publisher-name><publisher-loc>1752 N St., N.W., Washington, DC</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32611752</article-id><article-id pub-id-type=\"pmc\">PMC7431807</article-id><article-id pub-id-type=\"publisher-id\">00602-20</article-id><article-id pub-id-type=\"doi\">10.1128/JVI.00602-20</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Pathogenesis and Immunity</subject></subj-group></article-categories><title-group><article-title>Axl Deficiency Promotes the Neuroinvasion of Japanese Encephalitis Virus by Enhancing IL-1α Production from Pyroptotic Macrophages</article-title><alt-title alt-title-type=\"running-head\">Axl Deficiency Promotes Neuroinvasion of Japanese Encephalitis Virus</alt-title><alt-title alt-title-type=\"short-authors\">Wang et al.</alt-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Zhao-Yang</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhen</surname><given-names>Zi-Da</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Fan</surname><given-names>Dong-Ying</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-0632-2807</contrib-id><name><surname>Qin</surname><given-names>Cheng-Feng</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>b</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Han</surname><given-names>Dai-Shu</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>c</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhou</surname><given-names>Hong-Ning</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>d</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Wang</surname><given-names>Pei-Gang</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-2567-0380</contrib-id><name><surname>An</surname><given-names>Jing</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>a</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>e</sup></xref></contrib><aff id=\"aff1\"><label>a</label><addr-line>Department of Microbiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China</addr-line></aff><aff id=\"aff2\"><label>b</label><addr-line>Department of Virology, State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China</addr-line></aff><aff id=\"aff3\"><label>c</label><addr-line>Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China</addr-line></aff><aff id=\"aff4\"><label>d</label><addr-line>Yunnan Provincial Center of Arbovirus Research, Yunnan Provincial Key Laboratory of Vector-borne Diseases Control and Research, Yunnan Institute of Parasitic Diseases, Pu'er, Yunnan, China</addr-line></aff><aff id=\"aff5\"><label>e</label><addr-line>Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China</addr-line></aff></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Dutch</surname><given-names>Rebecca Ellis</given-names></name><role>Editor</role><aff>University of Kentucky College of Medicine</aff></contrib></contrib-group><author-notes><corresp id=\"cor1\">Address correspondence to Pei-Gang Wang, <email>pgwang@ccmu.edu.cn</email>, or Jing An, <email>anjing@ccmu.edu.cn</email>.</corresp><fn fn-type=\"other\"><p><bold>Citation</bold> Wang Z-Y, Zhen Z-D, Fan D-Y, Qin C-F, Han D-S, Zhou H-N, Wang P-G, An J. 2020. Axl deficiency promotes the neuroinvasion of Japanese encephalitis virus by enhancing IL-1α production from pyroptotic macrophages. J Virol 94:e00602-20. <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.1128/JVI.00602-20\">https://doi.org/10.1128/JVI.00602-20</ext-link>.</p></fn></author-notes><pub-date pub-type=\"epreprint\"><day>1</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><month>9</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>94</volume><issue>17</issue><elocation-id>e00602-20</elocation-id><history><date date-type=\"received\"><day>2</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Wang et al.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Wang et al.</copyright-holder><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">Creative Commons Attribution 4.0 International license</ext-link>.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"JVI.00602-20.pdf\"/><abstract abstract-type=\"precis\"><p>Japanese encephalitis virus (JEV) is a mosquito-borne flavivirus that causes Japanese encephalitis (JE), the most commonly diagnosed viral encephalitis worldwide. The fatality rate of JE is 20%, and nearly half of the surviving patients develop neuropsychiatric sequelae. Axl is a receptor tyrosine kinase that plays multiple roles in flaviviral infections. Currently, the involvement of Axl in JEV infection remains enigmatic. In this study, we demonstrate that Axl impedes the pathogenesis of severe JE in mice by maintaining blood-brain-barrier (BBB) integrity and restricting viral neuroinvasion. Furthermore, serum IL-1α is a key mediator of this process and is primarily released by JEV-infected pyroptotic macrophages to elicit BBB breakdown, while an IL-1α antagonist can effectively reduce the incidence of severe JE. Our work uncovers the protective role of Axl in antagonizing severe JE and shows that the use of an IL-1α antagonist may be a promising tactic to prevent severe JE.</p></abstract><abstract><title>ABSTRACT</title><p>Japanese encephalitis virus (JEV) is a flavivirus that causes Japanese encephalitis (JE), which has an unclear pathogenesis. Despite vaccination, thousands of deaths attributed to JE are reported annually. In this study, we report that mice deficient for Axl, a receptor tyrosine kinase that plays multiple roles in flaviviral infection, displayed greater mortality upon JEV infection. The effect of Axl deficiency on JEV infection was mediated by markedly elevated serum interleukin-1α (IL-1α) levels, which devastated the blood-brain-barrier and promoted viral neuroinvasion within 24 h postinfection. Using an <italic>in situ</italic> infection model, we showed that dead macrophages were the primary source of observed increased serum IL-1α levels. Axl deficiency enhanced cell death and caused pyroptosis in 80% of JEV-infected macrophages by disrupting phosphatidylinositol 3-kinase (PI3K)-Akt signaling. Intriguingly, the primary effector released by pyroptotic macrophages in our model was IL-1α rather than IL-1β. Finally, we assessed the effect of an IL-1α antagonist and demonstrated that it effectively prevented the incidence of JE. Our results indicate that Axl plays a protective role in JEV infection, identify IL-1α released by pyroptotic macrophages as a crucial factor promoting JEV neuroinvasion, and suggest that an IL-1α antagonist may be a candidate for JE therapy.</p><p><bold>IMPORTANCE</bold> Japanese encephalitis virus (JEV) is a mosquito-borne flavivirus that causes Japanese encephalitis (JE), the most commonly diagnosed viral encephalitis worldwide. The fatality rate of JE is 20%, and nearly half of the surviving patients develop neuropsychiatric sequelae. Axl is a receptor tyrosine kinase that plays multiple roles in flaviviral infections. Currently, the involvement of Axl in JEV infection remains enigmatic. In this study, we demonstrate that Axl impedes the pathogenesis of severe JE in mice by maintaining blood-brain-barrier (BBB) integrity and restricting viral neuroinvasion. Furthermore, serum IL-1α is a key mediator of this process and is primarily released by JEV-infected pyroptotic macrophages to elicit BBB breakdown, while an IL-1α antagonist can effectively reduce the incidence of severe JE. Our work uncovers the protective role of Axl in antagonizing severe JE and shows that the use of an IL-1α antagonist may be a promising tactic to prevent severe JE.</p></abstract><kwd-group><title>KEYWORDS</title><kwd>Axl</kwd><kwd>Japanese encephalitis virus</kwd><kwd>interleukin-1α</kwd><kwd>macrophages</kwd><kwd>pyroptosis</kwd></kwd-group><funding-group><award-group id=\"award1\"><funding-source><institution-wrap><institution>Key Project of Beijing Natural Science Foundation B</institution></institution-wrap></funding-source><award-id>KZ201810025035</award-id><principal-award-recipient><name><surname>An</surname><given-names>Jing</given-names></name></principal-award-recipient></award-group><award-group id=\"award2\"><funding-source><institution-wrap><institution>Scientific Research Plan of Beijing Municipal Education Committe</institution></institution-wrap></funding-source><award-id>KM201710025002</award-id><principal-award-recipient><name><surname>Wang</surname><given-names>Peigang</given-names></name></principal-award-recipient></award-group><award-group id=\"award3\"><funding-source><institution-wrap><institution>National Natural Science Foundation of China (NSFC)</institution><institution-id>https://doi.org/10.13039/501100001809</institution-id></institution-wrap></funding-source><award-id>81671971</award-id><principal-award-recipient><name><surname>An</surname><given-names>Jing</given-names></name></principal-award-recipient></award-group><award-group id=\"award4\"><funding-source><institution-wrap><institution>National Natural Science Foundation of China (NSFC)</institution><institution-id>https://doi.org/10.13039/501100001809</institution-id></institution-wrap></funding-source><award-id>81871641</award-id><principal-award-recipient><name><surname>Wang</surname><given-names>Peigang</given-names></name></principal-award-recipient></award-group><award-group id=\"award5\"><funding-source><institution-wrap><institution>National Natural Science Foundation of China (NSFC)</institution><institution-id>https://doi.org/10.13039/501100001809</institution-id></institution-wrap></funding-source><award-id>81972979</award-id><principal-award-recipient><name><surname>An</surname><given-names>Jing</given-names></name></principal-award-recipient></award-group><award-group id=\"award6\"><funding-source><institution-wrap><institution>NSFC | National Natural Science Foundation of China-Yunnan Joint Fund (NSFC-Yunnan Joint Fund)</institution><institution-id>https://doi.org/10.13039/501100011002</institution-id></institution-wrap></funding-source><award-id>U1902210</award-id><principal-award-recipient><name><surname>An</surname><given-names>Jing</given-names></name></principal-award-recipient></award-group><award-group id=\"award7\"><funding-source><institution-wrap><institution>NSFC | National Natural Science Foundation of China-Yunnan Joint Fund (NSFC-Yunnan Joint Fund)</institution><institution-id>https://doi.org/10.13039/501100011002</institution-id></institution-wrap></funding-source><award-id>U1602223</award-id><principal-award-recipient><name><surname>Zhou</surname><given-names>Hongning</given-names></name></principal-award-recipient></award-group></funding-group><counts><fig-count count=\"14\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"43\"/><page-count count=\"26\"/><word-count count=\"13978\"/></counts><custom-meta-group><custom-meta><meta-name>cover-date</meta-name><meta-value>September 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>INTRODUCTION</title><p>Japanese encephalitis virus (JEV), a member of the same genus (<italic>Flavivirus</italic>) as dengue virus (DENV), Zika virus (ZIKV), and West Nile virus (WNV), is transmitted by <italic>Culex</italic> mosquitos and causes Japanese encephalitis (JE), which is endemic in 24 countries and is the most important viral encephalitis worldwide (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Although vaccination has dramatically reduced the incidence of JE, approximately 67,900 cases of JE still occur annually (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>). Unfortunately, even after surviving the encephalitis, nearly half of patients bear permanent neuropsychiatric sequelae (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Moreover, due to a number of unfavorable factors, such as insufficient vaccination coverage and global warming, the incidence of JE is still increasing in some developing countries (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Nevertheless, to date, we have neither enough knowledge regarding the mechanism of JEV infection nor a specific therapy for JE.</p><p>As a member of the receptor tyrosine kinase family <underline>T</underline>yro3, <underline>A</underline>xl, and <underline>M</underline>ertk (TAM), which recognizes phosphatidylserines (PtdSers) exposed on the surface of apoptotic cells and enveloped viruses (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>), Axl has been proposed to be an entry receptor for various enveloped viruses, such as Lassa virus (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>), vaccinia virus (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>), and ebolavirus (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Regarding flaviviruses, a group to which JEV belongs, Axl has been thought to serve as an entry receptor for ZIKV, DENV, and WNV (<xref rid=\"B9\" ref-type=\"bibr\">9</xref><xref ref-type=\"bibr\" rid=\"B10\">–</xref><xref rid=\"B12\" ref-type=\"bibr\">12</xref>). However, accumulating evidence has shown that its roles in flaviviral infection are diverse and vary based on the virus and host type. For example, Axl mediates DENV entry via Gas6 bridging (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>), while Axl knockout has no impact on WNV replication in target organs but increases its neuroinvasion (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). The involvement of Axl in ZIKV infection is more complicated and controversial. Although Axl promotes ZIKV infection in many cell types <italic>in vitro</italic>, its knockout has no impact on ZIKV replication or pathogenesis in mouse models, human neural progenitor cells, or cerebral organoids (<xref rid=\"B15\" ref-type=\"bibr\">15</xref><xref ref-type=\"bibr\" rid=\"B16\">–</xref><xref rid=\"B17\" ref-type=\"bibr\">17</xref>). Recently, Axl knockout mice were shown to be resistant to ZIKV pathogenesis in an age-dependent manner, corresponding to lower prointerleukin-1β (pro-IL-1β) production and decreased apoptosis in microglia (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). The complexity of the contributions of Axl to flaviviral infection is potentially associated with its versatile functions. Axl not only mediates the removal of dead cells by phagocytes and maintains immune and inflammatory homeostasis but also regulates all forms of programed cell death (<xref rid=\"B19\" ref-type=\"bibr\">19</xref><xref ref-type=\"bibr\" rid=\"B20\">–</xref><xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Therefore, Axl has additional roles beyond that of a receptor in flaviviral infection, and its roles in infection need to be investigated on an individual basis for each flavivirus.</p><p>In this study, using Axl-deficient mice, we discovered that Axl suppresses JE pathogenesis by inhibiting IL-1α production from pyroptotic macrophages induced by JEV. IL-1α mediates the early neuroinvasion of JEV by disrupting blood-brain-barrier (BBB) integrity, and an IL-1α antagonist was shown to potently reduce the incidence of severe JE. The results of our study revealed the crucial roles of Axl and IL-1α in JEV infection and suggest that IL-1α antagonists may be candidate drugs for JE therapy.</p></sec><sec sec-type=\"results\" id=\"s2\"><title>RESULTS</title><sec id=\"s2.1\"><title>Axl deficiency promotes higher mortality after JEV infection.</title><p>To study the role of Axl in JEV infection, 4-week-old Axl-deficient mice (Axl<sup>−/−</sup>) and their littermate control mice (Axl<sup>+/−</sup>) received an intraperitoneal (i.p.) injection of different doses of JEV (10<sup>4</sup>, 10<sup>5</sup>, and 10<sup>6</sup> PFU per mouse). Axl<sup>+/−</sup> mouse littermates were used as controls because they are more comparable with respect to genetic background, age, and nutrition status, all of which have important impacts on mortality upon JEV infection according to our experience. At 6 to 8 days postinfection (dpi), 17% to 91% of mice, which depended on the dose, began to lose weight and became sluggish. As the disease progressed, these mice gradually manifested ruffled fur, hunched back, and limb paralysis and finally died from encephalitis. At a dose of 10<sup>4</sup> PFU of JEV, the mortality of the Axl<sup>−/−</sup> mice was 50% (11/22), which was significantly higher than the 17% (2/12) observed in the Axl<sup>+/−</sup> mice (<italic>P</italic> < 0.05) (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1A</xref>), demonstrating that the Axl<sup>−/−</sup> mice were more susceptible to JEV infection. As the infection dose increased, the mortality gap between the Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice decreased and vanished when 10<sup>6</sup> PFU of JEV was injected, which led to death in more than 90% of mice (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1B</xref> and <xref ref-type=\"fig\" rid=\"F1\">C</xref>). Compared to the Axl<sup>+/−</sup> mice, the Axl<sup>−/−</sup> mice displayed faster and more severe body weight loss after disease onset at all doses of JEV (<italic>P</italic> < 0.01 for 10<sup>4</sup> and 10<sup>5</sup> PFU, <italic>P</italic> = 0.06 for 10<sup>6</sup> PFU) (<xref ref-type=\"fig\" rid=\"F1\">Fig. 1D</xref> to <xref ref-type=\"fig\" rid=\"F1\">F</xref>). These results indicated that Axl may be a protective factor against JE pathogenesis that improves the survival of mice after JEV infection.</p><fig id=\"F1\" orientation=\"portrait\" position=\"float\"><label>FIG 1</label><caption><p>Effects of Axl on Japanese encephalitis virus (JEV) infection. Four-week-old Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice received an intraperitoneal (i.p.) injection of different doses of JEV (10<sup>4</sup>, 10<sup>5</sup>, and 10<sup>6</sup> PFU). (A to C) Survival curves of Axl<sup>+/−</sup> and Axl<sup>−/−</sup> mice infected with 10<sup>4</sup> PFU (A) (<italic>n</italic> = 12 for Axl<sup>+/−</sup>, <italic>n</italic> = 22 for Axl<sup>−/−</sup>), 10<sup>5</sup> PFU (B) (<italic>n</italic> = 19 for Axl<sup>+/−</sup>, <italic>n</italic> = 14 for Axl<sup>−/−</sup>), and 10<sup>6</sup> PFU of JEV (C) (<italic>n</italic> = 11 for Axl<sup>+/−</sup>, <italic>n</italic> = 19 for Axl<sup>−/−</sup>). The survival curves were compared by log-rank test. (D to F) Body weight changes of Axl<sup>+/−</sup> and Axl<sup>−/−</sup> mice infected with 10<sup>4</sup> PFU (D) (<italic>n</italic> = 26 for Axl<sup>+/−</sup>, <italic>n</italic> = 19 for Axl<sup>−/−</sup>), 10<sup>5</sup> PFU (E) (<italic>n</italic> = 7 for Axl<sup>+/−</sup>, <italic>n</italic> = 9 for Axl<sup>−/−</sup>), and 10<sup>6</sup> PFU of JEV (F) (<italic>n</italic> = 11 for Axl<sup>+/−</sup>, <italic>n</italic> = 19 for Axl<sup>−/−</sup>). The body weight changes were compared by two-way ANOVA. Data are expressed as means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ns, no significance (<italic>P</italic> > 0.05); each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0001\"/></fig><p>One possible explanation for these results is that Axl inhibited JEV replication. To test this hypothesis, we measured viral RNA loads in serum and peripheral organs and analyzed JEV infection in primary macrophages, which are reported to be the primary target cells during JEV infection. After i.p. infection with JEV, viral RNA was detectable in serum at 6 h postinfection (hpi) and lasted for at least 7 days (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2A</xref>). The Axl<sup>−/−</sup> and control mice demonstrated similar levels of viral RNA in serum (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2A</xref>) and all assayed peripheral organs at different time points (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2B</xref> to <xref ref-type=\"fig\" rid=\"F2\">D</xref>). Peritoneal macrophages isolated from the Axl<sup>−/−</sup> and control mice were infected with an equivalent dose of JEV <italic>in vitro</italic> but showed no difference regarding viral infection rate and replication dynamics (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2E</xref> to <xref ref-type=\"fig\" rid=\"F2\">G</xref>). To further test if Axl is only a restrictive factor in the brain, we injected JEV directly into mouse brains. All mice died within 7 days (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2H</xref>), which was accompanied by similar body weight loss (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2I</xref>) and viral RNA loads in the brain (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2J</xref>). Thus, Axl is not likely to be a restrictive factor that directly inhibits JEV replication. Considering that the Axl<sup>−/−</sup> mice had similar viral loads in peripheral organs but were at a greater risk of developing encephalitis, Axl possibly plays a protective role by regulating the BBB permeability for JEV.</p><fig id=\"F2\" orientation=\"portrait\" position=\"float\"><label>FIG 2</label><caption><p>Effects of Axl on peripheral and brain JEV infection. (A to D) Four-week-old Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice received an i.p. injection of 10<sup>4</sup> PFU of JEV. Viral loads in serum (A), spleen (B), liver (C), and kidney (D) at the indicated time points postinfection. LOD (green dashed line), limit of detection. Each dot denotes a mouse, and the viral loads were compared by two-way ANOVA and multiple <italic>t</italic> tests. (E to G) Primary peritoneal macrophages were infected with JEV at an MOI of 1. (E) Immunofluorescent (IF) staining of JEV antigens (green) in peritoneal macrophages at 24 hpi. (F) Infection rates of peritoneal macrophages at 24 hpi. <italic>n</italic> = 4 for each group, and the infection rates were compared by unpaired <italic>t</italic> test. (G) Viral loads in supernatants of peritoneal macrophages. <italic>n</italic> = 4 for each group, and the data were compared by two-way ANOVA and multiple <italic>t</italic> tests. (H to J) Four-week-old Axl<sup>−/−</sup> mice (<italic>n</italic> = 8) and Axl<sup>+/−</sup> mice (<italic>n</italic> = 12) were intracerebrally injected with 10<sup>3</sup> PFU of JEV. (H) Survival curves were compared by log-rank test. (I) Body weight changes were compared by two-way ANOVA. (J) Viral loads in brain at 6 dpi, each dot denotes a mouse, and the viral loads were compared by unpaired <italic>t</italic> test. All the data are expressed as the means ± SEM; ns, no significance (<italic>P</italic> > 0.05); each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0002\"/></fig></sec><sec id=\"s2.2\"><title>Axl deficiency increases the probability of JEV brain infection.</title><p>To investigate if Axl protects brains from JEV infection, we measured the brain viral loads of the Axl<sup>−/−</sup> and control mice from 1 to 7 dpi after i.p. infection with JEV. Viral RNA was barely detectable in mouse brains until 5 dpi (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3A</xref>). The Axl<sup>−/−</sup> mice displayed a higher positive rate of JEV and greater viral loads in the brain than control mice (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3A</xref>), indicating that Axl<sup>−/−</sup> mouse brains were more accessible to JEV. We further observed the target cell usage of JEV in mouse brains (cerebral cortex) through immunofluorescence (IF) staining of NeuN (a specific marker for neurons), GFAP (a specific marker for astrocytes), Iba1 (a specific marker for microglia), and JEV antigens, but no differences between the Axl<sup>−/−</sup> and control mice were observed (data not shown), suggesting the protective role of Axl is achieved by inhibiting JEV entry into the brain rather than altering its cell tropism in the brain.</p><fig id=\"F3\" orientation=\"portrait\" position=\"float\"><label>FIG 3</label><caption><p>Effects of Axl on JEV neuroinvasion and blood-brain barrier (BBB) permeability. Four-week-old Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice received an i.p. injection of 10<sup>4</sup> PFU of JEV (A and E to K) or 5 × 10<sup>4</sup> PFU of Rluc-JEV for bioluminescent imaging (BLI) (B to D). (A) Shows viral loads in brains; each dot denotes a mouse, and viral loads were compared by two-way ANOVA and multiple <italic>t</italic> tests. (B) Representative BLI image of Rluc-JEV infection. (C) Quantification of the BLI signal intensity in peritoneal cavity; <italic>n</italic> = 15 for each group, and the data were compared by two-way ANOVA and multiple <italic>t</italic> tests. (D) Cumulative percentage of the mice with Rluc-JEV entry into brain; <italic>n</italic> = 15 for each group, and the data were compared by Fisher’s exact test. (E) Representative image of Evans blue (EB) leakage into brain parenchyma. (F) Quantification of EB leakage into brain parenchyma after JEV infection; <italic>n</italic> = 5 to 8 for each group, and the data were compared by two-way ANOVA and multiple <italic>t</italic> tests. (G to I) Correlation analysis of BBB permeability and brain viral load in Axl<sup>+/−</sup> mice (G), Axl<sup>−/−</sup> mice (H), and mixed mice (I); each dot denotes a mouse, and the data were analyzed by linear regression model. (J) Representative image of sodium fluorescein (NaF) leakage into brain parenchyma. (K) Quantification of NaF leakage into brain parenchyma under uninfected and JEV-infected (7 dpi) conditions; each dot denotes a mouse, and the data were compared by multiple <italic>t</italic> tests. All the data are expressed as the means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ns, no significance (<italic>P</italic> > 0.05); each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0003\"/></fig><p>To confirm the central nervous system (CNS) entry-inhibition role of Axl, mice received an i.p. injection of 5 × 10<sup>4</sup> PFU of Renilla luciferase-harboring JEV (Rluc-JEV), and the luciferase substrate was injected at different time points to allow viral replication and spread to be continuously monitored via bioluminescent imaging (BLI). Rluc-JEV was first detected in the abdomens (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3B</xref>), the site of infection, in all mice by 2 dpi and later in the brains of a few mice (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3B</xref>). The Axl<sup>−/−</sup> and control mice exhibited no difference in signal intensity in the abdomens (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3C</xref>). However, more Axl<sup>−/−</sup> mice showed luciferase-positive brains (7/15) than the control mice (1/15) by 10 dpi (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3D</xref>), in agreement with the results obtained with the wild-type JEV. Taken together, the results show that Axl deficiency remarkably increased the probability of JEV entry into the brain.</p></sec><sec id=\"s2.3\"><title>Axl deficiency increases BBB permeability at an early stage of JEV infection.</title><p>To understand the mechanism underlying the protective role of Axl against JE pathogenesis, we utilized an Evans blue (EB) leakage test to gauge the permeability of the BBB after JEV infection, which is the key structure preventing viral entry into the brain. In the uninfected groups, both the Axl<sup>−/−</sup> and control mice showed very little and comparable EB leakage into the brain parenchyma, indicating that Axl deficiency has no effect on BBB permeability under physiological conditions (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3E</xref> and <xref ref-type=\"fig\" rid=\"F3\">F</xref>, 0 dpi). After JEV infection, EB leakage into the brain parenchyma significantly increased and reached the first peak at 2 dpi, when the Axl<sup>−/−</sup> mice showed significantly enhanced EB leakage compared with that observed in control mice, indicating that Axl deficiency promoted the BBB permeability at the early phase of infection (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3E</xref> and <xref ref-type=\"fig\" rid=\"F3\">F</xref>, 2 dpi). After 2 dpi, both the Axl<sup>−/−</sup> and control mice showed gradually decreased EB leakage into the brain parenchyma until 6 dpi, when EB leakage in the Axl<sup>−/−</sup> mice increased again, while the control mice showed a continual decrease in EB leakage (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3E</xref> and <xref ref-type=\"fig\" rid=\"F3\">F</xref>, 2 to 7 dpi). The Axl<sup>−/−</sup> mice showed significantly enhanced EB leakage compared with the control mice at 6 and 7 dpi, indicating that Axl deficiency also promoted BBB permeability at the late phase of infection (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3E</xref> and <xref ref-type=\"fig\" rid=\"F3\">F</xref>, 6 and 7 dpi). Moreover, in both the Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice, there was a strong positive linear correlation between BBB permeability and brain viral load (slope = 0.6917, R<sup>2</sup> = 0.37, <italic>P</italic> < 0.05 for Axl<sup>−/−</sup> mice; slope = 0.7484, R<sup>2</sup> = 0.50, <italic>P</italic> < 0.05 for Axl<sup>+/−</sup> mice; and slope = 0.7375, R<sup>2</sup> = 0.55, <italic>P</italic> < 0.001 for all mice) (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3G</xref> to <xref ref-type=\"fig\" rid=\"F3\">I</xref>), suggesting that greater BBB permeability facilitates JEV entry into brains. We also used a fluorescein leakage test to confirm the results obtained in the EB leakage test. In the uninfected groups, all mice showed slight but comparable fluorescein leakage into the brain parenchyma. In contrast, the Axl<sup>−/−</sup> mice showed significantly greater fluorescein leakage than the Axl<sup>+/−</sup> mice after JEV infection (7 dpi) (<xref ref-type=\"fig\" rid=\"F3\">Fig. 3J</xref> and <xref ref-type=\"fig\" rid=\"F3\">K</xref>). These data indicate that Axl deficiency significantly increases BBB permeability and consequently promotes JEV entry into brain.</p><p>In the results described above, we noted that the Axl<sup>−/−</sup> mice displayed greater BBB permeability at an early stage of JEV infection, which is much earlier than the appearance of encephalitis. To confirm this phenomenon and investigate the underlying mechanisms, we elucidated the dynamic changes in the expression and distribution of three important tight junction (TJ) proteins in the brain, namely, claudin-5, occludin, and ZO-1, using IF staining. In the uninfected mice (0 dpi), claudin-5 displayed the strongest signal and was located at the interfaces between adjacent endothelial cells, as revealed using an antibody against CD31 (a specific marker of vascular endothelial cells) (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4A</xref>). Occludin and ZO-1 displayed a similar distribution as claudin-5 (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4A</xref>). No difference between the Axl<sup>−/−</sup> and control mice was observed with respect to the IF staining intensity of all three TJ proteins (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4B</xref>). After JEV infection, in both the Axl<sup>−/−</sup> and control mice, the IF staining intensity of the three TJ proteins was apparently reduced (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4C</xref>). Remarkably, the Axl<sup>−/−</sup> mice showed a lower IF staining intensity of claudin-5 than the control mice at 1 dpi (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4D</xref>). At 2 dpi, the Axl<sup>−/−</sup> mice displayed an even lower IF staining intensity of claudin-5, occludin, and ZO-1 than the control mice (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4E</xref> and <xref ref-type=\"fig\" rid=\"F4\">F</xref>). We also detected the levels of claudin-5, occludin, and ZO-1 mRNA in the brains of the Axl<sup>−/−</sup> and control mice at 0, 1, and 2 dpi using quantitative real-time PCR (qRT-PCR), and we observed that the RNA abundance of the three TJ proteins was consistent with their protein levels detected by IF staining (<xref ref-type=\"fig\" rid=\"F4\">Fig. 4G</xref> to <xref ref-type=\"fig\" rid=\"F4\">I</xref>). Taken together, our results suggest that prior to the occurrence of encephalitis, the expression of TJ proteins in the brains of Axl<sup>−/−</sup> mice have already been disrupted, making it easier for JEV to penetrate the BBB.</p><fig id=\"F4\" orientation=\"portrait\" position=\"float\"><label>FIG 4</label><caption><p>Effects of Axl on the integrity of tight junctions in the brain. Four-week-old Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice received an i.p. injection of 10<sup>4</sup> PFU of JEV. (A, C, and E) IF staining of claudin-5, occludin, and ZO-1 (tight junction proteins, red); CD31 (microvascular endothelial cell marker, green); and 4′,6-diamidino-2-phenylindole (DAPI; nucleus, blue) in brain at 0 dpi (A), 1 dpi (C), and 2 dpi (E), where each image is the representative from 3 to 5 mice; scale bar = 20 μm. (B, D, and F) Quantification of the fluorescent intensity of claudin-5, occludin, and ZO-1 in brain at 0 dpi (B), 1 dpi (D), and 2 dpi (F); each dot denotes a mouse, and the data were compared by unpaired <italic>t</italic> test. (G to I) Relative mRNA abundance of claudin-5, occludin, and ZO-1 in mouse brains at 0 dpi (G), 1 dpi (H), and 2 dpi (I), normalized to the average of Axl<sup>+/−</sup> mice; each dot denotes a mouse, and the data were compared by unpaired <italic>t</italic> test. All the data are expressed as the means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0004\"/></fig></sec><sec id=\"s2.4\"><title>IL-1α mediates early JEV neuroinvasion in Axl-deficient mice.</title><p>As changes in BBB permeability and TJ protein levels rapidly occurred after JEV infection, we wondered whether Axl<sup>−/−</sup> mice triggered the release of some humoral factors to disrupt BBB integrity soon after JEV infection. To address the question, we measured the serum levels of a panel of cytokines (IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-10, tumor necrosis factor alpha [TNF-α], interferon gamma [IFN-γ], CCL2, and CCL5) that may affect BBB permeability at 1 and 7 dpi. Without JEV infection, no obvious difference was observed in the serum levels for any of the assayed cytokines between the Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5</xref>, mock). In contrast, upon JEV infection, the serum levels of all measured cytokines increased at 1 dpi and returned to a lower level at 7 dpi that was comparable to those observed in the mock-treated group (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5</xref>, mock, 1 and 7 dpi). Compared with the Axl<sup>+/−</sup> mice, the Axl<sup>−/−</sup> mice showed higher levels of IL-1α, IL-1β, IL-2, IL-4, IL-6, and IFN-γ but lower levels of CCL2 and CCL5 in serum at 1 dpi (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5</xref>, 1 dpi). Of the six cytokines with increased levels, IL-1α (1 dpi) and IL-6 (1 dpi) were greatly increased in Axl<sup>−/−</sup> mice (<xref ref-type=\"fig\" rid=\"F5\">Fig. 5</xref>, 1 dpi), suggesting that they may be the mediators responsible for the observed BBB breakdown.</p><fig id=\"F5\" orientation=\"portrait\" position=\"float\"><label>FIG 5</label><caption><p>Cytokine profiling in serum. Four-week-old Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice received an i.p. injection of 10<sup>4</sup> PFU of JEV or 200 μl of PBS (mock) per mouse, and serum samples were obtained at 1 and 7 dpi. A panel of cytokines, including IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ, CCL2, and CCL5 in sera were detected by a bead-based immunoassay (Aimplex); <italic>n</italic> = 2 for mock, and <italic>n</italic> = 6 for 1 and 7 dpi; and the data were compared by two-way ANOVA and multiple <italic>t</italic> tests. All the data are expressed as means ± SEM; *, <italic>P</italic> < 0.05; this result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0005\"/></fig><p>To further investigate the effect of IL-1α and IL-6 on JE pathogenesis, we treated JEV-infected Axl<sup>−/−</sup> mice with IL-1α, IL-6, or vehicle (sterile normal saline) daily. Compared with the vehicle-treated mice, the IL-6-treated mice showed slightly higher body weights (<xref ref-type=\"fig\" rid=\"F6\">Fig. 6A</xref>) but no differences in mortality (<xref ref-type=\"fig\" rid=\"F6\">Fig. 6B</xref>) or BBB permeability (<xref ref-type=\"fig\" rid=\"F6\">Fig. 6C</xref> and <xref ref-type=\"fig\" rid=\"F6\">D</xref>), which excluded it as a crucial contributor to JE pathogenesis in Axl<sup>−/−</sup> mice.</p><fig id=\"F6\" orientation=\"portrait\" position=\"float\"><label>FIG 6</label><caption><p>Effects of IL-6 on JEV infection outcome and BBB permeability. (A and B) Four-week-old Axl<sup>−/−</sup> mice received an i.p. injection of recombinant mouse IL-6 (60 ng per mouse, <italic>n</italic> = 10) or vehicle (sterile normal saline, <italic>n</italic> = 10) 30 min prior to an i.p. injection of 10<sup>4</sup> PFU of JEV, and then they were treated daily with IL-6 (60 ng per mouse) or vehicle (sterile normal saline). (A) Body weight changes were compared by two-way ANOVA. (B) Survival curves were compared by log-rank test. (C and D) Four-week-old Axl<sup>−/−</sup> mice received a single i.p. injection of IL-6 (60 ng per mouse) or vehicle (sterile normal saline), and BBB permeability was measured 6 h later by an EB leakage test. (C) Representative image showing EB leakage into brain parenchyma. (D) Quantification of EB content in brain, normalized to the average of vehicle-treated mice, where each dot denotes a mouse, and the data were compared by unpaired <italic>t</italic> test. All the data are expressed as means ± SEM; **, <italic>P</italic> < 0.01; ns, no significance (<italic>P</italic> > 0.05); each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0006\"/></fig><p>In contrast, mice treated with IL-1α showed more severe body weight loss and JE signs than vehicle-treated mice (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7A</xref> and <xref ref-type=\"fig\" rid=\"F7\">B</xref>), and the mortality rate in the IL-1α-treated group (11/14) was significantly greater than that observed for the vehicle-treated group (5/13) (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7C</xref>). In microscopic observations, JEV infection induced obvious neuroinflammation, including neuron cell death, angiectasis, perivascular cuffing, and inflammatory cell infiltration (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7D</xref> and <xref ref-type=\"fig\" rid=\"F7\">E</xref>). Moreover, IL-1α-treated mice showed significantly larger areas of neuron death (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7D</xref> and <xref ref-type=\"fig\" rid=\"F7\">F</xref>) and inflammatory cell infiltration than the vehicle-treated mice (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7E</xref> and <xref ref-type=\"fig\" rid=\"F7\">G</xref>) at 7 dpi, suggesting that IL-1α treatment enhanced JEV-induced brain lesions. We also detected the viral load in mouse brains by qRT-PCR and observed that the IL-1α-treated mice showed greater viral loads in the brain than the vehicle-treated mice (<xref ref-type=\"fig\" rid=\"F7\">Fig. 7H</xref>). Taken together, these results show that IL-1α promotes the pathogenesis of severe JE by promoting the invasion of JEV into the brain.</p><fig id=\"F7\" orientation=\"portrait\" position=\"float\"><label>FIG 7</label><caption><p>Effects of IL-1α on JEV infection outcome. Four-week-old Axl<sup>−/−</sup> mice received an i.p. injection of recombinant mouse IL-1α (200 ng per mouse, <italic>n</italic> = 14) or vehicle (sterile normal saline, <italic>n</italic> = 13) 30 min prior to an i.p. injection of 10<sup>4</sup> PFU of JEV and then were treated daily with IL-1α or vehicle. (A) Body weight changes were compared by two-way ANOVA. (B) Scores of the infection signs, which were compared by rank-sum test. (C) Survival curves were compared by log-rank test. (D) Pathological lesions in cerebral cortex and hippocampus at 7 dpi. (E) Perivascular infiltration of inflammatory cells in cerebral cortex at 7 dpi. (F and G) Quantification of the area of neuron death (F) and the area of inflammatory cell infiltration (G) in cerebral cortex per high power filed (HPF), normalized to the average of vehicle-treated mice; <italic>n</italic> = 5 for each group, and the data were compared by unpaired <italic>t</italic> test. (H) Viral load in mouse brains at 7 dpi; green dashed line denotes the limit of detection, <italic>n</italic> = 5 for each group, and the data were compared by unpaired <italic>t</italic> test. All the data are expressed as the means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0007\"/></fig><p>To elucidate the impact of IL-1α on BBB permeability in JEV-infected mice, we evaluated BBB permeability by quantifying endogenous IgG leakage and assessing the expression and distribution of claudin-5, occludin, and ZO-1 in mouse brains by IF staining. In contrast to the vehicle-treated mice, the IL-1α-treated mice showed significantly increased endogenous IgG leakage into the brain parenchyma (<xref ref-type=\"fig\" rid=\"F8\">Fig. 8A</xref> and <xref ref-type=\"fig\" rid=\"F8\">B</xref>). In agreement with this result, the expression of claudin-5, occludin, and ZO-1 in the IL-1α-treated mice was conspicuously lower than that observed in the vehicle-treated mice, and the distribution of the three TJ proteins was more severely disrupted in the IL-1α-treated mice than in the vehicle-treated mice (<xref ref-type=\"fig\" rid=\"F8\">Fig. 8C</xref> and <xref ref-type=\"fig\" rid=\"F8\">D</xref>). Taken together, these results indicate that IL-1α but not IL-6 disrupted the BBB integrity and promoted JEV invasion into brains.</p><fig id=\"F8\" orientation=\"portrait\" position=\"float\"><label>FIG 8</label><caption><p>Effects of IL-1α on BBB permeability and tight junction integrity during JEV infection. (A) Representative image displaying endogenous IgG (green) leakage into brain parenchyma. Claudin-5 (red) was used to position BBB; DAPI was used to show nucleus; scale bar = 20 μm. (B) Quantification of endogenous IgG leakage into brain parenchyma at 7 dpi; <italic>n</italic> = 5, and the data were compared by unpaired <italic>t</italic> test. (C) Representative image depicting brain claudin-5, occludin, and ZO-1 (red); CD31 (microvascular endothelial cell marker, green); and DAPI (nucleus, blue); scale bar = 20 μm. (D) Quantification of the staining intensity of claudin-5, occludin, and ZO-1 in the cerebral cortex at 7 dpi; <italic>n</italic> = 5, and the data were compared by unpaired <italic>t</italic> test. All the data are expressed as the means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0008\"/></fig><p>To ensure IL-1α was the factor inducing BBB and TJ breakdown at the early stage of JEV infection, an i.p. injection of a mouse recombinant IL-1α was administered to Axl<sup>−/−</sup> mice, and its direct effect on BBB permeability and brain TJs was assessed at 6 h posttreatment, when viral RNA was first detected in serum. BBB permeability was measured by EB leakage assays and by quantifying the levels of claudin-5, occludin, and ZO-1 mRNA by qRT-PCR and assessing their distribution by IF staining. Compared with the vehicle-treated mice, the IL-1α-treated mice showed greater EB leakage into the brain parenchyma (2.6-fold), indicating that IL-1α alone could immediately increase BBB permeability (<xref ref-type=\"fig\" rid=\"F9\">Fig. 9A</xref> and <xref ref-type=\"fig\" rid=\"F9\">B</xref>). Furthermore, the IL-1α-treated mice showed significantly lower mRNA levels of claudin-5, occludin, and ZO-1 (<xref ref-type=\"fig\" rid=\"F9\">Fig. 9C</xref>) and much weaker IF staining intensity for these TJ proteins than was observed in the vehicle-treated mice (<xref ref-type=\"fig\" rid=\"F9\">Fig. 9D</xref> and <xref ref-type=\"fig\" rid=\"F9\">E</xref>). Moreover, unlike in the vehicle-treated mice, where the TJ proteins clearly located at the interface between two neighboring endothelial cells, the TJ proteins in the IL-1α-treated mice were dispersed and lost their typical distribution patterns (<xref ref-type=\"fig\" rid=\"F9\">Fig. 9D</xref>), indicating that IL-1α alone could rapidly disrupt the distribution of TJ proteins and increase the BBB permeability.</p><fig id=\"F9\" orientation=\"portrait\" position=\"float\"><label>FIG 9</label><caption><p>Effects of IL-1α alone on BBB permeability and tight junction integrity. Four-week-old Axl<sup>−/−</sup> mice received an i.p. injection of recombinant mouse IL-1α (200 ng per mouse) or vehicle (sterile normal saline). At 6 h posttreatment, BBB permeability was gauged by an EB leakage test, and brain tight junction proteins were observed by IF staining. (A) Representative image showing EB leakage into brain parenchyma. (B) BBB permeability change after IL-1α treatment; <italic>n</italic> = 5, and the data were compared by unpaired <italic>t</italic> test. (C) mRNA abundance of claudin-5, occludin, and ZO-1 in mouse brains after IL-1α treatment, normalized to the average of vehicle-treated mice; <italic>n</italic> = 5, and the data were compared by unpaired <italic>t</italic> test. (D) IF staining of claudin-5, occludin, and ZO-1 (red); CD31 (microvascular endothelial cell marker, green); and DAPI (nucleus, blue); scale bar = 20 μm. (E) Quantification of the IF staining intensity of claudin-5, occludin, and ZO-1; <italic>n</italic> = 5 for each group, and the data were compared by unpaired <italic>t</italic> test. All the data are expressed as the means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0009\"/></fig></sec><sec id=\"s2.5\"><title>IL-1α is released by dead JEV-infected <italic>in situ</italic> peritoneal macrophages.</title><p>To identify the source of IL-1α in the JEV-infected mice, we measured the IL-1α content in the brain, serum, and peritoneal wash at 24 hpi. No significant alteration in the brain IL-1α content was observed (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10A</xref>). Consistent with previous measurements, serum IL-1α levels sharply increased (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10B</xref>). Remarkably, peritoneal wash levels of IL-1α showed a decreasing tendency after JEV infection (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10C</xref>), and there was a negative correlation with respect to the IL-1α content between the serum and peritoneal wash (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10D</xref>). The above data suggested that serum IL-1α was primarily derived from the peritoneal cavity rather than the brain, and it was probably achieved by releasing the preexisting IL-1α inside cells rather than increasing IL-1α expression. The serum IL-1α content in Axl<sup>−/−</sup> mice was much greater than that observed in the control mice (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10B</xref>), whereas the IL-1α content in the peritoneal wash samples from Axl<sup>−/−</sup> mice was lower than that detected in the control mice (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10C</xref>), suggesting that Axl deficiency accelerated the release of IL-1α by peritoneal cells.</p><fig id=\"F10\" orientation=\"portrait\" position=\"float\"><label>FIG 10</label><caption><p>Effect of Axl on IL-1α production during JEV infection. (A to D) Four-week-old Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice received an i.p. injection of 10<sup>4</sup> PFU of JEV. IL-1α content in the brain (A), serum (B), and peritoneal wash (C) at 24 hpi; each dot denotes a mouse, and the data were compared by unpaired <italic>t</italic> test. (D) Correlation analysis of IL-1α content in serum and peritoneal wash at 24 hpi; the data were analyzed by linear regression model. (E to H) Peritoneal macrophages were isolated from Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice, cultured, and infected with JEV (MOI = 1) or stimulated with LPS (1 μg/ml) <italic>in vitro</italic>. (E) IF staining of F4/80 (marker of macrophages) in untreated Axl<sup>−/−</sup> and Axl<sup>+/−</sup> peritoneal macrophages. Scale bar = 20 μm. (F) Dynamics of IL-1α contents in the supernatants of peritoneal macrophages; <italic>n</italic> = 4 for each group, and the data were compared by two-way ANOVA. (G) IF staining of intracellular IL-1α in peritoneal macrophages at 24 hpi. Scale bar = 20 μm. (H) IL-1α contents in the supernatants of peritoneal macrophages at 24 hpi; <italic>n</italic> = 3 to 6 for each group, and the data were compared by unpaired <italic>t</italic> test. (I and J) Murine cerebral microvascular endothelial cells (bEND.3) and primary splenocytes were infected with JEV (MOI, 1). (I) IL-1α contents in the supernatants of bEND.3 cells at 24 hpi; <italic>n</italic> = 5, and the data were compared by unpaired <italic>t</italic> test. (J) IL-1α contents in the supernatants of primary splenocytes at 24 hpi; <italic>n</italic> = 6, and the data were compared by unpaired <italic>t</italic> test. (K) IF staining of JEV antigens and IL-1α inside the <italic>in situ</italic> peritoneal macrophages at 24 hpi. Scale bar = 20 μm. (L) Infection rates of the <italic>in situ</italic> peritoneal macrophages at 24 hpi; <italic>n</italic> = 3 for Axl<sup>+/−</sup>, and <italic>n</italic> = 5 for Axl<sup>−/−</sup>; the data were compared by unpaired <italic>t</italic> test. (M) Viral loads in peritoneal washes at 24 hpi; <italic>n</italic> = 7 for each group, and the data were compared by unpaired <italic>t</italic> test. (N) IF staining intensity of intracellular IL-1α in the <italic>in situ</italic> peritoneal macrophages at 24 hpi; <italic>n</italic> = 4 to 7 for each group, and the data were compared by unpaired <italic>t</italic> test. (O) Trypan blue staining of the <italic>in situ</italic> peritoneal macrophages at 24 hpi. Scale bar = 20 μm. (P) Cell death rates of the <italic>in situ</italic> peritoneal macrophages at 24 hpi; <italic>n</italic> = 5 to 7, and the data were compared by unpaired <italic>t</italic> test. (Q) IL-1α content in the supernatant of live and hypotonically dead Axl<sup>−/−</sup> peritoneal macrophages; <italic>n</italic> = 8 for each group, and the data were compared by unpaired <italic>t</italic> test. All the data are expressed as the means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; ns, no significance (<italic>P</italic> > 0.05); each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0010\"/></fig><p>To elucidate the mechanism underlying the Axl-mediated regulation of IL-1α release by peritoneal cells, we isolated mouse peritoneal cells and infected them with JEV <italic>in vitro</italic>. Most of the peritoneal cells were F4/80 positive (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10E</xref>), suggesting they were macrophages. In the uninfected groups, Axl<sup>−/−</sup> and control macrophages showed similar IL-1α content in the supernatant. However, the IL-1α content in the supernatant of Axl<sup>−/−</sup> macrophages immediately increased after infection, which was maintained at higher levels than that of the control macrophages at all tested time points, suggesting that Axl deficiency promotes the release of IL-1α (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10F</xref>). However, the magnitude of the increase of IL-1α in the supernatant of Axl<sup>−/−</sup> macrophages was not enough to explain the drastic rise in serum IL-1α levels. Moreover, at 24 hpi, the expression of IL-1α inside peritoneal macrophages did not decrease significantly (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10G</xref>), and the IL-1α content in the supernatant was slightly elevated (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10H</xref>). We also measured IL-1α production by mouse brain microvascular endothelial cells (bEND.3) and primary splenocytes <italic>in vitro</italic> in the absence of Axl signaling, which was achieved by Axl deficiency or inhibition with R428, but these cells produced even less IL-1α than the peritoneal macrophages (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10I</xref> and <xref ref-type=\"fig\" rid=\"F10\">J</xref>). Compared with the JEV-infected cells, lipopolysaccharide (LPS)-stimulated peritoneal macrophages showed a conspicuous decrease in intracellular IL-1α expression (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10G</xref>) and a sharp increase in the IL-1α content in the supernatant (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10H</xref>). IL-1α can be released by either LPS induction or cell death (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Since IL-1α release by peritoneal macrophages after JEV infection occurred through a different mechanism than LPS stimulation, it was speculated that it may have been released by dead cells as an alarm molecule.</p><p>We noticed that the peritoneal macrophages cultured <italic>in vitro</italic> were not highly susceptible to JEV infection (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2E</xref> and <xref ref-type=\"fig\" rid=\"F2\">F</xref>), and no detectable death was observed after infection. Thereafter, we avoided the <italic>in vitro</italic> infection model and instead directly isolated the peritoneal macrophages from JEV-infected mice and made smears, which closely reflected the <italic>in situ</italic> infection characteristics of peritoneal macrophages. We observed that <italic>in situ</italic> macrophages were highly susceptible to JEV infection, with an infection rate of up to 80% (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10K</xref> and <xref ref-type=\"fig\" rid=\"F10\">L</xref>). There was no significant difference in the infection rate between the Axl<sup>−/−</sup> and control macrophages (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10L</xref>), which was in accordance with the similar viral titers observed in the peritoneal wash samples (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10M</xref>) and with the previous results (<xref ref-type=\"fig\" rid=\"F2\">Fig. 2F</xref> and <xref ref-type=\"fig\" rid=\"F2\">G</xref>). In both the Axl<sup>−/−</sup> and control macrophages, IL-1α was largely localized to the nucleus, which was consistent with previous reports (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). At 24 hpi, the expression of intracellular IL-1α significantly increased, and the Axl<sup>−/−</sup> macrophages showed slightly greater expression of IL-1α than that observed in the control macrophages (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10K</xref> and <xref ref-type=\"fig\" rid=\"F10\">N</xref>). These results suggest that IL-1α is released not only by induction but also by immediately liberating preexisting intracellular IL-1α.</p><p>To assess whether serum IL-1α was released by dead macrophages after JEV infection, we first used the Trypan blue (TB) staining method to discriminate live cells (unstained, TB<sup>−</sup>) and dead cells (blue stained, TB<sup>+</sup>) (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10O</xref>), At 24 hpi, the proportion of TB<sup>+</sup> cells in the peritoneal wash samples of the Axl<sup>−/−</sup> mice was significantly increased, much more than that observed for the control mice (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10O</xref> and <xref ref-type=\"fig\" rid=\"F10\">P</xref>). To determine whether additional IL-1α can be released after cell death, we used a hypotonic treatment to induce the death of Axl<sup>−/−</sup> peritoneal macrophages, observing that IL-1α levels in the supernatant increased by more than 2 orders of magnitude (<xref ref-type=\"fig\" rid=\"F10\">Fig. 10Q</xref>). Taken together, these results suggest that macrophages lacking Axl die more readily after JEV infection, which triggers a substantial release of IL-1α from cells.</p></sec><sec id=\"s2.6\"><title>Axl deficiency promotes the pyroptosis of JEV-infected peritoneal macrophages.</title><p>To further elucidate the mode of death of JEV-infected macrophages, we isolated peritoneal macrophages from JEV-infected mice at 24 hpi and analyzed transcriptomic changes through transcriptome sequencing (RNA-Seq) analysis. Hundreds of differentially expressed genes were identified between the Axl<sup>−/−</sup> and control macrophages after infection (<xref ref-type=\"fig\" rid=\"F11\">Fig. 11A</xref>). Many differentially expressed genes were enriched in cell death-related biological processes, such as necrosis, apoptosis, and pyroptosis (<xref ref-type=\"fig\" rid=\"F11\">Fig. 11B</xref>). Remarkably, compared with the control macrophages, most of these cell death-related genes in the Axl<sup>−/−</sup> macrophages were upregulated after infection, suggesting that Axl deficiency enhanced the transcription of cell death-related genes (<xref ref-type=\"fig\" rid=\"F11\">Fig. 11B</xref>). We also assessed the transcription of IL-1α, IL-1β, and IL-1 receptor antagonist (IL-1Ra), observing that only IL-1β was upregulated in JEV-infected Axl<sup>−/−</sup> macrophages (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). After infection, the transcription of caspase-1 and fasdermin D (GSDMD) (key mediators of pyroptosis) was notably enhanced but showed no difference between the Axl<sup>−/−</sup> and control macrophages (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Although the transcription of RIPK1 (a key mediator of necroptosis) was unchanged, its suppressor BIRC3 showed greater transcription in Axl<sup>−/−</sup> macrophages than in control macrophages after infection (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). The transcription of caspase-8, caspase-9, and caspase-3 (all are crucial regulators of apoptosis and pyroptosis) was enhanced after infection, but no differences were observed between the Axl<sup>−/−</sup> and control macrophages (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). The expression of the proapoptotic gene <italic>BCL-10</italic> was upregulated in the Axl<sup>−/−</sup> macrophages after infection (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). In addition, <italic>Noxo1</italic>, which promotes the generation of ROS and cell death, was more expressed in the Axl<sup>−/−</sup> macrophages than that observed in the control macrophages after infection (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). PI3K-Akt signaling is an important pathway that promotes cell survival. After infection, the transcription levels of PI3K and Akt were similar in the Axl<sup>−/−</sup> and control macrophages (data not shown), but the PI3K inhibitors PI3KIP1 and WDR91 as well as the Akt inhibitor TRIB3 showed increased expression in the Axl<sup>−/−</sup> macrophages compared with that observed in the control macrophages (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>), suggesting that during infection, PI3K-Akt signaling may be inhibited in Axl<sup>−/−</sup> macrophages.</p><fig id=\"F11\" orientation=\"portrait\" position=\"float\"><label>FIG 11</label><caption><p>Transcriptomic analysis of the cell death pathways in the <italic>in situ</italic> peritoneal macrophages. Four-week-old Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice received an i.p. injection of 10<sup>4</sup> PFU of JEV. At 24 hpi, the <italic>in situ</italic> peritoneal macrophages were isolated and subjected to transcriptomic sequencing. (A) Volcano plot showing the upregulated, downregulated, and unchanged genes. JEV-infected Axl<sup>−/−</sup> macrophages versus JEV-infected Axl<sup>+/−</sup> macrophages; each dot denotes a specific gene; <italic>n</italic> = 5 for each group. (B) Gene Ontology (GO) enrichment analysis of the cell death-related biological processes; JEV-infected Axl<sup>−/−</sup> macrophages versus JEV-infected Axl<sup>+/−</sup> macrophages; <italic>n</italic> = 5 for each group.</p></caption><graphic xlink:href=\"JVI.00602-20-f0011\"/></fig><table-wrap id=\"T1\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>Transcription levels of IL-1 signaling-related genes and cell death-related genes<xref ref-type=\"table-fn\" rid=\"T1F1\"><sup><italic>a</italic></sup></xref></p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th rowspan=\"3\" colspan=\"1\">Gene</th><th colspan=\"4\" rowspan=\"1\">Transcription level by treatment group<hr/></th></tr><tr><th colspan=\"2\" rowspan=\"1\">Mock<hr/></th><th colspan=\"2\" rowspan=\"1\">JEV<hr/></th></tr><tr><th rowspan=\"1\" colspan=\"1\">Axl<sup>+/−</sup></th><th rowspan=\"1\" colspan=\"1\">Axl<sup>−/−</sup></th><th rowspan=\"1\" colspan=\"1\">Axl<sup>+/−</sup></th><th rowspan=\"1\" colspan=\"1\">Axl<sup>−/−</sup></th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">IL-1α</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.19</td><td rowspan=\"1\" colspan=\"1\">1.85 ± 0.48</td><td rowspan=\"1\" colspan=\"1\">3.63 ± 0.59</td><td rowspan=\"1\" colspan=\"1\">3.89 ± 0.77</td></tr><tr><td rowspan=\"1\" colspan=\"1\">IL-1β</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.18</td><td rowspan=\"1\" colspan=\"1\">1.07 ± 0.55</td><td rowspan=\"1\" colspan=\"1\">6.91 ± 0.84</td><td rowspan=\"1\" colspan=\"1\">23.55 ± 10.02<xref ref-type=\"table-fn\" rid=\"T1F2\"><sup><italic>b</italic></sup>\n</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\">IL-1Ra</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.10</td><td rowspan=\"1\" colspan=\"1\">1.04 ± 0.11</td><td rowspan=\"1\" colspan=\"1\">15.02 ± 1.63</td><td rowspan=\"1\" colspan=\"1\">16.92 ± 5.71</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Caspase-1</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.14</td><td rowspan=\"1\" colspan=\"1\">0.80 ± 0.04</td><td rowspan=\"1\" colspan=\"1\">2.33 ± 0.08</td><td rowspan=\"1\" colspan=\"1\">2.03 ± 0.19</td></tr><tr><td rowspan=\"1\" colspan=\"1\">GSDMD</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.11</td><td rowspan=\"1\" colspan=\"1\">0.93 ± 0.06</td><td rowspan=\"1\" colspan=\"1\">2.11 ± 0.02</td><td rowspan=\"1\" colspan=\"1\">2.00 ± 0.13</td></tr><tr><td rowspan=\"1\" colspan=\"1\">RIPK1</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.05</td><td rowspan=\"1\" colspan=\"1\">0.94 ± 0.05</td><td rowspan=\"1\" colspan=\"1\">1.05 ± 0.02</td><td rowspan=\"1\" colspan=\"1\">1.02 ± 0.06</td></tr><tr><td rowspan=\"1\" colspan=\"1\">BIRC3</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.05</td><td rowspan=\"1\" colspan=\"1\">0.94 ± 0.08</td><td rowspan=\"1\" colspan=\"1\">0.69 ± 0.02</td><td rowspan=\"1\" colspan=\"1\">0.84 ± 0.03<xref ref-type=\"table-fn\" rid=\"T1F3\"><sup><italic>c</italic></sup>\n</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\">Caspase-8</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.07</td><td rowspan=\"1\" colspan=\"1\">0.95 ± 0.05</td><td rowspan=\"1\" colspan=\"1\">2.19 ± 0.11</td><td rowspan=\"1\" colspan=\"1\">2.17 ± 0.11</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Caspase-9</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.01</td><td rowspan=\"1\" colspan=\"1\">0.96 ± 0.01</td><td rowspan=\"1\" colspan=\"1\">1.13 ± 0.04</td><td rowspan=\"1\" colspan=\"1\">1.10 ± 0.02</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Caspase-3</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.04</td><td rowspan=\"1\" colspan=\"1\">1.20 ± 0.08</td><td rowspan=\"1\" colspan=\"1\">2.37 ± 0.09</td><td rowspan=\"1\" colspan=\"1\">2.21 ± 0.20</td></tr><tr><td rowspan=\"1\" colspan=\"1\">BCL-10</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.04</td><td rowspan=\"1\" colspan=\"1\">0.99 ± 0.06</td><td rowspan=\"1\" colspan=\"1\">0.95 ± 0.02</td><td rowspan=\"1\" colspan=\"1\">1.08 ± 0.03<xref ref-type=\"table-fn\" rid=\"T1F2\"><sup><italic>b</italic></sup>\n</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\">Noxo1</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.06</td><td rowspan=\"1\" colspan=\"1\">1.17 ± 0.11</td><td rowspan=\"1\" colspan=\"1\">0.46 ± 0.05</td><td rowspan=\"1\" colspan=\"1\">0.80 ± 0.04<xref ref-type=\"table-fn\" rid=\"T1F3\"><sup><italic>c</italic></sup>\n</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\">PI3KIP1</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.09</td><td rowspan=\"1\" colspan=\"1\">0.88 ± 0.12</td><td rowspan=\"1\" colspan=\"1\">0.48 ± 0.03</td><td rowspan=\"1\" colspan=\"1\">0.72 ± 0.12<xref ref-type=\"table-fn\" rid=\"T1F2\"><sup><italic>b</italic></sup>\n</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\">WDR91</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.03</td><td rowspan=\"1\" colspan=\"1\">0.92 ± 0.02</td><td rowspan=\"1\" colspan=\"1\">0.87 ± 0.02</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.03<xref ref-type=\"table-fn\" rid=\"T1F2\"><sup><italic>b</italic></sup>\n</xref></td></tr><tr><td rowspan=\"1\" colspan=\"1\">TRIB3</td><td rowspan=\"1\" colspan=\"1\">1.00 ± 0.34</td><td rowspan=\"1\" colspan=\"1\">0.41 ± 0.11</td><td rowspan=\"1\" colspan=\"1\">0.60 ± 0.05</td><td rowspan=\"1\" colspan=\"1\">1.15 ± 0.24<xref ref-type=\"table-fn\" rid=\"T1F2\"><sup><italic>b</italic></sup>\n</xref></td></tr></tbody></table><graphic xlink:href=\"JVI.00602-20-t0001\"/></alternatives><table-wrap-foot><fn fn-type=\"other\" id=\"T1F1\"><label>a</label><p>The transcription levels of specific genes were normalized to the average vFPKM (fragments per kilobases of transcript per million fragments mapped) of mock Axl<sup>+/−</sup> macrophages and are expressed as mean ± SEM. Data between two groups were analyzed by unpaired <italic>t</italic> test (JEV, Axl<sup>−/−</sup> versus Axl<sup>+/−</sup>). <italic>n</italic> = 5 for each group.</p></fn><fn fn-type=\"other\" id=\"T1F2\"><label>b</label><p><italic>P</italic> < 0.05.</p></fn><fn fn-type=\"other\" id=\"T1F3\"><label>c</label><p><italic>P</italic> < 0.01.</p></fn></table-wrap-foot></table-wrap><p>Subsequently, we verified the RNA-Seq revealed cell death pathways (apoptosis, pyroptosis, and necroptosis) using IF staining. At 24 hpi, the proportion of GSDMD-N-terminal<sup>+</sup> (a marker of pyroptosis) cells and terminal deoxynucleotidyltransferase-mediated dUTP-biotin nick end labeling (TUNEL)<sup>+</sup> (a marker of apoptosis) cells increased significantly (<xref ref-type=\"fig\" rid=\"F12\">Fig. 12A</xref> to <xref ref-type=\"fig\" rid=\"F12\">C</xref>) but that of RIPK1<sup>+</sup> (a marker of necroptosis) cells did not change significantly (<xref ref-type=\"fig\" rid=\"F12\">Fig. 12D</xref> and <xref ref-type=\"fig\" rid=\"F12\">E</xref>), indicating that the JEV-infected peritoneal macrophages primarily underwent pyroptosis and apoptosis rather than necroptosis, which was consistent with the RNA-Seq results. The pyroptosis and apoptosis rates in the Axl<sup>−/−</sup> macrophages were much greater than those observed in the control mice (<xref ref-type=\"fig\" rid=\"F12\">Fig. 12B</xref> and <xref ref-type=\"fig\" rid=\"F12\">C</xref>), suggesting that Axl deficiency promotes the pyroptosis and apoptosis of JEV-infected macrophages. Remarkably, pyroptosis was the predominant death mode, with approximately 80% of JEV-infected Axl<sup>−/−</sup> macrophages undergoing pyroptosis (<xref ref-type=\"fig\" rid=\"F12\">Fig. 12F</xref>). We also assessed the expression of cleaved caspase-1 (another maker of pyroptosis) and GSDMD-N in the <italic>in situ</italic> peritoneal macrophages by immunoblotting and observed that, consistent with the IF staining results, Axl<sup>−/−</sup> macrophages showed greater expression of the cleaved caspase-1 and GSDMD-N than the control macrophages after JEV infection (<xref ref-type=\"fig\" rid=\"F12\">Fig. 12G</xref> to <xref ref-type=\"fig\" rid=\"F12\">I</xref>). In subsequent experiments, caspase-8, a key molecule regulating cell death mode, was observed to be significantly upregulated in JEV-infected macrophages, but no significant difference in caspase-8 expression was observed between the Axl<sup>−/−</sup> and control macrophages (<xref ref-type=\"fig\" rid=\"F13\">Fig. 13A</xref>). The changes in caspase-9 and caspase-3 levels were similar to that observed for caspase-8 (<xref ref-type=\"fig\" rid=\"F13\">Fig. 13B</xref> and <xref ref-type=\"fig\" rid=\"F13\">C</xref>).</p><fig id=\"F12\" orientation=\"portrait\" position=\"float\"><label>FIG 12</label><caption><p>Effect of Axl on the cell death mode of <italic>in situ</italic> peritoneal macrophages. Four-week-old Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice received an i.p. injection of 10<sup>4</sup> PFU of JEV. Specifically, for PI3K inhibition in the <italic>in situ</italic> Axl<sup>+/−</sup> peritoneal macrophages, an i.p. injection of LY294002 (1.25 mg per mouse) was administered 1 h prior to i.p. infection. At 24 hpi, the <italic>in situ</italic> peritoneal macrophages were isolated. (A) IF staining of GSDMD-N (marker of pyroptosis) and TUNEL staining (marker of apoptosis) of the <italic>in situ</italic> peritoneal macrophages. (B) Pyroptosis (GSDMD-N<sup>+</sup>) rates of the <italic>in situ</italic> peritoneal macrophages; <italic>n</italic> = 3 to 4 for each group; the data were compared by unpaired <italic>t</italic> test. (C) Apoptosis (TUNEL<sup>+</sup>) rates of the <italic>in situ</italic> peritoneal macrophages; <italic>n</italic> = 3 to 4 for each group; the data were compared by unpaired <italic>t</italic> test. (D) IF staining of RIPK1 (marker of necroptosis) in the <italic>in situ</italic> peritoneal macrophages. (E) Quantification of the fluorescence intensity of RIPK1 in the <italic>in situ</italic> peritoneal macrophages; <italic>n</italic> = 3 for each group; the data were compared by unpaired <italic>t</italic> test. (F) Pyroptosis (GSDMD-N<sup>+</sup>) rate of JEV-infected (JEV<sup>+</sup>) <italic>in situ</italic> peritoneal macrophages; <italic>n</italic> = 3 to 4 for each group; the data were compared by unpaired <italic>t</italic> test. (G) Immunoblotting detection of cleaved caspase-1 and GSDMD-N in the <italic>in situ</italic> peritoneal macrophages at 24 hpi. GAPDH was used as a loading control; <italic>n</italic> = 3. (H and I) Quantification of the gray density of cleaved caspase-1 (H) and GSDMD-N (I), normalized to the average of mock-treated Axl<sup>+/-</sup> macrophages; <italic>n</italic> = 3 for each group; the data were compared by unpaired <italic>t</italic> test. All the data are expressed as the means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0012\"/></fig><fig id=\"F13\" orientation=\"portrait\" position=\"float\"><label>FIG 13</label><caption><p>Effects of Axl on the expression of cell death regulatory proteins in the <italic>in situ</italic> peritoneal macrophages. Four-week-old Axl<sup>−/−</sup> and Axl<sup>+/−</sup> mice received an i.p. injection of 10<sup>4</sup> PFU of JEV. At 24 hpi, the <italic>in situ</italic> peritoneal macrophages were isolated. (A to C) IF staining and quantification of caspase-8 (extrinsic apoptosis initiator) (A), caspase-9 (intrinsic apoptosis initiator) (B), and caspase-3 (apoptosis executor) (C), where these genes are also crucial regulators of pyroptosis; <italic>n</italic> = 3 for each group; the data were compared by unpaired <italic>t</italic> test. Scale bar = 20 μm. (D and E) IF staining of PI3K-p110 (D) and phosphorylated Akt (pAkt) (E) of the <italic>in situ</italic> peritoneal macrophages. Scale bar = 20 μm. (F and G) Quantification of the fluorescent intensity of PI3K-p110 (F) and pAkt (G) of the <italic>in situ</italic> peritoneal macrophages; <italic>n</italic> = 3 to 4 for each group; the data were compared by unpaired <italic>t</italic> test. (H) Immunoblotting detection of PI3K-p110 in the <italic>in situ</italic> peritoneal macrophages at 24 hpi. GAPDH is used as a loading control. (I) Quantification of the gray density of PI3K-p110, normalized to the average of mock-treated Axl<sup>+/-</sup> macrophages; <italic>n</italic> = 3 for each group; the data were compared by unpaired <italic>t</italic> test. All the data are expressed as the means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0013\"/></fig><p>Axl has been shown to promote cell survival and inhibit cell apoptosis and pyroptosis by activating PI3K-Akt signaling (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>), and the RNA-Seq results also indicated that PI3K-Akt signaling was inhibited in Axl<sup>−/−</sup> macrophages. To confirm this result, we examined the expression of PI3K-p110 and phosphorylated Akt (pAkt) in JEV-infected <italic>in situ</italic> macrophages using IF staining (<xref ref-type=\"fig\" rid=\"F13\">Fig. 13D</xref> and <xref ref-type=\"fig\" rid=\"F13\">E</xref>), observing that the expression of PI3K-p110 (<xref ref-type=\"fig\" rid=\"F13\">Fig. 13D</xref> and <xref ref-type=\"fig\" rid=\"F13\">F</xref>) and pAkt (<xref ref-type=\"fig\" rid=\"F13\">Fig. 13E</xref> and <xref ref-type=\"fig\" rid=\"F13\">G</xref>) were significantly lower in Axl<sup>−/−</sup> macrophages than in control macrophages. Moreover, the PI3K inhibitor LY294002 could effectively reduce the differences in the pyroptosis (<xref ref-type=\"fig\" rid=\"F12\">Fig. 12A</xref> and <xref ref-type=\"fig\" rid=\"F12\">B</xref>) and apoptosis rates (<xref ref-type=\"fig\" rid=\"F12\">Fig. 12A</xref> and <xref ref-type=\"fig\" rid=\"F12\">C</xref>) between the Axl<sup>−/−</sup> macrophages and control macrophages. We also verified the expression of PI3K-p110, cleaved caspase-1, and GSDMD-N after JEV infection and LY294002 treatment by immunoblotting, the results of which were consistent to those of IF staining (<xref ref-type=\"fig\" rid=\"F12\">Fig. 12G</xref> to <xref ref-type=\"fig\" rid=\"F12\">I</xref>, <xref ref-type=\"fig\" rid=\"F13\">Fig. 13H</xref> and <xref ref-type=\"fig\" rid=\"F13\">I</xref>). Taken together, these results suggest that Axl impedes the pyroptosis and apoptosis of JEV-infected peritoneal macrophages via activating PI3K-Akt signaling.</p></sec><sec id=\"s2.7\"><title>IL-1α antagonist prevents JE pathogenesis.</title><p>Since IL-1α promoted JE pathogenesis, we wondered if its antagonist could hinder the incidence of JE. To test this hypothesis, Axl<sup>−/−</sup> mice were challenged with 10<sup>4</sup> PFU of JEV and then treated with IL-1Ra (a natural IL-1α antagonist) or vehicle (sterile normal saline) every 2 days. The IL-1Ra-treated mice showed no occurrence of JE or mortality (0/13), whereas the vehicle-treated mice showed severe signs of (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14A</xref> and <xref ref-type=\"fig\" rid=\"F14\">B</xref>) and increased mortality by JE (7/14) (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14C</xref>), suggesting the IL-1α antagonist potently reduced the incidence of JE. Next, we assessed the pathological changes in the brain, observing that JEV infection in the mouse brain caused neuron death, angiectasis, and inflammatory cell infiltration. Compared with the vehicle-treated mice, the IL-1Ra-treated mice showed significantly fewer areas of neuron death (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14D</xref> and <xref ref-type=\"fig\" rid=\"F14\">E</xref>) and inflammatory cell infiltration (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14D</xref> and <xref ref-type=\"fig\" rid=\"F14\">F</xref>), suggesting that the IL-1α antagonist potently relieved brain lesions during JEV infection. To elucidate the mechanism by which IL-1Ra protects mice from JE, we assessed the viral load and BBB permeability in mouse brains, observing that the IL-1Ra-treated mice exhibited reduced viral loads in the brain (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14G</xref>) and significantly decreased endogenous IgG leakage into the brain parenchyma (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14H</xref> and <xref ref-type=\"fig\" rid=\"F14\">I</xref>). These data indicate that IL-1Ra may prevent JE pathogenesis by inhibiting increased BBB permeability and inhibiting JEV invasion of the brain during infection.</p><fig id=\"F14\" orientation=\"portrait\" position=\"float\"><label>FIG 14</label><caption><p>Effects of an IL-1α antagonist on JEV infection. Four-week-old Axl<sup>−/−</sup> mice received an i.p. injection of recombinant rat IL-1Ra (10 μg per mouse, <italic>n</italic> = 13) or vehicle (sterile normal saline, <italic>n</italic> = 14) 1 h prior to receiving an i.p. injection of 10<sup>4</sup> PFU of JEV and then were treated with IL-1Ra (5 μg per mouse) or vehicle every 2 days. (A) Representative image showing clinical manifestations in the IL-1Ra- and vehicle-treated mice with JEV infection. The IL-1Ra-treated mice showed no apparent signs of JE, while vehicle-treated mice displayed typical signs of JE, such as ruffled fur and limb paralysis. (B) Sign scores of IL-1Ra- and vehicle-treated mice with JEV infection, which were compared by rank-sum test. (C) Survival curves, which were compared by log-rank test. (D) Pathological changes in cerebral cortex and hippocampus at 7 dpi; red arrow denotes neuron death, blue arrow denotes inflammatory cell infiltration, and black arrow denotes angiectasis; scale bar = 100 μm. (E and F) Quantification of the area of neuron death (E) and the area of inflammatory cell infiltration (F) in cerebral cortex, normalized to the average of vehicle-treated mice; <italic>n</italic> = 5, and the data were compared by unpaired <italic>t</italic> test. (G) Viral load in mouse brains at 7 dpi; green dashed line denotes the limit of detection; <italic>n</italic> = 5, and the data were compared by Mann-Whitney test. (H) Quantification of the endogenous IgG leakage into brain parenchyma at 7 dpi; <italic>n</italic> = 5, and the data were compared by unpaired <italic>t</italic> test. (I) Representative image delineating endogenous IgG (green) leakage into brain parenchyma at 7 dpi. Claudin-5 (red) was used to position BBB; DAPI was used to show nucleus; scale bar = 20 μm. (J) Representative images depicting brain claudin-5, occludin, and ZO-1 (red); CD31 (microvascular endothelial cell marker, green); and DAPI (nucleus, blue); scale bar = 20 μm. (K) Quantification of the fluorescent intensity of claudin-5, occludin, and ZO-1 in brain; <italic>n</italic> = 5, and the data were compared by unpaired <italic>t</italic> test. (L to N) Four-week-old wild-type BALB/c mice received an i.p. injection of recombinant rat IL-1Ra (10 μg per mouse, <italic>n</italic> = 10) or vehicle (sterile normal saline, <italic>n</italic> = 10) 1 h prior to receiving an i.p. injection of 10<sup>5</sup> PFU of JEV and then were treated with IL-1Ra (5 μg per mouse) or vehicle every 2 days. (L) Survival curves, which were compared by log-rank test. (M) Body weight changes, which were compared by two-way ANOVA. (N) Scores of the infection signs, which were compared by rank-sum test. All the data are expressed as the means ± SEM; *, <italic>P</italic> < 0.05; **, <italic>P</italic> < 0.01; each result is the representative of three independent experiments.</p></caption><graphic xlink:href=\"JVI.00602-20-f0014\"/></fig><p>Finally, we assessed the expression and distribution of brain claudin-5, occludin, and ZO-1, observing that in the vehicle-treated mice, the expression of claudin-5 and occludin were significantly reduced and exhibited a sparse distribution along the microvascular endothelial cells (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14J</xref>). The expression of ZO-1 was also reduced, and its distribution became dispersed (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14J</xref>). Notwithstanding, in the IL-1Ra-treated mice, the expression and distribution of the three TJ proteins were comparable to that observed in the uninfected mice, where they were regularly spaced at the interfaces of microvascular endothelial cells (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14J</xref>). The above results suggest that the IL-1α antagonist can efficiently safeguard the expression and distribution of TJ proteins. In summary, the IL-1α antagonist protected mice from developing JE by reducing BBB permeability and viral invasion into brain during JEV infection.</p><p>We also verified the effect of the IL-1α antagonist on JE pathogenesis in wild-type BALB/c mice. BALB/c mice were administered IL-1Ra (<italic>n</italic> = 10) or vehicle (<italic>n</italic> = 10) in the same manner as described for the Axl<sup>−/−</sup> mice and then were challenged with an even higher dose of JEV (10<sup>5</sup> PFU per mouse). Compared with the vehicle-treated mice, the IL-1Ra-treated mice showed significant reductions in mortality, body weight loss, and sign scores after JEV infection (<xref ref-type=\"fig\" rid=\"F14\">Fig. 14K</xref> to <xref ref-type=\"fig\" rid=\"F14\">M</xref>), suggesting that the IL-1α antagonist also impedes JE pathogenesis in wild-type BALB/c mice.</p></sec></sec><sec sec-type=\"discussion\" id=\"s3\"><title>DISCUSSION</title><p>In this study, we discovered that Axl deficiency causes increased mortality in mice infected with JEV. Axl deficiency did not directly affect JEV replication; rather, it significantly increased the neuroinvasion of JEV, which was correlated with an enhanced BBB permeability. IL-1α, which was primarily produced by the JEV-infected peritoneal macrophages, was shown to be the key mediator that induced BBB breakdown and promoted JEV neuroinvasion at the early phase of infection. Furthermore, an IL-1α antagonist effectively reduced the incidence of severe JE by antagonizing JEV-induced brain TJ impairment and increased BBB permeability. The results of our study suggest that Axl is a protective factor that reduces the incidence of JE and that IL-1α promotes early JEV neuroinvasion.</p><p>Axl is a crucial and complicated host factor in flaviviral infection. Although many studies have proposed that Axl serves as an entry receptor for several flaviviruses, accumulating evidence suggests that Axl plays unique roles in a virus type-dependent manner. For example, Axl mediates the entry of DENV (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>), inhibits WNV neuroinvasion without affecting viral replication in target organs (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>), and also enhances ZIKV pathogenesis in an age-dependent manner by exacerbating IL-1β production and apoptosis in microglia (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Our results show that Axl impedes JEV neuroinvasion by antagonizing viral-induced pyroptosis in peritoneal macrophages and subsequent IL-1α release. These results further suggest that Axl plays unique roles in the infection processes of different flaviviruses.</p><p>In our study, we identified IL-1α as a pivotal mediator of JEV infection and demonstrated that it functions at an early rather than late stage of JEV infection. Cytokine screening results showed that IL-1α was the most increased proinflammatory cytokine in serum from Axl-deficient mice at 1 dpi, and it potently inhibited brain TJ protein expression and distribution, resulting in increased BBB permeability. Importantly, an IL-1α antagonist effectively restored these TJ impairments and BBB disruptions and subsequently protected mice from developing severe JE. Currently, there are few reports regarding the specific role of IL-1α in modulating BBB integrity, and they primarily focus on nonviral diseases. In an <italic>in vitro</italic> human BBB model, IL-1α immediately increased BBB permeability within 6 h by disrupting the distribution and expression of TJ proteins, which was accompanied by cytoskeleton alteration, and its blockage by IL-1Ra could prevent such changes (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Gloor et al. reported that IL-1α increased BBB permeability in an <italic>in vitro</italic> porcine BBB model by downregulating plasma membrane-associated tyrosine phosphatase activity (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Indeed, we also observed that IL-1α immediately downregulated the expression of claudin-5, occludin, and ZO-1 in an <italic>in vitro</italic> mouse BBB model, which is related to actin filament and MyD88 alterations (data not shown). In addition to acting on brain microvascular endothelial cells, IL-1α also accounted for increasing pulmonary vascular permeability by downregulating the TJ protein cadherin during acute lung injury (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). IL-1β is a homolog of IL-1α and uses the same receptor as IL-1α. Pan et al. reported that DENV infection induces IL-1β secretion from macrophages in patients and mice, and IL-1β induces vascular leakage in both <italic>in vitro</italic> vascular endothelial monolayers and in mice, while IL-1Ra alleviates these IL-1β-mediated effects (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). In agreement with the above studies, the results of our work suggest that IL-1α is a crucial mediator that promotes the incidence of JE by disrupting BBB integrity at a very early stage of infection. Moreover, IL-1α is a promising target for JE therapy and IL-1Ra is an effective antagonist.</p><p>In our results, pyroptosis, rather than apoptosis or necroptosis, was identified as the predominant cell death mode in JEV-infected <italic>in situ</italic> macrophages, suggesting its pivotal role in JEV infection. Interestingly, pyroptosis is also observed in the infection of other flaviviruses. DENV infection in macrophages induces pyroptosis together with the release of IL-1β (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>). ZIKV infection elicits pyroptosis in the brains of fatal microcephaly cases and increases IL-1β expression (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). In addition, WNV infection in the brain also provokes the expression of pyroptosis markers (caspase-1, IL-1β, and IL-18) (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). Elucidation of the significance of viral infection-induced pyroptosis will deepen our understanding of the nature of flavivirus infections.</p><p>Intriguingly, our investigations showed that JEV-induced pyroptosis was accompanied by the release of large amounts of IL-1α rather than IL-1β. The cleavage of pro-IL-1β into mature IL-1β is a key step in pyroptosis that promotes inflammation and is also a feature that distinguishes pyroptosis from other types of cell death (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). In contrast to IL-1β, the release of IL-1α has been largely less studied in pyroptosis. Indeed, IL-1α and IL-1β share the same receptor and have similar biological functions, but they have distinct differences in the manner they are released (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). IL-1α is typically preexpressed in cells and immediately released to serve as an alarm factor by dead cells (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). In our study, IL-1α levels increased more significantly than IL-1β in Axl-deficient mice. We isolated <italic>in situ</italic> peritoneal macrophages from JEV-infected mice and observed that both Axl-deficient macrophages and control macrophages expressed large amounts of IL-1α in the cytosol and nucleus. However, Axl-deficient macrophages died more readily (pyroptosis and apoptosis) than control cells after JEV infection, and the dead macrophages readily released the preexpressed intracellular IL-1α and boosted serum IL-1α levels. The results of mechanistic studies indicated that Axl could activate PI3K-Akt signaling, which downregulated the expression of cleaved caspase-1 and GSDMD-N and thus inhibited the pyroptotic death of macrophages, averting large amounts of IL-1α being released by dead cells. Since IL-1α is preexpressed in macrophages and IL-1β is merely expressed upon stimulation, IL-1α is likely to act earlier and faster than IL-1β, which may explain why IL-1α is more crucial in the early phase of JEV infection. Previous studies on pyroptosis in flaviviral infection typically focused on IL-1β and not IL-1α. Therefore, whether the release of IL-1α prior to IL-1β is a universal phenomenon during pyroptosis is an interesting issue to be addressed in future studies.</p><p>In summary, in this study, we discovered that Axl is a protective factor that impedes the occurrence of JE by suppressing IL-1α production from pyroptotic macrophages and IL-1α-induced BBB breakdown. Neuroinvasive pathogens penetrate BBB in unique ways, and our results reveal a novel mechanism in which BBB penetration is driven by IL-1α. In view of the apparent protection of IL-1α blockage in JEV infection, IL-1α can thus be a potential therapeutic target to prevent severe JE. Although the above findings are interesting, there are still some issues that need to be solved. JEV is often transmitted by mosquito bites, but our experimental system used an i.p. injection, and while macrophages are one of the primary target cells for both infection routes, whether Axl and IL-1α play the same roles in JEV infection resulting from mosquito bites or a subcutaneous route remains to be elucidated.</p></sec><sec sec-type=\"materials|methods\" id=\"s4\"><title>MATERIALS AND METHODS</title><sec id=\"s4.1\"><title>Cells and viruses.</title><p>C6/36 cells (<named-content content-type=\"genus-species\">Aedes albopictus</named-content> lava cells) were grown in RPMI 1640 medium (Gibco, USA) supplemented with 10% fetal bovine serum (FBS; PAN, Germany) and maintained at 28°C. Vero cells (African green monkey kidney cells) were raised in minimum essential medium (MEM; Gibco) supplemented with 5% FBS. bEND.3 cells (murine brain microvascular endothelial cells) were maintained in Dulbecco’s modified eagle medium (DMEM; Gibco) supplemented with 10% FBS.</p><p>JEV Beijing strain-1 (JEV) (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>) and <italic>Renilla reniformis</italic> luciferase reporter JEV (Rluc-JEV) (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>) were propagated in C6/36 cells and titrated on Vero cells by plaque assay as previously reported (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>).</p></sec><sec id=\"s4.2\"><title>Mice.</title><p>Axl-deficient (Axl<sup>−/−</sup>) mice were kindly provided by Greg Lemke (Salk Institute for Biological Studies, La Jolla, CA). The F<sub>0</sub> Axl<sup>−/−</sup> mice were generated from C57BL/6J mice and were mated with F<sub>0</sub> wild-type (Axl<sup>+/+</sup>) C57BL/6J mice to generate F<sub>1</sub> heterozygous (Axl<sup>+/−</sup>) mice. The F<sub>0</sub> Axl<sup>−/−</sup> mice were then mated with F<sub>1</sub> Axl<sup>+/−</sup> mice to generate F<sub>2</sub> Axl<sup>−/−</sup> mice (experimental group) and littermate F<sub>2</sub> Axl<sup>+/−</sup> mice (control group). The littermate Axl<sup>+/−</sup> mice are ideal controls for Axl<sup>−/−</sup> mice due to them being the same age and having similar nutritional status, living status, and genetic background. The mice were bred and maintained under a specific-pathogen-free animal facility at Capital Medical University, Beijing. The genotypes were identified using PCR, and the following three primers were used: Wt, 5′-GCCGAGGTATAGTCTGTCACAG-3′; Mut, 5′-TTTGCCAAGTTCTAATTCCATC-3′; and WtMut, 5′-AGAAGGGGTTAGATGAGGAC-3′. The sizes of PCR products are 350 bp for Axl<sup>+/+</sup> mice, 350 and 200 bp for Axl<sup>+/−</sup> mice, and 200 bp for Axl<sup>−/−</sup> mice.</p><p>Four-week-old female BALB/c mice were purchased from Vital River Laboratories (Beijing, China) and used for validating the protection of IL-1Ra during JEV infection.</p></sec><sec id=\"s4.3\"><title>Animal infection.</title><p>Axl<sup>+/−</sup> and Axl<sup>−/−</sup> mice were i.p. injected with 10<sup>4</sup>, 10<sup>5</sup>, or 10<sup>6</sup> PFU of JEV in 200 μl of phosphate-buffered saline (PBS) or just 200 μl of PBS in mock-treated mice. For intracerebral infection, mice were intracerebrally injected with 10<sup>3</sup> PFU of JEV in 20 μl of PBS. For bioluminescent imaging, mice were i.p. injected with 5 × 10<sup>4</sup> PFU of Rluc-JEV in 200 μl of PBS.</p></sec><sec id=\"s4.4\"><title>Scoring the signs of JEV infection.</title><p>To quantitatively analyze the severity of JEV infection, signs were scored according to the following standard: well, 0; sluggish, 1; partially ruffled fur, 2; wholly ruffled fur, 3; hunched back, 4; one limb paralysis, 5; two limb paralysis, 6; more than two limb paralysis, 7; and death, 8.</p></sec><sec id=\"s4.5\"><title>H&E staining.</title><p>Brains of JEV-infected or mock-treated mice were collected at 7 dpi. The brains were immersed in 4% paraformaldehyde (PFA; in PBS) for paraffin embedding and sectioning. Paraffin-embedded brains were sagittally sectioned into 5-μm slides, which were then stained with hematoxylin and eosin (H&E) according to a standard protocol. The analyses of H&E staining results were completed by using Image-Pro Plus 6.0 software.</p></sec><sec id=\"s4.6\"><title>IF staining.</title><p>For immunofluorescent (IF) staining, tissue sections or cell monolayers were subjected to membrane permeability with 0.5% Triton (in PBS) for 5 min and blockage with 1% bovine serum albumin (BSA) for 1 h; then, they were incubated with specific primary antibodies (1:250 to 1:300) at 4°C overnight and incubated with Alexa Fluor 488-labeled donkey anti-mouse IgG antibody (1:500; catalog number A21202; Thermo) and/or Alexa Fluor 594-labeled donkey anti-rabbit IgG antibody (1:500; A21207; Thermo) at room temperature for 50 min. The primary antibodies used in IF staining include anti-JEV serum (self-made), anti-NeuN (ab104224; Abcam), anti-GFAP (ab7260; Abcam), anti-Iba1 (10904-1-AP; Proteintech), anti-CD31 (ab9498; Abcam), anti-claudin-5 (AF0130; Affinity Biosciences), anti-Occludin (71-1500; Thermo), anti-ZO-1 (61-7300; Thermo), anti-F4/80 (ab100790; Abcam), anti-IL-1α (DF6893; Affinity Biosciences), anti-GSDMD-N (AF4013; Affinity Biosciences), anti-RIPK1 (AF7377; Affinity Biosciences), anti-caspase-8 (ab227430; Abcam), anti-caspase-9 (ab202068; Abcam), anti-caspase-3 (9664S; Cell Signaling Technology [CST]), anti-PI3K-p110 (AF5112; Affinity Biosciences), and anti-pAkt (AF0016; Affinity Biosciences). The analyses of IF staining results were accomplished using Image-Pro Plus 6.0 software. Specifically, the IF staining images were collected under fixed excitation light intensity and exposure time. For each slide from a mouse, at least eight random scopes were collected under appropriate magnification. Then, the original images were subjected to fluorescent intensity gauge for specific staining signals by using the count/size function of Image-Pro Plus 6.0 software. The selected measurements were area and density (red). The mean density of all software-identified specific staining signals was extracted as the fluorescent intensity for an image (scope). The average fluorescent intensity of at least eight images (scopes) for each slide was defined as the fluorescent intensity for an independent mouse.</p></sec><sec id=\"s4.7\"><title>RNA extraction in serum and major target organs.</title><p>Serum, brain, spleen, liver, and kidney were collected at 1, 4, and 7 dpi. The total RNA in serum was extracted using the EasyPure viral DNA/RNA kit (ER201-01; TransGen). The total RNA in organs was extracted using the TransZol reagent (ET101-01; TransGen).</p></sec><sec id=\"s4.8\"><title>Quantitative real-time PCR.</title><p>For viral load detection, a previously reported (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>) 6-carboxyfluorescein (FAM)-6-carboxytetramethylrhodamine (TAMRA) probe-based quantitative real-time PCR (qRT-PCR) method was used to quantify JEV genome RNA copies in the cell supernatant, serum, and brain. We also developed an SYBR-based qRT-PCR method to reflect viral loads in spleen, liver, and kidney (in these organs, viral loads were quite low), and the results were expressed as JEV copies/glyceraldehyde-3-phosphate dehydrogenase (GAPDH) copies. The primer set for JEV is sense, 5′-CGTTTCGTGCTGGCTCTTAT-3′; and antisense, 5′-CCAAGTTCTCGTTTGAAACT-3′. The primer set for GAPDH is sense, 5′-CCTGGAGAAACCTGCCAAGT-3′; and antisense, 5′-GGAGTTGCTGTTGAAGTCGC-3′. For brain tight junction RNA quantification, a previously reported (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>) qRT-PCR method was modified and used. Specifically, the primer sets for brain tight junctions (claudin-5, occludin, and ZO-1) were kept the same as the reported method, while the normalization control was GAPDH in our system rather than HRPT (hypoxanthine phosphoribosyltransferase gene) used by the reported method.</p></sec><sec id=\"s4.9\"><title>Isolation, culture, and infection of primary cells.</title><p>To isolate peritoneal macrophages, 4-week-old mice were euthanized and disinfected, followed by i.p. injection with 5 ml of DMEM, abdominal massage for 3 min, and incubation for 7 min. Then, the injected DMEM in abdominal cavity was collected and centrifuged at 2,000 rpm for 10 min. The resulting cell pellet was washed and recovered in DMEM supplemented with 10% FBS. The cells were cultured for 4 h and then washed with PBS to remove nonadherent cells; finally, fresh DMEM supplemented with 10% FBS was added. Isolated peritoneal macrophages were stained with an anti-F4/80 antibody to monitor the cell purity. And using this method, the macrophage purity can be no less than 98%. For infection, peritoneal macrophages were infected with JEV at a multiplicity of infection (MOI) of 1 for 1.5 h.</p><p>To isolate splenocytes, the spleen was collected from mice and ground on a 200-mesh filter. The filtrate was collected and subjected to density gradient centrifugation with 1× mouse lymphocyte separation medium (DKW33R0100; Dakewe). The splenocytes were cultured with 10% FBS RPMI 1640 medium. For infection, splenocytes were infected with JEV at an MOI of 1 for 1.5 h.</p></sec><sec id=\"s4.10\"><title>BLI of Rluc-JEV infection in mice.</title><p>Axl<sup>+/−</sup> (<italic>n</italic> = 15) and Axl<sup>−/−</sup>(<italic>n</italic> = 15) mice were i.p. injected with 5 × 10<sup>4</sup> PFU of Rluc-JEV. The mice were subjected to bioluminescence detection by a bioluminescence detecting system (PerkinElmer, USA) at 2, 4, 5, 8, and 10 dpi. Each mouse was i.p. injected with 200 μl of 0.148 mM ViviRen substrate (P1232; Promega) 10 min prior to immediate imaging for 90 s at 8 bins.</p></sec><sec id=\"s4.11\"><title>BBB permeability evaluation.</title><p>Three independent methods were used to measure BBB permeability, namely, Evans blue (EB) leakage test, sodium fluorescein (NaF) leakage test, and endogenous IgG leakage detection.</p><p>For EB brain leakage test, mice were intravenously injected with 100 μl of a 2% EB (E808783; Macklin) solution (in normal saline). After 45 min to allow for the circulation of EB, the mice were anesthetized with 200 μl of a 3.5% chloral hydrate solution. The anesthetized mice were then transcardially perfused with 30 ml of normal saline to completely remove EB in circulation. The mouse brains were then collected, weighed, and homogenized in dimethylformamide (DMFA) (200-mg brain tissues/500 μl DMFA). The homogenates were kept at 60°C for 24 h, followed by centrifugation at 12,000 × <italic>g</italic> for 5 min. A total of 100 μl of the resulting supernatant was transferred into a 96-well plate to measure absorbance at 620 nm. The content of EB in the brain was calculated according to the standard curve and expressed as EB content (μg) per brain tissues (g). To facilitate the linear correlation analysis between brain viral load (log<sub>10</sub>) and BBB permeability, the EB content was normalized to the average EB content of Axl<sup>+/−</sup> mice at 0 dpi.</p><p>A NaF leakage test was performed according to a method previously reported (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). Briefly, mice were i.p. injected with 100 μl of 100 mg/ml NaF. Then, circulating blood was gleaned to separate serum. The serum (60 μl) was mixed with 60 μl of 15% trichloroacetic acid (TCA) and centrifuged. The supernatant was then mixed with 30 μl of 5 M NaOH and 7.5% TCA, and NaF content was measured as described below. Euthanized mice were transcardially perfused with 30 ml of normal saline. The brain tissues (200 mg) were homogenized in 7.5% TCA (600 μl) and centrifuged to separate supernatant. The supernatant (120 μl) was mixed with 30 μl of 5 M NaOH. NaF content in the mixture was determined using a spectrophotometer (Multiskan spectrum; Thermo Scientific) with excitation at 485 nm and emission at 530 nm. A standard curve (0 to 25 μg/ml) was used to calculate the NaF content in samples. NaF leakage into brain parenchyma was expressed as brain NaF content/serum NaF content to normalize different blood levels of the dye.</p><p>For endogenous IgG detection, brains were embedded in OCT and cryo-sectioned into 6-μm slides, anti-claudin-5 antibody was used to demonstrate BBB, and Alexa Fluor 488-labeled donkey anti-mouse IgG was used to visualize endogenous IgG.</p></sec><sec id=\"s4.12\"><title>Multiplex immunoassays and enzyme-linked immunosorbent assay.</title><p>Axl<sup>+/−</sup> and Axl<sup>−/−</sup> mice were i.p. injected with 10<sup>4</sup> PFU of JEV, and sera were collected at 1 dpi and 7 dpi. Supernatants from cultured cells were collected at indicated time points. The sera were subjected to a bead-based immunoassay (Aimplex multiplex immunoassays for flow; Beijing Quantobio Co., Ltd.) to measure a panel of cytokines (IL-1α, IL-1β, IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ, CCL2, and CCL5). Specially, the contents of IL-1α in peritoneal washes, supernatants of peritoneal macrophages, and other cells were gauged by enzyme-linked immunosorbent assay (ELISA) (ELM-IL1a; Raybiotech).</p></sec><sec id=\"s4.13\"><title>R428 treatment.</title><p>bEND.3 cells were pretreated with culture medium containing 1 μM R428 (21523; Cayman Chemical) or vehicle (0.1% DMSO) for 30 min and then infected with JEV at an MOI of 1 for 1.5 h in the presence of 1 μM R428 or vehicle. After infection, the viral inoculates were completely removed and replaced with fresh culture medium containing 1 μM R428 or vehicle. The cell supernatants were collected at 24 hpi and subjected to IL-1α measurement.</p></sec><sec id=\"s4.14\"><title>Trypan blue staining.</title><p>The isolated peritoneal macrophages were stained with 0.04% of a TB solution (final concentration) for 3 min and then immediately smeared on a slide for cell death counting and photographing.</p></sec><sec id=\"s4.15\"><title>TUNEL staining.</title><p><italic>In situ</italic> peritoneal macrophages were isolated from JEV-infected mice and immediately made into smears, followed by TUNEL staining with the one-step TUNEL apoptosis assay kit (C1086; Beyotime).</p></sec><sec id=\"s4.16\"><title>Immunoblotting.</title><p>The isolated peritoneal macrophages were lysed with 100 μl of 1× cell lysis buffer (9803S; CST), ultrasonicated, mixed with 25 μl of 5× SDS loading buffer (P0015; Beyotime), and finally boiled for 15 min. The resulting samples were subjected to an immunoblotting protocol (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>) for the detection of specific proteins.</p></sec><sec id=\"s4.17\"><title>Drug administration.</title><p>Mice (Axl<sup>−/−</sup>) in the IL-1α- or IL-6-treated group were i.p. injected with 200 ng of a recombinant mouse IL-1α (50114-MNAE, Sino Biological) or 60 ng of a recombinant mouse IL-6 (50136-MNAE; Sino Biological) per mouse 30 min prior to i.p. injection with 10<sup>4</sup> PFU of JEV; then, a daily i.p. injection of 200 ng of IL-1α or 60 ng of IL-6 was administered. Mice (including wild-type BALB/c mice and Axl<sup>−/−</sup> mice) in IL-1Ra-treated group were i.p. injected with 10 μg of a recombinant rat IL-1Ra (a natural IL-1α antagonist; 80073-R01H; Sino Biological) per mouse 1 h prior to i.p. injection with 10<sup>4</sup> PFU of JEV; then, an every-2-day i.p. injection of 5 μg of IL-1Ra was administered. Mice (including wild-type BALB/c mice and Axl<sup>−/−</sup> mice) in vehicle-treated group were i.p. injected with 200 μl of sterile normal saline 30 min prior to i.p. injection with 10<sup>4</sup> PFU of JEV; then, a daily i.p. injection of 200 μl of sterile normal saline was administered. Axl<sup>+/−</sup> mice were i.p. injected with 1.25 mg of LY294002 (PI3K inhibitor; HY-10108; MCE) per mouse 1 h prior to i.p. injection with 10<sup>4</sup> PFU of JEV. At 24 hpi, peritoneal macrophages were isolated and made into smears for cell death studies.</p></sec><sec id=\"s4.18\"><title>Statistical analysis.</title><p>The quantitative data were expressed as mean ± SEM, and all the statistical analyses were performed on GraphPad Prism 7.00 software. Student’s <italic>t</italic> test or Welch’s correction and Mann-Whitney test were used to compare quantitative data between two groups. Two-way analysis of variance (ANOVA) and multiple <italic>t</italic> tests were used to compare grouped quantitative data. Specially, the log-rank test was used to compare survival curves. Fisher’s exact test was used to compare the probability of JEV entry into the brain. The rank-sum test was used to compare sign scores. A linear regression model was used to analyze the correlation between brain viral load and BBB permeability and the correlation between IL-1α content in serum and IL-1α content in peritoneal wash. Comparisons were considered statistically different when the <italic>P</italic> value was <0.05.</p></sec><sec id=\"s4.19\"><title>Ethical statement.</title><p>All the animal experiments were reviewed and approved by the Experimental Animal Welfare and Animal Ethics Committee of Capital Medical University, Beijing, China (permission code, EEI-2015-048; permission date, 20 April 2015).</p></sec></sec></body><back><ack><title>ACKNOWLEDGMENTS</title><p>This study was supported by National Natural Science Foundation of China (81671971, 81871641, 81972979, U1902210, and U1602223), Scientific Research Plan of Beijing Municipal Education Committee (KM201710025002), and Key Project of Beijing Natural Science Foundation B (KZ201810025035). The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.</p><p>Z.Y.W. designed and performed the experiments, analyzed the data, and wrote and reviewed the manuscript. Z.D.Z. helped perform some of animal experiments. D.Y.F. maintained cells, viruses, and reagents. C.F.Q. and D.S.H. helped to design the experiments and analyzed the data. H.N.Z., P.G.W., and J.A. conceived the project, analyzed the data, and reviewed the manuscript. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849210</article-id><article-id pub-id-type=\"pmc\">PMC7431816</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00748</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Unfavorable Outcomes Related to Endovascular Treatment of Giant Vertebrobasilar Aneurysms</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Miao</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Liang</surname><given-names>Huaxin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Jie</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/744996/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Neurosurgery, The China-Japan Union Hospital of Jilin University</institution>, <addr-line>Changchun</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Neurology, The China-Japan Union Hospital of Jilin University</institution>, <addr-line>Changchun</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Osama O. Zaidat, Northeast Ohio Medical University, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Diogo C. Haussen, Emory University, United States; Waldo Rigoberto Guerrero, University of South Florida, United States</p></fn><corresp id=\"c001\">*Correspondence: Jie Wang <email>wangjie77@163.com</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Endovascular and Interventional Neurology, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>748</elocation-id><history><date date-type=\"received\"><day>14</day><month>7</month><year>2019</year></date><date date-type=\"accepted\"><day>17</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Li, Liang and Wang.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Li, Liang and Wang</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Background:</bold> Giant vertebrobasilar aneurysms (GVBAs) have an unfavorable natural history if left untreated and often pose a sizeable challenge to endovascular treatment. The aim of this study was to analyze the angiographic and clinical outcomes of GVBAs treated by various endovascular procedures.</p><p><bold>Methods:</bold> Between January 2010 and September 2018, 27 patients with 27 GVBAs treated endovascularly were enrolled in this consecutive study. The clinical and angiographic features, treatment modalities, and outcomes were analyzed.</p><p><bold>Results:</bold> The patient cohort comprised 21 men (77.8%) and 6 women (22.2%) of mean age 42.7 ± 18.9 years (range, 6–65 years). The most common presenting symptom was compressive symptoms, present in 15 patients (55.6%). None of the GVBAs was ruptured. Of the 27 GVBAs, 23 aneurysms were dissecting aneurysm with intramural hematoma and 4 aneurysms were saccular. Regarding treatment approach, internal trapping was used in 5 aneurysms, stent-assisted coil embolization in 10, sole stenting in 4, and flow diverters in 8. Overall, 12 patients (44.4%) had an unfavorable angiographic or clinical outcome: 3 patients presented with post-operative complications and subsequent death, and 9 with poor prognosis during follow-up.</p><p><bold>Conclusions:</bold> Patients with GVBAs may not benefit from endovascular treatment. Newer-generation devices are necessary to provide more optimal therapy for the management of these complex lesions.</p></abstract><kwd-group><kwd>giant vertebrobasilar aneurysms</kwd><kwd>endovascular treatment</kwd><kwd>outcome</kwd><kwd>complications</kwd><kwd>poor prognosis</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">National Natural Science Foundation of China<named-content content-type=\"fundref-id\">10.13039/501100001809</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"3\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"30\"/><page-count count=\"8\"/><word-count count=\"4726\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Giant vertebrobasilar aneurysms (GVBAs), intracranial aneurysms with a maximum diameter of at least 25 mm originating from the vertebral and basilar artery, are rare and always challenging because of their complex neuroanatomy and pathophysiologic features (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>). Owing to minimal invasiveness and lower risk, endovascular treatment of GVBAs is considered to be safer than open surgery (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Although previous studies showed that endovascular treatment of GVBAs was always associated with high recurrence rates (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>), these studies did not include cases with implantation of a flow diverter. Therefore, the aim of this study was to analyze the angiographic and clinical outcome of GVBAs treated endovascularly with stent-assisted coiling, overlapping stents, internal trapping, or flow diverters. The findings of this study should expand the knowledge base regarding preferences in clinical practice.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>Patient Selection</title><p>This retrospective study was approved by our institutional ethics committee. Written informed consent was provided by patients or their relatives during hospitalization, and the privacy of the patients was strictly protected. Between January 2010 and September 2018, a total of 27 patients with 27 GVBAs treated endovascularly were enrolled in this study. The exclusion criteria included the following: (1) pre-existing diagnoses of arteritis, fibromuscular dysplasia, iatrogenic aneurysms, or pseudoaneurysms; (2) history of traumatic and iatrogenic injury; (3) extracranial dissecting aneurysms extending into the intracranial segment; (4) GVBAs without endovascular treatment; (5) aneurysm size <25 mm; (6) vertebrobasilar dolichoectasia. The information collected and analyzed included patient demographics (age and sex), location and angiographic features of the GVBAs, endovascular treatment selected, treatment complications, follow-up interval, and angiographic and clinical follow-up outcomes.</p></sec><sec><title>Endovascular Procedures</title><p>Endovascular treatment was performed under general anesthesia and systemic intravenous heparin. Patients were treated with internal trapping, overlapping stents, stent-assisted coiling, or flow diverters as appropriate. For internal trapping, various platinum coils were used to occlude the dissecting aneurysm and the parent artery. Balloon occlusion test was used to determine whether sufficient collateral circulation compensate after the vessel sacrificed. Internal trapping was our first choice if the GVBAs did not involve the dominant vertebral artery or the important arterial branches (such as posterior inferior cerebellar artery, anterior inferior cerebellar artery, or other large perforating arteries), and the collateral blood supply were confirmed to be away from the section of the blood vessel harboring the aneurysm. In contrast, if a GVBA was dominant without sufficient collaterals, the GVBA was treated with reconstructive methods using stents alone and flow diverter or with coiling. However, flow diverter was the first choice if a GVBA did not involve important arterial branches (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). If reconstructive endovascular procedures were chosen, patients were premedicated with a dual-antiplatelet regimen (75 mg of clopidogrel and 100 mg of aspirin daily) at least 5 days before the procedure. After the procedure, patients treated with a conventional stent were given 75 mg/d clopidogrel for 6 weeks and 100 mg/d aspirin for 6 months, while patients treated with a flow diverter were given 75 mg/d clopidogrel for 3 months and 100 mg/d aspirin thereafter.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Flow chart of the decision concerning endovascular treatment methods for giant vertebrobasilar aneurysms.</p></caption><graphic xlink:href=\"fneur-11-00748-g0001\"/></fig></sec><sec><title>Materials</title><p>Various types of embolic materials used in the endovascular treatment included detachable coils such as the Matrix coil (Cordis, New Brunswick, NJ, USA) and Microplex coil (MicroVention, Aliso Viejo, CA, USA). Neurovascular stents were used to reconstruct the dissected artery, such as Enterprise (Cordis Neurovascular, Miami, FL, USA), Solitaire AB (Ev3, Irvine, CA, USA), and Low-profile Visualized Intraluminal Support (MicroVention Terumo, Tustin, CA, USA) stents, a silk flow-diverter stent (Balt Extrusion, Montmorency, France), and a pipeline embolization device (Covidien/ev3 Neurovascular, Irvine, CA, USA).</p></sec><sec><title>Follow-Up</title><p>All patients were recommended to undergo a 6-month angiographic follow-up, and a magnetic resonance (MR) angiogram or computed tomography angiogram performed annually thereafter. Any aneurysm that displayed an increasing percentage of contrast filling of the aneurysmal sac on follow-up angiography or presented with more than 1-mm enlargement on MR imaging was considered an unfavorable angiographic outcome (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). The occlusion rate (<90%) for the aneurysms with flow diverter and re-patency of parent artery for the aneurysms with internal trapping were defined as unfavorable angiographic outcomes (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Otherwise, the aneurysm was regarded as a favorable angiographic outcome. Patients' clinical outcomes were measured by the Glasgow Outcome Scale score at follow-up visits or by a telephone interview. The GOS score of 5 or 4 was considered a favorable clinical outcome, and scores of 3, 2, or 1 was unfavorable (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><p>Between January 2010 and September 2018, 27 patients with 27 GVBAs were enrolled in the study. The clinical characteristics, imaging features, endovascular treatment therapies, and follow-up outcomes are listed in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>. Among the 27 patients, 12 (44.4%) had an unfavorable angiographic or clinical outcome. Three of these patients presented with post-operative complications and subsequent death while 9 carried a poor prognosis during follow-up.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Details of 27 patients with giant vertebrobasilar aneurysms.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Case No</bold>.</th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Gender</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Age</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Location</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Size (mm)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Aneurysm type</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Initial symptom</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Endovascular treatment</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Stents</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Angiographic follow-up result</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Angiographic follow-up time(m)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Clinical follow-up result (GOS)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Clinical follow-up time(m)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">54</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">39.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Solitaire × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">65</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compessive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 1 + Solitaire × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">27.7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compessive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sole stenting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 1 + Solitaire × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">49</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compessive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 1 + Solitaire × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sole stenting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">54</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ischemic stroke</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 1 + Solitaire × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 2 + LVIS × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">51</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36.5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">61</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal trapping</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">52</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38.3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal trapping</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">49</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sole stenting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sole stenting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LVIS × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">58</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">52</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Incidental</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enterprise × 2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">54</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Saccular</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diverter with adjunctive coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PED × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VBJ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Saccular</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diverter with adjunctive coiling + contralateral VA sacrifice</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PED × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Saccular</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diverters</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PED × 4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal trapping</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">26.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ischemic stroke</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diverter</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PED × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal trapping</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal trapping</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">23</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">57</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diverter</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PED × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VBJ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Saccular</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diverter with adjunctive coiling + contralateral VA sacrifice</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Silk × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal trapping</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal trapping</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">26</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diverter with adjunctive coiling</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PED × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">27</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">M</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VBJ</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dissecting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Compressive symptom</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diverter with adjunctive coiling + contralateral VA sacrifice</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PED × 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Favorable</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td></tr></tbody></table><table-wrap-foot><p><italic>M, male; F, female; VA, vertebral artery; BA, basilar artery; VBJ, vertebrobasilar junction; N/A, not applicable; GOS, Glasgow Outcome Scale score</italic>.</p></table-wrap-foot></table-wrap><sec><title>Clinical and Imaging Characteristics</title><p>The patient cohort comprised 21 men (77.8%) and six women (22.2%). The mean age of the patients was 42.7 ± 18.9 years (ranging from 6 to 65 years). The most common presenting symptom was compressive symptoms, present in 15 patients (55.6%). Nine patients (33.3%) presented with headache, two patients (7.4%) with ischemic stroke, and one (3.7%) with asymptomatic lesion. None of the GVBAs was ruptured. Among the 27 GVBAs, 23 aneurysms were fusiform aneurysm with intramural hematoma (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>) and four aneurysms were saccular type (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>). The average maximal diameter of aneurysms was 29.2 ± 4.4 mm (range, 25.0–39.4 mm). The location of GVBAs was the vertebral artery in 21 cases, basilar artery in three, and vertebral–basilar junction in three.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>A 6-year-old girl with a giant vertebrobasilar dissecting aneurysm. <bold>(A)</bold> Magnetic resonance imaging showed a giant vertebrobasilar dissecting aneurysm with intramural hematoma. <bold>(B)</bold> Left vertebral anteroposterior angiogram revealed the aneurysm. <bold>(C)</bold> Left vertebral artery and the aneurysm were occluded with coils at lateral angiogram. <bold>(D)</bold> Right vertebral was patency after the procedure at lateral angiogram. <bold>(E,F)</bold> Left and right vertebral lateral angiogram at follow-up showed the aneurysm recanalized (arrows).</p></caption><graphic xlink:href=\"fneur-11-00748-g0002\"/></fig><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>A 55-year-old woman with a giant saccular aneurysm at the vertebrobasilar junction. <bold>(A,B)</bold> Bilateral vertebral angiogram showed the aneurysm. <bold>(C)</bold> The right vertebral and basilar arteries were reconstructed with a pipeline embolization device. <bold>(D)</bold> The left vertebral artery and the aneurysm were occluded with coils. <bold>(E,F)</bold> Right vertebral angiogram showed complete occlusion of the aneurysm after the procedure. However, the patient died of brainstem function failure 3 h later after worsening of mass effect.</p></caption><graphic xlink:href=\"fneur-11-00748-g0003\"/></fig></sec><sec><title>Endovascular Treatment Modality and Outcome</title><p>Endovascular treatment was technically feasible in all 27 cases. Five dissecting GVBAs received internal trapping. Ten aneurysms underwent stent-assisted coil embolization (single stent, <italic>n</italic> = 1; two stents, <italic>n</italic> = 6; three stents, <italic>n</italic> = 3). Four aneurysms were treated with sole stenting (single stent, <italic>n</italic> = 1; two stents, <italic>n</italic> = 2; three stents, <italic>n</italic> = 1). Eight aneurysms were treated with flow diverters (single flow diverter, <italic>n</italic> = 2; single flow diverter with coils, <italic>n</italic> = 5; four flow diverters, <italic>n</italic> = 1).</p><p>None of the patients had intraoperative complications. Among the 27 patients, three patients who underwent flow-diverter deployment suffered from periprocedural complications and subsequent death: one patient died from aneurysmal hemorrhage 4 days with four overlapping flow diverters and the other two died of brainstem failure resulting from compression with single flow diverter adjunctive coils. Clinical follow-up was established for the remaining 24 patients with a mean duration of 14.5 ± 7.1 months (range, 6–32 months); however, angiographic follow-up was provided only for 23 (92.3%) patients (6.9 ± 4.0 months), as one patient died of longer complication (brainstem failure resulting from compression) relatively early in the follow-up period. Of the 24 patients, 20 had a favorable clinical outcome and four with unfavorable clinical outcome during the follow-up period. Eight presented with an unfavorable angiographic outcome, three of whom ultimately died because of severe brainstem compression. Of the eight angiographically unfavorable patients, two were treated initially with internal trapping, three with stent-assisted coils, and three with overlapping stents. Four patients underwent repeat procedures. Despite two patients undergoing repeat internal trapping, the MR image still showed enlargement of the GVBAs at 2-year follow-up. The other two patients were re-treated by stent-assisted coiling and overlapping stents, respectively. We compared and summarized the unfavorable outcomes with different treatments for GVBAs (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>).</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Comparison of unfavorable outcomes with different treatments for giant vertebrobasilar aneurysms.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Stent-assisted coiling, <italic>n</italic> = 10</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Sole stenting, <italic>n</italic> = 4</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Internal trapping, <italic>n</italic> = 5</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Flow diverter, <italic>n</italic> = 8</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable angiographic outcomes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (30.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (75.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (40.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0.0%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable clinical outcomes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (30.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (20.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (37.5%)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unfavorable angiographic or clinical outcomes</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (40.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (75.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (40.0%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (37.5%)</td></tr></tbody></table></table-wrap></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>GVBAs are always associated with high morbidity and mortality during the course of natural history if left untreated (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). Although these lesions pose an increased risk to treatment, intervention is usually considered necessary because of the rapidly changing morphology and progressive mass effect (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Therefore, the primary purpose of this study was to evaluate the safety and efficacy of endovascular treatment for GVBAs. Of the 27 patients with 27 GVBAs included, 12 patients presented with an unfavorable angiographic or clinical outcome. Of these 12 patients, eight GVBAs demonstrated an unfavorable angiographic outcome at follow-up and six patients died of post-operative complications and brainstem compression during the follow-up period. The outcome of treating GVBAs with current endovascular modalities therefore seems questionable and unpredictable. Our results suggest that patients with GVBAs may not derive optimal benefit from endovascular treatment.</p><p>At present, appropriate endovascular treatment of GVBAs is very challenging. As summarized in <xref rid=\"T3\" ref-type=\"table\">Table 3</xref>, there is significant high mortality, permanent morbidity rate of GVBAs with endovascular treatment. Treatment decisions about endovascular modalities are based on evaluation of the location, collateral blood supply, and the important arterial branches if involved (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). All features of GVBAs must be taken into account to undertake the best approach for each patient. If there is a sufficient compensatory blood supply, internal trapping with the occlusion of aneurysm, and parent artery is our first choice. Although previous studies have proved that internal trapping is an effective therapy for this lesion, with a satisfactory long-term outcome (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>), it may be not effective in some cases. For instance, GVBAs could continue to enlarge even after deployment of internal trapping. Iihara et al. (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>) reported that after a partially thrombosed giant aneurysm of the vertebral artery was treated by internal trapping, the aneurysm continued to enlarge. Formation of intramural hematoma may be a necessary critical event for GVBAs to become progressive (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). The hypothesis for recurrent GVBAs after treatment with internal trapping states that the vasa vasorum in the intramural hematoma is fragile and could cause repetitive intramural hemorrhage, resulting in enlargement of the aneurysm (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). In line with this implication, in our study aneurysm recurrence was observed in four GVBAs that were treated or re-treated by internal trapping.</p><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Literature review of endovascular treatment for giant vertebrobasilar aneurysms.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Author, year</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>No of Pts/GVBAs</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean (range) age, years</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Size(mm) Median (range)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Treatment method (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Perioperative IE (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Radiologic follow-up (months) median (range)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>CO at final follow-up (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Retreatment (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Good clinical outcome at follow-up (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Delayed IE (%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Morbidity, % (mortality, %)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Limaye et al. (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22/22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37 (13–63)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">>25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal trapping (59.0), Flow reversal (22.7), Flow diversion (9.1), Coiling (4.6), Stent-assisted coiling (4.6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(9.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(1–60)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 (94%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14 (63.6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22.7 (18.2)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ge et al. (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7/7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43.9 (2–77)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31 (25–40)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stent-assisted coiling (71.4), Flow diversion (14.3), Coiling (14.3),</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.5 (7–22)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (14.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (14.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (85.7)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lubicz et al. (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13/13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47.9 (15–65)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">35 (25–60)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Internal trapping (100)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (7.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.9 (12–48)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (92.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (7.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12 (92.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (7.7)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Siddiqui et al. (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2/2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42 (42)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36.4 (35.6–37.1)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diversion (100)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (100)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (100)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Meckel et al. (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8/8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55.1 (34–72)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">> 25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diversion (100)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (12.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16.6 (0.5–34)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (37.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (12.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (50)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (12.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (50)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Toth et al. (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3/3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">46.6 (30–60)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.3 (27–29)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Flow diversion (100)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (66.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 (9–18)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2 (66.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (33.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.3 (33.3)</td></tr></tbody></table><table-wrap-foot><p><italic>Pts, patients; GVBAs, giant vertebrobasilar aneurysms; IE, ischemic events; CO, complete occlusion</italic>.</p></table-wrap-foot></table-wrap><p>GVBAs treated by conventional stents with or without coiling had a high rate of recanalization, for several possible reasons (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>–<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). First, total occlusion with dense packing was difficult in the case of dissecting aneurysms because of the complex geometry and irregular shape, without a definitive aneurysm neck and a fragile vessel wall (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Second, owing to their high porosity, conventional stents have a limited flow-diversion effect (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Although conventional stents are able to maintain the patency of the parent artery, they cannot completely occlude the persistent blood flow into the aneurysm, which could result in coil compaction. Third, GVBAs were physiologically active and dynamic (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B28\" ref-type=\"bibr\">28</xref>), as rapid change or expansion of GVBAs was observed (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). This may be a reason for the aneurysmal growth and the failure of endovascular treatment. In our study, of the 14 cases treated by conventional stents with or without coil embolization, seven presented with a poor prognosis during the follow-up period.</p><p>Flow diverters have emerged as a promising option for GVBAs, and some studies have reported a satisfactory outcome using flow diverters (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Compared with conventional stents, flow diverters are characterized by more metal coverage, providing a scaffold for neointimal tissue formation and thus completing reconstruction of the parent artery. Moreover, flow diverters could cause stagnation of blood flow and promote thrombosis within the aneurysmal sac by its fluid-diverting effect. However, flow diverters have been associated with the risk of catastrophic complications after endovascular treatment. Siddiqui et al. (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>) reported that among seven patients with symptomatic vertebrobasilar aneurysms who underwent endovascular treatment with flow diverters, at the last follow-up evaluation four patients had died (two patients with post-treatment aneurysm rupture and the other two lacking improvement in neurologic status) and one presented with severe disability. Similarly, Meckel et al. (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>) reported that four of 10 patients with complex vertebrobasilar junction aneurysms treated with flow diverters died as a result of sequelae of subarachnoid hemorrhage, late flow-diverter thrombosis, progressive mass effect, and delayed intracranial hemorrhage, respectively. In our study, among the seven patients treated with flow diverters, three died of subarachnoid hemorrhage or progressive mass effect while the other four had a good prognosis at follow-up. Therefore, clinicians should be cautious in the decision-making process regarding whether or when a flow diverter should be applied in the posterior circulation.</p><p>Treatment of GVBAs is usually necessary because of their unfavorable natural history. However, previous studies observed that mortality and morbidity seem to be higher in symptomatic posterior circulation aneurysms after endovascular treatment (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Perhaps the best strategy for GVBAs is timely discovery and timely treatment before these lesions become symptomatic and chronically enlarged. Regular physical examination including MRI and MRA for the patients with high risk factor (such as family history of GVBAs), might be an optional method for discovery of these lesions early. Moreover, the assessment of HRMRI in GVBAs patients might be identified the high-risk patients with evidence of progression on imaging (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). However, future studies are necessary to clarify these points and provide optimal treatment for patients with GVBAs and further advancements are necessary to provide optimal treatment for patients with GVBAs.</p><p>There are limitations to this study. As it is a retrospective study with a limited number of patients, more data on larger numbers of GVBAs are required. Moreover, different interventional materials were used, and there may be patient selection bias.</p></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>The ideal approach to the treatment of GVBAs remains debatable. Endovascular treatment may not halt the progressive course of GVBAs, and continuous follow-up is required. Newer-generation devices may provide more optimal therapy for the management of these complex lesions.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The datasets analyzed in this article are not publicly available. Requests to access the datasets should be directed to Miao Li, <email>limiao@jlu.edu.cn</email>.</p></sec><sec id=\"s7\"><title>Ethics Statement</title><p>Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>ML contributed to the preparation of the manuscript and data collection. HL contributed to data analysis and interpretation. JW contributed to the experimental design and manuscript revision. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by the National Natural Science Foundation of China (Grant Numbers: 81801158, 81801156, 81471167, and 81671139) and the Special Research Project for Capital Health Development (Grant Number: 2018-4-1077).</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Cell Dev Biol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Cell Dev Biol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Cell Dev. Biol.</journal-id><journal-title-group><journal-title>Frontiers in Cell and Developmental Biology</journal-title></journal-title-group><issn pub-type=\"epub\">2296-634X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850868</article-id><article-id pub-id-type=\"pmc\">PMC7431817</article-id><article-id pub-id-type=\"doi\">10.3389/fcell.2020.00776</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Cell and Developmental Biology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Comprehensive Model for Epidermal Growth Factor Receptor Ligand Binding Involving Conformational States of the Extracellular and the Kinase Domains</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Hajdu</surname><given-names>Tímea</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/956058/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Váradi</surname><given-names>Tímea</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1046886/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Rebenku</surname><given-names>István</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kovács</surname><given-names>Tamás</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Szöllösi</surname><given-names>János</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Nagy</surname><given-names>Peter</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/286086/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Biophysics and Cell Biology, Faculty of Medicine, University of Debrecen</institution>, <addr-line>Debrecen</addr-line>, <country>Hungary</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Doctoral School of Molecular Medicine, Faculty of Medicine, University of Debrecen</institution>, <addr-line>Debrecen</addr-line>, <country>Hungary</country></aff><aff id=\"aff3\"><sup>3</sup><institution>MTA-DE Cell Biology and Signaling Research Group, Faculty of Medicine, University of Debrecen</institution>, <addr-line>Debrecen</addr-line>, <country>Hungary</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Venkaiah Betapudi, Chemical and Biological Defense Division (DHS), United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Sarvenaz Sarabipour, Johns Hopkins University, United States; Paul Van Bergen En Henegouwen, Utrecht University, Netherlands</p></fn><corresp id=\"c001\">*Correspondence: Peter Nagy, <email>nagyp@med.unideb.hu</email>; <email>peter.v.nagy@gmail.com</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Cellular Biochemistry, a section of the journal Frontiers in Cell and Developmental Biology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>8</volume><elocation-id>776</elocation-id><history><date date-type=\"received\"><day>17</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Hajdu, Váradi, Rebenku, Kovács, Szöllösi and Nagy.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Hajdu, Váradi, Rebenku, Kovács, Szöllösi and Nagy</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The epidermal growth factor (EGF) receptor (EGFR) undergoes ligand-dependent dimerization to initiate transmembrane signaling. Although crystallographic structures of the extracellular and kinase domains are available, ligand binding has not been quantitatively analyzed taking the influence of both domains into account. Here, we developed a model explicitly accounting for conformational changes of the kinase and extracellular domains, their dimerizations and ligand binding to monomeric and dimeric receptor species. The model was fitted to ligand binding data of suspended cells expressing receptors with active or inactive kinase conformations. Receptor dimers with inactive, symmetric configuration of the kinase domains exhibit positive cooperativity and very weak binding affinity for the first ligand, whereas dimers with active, asymmetric kinase dimers are characterized by negative cooperativity and subnanomolar binding affinity for the first ligand. The homodimerization propensity of EGFR monomers with active kinase domains is ∼100-times higher than that of dimers with inactive kinase domains. Despite this fact, constitutive, ligand-independent dimers are mainly generated from monomers with inactive kinase domains due to the excess of such monomers in the membrane. The experimental finding of increased positive cooperativity at high expression levels of EGFR was recapitulated by the model. Quantitative prediction of ligand binding to different receptor species revealed that EGF binds to receptor monomers and dimers in an expression-level dependent manner without significant recruitment of monomers to dimers upon EGF stimulation below the phase transition temperature of the membrane. Results of the fitting offer unique insight into the workings of the EGFR.</p></abstract><kwd-group><kwd>EGF receptor</kwd><kwd>ligand binding</kwd><kwd>cooperativity</kwd><kwd>dimerization</kwd><kwd>kinase domain</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Nemzeti Kutatási Fejlesztési és Innovációs Hivatal<named-content content-type=\"fundref-id\">10.13039/501100011019</named-content></funding-source><award-id rid=\"cn001\">K120302</award-id><award-id rid=\"cn001\">GINOP-2.3.2-15-2016-00020</award-id><award-id rid=\"cn001\">GINOP-2.3.2-15-2016-00044</award-id></award-group></funding-group><counts><fig-count count=\"5\"/><table-count count=\"2\"/><equation-count count=\"16\"/><ref-count count=\"69\"/><page-count count=\"16\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>The ErbB/HER family of receptor tyrosine kinases (RTKs) comprises four transmembrane receptors activated by a large number and variety of peptide growth factors (<xref rid=\"B62\" ref-type=\"bibr\">Yarden and Sliwkowski, 2001</xref>). ErbB1, also known as the epidermal growth factor (EGF) receptor (EGFR), is activated by ligand binding followed by tyrosine phosphorylation of its C-terminal domain and recruitment of SH2 and PTB domain-containing proteins (<xref rid=\"B57\" ref-type=\"bibr\">Wagner et al., 2013</xref>). While EGFR is one of the most studied transmembrane receptors, perplexing questions remain about the fine details of its activation (<xref rid=\"B21\" ref-type=\"bibr\">Jovin, 2014</xref>). Although larger clusters of EGFR has also been described (<xref rid=\"B52\" ref-type=\"bibr\">Szabó et al., 2008</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Needham et al., 2016</xref>), dimerization is believed to be the key in regulating receptor activation, a notion strongly supported by crystallographic structures of the extracellular and kinase domains of the receptor (<xref rid=\"B45\" ref-type=\"bibr\">Ogiso et al., 2002</xref>; <xref rid=\"B68\" ref-type=\"bibr\">Zhang et al., 2006</xref>). The tethered or closed structure of the extracellular domain (ECD) undergoes a rearrangement to an extended conformation forming back-to-back, ligand-bound dimers stabilized by the exposed dimerization arm (<xref rid=\"B30\" ref-type=\"bibr\">Lemmon, 2009</xref>; <xref rid=\"B69\" ref-type=\"bibr\">Ziomkiewicz et al., 2013</xref>). Besides this dimeric species, ligand-bound head-to-head dimers (<xref rid=\"B18\" ref-type=\"bibr\">Garrett et al., 2002</xref>) and ligand-free dimeric species, including side-to-side dimers have also been reported (<xref rid=\"B42\" ref-type=\"bibr\">Moriki et al., 2001</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Zanetti-Domingues et al., 2018</xref>). Dimerization is not only mediated by the ECD, but also by the transmembrane (TMD) and intracellular domains under the influence of the lipid environment of the plasma membrane (<xref rid=\"B26\" ref-type=\"bibr\">Kovacs et al., 2015</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Kovács et al., 2016</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Zákány et al., 2020</xref>). The TMD of EGFR contains two dimerization motifs, with the N- and C-terminal ones suggested to stabilize active and inactive dimers, respectively (<xref rid=\"B14\" ref-type=\"bibr\">Fleishman et al., 2002</xref>). The kinase domain (KD) is also capable of forming at least two different dimeric species. The kinase is activated in an asymmetric dimer by a mechanism recapitulating how cyclins activate cyclin-dependent kinases, while the symmetric dimer, although harboring KDs in their active conformation, is unlikely to be capable of signal transduction (<xref rid=\"B29\" ref-type=\"bibr\">Landau et al., 2004</xref>; <xref rid=\"B68\" ref-type=\"bibr\">Zhang et al., 2006</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Jura et al., 2009</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Kovacs et al., 2015</xref>). Most tyrosine kinase inhibitors bind to the ATP-binding pocket of the kinase. Type I inhibitors, including erlotinib, stabilize the active conformation of the kinase, while type II inhibitors, e.g., lapatinib, stabilize its inactive structure (<xref rid=\"B51\" ref-type=\"bibr\">Stamos et al., 2002</xref>; <xref rid=\"B59\" ref-type=\"bibr\">Wood et al., 2004</xref>; <xref rid=\"B67\" ref-type=\"bibr\">Zhang et al., 2009</xref>). Interactions of the juxtamembrane segment with negatively charged lipids in the inner leaflet of the plasma membrane prevent formation of active kinase dimers, while the juxtamembrane segment also forms activating interactions with the KD (<xref rid=\"B22\" ref-type=\"bibr\">Jura et al., 2009</xref>). Although the large variety of dimeric structures is complicated, a scheme seems to emerge in which the asymmetric, active kinase dimer, the TMD dimer stabilized by its N-terminal dimerization motif and the liganded, back-to-back ECD dimer mutually favor each other, since this conformation of the ECD holds the C-terminal dimerization motifs of the TMD apart and the asymmetric KD dimer pulls the N-terminal dimerization motifs of the TMD together (<xref rid=\"B13\" ref-type=\"bibr\">Diwanji et al., 2019</xref>). The finding of coupling between the KD activated by the L858R mutation and the unliganded, dimerization competent ECD also supports the previous model (<xref rid=\"B54\" ref-type=\"bibr\">Valley et al., 2015</xref>). Interactions between different parts of inactive receptors is more controversial. While coupling of the symmetric kinase dimer to the TMD dimer stabilized by its C-terminal dimerization motif is widely accepted, conformational coupling of this symmetric kinase dimer and the transmembrane domains to the ECD is debated. While some evidence suggests that the liganded ECD can couple to both the symmetric and asymmetric kinase dimers (<xref rid=\"B40\" ref-type=\"bibr\">Mi et al., 2011</xref>), a strict linkage between symmetric kinase dimers and the closed conformation of the ECD has also been put forward (<xref rid=\"B4\" ref-type=\"bibr\">Bessman et al., 2014</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Macdonald-Obermann and Pike, 2018</xref>). In addition, the ECD of inactive receptors has been suggested to form alternative structures as well, like side-to-side dimers (<xref rid=\"B42\" ref-type=\"bibr\">Moriki et al., 2001</xref>) or head-to-head oligomers (<xref rid=\"B66\" ref-type=\"bibr\">Zanetti-Domingues et al., 2018</xref>).</p><p>Although significant progress has been made in revealing conformational transitions of isolated receptor parts upon activation, correlations between these receptor states in the full-length protein have not been described due to the flexibility of the juxtamembrane domains and the difficulty of studying structurally homogenous EGFR populations (<xref rid=\"B40\" ref-type=\"bibr\">Mi et al., 2011</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Diwanji et al., 2019</xref>). These correlations implicitly determine the concentration dependence of ligand binding. Oligomerization linkage takes place when a liganded monomer undergoes dimerization differently than its unliganded counterpart revealing ligand-induced conformational transitions. Throughout the paper this phenomenon will be briefly referred to as linkage. When ligands bind cooperatively to a dimer, the dissociation constants for consecutive ligand bindings are not identical implying conformational changes taking place in the dimer. Although this phenomenon has been named homotropic ligand linkage, the term cooperativity has been so widely accepted that it will be used for referring to this phenomenon (<xref rid=\"B60\" ref-type=\"bibr\">Wyman and Gill, 1990</xref>; <xref rid=\"B47\" ref-type=\"bibr\">Pike, 2012</xref>). Analysis of equilibrium ligand binding is often carried out by fitting the Hill equation to data providing the Hill cooperativity coefficient. Although a Hill coefficient different from one is interpreted as a sign of cooperativity, we are going to refer to this phenomenon as apparent or phenomenological cooperativity since linkage and molecular cooperativity collectively determine the Hill coefficient.</p><p>Quantitative EGF binding assays may produce concave-up Scatchard plots, which were attributed to two independent binding sites for decades (<xref rid=\"B12\" ref-type=\"bibr\">Defize et al., 1988</xref>; <xref rid=\"B3\" ref-type=\"bibr\">Bellot et al., 1990</xref>; <xref rid=\"B46\" ref-type=\"bibr\">Özcan et al., 2006</xref>). However, negative cooperativity has been invoked to account for the apparent heterogeneity of EGF binding sites (<xref rid=\"B37\" ref-type=\"bibr\">Macdonald and Pike, 2008</xref>). This phenomenon requires that binding of the first ligand to an EGFR dimer decreases the ligand binding affinity of the other subunit by inducing an asymmetric ECD dimer with a constrained ligand binding site on the unoccupied receptor. While the drosophila EGFR, whose isolated ECD retains negative cooperativity, has indeed been reported to form such asymmetric dimers (<xref rid=\"B1\" ref-type=\"bibr\">Alvarado et al., 2010</xref>), its human counterpart only exhibits negative cooperativity when ligands bind to full-length receptors in cells. Although low affinity ligands bound to human EGFR stabilize an asymmetric ECD dimer (<xref rid=\"B15\" ref-type=\"bibr\">Freed et al., 2017</xref>), such an asymmetry has not been observed when high affinity ligands, like EGF, are complexed with the receptor (<xref rid=\"B39\" ref-type=\"bibr\">Martin-Fernandez, 2012</xref>). These facts suggest that other receptor regions, conformations and/or unknown cellular components must be involved in the regulation of ligand affinity of EGFR in the cellular environment. Although negative cooperativity became the dogma in EGF binding studies, several investigators reported positive cooperative ligand binding to EGFR (<xref rid=\"B50\" ref-type=\"bibr\">Sherrill and Kyte, 1996</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Lemmon et al., 1997</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Teramura et al., 2006</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Chung et al., 2010</xref>) with a recent report linking positive cooperativity to receptor dimers with symmetric kinase dimers, but without quantitative analysis based on molecular structures (<xref rid=\"B38\" ref-type=\"bibr\">Macdonald-Obermann and Pike, 2018</xref>). The present study was undertaken to generate a structure-based model of EGF binding involving conformational transitions of the extracellular and intracellular domains. The obtained equations not only fit the experimental data well, but are in accordance with most published structural results and can resolve the aforementioned contradictions regarding cooperativity of ligand binding.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Cells</title><p>The CHO-K1 cell line (ATCC CCL-61) was obtained from the American Type Culture Collection (Manassas, VA, United States). It’s subline, F1-4, stably expresses EGFR-GFP (<xref rid=\"B5\" ref-type=\"bibr\">Brock et al., 1999</xref>). F1-4 cells were maintained at 37°C and 5% CO<sub>2</sub> in Dulbecco’s Modified Eagle’s medium (DMEM) supplemented with 10% fetal calf serum and 50 μg/ml gentamicin. The number of passages of cells used for the experiments never exceeded 15. The expression level of EGFR was determined by flow cytometry using Qifikit (Agilent Technologies, Santa Clara, CA, United States). For stimulation of EGFR, cells were cultured in DMEM containing 0.1% fetal calf serum and 50 μg/ml gentamicin for 12 h followed by incubation with 130 nM EGF (R&D Systems, Minneapolis, MN, United States) in Hank’s balanced salt solution at 37°C or 4°C for 15 min or 1 h.</p></sec><sec id=\"S2.SS2\"><title>Reagents</title><p>For microscopic experiments cells were cultured on μ-slide 8-well chambered coverglass (Ibidi, Martinsried, Germany). For flow cytometry cells were harvested by trypsinization. Human EGF (cat. no: 236-EG) was purchased from R&D Systems (Minneapolis, MN, United States). Tetramethylrhodamine-labeled EGF (cat. no: E3481) was purchased from Thermo Fisher Scientific (Waltham, MA, United States). EGF-stimulated tyrosine phosphorylation was visualized by labeling with an antibody against phosphotyrosine (PY99, cat. no: sc-7020; Santa Cruz, Dallas, TX, United States) followed by secondary staining with AlexaFluor647-conjugated goat-anti-mouse IgG (cat. no: A-21235; Thermo Fisher Scientific). Tunicamycin (cat. no: T7765), latrunculin-B (cat. no: L5288) and jasplakinolide (Cat. no: J4580) were purchased from Sigma-Aldrich (St. Louis, MO, United States). Tetramethylrhodamine B isothiocyanate-labeled phalloidin (TRITC-phalloidin, cat. no: P1951) and 4′,6-diamidino-2′-phenylindole (DAPI, cat. no: D9542) were obtained from Sigma Aldrich. Lapatinib (cat. no: S2111) and erlotinib (cat. no: S7786) were purchased from Selleckchem (Houston, TX, United States).</p></sec><sec id=\"S2.SS3\"><title>Labeling of Cells With Fluorescent EGF</title><p>Tetramethylrhodamine-conjugated EGF (TAMRA-EGF) was reconstituted at a concentration of 10 μM. A two-fold dilution series of TAMRA-EGF was prepared with each vial containing 500 μl TAMRA-EGF dissolved in phosphate-buffered saline supplemented with 1% (w/v) BSA. The solutions were kept on ice before adding 20 μl of a cold cell suspension containing 100,000 cells. Cells were incubated in the presence of TAMRA-EGF for 1 h on ice with shaking. The fluorescence intensity of the samples was measured on a FACS Aria III flow cytometer (BD Biosciences, San Jose, CA, United States) without washing to prevent the dissociation of fluorescent EGF. TAMRA was excited at 561 nm, and its emission was detected through a 595 nm band-pass filter. Analysis of flow cytometric data was carried out with FCS Express (<italic>Denovo</italic> Software, Thornhill, ON, Canada). Mean fluorescence intensities were background-corrected followed by fitting the Hill equation to the binding curves:</p><p>\n<disp-formula id=\"S2.E1\"><label>(1)</label><mml:math id=\"M1\"><mml:mrow><mml:mi>I</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi>min</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mi>max</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mi>min</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mn>10</mml:mn><mml:mrow><mml:mi>n</mml:mi><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mrow><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mi>d</mml:mi></mml:msub><mml:mo>⁢</mml:mo><mml:mrow/></mml:mrow></mml:mrow><mml:mo>-</mml:mo><mml:mrow><mml:mrow/><mml:mo>⁢</mml:mo><mml:mrow><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mi>c</mml:mi></mml:mrow></mml:mrow></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>where <italic>I</italic> is the intensity of the sample labeled with concentration <italic>c</italic> of fluorescent EGF, <italic>I</italic><sub><italic>min</italic></sub> and <italic>I</italic><sub><italic>max</italic></sub> are the minimal and maximal intensities, respectively. <italic>K</italic><sub><italic>d</italic></sub> and <italic>n</italic> are the dissociation constant and the Hill coefficient, respectively. The same datasets were also used for fitting of the model described in the present manuscript.</p></sec><sec id=\"S2.SS4\"><title>Treatment of Cells With Kinase Inhibitors</title><p>In order to measure the effect of kinase inhibitors on EGF-induced phosphorylation cells were grown on μ-slide 8-well chambered coverglass and were serum-starved for 12 h. Then, they were exposed to erlotinib or lapatinib at a concentration of 5 μM at 37°C for 1 h followed by stimulation with EGF at a concentration of 130 nM for 15 min at 37°C. Cells were fixed in 3.7% (v/v) formaldehyde, permeabilized with a solution of 0.1% (v/v) Triton-X100 containing 1% (w/v) BSA in PBS followed by secondary staining with a pan-phosphotyrosine antibody (PY99) and AlexaFluor647 goat anti-mouse IgG. Samples were observed using a Zeiss LSM880 confocal microscope with a C-Apochromat 40 × (N.A. = 1.2) water immersion objective. GFP and AlexaFluor647 were excited at 488 and 633 nm, respectively. Emissions of GFP and AlexaFluor647 were measured in the wavelength range of 490–570 nm and 635–755 nm, respectively. In order to measure the effect of kinase inhibitors on EGF binding trypsinized cells were treated with erlotinib or lapatinib at a concentration of 5 μM at 37°C for 1 h followed by labeling with TAMRA-EGF as described previously.</p></sec><sec id=\"S2.SS5\"><title>Inhibition of Glycosylation of EGFR</title><p>F1-4 cells were treated with tunicamycin at a concentration of 1 μg/ml for 24 h followed by trypsinization and labeling with TAMRA-EGF as described above. In order to show the effect of deglycosylation on the molecular weight of EGFR control and tunicamycin-treated cells were lysed and scraped followed by running the lysate in a 7% SDS-PAGE gel. Proteins were transferred to PVDF membranes, and the blots were incubated with an antibody against EGFR (anti-EGFR, clone F4, Thermo Fisher Scientific; Cat. no: MA1-24226) at 4°C overnight followed by labeling with anti-mouse IgG-peroxidase (Sigma-Aldrich; cat no: AP124P) for detection with a chemiluminescence kit (Thermo Fisher Scientific; cat no: 34577, 34095).</p></sec><sec id=\"S2.SS6\"><title>Inhibition or Promotion of Actin Polymerization</title><p>Cells were treated with latrunculin-B at a concentration of 2 μM at 37°C for 10 min or by 1 μM jasplakinolide at 37°C for 30 min followed by labeling with a concentration series of TAMRA-EGF as described above. In order to visualize the effect of latrunculin-B or jasplakinolide on microfilaments, control and treated cells were permeabilized with acetone followed by staining with 4 μg/ml TRITC-phalloidin and 10 μg/ml DAPI. Samples were observed with a Zeiss LSM880 confocal microscope using a C-Apochromat 40 × (N.A. = 1.2) water immersion objective. DAPI and TRITC were excited at 405 nm and 543 nm, respectively. The emissions of DAPI and TRITC were detected in the wavelength range of 410–482 nm and 557–655 nm, respectively.</p></sec><sec id=\"S2.SS7\"><title>Homo-FRET Measurements</title><p>F1-4 cells were seeded at a density of 5 × 10<sup>4</sup> cells/well on μ-slide 8-well chambered coverglass, and serum-starved overnight. After washing with Hank’s balanced salt solution, cells were treated with erlotinib or lapatinib at a concentration of 5 μM at 37°C for 1 h followed by stimulation with EGF at a concentration of 130 nM for 15 min at 37°C or 4°C. Imaging was carried out with a Zeiss LSM880 confocal microscope without washing or diluting the samples. Samples were examined with a C-Apochromat 40 × (N.A. = 1.2) water immersion objective. Stepwise bleaching of GFP was accomplished by a 405-nm laser line till the GFP signal completely faded. Fluorescence was observed in two tracks detecting the fluorescence emission polarized parallel (<italic>I</italic><sub>∥</sub>) and perpendicular (<italic>I</italic><sub>⊥</sub>) to the excitation beam by a Quasar detector in the L-format arrangement. Intensities were summed in membrane pixels, identified by manually seeded watershed segmentation, after subtracting background fluorescence determined in a cell-free area of the image. Intensities were corrected for high numerical aperture detection followed by calculating anisotropy (<italic>r</italic>) as follows (<xref rid=\"B20\" ref-type=\"bibr\">Jovin, 1979</xref>):</p><p>\n<disp-formula id=\"S2.E2\"><label>(2)</label><mml:math id=\"M2\"><mml:mrow><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mo>∥</mml:mo></mml:msub><mml:mo>-</mml:mo><mml:mrow><mml:mi>G</mml:mi><mml:mo>⁢</mml:mo><mml:mo>⁢</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mo>⊥</mml:mo></mml:msub></mml:mrow></mml:mrow><mml:mrow><mml:msub><mml:mi>I</mml:mi><mml:mo>∥</mml:mo></mml:msub><mml:mo>+</mml:mo><mml:mrow><mml:mn>2</mml:mn><mml:mo>⁢</mml:mo><mml:mo>⁢</mml:mo><mml:mi>G</mml:mi><mml:mo>⁢</mml:mo><mml:mo>⁢</mml:mo><mml:msub><mml:mi>I</mml:mi><mml:mo>⊥</mml:mo></mml:msub></mml:mrow></mml:mrow></mml:mfrac></mml:mrow></mml:math></disp-formula></p><p>The G factor, characterizing the sensitivity of the detection system to the parallel and perpendicular intensity components, was determined by imaging a GFP solution using microscope settings identical to the ones used for the cells.</p><p>Homo-FRET implemented in microscopy or flow cytometry has been used previously to analyze receptor clustering (<xref rid=\"B34\" ref-type=\"bibr\">Lidke et al., 2003</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Yeow and Clayton, 2007</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Szabó et al., 2008</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Hofman et al., 2010</xref>). In order to estimate homoclustering of EGFR, the anisotropy, calculated from membrane pixels, of each image in the bleaching sequence was plotted against the residual fractional intensity of GFP, and an equation describing the anisotropy of a mixture of monomers and homoclustersed receptors was fitted to the measured data points. We assumed that a fraction of proteins (<italic>mon</italic>) is unclustered, whereas the rest of them form clusters consisting of <italic>N</italic> fluorophores. The equation describing the anisotropy of such a population of fluorophores (<italic>r</italic><sub><italic>s,N</italic></sub>) was fitted to the measured data (<xref rid=\"B52\" ref-type=\"bibr\">Szabó et al., 2008</xref>):</p><p>\n<disp-formula id=\"S2.E3\"><label>(3)</label><mml:math id=\"M3\"><mml:mtable columnalign=\"left\"><mml:mtr><mml:mtd><mml:msub><mml:mi>r</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mo>,</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>N</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:mfrac><mml:munderover><mml:mstyle mathsize=\"140%\" displaystyle=\"true\"><mml:mo>∑</mml:mo></mml:mstyle><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:munderover><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfrac><mml:mi>N</mml:mi><mml:mi>k</mml:mi></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:msup><mml:mi>s</mml:mi><mml:mi>k</mml:mi></mml:msup><mml:msup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>s</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:msup><mml:mi>k</mml:mi></mml:mrow></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>          </mml:mtext><mml:mrow><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mi>r</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mfrac><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mi>d</mml:mi><mml:mn>6</mml:mn></mml:msup></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:mi>k</mml:mi><mml:msup><mml:mi>d</mml:mi><mml:mn>6</mml:mn></mml:msup></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:msub><mml:mi>r</mml:mi><mml:mrow><mml:mi>F</mml:mi><mml:mi>R</mml:mi><mml:mi>E</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:msub><mml:mfrac><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:msup><mml:mi>d</mml:mi><mml:mn>6</mml:mn></mml:msup></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:mi>k</mml:mi><mml:msup><mml:mi>d</mml:mi><mml:mn>6</mml:mn></mml:msup></mml:mrow></mml:mfrac></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>r</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula></p><p>where <italic>s</italic> is the fraction of unbleached fluorophores, <italic>r</italic><sub>1</sub> and <italic>r</italic><sub><italic>FRET</italic></sub> are the anisotropies of an isolated fluorophore and a fluorophore excited by homo-FRET, respectively, and <italic>d</italic> is the reciprocal of the distance between the fluorophores in the clusters normalized to <italic>R</italic><sub>0</sub>. <italic>r</italic><sub>1</sub> was assumed to be 0.34 (<xref rid=\"B56\" ref-type=\"bibr\">Volkmer et al., 2000</xref>), while the anisotropy of fluorophores excited by homo-FRET (<italic>r</italic><sub><italic>FRET</italic></sub>) was assumed to be zero (<xref rid=\"B48\" ref-type=\"bibr\">Runnels and Scarlata, 1995</xref>). The distance between two fluorophores in a cluster was assumed to be equal to the Förster distance for a GFP-GFP homo-FRET pair (4.8 nm), which is in the same order of magnitude as the size of a GFP barrel (<xref rid=\"B61\" ref-type=\"bibr\">Yang et al., 1996</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Kremers and Goedhart, 2009</xref>). Fitting provided the number of proteins in a homocluster (<italic>N</italic>, cluster size) and the fraction of monomeric receptors (<italic>mon</italic>). The reliability of the estimation at the given biological variability and measurement error was determined by Monte Carlo simulation. Five hundred anisotropy vs. fractional residual intensity curves were generated using the mean and the standard deviation of the anisotropies. All curves were fitted generating 500 estimations for the cluster size and monomeric fraction. The histograms of these values and their 95% confidence intervals provided an estimation for the reliability of the fitting (<xref rid=\"B52\" ref-type=\"bibr\">Szabó et al., 2008</xref>).</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Equilibrium Binding of EGF to Cells With or Without Kinase Inhibitor Treatment</title><p>Coupling between the conformations of the intra- and extracellular parts of EGFR are expected to affect the apparent cooperativity and affinity of EGF binding, which can be estimated by analyzing the dependence of these parameters on the expression level of the receptor. In order to look at cell populations having different EGFR expression levels without disturbing the homeostasis of cells significantly, we used F1-4 cells, a CHO subline stably transfected with EGFR-GFP. Flow cytometric gating on the GFP fluorescence makes selection of subpopulations with different expression levels of EGFR possible (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 1</xref>). Although quantitative imaging of fluorescent EGF bound to attached cells is possible (<xref rid=\"B53\" ref-type=\"bibr\">Teramura et al., 2006</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Chung et al., 2010</xref>), the statistical reliability of such measurements, especially when restricted to a subpopulation with a certain receptor expression level, is poor due to the low number of analyzed cells. We expected that such noisy data would have prevented successful fitting of a complex model. In order for our flow cytometric approach to be relevant for the physiological state of EGFR, we showed that trypsinization, used for detaching cells from culture flasks, had negligible effect on the expression level and molecular weight of EGFR on the cell surface (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 2</xref>). In spite of these findings, it must be pointed out that all binding measurements were carried out with suspended cells. We also established that the EGF binding characteristics of the EGFR-GFP fusion construct are identical to those of native EGFR (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 3</xref>). Fitting of the Hill equation to the equilibrium EGF binding data in F1-4 cells revealed nanomolar apparent <italic>K</italic><sub><italic>d</italic></sub> and positive apparent cooperativity, which increased as a function of receptor expression (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> and <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Throughout the analyses presented in the manuscript ligand depletion was assumed to be negligible. The validity of this assumption is shown in the <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Information</xref>. In order to test explicitly the influence of different states of the KD on EGF binding, inhibitors stabilizing the kinase in the active and inactive conformations were used. Their effect on EGF-induced tyrosine phosphorylation is shown in <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 4</xref>. Erlotinib, binding and stabilizing the active conformation of the kinase, increased the affinity of the receptor for EGF and reduced the Hill coefficient to a range characteristic of negative apparent cooperativity in all cell populations except for those exhibiting the highest expression. Cells treated with lapatinib, an inhibitor stabilizing the inactive conformation of the KD, were characterized by positive cooperative EGF binding with reduced apparent affinity (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> and <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). The results revealed that EGFR exhibits an expression level-dependent tendency for positive apparent cooperativity in ligand binding, which is also influenced by the conformation of the KD.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Equilibrium EGF binding and their fits according to the Hill equation. Control F1-4 cells and those treated with 5 μM erlotinib or lapatinib for 1 h were incubated with a 2-fold concentration series of EGF for an hour on ice. Cells pretreated with the kinase inhibitors were incubated with EGF in the continued presence of the inhibitors. The fluorescence intensity of cells was measured by flow cytometry without washing or diluting the samples. The whole cell population and three subpopulations identified according to their different EGFR-GFP expressions were separately analyzed. The symbols represent the mean of three independent measurements with the error bars showing the standard error of the mean. The lines show fits of the Hill equation to the experimental data. Results of the fit are presented in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>.</p></caption><graphic xlink:href=\"fcell-08-00776-g001\"/></fig><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>EGF binding fitted according to the Hill equation.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Low expresser</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Medium expresser</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">High expresser</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">All cells</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Untreated</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 1.14 ± 0.004</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 1.33 ± 0.008</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 1.41 ± 0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 1.26 ± 0.006</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 4.4 ± 0.005 nM <italic>N</italic> = 390,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 3.8 ± 0.01 nM <italic>N</italic> = 630,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 3.8 ± 0.01 nM <italic>N</italic> = 1,070,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 3.9 ± 0.007 nM <italic>N</italic> = 560,000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Erlotinib</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 0.92 ± 0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 0.99 ± 0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 1.03 ± 0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 0.95 ± 0.01</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 2 ± 0.02 nM <italic>N</italic> = 390,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 1.8 ± 0.01 nM <italic>N</italic> = 630,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 1.7 ± 0.02 nM <italic>N</italic> = 1,000,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 1.9 ± 0.01 nM <italic>N</italic> = 500,000</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lapatinib</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 1.04 ± 0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 1.21 ± 0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 1.36 ± 0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>n</italic> = 1.2 ± 0.01</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 6.3 ± 0.01 nM <italic>N</italic> = 390,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 5.1 ± 0.01 nM <italic>N</italic> = 630,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 4.9 ± 0.02 nM <italic>N</italic> = 1,010,000</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> = 4.9 ± 0.02 nM <italic>N</italic> = 520,000</td></tr></tbody></table><table-wrap-foot><attrib><italic>Equilibrium EGF binding data was fitted with the Hill equation. The apparent dissociation constant (<italic>K</italic><sub><italic>d</italic></sub>) and the Hill coefficient (<italic>n</italic>, ± their standard deviations) are displayed for the control sample and cells treated with the kinase inhibitors. The standard deviations were estimated by multiplying the inverse of the Hessian with the standard deviation of the fitting error. The expression level of EGFR of the whole cell population was measured by flow cytometric calibration. EGFR expressions of the low, medium and high expresser subpopulations were determined by assuming that the maximum intensity of EGF fluorescence, determined from the Hill fits, is proportional to the receptor expression level. The number of receptors/cell, designated by N, is shown at the bottom of each cell. The experimental data and the fits are shown in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>.</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS2\"><title>Development of a Model Involving Conformational States of the Ligand-Binding and Kinase Domains</title><p>Given the results obtained with cells treated with the two kinase inhibitors, establishing that the KD exerts significant effects on the characteristics of EGF binding, and structural evidence pointing at dimerization of the KD, we developed a molecular model of EGF binding based on the following assumptions. 1. The closed conformation of the ECD is in equilibrium with its extended structure (<xref rid=\"B25\" ref-type=\"bibr\">Klein et al., 2004</xref>). Although the ligandless ECD has been found to be dynamic, assuming other conformations as well (<xref rid=\"B23\" ref-type=\"bibr\">Kaplan et al., 2016</xref>), they were not included in the model in order to limit the number of free parameters. 2. Receptor dimerization only takes place with receptors whose ECD is in the extended conformation (<xref rid=\"B18\" ref-type=\"bibr\">Garrett et al., 2002</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Ogiso et al., 2002</xref>). 3. The KD is assumed to adopt either an inactive or active conformation in receptor monomers. Although other conformations of the KD have also been reported (<xref rid=\"B49\" ref-type=\"bibr\">Shan et al., 2012</xref>), the number of molecular species had to be minimized so that the model remains tractable. Therefore, these intermediate conformations were excluded from the model. 4. The conformations of the intra- and extracellular domains are uncoupled from each other in monomeric receptors. 5. The extended conformation of the ECD dimers can couple with both symmetric and asymmetric KD dimers, in accordance with high resolution electron microscopic evidence (<xref rid=\"B40\" ref-type=\"bibr\">Mi et al., 2011</xref>). While assignment of symmetric and asymmetric configurations to the two different kinds of kinase dimers in the model is not based on firm structural evidence, the assumption of two different receptor dimerization pathways beginning from receptors harboring inactive and active KDs was absolutely required for successful fitting of the data. With this limitation in mind, we still refer to these kinase dimers as symmetric and asymmetric. The structure of kinase domain dimers assigned to these two different dimerization pathways will be further addressed in the Discussion. 6. Dimers with symmetric and asymmetric KD dimers are characterized by different affinities for EGF. Incorporation of this assumption was required so that the model could reproduce the significant dependence of apparent cooperativity on expression levels and on the presence of the two kinase inhibitors. A possible explanation for the validity of this assumption is provided in the Discussion. 7. Ligand binds only to the extended conformation of the ECD. Models describing EGF binding including or neglecting ligand binding to the closed conformation of the ECD have both been published (<xref rid=\"B25\" ref-type=\"bibr\">Klein et al., 2004</xref>; <xref rid=\"B37\" ref-type=\"bibr\">Macdonald and Pike, 2008</xref>). Since the affinity of a dimer harboring symmetric kinase dimers for binding of the first ligand turned out to be low, incorporation of another low affinity binding site, the closed ECD, would have made the model less reliable. Besides this model, several alternative ones have been tested. Receptor dimerization beginning from a monomer with extended ECD and an asymmetric kinase dimer was included in all of them. Without inclusion of another dimerization pathway the result of the fitting was poor. If the second dimerization pathway began from either of the monomers with closed ECD (“CA” or “CI”), fittings were also of poor quality. The approach taken to construct the model is a compromise between an even more detailed, but mathematically intractable model taking every possible state and transition into consideration and a biologically unrealistic, simplistic model, which is easy to handle. Such a golden mean has been suggested to result in realistic and experimentally verifiable models (<xref rid=\"B63\" ref-type=\"bibr\">Yates et al., 2001</xref>).</p><p>The model, presented in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>, involves twelve molecular species, whose equilibrium state is described by eleven equations and nine constants. The following equations characterize the conformational transitions and ligand binding of monomeric species:</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Model for EGF binding involving conformations of the ligand binding and kinase domains. The extracellular part of the receptor is present in either a closed (C) or extended (E) conformation with both of these states coupled to either an active (A) or inactive (I) kinase domain generating four possible monomers (CI, CA – closed extracellular domain with inactive and active kinase domain, respectively; EI, EA – extended extracellular domain with inactive and active kinase domain, respectively). Receptors whose extracellular domain is in the extended conformation can bind EGF (red circles). Liganded species are designated by an ‘L’ at the end of their names. Receptors with extended extracellular domains can form dimers designated by a ‘D’ at the beginning of their names. Monomeric and dimeric species, whose kinase domain is in an active or inactive conformation, are shown in blue and black, respectively. Three different kinds of dimers with inactive, symmetric kinase dimers are present according to their ligand binding state (DES, DESL, DES2L – dimers with inactive, symmetric kinase dimers with 0, 1 and 2 bound EGF, respectively). The same designation principle applies to dimeric species with active, asymmetric kinase dimers (DEA, DEAL, DEA2L). The double arrows (continuous lines) show those transitions, which are explicitly included in describing the equilibrium. The descriptions beside the arrows show the equation and constant describing the equilibrium corresponding to the arrow. The equilibrium of all other conformational changes, ligand binding and dimerization events not explicitly shown in the figure are fully described by the equation set discussed in the main text since the equilibrium constants determine the standard Gibbs energy change of a reaction and consequently the ratio of the equilibrium concentrations of any pairwise selection of species. Although the dashed double arrow labeling the dimerization of liganded monomers is not explicitly included in the model, it is required for determining linkage in the system.</p></caption><graphic xlink:href=\"fcell-08-00776-g002\"/></fig><p>\n<disp-formula id=\"S3.E4\"><label>(4)</label><mml:math id=\"M4\"><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>C</mml:mi><mml:mo>⁢</mml:mo><mml:mi>I</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>C</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>\n<disp-formula id=\"S3.E5\"><label>(5)</label><mml:math id=\"M5\"><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>C</mml:mi><mml:mo>⁢</mml:mo><mml:mi>I</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>I</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>\n<disp-formula id=\"S3.E6\"><label>(6)</label><mml:math id=\"M6\"><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>I</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>\n<disp-formula id=\"S3.E7\"><label>(7)</label><mml:math id=\"M7\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>I</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>G</mml:mi><mml:mo>⁢</mml:mo><mml:mi>F</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>I</mml:mi><mml:mo>⁢</mml:mo><mml:mi>L</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>\n<disp-formula id=\"S3.E8\"><label>(8)</label><mml:math id=\"M8\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>G</mml:mi><mml:mo>⁢</mml:mo><mml:mi>F</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi><mml:mo>⁢</mml:mo><mml:mi>L</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>where <italic>K</italic><sub>1</sub> and <italic>K</italic><sub>2</sub> are the equilibrium constants for the conformational transitions of the KD and the ECD, respectively, and <italic>K</italic><sub>3</sub> is the dissociation constant of EGF binding to a monomeric receptor having an extended ECD. The abbreviations of molecular species are defined in the legend to <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>. The following equations describe the dimerization of monomers leading to the formation of dimers with symmetric kinase dimers:</p><p>\n<disp-formula id=\"S3.E9\"><label>(9)</label><mml:math id=\"M9\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>I</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>I</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>S</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>\n<disp-formula id=\"S3.E10\"><label>(10)</label><mml:math id=\"M10\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>S</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>G</mml:mi><mml:mo>⁢</mml:mo><mml:mi>F</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>S</mml:mi><mml:mo>⁢</mml:mo><mml:mi>L</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>\n<disp-formula id=\"S3.E11\"><label>(11)</label><mml:math id=\"M11\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>S</mml:mi><mml:mo>⁢</mml:mo><mml:mi>L</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>G</mml:mi><mml:mo>⁢</mml:mo><mml:mi>F</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>6</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>S</mml:mi><mml:mo>⁢</mml:mo><mml:mn>2</mml:mn><mml:mo>⁢</mml:mo><mml:mi>L</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>where <italic>K</italic><sub>4</sub> is the dissociation constant of an unliganded dimer harboring extended ECD and symmetric KD dimer, and the dissociation constants of the first and second EGF binding to this dimer are denoted by <italic>K</italic><sub>5</sub> and <italic>K</italic><sub>6</sub>, respectively. The dimerization and ligand binding pathway of species with asymmetric KD dimers are described by the equations below:</p><p>\n<disp-formula id=\"S3.E12\"><label>(12)</label><mml:math id=\"M12\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>7</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>\n<disp-formula id=\"S3.E13\"><label>(13)</label><mml:math id=\"M13\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>G</mml:mi><mml:mo>⁢</mml:mo><mml:mi>F</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>8</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi><mml:mo>⁢</mml:mo><mml:mi>L</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>\n<disp-formula id=\"S3.E14\"><label>(14)</label><mml:math id=\"M14\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi><mml:mo>⁢</mml:mo><mml:mi>L</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>G</mml:mi><mml:mo>⁢</mml:mo><mml:mi>F</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>9</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>D</mml:mi><mml:mo>⁢</mml:mo><mml:mi>E</mml:mi><mml:mo>⁢</mml:mo><mml:mi>A</mml:mi><mml:mo>⁢</mml:mo><mml:mn>2</mml:mn><mml:mo>⁢</mml:mo><mml:mi>L</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>where <italic>K</italic><sub>7</sub> is the dissociation constant of an unliganded dimer with extended ECD and asymmetric KD dimer, while <italic>K</italic><sub>8</sub> and <italic>K</italic><sub>9</sub> denote the dissociation constants of the first and second EGF binding to this dimer, respectively. The relationships describing the quantity of cell-bound EGF and the conservation of receptor number constitute the final two equations of the system:</p><p>\n<disp-formula id=\"S3.E15\"><label>(15)</label><mml:math id=\"M15\"><mml:mtable columnalign=\"left\"><mml:mtr><mml:mtd><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>E</mml:mi><mml:mi>G</mml:mi><mml:msub><mml:mi>F</mml:mi><mml:mrow><mml:mi>b</mml:mi><mml:mi>o</mml:mi><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>d</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>E</mml:mi><mml:mi>I</mml:mi><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>S</mml:mi><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>                           </mml:mtext><mml:mo>+</mml:mo><mml:mn>2</mml:mn><mml:mo stretchy=\"false\">(</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>S</mml:mi><mml:mn>2</mml:mn><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mn>2</mml:mn><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo stretchy=\"false\">)</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula></p><p>\n<disp-formula id=\"S3.E16\"><label>(16)</label><mml:math id=\"M16\"><mml:mtable columnalign=\"left\"><mml:mtr><mml:mtd><mml:mo stretchy=\"false\">[</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>C</mml:mi><mml:mi>I</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>C</mml:mi><mml:mi>A</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>E</mml:mi><mml:mi>I</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>E</mml:mi><mml:mi>I</mml:mi><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>                </mml:mtext><mml:mo>+</mml:mo><mml:mn>2</mml:mn><mml:mo stretchy=\"false\">(</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>S</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>S</mml:mi><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>S</mml:mi><mml:mn>2</mml:mn><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>                </mml:mtext><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mn>2</mml:mn><mml:mi>L</mml:mi><mml:mo stretchy=\"false\">]</mml:mo><mml:mo stretchy=\"false\">)</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula></p><p>where <italic>R</italic><sub><italic>tot</italic></sub> is the number of receptors/cell. The unit of ligand dissociation constants in the model is nM, whereas the unit of receptor dissociation constants is number of receptors/cell. Equations (4)-(16), constituting a quadratic equation set containing 13 unknowns (the concentration of the 12 molecular species and [<italic>EGF</italic><sub><italic>bound</italic></sub>]), were solved with Mathematica (Wolfram Research, Champaign, IL). The set of roots in which all 13 concentrations were positive was selected as the meaningful solution, which is presented as a Matlab (Mathworks, Natick, MA) file in the <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Information</xref>.</p><p>Control cells and the samples treated with erlotinib or lapatinib were divided into three subpopulations corresponding to low, medium and high expressers of EGFR. In this way there were four cell populations for each experimental condition (the three gated subpopulations and the whole cell population), altogether constituting 12 experimental conditions. The sum of squared deviations between the fitted equations and the experimental data was minimized by an algorithm, which was global in two respects: (1) All 12 data sets were fitted simultaneously with parameters <italic>K</italic><sub>2</sub>-<italic>K</italic><sub>9</sub> shared between all of them, while <italic>K</italic><sub>1</sub> characterizing the conformational equilibrium between the active and inactive KDs was allowed to have three different values for the control, erlotinib- and lapatinib-treated samples. In this way, the 11 free parameters in the model were globally fitted to 156 data points from the 12 data sets. (2) The Global Search algorithm of Matlab was used for finding the global minimum of the norm. In order to define the confidence interval of the fitted parameters the optimization procedure was repeated 100-times.</p><sec id=\"S3.SS2.SSS1\"><title>Fitting of the Model to Equilibrium EGF Binding Data</title><p>The data set analyzed previously with the Hill equation was fitted with the model described in the previous section (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>, <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). The confidence intervals of the fitted parameters are shown in <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 5</xref>. The model reveals that the inactive conformation of the KD is favored under all experimental conditions in the absence of EGF, with the kinase inhibitors shifting this equilibrium in accordance with their presumed tendency to stabilize one of the kinase structures. In the absence of ligand, the ECD is preferentially in the closed conformation. Monomers with an inactive KD dimerize much less efficiently than monomers harboring an active KD. While the ligand binding affinity of EGFR monomers is in the nanomolar range, the KD exerts significant effects on the affinity and cooperativity of dimers. Dimeric structures with asymmetric kinase dimers exhibit subnanomolar affinity for the first ligand, but the second EGF binds with a ∼30-times lower affinity due to significant negative cooperativity. Dimers with symmetric kinase dimers have an extremely low affinity for the first ligand, but a subnanomolar binding constant is found for the second EGF. The model parameters revealed that the KD is strongly coupled to ligand binding suggesting that its involvement cannot be neglected in analyzing EGF binding data.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Fitting of equilibrium EGF binding according to the model involving structures of the extracellular and intracellular receptor parts. Experimental data, the same as shown in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>, were fitted to the model introduced in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>. The fitted model parameters are displayed in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>.</p></caption><graphic xlink:href=\"fcell-08-00776-g003\"/></fig><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Fitting of the proposed model to the equilibrium binding of EGF.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Parameter</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Description</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Scope of parameter</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Value (95% CI)</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub>1</sub></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">equilibrium constant for inactive and active KD (>1 favors the inactive conformation)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">untreated</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">104 (67–155)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">erlotinib (active KD)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25 (17–38)</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">lapatinib (inactive KD)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">162 (101–243)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub>2</sub></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">equilibrium constant for the closed and extended conformations of the ECD (>1 favors the closed conformation)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">global</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.9 (1.6–4.4)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub>3</sub></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> of EGF binding to a monomer with extended ECD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">global</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3 nM (0.8–1.8 nM)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub>4</sub></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">dimerization of monomers with inactive KD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">global</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36,900 (15,600–68,700)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub>5</sub></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> of binding of the first EGF to a dimer with inactive, symmetric kinase dimer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">global</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">95 nM (69–99 nM)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub>6</sub></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> of binding of the second EGF to a dimer with inactive, symmetric kinase dimer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">global</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.45 nM (0.34–0.53 nM)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub>7</sub></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">dimerization of monomers with active KD</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">global</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">480 (86–2,400)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub>8</sub></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> of binding of the first EGF to a dimer with active, asymmetric kinase dimer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">global</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1 nM (0.02–0.16 nM)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub>9</sub></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>K</italic><sub><italic>d</italic></sub> of binding of the second EGF to a dimer with active, asymmetric kinase dimer</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">global</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.96 nM (2.93–2.97 nM)</td></tr></tbody></table><table-wrap-foot><attrib><italic>The model involving conformational transitions of the kinase domain (KD) and extracellular domains (ECD) was fitted to equilibrium EGF binding. The measured data points and the fitted lines are shown in <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>. All twelve curves were fitted globally with the same value of parameters <italic>K</italic><sub>2</sub>-<italic>K</italic><sub>9</sub>, while three different values of <italic>K</italic><sub>1</sub> were used for the three experimental conditions (untreated/erlotinib/lapatinib). The confidence interval was estimated by repeating the fitting 100-times. Constants describing conformational equilibria (<italic>K</italic><sub>1</sub>, <italic>K</italic><sub>2</sub>) express concentration ratios according to equations (4) and (5), therefore they do not have a unit. The unit of equilibrium constants describing dimerizations (<italic>K</italic><sub>4</sub>, <italic>K</italic><sub>7</sub>) is number of receptors/cell.</italic></attrib></table-wrap-foot></table-wrap></sec></sec><sec id=\"S3.SS3\"><title>Predictions of the Model for Different Molecular Species</title><p>The fitted model parameters not only describe the amount of cell-bound EGF, but they also predict the dependence of different receptor species on ligand concentration and receptor expression level. Calculation of the concentration of liganded receptor species revealed that a substantial fraction of EGF binds to monomeric EGFR with inactive KD, while monomers with active KDs do not contribute to ligand binding (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 6</xref>). The fraction of EGF bound to monomer receptors decreases with increased EGFR expression, a feature, which could account for increased apparent positive cooperativity at high receptor expression levels. Dimers with both symmetric and asymmetric kinase dimers participate in ligand binding with the relative contribution of the latter increasing as a function of EGFR expression. Due to favoring the active conformation of the KD, erlotinib eliminates the binding of EGF to dimers with symmetric kinase dimers almost completely and significantly decreases the fraction of EGF binding to monomeric receptors. As a result, EGF binds almost exclusively to dimers harboring asymmetric kinase dimers in the presence of erlotinib. The model predicts that monomeric receptors with inactive KDs become the dominant EGF binding species in the presence of lapatinib at all receptor expression levels tested. In accordance with its ability to stabilize the inactive conformation of the kinase, lapatinib significantly decreases the percentage of liganded receptor dimers with active KDs.</p><p>Apparent cooperativity of ligand binding, captured by the Hill coefficient, is not only determined by the affinity of receptor dimers for the first and second EGF, but also by linkage. Linkage was determined by considering the equivalence of two pathways leading to the formation of doubly liganded receptor dimers: (i) dimerization of liganded receptor monomers, and (ii) dimerization of unliganded receptor monomers followed by successive binding of two EGFs (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). The dissociation constant for dimerization of a ligand-bound EGFR with inactive KD was found to be <inline-formula><mml:math id=\"INEQ15\"><mml:mrow><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mo>⁢</mml:mo><mml:msub><mml:mi>K</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mo>⁢</mml:mo><mml:msub><mml:mi>K</mml:mi><mml:mn>6</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mi mathvariant=\"normal\">/</mml:mi><mml:mo>⁢</mml:mo><mml:msubsup><mml:mi>K</mml:mi><mml:mn>3</mml:mn><mml:mn>2</mml:mn></mml:msubsup></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mn>9.3</mml:mn><mml:mo>⋅</mml:mo><mml:msup><mml:mn>10</mml:mn><mml:mn>5</mml:mn></mml:msup></mml:mrow></mml:mrow></mml:math></inline-formula>, which implies negative linkage and decreased homodimerization tendency compared to unliganded monomers with inactive KDs. In contrast, the dissociation constant for homodimerization of liganded receptor monomers with active KD, described by the term <inline-formula><mml:math id=\"INEQ16\"><mml:mrow><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>7</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mo>⁢</mml:mo><mml:msub><mml:mi>K</mml:mi><mml:mn>8</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mo>⁢</mml:mo><mml:msub><mml:mi>K</mml:mi><mml:mn>9</mml:mn></mml:msub><mml:mo>⁢</mml:mo><mml:mi mathvariant=\"normal\">/</mml:mi><mml:mo>⁢</mml:mo><mml:msubsup><mml:mi>K</mml:mi><mml:mn>3</mml:mn><mml:mn>2</mml:mn></mml:msubsup></mml:mrow><mml:mo>=</mml:mo><mml:mn>84</mml:mn></mml:mrow></mml:math></inline-formula>, is significantly smaller than the same constant for unliganded monomers with active KD implying positive linkage. Due to the very strong dimerization tendency of unliganded and liganded EGFR with active KDs, EGF-bound receptor monomers with active KDs are predicted not to exist since they immediately dimerize upon their formation.</p><p>According to the model calculations only four molecular species bind EGF significantly under any of the experimental conditions: monomeric EGFR with inactive KD, singly liganded receptor dimer with asymmetric KD dimer and both kinds of doubly liganded receptor dimers (with symmetric and asymmetric KD dimers). The singly liganded dimer with asymmetric KD dimer exhibits the highest, subnanomolar affinity for EGF, while binding to the other three molecular species is saturated above 10 nM EGF (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 7</xref>). Although there are six different, EGF-binding receptor species (two kinds of monomers with active and inactive KDs, singly and doubly liganded dimers of both kinds), only four of them bind EGF to a significant extent. The EGF-binding affinities of these binding sites is characterized by the dissociation constants in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref> (<italic>K</italic><sub>3</sub>, <italic>K</italic><sub>5</sub>, <italic>K</italic><sub>6</sub>\n<italic>K</italic><sub>8</sub>, <italic>K</italic><sub>9</sub>). It is worth pointing out that the ECD is assumed to adopt an extended conformation in all of these EGF-binding receptor species in the model. Therefore, subtle alterations in the conformations and in the stability of the conformations may account for the different ligand-binding affinities. Along this line, the extended ECD of a receptor monomer exhibits nanomolar EGF affinity (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>), but this conformation of the ECD in a dimer with asymmetric KD dimers is characterized by subnanomolar EGF affinity, most likely because the dimeric structure stabilizes the extended conformation. The issue of different cooperativities of the two different receptor dimers with asymmetric and symmetric KD dimers will be further considered in the Discussion. Two of the six EGF binding receptor species do not reach significant concentration for the following reason: (i) the liganded receptor monomer with active KD immediately dimerizes; (ii) the singly liganded receptor dimer with symmetric KD dimer immediately binds the second ligand due to the strong positive cooperativity.</p><p>Besides confirming the previous conclusions, calculation of the amount of all kinds of receptor species for all experimental conditions also predicts that a substantial fraction of ligand-independent preformed dimers exist (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 8</xref>). These constitutive dimers harbor kinase domains in a symmetric configuration. A peculiar prediction of these calculations is the lack of dependence of the total amount of receptor dimers on EGF concentration in the control and lapatinib-treated samples. In contrast, EGF induces a slight increase in the fraction of receptor dimers in erlotinib-treated cells.</p></sec><sec id=\"S3.SS4\"><title>EGF-Induced Changes in EGFR Clustering Revealed by Homo-FRET Experiments Are in Agreement With the Model</title><p>Although the lack of EGF-dependent recruitment of EGFRs to dimers may sound unexpected, one must bear in mind that the experiments were performed at 4°C, below the phase transition temperature of the plasma membrane, to prevent internalization. In order to confirm the predictions of the model, homo-FRET experiments were performed. Since the donor and the acceptor are spectroscopically identical in homo-FRET, energy migrates in a cluster of such fluorophores (<xref rid=\"B34\" ref-type=\"bibr\">Lidke et al., 2003</xref>). Therefore, homo-FRET has already been used extensively for characterizing homoclustering of receptors (<xref rid=\"B34\" ref-type=\"bibr\">Lidke et al., 2003</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Yeow and Clayton, 2007</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Szabó et al., 2008</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Hofman et al., 2010</xref>). The extent of energy migration is inversely related to fluorescence anisotropy, the only read-out parameter influenced by homo-FRET. Since anisotropy is not only influenced by homo-FRET, the dependence of anisotropy on the density of fluorophores was utilized to determine the cluster size (the number of fluorophores in a cluster) and the fraction of monomeric, unclustered fluorophores according to a method developed previously (<xref rid=\"B52\" ref-type=\"bibr\">Szabó et al., 2008</xref>). Different fluorophore densities were generated by gradual photobleaching of EGFR-GFP.</p><p>In control cells without pretreatment with kinase inhibitors, EGF did not induce any change in EGFR homoclustering at 4°C (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 9</xref>). We have shown that 15-min and 60-min incubations were equally ineffective in bringing about changes in EGFR clustering at 4°C (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 10</xref>). These results are in accordance with previous reports revealing that tyrosine phosphorylation of EGFR takes place and reaches saturation in ∼5 min at 4°C, while effects requiring significant lateral diffusion (e.g., internalization) are blocked below the phase transition temperature of the membrane (<xref rid=\"B8\" ref-type=\"bibr\">Campos-Gonzalez and Glenney, 1991</xref>; <xref rid=\"B41\" ref-type=\"bibr\">Moehren et al., 2002</xref>). Therefore, all homo-FRET experiments were carried out with cells stimulated with EGF for 15 min. As opposed to no effect at 4°C, EGF induced a substantial increase in the cluster size and in the fraction of clustered receptors at 37°C (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 9</xref>). The confidence intervals of the estimations are shown in <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 11</xref>, and representative anisotropy images are displayed in <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 12</xref>. Erlotinib-pretreated cells responded to EGF with a slight increase in the fraction of clustered receptors at 4°C corroborating this prediction of the model as well. The EGF-induced increase in EGFR clustering at 37°C was augmented by erlotinib pretreatment in agreement with previous results (<xref rid=\"B11\" ref-type=\"bibr\">Coban et al., 2015</xref>). While lapatinib did not alter the lack of EGF-induced clustering at 4°C, it slightly decreased the EGF-elicited clustering of EGFR as evidenced by the smaller cluster size.</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Homoclustering of EGFR in quiescent and EGF-stimulated cells determined by microscopic homo-FRET experiments. Cells were serum-starved overnight followed by a 1-h pretreatment with kinase inhibitors (5 μM, 1 h, 37°C) if indicated. Cells were incubated with 130 nM EGF at 4°C or 37°C for 15 min followed by confocal microscopic determination of the fluorescence anisotropy of EGFR-GFP at different levels of GFP intensity achieved by repeated photobleaching. Fitting of an equation describing the dependence of anisotropy on cluster properties revealed the number of proteins in a homocluster (cluster size) and the fraction of monomeric receptors. The error bars indicate the standard error of the mean determined from four image series.</p></caption><graphic xlink:href=\"fcell-08-00776-g004\"/></fig><p>While the model describing EGF binding only differentiates between monomers and dimers, homo-FRET detects larger clusters as well (<xref rid=\"B52\" ref-type=\"bibr\">Szabó et al., 2008</xref>). Therefore, the fraction of dimeric EGFRs and the fraction of clustered receptors in the homo-FRET experiments are not directly comparable. However, changes in dimerization and large-scale clustering are correlated. EGF-induced changes in large-scale clustering only took place at 4°C in our experiments, similar to the requirement for EGF stimulation to be carried out at room temperature or at 37°C so that the growth factor induces dimerization (<xref rid=\"B17\" ref-type=\"bibr\">Gadella and Jovin, 1995</xref>). The remarkable correspondence between the homo-FRET experiments and the EGF-dependent changes in EGFR dimerization, shown in <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 8</xref>, lends significant support to the model describing ligand binding.</p></sec><sec id=\"S3.SS5\"><title>Experimental Conditions Eliminating Positive Apparent Cooperativity of EGF Binding</title><p>Since cytoskeletal anchoring has repeatedly been observed to affect ligand binding and activation of EGFR (<xref rid=\"B58\" ref-type=\"bibr\">Wiegant et al., 1986</xref>; <xref rid=\"B33\" ref-type=\"bibr\">Lidke et al., 2005</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Low-Nam et al., 2011</xref>), we tested whether disassembling actin fibers exerts any effect on EGF binding. Latrunculin B treatment disrupted actin fibers and led to a decreased affinity and apparent cooperativity of EGF binding (control cells: <italic>K</italic><sub><italic>d</italic></sub> = 4 nM (3.9–4.1), <italic>n</italic> = 1.24 (1.21–1.26); latrunculin B-treated cells: <italic>K</italic><sub><italic>d</italic></sub> = 8.2 nM (7.9–8.5), <italic>n</italic> = 1.03 (1–1.05); the 95% confidence interval is displayed in the parentheses; <xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 13</xref>). We also tested if induction of nucleation of actin polymerization by jasplakinolide affects EGF binding. The affinity and apparent cooperativity of EGF binding were not altered by the treatment (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 14</xref>), most likely as a result of jasplakinolide exerting minimal effects on the overall organization of actin filaments and on the subcortical actin meshwork (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 13</xref>). Glycosylation of the ECD has been shown to alter the structure of EGFR significantly (<xref rid=\"B24\" ref-type=\"bibr\">Kaszuba et al., 2015</xref>). Tunicamycin treatment successfully deglycosylated EGFR, as evidenced by the decreased molecular weight of the protein (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 15</xref>), and led to an even more pronounced reduction in EGF binding affinity and apparent cooperativity than disrupting actin fibers (<italic>K</italic><sub><italic>d</italic></sub> = 15 nM (14.6–15.4), <italic>n</italic> = 0.77 (0.75–0.78); <xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref>). Although the model presented in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> was fitted to the EGF binding data of tunicamycin- and latrunculin B-treated cells, the results of the fitting were unreproducible and unreliable. We attribute the failure to two circumstances: (i) Since both conditions compromised cell viability, we could not combine these treatments with kinase inhibitors leading to a low number of data points to be fitted. (ii) The low affinity of EGF binding, especially in the tunicamycin-treated cells, resulted in the lack of saturation. Although fitting the Hill equation to the data points does not reveal the molecular background of the observed changes, the results still show that both glycosylation and an intact cytoskeleton are required for positive cooperative EGF binding and for maintaining the affinity of the binding site.</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>Experimental conditions disturbing positive cooperative binding of EGF. <bold>(A)</bold> The effect of tunicamycin and latrunculin B treatment on apparent cooperativity of EGF binding. Control samples or cells pretreated with tunicamycin or latrunculin B were incubated with a concentration series of EGF to measure the equilibrium binding of the growth factor. The measured data points along with their standard error of the mean and fits according to the Hill equation are shown in the figure. <bold>(B)</bold> Cells were incubated with a concentration series of EGF for 1 h or 24 h, and the equilibrium binding of the growth factor was measured by flow cytometry. The inserts display the Scatchard diagrams.</p></caption><graphic xlink:href=\"fcell-08-00776-g005\"/></fig><p>In all of the binding assays reported in the manuscript cells were incubated with EGF on ice for 1 h. The low temperature incubation “freezes” the membrane inhibiting internalization, which would completely invalidate the assumption of equilibrium. However, the low temperature also slows down the rate with which equilibrium binding at the plasma membrane is reached prompting some authors to increase the incubation time to 12–24 h (<xref rid=\"B37\" ref-type=\"bibr\">Macdonald and Pike, 2008</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Macdonald-Obermann and Pike, 2018</xref>). In order to analyze the effect of a long incubation time EGF binding data obtained with a 1-h and a 24-h incubation with the growth factor were fitted by the Hill equation. The comparison revealed that the long incubation time substantially decreased the apparent cooperativity of EGF binding (1-h incubation: <italic>K</italic><sub><italic>d</italic></sub> = 3.4 nM (3.3–3.6); <italic>n</italic> = 1.26 (1.23–1.29); 24-h incubation: <italic>K</italic><sub><italic>d</italic></sub> = 5.2 nM (5–5.5); <italic>n</italic> = 1.02 (0.99–1.05); <xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref>). Next, we recorded microscopic images of cells incubated with EGF for 1 h or 24 h, and compared the relative amount of fluorescence of TAMRA-EGF from within the intracellular space. Although both incubations were performed on ice, the 24-h incubation led to a significant increase in the intracellular concentration of EGF (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 16</xref>). We concluded that long incubation times allow EGF to be internalized even at low temperatures questioning the assumption that equilibrium binding of EGF to membrane receptors is measured under such experimental conditions.</p><p>Since measuring the cell-bound fraction of EGF before reaching equilibrium can increase the apparent cooperativity of ligand binding (see <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Information</xref> for a detailed derivation), it was essential to show that the 1-h incubation time used in the experiments is sufficient for EGF binding to reach equilibrium. Time-correlated analysis of flow cytometric data enabled us to conclude that EGF binding is saturated in less than 1 h (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 17</xref>).</p></sec></sec><sec id=\"S4\"><title>Discussion</title><p>In this manuscript we developed and tested a structure-based model for the ligand binding and dimerization of EGFR, whose defining feature is the different dimerization and ligand binding propensity of receptors harboring a kinase in its active or inactive conformation. In order to keep the number of model parameters as low as possible, several kinds of molecular species and processes were neglected, e.g., conformations of the ECD other than the extended and the closed ones, dimerization of monomers with an ECD conformation other than the extended structure, dimers formed only by interactions between the kinase domains, conformational states of the juxta- and transmembrane domains, interactions of the juxtamembrane domain with the membrane and local heterogeneity of receptor concentrations. Oligomerization is not explicitly involved in the model either, although interpretation of the different ligand binding affinities of EGFR dimers harboring active and inactive KDs involves higher-order receptor complexes, a feature of the model to be explained later. Therefore, although the absolute values of the fitted parameters may be inaccurate, the general tendencies and properties have reliably been identified, a conclusion supported by the fact that our experimental data were successfully fitted and that the predictions of the model are in accordance with most published data. Analysis of the fitted model allowed us to reach the following major conclusions: (i) dimers harboring asymmetric and symmetric KD dimers exhibit negative and positive cooperative ligand binding, respectively; (ii) the homodimerization tendency of erlotinib-stabilized active KDs is higher than that of lapatinib-bound, inactive KDs; (iii) the dimerization tendency of liganded EGFR with active KD is stronger than that of unliganded monomers (positive linkage), while the opposite tendency applies to the inactive KD; (iv) both receptor monomers and dimers contribute to EGF binding with the importance of dimers increasing at high receptor expression levels; (v) A significant amount of preformed, ligand-independent dimers harboring inactive, symmetric KD dimers are present, and the fraction of receptor dimers does not change significantly upon ligand binding. While this latter conclusion may seem to be at odds with common sense, the gel-like state of the plasma membrane at the temperature of the experiments (4°C) most likely prevents diffusion-driven alterations of the monomer-dimer equilibrium in the plasma membrane. This prediction is also in accordance with our homo-FRET experiments and with previous results showing that EGF bound to cells on ice only induces receptor dimerization upon elevation of the temperature to 20°C or 37°C (<xref rid=\"B17\" ref-type=\"bibr\">Gadella and Jovin, 1995</xref>). The quantitative model proposed by Macdonald and Pike, neglecting structural transitions of the ECD and different conformations of the KD, predicts that EGF binding leads to a decreased fraction of receptor dimers (see recalculation of the model in the <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Material</xref>). This feature is the consequence of the lower dimerization propensity of liganded monomers compared to their unliganded counterparts. We believe that predictions of our model are in better agreement with experimental findings than those of the previous model. The fact that our homo-FRET experimental results are in agreement also with the prediction of increased dimerization of EGFRs, whose KD is locked in the active conformation, confirms the major properties of the proposed model. This erlotinib-induced increase in EGFR dimerization has also been reported by <xref rid=\"B11\" ref-type=\"bibr\">Coban et al. (2015)</xref>.</p><p>According to our model, dimerization of EGFR monomers with active and inactive KDs leads to the formation of dimers harboring asymmetric and symmetric KD dimers, respectively. While the formation of asymmetric KD dimers from the active conformation of the kinase is in accordance with the proposed resemblance of EGFR activation to cyclin-induced activation of cyclin-dependent kinases (<xref rid=\"B68\" ref-type=\"bibr\">Zhang et al., 2006</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Kovacs et al., 2015</xref>), the structural identity of the other KD dimer is dubious. The assumption of two different KD dimers was required for fitting the measurement data successfully. The hypothesis that the KD dimer other than the asymmetric dimer is identical to symmetric KD dimers is somewhat arbitrary. While symmetric KD dimers have been suggested to contain the kinase in the active conformation (<xref rid=\"B51\" ref-type=\"bibr\">Stamos et al., 2002</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Landau et al., 2004</xref>), our observation that this dimeric species increased in abundance in the presence of lapatinib (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 8</xref>) argues that they contain the kinase in its inactive conformation. While assignment of a specific structure to this KD dimer is arguable based on our experiments, the fact that a KD dimer different from the asymmetric dimer must exist and that this KD dimer is coupled to an ECD whose ligand binding properties are markedly different from the negatively cooperative, high affinity binding site seems certain. We also tested a model in which formation of this dimeric species containing a symmetric kinase dimer began from the “CI” monomer (containing inhibited kinase and closed ECD), but fitting of this model to the experimental data was not successful (data not shown).</p><p>Other aspects of the predictions of the proposed model are also in agreement with previous literature data. The fact that a significant fraction of EGF binds to monomeric receptor species may be the consequence of hindered long-range diffusion in the gel-like membrane, an inherent consequence of the experimental condition, but such a phenomenon has repeatedly been reported previously (<xref rid=\"B53\" ref-type=\"bibr\">Teramura et al., 2006</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Szabó et al., 2008</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Liu et al., 2012</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Vámosi et al., 2019</xref>). Coupling of a liganded ECD dimer to both active and inactive KD dimers, a key feature in the proposed model, has been experimentally observed in electron microscopy (<xref rid=\"B40\" ref-type=\"bibr\">Mi et al., 2011</xref>). Substantial controversy exists in the literature regarding constitutive, ligand-independent EGFR dimers. While several pieces of evidence point at their existence (<xref rid=\"B10\" ref-type=\"bibr\">Clayton et al., 2007</xref>; <xref rid=\"B52\" ref-type=\"bibr\">Szabó et al., 2008</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Zanetti-Domingues et al., 2018</xref>), their transient nature and dependence on receptor expression levels has also been emphasized (<xref rid=\"B43\" ref-type=\"bibr\">Nagy et al., 2010</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Low-Nam et al., 2011</xref>). Our model calculations show that such constitutive dimers harbor receptors with symmetric, inactive kinase dimers. This conclusion is in agreement with a recent study showing that mutations introduced into the active, asymmetric kinase dimer interface do not significantly affect the stability of ligand-independent, preformed dimers (<xref rid=\"B7\" ref-type=\"bibr\">Byrne et al., 2020</xref>). Due to the very low affinity of these dimeric species for binding of the first ligand, they hardly bind EGF at low ligand concentrations, i.e., the fraction of these preformed dimers is constant in this concentration range of EGF. When they do bind EGF beginning from the 1–10 nM range, they do so with positive cooperativity, a prediction, which is in accordance with previous single-molecule experiments (<xref rid=\"B53\" ref-type=\"bibr\">Teramura et al., 2006</xref>). There is a perplexing contradiction regarding the cooperativity of ligand binding in the EGFR system. While negative cooperativity has become widely accepted (<xref rid=\"B37\" ref-type=\"bibr\">Macdonald and Pike, 2008</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Alvarado et al., 2010</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Martin-Fernandez, 2012</xref>), positive cooperativity has also been repeatedly observed (<xref rid=\"B50\" ref-type=\"bibr\">Sherrill and Kyte, 1996</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Lemmon et al., 1997</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Teramura et al., 2006</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Chung et al., 2010</xref>; <xref rid=\"B38\" ref-type=\"bibr\">Macdonald-Obermann and Pike, 2018</xref>). Our model predicts that the type of apparent cooperativity of EGF binding depends on receptor expression levels, and it is attributable to the different ligand binding properties of receptor dimers with the two different kinase dimers. Our observation and prediction that positive cooperativity increases as a function of receptor expression levels have already been reported (<xref rid=\"B31\" ref-type=\"bibr\">Lemmon et al., 1997</xref>). The fact that cooperativity of EGF binding depends on the relative abundance of receptors with active and inactive KDs and on receptor expression levels may account for the inconsistency in the literature in this regard.</p><p>Among dimers those harboring an inactive, symmetric kinase dimer are the dominant species in the absence or at low concentrations of EGF, while dimers with active, asymmetric kinase dimers are preferred at high ligand concentrations (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Figure 8</xref>). This tendency seems to prevent unintended kinase activation in the absence of stimulation, and to favor initiation of signaling at sufficiently high EGF concentrations. Although the dimerization tendency of unliganded monomers with an inactive KD is weaker than that of unliganded monomers with an active KD, dimers with an inactive KD in the absence or at low concentrations of EGF are favored for the following reasons: (i) the equilibrium between the inactive and active KDs is shifted toward the inactive conformation (<italic>K</italic><sub>1</sub> in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>); (ii) the binding affinity of dimers with inactive, symmetric kinase dimers for the first ligand is very weak. In contrast, dimers with active, asymmetric KD dimers are favored at high ligand concentrations, which is brought about by the positive linkage between ligand binding and dimer assembly if the KD is in the active conformation and by the inherently stronger dimerization tendency of monomers harboring an active KD.</p><p>We identified two conditions, depolymerization of actin filaments and inhibition of glycosylation, which reduced receptor affinity and abolished positive apparent cooperativity of EGF binding. Since the quality of these data did not allow for model fitting, only speculations can be put forward regarding the explanation. Both treatments may inhibit positive apparent cooperativity by reducing the local receptor concentration. Actin depolymerization may achieve this effect by abolishing confinement (<xref rid=\"B36\" ref-type=\"bibr\">Low-Nam et al., 2011</xref>), while deglycosylation may reduce the affinity of EGFR to putative raft-like domains or glycolipids (<xref rid=\"B16\" ref-type=\"bibr\">Furukawa et al., 2012</xref>). Since deglycosylation has been shown to alter the conformation of EGFR ECD and its orientation relative to the membrane, its affinity for dimerization and ligand binding is expected to be altered (<xref rid=\"B24\" ref-type=\"bibr\">Kaszuba et al., 2015</xref>). Since tunicamycin is a general inhibitor of N-glycosylation, it is also possible that the effect of this treatment is attributable to effects on the glycosylation of other proteins.</p><p>While the conformations of the extra- and intracellular domains are unlikely to be coupled in monomeric receptors, they can be indirectly linked to each other in dimers or higher order clusters. A possible explanation for the assignment of different EGF affinities to dimers with symmetric and asymmetric KD dimers invoking higher order clusters is provided below. Preformed, unliganded EGFR dimers harbor inactive, symmetric kinase dimers as revealed by the fitting and also supported by literature data (<xref rid=\"B7\" ref-type=\"bibr\">Byrne et al., 2020</xref>). Such preformed dimers have been found to form chains or polymers of dimers (<xref rid=\"B66\" ref-type=\"bibr\">Zanetti-Domingues et al., 2018</xref>). It is reasonable to assume, as suggested previously, that access of EGF to the ligand binding site of such receptors is blocked explaining their very low affinity for binding the first ligand. Ligand binding must remove these preformed dimers from these receptor polymers since their orientation would not allow cross-phosphorylation to happen. Once they are removed from the receptor polymers to form dimers (not forming larger clusters) (<xref rid=\"B44\" ref-type=\"bibr\">Needham et al., 2016</xref>), binding of the second ligand takes place much more easily leading to positive cooperativity. Dimers harboring active kinase domains (asymmetric KD dimers) are not incorporated to dimer chains, therefore the inherent negative cooperativity of the ECD is manifested in their case. Successful fitting of the experimental data using these assumptions implies that the influence of higher-order clusters on the affinity of EGF binding to receptors should not be overlooked.</p><p>The proposed model has important implications for interpreting the action of tyrosine kinase inhibitors. These inhibitors not only block the enzymatic activity of the KD, but they also alter the abundance of different molecular species. In particular, EGFR with an active kinase domain has a stronger dimerization tendency than receptors with inactive kinase domains (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). In addition, they also differ in terms of their propensity to form receptor oligomers as explained in the previous paragraph. Therefore, the effect of kinase inhibitors on EGF binding is determined by how they shift the concentration of different receptor states. Inhibitors stabilizing the active conformation of the kinase (e.g., erlotinib) enhance dimerization, a proposition supported by our homo-FRET experiments and previous data (<xref rid=\"B32\" ref-type=\"bibr\">Lichtner et al., 2001</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Anido et al., 2003</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Coban et al., 2015</xref>). In contrast, lapatinib, stabilizing the inactive conformation of the kinase, does not bring about such an increase in receptor dimerization in agreement with previous data (<xref rid=\"B6\" ref-type=\"bibr\">Bublil et al., 2010</xref>). Since the kinase in the majority of receptors is already in the inactive conformation in the absence of inhibitors (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>), the effect of lapatinib, stabilizing the inactive conformation of the KD, on the monomer/dimer equilibrium is much less pronounced than that of erlotinib. In light of the effect of kinase inhibitors on the monomer/dimer equilibrium, inhibitors stabilizing the inactive conformation of the kinase seem to be more potent and safer from a theoretical point of view.</p><p>In conclusion, the model developed in the current manuscript provides a comprehensive view on the molecular transitions taking place upon EGF binding to its receptor. Although different experimental approaches can capture distinct steps of the ligand-induced alterations in the conformation and assembly of receptor dimers, practically none of them can decipher all of them in a quantitative manner. Global analysis of EGF binding equilibria allowed us to generate a model providing insight into most steps of the activation pathway at a pseudo-molecular level.</p></sec><sec sec-type=\"data-availability\" id=\"S5\"><title>Data Availability Statement</title><p>All datasets presented in this study are included in the article/<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Material</xref>.</p></sec><sec id=\"S6\"><title>Author Contributions</title><p>TH carried out and analyzed most of the experiments and wrote the initial version of the manuscript. TV and IR contributed to the flow cytometric and confocal microscopic experiments, respectively. TK performed part of the experiments with compounds modifying actin polymerization and protein glycosylation. JS advised about the flow cytometric experiments and revised the manuscript. PN conceived, supervised and funded the project, developed the mathematical model and revised the manuscript. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> The work was supported by research grants from the National Research, Development and Innovation Office, Hungary (K120302, GINOP-2.3.2-15-2016-00020, GINOP-2.3.2-15-2016-00044, EFOP-3.6.3-VEKOP-16-2017-00009).</p></fn></fn-group><ack><p>We would like to express our gratitude to Thomas M. Jovin (Max Planck Institute for Biophysical Chemistry, Göttingen) for his mentorship and scientific advice during the initial stages of this project.</p></ack><sec id=\"S9\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fcell.2020.00776/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fcell.2020.00776/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM2\"><media xlink:href=\"Data_Sheet_2.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Alvarado</surname><given-names>D.</given-names></name><name><surname>Klein</surname><given-names>D. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">33089974</article-id><article-id 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contrib-type=\"author\"><name><surname>Qian</surname><given-names>Zhen</given-names></name><address><email>qianzhen_YFY@126.com</email></address><xref ref-type=\"aff\" rid=\"acel13185-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13185-cr-0006\" contrib-type=\"author\"><name><surname>Wu</surname><given-names>Li</given-names></name><address><email>14_wuli@tongji.edu.cn</email></address><xref ref-type=\"aff\" rid=\"acel13185-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13185-cr-0007\" contrib-type=\"author\"><name><surname>Xu</surname><given-names>Xiaojun</given-names></name><address><email>xiaojunxu@cpu.edu.cn</email></address><xref ref-type=\"aff\" rid=\"acel13185-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13185-cr-0008\" contrib-type=\"author\"><name><surname>Wan</surname><given-names>Xiaoping</given-names></name><address><email>wanxiaoping@tongji.edu.cn</email></address><xref ref-type=\"aff\" rid=\"acel13185-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13185-cr-0009\" contrib-type=\"author\"><name><surname>Jiang</surname><given-names>Ying</given-names></name><address><email>ying_jiang@tongji.edu.cn</email></address><xref ref-type=\"aff\" rid=\"acel13185-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13185-cr-0010\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Zhang</surname><given-names>Jian</given-names></name><xref ref-type=\"aff\" rid=\"acel13185-aff-0004\">\n<sup>4</sup>\n</xref><address><email>jian.zhang@sjtu.edu.cn</email></address></contrib><contrib id=\"acel13185-cr-0011\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Gao</surname><given-names>Shaorong</given-names></name><xref ref-type=\"aff\" rid=\"acel13185-aff-0001\">\n<sup>1</sup>\n</xref><address><email>gaoshaorong@tongji.edu.cn</email></address></contrib><contrib id=\"acel13185-cr-0012\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Mao</surname><given-names>Zhiyong</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-5298-1918</contrib-id><xref ref-type=\"aff\" rid=\"acel13185-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13185-aff-0002\">\n<sup>2</sup>\n</xref><address><email>zhiyong_mao@tongji.edu.cn</email></address></contrib></contrib-group><aff id=\"acel13185-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Clinical and Translational Research Center of Shanghai First Maternity & Infant Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology</named-content>\n<institution>Tongji University</institution>\n<city>Shanghai</city>\n<country country=\"CN\">China</country>\n</aff><aff id=\"acel13185-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Tsingdao Advanced Research Institute</named-content>\n<institution>Tongji University</institution>\n<city>Qingdao</city>\n<country country=\"CN\">China</country>\n</aff><aff id=\"acel13185-aff-0003\">\n<label><sup>3</sup></label>\n<named-content content-type=\"organisation-division\">State Key Laboratory of Natural Medicines</named-content>\n<institution>China Pharmaceutical University</institution>\n<city>Nanjing</city>\n<country country=\"CN\">China</country>\n</aff><aff id=\"acel13185-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Ministry of Education</named-content>\n<institution>Shanghai Jiao‐Tong University School of Medicine</institution>\n<city>Shanghai</city>\n<country country=\"CN\">China</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nZhiyong Mao and Shaorong Gao, Clinical and Translational Research Center of Shanghai First Maternity & Infant Hospital, Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.<break/>\nEmails: <email>zhiyong_mao@tongji.edu.cn</email> (ZM); <email>gaoshaorong@tongji.edu.cn</email> (SG)<break/>\nJian Zhang, Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao‐Tong University School of Medicine, Shanghai 200025, China.<break/>\nEmail: <email>jian.zhang@sjtu.edu.cn</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>21</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13185</elocation-id><history><date date-type=\"received\"><day>11</day><month>2</month><year>2020</year></date><date date-type=\"rev-recd\"><day>12</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>06</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13185.pdf\"/><abstract id=\"acel13185-abs-0001\"><title>Abstract</title><p>Cellular reprogramming is an emerging strategy for delaying the aging processes. However, a number of challenges, including the impaired genome integrity and decreased pluripotency of induced pluripotent stem cells (iPSCs) derived from old donors, may hinder their potential clinical applications. The longevity gene, Sirtuin 6 (SIRT6), functions in multiple biological processes such as the maintenance of genome integrity and the regulation of somatic cell reprogramming. Here, for the first time, we demonstrate that MDL‐800, a recently developed selective SIRT6 activator, improved genomic stability by activating two DNA repair pathways—nonhomologous end joining (NHEJ) and base excision repair (BER) in old murine‐derived iPSCs. More interestingly, we found that pretreating old murine iPSCs, which normally exhibit a restricted differentiation potential, with MDL‐800 promoted the formation of teratomas comprised of all three germ layers and robustly stimulated chimera generation. Our findings suggest that pharmacological activation of SIRT6 holds great promise in treating aging‐associated diseases with iPSC‐based cell therapy.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13185-abs-0002\"><p>MDL‐800, a selective SIRT6 activator, improves genomic stability by activating nonhomologous end joining and base excision repair, and enhances the pluripotency of old murine‐derived iPSCs.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13185-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13185-g003.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13185-kwd-0001\">aging</kwd><kwd id=\"acel13185-kwd-0002\">DNA repair</kwd><kwd id=\"acel13185-kwd-0003\">genome integrity</kwd><kwd id=\"acel13185-kwd-0004\">MDL‐800</kwd><kwd id=\"acel13185-kwd-0005\">pluripotency</kwd><kwd id=\"acel13185-kwd-0006\">SIRT6</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>National Key R&D Program of China </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100001809</institution-id></institution-wrap></funding-source><award-id>2018YFC2000100</award-id><award-id>2017YFA0103300</award-id><award-id>2016YFA0100400</award-id></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>National Natural Science Foundation of China </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100001809</institution-id></institution-wrap></funding-source><award-id>31871438</award-id><award-id>81972457</award-id><award-id>31721003</award-id><award-id>81630035</award-id><award-id>31871446</award-id><award-id>31801243</award-id></award-group><award-group id=\"funding-0003\"><funding-source>“Shu Guang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation</funding-source><award-id>19SG18</award-id></award-group><award-group id=\"funding-0004\"><funding-source>Program of Shanghai Academic Research Leader</funding-source><award-id>19XD1403000</award-id></award-group><award-group id=\"funding-0005\"><funding-source>the Young Elite Scientist Sponsorship Program by CAST</funding-source><award-id>2018QNRC001</award-id></award-group><award-group id=\"funding-0006\"><funding-source>key project of the Science and Technology of Shanghai Municipality</funding-source><award-id>19JC1415300</award-id></award-group><award-group id=\"funding-0007\"><funding-source>Shanghai Rising‐Star Program</funding-source><award-id>19QA1409600</award-id></award-group><award-group id=\"funding-0008\"><funding-source>Shanghai Chenguang Program</funding-source><award-id>16CG17</award-id></award-group><award-group id=\"funding-0009\"><funding-source>Shanghai municipal medical and health discipline construction projects</funding-source><award-id>2017ZZ02015</award-id></award-group><award-group id=\"funding-0010\"><funding-source>Fundamental Research Funds for the Central Universities</funding-source></award-group><award-group id=\"funding-0011\"><funding-source>Open Project Program of State Key Laboratory of Natural Medicines</funding-source><award-id>SKLNMKF201905</award-id></award-group></funding-group><counts><fig-count count=\"2\"/><table-count count=\"0\"/><page-count count=\"7\"/><word-count count=\"4470\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13185-cit-1001\">\n<string-name>\n<surname>Chen</surname>\n<given-names>Y</given-names>\n</string-name>, <string-name>\n<surname>Chen</surname>\n<given-names>J</given-names>\n</string-name>, <string-name>\n<surname>Sun</surname>\n<given-names>X</given-names>\n</string-name>, et al. <article-title>The SIRT6 activator MDL-800 improves genomic stability and pluripotency of old murine-derived iPS cells</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13185</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13185</pub-id>\n</mixed-citation>\n</p><fn-group id=\"acel13185-ntgp-0001\"><fn id=\"acel13185-note-0001\"><p>Yu Chen, Jiayu Chen, and Xiaoxiang Sun contributed equally to this work.</p></fn></fn-group></notes></front><body id=\"acel13185-body-0001\"><sec id=\"acel13185-sec-0001\"><label>1</label><title>INTRODUCTION, RESULTS, AND DISCUSSION</title><p>Aging is a complex process characterized by a time‐dependent decline in physiological function and an increased vulnerability to disease (Chalkiadaki & Guarente, <xref rid=\"acel13185-bib-0002\" ref-type=\"ref\">2015</xref>; Kennedy et al., <xref rid=\"acel13185-bib-0009\" ref-type=\"ref\">2014</xref>). The loss of tissue homeostasis contributes to the onset of aging and age‐related diseases (Sullivan et al., <xref rid=\"acel13185-bib-0026\" ref-type=\"ref\">2018</xref>). Restoring the functionality of aged tissues with adult stem cells is an emerging strategy in regenerative medicine (Cerletti et al., <xref rid=\"acel13185-bib-0001\" ref-type=\"ref\">2008</xref>; Chhabra & Brayman, <xref rid=\"acel13185-bib-0004\" ref-type=\"ref\">2013</xref>). For instance, hematopoietic progenitor cell transplantation is an FDA‐approved treatment for reconstituting the hematopoietic and immunologic systems in patients. In addition, several other types of stem cell therapies are under investigation in clinical trials to evaluate their safety and effectiveness in treating diseases, including type 2 diabetes mellitus, Parkinson's disease, and osteoarthritis (<ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov\">https://clinicaltrials.gov</ext-link>). However, only a limited number of types of adult stem cells can be successfully isolated, maintained <italic>in vitro</italic>, and applied in the clinic (Mount, Ward, Kefalas, & Hyllner, <xref rid=\"acel13185-bib-0018\" ref-type=\"ref\">2015</xref>). Induced pluripotent stem cells (iPSCs) derived from autologous somatic cells transduced with Yamanaka factors can be subsequently differentiated into desired cell types <italic>in vitro</italic> and further applied to treat age‐related diseases in a variety of types of tissues (Park et al., <xref rid=\"acel13185-bib-0020\" ref-type=\"ref\">2008</xref>; Si‐Tayeb et al., <xref rid=\"acel13185-bib-0024\" ref-type=\"ref\">2010</xref>; Takebe et al., <xref rid=\"acel13185-bib-0027\" ref-type=\"ref\">2013</xref>). Additionally, autologous therapy with iPSCs may avoid certain ethical concerns and potentially minimize the risk of immune rejection associated with allogenic stem cell products. However, the potential clinical application of iPSCs in regenerative medicine faces a dilemma as it is likely that the iPSCs used to treat any age‐related diseases would be derived from somatic cells isolated from patients at old ages while the quality of iPSCs derived from old donors is not as high as young iPSCs or embryonic stem cells (ESCs) (Lo Sardo & Ferguson, <xref rid=\"acel13185-bib-0011\" ref-type=\"ref\">2017</xref>; Skamagki et al., <xref rid=\"acel13185-bib-0025\" ref-type=\"ref\">2017</xref>). Genome integrity and pluripotent potential are two critical parameters in evaluating the quality of iPSCs (Sullivan et al., <xref rid=\"acel13185-bib-0026\" ref-type=\"ref\">2018</xref>). Developing novel methods to improve the genome integrity and pluripotency of iPSCs derived from old subjects would help achieve therapeutic goals in treating age‐related diseases.</p><p>The longevity gene SIRT6 is an enzyme possessing both NAD<sup>+</sup>‐dependent protein deacetylase activity and mono (ADP‐ribosyl) transferase activity. Loss of SIRT6 leads to genomic instability and severe phenotypes consistent with premature aging, including osteopenia, reduced subcutaneous fat, and shortened life span in mice (Mostoslavsky et al., <xref rid=\"acel13185-bib-0017\" ref-type=\"ref\">2006</xref>), while overexpression of SIRT6 extends murine life span (Kanfi et al., <xref rid=\"acel13185-bib-0008\" ref-type=\"ref\">2012</xref>). Several reports demonstrate that SIRT6 is a pivotal regulator of different DNA repair pathways, including the DNA double‐strand break (DSB) repair pathways—canonical nonhomologous end joining (c‐NHEJ), alternative NHEJ (alt‐NHEJ), homologous recombination (HR)—and base excision repair (BER) by targeting DNA‐PKcs, PARP1 and SNF2H (Mao et al., <xref rid=\"acel13185-bib-0014\" ref-type=\"ref\">2011</xref>, <xref rid=\"acel13185-bib-0015\" ref-type=\"ref\">2012</xref>; McCord et al., <xref rid=\"acel13185-bib-0016\" ref-type=\"ref\">2009</xref>; Toiber et al., <xref rid=\"acel13185-bib-0028\" ref-type=\"ref\">2013</xref>; Xu et al., <xref rid=\"acel13185-bib-0030\" ref-type=\"ref\">2015</xref>). As a chromatin associated epigenetic factor, SIRT6 also participates in regulating the expression of the pluripotency genes that determine the balance between pluripotency and differentiation (Etchegaray et al., <xref rid=\"acel13185-bib-0005\" ref-type=\"ref\">2015</xref>; O'Callaghan & Vassilopoulos, <xref rid=\"acel13185-bib-0019\" ref-type=\"ref\">2017</xref>). Although expression of SIRT6 protein gradually increases during reprogramming (Xu et al., <xref rid=\"acel13185-bib-0029\" ref-type=\"ref\">2019</xref>), our previous study found that SIRT6 protein level is significantly lower in old murine‐derived iPSCs and that the low expression of SIRT6 resulted in the decline of NHEJ and genomic stability in old murine‐derived iPSCs compared to those derived from young mice (Chen et al., <xref rid=\"acel13185-bib-0003\" ref-type=\"ref\">2017</xref>). However, whether SIRT6 activators can be utilized to enhance DNA repair to stabilize genomes and improve the pluripotency of old iPSCs remains to be determined.</p><p>MDL‐800 (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1a</xref>) is a selective allosteric activator of SIRT6. It stimulates SIRT6 catalytic activity and promotes the binding affinities of substrate to SIRT6 (Huang et al., <xref rid=\"acel13185-bib-0006\" ref-type=\"ref\">2018</xref>). We therefore tested whether MDL‐800 treatment improved the quality of old murine (2‐year‐old)‐derived iPSCs. We first validated that MDL‐800 enhances the enzymatic activity of mouse SIRT6. We pretreated the iPSCs derived from the old mice with MDL‐800 at concentrations of 5 μM and 20 μM for 24 hr, and then analyzed the level of a SIRT6 substrate H3K56Ac. We found that, consistent with previous reports on human SIRT6, treating the mouse iPSCs with MDL‐800 at 20 μM promoted the deacetylation of H3K56Ac (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1b</xref>), and prolonging the incubation time to 48 hr led to a further reduction in acetylation level of H3K56Ac (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1c</xref>). These data indicate that MDL‐800 might also directly activate the catalytic activity of mouse SIRT6. To further demonstrate that MDL‐800 affects the acetylation level of H3K56Ac through monitoring mouse SIRT6 activity, we treated iPSCs derived from <italic>Sirt6</italic>\n<sup>+/+</sup> mouse embryonic fibroblasts (MEFs) or <italic>Sirt6</italic>\n<sup>−/−</sup> MEFs with MDL‐800. We found that <italic>Sirt6</italic> deficiency abrogated the MDL‐800 mediated promotion of H3K56Ac deacetylation (Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S1</xref>a,b). Taken together, our results indicate that MDL‐800 specifically activates SIRT6 enzymatic activity in mouse iPSCs.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13185-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>MDL‐800 promotes genome integrity by enhancing NHEJ and BER in old murine‐derived iPSCs. (a) Chemical structure of MDL‐800. (b) H3K56Ac levels in old murine‐derived iPSCs treated with the indicated doses of MDL‐800 for 24 hr. (c) H3K56Ac levels in old murine‐derived iPSCs treated with 20 μΜ MDL‐800 for indicated time period. (d‐e) Analysis of genome integrity in old murine‐derived iPSCs treated with the indicated doses of MDL‐800 for 5 passages by alkaline comet assay. Data in (d) and (e) are from the same experiment. The average tail moments are shown in (d), and the percentage of DNA content in tails are shown in (e). At least 50 cells per group were included for analysis. Error bars represent <italic>SEM</italic>. <italic>p</italic> value in (e) was determined by ANOVA. (f) Western blot analysis of γH2AX level. Old murine‐derived iPSCs were treated with 20 μΜ MDL‐800 for 5 passages before X‐ray irradiation at 8 Gy. Cells were lysed for protein extraction at the indicated time point post‐X‐ray treatment. (g) The schematic depictions of NHEJ and BER efficiency assay. For NHEJ efficiency analysis, the NHEJ reporter was linearized by I‐SceI endonuclease <italic>in vitro</italic> to mimic DSBs. For BER efficiency analysis, the pEGFP‐N1 plasmid was mixed with methylene blue and exposed to visible light produced by a 100‐W bulb for 120 min to induce base damage. The purified linearized NHEJ reporter (0.4 μg) or damaged pEGFP‐N1 reporter (0.2 μg), along with 0.1 μg pCAG‐DsRed vector, was transfected into 2 × 10<sup>5</sup> mouse iPSCs. FACS analysis was performed at 48‐hr post‐transfection. (h‐i) Analysis of NHEJ and BER efficiency of old murine‐derived iPSCs treated with indicated doses of MDL‐800 for 5 passages. Error bars represent s.d.. All experiments were repeated at least three times. **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001, ****<italic>p</italic> < 0.0001, n.s. not significant</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13185-g001\"/></fig><p>To evaluate the effects of MDL‐800 on genomic stability in iPSCs derived from old mice, we performed alkaline comet assays. We observed a dosage‐dependent decline in tail moment, which reflects genomic instability, in MDL‐800 treated iPSCs derived from old mice (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1d</xref>). A similar result assayed by the percentage of DNA content in the tail was also obtained (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1e</xref>). Moreover, we also examined genomic stability using comet assays in two additional clones of old murine‐derived iPSCs (Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S2</xref>a,b), and we observed a nearly identical decrease in tail moment, ruling out the possibility that the function of MDL‐800 is clone‐specific. Consistent with these observations, γH2AX (S139) level, a classical marker of DNA damage, was also reduced in old murine‐derived iPSCs treated with MDL‐800 in the absence or presence of X‐ray irradiation (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1f</xref>). These results suggest that treating old murine‐derived iPSCs with MDL‐800 is an effective method to stabilize the genome.</p><p>Efficient DNA repair is critical to the maintenance of genome integrity. Defects in different types of DNA repair pathways such as c‐NHEJ or BER often lead to phenotypes of premature aging (Li et al., <xref rid=\"acel13185-bib-0010\" ref-type=\"ref\">2018</xref>; Lombard et al., <xref rid=\"acel13185-bib-0012\" ref-type=\"ref\">2005</xref>). We hypothesized that MDL‐800 promotes genomic stability by boosting DNA repair. Considering SIRT6 participates in repairing both DSBs and DNA damage at bases, we set out to investigate which pathway could be activated post‐MDL‐800 treatment using our previously reported extrachromosomal repair assay (Seluanov, Mao, & Gorbunova, <xref rid=\"acel13185-bib-0022\" ref-type=\"ref\">2010</xref>; Zhang et al., <xref rid=\"acel13185-bib-0031\" ref-type=\"ref\">2020</xref>). The GFP‐based NHEJ or HR cassettes were linearized by I‐SceI endonuclease <italic>in vitro</italic> to mimic DSBs. Then, the linearized NHEJ (0.4 μg) or HR (0.5 μg) reporter, along with 0.1 μg pCAG‐DsRed vector for monitoring the difference in transfection efficiency between experiments, was transfected into 2 × 10<sup>5</sup> mouse iPSCs. FACS analysis was performed at 48‐hr post‐transfection (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1g</xref> and Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S3</xref>a). We found that 20 μM MDL‐800 treatment significantly promoted NHEJ efficiency by 4‐fold, while it did not influence HR repair (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1h</xref> and Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S3</xref>b). Treatment with a lower dosage (5 μM) also showed a trend of NHEJ enhancement, although the difference was not statistically significant (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1h</xref>). This result is consistent with our previous study which demonstrated that different from its function in somatic cells, SIRT6 regulates NHEJ rather than HR in mouse iPSCs (Chen et al., <xref rid=\"acel13185-bib-0003\" ref-type=\"ref\">2017</xref>), suggesting a cell type‐specific role for SIRT6 in regulating DSB repair. The efficiency of DSB repair was also analyzed in iPSCs generated from <italic>Sirt6</italic>\n<sup>+/+</sup> and <italic>Sirt6</italic>\n<sup>−/−</sup> MEFs (Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S5</xref>a,b), and our data demonstrated that MDL‐800 activated NHEJ in a SIRT6‐dependent manner. Previous studies have indicated that the choice of DSB repair pathways is determined by cell cycle stage (Hustedt & Durocher, <xref rid=\"acel13185-bib-0007\" ref-type=\"ref\">2016</xref>; Mao, Bozzella, Seluanov, & Gorbunova, <xref rid=\"acel13185-bib-0013\" ref-type=\"ref\">2008</xref>). To rule out the possibility that the MDL‐800‐mediated stimulatory effect on NHEJ is dependent on cell cycle arrest, we performed EdU incorporation assays (Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S4</xref>). We did not find any difference in cell cycle distribution between control and 20 μM MDL‐800‐treated old murine‐derived iPSCs (Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S4</xref>), suggesting that the SIRT6 activator MDL‐800 promotes NHEJ in old murine‐derived iPSCs in a cell cycle‐independent manner.</p><p>Moreover, SIRT6 was reported to regulate BER in both mice and humans (Mostoslavsky et al., <xref rid=\"acel13185-bib-0017\" ref-type=\"ref\">2006</xref>; Xu et al., <xref rid=\"acel13185-bib-0030\" ref-type=\"ref\">2015</xref>). We then investigated whether MDL‐800 treatment also influences the BER pathway using our previously established plasmid reactivation assay (Xu et al., <xref rid=\"acel13185-bib-0030\" ref-type=\"ref\">2015</xref>; Zhang et al., <xref rid=\"acel13185-bib-0031\" ref-type=\"ref\">2020</xref>). Briefly, 10 μg pEGFP‐N1 plasmid was mixed with methylene blue, followed by a 120‐min exposure to visible light generated by a 100‐W bulb, which induces 8‐hydroxyguanine damage on plasmids. Then, 0.2 μg purified damaged pEGFP‐N1 plasmid, together with 0.1 μg pCAG‐DsRed vector for normalizing transfection efficiency, was transfected into 2 × 10<sup>5</sup> mouse iPSCs, followed by FACS analysis at 48‐hr post‐transfection (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1g</xref>). We found that the efficiency of BER also showed a 2‐fold increase post‐20 μM MDL‐800 treatment in old murine‐derived iPSCs (Figure <xref rid=\"acel13185-fig-0001\" ref-type=\"fig\">1i</xref>). Similarly, the MDL‐800‐mediated stimulatory effect on BER efficiency was only observed in <italic>Sirt6</italic>\n<sup>+/+</sup> mouse iPSCs, but not in <italic>Sirt6</italic>\n<sup>−/−</sup> mouse iPSCs, which further validated that MDL‐800 functions through activating SIRT6 (Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S5</xref>c).</p><p>Taken together, these data reveal that in a SIRT6‐dependent fashion, MDL‐800 stimulates both NHEJ and BER in iPSCs derived from old mice, therefore stabilizing genomes of these iPSCs.</p><p>In addition to genome integrity, the pluripotency of iPSCs is another critical quality attribute (Sullivan et al., <xref rid=\"acel13185-bib-0026\" ref-type=\"ref\">2018</xref>). Previous studies have reported that SIRT6 participates in the regulation of pluripotency in both iPSCs and ESCs (Etchegaray et al., <xref rid=\"acel13185-bib-0005\" ref-type=\"ref\">2015</xref>; Xu et al., <xref rid=\"acel13185-bib-0029\" ref-type=\"ref\">2019</xref>). Thus, we set out to test whether MDL‐800 treatment could positively regulate the pluripotency and differentiation potential of old murine‐derived iPSCs. Mouse iPSCs pretreated with or without MDL‐800 were injected subcutaneously into the groin of immunodeficient nude mice. One month after injection, teratomas were dissected for further analysis. Hematoxylin–eosin (HE) staining demonstrated that MDL‐800 treated mouse iPSCs supported the formation of teratomas with all three germ layers, whereas untreated cells showed a neuroectoderm‐skewed differentiation phenotype (Figure <xref rid=\"acel13185-fig-0002\" ref-type=\"fig\">2a</xref> and Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S6</xref>), which was similar to that of <italic>Sirt6</italic>\n<sup>−/−</sup> ESCs (Etchegaray et al., <xref rid=\"acel13185-bib-0005\" ref-type=\"ref\">2015</xref>). Moreover, the pluripotency and <italic>in vivo</italic> differentiation potential of mouse iPSCs were evaluated by chimera experiments. Old C57BL/6 murine‐derived iPSCs were first infected with lentivirus bearing vectors encoding GFP for labeling, and the GFP<sup>+</sup> cells were sorted by FACS. GFP<sup>+</sup> mouse iPSCs were pretreated with MDL‐800 before microinjection into blastocysts. Then, the embryos were transplanted into the uteruses of pseudo‐pregnant ICR mice. E14.5 chimeric embryos were obtained by cesarean sections and used for chimerism analysis. Strikingly, we found that embryos generated from mouse iPSCs treated with MDL‐800 showed a stronger green fluorescence, indicating a higher capacity to differentiate into three lineages in chimeric mice (Figure <xref rid=\"acel13185-fig-0002\" ref-type=\"fig\">2b</xref>). In addition, the skins of embryos were dissociated into single cells and assessed by FACS for quantitative analysis. The result clearly showed that the percentage of GFP<sup>+</sup> cells were approximately 3‐fold higher in MDL‐800 treated group than in the control group (Figure <xref rid=\"acel13185-fig-0002\" ref-type=\"fig\">2c</xref>), indicating an improvement in <italic>in vivo</italic> differentiation of the same cell line upon MDL‐800 treatment. Further evidence from agouti coat color of the adult mice gave a similar result. There was a remarkable increase in the percentage of mice with a chimeric black coat color in the MDL‐800 treated group as compared to control group (Figure <xref rid=\"acel13185-fig-0002\" ref-type=\"fig\">2d,e</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13185-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>MDL‐800 improves the differentiation potential of old murine‐derived iPSCs. (a) MDL‐800 promotes the formation of teratomas comprised of all three germ layers from old murine‐derived iPSCs while teratomas in the control group show a neuroectoderm‐skewed differentiation phenotype. Old murine iPSCs were treated with 20 μΜ MDL‐800 for 5 passages before injection. Teratomas were dissected for HE staining. Ectoderm: neural tube, keratinized epithelium; Mesoderm: myofiber, adipocyte; Endoderm: cylindrical epithelium. (b‐c) MDL‐800 promotes chimera formation from GFP‐tagged old murine‐derived iPSCs. GFP‐tagged old murine‐derived iPSCs were treated with 20 μΜ MDL‐800 for 5 passages before blastocyst microinjection. Representative fluorescent images of E14.5 chimeric mouse embryos are shown in (b). Scale bar: 2 mm. The percentage of GFP positive cells in E14.5 embryos (left panel) and the representative FACS traces (right panel) are shown in (c). (d‐e) MDL‐800 promotes the generation of mice with higher chimerism from old murine‐derived iPSCs. iPSCs were treated with 20 μΜ MDL‐800 for 5 passages before blastocyst microinjection. Representative images of adult chimeric mice (upper panel) and the rate of chimera formation (lower panel) are shown in (d). The degree of chimerism was evaluated by the coat color in (e). Error bars represent <italic>SEM</italic>, *<italic>p</italic> < 0.05</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13185-g002\"/></fig><p>To further validate that MDL‐800 promotes pluripotency in old murine‐derived iPSCs in a SIRT6‐dependent manner, <italic>Sirt6</italic>\n<sup>+/+</sup> and <italic>Sirt6</italic>\n<sup>−/−</sup> mouse iPSCs were also labeled in green fluorescence for chimera analysis. Embryos post‐transplantation at E14.5 were obtained for imaging under a fluorescent microscope (Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S7</xref>a) and were further digested for analyzing the percentage of GFP<sup>+</sup> cells by FACS (Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S7</xref>b). We found that chimera formation was significantly promoted by 2.4‐fold from <italic>Sirt6</italic>\n<sup>+/+</sup> mouse iPSCs but not from <italic>Sirt6</italic>\n<sup>−/−</sup> mouse iPSCs upon MDL‐800 treatment (Figure <xref rid=\"acel13185-sup-0001\" ref-type=\"supplementary-material\">S7</xref>b). Collectively, these data demonstrated that MDL‐800 promotes pluripotency of old murine‐derived iPSCs in a SIRT6‐dependent manner.</p><p>Cumulatively, we demonstrate that activating SIRT6 with the recently developed potent SIRT6 activator, MDL‐800, improves the quality of iPSCs derived from old mice. It activates different pathways of DNA repair including NHEJ and BER, thereby promoting genome integrity; and it also improves the differentiation potential of old murine‐derived iPSCs. Our data imply that the safe and controlled pharmaceutical activation of SIRT6 with MDL‐800 holds great potentials in iPSC‐based cell therapy in treating aging‐associated diseases. Nevertheless, whether it has similar functions in iPSCs derived from old patient cells needs to be further determined, although a number of reports have indicated that the age‐associated decline in SIRT6 expression is possibly the determining factor causing the rise in genomic instability in human cells (Rohani, Johnson, Arnold, & Stolzing, <xref rid=\"acel13185-bib-0021\" ref-type=\"ref\">2014</xref>; Sharma et al., <xref rid=\"acel13185-bib-0023\" ref-type=\"ref\">2013</xref>; Xu et al., <xref rid=\"acel13185-bib-0030\" ref-type=\"ref\">2015</xref>).</p></sec><sec sec-type=\"COI-statement\" id=\"acel13185-sec-0003\"><title>CONFLICT OF INTEREST</title><p>None declared.</p></sec><sec id=\"acel13185-sec-0004\"><title>AUTHOR CONTRIBUTIONS</title><p>Y.C., J.C., and X.S. performed experiments, analyzed data, and wrote manuscript. J.Y., Z.Q., L.W., X.X., X.W., and Y.J. were involved in collection of data. J.Z., S.G., and Z.M. were involved in the conception and design, data interpretation, manuscript writing, and final approval of manuscript.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13185-sup-0001\"><caption><p>Fig S1‐S7</p></caption><media xlink:href=\"ACEL-19-e13185-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13185-sup-0002\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13185-s002.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13185-sec-0002\"><title>ACKNOWLEDGMENTS</title><p>We thank Dr. Michael Van Meter for critically reading the manuscript. This work was primarily supported by the National Key R&D Program of China (2018YFC2000100, 2017YFA0103300, 2016YFA0100400) and the National Natural Science Foundation of China (31871438, 81972457, 31721003, 81630035, 31871446, 31801243), and “Shu Guang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation (19SG18), Program of Shanghai Academic Research Leader (19XD1403000), the Young Elite Scientist Sponsorship Program by CAST (2018QNRC001), the key project of the Science and Technology of Shanghai Municipality (19JC1415300), the Shanghai Rising‐Star Program (19QA1409600), the Shanghai Chenguang Program (16CG17), the Shanghai municipal medical and health discipline construction projects (no. 2017ZZ02015), the Fundamental Research Funds for the Central Universities, and the Open Project Program of State Key Laboratory of Natural Medicines (SKLNMKF201905).</p></ack><sec sec-type=\"data-availability\" id=\"acel13185-sec-0006\"><title>DATA AVAILABILITY STATEMENT</title><p>Data sharing is not applicable to this article as no new data were created or analyzed in this study.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13185-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13185-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13185-cit-0001\">\n<string-name>\n<surname>Cerletti</surname>, <given-names>M.</given-names>\n</string-name>, <string-name>\n<surname>Jurga</surname>, <given-names>S.</given-names>\n</string-name>, <string-name>\n<surname>Witczak</surname>, <given-names>C. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32710526</article-id><article-id pub-id-type=\"pmc\">PMC7431820</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13197</article-id><article-id pub-id-type=\"publisher-id\">ACEL13197</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>A decade of epigenetic change in aging twins: Genetic and environmental contributions to longitudinal DNA methylation</article-title><alt-title alt-title-type=\"left-running-head\">REYNOLDS et al.</alt-title></title-group><contrib-group><contrib id=\"acel13197-cr-0001\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Reynolds</surname><given-names>Chandra A.</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-6502-7173</contrib-id><xref ref-type=\"aff\" rid=\"acel13197-aff-0001\">\n<sup>1</sup>\n</xref><address><email>chandra.reynolds@ucr.edu</email></address></contrib><contrib id=\"acel13197-cr-0002\" contrib-type=\"author\"><name><surname>Tan</surname><given-names>Qihua</given-names></name><xref ref-type=\"aff\" rid=\"acel13197-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13197-cr-0003\" contrib-type=\"author\"><name><surname>Munoz</surname><given-names>Elizabeth</given-names></name><xref ref-type=\"aff\" rid=\"acel13197-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13197-curr-0001\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13197-cr-0004\" contrib-type=\"author\"><name><surname>Jylhävä</surname><given-names>Juulia</given-names></name><xref ref-type=\"aff\" rid=\"acel13197-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13197-cr-0005\" contrib-type=\"author\"><name><surname>Hjelmborg</surname><given-names>Jacob</given-names></name><xref ref-type=\"aff\" rid=\"acel13197-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13197-cr-0006\" contrib-type=\"author\"><name><surname>Christiansen</surname><given-names>Lene</given-names></name><xref ref-type=\"aff\" rid=\"acel13197-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13197-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13197-cr-0007\" contrib-type=\"author\"><name><surname>Hägg</surname><given-names>Sara</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-2452-1500</contrib-id><xref ref-type=\"aff\" rid=\"acel13197-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13197-cr-0008\" contrib-type=\"author\"><name><surname>Pedersen</surname><given-names>Nancy L.</given-names></name><xref ref-type=\"aff\" rid=\"acel13197-aff-0003\">\n<sup>3</sup>\n</xref></contrib></contrib-group><aff id=\"acel13197-aff-0001\">\n<label><sup>1</sup></label>\n<institution>University of California ‐ Riverside</institution>\n<city>Riverside</city>\n<named-content content-type=\"country-part\">CA</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13197-aff-0002\">\n<label><sup>2</sup></label>\n<institution>University of Southern Denmark</institution>\n<city>Odense</city>\n<country country=\"DK\">Denmark</country>\n</aff><aff id=\"acel13197-aff-0003\">\n<label><sup>3</sup></label>\n<institution>Karolinska Institutet</institution>\n<city>Stockholm</city>\n<country country=\"SE\">Sweden</country>\n</aff><aff id=\"acel13197-aff-0004\">\n<label><sup>4</sup></label>\n<institution>Copenhagen University Hospital, Rigshospitalet</institution>\n<city>Copenhagen</city>\n<country country=\"DK\">Denmark</country>\n</aff><aff id=\"acel13197-curr-0001\"><label><sup>5</sup></label>Present address:\n<institution>University of Texas at Austin</institution>\n<city>Austin</city>\n<named-content content-type=\"country-part\">TX</named-content>\n<country country=\"US\">USA</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nChandra A. Reynolds, Department of Psychology, University of California Riverside, 900 University Avenue, Riverside, CA 92521.<break/>\nEmail: <email>chandra.reynolds@ucr.edu</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>24</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13197</elocation-id><history><date date-type=\"received\"><day>23</day><month>12</month><year>2019</year></date><date date-type=\"rev-recd\"><day>07</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13197.pdf\"/><abstract id=\"acel13197-abs-0001\"><title>Abstract</title><sec id=\"acel13197-sec-0001\"><title>Background</title><p>Epigenetic changes may result from the interplay of environmental exposures and genetic influences and contribute to differences in age‐related disease, disability, and mortality risk. However, the etiologies contributing to stability and change in DNA methylation have rarely been examined longitudinally.</p></sec><sec id=\"acel13197-sec-0002\"><title>Methods</title><p>We considered DNA methylation in whole blood leukocyte DNA across a 10‐year span in two samples of same‐sex aging twins: (a) Swedish Adoption Twin Study of Aging (SATSA; <italic>N</italic> = 53 pairs, 53% female; 62.9 and 72.5 years, <italic>SD </italic>= 7.2 years); (b) Longitudinal Study of Aging Danish Twins (LSADT; <italic>N</italic> = 43 pairs, 72% female, 76.2 and 86.1 years, <italic>SD</italic>=1.8 years). Joint biometrical analyses were conducted on 358,836 methylation probes in common. Bivariate twin models were fitted, adjusting for age, sex, and country.</p></sec><sec id=\"acel13197-sec-0003\"><title>Results</title><p>Overall, results suggest genetic contributions to DNA methylation across 358,836 sites tended to be small and lessen across 10 years (broad heritability <italic>M</italic> = 23.8% and 18.0%) but contributed to stability across time while person‐specific factors explained emergent influences across the decade. Aging‐specific sites identified from prior EWAS and methylation age clocks were more heritable than background sites. The 5037 sites that showed the greatest heritable/familial–environmental influences (<italic>p </italic>< 1E−07) were enriched for immune and inflammation pathways while 2020 low stability sites showed enrichment in stress‐related pathways.</p></sec><sec id=\"acel13197-sec-0004\"><title>Conclusions</title><p>Across time, stability in methylation is primarily due to genetic contributions, while novel experiences and exposures contribute to methylation differences. Elevated genetic contributions at age‐related methylation sites suggest that adaptions to aging and senescence may be differentially impacted by genetic background.</p></sec></abstract><abstract abstract-type=\"graphical\" id=\"acel13197-abs-0002\"><p>Individual differences in late‐life methylation for 358,836 cytosine–guanine dinucleotide (CpG) probes are due partly to genetic influences that contribute to stability across 10 years while non‐shared factors, including environmental experiences unique to individuals, contribute to new influences on methylation patterns, as indicated by the r<sub>A</sub>, r<sub>D</sub>, and r<sub>E</sub>. Aging‐related CpG sites show greater heritable influences on methylation consistent with genetic regulation of biological aging rates. \n<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13197-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13197-g005.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13197-kwd-0001\">aging</kwd><kwd id=\"acel13197-kwd-0002\">DNA methylation</kwd><kwd id=\"acel13197-kwd-0003\">heritability</kwd><kwd id=\"acel13197-kwd-0004\">longitudinal</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>Vetenskapsrådet </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100004359</institution-id></institution-wrap></funding-source><award-id>521‐2013‐8689</award-id><award-id>825‐2007‐7460</award-id><award-id>825‐2009‐6141</award-id><award-id>2015‐03255</award-id></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>Forskningsrådet för Arbetsliv och Socialvetenskap </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100001861</institution-id></institution-wrap></funding-source><award-id>97:0147:1B</award-id><award-id>2009‐0795</award-id></award-group><award-group id=\"funding-0003\"><funding-source><institution-wrap><institution>Forskningsrådet om Hälsa, Arbetsliv och Välfärd </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100006636</institution-id></institution-wrap></funding-source><award-id>2013‐2292</award-id></award-group><award-group id=\"funding-0004\"><funding-source><institution-wrap><institution>National Institute on Aging </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100000049</institution-id></institution-wrap></funding-source><award-id>AG028555</award-id><award-id>AG037985</award-id><award-id>AG04563</award-id><award-id>AG10175</award-id><award-id>AG17561</award-id><award-id>P01‐AG08761</award-id></award-group><award-group id=\"funding-0005\"><funding-source>the European Union's Seventh Framework Programme</funding-source><award-id>FP7/2007‐2011</award-id><award-id>259679</award-id></award-group></funding-group><counts><fig-count count=\"4\"/><table-count count=\"2\"/><page-count count=\"12\"/><word-count count=\"9200\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13181-cit-1001\">\n<string-name>\n<surname>Reynolds</surname>\n<given-names>CA</given-names>\n</string-name>, <string-name>\n<surname>Tan</surname>\n<given-names>Q</given-names>\n</string-name>, <string-name>\n<surname>Munoz</surname>\n<given-names>E</given-names>\n</string-name>, et al. <article-title>A decade of epigenetic change in aging twins: Genetic and environmental contributions to longitudinal DNA methylation</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13197</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13197</pub-id>\n</mixed-citation>\n</p><fn-group id=\"acel13197-ntgp-0001\"><fn fn-type=\"funding\" id=\"acel13197-note-0001\"><p>\n<bold>Funding information</bold>\n</p><p>SATSA has been supported by the National Institute on Aging (AG04563, AG10175), the MacArthur Foundation Research Network on Successful Aging, the Swedish Council for Working Life and Social Research (FAS) (97:0147:1B, 2009‐0795), and the Swedish Research Council (825‐2007‐7460, 825‐2009‐6141). DNA extraction was partly supported by AG028555, AG17561. Methylation work was supported by FORTE 2013‐2292, the Swedish Research Council (521‐2013‐8689, 2015‐03255) and a Distinguished Professor Award from the KI to NLP. LSADT has been supported by grants from the VELUX FOUNDATION, the U.S. National Institute on Aging (P01‐AG08761), the European Union's Seventh Framework Programme (FP7/2007‐2011) under grant agreement no. 259679, and The Danish National Program for Research Infrastructure 2007 [09‐063256]. Collaborative work was supported in part by the National Institute on Aging (AG037985). The manuscript content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p></fn></fn-group></notes></front><body id=\"acel13197-body-0001\"><sec id=\"acel13197-sec-0005\"><label>1</label><title>INTRODUCTION</title><p>The functional profiles of genes are not static and vary across time, and indeed across the lifespan, in part as a result of different environmental exposures and contexts (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>; Jones, Goodman, & Kobor, <xref rid=\"acel13197-bib-0020\" ref-type=\"ref\">2015</xref>; Lappe & Landecker, <xref rid=\"acel13197-bib-0027\" ref-type=\"ref\">2015</xref>; McClearn, <xref rid=\"acel13197-bib-0031\" ref-type=\"ref\">2006</xref>). Measurable gene–environment dynamics for behavioral traits are possible due to advances in biotechniques for global epigenetic profiling at, for example, specific methylation sites in the human genome. Epigenetic changes may be critical to the development of complex diseases, accelerated aging, or steeper declines in cognitive and physical functioning with age (Lappe & Landecker, <xref rid=\"acel13197-bib-0027\" ref-type=\"ref\">2015</xref>). Understanding epigenetic changes over time in the elderly may identify pathways of decline or plasticity (e.g., maintenance or even boosts in functioning) during the aging process and help with elucidating the biology of aging and survival.</p><p>Epigenetic modifications resulting in altered gene expression may occur due to a number of processes, including direct methylation of DNA (Jones & Takai, <xref rid=\"acel13197-bib-0022\" ref-type=\"ref\">2001</xref>). DNA methylation results from intrinsic‐programmed factors as well as non‐genetic processes that may arise due to prenatal or early life exposures or at later points in development (Gottesman & Hanson, <xref rid=\"acel13197-bib-0014\" ref-type=\"ref\">2005</xref>; Kanherkar, Bhatia‐Dey, & Csoka, <xref rid=\"acel13197-bib-0025\" ref-type=\"ref\">2014</xref>; Torano, Garcia, Fernandez‐Morera, Nino‐Garcia, & Fernandez, <xref rid=\"acel13197-bib-0041\" ref-type=\"ref\">2016</xref>). DNA methylation is characteristically produced by the addition of a methyl group to the DNA molecule cytosine within cytosine–guanine dinucleotides (CpGs), at an estimated 28 million sites across the human genome (Lovkvist, Dodd, Sneppen, & Haerter, <xref rid=\"acel13197-bib-0030\" ref-type=\"ref\">2016</xref>). Dense regions of CpGs referred to as “islands” represent about 5% of CpGs occurring in the genome (about 20,000 total) and often reside in promotor regions (Vinson & Chatterjee, <xref rid=\"acel13197-bib-0045\" ref-type=\"ref\">2012</xref>); in addition, surrounding “shores” and “shelves” to these islands are of interest and may be differentially methylated compared to islands (Jones et al., <xref rid=\"acel13197-bib-0020\" ref-type=\"ref\">2015</xref>). The addition of methylation tags to CpG sites is associated with altered gene expression, typically by interfering with or silencing gene transcription although upregulation of gene expression has been documented (Wang, Chen, Yang, Zhang, & Wong, <xref rid=\"acel13197-bib-0047\" ref-type=\"ref\">2019</xref>), and may differentially occur in cells across multiple tissue types including brain, muscle, and leukocytes (Fernandez et al., <xref rid=\"acel13197-bib-0009\" ref-type=\"ref\">2012</xref>). Methylation tags can be removed as a consequence of exposures as well, leading to dynamics in expression across time (Kanherkar et al., <xref rid=\"acel13197-bib-0025\" ref-type=\"ref\">2014</xref>).</p><p>Although epigenetic variation is largely attributed to environmental factors (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>; Hannon et al., <xref rid=\"acel13197-bib-0016\" ref-type=\"ref\">2018</xref>; Torano et al., <xref rid=\"acel13197-bib-0041\" ref-type=\"ref\">2016</xref>), there is evidence for genetic contributions to variation in methylation across the epigenome (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>; Hannon et al., <xref rid=\"acel13197-bib-0016\" ref-type=\"ref\">2018</xref>; Torano et al., <xref rid=\"acel13197-bib-0041\" ref-type=\"ref\">2016</xref>). Average heritabilities of 16.5%–19.0% have been reported across sites in the Illumina 450 k chip array from whole blood and common environmental influences of 3.0%–12.6% (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>; Hannon et al., <xref rid=\"acel13197-bib-0016\" ref-type=\"ref\">2018</xref>). Stronger evidence of common environment has been reported in young adulthood (18 years) at 12.6% (after correction for cell types; Hannon et al., <xref rid=\"acel13197-bib-0016\" ref-type=\"ref\">2018</xref>). Moreover, cross‐sectional work suggests that there may be smaller heritable components by mid‐adulthood (18%) than young adulthood (21%) (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>).</p><p>Epigenetic changes may accelerate over time, whereby changes in gene expression due to exposures become more abundant and salient to phenotypic changes, hence potentiating the development of health and aging conditions earlier in life. Indeed, methylation is correlated with age (Ciccarone, Tagliatesta, Caiafa, & Zampieri, <xref rid=\"acel13197-bib-0006\" ref-type=\"ref\">2018</xref>; van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>), is used to define biological clocks that may more closely track biological aging (Field et al., <xref rid=\"acel13197-bib-0010\" ref-type=\"ref\">2018</xref>), and is associated with mortality (Zhang et al., <xref rid=\"acel13197-bib-0050\" ref-type=\"ref\">2017</xref>) and a number of physical and neuropsychiatric health traits (Kanherkar et al., <xref rid=\"acel13197-bib-0025\" ref-type=\"ref\">2014</xref>; Lappe & Landecker, <xref rid=\"acel13197-bib-0027\" ref-type=\"ref\">2015</xref>). Longitudinal studies of twins represent a valuable approach to evaluate genetic and environmental contributions to stability and change in methylation across the methylome (Tan, Christiansen, von Bornemann Hjelmborg, & Christensen, <xref rid=\"acel13197-bib-0037\" ref-type=\"ref\">2015</xref>). Investigations of etiological contributions have relied primarily on cross‐sectional data (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>; Hannon et al., <xref rid=\"acel13197-bib-0016\" ref-type=\"ref\">2018</xref>) and have addressed age‐related differences (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>) but not change. We evaluate individual differences in DNA methylation at individual CpG sites across the methylome across 10 years in two Scandinavian samples of same‐sex aging twins, estimating the genetic and environmental contributions to stability as well as to novel influences that emerge. Moreover, we examine whether surrounding “shores” and “shelves” are differentially heritable compared to islands and whether sites identified as associated with rate of aging in epigenome‐wide association study (EWAS) or individual CpG clock sites are differentially heritable. In a combined sample of aging twins, assessed a decade apart in late‐life, we test two competing hypotheses about the longitudinal stability and change in DNA methylation that stem from prior cross‐sectional work (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>): (a) the contribution of genetic influences changes with age, reflecting diminishing influence across time, and (b) non‐shared factors accumulate in importance, signaling an increasing diversity of response to environmental exposures.</p></sec><sec sec-type=\"methods\" id=\"acel13197-sec-0006\"><label>2</label><title>METHODS</title><sec id=\"acel13197-sec-0007\"><label>2.1</label><title>Sample</title><p>We considered DNA methylation across a 10‐year span in 96 pairs of same‐sex aging twins (40 monozygotic, MZ pairs; 56 dizygotic, DZ pairs). Across two samples, the average age at time 1 was 68.89 years (<italic>SD </italic>= 8.58) and at time 2 was 78.59 years (<italic>SD </italic>= 8.70). Specifically, the Swedish Adoption Twin Study of Aging (SATSA) included 53 pairs (22 MZ, and 31 DZ pairs; 53% female), selected with measurements about 10 years apart (range = 8.00 to 11.82 years) at ages 62.9 and 72.5 years at time 1 and time 2, respectively (<italic>SD</italic> = 7.2). In 4 of 53 SATSA pairs, one twin partner had methylation data from one timepoint instead of both timepoints, but all data were included for these pairs. The Longitudinal Study of Aging Danish Twins (LSADT) included 43 pairs (18 MZ, and 25 DZ pairs; 72% female) at ages 76.2 and 86.1 years at time 1 and time 2 (<italic>SD</italic> = 1.8).</p></sec><sec sec-type=\"materials\" id=\"acel13197-sec-0008\"><label>2.2</label><title>Materials</title><p>Methylation measurements from the Illumina HumanMethylation450 array (Illumina) were preprocessed and normalized with adjustments for cell counts and batch effects. Processing of the SATSA sample probes has been described previously (Jylhävä et al., <xref rid=\"acel13197-bib-0024\" ref-type=\"ref\">2019</xref>; Wang et al., <xref rid=\"acel13197-bib-0048\" ref-type=\"ref\">2018</xref>) and in brief included the following: (a) preprocessing with the R package <italic>RnBeads</italic> (Assenov et al., <xref rid=\"acel13197-bib-0003\" ref-type=\"ref\">2014</xref>) where filtering of samples and probes proceeded with a greedy‐cut algorithm maximizing false‐positive rate versus sensitivity at a detection <italic>p</italic>‐value of 0.05; (b) removal of sites that overlap with a known SNP site or reside on sex chromosomes; (c) normalization of data using <italic>dasen</italic> (Pidsley et al., <xref rid=\"acel13197-bib-0033\" ref-type=\"ref\">2013</xref>); (d) applying a Sammon mapping method (Sammon, <xref rid=\"acel13197-bib-0035\" ref-type=\"ref\">1969</xref>) to remove technical variance; (e) adjustment for cell counts (Jones, Islam, Edgar, & Kobor, <xref rid=\"acel13197-bib-0021\" ref-type=\"ref\">2017</xref>); (f) correction for batch effects using the ComBat approach in the <italic>sva</italic> package (Leek, Johnson, Parker, Jaffe, & Storey, <xref rid=\"acel13197-bib-0028\" ref-type=\"ref\">2012</xref>).</p><p>Processing of the LSADT data has been described previously (Svane et al., <xref rid=\"acel13197-bib-0036\" ref-type=\"ref\">2018</xref>) and in brief included the following: (a) preprocessing with the R package <italic>MethylAid</italic> (van Iterson et al., <xref rid=\"acel13197-bib-0044\" ref-type=\"ref\">2014</xref>) where samples below quality requirements were excluded and probes with detection <italic>p</italic>‐value>0.01, no signal, or bead count <3 were filtered out; (b) removal of probes with >5% missing values, removal of sites that reside on sex chromosomes or cross‐reactive probes; (c) normalization and batch correction using functional normalization(Fortin et al., <xref rid=\"acel13197-bib-0011\" ref-type=\"ref\">2014</xref>) with four principal components.</p><p>Although Beta‐values are preferred for interpretation of methylation, Beta‐value units were translated into <italic>M</italic>‐values via a log2 ratio for improved distributional properties for the analysis of individual differences (Du et al., <xref rid=\"acel13197-bib-0008\" ref-type=\"ref\">2010</xref>). After performing the preprocessing steps, 390,894 probes remained for SATSA and 452,920 CpG sites remained for LSADT.</p><p>Altogether 368,391 sites were in common across the Swedish and Danish samples. After the described QC preprocessing in SATSA, 49 of 53 pairs had methylation data available for both members of each pair at both timepoints, while in 4 pairs one cotwin member had data at both timepoints while their twin partner had data at one timepoint but not both. After preprocessing, LSADT sample had methylation data represented for both cotwins at both timepoints among the 43 pairs.</p></sec><sec id=\"acel13197-sec-0009\"><label>2.3</label><title>Filtering of sites post‐analysis</title><p>We conducted additional filtering of probes where model‐fitting results evidenced means or variances outside of expected values. Specifically, we filtered based on the typical range of <italic>M</italic>‐values (c.f., Du et al., <xref rid=\"acel13197-bib-0008\" ref-type=\"ref\">2010</xref>), with expected mean values falling outside the range −6.25 to 6.25 for 1812 sites under either ACE or ADE models at either timepoint. Likewise, we filtered based on expected standard deviations exceeding 1.5 under either ACE or ADE models (Du et al., <xref rid=\"acel13197-bib-0008\" ref-type=\"ref\">2010</xref>) resulting in 9554 sites out of range under either ACE or ADE models at either timepoint. The effective reduction in sites was from 368,391 to 358,836 after dropping 9555 unique sites from the analysis set.</p></sec><sec id=\"acel13197-sec-0010\"><label>2.4</label><title>Analysis</title><p>Bivariate biometrical twin models of <italic>M</italic>‐values were fitted to all available data across the pairs using full‐information maximum likelihood (FIML), adjusting for centered age (centered at the average age across time = age ‐ 74 years), sex (0 = males, 1 = females), and country (0 = Sweden, 1 = Denmark). Bivariate ACE and ADE Cholesky models evaluated the degree to which additive genetic (A), dominance or non‐additive genetic (D), common environmental (C), and non‐shared factors (E), encompassing non‐shared environmental influences, measurement error, and stochastic factors, contributed to variation and covariation in <italic>M</italic>‐values within and across time (see Figure <xref rid=\"acel13197-fig-0001\" ref-type=\"fig\">1</xref>).The resolution of the genetic and environmental effects are done by comparing the relative similarity of monozygotic (MZ) twins who share 100% of their genes in common, including all additive effects and dominance deviations, versus dizygotic (DZ) twins who share on average 50% of segregating genes in common leading to expectations of 50% for additive effects and 25% for dominance deviations. Both twin types are presumed to have the same contribution of common environmental effects that contribute to similarity. We fitted ADE and ACE models as dominance (D), and common environment (C) could not be simultaneously estimated (see Figure <xref rid=\"acel13197-fig-0001\" ref-type=\"fig\">1</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13197-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Bivariate Cholesky model. Note. ACE and ADE models were separately fitted to <italic>M</italic>‐values at two waves 10 years apart</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13197-g001\"/></fig><p>Fit comparison between the ACE and ADE models was done via Akaike information criterion (AIC; Akaike, <xref rid=\"acel13197-bib-0002\" ref-type=\"ref\">1974</xref>). If the fit of the ADE model was as good or better than the ACE model, it was retained as “best” fitting, and otherwise, the ACE model was retained as best. We evaluated submodels including AE, CE, and E models. Differences in nested model deviance statistics [−2ln(L)] are distributed as chi‐square (<italic>χ</italic>\n<sup>2</sup>) with the difference in the number of parameters between the full and constrained models as the degrees‐of‐freedom (<italic>df</italic>). LSADT samples tended to show lower variability in methylation at any given probe compared to SATSA; hence, we allowed for scalar differences at each timepoint (<italic>k</italic>\n<sub>1</sub>, <italic>k</italic>\n<sub>2</sub>) in standard deviations between the two samples (see Figure <xref rid=\"acel13197-fig-0001\" ref-type=\"fig\">1</xref>). Thus, the relative contributions of A, C or D, and E were equated across LSADT and SATSA, but the scalar allowed for the variance components to differ by a constant at each assessment. Scalar differences in standard deviations were on average <italic>k</italic>\n<sub>1</sub> = 0.90 (<italic>SD </italic>= 0.93) and <italic>k</italic>\n<sub>2</sub> = 0.88 (<italic>SD</italic> = 0.89).</p><p>Annotation of CpG sites with respect to UCSC CpG Island information (Gardiner‐Garden & Frommer, <xref rid=\"acel13197-bib-0012\" ref-type=\"ref\">1987</xref>) was done by merging analysis results to the manifest file available for the Infinium HumanMethylation450 v1.2 BeadChip (Illumina). Annotations included ‘Island’, ‘North Shore’, ‘South Shore’, ‘North Shelf’, ‘South Shelf’, and a blank annotation field was treated as ‘Open Seas’.</p><p>In comparing relative heritabilities across sites by location, as well as aging/clock CPGs sets to remaining CpGs, we fitted random effects regression models to ages 69 and 79 biometrical estimates using <italic>lme</italic> (version 1.1‐21; Bates, Mächler, Bolker, & Walker, <xref rid=\"acel13197-bib-0005\" ref-type=\"ref\">2015</xref>). We allowed for random effects between and within sites, reflecting consistency of effects by CpG sites across time and non‐systematic variation within time.</p><p>To compare time1‐time2 correlations from the biometrical estimates, we rescaled the a<sub>12,</sub> d<sub>12</sub> or c<sub>12</sub>, and e<sub>12</sub> paths into correlations (r<sub>A</sub>, r<sub>D</sub> or r<sub>C</sub>, and r<sub>E</sub>) and performed Fisher Z‐transformations before submitting each to a skew‐normal regression analysis using the <italic>sn</italic> package (Azzalini, <xref rid=\"acel13197-bib-0004\" ref-type=\"ref\">2020</xref>). Regression analyses compared low stability sites to remaining CpGs, after which regression weights were inverse‐transformed into correlation units for interpretation.</p><p>Enrichment analyses were conducted using the GREAT 4.0.4 tool (McLean et al., <xref rid=\"acel13197-bib-0032\" ref-type=\"ref\">2010</xref>). Selected sites were mapped to the Human GRCh37 build and default settings were used for association rules (i.e., basal + extension: 5000 bp upstream, 1000 bp downstream, 1,000,000 bp max extension, curated regulatory domains included). We present results of both biomial and hypergeometric tests where the false discovery rate (FDR) achieved <italic>p</italic> < .05 and where fold enrichment (FE) tests exceeded 2.0. We followed up the enrichment analyses using the mQTL Database (Gaunt et al., <xref rid=\"acel13197-bib-0013\" ref-type=\"ref\">2016</xref>) to annotate associations with methylation quantitative trait loci, noting the number of <italic>cis</italic> or <italic>trans</italic> variants.</p></sec></sec><sec sec-type=\"results\" id=\"acel13197-sec-0011\"><label>3</label><title>RESULTS</title><p>We first evaluated the extent to which heritable and environmental influences contributed to each CpG site. Bivariate biometrical twin model results, comparing MZ twin similarity to DZ twin similarity within and across time, suggest under an ADE model that broad‐sense heritable contributions (A + D, <italic>N</italic> = 358,836) were on average small at age 69 years (<italic>M</italic> = 0.238 * 100 = 23.8%, time 1) and decreased across 10 years (<italic>M</italic> = 0.180 * 100 = 18.0%, time 2) (see Table <xref rid=\"acel13197-tbl-0001\" ref-type=\"table\">1</xref>, Variance Components). The decrease in broad heritability across time is significant within site, <italic>M</italic>\n<sub>t2‐t1</sub> = −.058 (<italic>t</italic> = −232.0, <italic>df </italic>= 358,835, CI<sub>95</sub> = −0.058, −0.057). The decrease in heritability is due to an absolute increase in non‐shared factors (E) compared to genetic influences (A, D) (see Table <xref rid=\"acel13197-tbl-0001\" ref-type=\"table\">1</xref>, Absolute Variances). Patterns of decline were observed for heritabilities (A) under the ACE model (0.150 and 0.109, respectively), and under best‐fitting ADE or ACE models (see Table <xref rid=\"acel13197-tbl-0001\" ref-type=\"table\">1</xref>, Variance Components). Common environmental influences were generally stable in overall ACE results at over 5% (0.057, 0.054) and in best‐fitting ACE results at 10% (0.106, 0.098) (see Table <xref rid=\"acel13197-tbl-0001\" ref-type=\"table\">1</xref>, Variance Components).</p><table-wrap id=\"acel13197-tbl-0001\" xml:lang=\"en\" content-type=\"TABLE\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>Variance components and absolute variances at time 1 (69 years) and time 2 (79 years)</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"bottom\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" rowspan=\"2\" valign=\"bottom\" colspan=\"1\">Variance Components</th><th align=\"left\" rowspan=\"2\" valign=\"bottom\" colspan=\"1\">\n<italic>N</italic> sites</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">A1</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">D1/C1</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">E1</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">A2</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">D2/C2</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">E2</th></tr><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ADE</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">358,836</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.111</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.142</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.127</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.160</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.762</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.175</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.091</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.125</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.089</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.130</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.820</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.158</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ADE best</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">187,535</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.057</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.106</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.217</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.168</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.725</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.182</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.048</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.092</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.152</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.148</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.800</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.168</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ACE</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">358,836</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.150</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.166</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.057</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.083</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.793</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.163</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.109</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.142</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.054</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.080</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.837</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.147</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ACE best</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">171,301</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.076</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.122</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.106</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.093</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.817</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.150</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.055</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.103</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.098</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.091</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.846</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.137</td></tr></tbody></table><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"bottom\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" rowspan=\"2\" valign=\"bottom\" colspan=\"1\">Absolute Variances</th><th align=\"left\" rowspan=\"2\" valign=\"bottom\" colspan=\"1\">\n<italic>N</italic> sites</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">A1</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">D1/C1</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">E1</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">A2</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">D2/C2</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">E2</th></tr><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>M</italic>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>SD</italic>\n</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ADE</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">358,836</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.029</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.079</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.038</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.088</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.162</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.144</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.028</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.079</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.032</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.086</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.214</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.195</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ADE best</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">187,535</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.019</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.063</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.067</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.112</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.174</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.151</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.018</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.063</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.057</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.111</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.235</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.206</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ACE</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">358,836</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.047</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.109</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.012</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.034</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.170</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.152</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.042</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.109</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.014</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.038</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.220</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.200</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ACE best</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">171,301</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.021</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.074</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.023</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.045</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.152</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.137</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.019</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.072</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.025</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.050</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.193</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.180</td></tr></tbody></table><table-wrap-foot id=\"acel13197-ntgp-0002\"><title>Note</title><fn id=\"acel13197-note-0002\"><p>A = additive genetic, D = non‐additive genetic (dominance), C = common environment, E = non‐shared factors.</p></fn></table-wrap-foot><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>Across time, heritabilities showed divergence by location [ADE best (A + D): <italic>χ</italic>\n<sup>2</sup> (5) = 618.3, <italic>p</italic> = 2.25E−131; ACE best (A): <italic>χ</italic>\n<sup>2</sup> (5) = 339.5, <italic>p</italic> = 3.19E−71] (see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13197-fig-0002\" ref-type=\"fig\">2</xref>). In ADE best results, islands and shelves showed lower broad (A + D) heritabilities than open seas by −0.01 or −1% (<italic>p</italic> ≤ 1.55E−07), whereas shores were higher by 0.01 or 1% than open seas (<italic>p</italic> ≤ 3.79E−15). In ACE best results, comparably lower heritabilities (A) were observed for islands versus open seas (<italic>p</italic> = 3.54E−58).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13197-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>Broad‐sense heritability by location across 10 years (ADE results, 358,836 CpGs). Note. Site differences shown are significant across time: <italic>χ</italic>\n<sup>2</sup>(5) = 995.48, <italic>p</italic> = 5.72E−213</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13197-g002\"/></fig><p>Next, we evaluated the number of CpG sites that achieved significant heritable or familial–environmental effects. At epigenome‐wide significance (<italic>p </italic>< 1E−07), 5037 CpG sites (1.4%) showed broad genetic (A, D) or familial–environmental effects (A, C) within or across time (<italic>df </italic>= 6), and 35,762 sites (10.0%) met <italic>p </italic>< 1E−02. Among the 358,836 sites, 52% of sites showed the better‐fitting model was ADE (<italic>N</italic> = 187,535) while 48% showed ACE as better‐fitting (<italic>N</italic> = 171,301) (see Table <xref rid=\"acel13197-tbl-0001\" ref-type=\"table\">1</xref>, Figure <xref rid=\"acel13197-fig-0003\" ref-type=\"fig\">3</xref>). A total of 58,676 sites (16.4%) achieved nominal significance comparing the ADE or ACE versus an E model (<italic>p</italic> < .05, 6 <italic>df</italic>; <italic>N</italic> = 32,685 ADE best, <italic>N</italic> = 25,991 ACE best), and 91,380 sites (25.5%) achieved nominal significance of an AE model over an E model (<italic>p</italic> < .05, <italic>df </italic>= 3). Given that power is low for C even in large samples, as well as to distinguish D from A, we present full model estimates (Visscher, Gordon, & Neale, <xref rid=\"acel13197-bib-0046\" ref-type=\"ref\">2008</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13197-fig-0003\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>Best‐fitting models: ADE (52%) or ACE (48%)</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13197-g003\"/></fig><p>In terms of contributions to stability and change in methylation due to genetic or environmental influences, across 358,836 sites, 58.5% showed cross‐time associations at <italic>p</italic> < .05 (<italic>df</italic> = 3, where a<sub>12</sub> = [d<sub>12</sub> or c<sub>12</sub>] = e<sub>12</sub> = 0) indicative of stability over time due to either genetic and/or environmental mechanisms. As shown in Figure <xref rid=\"acel13197-fig-0003\" ref-type=\"fig\">3</xref>, the cross‐time stability was largely due to genetic effects in both the ADE best and ACE best models which were most often perfect in correlation.</p><p>As cross‐sectional twin studies have reported that heritability may be higher for variable methylated sites (e.g., Hannon et al., <xref rid=\"acel13197-bib-0016\" ref-type=\"ref\">2018</xref>), we report the correlation between the estimated standard deviations of <italic>M</italic>‐values and the extent to which heritable effects were observed at time 1 and 2, respectively: (a) <italic>r<sub>SD</sub></italic>\n<sub>,</sub>\n<italic><sub>A</sub></italic>\n<sub>+</sub>\n<italic><sub>D</sub></italic> = 0.33 and 0.27 (187,535 sites) for ADE best, and (b) <italic>r<sub>SD</sub></italic>\n<sub>,</sub>\n<italic><sub>A</sub></italic> = 0.25 and 0.20 (171,301 sites) for ACE best. Sites in which non‐shared factors, E, explained all of the variability of <italic>M</italic>‐values (>99%) at both timepoints included 8268 total sites (5520 ADE best, 2748 ACE best). In all these cases, we observed that either the MZ twin correlations of <italic>M</italic>‐values were negative (<0), or the DZ correlations were sufficiently negative (<−0.05), or the difference between MZ and DZ correlations at each timepoint were sufficiently negative (<−0.1).</p><sec id=\"acel13197-sec-0012\"><label>3.1</label><title>Age‐related sites</title><p>We evaluated the best‐fitting ADE and ACE results of two published CpG sets that were identified in EWAS as related to age that overlap with the samples used in the presented analysis: (I) 1217 sites from Wang et al. (<xref rid=\"acel13197-bib-0048\" ref-type=\"ref\">2018</xref>); (II) 1934 sites from Tan et al. (<xref rid=\"acel13197-bib-0038\" ref-type=\"ref\">2016</xref>). Multilevel regression models compared heritabilities by location from the ADE best or ACE best model, fitted to both ages 69 and 79 estimates in set I [ADE best (A + D): <italic>χ</italic>\n<sup>2</sup> (5) =43.7, <italic>p</italic> = 2.66E−08; ACE best (A): <italic>χ</italic>\n<sup>2</sup> (5) = 27.9, <italic>p</italic> = 3.81E−05], with Islands under ADE or ACE models showing lower heritabilities by 0.09–0.10 or up to a 10% difference than open seas (both <italic>p</italic> ≤ 5.13E−07; see Figure <xref rid=\"acel13197-fig-0004\" ref-type=\"fig\">4</xref>, Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S1</xref>). In set II, ages 69 and 79 heritability estimates also showed divergence by location [ADE best (A + D): <italic>χ</italic>\n<sup>2</sup> (5) =16.8, <italic>p</italic> = 4.90E−03; ACE best (A): <italic>χ</italic>\n<sup>2</sup>(5) = 19.4, <italic>p</italic> = 1.62E−03], with Shores showing higher heritabilities by about 0.04 or 4% than open seas under ADE or ACE models (all <italic>p</italic> ≤ 2.56E−02; see Figure <xref rid=\"acel13197-fig-0004\" ref-type=\"fig\">4</xref>, Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S1</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13197-fig-0004\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>Age‐related CpG Sets: broad heritability by CpG Location</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13197-g004\"/></fig><p>Multilevel regression models were fitted to ages 69 and 79 biometrical estimates to compare the Aging sets’ CpG sites to the remaining background CpGs. Stronger heritable influences were apparent for Aging Set I (1217 sites) compared to remaining CpGs with 0.16 higher broad heritability (0.39 vs. 0.24, ADE best; <italic>p</italic> = 5.43E−162) and 0.12 higher narrow heritability (0.18 vs. 0.06, ACE best; <italic>p</italic> = 3.87E−146) and 0.05 higher common environmentality (0.15 vs. 0.10; <italic>p</italic> = 1.88E−40) (see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>, Variance Components). Patterns in the absolute variances suggested the greater heritability was due primarily to lower non‐shared factors (<italic>p</italic> ≤ 1.35E−07) and for ACE models coupled with higher additive genetic and common environmental influences (<italic>p</italic> ≤ 2.03E−04; see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>, Absolute Variances). Significantly higher heritabilities and common environmentality were also observed for Aging Set II (1934 sites) where the increased heritable and common environmental influences (all <italic>p</italic> ≤ 1.55E−31) were driven mainly by amplified genetic and common environmental influences (<italic>p</italic> ≤ 1.01E−11) and otherwise comparable non‐shared factors between the Aging II set and remaining background CpGs (see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). Thus, the age‐related sites showed a significantly higher proportion of variance attributed to heritable and shared environmental influences due to lower non‐shared factors in Aging set I and due to higher genetic and common environmental influences in Aging set II.</p></sec><sec id=\"acel13197-sec-0013\"><label>3.2</label><title>Methylation clock sites</title><p>Available CpG sites from four epigenetic clocks were evaluated in similar fashion using multilevel regression models fitted to ages 69 and 79 biometrical estimates: (a) 59 of 71 sites Hannum clock (Hannum et al., <xref rid=\"acel13197-bib-0017\" ref-type=\"ref\">2013</xref>), (b) 312 of 353 sites Horvath clock (Horvath, <xref rid=\"acel13197-bib-0019\" ref-type=\"ref\">2013</xref>), (c) 443 of 513 sites Levine clock (Levine et al., <xref rid=\"acel13197-bib-0029\" ref-type=\"ref\">2018</xref>), and 455 of 514 sites from the Zhang clock (Zhang et al., <xref rid=\"acel13197-bib-0049\" ref-type=\"ref\">2019</xref>). Significantly higher heritabilities (A + D, A) and common environmentality (C) were observed for the 1190 unique clock sites compared to all remaining CpGs (0.02–0.05 higher, <italic>p</italic> ≤ 8.68E−10, see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>, Variance Components). Comparisons of absolute variances suggested amplified genetic and common environmental influences (<italic>p</italic> ≤ 3.94E−05) as well as non‐shared factors (<italic>p</italic> ≤ 3.90E−08) between the clock sites and remaining background CpGs (see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>, Absolute Variances). Thus, the clock sites showed greater overall variability across sources of variance suggesting greater individual differences in these sites, with a significantly higher portion of variance attributed to heritable and shared environmental influences.</p><p>Among the 1190 unique CpG sites compared to one another, the Zhang clock sites tended to show stronger broad (A + D) genetic (0.07–0.08 higher, <italic>p</italic> ≤ 4.00E−07) and shared environmental (C) contributions (0.06 higher, <italic>p</italic> ≤ 2.19E−08) than Horvath or Levine clock sites, while Hannum sites were comparable to Zhang sites (within −0.018 to 0.018, <italic>p</italic> ≥ 4.71E−01) (see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S3</xref>). The ratio of intercept variance to total variance (<italic>ρ</italic>) in heritability estimates was 0.559 for ADE best models and 0.680 for ACE best models suggesting 56% and 68% of the variation in heritability, respectively, was CpG site‐specific across time and less than half of the variation was unique to CpG site and time, consistent with analyses by location (see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S1</xref>). Likewise, absolute variances showed strong between‐site variations (66%–88%, see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S3</xref>).</p></sec><sec id=\"acel13197-sec-0014\"><label>3.3</label><title>Low stability sites</title><p>We identified 2020 CpGs with low stability but meaningful genetic or common environmental contributions at one or both timepoints, that is, <italic>p</italic> > 0.01 (<italic>df </italic>= 3, where a<sub>12</sub>\n<italic> </italic>= [d<sub>12</sub> or c<sub>12</sub>]<italic> </italic>= e<sub>12</sub>\n<italic> </italic>= 0) and where e<sub>1</sub> or e<sub>2</sub> accounted for <50% of the total variation (1638 ADE best, 382 ACE best). Based on skew‐normal analyses, low stability CpGs had lower correlations among non‐shared factors across time than background CpGs (ADE best: <italic>r</italic>\n<sub>E,background</sub> = 0.24 vs. <italic>r</italic>\n<sub>E</sub>,<sub>low</sub> = 0.10, <italic>p</italic> = 2.80E−154; ACE best: <italic>r</italic>\n<sub>E,background</sub> = 0.17 vs. <italic>r</italic>\n<sub>E</sub>,<sub>low</sub> = 0.07, <italic>p</italic> ≤ 3.09E−27). The correlations of genetic (r<sub>A</sub>, r<sub>D</sub>) and common environmental influences (r<sub>C</sub>) across time were comparable (within 0.02 units) between background and low stability CpGs, albeit significant (<italic>p</italic> ≤ 1.02E−03), and otherwise very strong based on skew‐normal analyses (<italic>r</italic>\n<sub>background</sub> = 0.97–0.99 vs. <italic>r</italic>\n<sub>low</sub> = 0.95–0.99). Variability in these low stability CpGs increased across time with a ratio of SD<sub>2</sub>/SD<sub>1</sub> of 1.08 to 1.09 (SD<sub>ratio</sub> = 0.13) for ACE and ADE best models, respectively. Moreover, heritabilities decreased across time while non‐shared components tended to increase (see Figure <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S1</xref>). Compared to background CpGs, low stability CpGs tended to show higher A + D or A and C components (all <italic>p</italic> ≤ 2.03E−14) but generally lower overall absolute variances for A + D and E variances (<italic>p</italic> ≤ 15.51E−09) in ADE models (see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). Higher absolute variance for A but lower variance for E was observed in ACE models (<italic>p</italic> ≤ 4.94E−03) (see Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). Altogether, results suggest lower overall phenotypic variance in methylation among the low stability versus background CpGs across time (c.f., Table <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). However, within the set of lower stability CpGs, variance in methylation increased at time 2 mainly due to novel non‐shared factors (c.f., Figure <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S1</xref>).</p></sec><sec id=\"acel13197-sec-0015\"><label>3.4</label><title>Enrichment analysis: High heritability/familiality</title><p>The set of 5037 CpGs achieving epigenome significance (<italic>p </italic>< 1E–07) when evaluating tests of heritability (AD vs. E; <italic>N</italic> = 2049) or familiality (AC vs. E; <italic>N</italic> = 2988) across time were submitted to GREAT 4.0.4 to identify functions of cis‐regulatory regions (McLean et al., <xref rid=\"acel13197-bib-0032\" ref-type=\"ref\">2010</xref>). Specifically, we report the binomial and hypergeometric tests over genomic regions covered by the 5037 CpGs, reporting those that achieved region‐based fold enrichment (FE) >2 and both binomial and hypergeometric FDR <italic>Q</italic>‐Values < 0.05 (see Table <xref rid=\"acel13197-tbl-0002\" ref-type=\"table\">2</xref>; for full ontology results, see Table <xref rid=\"acel13197-sup-0002\" ref-type=\"supplementary-material\">S4</xref>). The sites that showed the greatest heritabilities showed enrichment in immune and inflammation pathways as well as neurotransmitter activity pathways. For example, the MHC protein complex pathway in the GO Cellular ontology list includes HLA region genes that code for HLA class II histocompatibility antigens in humans (c.f., GO:0042611, Table <xref rid=\"acel13197-sup-0002\" ref-type=\"supplementary-material\">S4</xref>). Moreover, the interferon‐gamma‐mediated signaling pathway in the GO Biological ontology list includes numerous genes associated with altered cytokine signaling and genes in the HLA region (c.f., GO:0060333, Table <xref rid=\"acel13197-sup-0002\" ref-type=\"supplementary-material\">S4</xref>).</p><table-wrap id=\"acel13197-tbl-0002\" xml:lang=\"en\" content-type=\"TABLE\" orientation=\"portrait\" position=\"float\"><label>TABLE 2</label><caption><p>GREAT 4.0.4 annotations using binomial and hypergeometric tests over genomic regions covered by the 5037 CpGs showing significant heritability/familiality <italic>p </italic>< 1E−07</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"bottom\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" rowspan=\"2\" valign=\"bottom\" colspan=\"1\">Ontology</th><th align=\"left\" colspan=\"6\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">Binomial</th><th align=\"left\" colspan=\"6\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">Hypergeometric</th></tr><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<p>Raw</p>\n<p>\n<italic>p</italic>‐Value</p>\n</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">FDR Q‐Val</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Fold Enrichment</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Observed Region Hits</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Region Set Coverage</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">FDR Q‐Val</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Fold Enrichment</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Observed Gene Hits</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Total Genes</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Gene Set Coverage</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GO Biological Process</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Interferon‐gamma‐mediated signaling pathway</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.71E−17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.48E−14</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">3.23</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">74</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.015</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">105</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.55E−02</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1.82</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">64</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.006</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Osteoblast development</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">104</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.04E−07</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.37E−05</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.48</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">39</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.008</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">57</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.91E−03</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.98</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GO Cellular Component</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">MHC protein complex</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.53E−45</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.47E−41</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">17.69</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">51</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.010</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.81E−05</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">3.31</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">20</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.004</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Integral component of lumenal side of endoplasmic reticulum membrane</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9.52E−31</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.11E−28</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">10.42</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">46</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.009</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.61E−02</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.48</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">MHC class II protein complex</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.78E−27</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9.58E−25</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">19.42</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">29</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.006</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.32E−05</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">3.63</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">15</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.003</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">GO Molecular Function</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Neurotransmitter:sodium symporter activity</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">53</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.16E−06</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.90E−04</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.90</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.005</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.80E−02</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.66</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.003</td></tr></tbody></table><table-wrap-foot id=\"acel13197-ntgp-0003\"><title>Note</title><fn id=\"acel13197-note-0003\"><p>Shown ontology from GREAT 4.0.4. FDR = false discovery rate.</p></fn></table-wrap-foot><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>The set of 5037 CpGs were then submitted to the mQTL Database (Gaunt et al., <xref rid=\"acel13197-bib-0013\" ref-type=\"ref\">2016</xref>). The search resulted in 1435 unique CpG matches to 155,177 SNP variants from the Middle Age timepoint (see Table <xref rid=\"acel13197-sup-0002\" ref-type=\"supplementary-material\">S6</xref>). Of the 1435 CpG matches, 1256 were associated with <italic>cis</italic>‐mQTLs and 304 were associated with <italic>trans</italic>‐mQTLs suggesting an abundance of associations with <italic>cis</italic>‐mQTLs. The maximum number of mQTLs associated with any given CpG was for cg03202060 with 5230 <italic>cis</italic>‐mQTLs variants plus 575 <italic>trans</italic>‐mQTLs. The <italic>cis</italic>‐mQTLs for cg03202060 reside in the HLA region on chromosome 6 (e.g., <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.genecards.org/cgi-bin/carddisp.pl?gene=HLA-DQB1%26keywords=HLA-DQB1\">https://www.genecards.org/cgi‐bin/carddisp.pl?gene=HLA‐DQB1&keywords=HLA‐DQB1</ext-link>), and the <italic>trans</italic>‐mQTLs traverse genes such as <italic>DDAH2</italic> related to metabolism of nitric oxide (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.genecards.org/cgi-bin/carddisp.pl?gene=DDAH2\">https://www.genecards.org/cgi‐bin/carddisp.pl?gene=DDAH2</ext-link>) and <italic>BAG6</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.genecards.org/cgi-bin/carddisp.pl?gene=BAG6%26keywords=BAG6\">https://www.genecards.org/cgi‐bin/carddisp.pl?gene=BAG6&keywords=BAG6</ext-link>) residing within the major histocompatibility class III region (MHCIII) and involved in the control of apoptosis. A scatterplot of cg03202060 <italic>M</italic>‐values of twin 1 by twin 2 across time is shown in Figure <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>a,b showing greater similarity for MZ than DZ pairs.</p><p>As polycomb repression may relate to age‐related changes in DNA methylation, we filtered our set of 5037 CpGs to reflect genes annotated on the 27 k array and evaluated whether our set mapped to 1861 PolyComb Group Target genes (PCGTs) identified using the Illumina 27 k chip probes (Zhuang et al., <xref rid=\"acel13197-bib-0051\" ref-type=\"ref\">2012</xref>). We observed 493 CpGs within a set of 293 PCGTs overlapped, or a 15.7% overlap of PCGTs (see Table <xref rid=\"acel13197-sup-0002\" ref-type=\"supplementary-material\">S6</xref>). A hypergeometric test of the 293 overlapping PCGTs was significant at <italic>p</italic> = 1.004E−11 suggesting overrepresentation, when considering the number of unique PCGTs in Zhuang et al. (<xref rid=\"acel13197-bib-0051\" ref-type=\"ref\">2012</xref>), and the number of genes represented in the Illumina 27 k chip.</p></sec><sec id=\"acel13197-sec-0016\"><label>3.5</label><title>Enrichment analysis: Low stability sites</title><p>The 2020 low stability CpGs were submitted to GREAT 4.0.4, showing enrichment for stress‐related DNA and RNA transcription pathways (see Tables <xref rid=\"acel13197-sup-0002\" ref-type=\"supplementary-material\">S7‐S8</xref>). Hence, these sites may lie in genes/gene pathways that are sensitive to exogenous exposures to stress leading to increasing divergence in methylation profiles across time. The GO Biological RNA and DNA pathways noted relate to heat shock and response to hypoxia in a number of plant and animal species, including humans (c.f., annotations GO:0043620, GO:0061418; Table <xref rid=\"acel13197-sup-0002\" ref-type=\"supplementary-material\">S8</xref>).</p><p>The low stability CpGs were submitted in kind to the mQTL Database (Gaunt et al., <xref rid=\"acel13197-bib-0013\" ref-type=\"ref\">2016</xref>) producing 397 unique CpG matches to 7103 mQTLs at the Midlife timepoint. Of the 397 CpG matches, 58 annotations were to <italic>cis</italic>‐mQTLs and 347 were to <italic>trans</italic>‐mQTLs (see Table <xref rid=\"acel13197-sup-0002\" ref-type=\"supplementary-material\">S9</xref>), suggesting an abundance of associations with <italic>trans</italic>‐mQTLs. The maximum number of mQTLs linked with any given CpG was for cg07677296 matched with 576 <italic>cis</italic>‐mQTLs. The <italic>cis</italic>‐mQTLs variants associated with cg07677296 traverse <italic>FAHD1</italic> and <italic>NUBP2</italic> on chromosome 16 and have been implicated in aging pathways related to insulin‐like growth factor (Teumer et al., <xref rid=\"acel13197-bib-0040\" ref-type=\"ref\">2016</xref>). A scatterplot of cg07677296 <italic>M</italic>‐values of twin 1 by twin 2 across time shows comparable similarity for MZ and DZ pairs (see Figure <xref rid=\"acel13197-sup-0001\" ref-type=\"supplementary-material\">S2</xref>c,d).</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13197-sec-0017\"><label>4</label><title>DISCUSSION</title><p>Overall, results suggest genetic contributions to DNA methylation tended to be small, vary by location, and decrease across a decade; however, genetic influence mainly contributed to the stability of methylation. Unique person‐specific influences not shared by cotwins were emergent across 10 years suggesting that non‐shared factors become more salient to DNA methylation in late life. The extent of variation in methylation at any given CpG site was positively correlated with observing stronger heritable effects. Moreover, 58% of sites showed stability across time due to strongly correlated genetic influences and modestly correlated non‐shared factors, suggesting continuity of influences across 10 years for more than half the CpG sites. The sites that showed the greatest heritabilities showed enrichment in immune and inflammation pathways and neurotransmitter transporter activity pathways. Low stability sites meanwhile showed increased expression variability across time due to novel non‐shared factors, with enrichment in stress‐related pathways, suggesting that these sites are responsive to “new” environmental cues even in old age.</p><p>Prior studies report average heritabilities of 16.5%–19.0% across adulthood (17‐79 years) (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>; Hannon et al., <xref rid=\"acel13197-bib-0016\" ref-type=\"ref\">2018</xref>) and common environmental influences of 3.0%–12.6%, that are stronger in young adulthood (Hannon et al., <xref rid=\"acel13197-bib-0016\" ref-type=\"ref\">2018</xref>). Our results of weakening heritable influences across age are consistent with the Dutch cross‐sectional study reporting average heritabilities of 21% and 18% at ages 25 and 50 assuming an AE model (van Dongen et al., <xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>), whereas our estimates of broad heritability under an ADE model are 24% and 18% many decades later at ages 69 and 79 years, respectively. Where non‐additive genetic effects fit best, the average broad heritability was 24% across age. For sites where including common environment fit best (ACE), lower average heritabilities were observed at 7% whereas common environment contributed 10% to variation in methylation across age; common environment is higher in 18‐year‐old UK adults at 12.6% (Hannon et al., <xref rid=\"acel13197-bib-0016\" ref-type=\"ref\">2018</xref>). We directly compared our heritabilities with those available from Van Dongen et al. (<xref rid=\"acel13197-bib-0043\" ref-type=\"ref\">2016</xref>) where twins were on average 37.2 years (17‐79 years). For 337,322 matching sites, our A + D estimates at time 1 (69 years) were strongly correlated with their AE results (<italic>r</italic> = .568, <italic>df </italic>= 337,320, CI<sub>95</sub> = 0.566, 0.570) and with their total heritability estimates where age interactions were estimated (<italic>r</italic> = .556, <italic>df</italic> = 334,657, CI<sub>95</sub> = 0.554, 0.559).</p><p>CpG sites related to age show a greater impact of heritable influences consistent with genetic regulation of the rate of biological aging. Sites associated with age and longevity generally show higher heritabilities than the total background sites and varied in magnitude of heritabilities by location, where “islands,” which often reside in promotor regions (Vinson & Chatterjee, <xref rid=\"acel13197-bib-0045\" ref-type=\"ref\">2012</xref>), typically showed lower heritability than those sites residing in surrounding “shores” and “shelves,” which have been shown to be differentially methylated compared to islands (Jones et al., <xref rid=\"acel13197-bib-0020\" ref-type=\"ref\">2015</xref>).</p><p>Moreover, the set of methylation clock sites are likewise more heritable than background CpG sites, with Zhang sites more heritable than Horvath and Levine sites, and Hannum sites comparable to the Zhang sites. We have recently reported heritability estimates of methylation clock ages of 52% for the Horvath clock and 36% for the Levine clock (Jylhava et al., <xref rid=\"acel13197-bib-0023\" ref-type=\"ref\">2019</xref>), where, consistent with our current site‐specific effects, stability across time was mediated primarily by genetic factors, whereas the person‐specific environmental factors contributed to differences across time. The 353 Horvath clock sites were selected as best predictors of chronological age using multiple tissues (Horvath, <xref rid=\"acel13197-bib-0019\" ref-type=\"ref\">2013</xref>) similar to the 513 Levine clock sites that were selected based on prediction of chronological age and nine biomarkers of phenotypic aging with models trained on multiple tissues (Levine et al., <xref rid=\"acel13197-bib-0029\" ref-type=\"ref\">2018</xref>). The 71 Hannum clock sites best predicted age (adjusted for sex, BMI) based on methylation observed in whole blood while the 514 sites from the Zhang prediction model relied on methylation observed in blood and saliva samples (Zhang et al., <xref rid=\"acel13197-bib-0049\" ref-type=\"ref\">2019</xref>). The current findings of moderately higher heritabilities in the Zhang and Hannum sites versus the other clock sites may be in part due to our use of blood tissue.</p><p>Enrichment analyses of the 1.4% of sites meeting <italic>p </italic>< 1E−07 suggest immune and inflammation pathways and neurotransmitter transporter activity pathways may feature in sites with strong heritable or familial–environmental components. Moreover, the analysis of mQTL associations suggests that a number these high heritability CpGs are associated largely with <italic>cis</italic>‐mQTLs, including those in the HLA region. Previous studies have identified methylation changes associated with altered immune functioning, including age‐related hypermethylation and reduced expression in CD8+ cells for genes involved in T‐cell‐mediated immune response and differentiation (Tserel et al., <xref rid=\"acel13197-bib-0042\" ref-type=\"ref\">2015</xref>). Indeed, five CpGs in our set identified as associated with <italic>cis</italic>‐mQTLs at midlife lie within the <italic>BCL11</italic> gene (cg26396443) or <italic>RUNX3</italic> gene (cg05162523, cg13566436, cg20674490, cg22509179) involved in T cell differentiation (Tserel et al., <xref rid=\"acel13197-bib-0042\" ref-type=\"ref\">2015</xref>). A related study of German and Danish individuals (including an overlapping sample of twins herein) evaluating RNA‐sequencing expression patterns and longevity identified expression patterns in biological processes contributing to immune system and response pathways (Häsler et al., <xref rid=\"acel13197-bib-0018\" ref-type=\"ref\">2017</xref>) and observed high heritabilities (30%–99%) among 20% of cis‐eQTLS. Immunosenescence describes an age‐associated decline in elderly individuals’ immune functioning, such as mounting less effective responses to vaccines and lowered resistance to illnesses, with concomitant upregulation of pro‐inflammatory cytokines, among several other cellular and physiological changes in the immune system (Accardi & Caruso, <xref rid=\"acel13197-bib-0001\" ref-type=\"ref\">2018</xref>). It has been proposed that heritable factors may be partly associated with differential immune responses (Derhovanessian et al., <xref rid=\"acel13197-bib-0007\" ref-type=\"ref\">2010</xref>; Poland, Ovsyannikova, Kennedy, Lambert, & Kirkland, <xref rid=\"acel13197-bib-0034\" ref-type=\"ref\">2014</xref>) and may predict influenza‐related susceptibility and mortality (Poland et al., <xref rid=\"acel13197-bib-0034\" ref-type=\"ref\">2014</xref>), for example, and, broadly, successful aging and longevity (Derhovanessian et al., <xref rid=\"acel13197-bib-0007\" ref-type=\"ref\">2010</xref>). Hence, differential adaptions to aging processes including immunosenescence reflect gene–environment dynamics with some individuals showing better adaptions than others due to genetic influences.</p><p>High heritability CpGs were also enriched for PCGTs—a group of genes that are epigenetically regulated by polycomb‐group proteins and involved in developmental processes and cell‐fate decisions (Lanzuolo & Orlando, <xref rid=\"acel13197-bib-0026\" ref-type=\"ref\">2012</xref>). Enrichment of hypermethylated of PCGT has also been implicated in cancer and aging and show consistent patterns across different cell types (Teschendorff et al., <xref rid=\"acel13197-bib-0039\" ref-type=\"ref\">2010</xref>). Our findings would thus support the role of heritable/familial–environmental factors in the epigenetic regulation of these fundamental cellular processes.</p><p>Enrichment analyses of low stability CpG sites suggest that stress‐related DNA and RNA transcription pathways may be relevant for these environmentally responsive sites which showed increased novel environmental contributions to methylation. It is notable that unlike the high heritability set, the low stability set showed more associations with <italic>trans</italic>‐mQTLs. That said, cg07677296 matched with 576 <italic>cis</italic>‐mQTLs, with variants spanning <italic>FAHD1</italic> and <italic>NUBP2</italic>, both implicated in metabolic and aging pathways related to insulin‐like growth factor (IGF) (Teumer et al., <xref rid=\"acel13197-bib-0040\" ref-type=\"ref\">2016</xref>). Specifically, <italic>FAHD1</italic> was identified as a <italic>cis</italic>‐eQTL associated with a variant in <italic>NUBP2</italic> (rs1065656) that may contribute to circulating IGF‐I and IGFBP‐3 concentrations (Teumer et al., <xref rid=\"acel13197-bib-0040\" ref-type=\"ref\">2016</xref>). Moreover, IGF‐I is implicated in oxidative stress pathways (Gubbi, Quipildor, Barzilai, Huffman, & Milman, <xref rid=\"acel13197-bib-0015\" ref-type=\"ref\">2018</xref>).</p><p>The current study establishes the extent to which the genetic and environmental influences contribute to site‐specific methylation across a 10‐year span in a longitudinal sample of Swedish and Danish twins. While stability of methylation was largely due to genetic influences, person‐specific environmental influences were emergent across time and explained change. By and large, the dynamics of methylation may be influenced by experiences and exposures, suggesting possible mediation of gene expression; however, the most heritable sites may participate in immune and inflammation pathways and neurotransmitter transporter activity pathways which suggest that adaptions to aging and senescence may be differentially impacted by genetic background.</p></sec><sec sec-type=\"COI-statement\" id=\"acel13197-sec-0018\"><title>CONFLICT OF INTEREST</title><p>None declared.</p></sec><sec id=\"acel13197-sec-0019\"><title>AUTHOR CONTRIBUTIONS</title><p>CAR drafted the manuscript. CAR and EM analyzed data. QT and JH contributed to scripting and QT, SH, and JJ advised on enrichment analyses. NLP and SH contributed to the coordination of the study and acquisition of the SATSA methylation data. JJ contributed to preparation of SATSA data and interpretation of results. LC, QT, and JH coordinated the LSADT data acquisition. All authors participated in interpretation of the data, have read and commented on the manuscript, and approved the final version.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13197-sup-0001\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13197-s001.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13197-sup-0002\"><caption><p>Table S4‐S9</p></caption><media xlink:href=\"ACEL-19-e13197-s002.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><sec sec-type=\"data-availability\" id=\"acel13197-sec-0021\"><title>DATA AVAILABILITY STATEMENT</title><p>Methylation data for SATSA are available at EMBL‐EBI (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/arrayexpress\" specific-use=\"software is-supplemented-by\">www.ebi.ac.uk/arrayexpress</ext-link>) under accession number E‐MTAB‐7309 (see Wang et al., 2018). For the LSADT study, legal restrictions prevent the deposit of data into a public database and transfer and sharing of individual‐level data requires prior approval from the Danish Data Protection Agency. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32910507</article-id><article-id pub-id-type=\"pmc\">PMC7431821</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13200</article-id><article-id pub-id-type=\"publisher-id\">ACEL13200</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Short Take</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Short Take</subject></subj-group></article-categories><title-group><article-title>Targeting RAS‐converting enzyme 1 overcomes senescence and improves progeria‐like phenotypes of ZMPSTE24 deficiency</article-title><alt-title alt-title-type=\"left-running-head\">YAO et al.</alt-title></title-group><contrib-group><contrib id=\"acel13200-cr-0001\" contrib-type=\"author\"><name><surname>Yao</surname><given-names>Haidong</given-names></name><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13200-cr-0002\" contrib-type=\"author\"><name><surname>Chen</surname><given-names>Xue</given-names></name><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13200-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13200-cr-0003\" contrib-type=\"author\"><name><surname>Kashif</surname><given-names>Muhammad</given-names></name><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13200-cr-0004\" contrib-type=\"author\"><name><surname>Wang</surname><given-names>Ting</given-names></name><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13200-cr-0005\" contrib-type=\"author\"><name><surname>Ibrahim</surname><given-names>Mohamed X.</given-names></name><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13200-cr-0006\" contrib-type=\"author\"><name><surname>Tüksammel</surname><given-names>Elin</given-names></name><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13200-cr-0007\" contrib-type=\"author\"><name><surname>Revêchon</surname><given-names>Gwladys</given-names></name><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13200-cr-0008\" contrib-type=\"author\"><name><surname>Eriksson</surname><given-names>Maria</given-names></name><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13200-cr-0009\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Wiel</surname><given-names>Clotilde</given-names></name><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref><address><email>clotilde.wiel@ki.se</email></address></contrib><contrib id=\"acel13200-cr-0010\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Bergo</surname><given-names>Martin O.</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-6915-7140</contrib-id><xref ref-type=\"aff\" rid=\"acel13200-aff-0001\">\n<sup>1</sup>\n</xref><address><email>martin.bergo@ki.se</email></address></contrib></contrib-group><aff id=\"acel13200-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Department of Biosciences and Nutrition</named-content>\n<institution>Karolinska Institutet</institution>\n<city>Huddinge</city>\n<country country=\"SE\">Sweden</country>\n</aff><aff id=\"acel13200-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Department of Plastic and Cosmetic Surgery</named-content>\n<named-content content-type=\"organisation-division\">Tongji Hospital</named-content>\n<named-content content-type=\"organisation-division\">Tongji Medical College</named-content>\n<institution>Huazhong University of Science and Technology</institution>\n<city>Wuhan</city>\n<country country=\"CN\">China</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nClotilde Wiel and Martin O. Bergo, Department of Biosciences and Nutrition, Karolinska Institutet, NEO Building, 6th fl., SE‐141 83 Huddinge, Sweden.<break/>\nEmail: <email>clotilde.wiel@ki.se</email> (CW); <email>martin.bergo@ki.se</email> (MOB)<break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>24</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13200</elocation-id><history><date date-type=\"received\"><day>03</day><month>4</month><year>2020</year></date><date date-type=\"rev-recd\"><day>11</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>7</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13200.pdf\"/><abstract id=\"acel13200-abs-0001\"><title>Abstract</title><p>Several progeroid disorders are caused by deficiency in the endoprotease ZMPSTE24 which leads to accumulation of prelamin A at the nuclear envelope. ZMPSTE24 cleaves prelamin A twice: at the third carboxyl‐terminal amino acid following farnesylation of a <italic>–CSIM</italic> motif; and 15 residues upstream to produce mature lamin A. The carboxyl‐terminal cleavage can also be performed by RAS‐converting enzyme 1 (RCE1) but little is known about the importance of this cleavage for the ability of prelamin A to cause disease. Here, we found that knockout of <italic>RCE1</italic> delayed senescence and increased proliferation of <italic>ZMPSTE24</italic>‐deficient fibroblasts from a patient with non‐classical Hutchinson‐Gilford progeria syndrome (HGPS), but did not influence proliferation of classical <italic>LMNA</italic>‐mutant HGPS cells. Knockout of <italic>Rce1</italic> in <italic>Zmpste24</italic>‐deficient mice at postnatal week 4–5 increased body weight and doubled the median survival time. The absence of <italic>Rce1</italic> in <italic>Zmpste24</italic>‐deficient fibroblasts did not influence nuclear shape but reduced an interaction between prelamin A and AKT which activated AKT‐mTOR signaling and was required for the increased proliferation. Prelamin A levels increased in <italic>Rce1</italic>‐deficient cells due to a slower turnover rate but its localization at the nuclear rim was unaffected. These results strengthen the idea that the presence of misshapen nuclei does not prevent phenotype improvement and suggest that targeting RCE1 might be useful for treating the rare progeroid disorders associated with <italic>ZMPSTE24</italic> deficiency.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13200-abs-0002\"><p>A deficiency in the protease ZMPSTE24 causes rare forms of accelerated aging. Here, we show that knockout of a related protease, RCE1, prevents carboxyl‐terminal processing of their shared substrate prelamin A, which disrupts an interaction with AKT and thereby stimulates AKT‐mTOR signaling. Knockout of RCE1 thus overcame senescence of cultured <italic>ZMPSTE24</italic>‐deficient cells and improved survival of <italic>Zmpste24</italic>‐deficient mice. <boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13200-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13200-g003.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13200-kwd-0001\">mouse models</kwd><kwd id=\"acel13200-kwd-0002\">prelamin A</kwd><kwd id=\"acel13200-kwd-0003\">progeria</kwd><kwd id=\"acel13200-kwd-0004\">RCE1</kwd><kwd id=\"acel13200-kwd-0005\">ZMPSTE24</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>Vetenskapsrådet </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100004359</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>Knut and Alice Wallenberg Foundation </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100004063</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0003\"><funding-source>Center for Innovative Medicine (CIMED)</funding-source></award-group><award-group id=\"funding-0004\"><funding-source>The Swedish Medical Research Council</funding-source></award-group><award-group id=\"funding-0005\"><funding-source>The Swedish Children’s Cancer Fund</funding-source></award-group><award-group id=\"funding-0006\"><funding-source>Alex and Eva Wallström Foundation</funding-source></award-group></funding-group><counts><fig-count count=\"2\"/><table-count count=\"0\"/><page-count count=\"6\"/><word-count count=\"3504\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13200-cit-1001\">\n<string-name>\n<surname>Yao</surname>\n<given-names>H</given-names>\n</string-name>, <string-name>\n<surname>Chen</surname>\n<given-names>X</given-names>\n</string-name>, <string-name>\n<surname>Kashif</surname>\n<given-names>M</given-names>\n</string-name>, et al. <article-title>Targeting RAS‐converting enzyme 1 overcomes senescence and improves progeria‐like phenotypes of ZMPSTE24 deficiency</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13200</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13200</pub-id>\n</mixed-citation>\n</p><fn-group id=\"acel13200-ntgp-0001\"><fn fn-type=\"equal\" id=\"acel13200-note-0001\"><p>Yao and Chen contributed equally.</p></fn></fn-group></notes></front><body id=\"acel13200-body-0001\"><p>Hutchinson‐Gilford progeria syndrome (HGPS) is typically caused by <italic>LMNA</italic> mutations that lead to accumulation at the nuclear rim of a shortened form of prelamin A called progerin (Eriksson et al., <xref rid=\"acel13200-bib-0008\" ref-type=\"ref\">2003</xref>; De Sandre‐Giovannoli et al., <xref rid=\"acel13200-bib-0007\" ref-type=\"ref\">2003</xref>). However, atypical HGPS can be caused by mutations in the endoprotease ZMPSTE24 which lead to accumulation of full‐length prelamin A (Barrowman and Michaelis, <xref rid=\"acel13200-bib-0002\" ref-type=\"ref\">2009</xref>). <italic>ZMPSTE24</italic> mutations also underlie mandibuloacral dysplasia (MAD) and restrictive dermopathy (RD), which is a mild progeroid disorder, and a severe developmental disorder, respectively (Barrowman and Michaelis, <xref rid=\"acel13200-bib-0002\" ref-type=\"ref\">2009</xref>; Michaelis and Hrycyna, <xref rid=\"acel13200-bib-0014\" ref-type=\"ref\">2013</xref>).</p><p>Prelamin A undergoes four modifications at a carboxyl‐terminal <italic>CSIM</italic> motif (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S1</xref>): farnesylation of the cysteine by farnesyltransferase (FTase); cleavage of the –<italic>SIM</italic> residues by either ZMPSTE24 or RAS‐converting enzyme 1 (RCE1); methylation of the cysteine by isoprenylcysteine carboxyl methyltransferase (ICMT); and removal of the last 15 amino acids by ZMPSTE24 (Barrowman, Hamblet, George, & Michaelis, <xref rid=\"acel13200-bib-0001\" ref-type=\"ref\">2008</xref>; Young, Fong, & Michaelis, <xref rid=\"acel13200-bib-0018\" ref-type=\"ref\">2005</xref>). Farnesylation and methylation are necessary for progerin's and prelamin A’s ability to cause progeria. Indeed, FTase inhibitors (FTIs) improve nuclear shape abnormalities of <italic>LMNA</italic>‐ and <italic>Zmpste24</italic>‐mutant cells and clinical phenotypes in HGPS patients (Gordon et al., <xref rid=\"acel13200-bib-0009\" ref-type=\"ref\">2018</xref>; Young et al., <xref rid=\"acel13200-bib-0018\" ref-type=\"ref\">2005</xref>). Targeting ICMT—explored only preclinically—does not affect nuclear shape but overcomes senescence and eliminates bone fractures and increases life span of <italic>Zmpste24</italic>‐deficient mice (Ibrahim et al., <xref rid=\"acel13200-bib-0011\" ref-type=\"ref\">2013</xref>).</p><p>However, nothing is yet known about the importance of the carboxyl‐terminal –<italic>SIM</italic> cleavage for prelamin A’s ability to cause disease. Because both ZMPSTE24 and RCE1 can catalyze this step, inhibiting it for therapeutic purposes would only be feasible in the setting of <italic>ZMPSTE24</italic> deficiency, where RCE1 activity would be rate limiting. Knockout of <italic>RCE1</italic> might be predicted to have a similar effect as knockout of <italic>ICMT</italic> in the context of <italic>ZMPSTE24</italic> deficiency since they act sequentially and both interventions would prevent methylation (Ibrahim et al., <xref rid=\"acel13200-bib-0011\" ref-type=\"ref\">2013</xref>). In this study, we used genetic strategies to address this issue.</p><p>We first analyzed cells from a 5‐year‐old male patient with atypical HGPS (PSADFN373) homozygous for an inactivating <italic>ZMPSTE24</italic> mutation (c.1274 T > C). Atypical HGPS and MAD‐B patients exhibit several clinical phenotypes including stunted growth, lipodystrophy, micrognathia, and hair loss, which overlap substantially, albeit not completely, with those of <italic>Zmpste24</italic>‐deficient mice (Bergo et al., <xref rid=\"acel13200-bib-0003\" ref-type=\"ref\">2002</xref>; Ibrahim et al., <xref rid=\"acel13200-bib-0011\" ref-type=\"ref\">2013</xref>). As expected from the loss of ZMPSTE24, the PSADFN373 cells expressed prelamin A and lamin C but no lamin A (Figure <xref rid=\"acel13200-fig-0001\" ref-type=\"fig\">1a</xref>). When <italic>RCE1</italic> expression in these cells was knocked out with CRISPR/CAS9, their proliferation increased (Figure <xref rid=\"acel13200-fig-0001\" ref-type=\"fig\">1b–d</xref>). However, <italic>RCE1</italic> knockout in progerin‐expressing cells (classical <italic>LMNA</italic>‐mutant HGPS) did not increase proliferation, likely because ZMPSTE24 can perform the –<italic>SIM</italic>‐cleavage in these cells (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S2</xref>a–c). Encouraged by these results, we used gene targeting in mice for further studies.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13200-fig-0001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Targeting RCE1 prevents premature senescence in <italic>ZMPSTE24</italic>\n<sup>−/−</sup> fibroblasts and improves survival of <italic>Zmpste24</italic>\n<sup>−/−</sup> mice. (a) Western blots showing accumulation of prelamin A in fibroblasts from a <italic>ZMPSTE24</italic>‐deficient patient (cell line PSADFN373) using lamin A/C antibodies (recognizing the amino terminus of prelamin A, lamin A, and lamin C) and prelamin A antibodies (recognizing the carboxyl terminus); Actin was the loading control. (b) TaqMan analyses showing <italic>RCE1</italic> mRNA levels in the <italic>ZMPSTE24</italic>‐deficient fibroblasts following CRISPR/CAS9‐mediated knockout of <italic>RCE1</italic> with two different sgRNAs; control cells were incubated with nonsense sgRNAs targeting dTomato (sgTOM). (c) Growth curves from population doubling assays of cells from panel b. Data are mean of three technical replicates per cell clone; cells were passage 30. (d) Growth curves from presto blue‐based cell viability assays. Data are mean of six replicates per clone; cells were passage 34. (e) Photograph of 22‐week‐old littermate male mice. (f) Body‐weight curves of male <italic>Zmpste24</italic> mice (<italic>n</italic> = 10) and <italic>Zmpste24</italic>\n<sup>−/−</sup>\n<italic>Rce1</italic>\n<sup>Δ/Δ</sup> (<italic>n</italic> = 5) mice. (g) Kaplan–Meier plot showing survival of <italic>Zmpste24</italic>\n<sup>−/−</sup>\n<italic>Rce1</italic>\n<sup>Δ/+</sup> (<italic>n</italic> = 17) and <italic>Zmpste24</italic>\n<sup>−/−</sup>\n<italic>Rce1</italic>\n<sup>Δ/Δ</sup> (<italic>n</italic> = 7) mice. (h) Growth curves from population doubling assays of primary fibroblasts isolated from two <italic>Zmpste24</italic>\n<sup>−/−</sup>\n<italic>Rce1</italic>\n<sup>fl/fl</sup> embryos (Cell line 1 and 2); <italic>Cre</italic>‐adenovirus was used to produce <italic>Zmpste24</italic>\n<sup>−/−</sup>\n<italic>Rce1</italic>\n<sup>Δ/Δ</sup> (i.e., <italic>Rce1</italic> knockout) cells from each parental <italic>Zmpste24</italic>\n<sup>−/−</sup>\n<italic>Rce1</italic>\n<sup>fl/fl</sup> cell line; β<italic>gal</italic>‐adenovirus was used as control. Data are mean of three replicates per cell line; cells were passage 4. (i) Growth curves from population doubling assays of primary fibroblasts isolated from two <italic>Zmpste24</italic>\n<sup>−/−</sup>\n<italic>Rce1</italic>\n<sup>+/+</sup> embryos incubated with 2 and 10 µM FTI. Data are mean of three technical replicates per condition; cells were passage 5. (j) SA‐β‐Gal staining assay. Data are mean of three independent cell lines (<italic>n</italic> = 3) assayed in triplicate; cells were passage 8. (k) Expression of senescence markers <italic>IL6</italic>, <italic>Cdkn2a</italic>, <italic>Lamin B1</italic> determined by TaqMan; <italic>β</italic>‐<italic>tubulin</italic> were used as the reference. Data are mean of three cell lines (<italic>n</italic> = 3) assayed in triplicate; cells were passage 9. (l) Seahorse analyses of basal and maximal respiration (Resp.), ATP production, and proton leak. Data are mean of three cell lines assayed in triplicate; cells were in passage 8. * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.005; **** <italic>p</italic> < 0.001</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13200-g001\"/></fig><p>\n<italic>Rce1</italic> expression in livers of tamoxifen‐injected <italic>Zmpste24</italic>\n<sup>–/–</sup>\n<italic>Rce1</italic>\n<sup>fl/fl</sup>\n<italic>Rosa26Cre</italic>\n<sup>ERT</sup> mice (designated <italic>Zmpste24</italic>\n<sup>−/−</sup>\n<italic>Rce1</italic>\n<sup>∆/∆</sup>) was ~65% lower than in livers of <italic>Zmpste24</italic>\n<sup>−/−</sup>\n<italic>Rce1</italic>\n<sup>∆/∆</sup> controls (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S2</xref>d,e). Increased body weight and prolonged survival accompanied the reduced <italic>Rce1</italic> expression (38 vs. 19 weeks), which are similar to effects observed with <italic>Icmt</italic> deficiency (Figure <xref rid=\"acel13200-fig-0001\" ref-type=\"fig\">1e</xref>–g) (Ibrahim et al., <xref rid=\"acel13200-bib-0011\" ref-type=\"ref\">2013</xref>). Because both ‐<italic>SIM</italic>‐cleaved unmethylated prelamin A (i.e., in <italic>Icmt</italic> deficiency) and non‐<italic>SIM</italic>‐cleaved unmethylated prelamin A (i.e., in <italic>Rce1</italic> deficiency) appear to be less toxic than methylated prelamin A, these results suggest that the methyl group contributes to prelamin A’s toxic effect. In contrast to <italic>Icmt</italic> deficiency, <italic>Rce1</italic> knockout did not affect grip strength and bone fractures (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S2</xref>f,g).</p><p>We isolated <italic>Zmpste24</italic>\n<sup>–/–</sup>\n<italic>Rce1</italic>\n<sup>fl/fl</sup> embryonic fibroblasts and knocked out <italic>Rce1</italic> completely with <italic>Cre</italic>‐adenovirus (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S2</xref>h). Similar to the results with human cells (i.e., Figure <xref rid=\"acel13200-fig-0001\" ref-type=\"fig\">1c,d</xref>), <italic>Rce1</italic> knockout increased proliferation of <italic>Zmpste24</italic>\n<sup>–/–</sup> cells (Figure <xref rid=\"acel13200-fig-0001\" ref-type=\"fig\">1h</xref> and Supporting Information Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S2</xref>i), but had no impact on <italic>Zmpste24</italic>\n<sup>+/+</sup> cells (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S2</xref>j). An FTI dose‐dependently reduced proliferation of <italic>Zmpste24</italic>\n<sup>–/–</sup> cells and prevented the increase in proliferation induced by the <italic>Rce1</italic> knockout (Figure <xref rid=\"acel13200-fig-0001\" ref-type=\"fig\">1i</xref> and Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S2</xref>k). These results confirm earlier findings that <italic>Rce1</italic> deficiency is compatible with cell proliferation whereas FTase inhibition—and knockout of the FTase β subunit—reduces or blocks it (Lee et al., <xref rid=\"acel13200-bib-0012\" ref-type=\"ref\">2010</xref>; Liu et al., <xref rid=\"acel13200-bib-0013\" ref-type=\"ref\">2010</xref>; Wahlstrom et al., <xref rid=\"acel13200-bib-0017\" ref-type=\"ref\">2007</xref>).</p><p>Consistent with increased proliferation, <italic>Rce1</italic> knockout reduced senescence‐associated β‐galactosidase activity of <italic>Zmpste24</italic>\n<sup>–/–</sup> cells, and the expression of senescence markers <italic>Il6</italic> and <italic>Cdkn2a</italic>; and increased <italic>Lmnb1</italic> expression (Figure <xref rid=\"acel13200-fig-0001\" ref-type=\"fig\">1j</xref>,k). In line with earlier studies in HGPS cells (Rivera‐Torres et al., <xref rid=\"acel13200-bib-0015\" ref-type=\"ref\">2013</xref>), basal and maximal respiration and ATP production were lower in <italic>Zmpste24</italic>\n<sup>–/–</sup> than wild‐type cells. Knockout of <italic>Rce1</italic> increased oxygen consumption rates and normalized those metabolic parameters; they were even increased slightly but significantly above baseline (Figure <xref rid=\"acel13200-fig-0001\" ref-type=\"fig\">1l</xref>).</p><p>Misshapen nuclei are a hallmark of progerin‐ and prelamin A‐expressing cells in culture and FTIs improve this phenotype (Capell et al., <xref rid=\"acel13200-bib-0005\" ref-type=\"ref\">2005</xref>; Toth et al., <xref rid=\"acel13200-bib-0016\" ref-type=\"ref\">2005</xref>). <italic>Rce1</italic> knockout, however, did not influence nuclear shape of <italic>Zmpste24</italic>\n<sup>–/–</sup> cells (Figure <xref rid=\"acel13200-fig-0002\" ref-type=\"fig\">2a</xref>). Consistent with previous studies (Ibrahim et al., <xref rid=\"acel13200-bib-0011\" ref-type=\"ref\">2013</xref>), AKT‐mTOR signaling was low in <italic>Zmpste24</italic>\n<sup>–/–</sup> cells as judged by Western blots of phospho‐AKT and phospho‐S6 (Figure <xref rid=\"acel13200-fig-0002\" ref-type=\"fig\">2b</xref>). Knockout of <italic>Rce1</italic> restored phospho‐AKT and phospho‐S6 levels, and disrupted the prelamin A–AKT interaction (Figure <xref rid=\"acel13200-fig-0002\" ref-type=\"fig\">2b,c</xref>). Moreover, an AKT inhibitor prevented the proliferation increase induced by <italic>Rce1</italic> knockout (Figure <xref rid=\"acel13200-fig-0002\" ref-type=\"fig\">2d</xref>); and an AKT activator increased proliferation of naïve <italic>Zmpste24</italic>\n<sup>–/–</sup> cells (Figure <xref rid=\"acel13200-fig-0002\" ref-type=\"fig\">2e</xref>). These data suggest that prelamin A in <italic>Zmpste24</italic>‐deficient cells binds AKT and prevents its phosphorylation and signaling; when the last three amino acids of prelamin A are retained, as in <italic>Rce1</italic>‐knockout cells, the prelamin A–AKT interaction is disrupted and subsequent AKT activation drives increased proliferation.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13200-fig-0002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>\n<italic>Rce1</italic> knockout prevents premature senescence of <italic>Zmpste24</italic>\n<sup>−/−</sup> fibroblasts by increasing AKT pathway signaling, but has no impact on nuclear shape. (a). Left, confocal images of representative nuclei in primary mouse embryonic fibroblasts stained with LAP2β antibodies; FTI (FTI‐276) concentration was 2 µM. Right, quantification of misshapen nuclei. Data are mean of three cell lines (<italic>n</italic> = 3) per condition; cells were passage 8. (b) Left, Western blots of fibroblast lysates showing steady‐state levels of phosphorylated and total AKT and S6, and prelamin A; β‐tubulin was the loading control. Middle and right, ratio of phosphorylated and total AKT and S6 (middle) and ratio of prelamin A and β‐tubulin (right) determined by densitometry of protein bands. Data are mean of three cell lines (<italic>n</italic> = 3) per genotype; cells were passage 8. (c) Upper, immunoprecipitation (IP) and Western blot (WB) showing an <italic>Rce1</italic>‐dependent interaction between prelamin A and AKT in <italic>Zmpste24</italic>\n<sup>–/–</sup> fibroblasts. The lysates were also used directly for Western blot of total AKT and prelamin A levels (input). Lower, prelamin A–AKT interaction determined by densitometry of protein bands. Data are mean of three cell lines (<italic>n</italic> = 3) per genotype and normalized first to total AKT and then to control (<italic>Zmpste24</italic>\n<sup>–/–</sup>\n<italic>Rce1</italic>\n<sup>fl/fl</sup>); cells were passage 8. (d, e) Growth curves from population doubling assays of fibroblasts incubated with an AKT inhibitor (20 µM, GSK690693) (d) and an AKT activator (5 µM, SC‐79) (e). Data are mean of triplicate analyses per condition; similar results were obtained with two cell lines each analyzed in two experiments; cells were passage 4. Scale bar, 20 µm, * <italic>p</italic> < 0.05; ** <italic>p</italic> < 0.01; *** <italic>p</italic> < 0.005; **** <italic>p</italic> < 0.001</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13200-g002\"/></fig><p>Consistent with absent RCE1 activity, RAS proteins increased in the cytosolic fraction and decreased in the membrane fraction of <italic>Zmpste24</italic>\n<sup>–/–</sup>\n<italic>Rce1</italic>\n<sup>∆/∆</sup> cells; and RAS and prelamin A exhibited a slight electrophoretic mobility shift (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S3</xref>a,b). Prelamin A was primarily localized at the nuclear membrane in <italic>Zmpste24</italic>\n<sup>–/–</sup> fibroblasts and hepatocytes, and the localization was unaffected by the knockout of <italic>Rce1</italic> (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S3</xref>c,d).</p><p>Data in Figure <xref rid=\"acel13200-fig-0002\" ref-type=\"fig\">2b</xref> and Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S3</xref>d revealed that steady‐state levels of prelamin A were higher in <italic>Zmpste24</italic>\n<sup>–/–</sup>\n<italic>Rce1</italic>\n<sup>∆/∆</sup> than <italic>Zmpste24</italic>\n<sup>–/–</sup>\n<italic>Rce1</italic>\n<sup>fl/fl</sup> cells (Figure <xref rid=\"acel13200-fig-0002\" ref-type=\"fig\">2b</xref> and Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S3</xref>d). When protein synthesis was stopped with cycloheximide, prelamin A disappeared at a slower rate in <italic>Zmpste24</italic>\n<sup>–/–</sup>\n<italic>Rce1</italic>\n<sup>∆/∆</sup> than <italic>Rce1</italic>\n<sup>fl/fl</sup> cells (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S3</xref>e). This result suggests that retention of the –<italic>SIM</italic> amino acids reduces prelamin A turnover and increases steady‐state levels.</p><p>Previous studies revealed that active AKT can phosphorylate prelamin A at Ser404 and trigger prelamin A degradation (Bertacchini et al., <xref rid=\"acel13200-bib-0004\" ref-type=\"ref\">2013</xref>; Cenni et al., <xref rid=\"acel13200-bib-0006\" ref-type=\"ref\">2008</xref>). Therefore, the finding that <italic>Rce1</italic> deficiency was associated with increased AKT activity and reduced prelamin A degradation is surprising. However, this effect was also observed with <italic>Icmt</italic> deficiency. The reason behind the opposing results is unclear but one potential explanation is that the absence of the methyl group or retention of the last three amino acids in our two studies prevents binding to proteins that contribute to prelamin A degradation. Future studies should determine whether AKT‐induced Ser404 phosphorylation influences the stability of endogenous prelamin A and senescence in the setting of <italic>Zmpste24</italic> deficiency; and the impact of knocking out <italic>Rce1</italic> and <italic>Icmt</italic>.</p><p>Thus, targeting RCE1‐mediated endoproteolysis increased survival and alleviated some phenotypes of <italic>Zmpste24</italic> deficiency in vivo, but the effect was less than that observed by targeting <italic>Icmt</italic> (Ibrahim et al., <xref rid=\"acel13200-bib-0011\" ref-type=\"ref\">2013</xref>). A potential explanation is that <italic>Rce1</italic> was knocked out by ~65% in 4‐ to 5‐week‐old mice whereas <italic>Icmt</italic> was knocked out by ~85% throughout development using a hypomorphic allele (Ibrahim et al., <xref rid=\"acel13200-bib-0011\" ref-type=\"ref\">2013</xref>). Thus, it is possible that the effects of the <italic>Rce1</italic> knockout in vivo are underestimated. This argument is strengthened by the finding that the knockout of <italic>Rce1</italic> in vitro—which was near complete (Figure <xref rid=\"acel13200-sup-0001\" ref-type=\"supplementary-material\">S2</xref>h)—showed more robust effects, comparable to <italic>Icmt</italic> deficiency.</p><p>Interestingly, the phenotypes of <italic>Zmpste24</italic> deficiency improved following <italic>Rce1</italic> knockout despite increased steady‐state levels of farnesylated prelamin A; unaltered localization at the nuclear rim; and lack of effect on nuclear shape. The reduced toxicity of non‐<italic>SIM</italic>‐cleaved prelamin A could potentially be derived from altered protein–protein interactions, including the reduced interaction with AKT which was associated with increased AKT signaling and required for the increased proliferation. But we cannot rule out the possible involvement of other <italic>CAAX</italic>‐protein substrates of RCE1, aside from prelamin A.</p><p>A specific RCE1 inhibitor would be required to determine whether targeting this enzyme pharmacologically could be useful in treating disorders of <italic>ZMPSTE24</italic> deficiency—a strategy that would be relevant for MAD‐B and the extremely rare atypical form of HGPS, but not for RD as it is lethal at birth (Hampton, Dore, & Schmidt, <xref rid=\"acel13200-bib-0010\" ref-type=\"ref\">2018</xref>). However, such an inhibitor would not be required to completely inhibit RCE1 because 65% of reduced <italic>Rce1</italic> expression was sufficient to double the median survival of <italic>Zmpste24</italic>‐deficient mice.</p><sec sec-type=\"COI-statement\" id=\"acel13200-sec-0003\"><title>CONFLICT OF INTERESTS</title><p>The authors declare that no competing interests exist.</p></sec><sec id=\"acel13200-sec-0004\"><title>AUTHOR CONTRIBUTIONS</title><p>H.Y. designed the study, performed experiments, interpreted data, made figures, and wrote the manuscript; X.C. performed experiments, interpreted data, and made figures; M.K. designed and performed experiments; T.W. performed experiments; M.X.I. designed experiments; E.T. performed mouse experiments; G.R. performed experiments; M.E. designed experiments and interpreted data; C.W. designed and performed experiments, supervised, and wrote the manuscript; M.O.B. designed the study, provided funding, and wrote the manuscript.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13200-sup-0001\"><caption><p>Figure S1‐S3</p></caption><media xlink:href=\"ACEL-19-e13200-s001.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13200-sup-0002\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13200-s002.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13200-sec-0002\"><title>ACKNOWLEDGEMENTS</title><p>We thank Xiufeng Xu for CRISPR experiments and the SciLifeLab BioImage Informatics Facility for data analyses. Microscopy was performed at the LCI facility/Nikon Center of Excellence, Karolinska Institutet, supported by grants from the Knut and Alice Wallenberg Foundation, Swedish Research Council, KI infrastructure, Centre for Innovative Medicine, and Jonasson Center at the Royal Institute of Technology. The study was supported by grants from the Knut and Alice Wallenberg Foundation, Center for Innovative Medicine (CIMED), The Swedish Medical Research Council, and The Swedish Children’s Cancer Fund (to M.O.B.); and the Alex and Eva Wallström Foundation (to C.W.). C.W. was supported by a Marie Sklodowska‐Curie Individual Fellowship and a Swedish Cancer Society Postdoctoral Fellowship.</p></ack><sec sec-type=\"data-availability\" id=\"acel13200-sec-0006\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13200-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13200-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13200-cit-0001\">\n<string-name>\n<surname>Barrowman</surname>, <given-names>J.</given-names>\n</string-name>, <string-name>\n<surname>Hamblet</surname>, <given-names>C.</given-names>\n</string-name>, <string-name>\n<surname>George</surname>, <given-names>C. 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pub-id-type=\"pmc\">PMC7431822</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13190</article-id><article-id pub-id-type=\"publisher-id\">ACEL13190</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Glycome profiling by lectin microarray reveals dynamic glycan alterations during epidermal stem cell aging</article-title><alt-title alt-title-type=\"left-running-head\">OINAM et al.</alt-title></title-group><contrib-group><contrib id=\"acel13190-cr-0001\" contrib-type=\"author\"><name><surname>Oinam</surname><given-names>Lalhaba</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-3929-8222</contrib-id><address><email>s1530557@u.tsukuba.ac.jp</email></address><xref ref-type=\"aff\" rid=\"acel13190-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13190-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13190-cr-0002\" contrib-type=\"author\"><name><surname>Changarathil</surname><given-names>Gopakumar</given-names></name><address><email>gopkmr.c@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13190-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13190-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13190-cr-0003\" contrib-type=\"author\"><name><surname>Raja</surname><given-names>Erna</given-names></name><address><email>rajaerna@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13190-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13190-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13190-cr-0004\" contrib-type=\"author\"><name><surname>Ngo</surname><given-names>Yen Xuan</given-names></name><address><email>s1730546@s.tsukuba.ac.jp</email></address><xref ref-type=\"aff\" rid=\"acel13190-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13190-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13190-cr-0005\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Tateno</surname><given-names>Hiroaki</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-3006-1659</contrib-id><xref ref-type=\"aff\" rid=\"acel13190-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13190-aff-0005\">\n<sup>5</sup>\n</xref><address><email>h-tateno@aist.go.jp</email></address></contrib><contrib id=\"acel13190-cr-0006\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Sada</surname><given-names>Aiko</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-0984-4280</contrib-id><xref ref-type=\"aff\" rid=\"acel13190-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13190-aff-0004\">\n<sup>4</sup>\n</xref><address><email>aisada@kumamoto-u.ac.jp</email></address></contrib><contrib id=\"acel13190-cr-0007\" contrib-type=\"author\"><name><surname>Yanagisawa</surname><given-names>Hiromi</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-7576-9186</contrib-id><address><email>hkyanagisawa@tara.tsukuba.ac.jp</email></address><xref ref-type=\"aff\" rid=\"acel13190-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13190-aff-0006\">\n<sup>6</sup>\n</xref></contrib></contrib-group><aff id=\"acel13190-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Life Science Center for Survival Dynamics</named-content>\n<named-content content-type=\"organisation-division\">Tsukuba Advanced Research Alliance (TARA)</named-content>\n<institution>University of Tsukuba</institution>\n<city>Tsukuba</city>\n<country country=\"JP\">Japan</country>\n</aff><aff id=\"acel13190-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Ph.D. Program in Human Biology</named-content>\n<named-content content-type=\"organisation-division\">School of Integrative and Global Majors</named-content>\n<institution>University of Tsukuba</institution>\n<city>Tsukuba</city>\n<country country=\"JP\">Japan</country>\n</aff><aff id=\"acel13190-aff-0003\">\n<label><sup>3</sup></label>\n<named-content content-type=\"organisation-division\">Graduate School of Comprehensive Human Sciences</named-content>\n<institution>University of Tsukuba</institution>\n<city>Tsukuba</city>\n<country country=\"JP\">Japan</country>\n</aff><aff id=\"acel13190-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">International Research Center for Medical Sciences (IRCMS)</named-content>\n<institution>Kumamoto University</institution>\n<city>Kumamoto</city>\n<country country=\"JP\">Japan</country>\n</aff><aff id=\"acel13190-aff-0005\">\n<label><sup>5</sup></label>\n<named-content content-type=\"organisation-division\">Cellular and Molecular Biotechnology Research Institute</named-content>\n<institution>National Institute of Advanced Industrial Science and Technology</institution>\n<city>Tsukuba</city>\n<country country=\"JP\">Japan</country>\n</aff><aff id=\"acel13190-aff-0006\">\n<label><sup>6</sup></label>\n<named-content content-type=\"organisation-division\">Faculty of Medicine</named-content>\n<institution>University of Tsukuba</institution>\n<city>Tsukuba</city>\n<country country=\"JP\">Japan</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nAiko Sada, International Research Center for Medical Sciences (IRCMS), Kumamoto University, 2‐2‐1 Honjo, Chuo‐ku, Kumamoto City 860‐0811, Japan.<break/>\nEmail: <email>aisada@kumamoto-u.ac.jp</email><break/>\nHiroaki Tateno, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 6, 1‐1‐1 Higashi, Tsukuba, Ibaraki 305‐8566, Japan.<break/>\nEmail: <email>h-tateno@aist.go.jp</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>18</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13190</elocation-id><history><date date-type=\"received\"><day>04</day><month>7</month><year>2019</year></date><date date-type=\"rev-recd\"><day>01</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>06</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by Anatomical Society and John Wiley & Sons Ltd</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13190.pdf\"/><abstract id=\"acel13190-abs-0001\"><title>Abstract</title><p>Aging in the epidermis is marked by a gradual decline in barrier function, impaired wound healing, hair loss, and an increased risk of cancer. This could be due to age‐related changes in the properties of epidermal stem cells and defective interactions with their microenvironment. Currently, no biochemical tools are available to detect and evaluate the aging of epidermal stem cells. The cellular glycosylation is involved in cell–cell communications and cell–matrix adhesions in various physiological and pathological conditions. Here, we explored the changes of glycans in epidermal stem cells as a potential biomarker of aging. Using lectin microarray, we performed a comprehensive glycan profiling of freshly isolated epidermal stem cells from young and old mouse skin. Epidermal stem cells exhibited a significant difference in glycan profiles between young and old mice. In particular, the binding of a mannose‐binder rHeltuba was decreased in old epidermal stem cells, whereas that of an α2‐3Sia‐binder rGal8N increased. These glycan changes were accompanied by upregulation of sialyltransferase, <italic>St3gal2</italic> and <italic>St6gal1</italic> and mannosidase <italic>Man1a</italic> genes in old epidermal stem cells. The modification of cell surface glycans by overexpressing these glycogenes leads to a defect in the regenerative ability of epidermal stem cells in culture. Hence, our study suggests the age‐related global alterations in cellular glycosylation patterns and its potential contribution to the stem cell function. These glycan modifications detected by lectins may serve as molecular markers for aging, and further functional studies will lead us to a better understanding of the process of skin aging.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13190-abs-0002\"><p>Using lectin microarray, a platform for high‐throughput glycome analysis, we demonstrate that epidermal stem cells undergo an age‐related glycome shift from high mannose‐ to sialylated complex‐type N‐glycans. These glycan alterations are detected by lectin probes, rHeltuba and rGal8N, which serve as potential biomarkers of skin aging.\n<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13190-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13190-g008.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13190-kwd-0001\">epidermal stem cells</kwd><kwd id=\"acel13190-kwd-0002\">glycosylation</kwd><kwd id=\"acel13190-kwd-0003\">lectin microarray</kwd><kwd id=\"acel13190-kwd-0004\">mannose</kwd><kwd id=\"acel13190-kwd-0005\">sialylation</kwd><kwd id=\"acel13190-kwd-0006\">skin aging</kwd><kwd id=\"acel13190-kwd-0007\">stem cell aging</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source>AMED‐PRIME, AMED</funding-source><award-id>JP19gm6110016</award-id></award-group><award-group id=\"funding-0002\"><funding-source>The Nanotech Career‐up Alliance N.R.P</funding-source></award-group><award-group id=\"funding-0003\"><funding-source>The Mitsubishi Foundation</funding-source></award-group><award-group id=\"funding-0004\"><funding-source><institution-wrap><institution>The Nakatomi Foundation </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100004398</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0005\"><funding-source><institution-wrap><institution>The Sumitomo Foundation </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100008608</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0006\"><funding-source><institution-wrap><institution>Hoyu Science Foundation </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100008608</institution-id></institution-wrap></funding-source></award-group></funding-group><counts><fig-count count=\"7\"/><table-count count=\"0\"/><page-count count=\"13\"/><word-count count=\"7668\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13190-cit-1001\">\n<string-name>\n<surname>Oinam</surname>\n<given-names>L</given-names>\n</string-name>, <string-name>\n<surname>Changarathil</surname>\n<given-names>G</given-names>\n</string-name>, <string-name>\n<surname>Raja</surname>\n<given-names>E</given-names>\n</string-name>, et al. <article-title>Glycome profiling by lectin microarray reveals dynamic glycan alterations during epidermal stem cell aging</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13190</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13190</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13190-body-0001\"><sec id=\"acel13190-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>The epidermis is the first barrier of our body that protects us from infection and dehydration. The epidermis consists of the interfollicular epidermis (IFE) and its appendages (hair follicles: HFs, sebaceous and sweat glands) and is replenished by distinct populations of stem cells (Gonzales & Fuchs, <xref rid=\"acel13190-bib-0012\" ref-type=\"ref\">2017</xref>; Rognoni & Watt, <xref rid=\"acel13190-bib-0034\" ref-type=\"ref\">2018</xref>). The IFE is renewed by stem cells located in the basal layer, which give rise to stratified squamous epithelium. HF stem cells reside in a specialized structure, called the bulge, and contribute to cyclic regeneration of HFs. Stem cells in the IFE and HFs are largely independent of each other during homeostasis, but they possess plasticity to change their fates in response to injury (Gonzales & Fuchs, <xref rid=\"acel13190-bib-0012\" ref-type=\"ref\">2017</xref>; Rognoni & Watt, <xref rid=\"acel13190-bib-0034\" ref-type=\"ref\">2018</xref>). The epidermis is separated from the dermis by the basement membrane enriched in the extracellular matrix, which regulates stem cell property and fates (Chermnykh, Kalabusheva, & Vorotelyak, <xref rid=\"acel13190-bib-0007\" ref-type=\"ref\">2018</xref>; Watt & Fujiwara, <xref rid=\"acel13190-bib-0044\" ref-type=\"ref\">2011</xref>).</p><p>An age‐related decline in tissue regeneration and function could be attributed to an impaired stem cell function, a theory known as “stem cell aging” (López‐Otín, Blasco, Partridge, Serrano, & Kroemer, <xref rid=\"acel13190-bib-0025\" ref-type=\"ref\">2013</xref>); however, it remains elusive what are the crucial drivers for aging at cellular and molecular levels. An aged epidermis shows histological and functional changes, including a decreased proliferative capacity (Charruyer et al., <xref rid=\"acel13190-bib-0006\" ref-type=\"ref\">2009</xref>; Gilchrest, <xref rid=\"acel13190-bib-0011\" ref-type=\"ref\">1983</xref>) and lower success in epidermal engraftment (Piccin & Morshead, <xref rid=\"acel13190-bib-0033\" ref-type=\"ref\">2010</xref>), a decrease in epidermal thickness, flattening of epidermal–dermal junction (Changarathil, Ramirez, Isoda, Sada, & Yanagisawa, <xref rid=\"acel13190-bib-0005\" ref-type=\"ref\">2019</xref>; Giangreco, Goldie, Failla, Saintigny, & Watt, <xref rid=\"acel13190-bib-0009\" ref-type=\"ref\">2010</xref>; Langton, Halai, Griffiths, Sherratt, & Watson, <xref rid=\"acel13190-bib-0022\" ref-type=\"ref\">2016</xref>; Makrantonaki & Zouboulis, <xref rid=\"acel13190-bib-0027\" ref-type=\"ref\">2007</xref>), delayed wound healing (Keyes et al., <xref rid=\"acel13190-bib-0020\" ref-type=\"ref\">2016</xref>), decreased barrier function (Gonzales & Fuchs, <xref rid=\"acel13190-bib-0012\" ref-type=\"ref\">2017</xref>), increased risk of cancer (Adams, Jasper, & Rudolph, <xref rid=\"acel13190-bib-0001\" ref-type=\"ref\">2015</xref>; López‐Otín et al., <xref rid=\"acel13190-bib-0025\" ref-type=\"ref\">2013</xref>), impaired HF stem cell lineages (Matsumura et al., <xref rid=\"acel13190-bib-0028\" ref-type=\"ref\">2016</xref>), and interaction with their niche (Ge et al., <xref rid=\"acel13190-bib-0008\" ref-type=\"ref\">2020</xref>). Mutant mouse studies and transcriptome analyses have suggested that the age‐related epidermal dysfunction could be due to defects in IFE and HF stem cells to interact with other cell types or extracellular matrix in skin (Ge et al., <xref rid=\"acel13190-bib-0008\" ref-type=\"ref\">2020</xref>; Giangreco, Qin, Pintar, & Watt, <xref rid=\"acel13190-bib-0010\" ref-type=\"ref\">2008</xref>; Keyes et al., <xref rid=\"acel13190-bib-0020\" ref-type=\"ref\">2016</xref>; Liu et al., <xref rid=\"acel13190-bib-0024\" ref-type=\"ref\">2019</xref>; Matsumura et al., <xref rid=\"acel13190-bib-0028\" ref-type=\"ref\">2016</xref>; Watanabe et al., <xref rid=\"acel13190-bib-0043\" ref-type=\"ref\">2017</xref>). However, changes in gene expression at the transcription level may not fully explain the molecular aspects of stem cell aging in skin.</p><p>Glycosylation is a reaction that proteins or lipids are modified with glycans (Varki, <xref rid=\"acel13190-bib-0040\" ref-type=\"ref\">2009</xref>). The protein glycosylation involves stepwise addition and removal of glycans, primarily mediated by glycosyltransferases and glycosidases (Spiro, <xref rid=\"acel13190-bib-0036\" ref-type=\"ref\">2002</xref>). The presence of glycans determines the structure, stability, and localization of glycoproteins, which affect a wide variety of biological processes, such as development (Haltiwanger & Lowe, <xref rid=\"acel13190-bib-0013\" ref-type=\"ref\">2004</xref>), tumorigenesis (Ohtsubo & Marth, <xref rid=\"acel13190-bib-0032\" ref-type=\"ref\">2006</xref>) and inflammation (Varki & Gagneux, <xref rid=\"acel13190-bib-0041\" ref-type=\"ref\">2015</xref>). Glycans are required for stem cell regulations by modulating signaling molecules that govern self‐renewal and differentiation of stem cells (Nishihara, <xref rid=\"acel13190-bib-0031\" ref-type=\"ref\">2018</xref>). As glycans are located at the cell surface, they have been utilized as biomarkers, for example, pluripotent status of mouse embryonic stem cells (Adewumi et al., <xref rid=\"acel13190-bib-0002\" ref-type=\"ref\">2007</xref>; Muramatsu & Muramatsu, <xref rid=\"acel13190-bib-0030\" ref-type=\"ref\">2004</xref>; Muramatsu & Muramatsu, <xref rid=\"acel13190-bib-0030\" ref-type=\"ref\">2004</xref>) and human induced pluripotent stem cells (Tateno et al., <xref rid=\"acel13190-bib-0038\" ref-type=\"ref\">2011</xref>). Given the role of glycans in diverse biological and biochemical processes, glycosylation might play an important role in the process of stem cell aging. However, the glycosylation state of stem cells in aged mammalian tissues remains largely uncharacterized.</p><p>The glycome analysis of tissue stem cells has been challenging, as tissue stem cells are rare and large amounts of samples are required for the structural analysis of glycans by mass spectrometry. Lectin microarray, a platform for high‐throughput glycome analysis, enables a comprehensive glycan profiling even from a relatively small number of cells (Kuno et al., <xref rid=\"acel13190-bib-0021\" ref-type=\"ref\">2005</xref>). Lectins are a class of glycan‐binding proteins that recognize various glycan structures (Hirabayashi, <xref rid=\"acel13190-bib-0016\" ref-type=\"ref\">2004</xref>). In lectin microarrays, a series of lectins with various glycan‐binding specificities are immobilized on a glass slide (Hirabayashi, Yamada, Kuno, & Tateno, <xref rid=\"acel13190-bib-0017\" ref-type=\"ref\">2013</xref>). Lectin–glycan interactions are quantitatively measured as fluorescent signals after incubation with fluorescence‐labeled samples in the lectin microarray (Kuno et al., <xref rid=\"acel13190-bib-0021\" ref-type=\"ref\">2005</xref>). Using this technology, glycoproteins isolated from various biological samples can be utilized for glycome analysis without the liberation of glycans (Tateno et al., <xref rid=\"acel13190-bib-0039\" ref-type=\"ref\">2007</xref>).</p><p>In our current study, we performed a comprehensive glycome analysis of IFE and HF stem cells in the old mouse skin by using lectin microarray consisting of 96 lectins with various glycan‐binding specificities (Tateno et al., <xref rid=\"acel13190-bib-0038\" ref-type=\"ref\">2011</xref>). We found that epidermal stem cells undergo global changes in their glycosylation patterns during aging, with decreased mannose and increased sialic acid (Sia) modifications. By overexpressing glycogenes in vitro, we recapitulated the old‐type glycome patterns in epidermal stem cells, which led to a decline in the proliferation capacity. We thus propose functional implications of glycans in stem cell regulation.</p></sec><sec sec-type=\"results\" id=\"acel13190-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13190-sec-0003\"><label>2.1</label><title>Distinct glycosylation patterns between young and old epidermal stem cells</title><p>To analyze the glycosylation state of epidermal stem cells during aging, IFE and HF stem cells were isolated from wild‐type C57BL/6 mice at 2 months (young, <italic>N = 4)</italic> and 22‐24 months (old, <italic>N</italic> = 3) of age and subjected to lectin microarray (Figure <xref rid=\"acel13190-fig-0001\" ref-type=\"fig\">1a</xref>). IFE stem cells (α6‐integrin+/CD34−/Sca1+) and HF stem cells (α6‐integrin+/CD34+) were separated by flow cytometry based on their differential expression of cell surface markers (Figure <xref rid=\"acel13190-fig-0001\" ref-type=\"fig\">1b,c</xref>). In old mouse skin, we detected significantly lower number of HF stem cells as previously reported (Matsumura et al., <xref rid=\"acel13190-bib-0028\" ref-type=\"ref\">2016</xref>), whereas the number of IFE stem cells remained unchanged (Figure S1). We asked whether their cell surface glycans were affected by aging. The hierarchical clustering of lectin microarray data showed that young and old samples were clustered separately, indicating their distinct glycosylation patterns (Figure <xref rid=\"acel13190-fig-0002\" ref-type=\"fig\">2a</xref>). Stem cells in different epidermal compartments, the IFE and HF, also showed differential glycosylation patterns, compatible with their distinct transcriptome signatures (Joost et al., <xref rid=\"acel13190-bib-0019\" ref-type=\"ref\">2016</xref>). To further examine the correlation of each cell population, we performed a principal component analysis (PCA) to dissect the similarity or differentiates among the samples. Young and old epidermal stem cells were separated by the biplot of principal components 1 and 2 (Figure <xref rid=\"acel13190-fig-0002\" ref-type=\"fig\">2b</xref>), supporting their differential lectin patterns. Thus, these data suggest that mouse IFE and HF stem cells undergo global alterations in glycosylation during aging.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13190-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Schematic representation of lectin microarray using freshly isolated epidermal stem cells. (a) Schematic representation of lectin microarray analysis. Hydrophobic fractions containing membrane proteins are isolated, fluorescently labeled, and incubated with lectin microarray, in which 96 lectins are immobilized on glass slides. The lectin–glycan interactions are measured and quantified as signal intensities obtained from each lectin spot. (b) A schematic representation of epidermal cell types in mouse skin and cell surface markers used. (c) Flow cytometry dot plot and sorting gates for the isolation of skin epidermal subpopulations. Interfollicular epidermal (IFE) stem cells are defined as ⍺6‐integrin+/CD34−/Sca1+, and hair follicles (HF) stem cells are defined as ⍺6‐integrin+/CD34+.</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13190-g001\"/></fig><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13190-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>Glycome analysis of young and old epidermal stem cells. (a) Heat map and hierarchical clustering of lectin microarray signals. Each row represents different stem cell populations isolated from an individual mouse (<italic>N</italic> = 4 for young mice, <italic>N</italic> = 3 for old mice). Ninety‐six lectins are shown on columns. (b) Principal component analysis of the mean normalized signals obtained from lectin microarray. A scatter plot for principal component (PC) 1 and 2 is shown. Each dot represents the sample derived from an individual mouse. Different cell types are indicated by color.</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13190-g002\"/></fig></sec><sec id=\"acel13190-sec-0004\"><label>2.2</label><title>Classes of lectins that differentially identified glycans in young and old stem cells</title><p>For the identification of lectins that were differentially bound to glycan structures between young and old stem cells, statistical analysis was performed using the mean normalized signals obtained from lectin microarray. Several classes of lectins were significantly changed (<italic>p</italic> < 0.01) between young and old stem cells (Figure <xref rid=\"acel13190-fig-0003\" ref-type=\"fig\">3</xref> and Table <xref rid=\"acel13190-sup-0003\" ref-type=\"supplementary-material\">S1</xref>), and we categorized them based on their glycan‐binding specificities.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13190-fig-0003\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>List of lectins significantly changed between young and old epidermal stem cells. (a, b) Lectins bound differentially to the young or old interfollicular epidermis (IFE) (a) and hair follicles (HF) (b). Statistically significant differences are calculated by unpaired Student's <italic>t</italic> test and <italic>p</italic> < 0.01 are selected. Lectins are categorized based on their binding specificities. Data are shown with <italic>t</italic>‐values. Also, see Table <xref rid=\"acel13190-sup-0003\" ref-type=\"supplementary-material\">S1</xref>.</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13190-g003\"/></fig><p>In the IFE, 13 lectins were significantly higher in young stem cells, whereas 12 lectins were higher in old stem cells (Figure <xref rid=\"acel13190-fig-0003\" ref-type=\"fig\">3a</xref> and Table <xref rid=\"acel13190-sup-0003\" ref-type=\"supplementary-material\">S1</xref>). The lectins enriched in young IFE stem cells included mannose‐binding lectins (NPA, GNA, DBAI, rHeltuba, Heltuba) (Maupin, Liden, & Haab, <xref rid=\"acel13190-bib-0029\" ref-type=\"ref\">2012</xref>; Tateno et al., <xref rid=\"acel13190-bib-0038\" ref-type=\"ref\">2011</xref>), fucose‐binding lectins (AAL, rAAL, rBC2LCN, rAOL) and O‐glycan (Tn)‐binding lectins (HPA, DBAIII). Consistently, it has been shown that high mannose‐type N‐glycans were highly enriched in human embryonic stem cells (An et al., <xref rid=\"acel13190-bib-0003\" ref-type=\"ref\">2012</xref>), with a possible role in the maintenance of stemness. The fucose (α1‐2)‐binding rBC2LCN has previously been identified as a lectin biomarker for undifferentiated pluripotent stem cells (Tateno et al., <xref rid=\"acel13190-bib-0038\" ref-type=\"ref\">2011</xref>).</p><p>In contrast, the lectins enriched in old IFE stem cells included Sia‐binding lectins (ACG, rACG, rGal8N) (Itakura et al., <xref rid=\"acel13190-bib-0018\" ref-type=\"ref\">2016</xref>; Sasaki, Itakura, & Toyoda, <xref rid=\"acel13190-bib-0035\" ref-type=\"ref\">2017</xref>), fucose (α1‐6)‐binding lectins (rPTL and LCA) and O‐glycan (T antigen) binding lectins (MPA, Jacalin and HEA) (Figure <xref rid=\"acel13190-fig-0003\" ref-type=\"fig\">3a</xref>). Since sialylation has been implicated in the aging of muscle and fibroblasts (Hanisch et al., <xref rid=\"acel13190-bib-0014\" ref-type=\"ref\">2013</xref>; Itakura et al., <xref rid=\"acel13190-bib-0018\" ref-type=\"ref\">2016</xref>; Sasaki et al., <xref rid=\"acel13190-bib-0035\" ref-type=\"ref\">2017</xref>), it might have an universal role in the process of aging. In HFs, 11 and 15 lectins showed significant enrichment in young and old stem cells, respectively (Figure <xref rid=\"acel13190-fig-0003\" ref-type=\"fig\">3b</xref> and Table <xref rid=\"acel13190-sup-0003\" ref-type=\"supplementary-material\">S1</xref>). Notably, lectins of similar functional classes were detected with significant differences in both IFE and HFs. Taken together, our lectin microarray analysis identified common sets of lectins that recognize age‐dependent glycan changes in IFE and HF stem cells: decreased mannose‐binding lectins and increased Sia‐binding lectins during aging.</p></sec><sec id=\"acel13190-sec-0005\"><label>2.3</label><title>Old epidermal stem cells display decreased mannose and increased Sia modifications</title><p>To detect age‐related glycan changes in epidermal stem cells, a mannose‐binding rHeltuba and an α2‐3Sia‐binding rGal8N were selected as recombinant lectin probes for further analysis. Quantification of signals in lectin microarray confirmed significantly higher signals of rHeltuba in young stem cells compared with old stem cells both in the IFE and HFs (Figure <xref rid=\"acel13190-fig-0004\" ref-type=\"fig\">4a</xref>). In contrast, rGal8N showed higher signals in old stem cells than young stem cells (Figure <xref rid=\"acel13190-fig-0004\" ref-type=\"fig\">4b</xref>). These results were validated by lectin blotting. One microgram of membrane proteins was separated by SDS‐PAGE and blotted with two lectins conjugated with horseradish peroxidase (HRP). The lectin blotting using rHeltuba showed decreased signals in old IFE and HF stem cells compared with young counterparts (Figure <xref rid=\"acel13190-fig-0004\" ref-type=\"fig\">4c</xref>), consistent with lectin microarray results. The major bands were detected at 80 and 110 kDa in young IFE, and at 45 and 60 kDa in young HF in addition to 80 and 110 kDa bands, suggesting the mannose modification in multiple proteins (Figure <xref rid=\"acel13190-fig-0004\" ref-type=\"fig\">4c</xref>). In contrast, rGal8N showed higher signals in old stem cells compared with young stem cells and major bands around 60 and 80 kDa were detected (Figure <xref rid=\"acel13190-fig-0004\" ref-type=\"fig\">4d</xref>). Hence, the identified lectins, rHeltuba and rGal8N, successfully detected distinct glycosylation between young and old epidermal stem cells.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13190-fig-0004\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>Detection of young and old epidermal stem cells by rHeltuba and rGal8N lectins. (a, b) Signal intensities of rHeltuba (Manα1‐3Man, Manα1‐6Man) (a) and rGal8N (α2‐3Sia) (b) in the lectin microarray are shown. The lectin signals of individual mice are averaged and normalized to the average of 96 lectins. <italic>N</italic> = 4 for young mice, <italic>N</italic> = 3 for old mice. Data are shown as means ±<italic>SD</italic>. Student's <italic>t</italic> test. ***<italic>p</italic> < 0.001. **<italic>p</italic> < 0.01. *<italic>p</italic> < 0.05. (c, d) Lectin blotting using the horseradish peroxidase (HRP)‐labeled lectins, rHeltuba (c) and rGal8N (d). Young and old stem cells in the interfollicular epidermis (IFE) and hair follicles are used. <italic>N</italic> = 4 for young mice, <italic>N</italic> = 3 for old mice. One microgram of protein from a single mouse is applied in each lane. The signal intensities of bands with indicated size are quantified.</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13190-g004\"/></fig></sec><sec id=\"acel13190-sec-0006\"><label>2.4</label><title>Detection of age‐related glycan changes in epidermal stem cells by flow cytometry using rHeltuba and rGal8N</title><p>To test the ability of lectin‐directed detection of glycans in living stem cells, we employed flow cytometry analysis in young and old stem cells using rHeltuba and rGal8N. Fluorescent‐labeled lectins (rHeltuba and rGal8N) were incubated with freshly isolated epidermal stem cells from wild‐type skin at 2 months (young, <italic>N</italic> = 3) or 22‐24 months (old, <italic>N</italic> = 3) of age. Flow cytometry analysis using rHeltuba showed a higher peak of signals in young stem cells compared with old stem cells in both IFE and HFs (Figure <xref rid=\"acel13190-fig-0005\" ref-type=\"fig\">5a</xref>, upper graphs). Statistical analysis of the mean fluorescence intensity of rHeltuba signals showed a significant difference between young and old HF stem cells (Figure <xref rid=\"acel13190-fig-0005\" ref-type=\"fig\">5c</xref>). To verify the specificity of rHeltuba binding to stem cells, competition assays were performed by adding excess mannose during incubation of the lectin with epidermal stem cells. Indeed, the rHeltuba signals in both young and old epidermal stem cells were abrogated in the presence of mannose (Figure <xref rid=\"acel13190-fig-0005\" ref-type=\"fig\">5a</xref>, lower graphs), confirming that the lectin detected mannose modifications on the surface of stem cells.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13190-fig-0005\" orientation=\"portrait\" position=\"float\"><label>FIGURE 5</label><caption><p>The rHeltuba and rGal8N lectins differentially bind to freshly isolated young and old stem cells. (a, b) The histogram shows signal intensities of PE‐labeled rHeltuba (1 µg/ml) (a) or rGal8N (10 µg/ml) (b) in the interfollicular epidermis (IFE) and hair follicles (HF) detected by flow cytometry. <italic>N</italic> = 3 for young and old mice. Data from each mouse are shown as an individual line. For inhibition of rHeltuba and rGal8N, 0.1 M mannose or 0.1 M lactose is used, respectively. (c, d) Quantification of mean fluorescence intensity obtained by flow cytometry. Data are shown as means ± <italic>SD</italic>. Statistical analysis is performed using the unpaired Student's <italic>t</italic> test. **<italic>p</italic> < 0.01. *<italic>p</italic> < 0.05. ns: not significant; rGal8N in HF stem cells, <italic>p</italic> = 0.1412.</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13190-g005\"/></fig><p>Similar flow cytometry experiments with rGal8N, the α2‐3Sia‐binding lectin, in the IFE showed a shift toward the higher signal intensity in old stem cells compared with young stem cells (Figure <xref rid=\"acel13190-fig-0005\" ref-type=\"fig\">5b</xref>, upper left, and <xref rid=\"acel13190-fig-0005\" ref-type=\"fig\">5</xref>d, left). The signals of rGal8N were inhibited by lactose, confirming the specific binding of rGal8N to glycans. In the HFs, however, there were no significant differences in the rGal8N signal intensity between young and old stem cells (Figure <xref rid=\"acel13190-fig-0005\" ref-type=\"fig\">5b</xref>, upper right, and <xref rid=\"acel13190-fig-0005\" ref-type=\"fig\">5</xref>d, right). One possible interpretation is that glycolipids, which content may differ between HF and IFE stem cells, had been detected by rGal8N in live HF cells and masked the difference between young and old HF stem cells. Taken together, these data indicate that both rHeltuba and rGal8N lectin probes successfully detected glycan changes in freshly isolated IFE stem cells by flow cytometry.</p></sec><sec id=\"acel13190-sec-0007\"><label>2.5</label><title>Upregulation of sialyltransferase and mannosidase genes in old epidermal stem cells</title><p>To address which enzymes are responsible for age‐related glycosylation changes in epidermal stem cells, we performed gene expression analysis using RT<sup>2</sup> profiler PCR array of mouse glycosylation‐related genes. RNAs isolated from IFE stem cells at 2 months (young, <italic>N</italic> = 3) or 22‐24 months (old, <italic>N</italic> = 3) of age were used for quantitative PCR. Among 84 genes involved in the glycosylation pathway, 14 genes were ≥1.5 fold upregulated in old IFE stem cells, whereas 3 genes were ≥1.5 fold downregulated (Figure <xref rid=\"acel13190-fig-0006\" ref-type=\"fig\">6a</xref> and Table <xref rid=\"acel13190-sup-0004\" ref-type=\"supplementary-material\">S2</xref>). Among them, five genes were identified with statistically significant differences (<italic>p</italic> < 0.05). Sialyltransferase genes (<italic>St3gal2</italic> and <italic>St6gal1</italic>) were upregulated in old IFE stem cells (Figure <xref rid=\"acel13190-fig-0006\" ref-type=\"fig\">6a,b</xref>), consistent with our lectin microarray (Figure <xref rid=\"acel13190-fig-0003\" ref-type=\"fig\">3</xref>). St3gal2 catalyzes the transfer of Sia from cyclic monophosphate‐Sia to β‐galactosides and forms α‐2,3 sialylated glycoconjugates (Varki, <xref rid=\"acel13190-bib-0040\" ref-type=\"ref\">2009</xref>). Similarly, St6gal1 catalyzes the addition of Sia to a galactose‐containing substrate and form α‐2,6 sialylated glycoconjugates (Varki, <xref rid=\"acel13190-bib-0040\" ref-type=\"ref\">2009</xref>). We also found that mannosidase gene <italic>Man1a</italic> was increased in old stem cells (Figure <xref rid=\"acel13190-fig-0006\" ref-type=\"fig\">6b</xref>). Man1a is an α‐1,2 mannosidase and is responsible for the removal of mannose residues to initiate the complex‐type N‐glycan formation (Varki, <xref rid=\"acel13190-bib-0040\" ref-type=\"ref\">2009</xref>), which matches with the decreased signals of mannose‐binding lectins in old IFE stem cells (Figure <xref rid=\"acel13190-fig-0003\" ref-type=\"fig\">3</xref>). Similarly, we also found an increased expression of <italic>Man1a</italic>, <italic>St3gal2</italic>, <italic>St6gal1</italic> in the old HF stem cells (Figure S2 and Table <xref rid=\"acel13190-sup-0004\" ref-type=\"supplementary-material\">S2</xref>). Thus, glycan changes of epidermal stem cells during aging are possibly mediated by the changes in glycosyltransferase and glycosidase expressions with age.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13190-fig-0006\" orientation=\"portrait\" position=\"float\"><label>FIGURE 6</label><caption><p>Gene expression analysis of glycosylation‐related genes using RT<sup>2</sup> Profiler PCR array. (a) The volcano plot represents fold change and <italic>p</italic>‐values on <italic>x</italic>‐ and <italic>y</italic>‐axis, respectively. The vertical red and blue lines represent a fold‐change cutoff of ≥1.5. <italic>N</italic> = 3 for young mice, <italic>N</italic> = 3 for old mice. Also, see Table <xref rid=\"acel13190-sup-0004\" ref-type=\"supplementary-material\">S2</xref>. (b) Lists of differentially expressed sialyltransferase and mannosidase genes. (c) Schematic representation of the putative glycan changes during epidermal stem cell aging.</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13190-g006\"/></fig></sec><sec id=\"acel13190-sec-0008\"><label>2.6</label><title>Recapitulation of old‐type glycosylation pattern by overexpressing glycogenes causes functional impairment of epidermal stem cells in vitro</title><p>Finally, we addressed whether age‐related glycan changes are a consequence of aging or causal to induce age‐related phenotypes in epidermal stem cells. To mimic the glycosylation pattern of old epidermal stem cells, we overexpressed three glycogenes (<italic>Man1a</italic>,<italic> St3gal2</italic>,<italic> St6gal1</italic>) in primary epidermal keratinocytes, an in vitro model of epidermal stem cells, and modified cell surface glycans to aging‐like status (Figure <xref rid=\"acel13190-fig-0007\" ref-type=\"fig\">7a</xref>). Successful gene overexpression and changes of glycosylation were evaluated by qRT‐PCR (Figure <xref rid=\"acel13190-fig-0007\" ref-type=\"fig\">7b</xref>) and lectin blotting (Figure <xref rid=\"acel13190-fig-0007\" ref-type=\"fig\">7c,d</xref>). The keratinocytes showed decreased mannose and increased Sia modifications (Figure <xref rid=\"acel13190-fig-0007\" ref-type=\"fig\">7c,d</xref>), which are similar to the glycosylation pattern of old epidermal stem cells in vivo (Figure <xref rid=\"acel13190-fig-0004\" ref-type=\"fig\">4</xref>). Overexpression of three glycogenes resulted in significantly less ability to proliferate as compared to the control keratinocytes infected with EGFP, and detached from a dish within 5 days of culture (Figure <xref rid=\"acel13190-fig-0007\" ref-type=\"fig\">7e,f</xref>). The overexpression of <italic>Man1a</italic> alone showed milder effects than <italic>St3gal2</italic> or <italic>St6gal1</italic> alone (Figure <xref rid=\"acel13190-fig-0007\" ref-type=\"fig\">7f</xref>). These data indicate that age‐related glycan changes may in part be responsible for a decline in the proliferation ability of epidermal stem cells during aging.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13190-fig-0007\" orientation=\"portrait\" position=\"float\"><label>FIGURE 7</label><caption><p>Aging‐associated glycogene overexpression leads to an impaired keratinocyte growth. (a) Scheme of the glycogene overexpression using the lentivirus system. (b) The qRT‐PCR of <italic>Man1a</italic>,<italic> St3gal2</italic>,<italic> St6gal1</italic> mRNA expression at 4 days after blasticidin selection (<italic>N</italic> = 3). Lenti‐EGFP is used as a control. Data are shown as means ± <italic>SD</italic>. Mann–Whitney test. *<italic>p</italic> < 0.05. (c, d) Confirmation of glycan changes by lectin blotting using the horseradish peroxidase (HRP)‐labeled lectins, rHeltuba (c) and rGal8N (d). One microgram of protein from three independent experiments is applied on each lane. Data are shown as means ± <italic>SD</italic>. Students <italic>t</italic> test. ***<italic>p</italic> < 0.001. **<italic>p</italic> < 0.01. *<italic>p</italic> < 0.05. The signal intensities of bands with indicated size are quantified. (e) Proliferation assay of primary keratinocytes after overexpressing glycogenes. The <italic>x</italic>‐axis represents the time points, and the <italic>y</italic>‐axis represents the absorbance at 450 nm. Absorbance is measured at 0, 1, 3, and 5 days post‐infection. Data are shown as means ± <italic>SD</italic>. Students <italic>t</italic> test. ***<italic>p</italic> < 0.001. **<italic>p</italic> < 0.01. *<italic>p</italic> < 0.05. (f) Representative images of the primary keratinocytes infected with lenti‐EGFP, or a combination or single lenti‐<italic>Man1a</italic>, ‐<italic>St3gal2</italic>, and ‐<italic>St6gal1</italic> at day 0 and 5.</p></caption><graphic id=\"nlm-graphic-15\" xlink:href=\"ACEL-19-e13190-g007\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"acel13190-sec-0009\"><label>3</label><title>DISCUSSION</title><p>In vivo sign of aging in the skin can be observed at the tissue and organismal levels; however, the molecular aspects of aging at the stem cell level remains elusive. In our current study, we performed a high‐throughput lectin‐based glycan profiling on murine epidermal stem cells and revealed their dynamic glycan alterations during aging. We propose a concept, “glycome shift” as a new molecular factor of epidermal stem cell aging (Figure <xref rid=\"acel13190-fig-0006\" ref-type=\"fig\">6c</xref>): high mannose‐type N‐glycans are globally replaced by α2‐3/6 sialylated complex‐type N‐glycans with age. Intriguingly, overexpression of three glycogene(s) (<italic>Man1a</italic>,<italic> St3gal2</italic>,<italic> St6gal1</italic>) recapitulated the aging glycan patterns and impaired the growth of primary keratinocytes, suggesting that the glycans could be one of the drivers of age‐related decline in the proliferation ability of epidermal stem cells. The identified lectins, the mannose‐binding rHeltuba and the α2‐3Sia‐binding rGal8N can be used as probes to visualize, select, or remove aged stem cells, with implications in future applications for regenerative therapy and diagnosis of skin aging. We also provide a proof of concept that our lectin microarray platform (Tateno et al., <xref rid=\"acel13190-bib-0038\" ref-type=\"ref\">2011</xref>) can successfully analyze the glycome of adult tissue stem cells, which are rare in tissue (≤1% of total skin cells) and their biochemical properties are not well‐characterized due to technical difficulties.</p><p>As glycosylation plays a critical role in cell–cell and cell–matrix interactions, the changes in glycans on the surface of epidermal stem cells might affect their ability to interact with neighboring stem cells, other cell types (e.g., fibroblasts, immune cells, and blood vessels), basement membrane and signaling molecules, all of which are essential components for maintaining the skin integrity. It will be interesting in the future to identify core proteins in which differential glycosylation takes place and to reveal the functional importance and biological meaning of glycosylation in age‐related skin dysfunction.</p><p>An aged skin exhibits declined wound healing ability, which is in part caused by impaired crosstalk between epidermal stem cells and dendritic epithelial T cells (Keyes et al., <xref rid=\"acel13190-bib-0020\" ref-type=\"ref\">2016</xref>). Given that several immune cells, including dendritic cells, have mannose‐binding receptors in the epidermis (Wollenberg et al., <xref rid=\"acel13190-bib-0045\" ref-type=\"ref\">2002</xref>), the decreased mannose in old IFE stem cells that we observed here (Figure <xref rid=\"acel13190-fig-0006\" ref-type=\"fig\">6c</xref>) could be associated with the defective stem cell–immune cell interaction in aged skin.</p><p>Our study showed an increase in α2‐3 and α2‐6 sialylation along with the expression of the corresponding sialyltransferase (<italic>St3gal2</italic> and <italic>St6gal1</italic>) in old IFE stem cells (Figure <xref rid=\"acel13190-fig-0006\" ref-type=\"fig\">6c</xref>). In agreement with our findings, sialylation was reported to be increased in the aged mouse muscle (Hanisch et al., <xref rid=\"acel13190-bib-0014\" ref-type=\"ref\">2013</xref>). The upregulation of sialyltransferases has also been suggested as a potential aging marker in human, which shows a higher activity of <italic>St6gal1</italic> in the plasma of individuals above 80 years of age (Catera et al., <xref rid=\"acel13190-bib-0004\" ref-type=\"ref\">2016</xref>). In addition, an α2‐6 sialylation and the expression of <italic>St6gal1</italic> were upregulated during epithelial to mesenchymal transition and tumor formation (Lu et al., <xref rid=\"acel13190-bib-0026\" ref-type=\"ref\">2014</xref>; Swindall et al., <xref rid=\"acel13190-bib-0037\" ref-type=\"ref\">2013</xref>). By contrast, α2‐3/6 sialylation was reported to be decreased during senescence and aging of human dermal fibroblasts (Itakura et al., <xref rid=\"acel13190-bib-0018\" ref-type=\"ref\">2016</xref>). In human pluripotent or mesenchymal stem cells, a higher sialylation is associated with a greater potential of stem cells (Hasehira et al., <xref rid=\"acel13190-bib-0015\" ref-type=\"ref\">2012</xref>; Tateno et al., <xref rid=\"acel13190-bib-0038\" ref-type=\"ref\">2011</xref>; Wang et al., <xref rid=\"acel13190-bib-0042\" ref-type=\"ref\">2015</xref>). The observed differences in the sialylation patterns might be due to the differences in cell types, species, or target proteins, indicating a diverse role of sialylation in the process of aging. Future studies using conditional knock‐out or overexpression of differentially expressed glycosyltransferases in the mouse epidermis will directly address the role of sialylation in the context of epidermal stem cell aging.</p></sec><sec id=\"acel13190-sec-0010\"><label>4</label><title>EXPERIMENTAL PROCEDURES</title><sec id=\"acel13190-sec-0011\"><label>4.1</label><title>Mice</title><p>All animal procedures were conducted following animal experimentation guidelines approved by the Institutional Animal Experiment Committee at the University of Tsukuba. Young (2‐month‐old) and old (22‐24‐month‐old) C57BL/6 mice were purchased from Charles River Laboratories or Japan SLC. Both male and female mice were used for experiments. All the experimental mice were housed in Laboratory Animal Resource Center, University of Tsukuba prior to experiments.</p></sec><sec id=\"acel13190-sec-0012\"><label>4.2</label><title>Isolation of epidermal stem cells by flow cytometry</title><p>Mouse dorsal and ventral skin were dissected and the subcutaneous and fat tissues were removed from the dermal side of the skin. The skin was incubated in 0.25% trypsin/versene overnight at 4°C and for 30 min at 37°C. The single‐cell solution was prepared by scraping the epidermis and subsequent filtering with strainers (70 μm, followed by 40 μm). Cells were stained with the following antibodies for 30 min on ice: CD34‐biotin (1:50, eBioscience), Streptavidin‐APC (1:100, BD Biosciences), α6‐integrin‐BUV395 (1:100, BD Biosciences, custom order) and Sca1‐BV421 (1:100, BD Biosciences). The dead cells were excluded by staining with propidium iodide (Sigma‐Aldrich). Cell isolation was performed with FACS Aria (BD Biosciences), and the data were analyzed with the FlowJo software (BD, Franklin Lakes, NJ).</p></sec><sec id=\"acel13190-sec-0013\"><label>4.3</label><title>Membrane protein isolation and quantification</title><p>Hydrophobic fractions containing membrane proteins were prepared using the CelLytic MEM Protein Extraction kit (Sigma‐Aldrich) following the manufacturer's protocols. Proteins were quantified using a micro BCA assay kit (Thermo Fisher Scientific). Protein amounts ranging from 15 to 30 μg were obtained from 100,000–300,000 IFE or HF stem cells.</p></sec><sec id=\"acel13190-sec-0014\"><label>4.4</label><title>Lectin microarray</title><p>The high‐density lectin microarray was produced according to the method previously described (Tateno et al., <xref rid=\"acel13190-bib-0038\" ref-type=\"ref\">2011</xref>). The protein concentration was adjusted to 2 μg/ml with PBST [10 mM PBS (pH 7.4), 140 mM NaCl, 2.7 mM KCl, 1% Triton X‐100] and was labeled with Cy3‐N‐hydroxysuccinimide ester (GE Healthcare). Cy3‐labeled proteins were diluted with probing buffer [25 mM Tris‐HCl (pH 7.5), 140 mM NaCl, 2.7 mM KCl, 1 mM CaCl<sub>2</sub>, 1 mM MnCl<sub>2</sub>, and 1% Triton X‐100] to 0.5 μg/ml and were incubated with the lectin microarray at 20°C overnight. Samples were washed with probing buffer for three times, and fluorescence images were captured using a Bio‐Rex scan 200 evanescent‐field‐activated fluorescence scanner (Rexxam Co. Ltd.).</p><p>The obtained signals were mean‐normalized and subjected to unsupervised hierarchical clustering, followed by a heat map analysis. The lectin signals of triplicate spots were averaged for each sample and normalized relative to the mean value of 96 lectins. The mean normalized lectin microarray data were used for unsupervised clustering with the average linkage method using Cluster 3.0 software. The heat map with clustering was visualized using Java TreeView. Significant differences in lectin intensity were calculated by unpaired Student's <italic>t</italic> test. The principal component analysis was performed by using the mean normalized signals and was generated using IBM SPSS Statistics software (IBM Japan, Ltd.). Principal component analysis (PCA) was run on the mean normalized intensity of the 96 lectins of the lectin microarray obtained from the different populations of epidermal stem cells from young (<italic>N</italic> = 4) and old (<italic>N</italic> = 3) mouse samples. After which, a biplot graph was plotted using the first two components.</p></sec><sec id=\"acel13190-sec-0015\"><label>4.5</label><title>Lectin blotting</title><p>Recombinant lectins (rHeltuba, rGal8N) were prepared using <italic>Escherichia coli</italic> as previously described (Tateno et al., <xref rid=\"acel13190-bib-0038\" ref-type=\"ref\">2011</xref>). Lectins were conjugated with HRP by using HRP labeling kit (Dojindo, Rockville, MD) at the concentration of 0.5 mg/ml and adjusted to the final concentration for incubation at 0.1 μg/ml.</p><p>One microgram of proteins from each cell population was separated by SDS‐PAGE on a 5‐20% gel (Perfect NT Gel system, NTH‐676HP, DRC, Tokyo, Japan) and transferred onto polyvinylidene fluoride membranes (Millipore, Burlington, MA). After blocking the membrane in Carbo‐Free blocking solution (Vector Laboratories, Burlingame, CA) for 1 hr at room temperature, it was incubated with HRP‐conjugated lectins overnight at 4°C. The signals were detected by using Western Lighting Plus (NEL104001EA, PerkinElmer). Lectin blot intensities were quantified using ImageJ software (National Institute of Health). The high‐intensity band was selected for quantification. Statistical significance was calculated by unpaired Student's <italic>t</italic> test (GraphPad Prism8 software).</p></sec><sec id=\"acel13190-sec-0016\"><label>4.6</label><title>Detection of lectin binding to epidermal stem cells by flow cytometry</title><p>Recombinant lectins (rHeltuba, rGal8N) were labeled with R‐Phycoerythrin (PE) using Phycoerythrin Labeling Kit ‐ NH2 (Dojindo) according to the manufacturer's protocol. The single‐cell solution was prepared as described above and resuspended in 1% BSA (Sigma‐Aldrich, A3059) without using the serum. Cells were stained with Lectin‐PE for 1 hr at 4°C, at the following concentrations: 1 μg/ml for rHeltuba‐PE and 10 μg/ml for rGal8N‐PE. For the inhibition assays, 0.1 M D‐(+)‐Mannose (Sigma‐Aldrich, M2069) and 0.1 M lactose monohydrate sugar (Wako Pure Chemical Industries, 121‐00105, Ltd) were used. After washing, cells were stained with antibodies for 30 min on ice and analyzed by FACS Aria (BD Biosciences). Student's <italic>t</italic> tests were performed to compare lectin signal intensity of young versus old stem cells by using GraphPad Prism8 software.</p></sec><sec id=\"acel13190-sec-0017\"><label>4.7</label><title>RT<sup>2</sup> profiler mouse glycosylation PCR arrays</title><p>Total RNAs were isolated using the RNeasy micro kit (QIAGEN), according to the manufacturer's protocol. The integrity of the isolated RNA was assessed by using RNA Pico Chips and Agilent 2100 bioanalyzer (Agilent Technologies). RNA samples with the RNA integrity number above 8 were used for further analysis. The cDNA from IFE and HF stem cells were synthesized from 50 and 5 ng of mRNA, respectively, using the RT<sup>2</sup> PreAMP cDNA Synthesis Kit (QIAGEN, 330451) followed by pre‐amplification using the pathway‐specific primer mix for mouse glycosylation (QIAGEN, PBM‐046Z).</p><p>The relative mRNA expressions of 84 genes regulating mouse glycosylation were analyzed using RT<sup>2</sup> Profiler™ PCR Arrays (QIAGEN, PAMM‐046Z) according to the manufacturer's instructions. The cDNA template prepared above was mixed with RT<sup>2</sup> SYBR Green qPCR Master Mix (QIAGEN, 330501) and nuclease‐free water. The cDNA mixture of 25 μl was applied to each well of the PCR arrays that contain the preloaded primer mix for each gene. The real‐time PCR amplification and detection were performed using a Bio‐Rad CFX96 Touch™ Real‐Time PCR Detection System (Bio‐Rad). Amplification cycle was used as following: activation of DNA Taq polymerase at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 s and annealing for 1 min at 60°C. The threshold cycle (C<sub>t</sub>) was used for PCR array quantification. The threshold values were set similarly across all the PCR array used in the analysis, and the baseline was defined by using the automated baseline option of the machine. Gene whose C<sub>t</sub> cycle was more than 35 was set as undetectable. C<sub>t</sub> values of biological replicates obtained from the real‐time PCR array analysis were used for the ∆∆ C<sub>t</sub> –based fold‐change calculations. For the data analysis, web‐based PCR data analysis provided from the data analysis center of QIAGEN was used. Samples were normalized using automatic normalization from the five housekeeping genes (<italic>Actb</italic>,<italic> B2m</italic>,<italic> Gapdh</italic>,<italic> Gusb</italic>,<italic> Hsp90ab1</italic>) in the PCR array. An appropriate correction was also made during the web‐based data analysis for the pre‐amplification step. Gene expression whose fold is greater than 1.5 was selected.</p></sec><sec id=\"acel13190-sec-0018\"><label>4.8</label><title>Mouse primary keratinocytes isolation and culture</title><p>Mouse primary keratinocytes were isolated from 2‐day‐old C57BL6/J mouse skin as previously reported (Lichti, Anders, & Yuspa, <xref rid=\"acel13190-bib-0023\" ref-type=\"ref\">2008</xref>). Keratinocytes were seeded on mitomycin‐treated, mouse embryonic fibroblasts and grown in low‐calcium E‐medium (15% chelex‐treated FBS, 0.05 mM CaCl<sub>2</sub>). Keratinocytes were used from passage 6‐13 for the subsequent analysis.</p></sec><sec id=\"acel13190-sec-0019\"><label>4.9</label><title>Lentivirus production and infection</title><p>Mouse cDNA encoding <italic>Man1a</italic>, <italic>St3gal2</italic>, <italic>St6gal1</italic>, or EGFP were cloned into CSII‐CMV‐MCS‐IRES2‐Bsd vector (RIKEN, Tsukuba, Japan) and transfected to 293 T cells using Lipofectamine 3000 (Thermo Fisher Scientific) together with packaging vectors pRSV‐Rev, pMD2.G and pMDLg/pRRE (Addgene). The medium containing lentivirus was collected on day two and three post‐transfection and concentrated using a Lenti‐X concentrator (Takara Bio).</p><p>Mouse keratinocytes were seeded at 50,000 cells in a 24‐well culture plate coated with collagen IV (Sigma). One day later, keratinocytes were infected with lentivirus along with 4 µg/mL polybrene for 16 hr. The 300 µl of medium containing 100 µl each of the glycogenes (<italic>Man1a</italic>,<italic> St3 Gal2</italic>,<italic> St6gal1</italic>) or 300 µl of medium containing lenti‐EGFP were used. The medium was changed after 16 hr and the infected keratinocytes were selected by using blasticidin at a concentration of 1 µg/ml.</p></sec><sec id=\"acel13190-sec-0020\"><label>4.10</label><title>qRT‐PCR</title><p>Mouse keratinocytes RNA was isolated using the RNeasy micro kit (QIAGEN) and real‐time RT‐PCR was performed using iTaq Universal SYBR green supermix (Bio‐Rad) with the following primers; <italic>Man1a</italic> forward: 5′‐GAGACCCAGTCTTTGCCGAA‐3′, <italic>Man1a</italic> reverse: 5′‐CGACACATGATGTTGACCCC‐3′, <italic>St3gal2</italic> forward: 5′‐CCTAATGTGGATTGCCAGCG‐3′, <italic>St3gal2</italic> reverse: 5′‐TCTGGACCTTCTCTTTGTCCA‐3′, <italic>St6gal1</italic> forward: 5′‐GGGCACAAAAACTACCATCCG‐3′, <italic>St6gal1 </italic>reverse: 5′‐TGATACCACTGCGGAATGTCT‐3′.</p></sec><sec id=\"acel13190-sec-0021\"><label>4.11</label><title>Cell proliferation assay</title><p>Keratinocyte proliferation was measured by using the Cell‐Counting Kit‐8 (CCK‐8, Dojindo, Japan) following the manufacturer's instructions. In brief, blasticidin‐selected keratinocytes were seeded in triplicate in a collagen‐IV‐coated flat‐bottom 96‐well plate at 2,000 cells/well. Keratinocytes were grown in the E‐medium and analyzed at 0, 1, 3, and 5 days after infection. Ten microliter of CCK‐8 reagent was incubated for 2 hr, and the absorption of the samples was measured at 450 nm using xMark microplate reader (Bio‐Rad).</p><p>For microscope analysis, blasticidin‐selected keratinocytes were seeded at 5,000 cells/well in a collagen‐IV‐coated 24‐well plate. Images were acquired by using the Evos FL cell imaging system (Thermo Fisher Scientific) at indicated time points.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13190-sec-0023\"><title>CONFLICT OF INTEREST</title><p>The authors declare no conflict of interest.</p></sec><sec id=\"acel13190-sec-0024\"><title>AUTHOR CONTRIBUTIONS</title><p>A.S. and H.Y. conceptualized the project. A.S. provided knowledge and techniques for stem cell analysis. H.T. provided knowledge and techniques of lectin analysis. A.S., H.T., L.O., and E.R. designed the experiments. L.O., G.C., A.S., E.R., and Y.X.N. performed experiments and analyzed the results. A.S., H.T., and H.Y. interpreted the results and supervised the project. L.O., A.S., H.T., and H.Y. wrote the manuscript. A.S. acquired funding.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13190-sup-0001\"><caption><p>Fig S1</p></caption><media xlink:href=\"ACEL-19-e13190-s001.tif\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13190-sup-0002\"><caption><p>Fig S2</p></caption><media xlink:href=\"ACEL-19-e13190-s002.tif\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13190-sup-0003\"><caption><p>Table S1</p></caption><media xlink:href=\"ACEL-19-e13190-s003.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13190-sup-0004\"><caption><p>Table S2</p></caption><media xlink:href=\"ACEL-19-e13190-s004.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13190-sec-0022\"><title>ACKNOWLEDGEMENTS</title><p>We thank Dr. J. Kobayashi (Hokkaido University) and Ms. K. Kawazoe for their critical discussion and technical assistance, and the Animal Resource Center at the University of Tsukuba for excellent mouse care. We would like to thank Dr. M. Kato, Dr. H. Suzuki, and Dr. Y. Watanabe (University of Tsukuba) for their help in lentivirus production. This work was supported by AMED‐PRIME, AMED (JP19gm6110016), The Nanotech Career‐up Alliance N.R.P to A.S., and research grants from the Mitsubishi Foundation, the Nakatomi Foundation, the Sumitomo Foundation and Hoyu Science Foundation to A.S.</p></ack><ref-list content-type=\"cited-references\" id=\"acel13190-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13190-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13190-cit-0001\">\n<string-name>\n<surname>Adams</surname>, <given-names>P. 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pub-id-type=\"pmc\">PMC7431823</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13183</article-id><article-id pub-id-type=\"publisher-id\">ACEL13183</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Liver osteopontin is required to prevent the progression of age‐related nonalcoholic fatty liver disease</article-title><alt-title alt-title-type=\"left-running-head\">GÓMEZ‐SANTOS et al.</alt-title></title-group><contrib-group><contrib id=\"acel13183-cr-0001\" contrib-type=\"author\"><name><surname>Gómez‐Santos</surname><given-names>Beatriz</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-6607-7973</contrib-id><xref ref-type=\"aff\" rid=\"acel13183-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0002\" contrib-type=\"author\"><name><surname>Saenz de Urturi</surname><given-names>Diego</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0003\" contrib-type=\"author\"><name><surname>Nuñez‐García</surname><given-names>Maitane</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0004\" contrib-type=\"author\"><name><surname>Gonzalez‐Romero</surname><given-names>Francisco</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0005\" contrib-type=\"author\"><name><surname>Buque</surname><given-names>Xabier</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0006\" contrib-type=\"author\"><name><surname>Aurrekoetxea</surname><given-names>Igor</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0007\" contrib-type=\"author\"><name><surname>Gutiérrez de Juan</surname><given-names>Virginia</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0008\" contrib-type=\"author\"><name><surname>Gonzalez‐Rellan</surname><given-names>Maria J.</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0004\">\n<sup>4</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0009\" contrib-type=\"author\"><name><surname>García‐Monzón</surname><given-names>Carmelo</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0006\">\n<sup>6</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0007\">\n<sup>7</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0010\" contrib-type=\"author\"><name><surname>González‐Rodríguez</surname><given-names>Águeda</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0006\">\n<sup>6</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0007\">\n<sup>7</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0011\" contrib-type=\"author\"><name><surname>Mosteiro</surname><given-names>Lorena</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0012\" contrib-type=\"author\"><name><surname>Errazti</surname><given-names>Gaizka</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0013\" contrib-type=\"author\"><name><surname>Mifsut</surname><given-names>Patricia</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0014\" contrib-type=\"author\"><name><surname>Gaztambide</surname><given-names>Sonia</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0015\" contrib-type=\"author\"><name><surname>Castaño</surname><given-names>Luis</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0016\" contrib-type=\"author\"><name><surname>Martin</surname><given-names>Cesar</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0008\">\n<sup>8</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0017\" contrib-type=\"author\"><name><surname>Nogueiras</surname><given-names>Rubén</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0004\">\n<sup>4</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0018\" contrib-type=\"author\"><name><surname>Martinez‐Chantar</surname><given-names>María L.</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0003\">\n<sup>3</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0007\">\n<sup>7</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0019\" contrib-type=\"author\"><name><surname>Syn</surname><given-names>Wing‐Kin</given-names></name><xref ref-type=\"aff\" rid=\"acel13183-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0009\">\n<sup>9</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0010\">\n<sup>10</sup>\n</xref></contrib><contrib id=\"acel13183-cr-0020\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Aspichueta</surname><given-names>Patricia</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-3553-1755</contrib-id><xref ref-type=\"aff\" rid=\"acel13183-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13183-aff-0002\">\n<sup>2</sup>\n</xref><address><email>patricia.aspichueta@ehu.eus</email></address></contrib></contrib-group><aff id=\"acel13183-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Department of Physiology</named-content>\n<named-content content-type=\"organisation-division\">Faculty of Medicine and Nursing</named-content>\n<institution>University of Basque Country UPV/EHU</institution>\n<city>Leioa</city>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13183-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Biocruces Bizkaia Health Research Institute</named-content>\n<institution>Cruces University Hospital</institution>\n<city>Barakaldo</city>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13183-aff-0003\">\n<label><sup>3</sup></label>\n<named-content content-type=\"organisation-division\">Liver Disease Lab, Center for Cooperative Research in Bioscience (CIC bioGUNE), Basque Research and Technology Alliance (BRTA)</named-content>\n<institution>e</institution>\n<city>Derio</city>\n<named-content content-type=\"country-part\">Bizkaia</named-content>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13183-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Department of Physiology</named-content>\n<named-content content-type=\"organisation-division\">CIMUS</named-content>\n<institution>University of Santiago de Compostela‐Instituto de Investigación Sanitaria</institution>\n<city>Santiago de Compostela</city>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13183-aff-0005\">\n<label><sup>5</sup></label>\n<institution>CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn)</institution>\n<city>Madrid</city>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13183-aff-0006\">\n<label><sup>6</sup></label>\n<named-content content-type=\"organisation-division\">Liver Research Unit</named-content>\n<named-content content-type=\"organisation-division\">Santa Cristina University Hospital</named-content>\n<institution>Instituto de Investigación Sanitaria Princesa</institution>\n<city>Madrid</city>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13183-aff-0007\">\n<label><sup>7</sup></label>\n<institution>Centro de investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Carlos III National Health Institute</institution>\n<city>Madrid</city>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13183-aff-0008\">\n<label><sup>8</sup></label>\n<named-content content-type=\"organisation-division\">Department of Biochemistry and Molecular Biology</named-content>\n<named-content content-type=\"organisation-division\">Biofisika Institute (UPV/EHU, CSIC)</named-content>\n<institution>UPV/EHU</institution>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13183-aff-0009\">\n<label><sup>9</sup></label>\n<named-content content-type=\"organisation-division\">Section of Gastroenterology</named-content>\n<named-content content-type=\"organisation-division\">Ralph H Johnson</named-content>\n<institution>VAMC</institution>\n<city>Charleston</city>\n<named-content content-type=\"country-part\">SC</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13183-aff-0010\">\n<label><sup>10</sup></label>\n<named-content content-type=\"organisation-division\">Division of Gastroenterology and Hepatology</named-content>\n<institution>Medical University of South Carolina</institution>\n<city>Charleston</city>\n<named-content content-type=\"country-part\">SC</named-content>\n<country country=\"US\">USA</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nPatricia Aspichueta, Department of Physiology, Faculty of Medicine and Nursing, University of the Basque Country UPV/EHU, Barrio Sarriena s/n, 48940 Leioa, Spain.<break/>\nEmail: <email>patricia.aspichueta@ehu.eus</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>07</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13183</elocation-id><history><date date-type=\"received\"><day>11</day><month>12</month><year>2019</year></date><date date-type=\"rev-recd\"><day>14</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>06</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13183.pdf\"/><abstract id=\"acel13183-abs-0001\"><title>Abstract</title><p>Osteopontin (OPN), a senescence‐associated secretory phenotype factor, is increased in patients with nonalcoholic fatty liver disease (NAFLD). Cellular senescence has been associated with age‐dependent hepatosteatosis. Thus, we investigated the role of OPN in the age‐related hepatosteatosis. For this, human serum samples, animal models of aging, and cell lines in which senescence was induced were used. Metabolic fluxes, lipid, and protein concentration were determined. Among individuals with a normal liver, we observed a positive correlation between serum OPN levels and increasing age. This correlation with age, however, was absent in patients with NAFLD. In wild‐type (WT) mice, serum and liver OPN were increased at 10 months old (m) along with liver p53 levels and remained elevated at 20m. Markers of liver senescence increased in association with synthesis and concentration of triglycerides (TG) in 10m OPN‐deficient (KO) hepatocytes when compared to WT hepatocytes. These changes in senescence and lipid metabolism in 10m OPN‐KO mice liver were associated with the decrease of 78 kDa glucose‐regulated protein (GRP78), induction of ER stress, and the increase in fatty acid synthase and CD36 levels. OPN deficiency in senescent cells also diminished GRP78, the accumulation of intracellular TG, and the increase in CD36 levels. In 20m mice, OPN loss led to increased liver fibrosis. Finally, we showed that OPN expression in vitro and in vivo was regulated by p53. In conclusion, OPN deficiency leads to earlier cellular senescence, ER stress, and TG accumulation during aging. The p53‐OPN axis is required to inhibit the onset of age‐related hepatosteatosis.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13183-abs-0002\"><p>OPN is a protective factor required to preserve liver health during aging. As OPN‐deficient mice become older, increased levels of senescence, ER stress, hepatosteatosis, DNA damage, fibrosis, and inflammation appear.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13183-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13183-g008.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13183-kwd-0001\">aging</kwd><kwd id=\"acel13183-kwd-0002\">lipid metabolism</kwd><kwd id=\"acel13183-kwd-0003\">nonalcoholic fatty liver disease</kwd><kwd id=\"acel13183-kwd-0004\">Osteopontin</kwd><kwd id=\"acel13183-kwd-0005\">p53</kwd><kwd id=\"acel13183-kwd-0006\">senescence</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>Ministerio de Economía y Competitividad </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100003329</institution-id></institution-wrap></funding-source><award-id>SAF2017‐87301‐R, MCIU/AEI/FEDER</award-id><award-id>UE RTI2018‐095134‐B‐100</award-id><award-id>MCIU/AEI/FEDER, UE RTI2018‐099413‐B‐I00, </award-id><award-id>ISCIII‐FEDER PI17/0053, ISCIII‐FEDER CP14/00181 and PI16/00823</award-id></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>Eusko Jaurlaritza IT971‐16, Xunta de Galicia (2015‐CP080 and 2016‐ PG057). </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100003086</institution-id></institution-wrap></funding-source></award-group></funding-group><counts><fig-count count=\"7\"/><table-count count=\"1\"/><page-count count=\"16\"/><word-count count=\"8937\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13183-cit-1001\">\n<string-name>\n<surname>Gómez‐Santos</surname>\n<given-names>B</given-names>\n</string-name>, <string-name>\n<surname>Saenz de Urturi</surname>\n<given-names>D</given-names>\n</string-name>, <string-name>\n<surname>Nuñez‐García</surname>\n<given-names>M</given-names>\n</string-name>, et al. <article-title>Liver osteopontin is required to prevent the progression of age‐related nonalcoholic fatty liver disease</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13183</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13183</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13183-body-0001\"><sec id=\"acel13183-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of liver disease in Western countries and includes a spectrum of disorders (Friedman, Neuschwander‐Tetri, Rinella, & Sanyal, <xref rid=\"acel13183-bib-0011\" ref-type=\"ref\">2018</xref>). Hepatosteatosis is the earliest stage, and it is caused by the accumulation of fat in the hepatocytes. Increased fatty acid (FA) uptake from the diet and/or peripheral tissues, increased lipogenesis, defects in lipid oxidation, and/or export to circulation in VLDL particles induce their aberrant accumulation that can lead, in some patients, to progression to nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and/or hepatocellular carcinoma (HCC) (Friedman et al., <xref rid=\"acel13183-bib-0011\" ref-type=\"ref\">2018</xref>).</p><p>Epidemiological studies have demonstrated that NAFLD and NASH are common among the elderly (Bertolotti et al., <xref rid=\"acel13183-bib-0005\" ref-type=\"ref\">2014</xref>). In old age, fat is redistributed outside the usual fat deposits and lipids can accumulate in nonadipose tissues like skeletal muscle, heart, and liver. Thus, aging is associated with an increase in the lipid accumulation within the liver that may compromise the normal function due to lipotoxicity (Slawik & Vidal‐Puig, <xref rid=\"acel13183-bib-0048\" ref-type=\"ref\">2006</xref>).</p><p>Osteopontin (OPN) is a multifunctional protein that is expressed in a variety of tissues and has many functions, both physiological and pathological (Ashkar et al., <xref rid=\"acel13183-bib-0002\" ref-type=\"ref\">2000</xref>; Ramaiah & Rittling, <xref rid=\"acel13183-bib-0042\" ref-type=\"ref\">2007</xref>). It is involved in liver pathologies and acts as a modulator of liver lipid metabolism (Nunez‐Garcia et al., <xref rid=\"acel13183-bib-0034\" ref-type=\"ref\">2017</xref>; Nuñez‐Garcia et al., <xref rid=\"acel13183-bib-0035\" ref-type=\"ref\">2018</xref>). OPN is involved in NAFLD pathogenesis (Kiefer et al., <xref rid=\"acel13183-bib-0016\" ref-type=\"ref\">2011</xref>), and its liver expression is increased in obesity and correlates with steatosis and insulin resistance (Gómez‐Ambrosi et al., <xref rid=\"acel13183-bib-0013\" ref-type=\"ref\">2007</xref>). OPN expression is also upregulated in liver cancers such as in HCC and cholangiocarcinoma (Wen, Jeong, Xia, & Kong, <xref rid=\"acel13183-bib-0050\" ref-type=\"ref\">2016</xref>; Zheng et al., <xref rid=\"acel13183-bib-0052\" ref-type=\"ref\">2018</xref>). Regarding its role as a metabolic modulator, it controls the fate of acetyl‐CoA in liver (Nunez‐Garcia et al., <xref rid=\"acel13183-bib-0034\" ref-type=\"ref\">2017</xref>; Nuñez‐Garcia et al., <xref rid=\"acel13183-bib-0035\" ref-type=\"ref\">2018</xref>) and rewires liver lipid metabolism after partial hepatectomy (Nuñez‐Garcia et al., <xref rid=\"acel13183-bib-0035\" ref-type=\"ref\">2018</xref>). OPN is considered a senescence‐associated secretory phenotype (SASP) factor (Flanagan et al., <xref rid=\"acel13183-bib-0010\" ref-type=\"ref\">2017</xref>; Pazolli et al., <xref rid=\"acel13183-bib-0038\" ref-type=\"ref\">2009</xref>). SASP factors are mainly cytokines, chemokines, and proteases with autocrine and paracrine effects that affect the neighboring cells and are able to induce senescence (Coppé, Desprez, Krtolica, & Campisi, <xref rid=\"acel13183-bib-0008\" ref-type=\"ref\">2010</xref>; Malaquin, Martinez, & Rodier, <xref rid=\"acel13183-bib-0030\" ref-type=\"ref\">2016</xref>). The accumulation of senescent cells can lead to inflammation and in turn lead to further senescence of surrounding cells. SASP may be beneficial because it can help maintain homeostasis by clearing senescent cells and, thereby, reduce the tissue damage; however, if senescent cells are not cleared and accumulated, detrimental effects appear contributing to inflammation and to making surrounding cells senescent (Lau & David, <xref rid=\"acel13183-bib-0021\" ref-type=\"ref\">2019</xref>; Malaquin et al., <xref rid=\"acel13183-bib-0030\" ref-type=\"ref\">2016</xref>).</p><p>Senescence is a stable cell cycle arrest mediated through activation of p53/p21 and/or Rb/p16 pathways (Munoz‐Espin & Serrano, <xref rid=\"acel13183-bib-0032\" ref-type=\"ref\">2014</xref>). Senescent cells have been found in the livers of NAFLD and cirrhotic patients and of high‐fat diet fed and genetically obese mice (Aravinthan & Alexander, <xref rid=\"acel13183-bib-0001\" ref-type=\"ref\">2016</xref>; Ogrodnik et al., <xref rid=\"acel13183-bib-0036\" ref-type=\"ref\">2017</xref>). Cellular senescence also drives age‐dependent hepatosteatosis (Ogrodnik et al., <xref rid=\"acel13183-bib-0036\" ref-type=\"ref\">2017</xref>). However, the mechanisms involved in senescence‐induced NAFLD progression remain poorly understood. It is recognized that the aging liver is potentially at risk of injury because of its inability to respond to stresses. In the aging liver, a significant proportion of hepatocytes develop a senescent phenotype (Wang et al., <xref rid=\"acel13183-bib-0049\" ref-type=\"ref\">2009</xref>), and the aging liver exhibits decreased capacity for liver regeneration (Schmucker & Sanchez, <xref rid=\"acel13183-bib-0045\" ref-type=\"ref\">2011</xref>) and is less able to cope with oxidative stress (Schmucker, <xref rid=\"acel13183-bib-0044\" ref-type=\"ref\">2005</xref>). The aging liver also expresses lower levels of chaperone proteins (Erickson, Dunning, & Holtzman, <xref rid=\"acel13183-bib-0009\" ref-type=\"ref\">2006</xref>) and exhibits increased levels of cellular apoptosis (Zhong et al., <xref rid=\"acel13183-bib-0053\" ref-type=\"ref\">2017</xref>).</p><p>Here, we investigated the role of OPN in the aging‐related metabolic fatty liver disease. We used serum samples from obese and nonobese patients, animal models of aging, and cell lines and found that OPN is necessary to prevent age‐related liver disease. The results showed that the loss of OPN leads to earlier cellular senescence, ER stress, increased <italic>de novo</italic> lipogenesis, and increased lipid uptake, altogether promoting fatty liver disease. This study also demonstrated that expression of liver OPN is regulated, at least in part, by p53 in cellular models of senescence and NAFLD.</p></sec><sec sec-type=\"results\" id=\"acel13183-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13183-sec-0003\"><label>2.1</label><title>Aging increases osteopontin in liver and serum</title><p>As OPN is secreted into the bloodstream, we first assessed if OPN is associated with aging by measuring serum OPN levels in a cohort of individuals of varying ages and who were found to have a normal liver (NL) (<italic>n</italic> = 34) (Figure <xref rid=\"acel13183-fig-0001\" ref-type=\"fig\">1a</xref>) (Table <xref rid=\"acel13183-tbl-0001\" ref-type=\"table\">1</xref>). The results showed a positive correlation between serum OPN concentration and age (Figure <xref rid=\"acel13183-fig-0001\" ref-type=\"fig\">1a</xref>). Given that OPN is increased in liver (Lima‐Cabello et al., <xref rid=\"acel13183-bib-0025\" ref-type=\"ref\">2010</xref>) and serum of NAFLD patients (Nunez‐Garcia et al., <xref rid=\"acel13183-bib-0034\" ref-type=\"ref\">2017</xref>), OPN levels were also measured in a cohort of NAFLD patients (<italic>n</italic> = 89) (Figure <xref rid=\"acel13183-fig-0001\" ref-type=\"fig\">1b</xref>) (Table <xref rid=\"acel13183-tbl-0001\" ref-type=\"table\">1</xref>). The correlation between serum OPN concentration and age was lost in NAFLD patients, in which OPN levels were already high in younger patients (Figure <xref rid=\"acel13183-fig-0001\" ref-type=\"fig\">1b</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13183-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Osteopontin serum and liver levels increase during aging. (a) Serum samples from normal liver (NL) individuals (<italic>n</italic> = 34), (b) nonalcoholic fatty liver disease (NAFLD) patients (<italic>n</italic> = 89), and nonobese NAFLD patients (<italic>n</italic> = 13) were quantified by ELISA to asses circulating OPN levels. (c) Serum OPN from 3‐, 10‐, and 20‐month‐old (m) wild‐type (WT) mice was quantified by ELISA and liver OPN levels were assessed by immunoblotting using transferrin as loading control (<italic>n</italic> = 4–8). Correlation analysis was tested using the Pearson correlation test. Values are means ± <italic>SEM</italic>. Significant differences are denoted by *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, and ***<italic>p</italic> < 0.001 (Student's <italic>t</italic> test)</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13183-g001\"/></fig><table-wrap id=\"acel13183-tbl-0001\" xml:lang=\"en\" content-type=\"TABLE\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>Demographic, metabolic, biochemical, and histological characteristics of individuals with normal liver (NL), nonalcoholic fatty liver disease (NAFLD), and nonobese NAFLD. Obese patients were considered when the BMI was ≥30</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">NL (<italic>n</italic> = 34)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">NAFLD (<italic>n</italic> = 89)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">nonobese NAFLD (<italic>n</italic> = 13)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">BMI (kg/m<sup>2</sup>)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">28.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">39.42***</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25.96***</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Age(Years)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">48.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">52.92</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Male gender %</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">32.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31.7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">46</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Female gender %</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">67.6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">68.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">54</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TG (mg/dl)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">104.3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">144.51***</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">128.1*</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Glucose (mg/dl)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">95.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">93.7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">104</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CHOL (mg/dl)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">188.4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">169.41*</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">201.15***</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CHOL‐HDL (mg/dl)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">49.9</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">38.86***</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">53.15***</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ALT (IU/L)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19.2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">40.68***</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">34.620</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">AST (IU/L)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">18.1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">29.55***</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">25.690</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Insulin (mU/ml)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">11.76***</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.9</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Steatosis %</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Grade 0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">100</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Grade 1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">58.0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">84.6</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Grade 2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">27.0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15.4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Grade 3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">15.0</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0</td></tr></tbody></table><table-wrap-foot id=\"acel13183-ntgp-0001\"><title>Note</title><fn id=\"acel13183-note-0001\"><p>Data are the mean or percentage (%). Values are means of <italic>n</italic> = 34 for NL, <italic>n</italic> = 89 for NAFLD and <italic>n</italic> = 13 for nonobese NAFLD patients. Significant differences are denoted by *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, and ***<italic>p</italic> < 0.001 when comparing NL versus NAFLD, and when comparing NAFLD versus nonobese NAFLD (Student's <italic>t</italic> test).</p></fn><fn id=\"acel13183-note-0002\"><p>Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CHOL, cholesterol; HDL, high‐density lipoprotein; TG, triglyceride.</p></fn></table-wrap-foot><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>To investigate the involvement of OPN in age‐related development of fatty liver, animal models that recapitulate aging were generated. In this model, 3‐month‐old (3m) mice represent young mice, 10‐month‐old (10m) mice intermediate‐age mice, and 20‐month‐old mice (20m) aged mice. The results showed that serum OPN concentration increased from 3 to 10m, and the increase maintained in 20m mice (Figure <xref rid=\"acel13183-fig-0001\" ref-type=\"fig\">1c</xref>). Total Liver OPN levels were also analyzed, and a similar profile was obtained. OPN protein levels were increased in 10m mice as compared to 3m mice, and these levels were maintained at 20m (Figure <xref rid=\"acel13183-fig-0001\" ref-type=\"fig\">1c</xref>).</p></sec><sec id=\"acel13183-sec-0004\"><label>2.2</label><title>OPN deficiency in mice results in an early increase in liver lipid storage during aging</title><p>Given that OPN levels increase in liver during aging, we evaluated the role of OPN in liver lipid accumulation and NAFLD development. For this, we used 3m, 10m and 20m WT and OPN‐knockout (KO) mice. To study the role of OPN in NAFLD and aging, a separate group of WT and OPN‐KO mice were fed a high‐fat diet (HFD) from 16m until sacrifice at 20m (i.e., mice were 4 months on HFD).</p><p>Liver lipid analysis showed a premature increase in lipid concentration in 10m OPN‐KO mice compared to their age‐matched WTs (Figure <xref rid=\"acel13183-fig-0002\" ref-type=\"fig\">2a</xref>). In fact, there was a rise in triglycerides (TG), cholesteryl ester (CE), fatty acids (FA), and diglycerides (DG) concentration at 10m that maintained at 20m. Thus, during aging, fluctuations in liver lipid concentration differ between WT and OPN‐KO mice (Figure <xref rid=\"acel13183-fig-0002\" ref-type=\"fig\">2a</xref>); in WT mice, the peak of concentration for all lipids was observed at 20m, being lipid storage similar at 3 and 10m (Figure <xref rid=\"acel13183-fig-0002\" ref-type=\"fig\">2a,b</xref>); OPN‐KO mice on the other hand accumulated liver lipids at an earlier age. Even more, the results showed that serum TG was increased in 10m and 20m OPN‐KO mice when compared to their WT controls (Figure <xref rid=\"acel13183-fig-0002\" ref-type=\"fig\">2c</xref>). Serum FAs also increased in 10m OPN‐KO animals. However, serum total cholesterol (Chol) did not change when comparing age or genotype (Figure <xref rid=\"acel13183-fig-0002\" ref-type=\"fig\">2c</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13183-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>OPN deficiency leads to a premature increase of liver lipid accumulation. (a) Lipids were extracted from liver homogenates of (<italic>n</italic> = 4–8) 3‐, 10‐, and 20‐month‐old (m) WT and OPN‐KO mice fed a chow diet (CD) and a high‐fat diet (HFD). Triglycerides (TG), cholesteryl esters (CE), diglycerides (DG), and fatty acids (FA) were separated and quantified. (b) H&E staining was performed to study the histology of the liver. (c) Serum lipids TG, FAs, and total cholesterol (Cho) were quantified (<italic>n</italic> = 4–8) in 3, 10, and 20 m WT and OPN‐KO mice fed a CD and a HFD. Values are means ± <italic>SEM</italic>. Significant differences when comparing genotypes of the same age‐group are denoted by *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, and ***<italic>p</italic> < 0.001 and #<italic>p</italic> < 0.05, ##<italic>p</italic> < 0.01, and ###<italic>p</italic> < 0.001 when comparing with previous age‐group in the same genotype (Student's <italic>t</italic> test)</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13183-g002\"/></fig><p>Others have previously reported that young OPN‐KO mice are protected from diet‐induced hepatosteatosis (Kiefer et al., <xref rid=\"acel13183-bib-0016\" ref-type=\"ref\">2011</xref>; Lancha et al., <xref rid=\"acel13183-bib-0020\" ref-type=\"ref\">2014</xref>). However, given the observed early accumulation of lipid in the OPN‐KO mice liver during aging, lipid storage and hepatosteatosis were also measured in the HFD‐fed 20m mice. The lipid concentration (Figure <xref rid=\"acel13183-fig-0002\" ref-type=\"fig\">2a</xref>) and accumulation of lipid droplets (Figure <xref rid=\"acel13183-fig-0002\" ref-type=\"fig\">2b</xref>) showed that aged OPN‐KO mice were no longer protected from diet‐induced hepatosteatosis. The results showed that at 20m, the <italic>de novo</italic> lipogenesis was decreased (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S1</xref>A); however, increased esterification of fatty acids into more complex lipids (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S1</xref>B) associated with increased levels of the fatty acid transporter CD36 were observed (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S1</xref>C). Besides, serum TG levels were increased (Figure <xref rid=\"acel13183-fig-0002\" ref-type=\"fig\">2c</xref>). Although the final body weight of HFD‐fed 20m OPN‐KO mice was similar to their age‐matched HFD‐fed WT mice (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S1</xref>D), they developed a greater degree of insulin resistance as demonstrated by the insulin tolerance test (ITT) (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S1</xref>E), insulin levels, and HOMA‐IR (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S1</xref>F).</p></sec><sec id=\"acel13183-sec-0005\"><label>2.3</label><title>OPN deficiency induces senescence‐associated hepatosteatosis</title><p>Increased lipid storage in liver is due to a misbalance between processes that control lipid input and output. To ascertain the mechanisms involved in the increased lipid storage, <italic>de novo</italic> lipogenesis and the esterification of fatty acids were measured. We used hepatocytes from 10m WT and OPN‐KO mice because 10m OPN‐KO mice exhibited upregulation in liver and serum lipids. The results showed that <italic>de novo</italic> synthesis of TG, DG, and CE and the esterification of oleate into TGs were increased in OPN‐KO mice (Figure <xref rid=\"acel13183-fig-0003\" ref-type=\"fig\">3a</xref>), but no changes were observed in the incorporation of oleate into DG or CE (Figure <xref rid=\"acel13183-fig-0003\" ref-type=\"fig\">3a</xref>). Protein levels of fatty acid synthase (FAS) which is involved in fatty acid synthesis, and CD36 which is involved in fatty acid uptake (Figure <xref rid=\"acel13183-fig-0003\" ref-type=\"fig\">3b</xref>), were also augmented, but no significant changes were observed in the pACC and ACC levels (Figure <xref rid=\"acel13183-fig-0003\" ref-type=\"fig\">3b</xref>). Catabolism of fatty acids through beta oxidation (β‐oxidation) (a mechanism involved in lipid output) was also assessed by the production of acid soluble metabolites (ASM) and CO<sub>2</sub>. We did not detect any statistically significant difference in palmitate β‐oxidation between both groups of mice (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S2</xref>A).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13183-fig-0003\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>Increased lipid synthesis is linked to markers of senescence and ER stress in 10‐month‐old OPN‐KO mice liver. (a) Hepatocytes isolated from 10‐month‐old (m) WT and OPN‐KO mice were cultured in medium with [<sup>3</sup>H]acetate or [<sup>3</sup>H]oleate for 4 hr. Lipids were extracted and radioactivity incorporated into TG, DG, and CE was assessed by liquid scintillation (<italic>n</italic> = 5). (b) Fatty acid synthase (FAS), phosphorylated, and total acetyl‐CoA carboxylase (ACC) and CD36 protein levels of 10 m WT and OPN‐KO mice were assessed by immunoblotting using transferrin as loading control (<italic>n</italic> = 5–8). (c) Immunoblot analysis of 10 m mice of GRP78, total and phosphorylated eIF2α and IRe1a protein from liver extract was assessed using transferrin as loading control (<italic>n</italic> = 5–6). (d) Liver senescence‐associated (SA) β‐galactosidase‐positive area percentage was quantified in 10 m WT and OPN‐KO mice. Immunoblot analysis of p21 protein from liver extract was assessed using transferrin as loading control (<italic>n</italic> = 5). Significant differences when comparing genotypes of the same age‐group are denoted by *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, and ***<italic>p</italic> < 0.001 (Student's <italic>t</italic> test)</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13183-g003\"/></fig><p>Loss of proteostasis is a hallmark of aging (López‐Otín, Blasco, Partridge, Serrano, & Kroemer, <xref rid=\"acel13183-bib-0027\" ref-type=\"ref\">2013</xref>). The endoplasmic reticulum (ER) is an important organelle for regulating lipid homeostasis and protein synthesis (Han & Kaufman, <xref rid=\"acel13183-bib-0014\" ref-type=\"ref\">2016</xref>). Many insults can disturb ER homeostasis, which lead to ER stress and activation of the unfolded protein response (UPR). The UPR might lead to increased lipogenesis, thus contributing to lipotoxicity. On the other hand, lipid accumulation <italic>per se</italic> can also perturb ER function, which then generates ER stress (Basseri & Austin, <xref rid=\"acel13183-bib-0003\" ref-type=\"ref\">2012</xref>). ER stress, therefore, can activate pathways involved in accumulation of lipids in hepatocytes, which in turn exacerbates ER stress‐mediated increase in lipid storage and induce a vicious positive‐feedback cycle, and persistence of ER stress may lead to development and/or progression of liver disease (Lebeaupin et al., <xref rid=\"acel13183-bib-0023\" ref-type=\"ref\">2018</xref>).</p><p>The decrease in the amount of chaperone proteins is a hallmark of aging (Erickson et al., <xref rid=\"acel13183-bib-0009\" ref-type=\"ref\">2006</xref>) and may be an activator of ER stress (Klaips, Jayaraj, & Hartl, <xref rid=\"acel13183-bib-0018\" ref-type=\"ref\">2018</xref>). Thus, we next investigated whether the early lipid accumulation in OPN‐KO mice during aging could be associated with altered levels of chaperones. Interestingly, the results showed that in 10m OPN‐KO mice livers, levels of the chaperone GRP78 were decreased while levels of phosphorylated eIF2α (peIF2α) and the peIF2α/eIF2α ratio were increased (Figure <xref rid=\"acel13183-fig-0003\" ref-type=\"fig\">3c</xref>). No changes were observed in the IRE1α branch of UPR (Figure <xref rid=\"acel13183-fig-0003\" ref-type=\"fig\">3c</xref>). Altered mTOR signaling could be linked to the activation of ER stress (Liu & Sabatini, <xref rid=\"acel13183-bib-0026\" ref-type=\"ref\">2020</xref>), the results showed that phosphorylation of mTOR was decreased in OPN‐KO mice (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S2</xref>B), while there was a tendency toward decrease in that of S6 (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S2</xref>B).</p><p>It is also well recognized that proteostasis prevents cellular senescence (Kim et al., <xref rid=\"acel13183-bib-0017\" ref-type=\"ref\">2018</xref>), and senescent cells can contribute to age‐related tissue degeneration and fat accumulation (Ogrodnik et al., <xref rid=\"acel13183-bib-0036\" ref-type=\"ref\">2017</xref>). Therefore, we also analyzed markers of senescence in 10m mice livers. In 10m OPN‐KO mice livers, the increased β‐galactosidase (SA‐β‐galactosidase) and p21 levels (Figure <xref rid=\"acel13183-fig-0003\" ref-type=\"fig\">3d</xref>) showed the induction of a senescence phenotype in liver when compared to the age‐matched WT mice. Levels of γH2AX, indicating DNA damage, were also measured; however, they were undetectable (data not shown).</p><p>Considering that deficient autophagy in the liver impairs the mobilization of TGs into FAs, resulting in steatosis (Singh et al., <xref rid=\"acel13183-bib-0047\" ref-type=\"ref\">2009</xref>) and that it could have a direct effect on triggering senescence and quiescence (Rajendran et al., <xref rid=\"acel13183-bib-0041\" ref-type=\"ref\">2019</xref>), we analyzed levels of proteins involved in autophagy. We found no statistically significant differences in ATG5, ATG7, or LC3 levels in 10m OPN‐KO mice (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S2</xref>C).</p><p>To establish a causality of events, and to validate these results in an <italic>in vitro</italic> model of senescence, palbociclib (Palbo) (a cyclin‐dependent protein kinase 4/6 inhibitor) or hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) were used to induce senescence in HepG2 cells. As OPN is a SASP factor and senescence is a hallmark of aging, intracellular and secreted OPN levels were measured in HepG2 cells after the induction of senescence. Induction of senescence, as the SA‐β‐galactosidase staining showed (Figure <xref rid=\"acel13183-fig-0004\" ref-type=\"fig\">4a</xref>), increased intracellular and secreted OPN protein levels (Figure <xref rid=\"acel13183-fig-0004\" ref-type=\"fig\">4a</xref>). When OPN knocked‐down (siOPN) (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S3</xref>A) cells where treated with vehicle no differences were observed in SA‐β‐galactosidase‐positive cells as compared to their controls (Figure <xref rid=\"acel13183-fig-0004\" ref-type=\"fig\">4b</xref>). However, when cells were treated with palbo, siOPN cells showed higher percentage of SA‐β‐galactosidase‐positive cells as compared to the siCtrl cells (Figure <xref rid=\"acel13183-fig-0004\" ref-type=\"fig\">4b</xref>). Conversely, when cells were treated with rOPN, there was a significant decrease of SA‐β‐galactosidase‐positive cells (Figure <xref rid=\"acel13183-fig-0004\" ref-type=\"fig\">4b</xref>), and a reduction of RB phosphorylation (target of CDK4/6) (Figure <xref rid=\"acel13183-fig-0004\" ref-type=\"fig\">4c</xref>), thus confirming the role of OPN as a factor that prevents senescence.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13183-fig-0004\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>OPN deficient cells are more vulnerable to senescence. (a) HepG2 cells were treated with hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), Palbociclib (Palbo) (2 µM), or vehicle (Veh). Senescence induction was assessed with the senescence‐associated (SA)‐β‐galactosidase staining. OPN protein levels from HepG2 cells were measured by immunoblotting using glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH) as loading control. Secreted OPN in the media was measured by ELISA (<italic>n</italic> = 4–7). (b) OPN‐deficient cells (siOPN), their controls (siCtrl), control (Ctrl), and recombinant OPN (rOPN) treated HepG2 cells were incubated either with Palbo (2 µM) to induce senescence or with Veh. SA‐β‐galactosidase staining was performed, and the percentage of positive cells was evaluated (<italic>n</italic> = 4–6). (c) rOPN‐treated cells (rOPN) and their control cells (Ctrol) were treated with Palbo (2 µM), to induce senescence, or vehicle (Veh). Phosphorylated Rb protein (ppRB) levels were measured by immunoblotting to study palbociclib effect using GAPDH as loading control (<italic>n</italic> = 3). Values are means ± <italic>SEM</italic>. Significant differences are denoted by *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, and ***<italic>p</italic> < 0.001 (Student's <italic>t</italic> test)</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13183-g004\"/></fig><p>OPN knockdown (i.e., siOPN) during palbo‐associated senescence also led to decreased GRP78 but increased γH2AX and p21 level expression (Figure <xref rid=\"acel13183-fig-0005\" ref-type=\"fig\">5a</xref>). By contrast, GRP78 levels remained unaltered and γH2AX decreased, while p21 levels increased (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S3</xref>B) when OPN knockdown occurred under normal (i.e., non senescence) conditions. In sum, these results indicate that 1‐the induction of senescent cells <italic>in vitro</italic> promotes the generation and secretion of OPN; and 2‐the induction of senescence <italic>in vitro</italic> is more marked when OPN is absent.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13183-fig-0005\" orientation=\"portrait\" position=\"float\"><label>FIGURE 5</label><caption><p>Lack of OPN in senescent cells induces the decrease of GRP78 and the storage of triglycerides. OPN‐deficient cells (siOPN) and their controls (siCtrl) were treated with Palbociclib (Palbo) to induce senescence. (a) GRP78, total and phosphorylated eIF2α, p21, and γH2AX protein levels were measured by immunoblotting using glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH) as loading control (<italic>n</italic> = 4–8). (b) Intracellular TG levels were measured (C) FAS, total and phosphorylated acetyl‐CoA carboxylase (ACC), and CD36 protein levels were measured using GAPDH as a loading control (<italic>n</italic> = 4). Values are means ± <italic>SEM</italic>. Significant differences are denoted by *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, and ***<italic>p</italic> < 0.001 (Student's <italic>t</italic> test)</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13183-g005\"/></fig><p>Changes in levels of GRP78 and markers of senescence during palbo treatment were also associated with an increase in HepG2 TG concentration (Figure <xref rid=\"acel13183-fig-0005\" ref-type=\"fig\">5b</xref>) and the upregulation of CD36 (Figure <xref rid=\"acel13183-fig-0005\" ref-type=\"fig\">5c</xref>). No changes, however, were observed in levels of the pro‐lipogenic proteins, FAS or ACC (Figure <xref rid=\"acel13183-fig-0005\" ref-type=\"fig\">5c</xref> and Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S3</xref>D). It was recently reported that CD36 is rapidly upregulated in multiple cell types in response to senescent stimuli (Chong et al., <xref rid=\"acel13183-bib-0006\" ref-type=\"ref\">2018</xref>). By comparison, TG and CD36 levels maintained unchanged when HepG2 cells were not treated with the senescence inducer (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S3</xref>C,D). Thus, OPN deficiency increases cellular vulnerability to senescence and the senescent‐associated altered processes. These in vitro experiments recapitulate results obtained in vivo, and imply that reduced OPN levels in the setting of cellular senescence will increase lipid storage in vivo, will enhance activation of ER stress and de novo lipogenesis.</p></sec><sec id=\"acel13183-sec-0006\"><label>2.4</label><title>OPN deficiency promotes age‐related fibrosis</title><p>Human NASH and fibrosis are related to increased OPN levels (Glass et al., <xref rid=\"acel13183-bib-0012\" ref-type=\"ref\">2018</xref>), and the current results show that liver OPN prevents age‐related early accumulation of lipids, senescence, and ER stress. We next evaluated whether OPN was also required to prevent fibrosis in the aged mice. The results showed that at 3m and 10m, fibrosis in OPN‐KO mice was comparable with age‐matched WT mice (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S4</xref>A). Similar results were noted for F4/80, a marker of macrophages/ kupffer cells (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S4</xref>A), and SA‐β‐galactosidase (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S2</xref>D). 20m OPN‐KO mice however developed significantly more liver fibrosis (Figure <xref rid=\"acel13183-fig-0006\" ref-type=\"fig\">6a</xref>) and had higher levels of F4/80 protein (Figure <xref rid=\"acel13183-fig-0006\" ref-type=\"fig\">6b</xref>) compared with WT mice under HFD conditions. AST levels were also significantly higher in 20m HFD‐fed OPN‐KO mice (Figure <xref rid=\"acel13183-fig-0006\" ref-type=\"fig\">6c</xref>). Fibrosis was also upregulated in the CD‐fed 20m OPN‐KO mice while F4/80 immunostaining remained unaltered (Figure <xref rid=\"acel13183-fig-0006\" ref-type=\"fig\">6a,b</xref>). Even though fibrosis and F4/80 increased in the HFD‐fed 20m OPN‐KO mice, lipid storage maintained unaltered (Figure <xref rid=\"acel13183-fig-0002\" ref-type=\"fig\">2a</xref>). We next analyzed induction of ER stress and senescence. The results showed that while GRP78, activation of the PERK branch of UPR and p21 levels remained unaltered in HFD‐fed 20m OPN‐KO mice livers as compared to their HFD‐fed 20m WT mice (Figure <xref rid=\"acel13183-fig-0006\" ref-type=\"fig\">6d</xref>), levels of γH2AX, indicating DNA damage, were increased as compared to their controls (Figure <xref rid=\"acel13183-fig-0006\" ref-type=\"fig\">6d</xref>). The latter was not observed when mice were fed a chow diet (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S4</xref>B).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13183-fig-0006\" orientation=\"portrait\" position=\"float\"><label>FIGURE 6</label><caption><p>Liver fibrosis is increased in aged high‐fat diet fed OPN‐KO mice. (a) Sirius Red and Masson trichrome stainings were performed and evaluated to assess tissue fibrosis in 20‐month‐old (m) WT and OPN‐KO mice fed a chow (CD) and a high‐fat diet (HFD) (<italic>n</italic> = 4–6). (b) F4/80 staining was performed in 20 m mice fed a CD and a HFD for evaluating inflammation. (c) ALT and AST serum levels were evaluated in 20 m mice fed a HFD (<italic>n</italic> = 4–5). (d) Immunoblot analysis of GRP78, total and phosphorylated eIF2a, γH2Ax, and p21 protein levels from liver extract were assessed using transferrin as loading control (<italic>n</italic> = 4–5) in 20 m HFD‐fed WT and OPN‐KO mice. Values are means ± <italic>SEM</italic>. Significant differences are denoted by *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, and ***<italic>p</italic> < 0.001 when comparing WT and OPN‐KO mice (Student's <italic>t</italic> test)</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13183-g006\"/></fig></sec><sec id=\"acel13183-sec-0007\"><label>2.5</label><title>Liver OPN is p53 regulated</title><p>p53 signaling mediates cellular senescence, and it is involved in aging (Munoz‐Espin & Serrano, <xref rid=\"acel13183-bib-0032\" ref-type=\"ref\">2014</xref>; Rufini, Tucci, Celardo, & Melino, <xref rid=\"acel13183-bib-0043\" ref-type=\"ref\">2013</xref>). p53 and other p53 family members also regulate metabolic pathways (Napoli & Flores, <xref rid=\"acel13183-bib-0033\" ref-type=\"ref\">2017</xref>; Porteiro, Fondevila, Delgado, et al., <xref rid=\"acel13183-bib-0040\" ref-type=\"ref\">2017</xref>) and the development of hepatosteatosis (Porteiro, Fondevila, Buque, et al., <xref rid=\"acel13183-bib-0039\" ref-type=\"ref\">2017</xref>). OPN has been described as a p53 target in fibroblasts (Morimoto, Sasaki, Ishida, Imai, & Tokino, <xref rid=\"acel13183-bib-0031\" ref-type=\"ref\">2002</xref>). As both OPN and p53 play a role in senescence, lipid metabolism and hepatosteatosis, we analyzed if OPN could also be a target of p53 in liver. Interestingly, the data showed that liver p53 levels increased during aging, mirroring a similar pattern to OPN in the animal model of aging (Figure <xref rid=\"acel13183-fig-0007\" ref-type=\"fig\">7a</xref>), and that loss of p53 resulted in a dramatic decrease in OPN in both CD‐ and HFD‐fed mice; the decrease was particularly marked among HFD‐fed mice (Figure <xref rid=\"acel13183-fig-0007\" ref-type=\"fig\">7b</xref>). These data are consistent with prior reports, which showed that liver OPN increased with HFD (Bertola et al., <xref rid=\"acel13183-bib-0004\" ref-type=\"ref\">2009</xref>). Over‐expression of p53 in vivo, as others performed before (Porteiro, Fondevila, Delgado, et al., <xref rid=\"acel13183-bib-0040\" ref-type=\"ref\">2017</xref>), also increased liver OPN levels (Figure <xref rid=\"acel13183-fig-0007\" ref-type=\"fig\">7c</xref>), even in p53‐KO mice (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S5</xref>A).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13183-fig-0007\" orientation=\"portrait\" position=\"float\"><label>FIGURE 7</label><caption><p>Liver OPN is p53 regulated. (a) Protein levels of p53 were evaluated by immunohistochemistry in liver sections of 3‐, 10‐, and 20‐month‐old (m) wild‐type (WT) mice (<italic>n</italic> = 3–5). (b) OPN protein levels from liver homogenates were measured in WT and p53‐KO male and female mice fed a chow diet (CD) and a high‐fat diet (HFD) by immunoblotting using glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH) as loading control (<italic>n</italic> = 4–6). (c) Liver p53 and OPN protein levels were evaluated in WT mice fed a HFD injected with p53‐dominant positive adenovirus (adp53) and GFP (adGFP), using transferrin or GAPDH as a loading control (<italic>n</italic> = 4–6). (d) Protein levels of p53 and OPN in siCtrl and sip53 HepG2 cells were measured by immunoblotting using GAPDH as loading control. Extracellular OPN was measured using an ELISA (<italic>n</italic> = 4–5). (e) OPN protein levels from HepG2 cells silenced for p53 and Hep3B cells treated with palbociclib (Palbo) were measured by immunoblotting using GAPDH as loading control. OPN media levels from Hep3B cells treated either with vehicle (Veh) or with palbociclib (Palbo) were measured by ELISA (<italic>n</italic> = 4–8). Values are means ± <italic>SEM</italic>. Significant differences are denoted by *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, and ***<italic>p</italic> < 0.001 (Student's <italic>t</italic> test)</p></caption><graphic id=\"nlm-graphic-15\" xlink:href=\"ACEL-19-e13183-g007\"/></fig><p>Data obtained from the Cell Line Encyclopedia (© 2019 The Broad Institute of MIT & Harvard) showed that the cell line with the least p53 expression, the Hep3B, also had the lowest OPN expression. <italic>In vitro</italic> experiments showed that the decreased p53 levels in HepG2 cells using a siRNA led to diminish intracellular and secreted OPN levels (Figure <xref rid=\"acel13183-fig-0007\" ref-type=\"fig\">7d</xref>). Earlier, we showed that induction of senescence with Palbo increased OPN levels (Figure <xref rid=\"acel13183-fig-0004\" ref-type=\"fig\">4a</xref>). Here, we found that cells deficient in p53 (i.e., either p53‐silenced cells or p53‐null cells) were unable to upregulate OPN expression even after treatment with Palbo (Figure <xref rid=\"acel13183-fig-0007\" ref-type=\"fig\">7e</xref>; Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S5</xref>B) or H<sub>2</sub>O<sub>2</sub> (Figure <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S5</xref>C), confirming that p53 is a master regulator of hepatocyte OPN expression.</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13183-sec-0008\"><label>3</label><title>DISCUSSION</title><p>Aging is a complex multifunctional process, in which metabolism plays an important role (López‐Otín et al., <xref rid=\"acel13183-bib-0027\" ref-type=\"ref\">2013</xref>). Indeed, many aging‐related diseases have a metabolic component. Epidemiological studies have demonstrated that NAFLD and NASH are common among the elderly (Bertolotti et al., <xref rid=\"acel13183-bib-0005\" ref-type=\"ref\">2014</xref>) although some pieces of evidence suggest that very old age‐groups show a decrease in NAFLD and HCC prevalence (Sheedfar, Biase, Koonen, & Vinciguerra, <xref rid=\"acel13183-bib-0046\" ref-type=\"ref\">2013</xref>). Senescence is one of the hallmarks of aging and is a permanent state of cell cycle arrest in response to different stresses, thus, a cellular defense mechanism (López‐Otín et al., <xref rid=\"acel13183-bib-0027\" ref-type=\"ref\">2013</xref>). This process takes place in several tissues during different physiological and pathological conditions. Senescence of hepatocytes is a feature of chronic liver disease independent of etiology and plays an important role in the progression of chronic liver disease (Aravinthan & Alexander, <xref rid=\"acel13183-bib-0001\" ref-type=\"ref\">2016</xref>). Cellular senescence has been associated with age‐dependent hepatosteatosis, and it correlates with severity of NAFLD (Ogrodnik et al., <xref rid=\"acel13183-bib-0036\" ref-type=\"ref\">2017</xref>). Specifically, the accumulation of senescent hepatocytes has been shown to promote progression (Aravinthan & Alexander, <xref rid=\"acel13183-bib-0001\" ref-type=\"ref\">2016</xref>). Previous work has described osteopontin (OPN) as a senescence‐associated secretory phenotype (SASP) factor (Flanagan et al., <xref rid=\"acel13183-bib-0010\" ref-type=\"ref\">2017</xref>; Pazolli et al., <xref rid=\"acel13183-bib-0038\" ref-type=\"ref\">2009</xref>). Although senescence‐associated cell cycle exit likely evolved as an antitumor mechanism, SASP contains both anti‐ and pro‐tumorigenic potential (Lau & David, <xref rid=\"acel13183-bib-0021\" ref-type=\"ref\">2019</xref>). Thus, identification and characterization of the SASP factors that are pro‐ and those that are antitumorigenic depending on the contexts and tissue is crucial. Here, we investigated the role of OPN in the age‐related hepatosteatosis. Data showed a positive correlation between serum OPN levels and increasing age in humans. Correlation however, lost in NAFLD patients where OPN levels are already higher in younger patients. Similar results were obtained in mice, where liver and serum OPN increases with age, mirroring the same pattern as p53, a regulator of cellular senescence, and whose downregulation has been linked to hepatosteatosis and induction of ER stress (Porteiro, Fondevila, Delgado, et al., <xref rid=\"acel13183-bib-0040\" ref-type=\"ref\">2017</xref>). Finally, we also found that when senescence was induced in HepG2 cells, OPN levels were also increased in cells and culture medium.</p><p>Thus, to determine whether the age‐related increase in OPN was protective or deleterious for hepatosteatosis and liver disease progression; lipid storage, metabolic fluxes, ER stress, and senescence were also studied in OPN‐KO mice during aging. Young OPN‐KO mice are protected from diet‐induced hepatosteatosis (Kiefer et al., <xref rid=\"acel13183-bib-0016\" ref-type=\"ref\">2011</xref>; Lancha et al., <xref rid=\"acel13183-bib-0020\" ref-type=\"ref\">2014</xref>). Older mice, which lacked OPN however, accumulated liver lipids, developed liver steatosis, and had higher serum TG levels in association with insulin resistance at an earlier age than the WT mice.</p><p>We previously observed that in young mice, OPN is a regulator of liver lipid metabolism during regeneration after partial hepatectomy (Nuñez‐Garcia et al., <xref rid=\"acel13183-bib-0035\" ref-type=\"ref\">2018</xref>). As intermediate‐age (10m) OPN‐KO mice was noted to store more lipids, we investigated the mechanisms by which OPN regulate liver lipid metabolism. We found that increased lipid synthesis and uptake were linked to higher levels of fatty acid synthase (FAS) and CD36. We also found that markers of senescence and activation of the unfolded protein response PERK branch (linked to decreased GRP78 chaperone levels) were increased. Loss of proteostasis and senescence are hallmarks of aging (López‐Otín et al., <xref rid=\"acel13183-bib-0027\" ref-type=\"ref\">2013</xref>), and several studies have demonstrated that the decrease in chaperone number is associated with aging (Lee et al., <xref rid=\"acel13183-bib-0024\" ref-type=\"ref\">2017</xref>) and can be a cause of the increased ER stress with aging. Other studies have also shown that forced over‐expression of GRP78 attenuates steatosis by inhibiting sterol regulatory element‐binding protein (SREBP‐1c) (Kammoun et al., <xref rid=\"acel13183-bib-0015\" ref-type=\"ref\">2009</xref>). Thus, the decrease in GRP78 levels may activate SREBP‐1c (given that FAS is increased and is one of its downstream targets) and induce <italic>de novo</italic> lipid synthesis.</p><p>OPN has been linked to fibrosis and NASH in humans and animal models. In fact, OPN has been described as a profibrogenic factor and its deficiency avoids fibrosis (Coombes et al., <xref rid=\"acel13183-bib-0007\" ref-type=\"ref\">2015</xref>). The role of OPN however is quite different during aging. Liver fibrosis was increased in 20m (aged) OPN‐KO mice, and this was enhanced after a HFD. Induction of fibrosis by senescent hepatocytes is generally a way of limiting tissue injury as part of wound healing process. The accumulation of senescent hepatocytes leads to continued activation of hepatic stellate cells, which in turn, leads to liver fibrosis (Aravinthan & Alexander, <xref rid=\"acel13183-bib-0001\" ref-type=\"ref\">2016</xref>). Liver fibrosis (observed mainly in HFD‐fed OPN‐KO mice) was associated with inflammation, DNA damage, and insulin resistance. In combination, these factors contribute to the development of liver fibrosis. It is unclear however whether the inflammation and fibrosis observed in the aging mice are consequences of a premature liver lipid storage or are dependent on other dysregulated mechanisms that activate during aging. Supporting the protective role of OPN, other studies have also shown a protective role of OPN in alcoholic hepatitis (Lazaro et al., <xref rid=\"acel13183-bib-0022\" ref-type=\"ref\">2015</xref>; Magdaleno et al., <xref rid=\"acel13183-bib-0029\" ref-type=\"ref\">2018</xref>).</p><p>Finally, previous work has shown that OPN is a p53 target in fibroblasts (Morimoto et al., <xref rid=\"acel13183-bib-0031\" ref-type=\"ref\">2002</xref>). p53 represses some of the SASP in certain conditions and cell types (Wiley et al., <xref rid=\"acel13183-bib-0051\" ref-type=\"ref\">2018</xref>). However, p53 also plays a critical enhancing role in SASP production, as many of these SASP factors are directly stimulated by p53, among them the secretion of several cytokines (Pavlakis & Stiewe, <xref rid=\"acel13183-bib-0037\" ref-type=\"ref\">2020</xref>). SASP profile can greatly variate depending on the cell type and effector. Concerning liver disease, it has been demonstrated that p53 regulates SASP of hepatic stellate cells (Lujambio et al., <xref rid=\"acel13183-bib-0028\" ref-type=\"ref\">2013</xref>). SASP also regulates genes affecting macrophage function. In fact, p53 signaling, through SASP, influences macrophage polarization (Lujambio et al., <xref rid=\"acel13183-bib-0028\" ref-type=\"ref\">2013</xref>). Precisely, induction of senescence by p53 activation in malignant hepatocytes showed a reduction in the tumor size caused by SASP‐mediated recruitment of immune cells to the tumors (Lujambio et al., <xref rid=\"acel13183-bib-0028\" ref-type=\"ref\">2013</xref>). Here, the results showed in vivo and in vitro that normal expression of p53 is required to ensure the homeostasis of OPN in liver and in HepG2 cells. Thus, p53‐OPN axis is required for maintaining the liver health during aging.</p><p>In summary, OPN deficiency increases the susceptibility of the liver to aging and aging‐associated liver disease. As OPN‐deficient mice become older, increased levels of senescence, ER stress, hepatosteatosis, DNA damage, fibrosis, and inflammation appear. The in vivo results are supported by in vitro findings. In addition, liver OPN is p53 regulated, and it plays a role in ensuring OPN secretion in response to senescence. The overall data suggest that OPN is a protective factor that counteracts senescence.</p></sec><sec id=\"acel13183-sec-0009\"><label>4</label><title>EXPERIMENTAL PROCEDURES</title><sec id=\"acel13183-sec-0010\"><label>4.1</label><title>Human samples</title><p>This study included 123 patients; 42 were nonobese and 81 were obese. Individuals were considered nonobese when presenting a BMI lower than 30 kg/m<sup>2</sup> and obese when the BMI was higher than 30 kg/m<sup>2</sup>. Liver biopsy was performed during laparoscopic cholecystectomy or during bariatric surgery. According to Kleiner criteria (Kleiner et al., <xref rid=\"acel13183-bib-0019\" ref-type=\"ref\">2005</xref>), of the obese group, 76 individuals had a clinical diagnosis of nonalcoholic fatty liver disease (NAFLD) and 5 were obese control individuals with normal liver (NL). The nonobese group was comprised of 13 individuals with NAFLD and 29 control individuals with NL. Steatosis was assessed as outlined by Kleiner et al. (<xref rid=\"acel13183-bib-0019\" ref-type=\"ref\">2005</xref>). The study was performed in agreement with the Declaration of Helsinki and with local and national laws. The Human Ethics Committee of the University Hospital Santa Cristina and the Ethical committee of clinical research of the Basque Country approved the study procedures. Written informed consent was obtained from all patients before inclusion in the study.</p></sec><sec id=\"acel13183-sec-0011\"><label>4.2</label><title>Animals</title><p>3‐, 10‐, and 20‐month‐old WT and OPN‐KO female mice were used. Mice were maintained on a rodent chow diet (CD) (Teklad Global 18% Protein Rodent Diet 2018S; Harlan Laboratories INC., USA) or a rodent high‐fat diet (HFD) (Bioserv S3282, 60% Fat) and water ad libitum.</p><p>In addition, 3‐month‐old WT and p53‐KO mice were used to assess the role of p53 in OPN regulation. For assessing OPN expression in p53‐KO animals, mice were maintained on a rodent CD (Teklad Global 18% Protein Rodent Diet 2018S; Harlan Laboratories INC., USA) or a rodent HFD (Bioserv S3282, 60% Fat) and water ad libitum for 4 weeks. For assessing OPN expression after adenoviral over‐expression of p53, WT and p53‐KO mice were maintained in a rodent high‐fat diet (Research Diets D12,492; 60% fat, 5.24 kcal/g, Research Diets, New Brunswick, NJ) and water ad libitum for 11 weeks. Animal procedures were approved by the Ethics Committee for Animal Welfare of the University of the Basque Country UPV/EHU and were conducted in conformity with the EU Directives for animal experimentation.</p></sec><sec id=\"acel13183-sec-0012\"><label>4.3</label><title>In vivo adenoviral gene transfer</title><p>Wild‐type (WT) mice and p53‐KO mice were injected by tail vein injection with 100 ml of adenoviral vectors diluted in saline for over‐expression of hepatic p53. Adenoviral vectors activating p53 (SignaGen Laboratories, USA, ref # SL100,777) and GFP (SignaGen Laboratories, USA, ref # SL100,833) (1_109 VGml_1) were used.</p></sec><sec id=\"acel13183-sec-0013\"><label>4.4</label><title>Glucose and insulin tolerance tests</title><p>For glucose tolerance test (GTT), mice were fasted for 4 hr and then were administered a glucose solution (2 g/kg body weight) by oral gavage. Blood glucose levels were measured before glucose administration (time 0) and 15, 30, 60, and 120 min postgavage using blood strips. For insulin sensitivity test (ITT), mice were injected intraperitoneally 1 U insulin/kg body weight and glucose levels were measured as described above for glucose tolerance test.</p></sec><sec id=\"acel13183-sec-0014\"><label>4.5</label><title>Glucose and insulin measurement</title><p>Glucose blood levels were measured using blood strips. For insulin measurement, an ultrasensitive mouse Insulin ELISA kit (catalog #90080; Crystal Chem, Downers Grove, IL) was used following manufacturer's instructions.</p></sec><sec id=\"acel13183-sec-0015\"><label>4.6</label><title>\n<italic>In vitro</italic> studies</title><p>HepG2 and Hep3B cell lines were obtained from the American Type Culture Collection (ATCC). Cells were maintained in Dulbecco's modified Eagle's medium supplemented with fetal bovine serum 10% (v/v), penicillin‐streptomycin 1%, glutamine, and amphotericin B 1%. Cells were kept at 37°C within a 95% humidified atmosphere containing 5% carbon dioxide in an incubator. Depending on the experiment, cells were plated in 60‐mm culture dishes, 6‐well plates, or in 24‐well culture plates.</p></sec><sec id=\"acel13183-sec-0016\"><label>4.7</label><title>Cell transfection</title><p>For knocking‐down experiments, a commercial siRNA was used against osteopontin (SPP1 gene) and p53 (TP53 gene) (Ambion CA, USA). Negative controls were included in each assay by using Silencer™ Select Negative Control siRNA (Ambion CA, USA). Reverse transfection was performed using RNAiMAX lipofectamine (Invitrogen Life Technologies, USA) in Opti‐MEM media. Briefly, Lipofectamine and siRNA were diluted separately in Opti‐MEM and then dilutions were mixed and incubated for 20 min at RT. 24 hr after transfection, transfection media was removed and fresh treatment media was added. Gene silencing efficiency was confirmed by RNA expression analysis.</p></sec><sec id=\"acel13183-sec-0017\"><label>4.8</label><title>Palbociclib treatment</title><p>Senescence inductor Palbociclib 2 μM was dissolved in DMSO and filtered. 24 hr after transfection, HepG2 cells were exposed to palbociclib or vehicle, which was added to the regular complete media. Cells were collected 4 days after palbociclib treatment for their study.</p></sec><sec id=\"acel13183-sec-0018\"><label>4.9</label><title>Hydrogen peroxide treatment</title><p>Hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) was used for senescence induction. 24 hr after plating, cells were exposed to H<sub>2</sub>O<sub>2</sub> 700 μM or vehicle for 1 hr. Then, treatment media was replaced and fresh media was added. Cells were collected 4 days after H<sub>2</sub>O<sub>2</sub> treatment for their study.</p></sec><sec id=\"acel13183-sec-0019\"><label>4.10</label><title>Analysis of liver and serum lipid concentration</title><p>After homogenization of liver tissue or scraping cells, lipids were extracted as described before and detailed in Appendix <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13183-sec-0020\"><label>4.11</label><title>Immunoassays</title><p>For the immunoblots, samples were separated to SDS‐PAGE and proteins were transferred to nitrocellulose membranes. Western blotting was performed using the primary antibody of interest.</p></sec><sec id=\"acel13183-sec-0021\"><label>4.12</label><title>[<sup>3</sup>H]Acetate and [<sup>3</sup>H]oleate incorporation</title><p>For <italic>de novo</italic> lipogenesis analysis, primary hepatocytes (1.5 × 10<sup>6</sup> cells/plate) or liver slices (40 mg) were incubated with sodium [<sup>3</sup>H]acetate (84 mCi/ mmol;20 µCi/mL; 20 µM). For esterification analysis primary hepatocytes (1.5 × 10<sup>6</sup> cells/plate) were incubated with [<sup>3</sup>H]oleate (54 mCi/mmol; 2 µCi/mL; 20 µM) and liver slices (40 mg) were incubated with [<sup>3</sup>H]oleate (54 mCi/mmol; 2 µCi/mL; 800 µM). At 240 min, cells and mediums were collected. To quantify the radioactivity incorporated from [<sup>3</sup>H]acetate and [<sup>3</sup>H]oleate into lipids, lipids were extracted and separated. After the scraping of the corresponding bands into a vial containing scintillation, liquid radioactivity was determined.</p></sec><sec id=\"acel13183-sec-0022\"><label>4.13</label><title>Fatty acid oxidation measurement</title><p>Beta oxidation was assessed as described before and detailed in Appendix <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13183-sec-0023\"><label>4.14</label><title>Osteopontin ELISA</title><p>For quantification of serum and cell supernatant OPN concentration, a Human or Mouse OPN Quantikine ELISA kit (R&D Systems) was used following manufacturer's instructions.</p></sec><sec id=\"acel13183-sec-0024\"><label>4.15</label><title>Histochemistry</title><p>Paraffin‐embedded sections (5 μm thick) of formalin‐fixed liver samples or OCT‐embedded samples were used. All procedures are detailed in Appendix <xref rid=\"acel13183-sup-0006\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13183-sec-0025\"><label>4.16</label><title>Total protein measurement</title><p>Protein concentration was measured using commercially Bicinchoninic Acid Reagent(Thermo Fisher Scientific Inc).</p></sec><sec id=\"acel13183-sec-0026\"><label>4.17</label><title>Statistical analysis</title><p>Data are represented as mean ± <italic>SEM</italic>. Differences between groups were tested using Student's <italic>t</italic> test, two‐way ANOVA. Significance was defined as <italic>p</italic> < 0.05. Correlation analysis was tested using the Pearson correlation test. These analyses were performed using GraphPad Prism software.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13183-sec-0028\"><title>CONFLICT OF INTEREST</title><p>MLM‐Ch is consultant of Mitotherapeutix.</p></sec><sec id=\"acel13183-sec-0029\"><title>AUTHOR CONTRIBUTIONS</title><p>BG‐S and PA designed the study. BG‐S, DSDU, MN‐G, FG‐R, XB, IA, VGDJ, MJG‐R, LM, CM, and PA performed experiments and investigations. CG‐M, AG‐R, GE, PM, SG, MJG‐R, LC, and RN acquisition of samples and investigations. BG‐S, RN, MLM‐Ch, WS, and PA contributed to data analysis and discussion. BG‐S and PA wrote the paper, and all authors contributed to editing.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13183-sup-0001\"><caption><p>Figure S1</p></caption><media xlink:href=\"ACEL-19-e13183-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13183-sup-0002\"><caption><p>Figure S2</p></caption><media xlink:href=\"ACEL-19-e13183-s002.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13183-sup-0003\"><caption><p>Figure S3</p></caption><media xlink:href=\"ACEL-19-e13183-s003.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13183-sup-0004\"><caption><p>Figure S4</p></caption><media xlink:href=\"ACEL-19-e13183-s004.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13183-sup-0005\"><caption><p>Figure S5</p></caption><media xlink:href=\"ACEL-19-e13183-s005.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13183-sup-0006\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13183-s006.doc\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13183-sec-0027\"><title>ACKNOWLEDGMENTS</title><p>This work was supported by Ayudas para apoyar grupos de investigación del sistema Universitario Vasco (IT971‐16 to P.A.), MINECO‐FEDER (SAF2017‐87301‐R to M.L.M‐Ch) MCIU/AEI/FEDER, UE (RTI2018‐095134‐B‐100 to P.A. and RTI2018‐099413‐B‐I00 to RN, Asociación Española contra el Cáncer, Canceres raros (M.L.M‐Ch), La Caixa Foundation (to M.L.M‐Ch), Ayudas Fundación BBVA a equipos de Investigación Científica 2018 (to M.L.M‐Ch), Xunta de Galicia (RN: 2015‐CP080 and 2016‐ PG057), Fundación BBVA (RN), and European Foundation for the Study of Diabetes (RN). ISCIII‐FEDER PI17/00535 (to C.G‐M.), ISCIII‐FEDER CP14/00181 and PI16/00823 (to A.G‐ R.), and Francisco Cobos Foundation (to A.G‐R.). CiC bioGUNE thanks MINECO for the Severo Ochoa Excellence Accreditation (SEV‐2016‐ 0644).</p></ack><sec sec-type=\"data-availability\" id=\"acel13183-sec-0031\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13183-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13183-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13183-cit-0001\">\n<string-name>\n<surname>Aravinthan</surname>, <given-names>A. D.</given-names>\n</string-name>, & <string-name>\n<surname>Alexander</surname>, <given-names>G. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32610362</article-id><article-id pub-id-type=\"pmc\">PMC7431824</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13178</article-id><article-id pub-id-type=\"publisher-id\">ACEL13178</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Paper</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Papers</subject></subj-group></article-categories><title-group><article-title>The inherited methylome landscape is directly altered with paternal aging and associated with offspring neurodevelopmental disorders</article-title><alt-title alt-title-type=\"left-running-head\">DENOMME et al.</alt-title></title-group><contrib-group><contrib id=\"acel13178-cr-0001\" contrib-type=\"author\"><name><surname>Denomme</surname><given-names>Michelle M.</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-7876-0462</contrib-id><xref ref-type=\"aff\" rid=\"acel13178-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13178-cr-0002\" contrib-type=\"author\"><name><surname>Haywood</surname><given-names>Mary E.</given-names></name><xref ref-type=\"aff\" rid=\"acel13178-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13178-cr-0003\" contrib-type=\"author\"><name><surname>Parks</surname><given-names>Jason C.</given-names></name><xref ref-type=\"aff\" rid=\"acel13178-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13178-cr-0004\" contrib-type=\"author\"><name><surname>Schoolcraft</surname><given-names>William B.</given-names></name><xref ref-type=\"aff\" rid=\"acel13178-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13178-cr-0005\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Katz‐Jaffe</surname><given-names>Mandy G.</given-names></name><xref ref-type=\"aff\" rid=\"acel13178-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13178-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13178-aff-0003\">\n<sup>3</sup>\n</xref><address><email>MKatz-Jaffe@ColoCRM.com</email></address></contrib></contrib-group><aff id=\"acel13178-aff-0001\">\n<label><sup>1</sup></label>\n<institution>Fertility Labs of Colorado</institution>\n<city>Lone Tree</city>\n<named-content content-type=\"country-part\">CO</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13178-aff-0002\">\n<label><sup>2</sup></label>\n<institution>Fertility Genetics</institution>\n<city>Lone Tree</city>\n<named-content content-type=\"country-part\">CO</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13178-aff-0003\">\n<label><sup>3</sup></label>\n<institution>Colorado Center for Reproductive Medicine</institution>\n<city>Lone Tree</city>\n<named-content content-type=\"country-part\">CO</named-content>\n<country country=\"US\">USA</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nMandy G. Katz‐Jaffe, Fertility Labs of Colorado, Colorado Center for Reproductive Medicine, 10290 Ridgegate Circle, Lone Tree, CO 80124, USA.<break/>\nEmail: <email>MKatz-Jaffe@ColoCRM.com</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>01</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13178</elocation-id><history><date date-type=\"received\"><day>12</day><month>2</month><year>2020</year></date><date date-type=\"rev-recd\"><day>22</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>5</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13178.pdf\"/><abstract id=\"acel13178-abs-0001\"><title>Abstract</title><p>Paternal aging and the prevalence of neurodevelopmental disorders in offspring are well documented. Yet, the underlying mechanism and the mode of inheritance have not been conclusively established. Advancing paternal age is a subtle and varying phenotype. As such, it is likely that a threshold for cumulative risk may exist that, if surpassed, culminates in a predisposition to disease and ultimately an observed phenotype in offspring. Epigenetic regulation provides a plausible explanation for the nongenetic paternal transmission of disease susceptibility. With the use of whole‐genome methylation sequencing, the data described herein substantiate an increasingly compromised DNA methylation profile as sperm ages and, for the first time, also demonstrate a generational correlation in sperm and blastocyst of an altered methylome associated with advanced paternal age. Methylation alterations are not randomly distributed across the genome, but appear clustered at certain chromosomal locations, and significantly colocalize with regions of nucleosome retention. Genes associated with autism spectrum disorder, schizophrenia, and bipolar disorder are significantly enriched with causative methylation aberrations in both sperm and embryos from aged fathers. The long‐term health burden and societal economic impact of these conditions are substantial and will continue with increasingly prevalent diagnosis. This work provides a mechanistic link between the paternal age effect and offspring neurodevelopmental disorders leading to a better understanding of causation and investigation into potential future therapy.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13178-abs-0002\"><p>A generational epigenetic correlation was observed for human sperm and blastocyst methylomes in association with advanced paternal age at genes implicated in offspring neurodevelopmental disorders, including autism spectrum disorder, schizophrenia, and bipolar disorder. This work provides a mechanistic link between the paternal age effect and offspring neurodevelopmental disorders leading to a better understanding of causation and clinical management.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13178-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13178-g005.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13178-kwd-0001\">advanced paternal age</kwd><kwd id=\"acel13178-kwd-0002\">blastocyst</kwd><kwd id=\"acel13178-kwd-0003\">DNA methylation</kwd><kwd id=\"acel13178-kwd-0004\">epigenetics</kwd><kwd id=\"acel13178-kwd-0005\">offspring neurodevelopmental disorders</kwd><kwd id=\"acel13178-kwd-0006\">sperm</kwd></kwd-group><counts><fig-count count=\"4\"/><table-count count=\"6\"/><page-count count=\"13\"/><word-count count=\"8392\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13178-cit-1001\">\n<string-name>\n<surname>Denomme</surname>\n<given-names>MM</given-names>\n</string-name>, <string-name>\n<surname>Haywood</surname>\n<given-names>ME</given-names>\n</string-name>, <string-name>\n<surname>Parks</surname>\n<given-names>JC</given-names>\n</string-name>, <string-name>\n<surname>Schoolcraft</surname>\n<given-names>WB</given-names>\n</string-name>, <string-name>\n<surname>Katz‐Jaffe</surname>\n<given-names>MG</given-names>\n</string-name>. <article-title>The inherited methylome landscape is directly altered with paternal aging and associated with offspring neurodevelopmental disorders</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13178</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13178</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13178-body-0001\"><sec id=\"acel13178-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>The influence of advanced paternal age (APA) on adverse offspring health has been well documented, including increased disease risk of neurodevelopmental disorders (Frans et al., <xref rid=\"acel13178-bib-0009\" ref-type=\"ref\">2008</xref>; Hare & Moran, <xref rid=\"acel13178-bib-0014\" ref-type=\"ref\">1979</xref>; Miller et al., <xref rid=\"acel13178-bib-0026\" ref-type=\"ref\">2011</xref>). A recent meta‐analysis of paternal age and psychiatric disorders indicated significantly elevated risk estimates for children conceived by fathers over 50 years, with adjusted odds ratio (OR) for developing autism spectrum disorder ranging from 2.26 to 3.37, schizophrenia OR 1.59–3.80, and bipolar disorder OR 1.27–2.84, compared with children conceived by young fathers (de Kluiver, Buizer‐Voskamp, Dolan, & Boomsma, <xref rid=\"acel13178-bib-0006\" ref-type=\"ref\">2017</xref>). The long‐term health burden of these conditions for the affected children, as well as the economic impact on their families and to society, demands a better understanding of the causative link between paternal age and offspring neurodevelopmental disorders.</p><p>The causal mechanism underlying this paternal age effect has not been elucidated, nor has the mode of inheritance yet to be conclusively established. Observed phenotypes derived by mammalian non‐Mendelian genetics suggest that factors independent of DNA sequence play a significant role (Bassett, Chow, O'Neill, & Brzustowicz, <xref rid=\"acel13178-bib-0002\" ref-type=\"ref\">2001</xref>). Rather, modifiable and inheritable epigenetic information, such as DNA methylation, has this potential to be generationally transmitted. A series of epigenetic reprogramming events occur during gametogenesis and immediately after fertilization (Reik, Dean, & Walter, <xref rid=\"acel13178-bib-0033\" ref-type=\"ref\">2001</xref>). Erasure and gamete‐specific establishment of methylation marks occur during primordial germ cell development. Directly after fertilization, the male pronucleus undergoes a rapid, active demethylation process, while the female pronucleus undergoes passive replication‐dependent demethylation, prior to re‐establishment in the developing embryo. Imprinted genes, and perhaps other developmentally critical regions, elude the embryonic portion of epigenetic reprogramming, enabling epigenetic generational inheritance (Kobayashi et al., <xref rid=\"acel13178-bib-0020\" ref-type=\"ref\">2012</xref>). A longitudinal paired study of sperm collected one‐to‐two decades apart demonstrated age‐associated DNA methylation alterations in the paternal germline, with a portion of these changes located at genes associated with schizophrenia and bipolar disorder (Jenkins, Aston, Pflueger, Cairns, & Carrell, <xref rid=\"acel13178-bib-0017\" ref-type=\"ref\">2014</xref>). The impact of aging on the sperm methylome was also confirmed in a mouse model, with subsequent age‐associated methylation alterations observed in murine offspring, influencing behavioral phenotypes (Milekic et al., <xref rid=\"acel13178-bib-0025\" ref-type=\"ref\">2015</xref>).</p><p>Unlike genetic errors, epigenetic information can be disrupted by various external environmental and endogenous factors during reprogramming events, which could be inherited and lead to long‐term adverse consequences affecting offspring health (Kimura, Yoshizaki, & Osumi, <xref rid=\"acel13178-bib-0019\" ref-type=\"ref\">2018</xref>). Exposure to famine in utero, like the Dutch Hunger Winter, resulted in epigenetic silencing of certain genes in offspring, causing higher rates of obesity, diabetes, schizophrenia, and mortality (Lumey, Stein, & Susser, <xref rid=\"acel13178-bib-0022\" ref-type=\"ref\">2011</xref>; Lumey et al., <xref rid=\"acel13178-bib-0023\" ref-type=\"ref\">2012</xref>; Tobi et al., <xref rid=\"acel13178-bib-0041\" ref-type=\"ref\">2018</xref>). Epigenetic dysregulation is also prevalent in cancer development (Franco, Schoneveld, Georgakilas, & Panayiotidis, <xref rid=\"acel13178-bib-0008\" ref-type=\"ref\">2008</xref>). Oncogenesis is commonly associated with global hypomethylation, inducing genomic instability and activation of oncogenes, as well as promoter hypermethylation leading to inactivation of tumor suppressor genes (Kulis & Esteller, <xref rid=\"acel13178-bib-0021\" ref-type=\"ref\">2010</xref>). Notably, an increased incidence of pediatric cancers has been implicated in offspring of older fathers (Hemminki, Kyyronen, & Vaittinen, <xref rid=\"acel13178-bib-0015\" ref-type=\"ref\">1999</xref>; Yip, Pawitan, & Czene, <xref rid=\"acel13178-bib-0045\" ref-type=\"ref\">2006</xref>). The process of aging can modulate epigenetic regulation, in both somatic cells and the germline. Previously published work has confirmed these effects on the sperm methylome over time (Jenkins et al., <xref rid=\"acel13178-bib-0017\" ref-type=\"ref\">2014</xref>). Inherited epigenetic alterations in older males may be the mechanism leading to the increased disease susceptibility in their offspring.</p><p>Our data substantiate a compromised DNA methylation profile in aged sperm, supporting the hypothesis of incomplete reprogramming during spermatogenesis. For the first time, we also demonstrate a generational correlation in sperm and embryo of an altered human methylation landscape associated with advanced paternal age, contributing to nonequivalent efficiency of methylation re‐establishment throughout the human blastocyst genome. Aberrant epigenetic reprogramming is significantly enriched at genes essential for neurological development in both APA sperm and blastocysts, and provides a mechanistic link between the paternal age effect and offspring neurodevelopmental disorders.</p></sec><sec sec-type=\"results\" id=\"acel13178-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13178-sec-0003\"><label>2.1</label><title>Patient demographics</title><p>Advanced paternal age was defined as ≥ 50 years, while young men ≤ 35 years were selected as controls. Demographic data for all male participants are presented in Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S1</xref>. Only normozoospermic samples were utilized based on WHO criteria (Cooper et al., <xref rid=\"acel13178-bib-0003\" ref-type=\"ref\">2010</xref>) and/or internal clinical standards. In addition, only blastocysts donated from young fertile donor oocyte in vitro fertilization (IVF) cycles were utilized in order to effectively eliminate known female and male factor infertility.</p></sec><sec id=\"acel13178-sec-0004\"><label>2.2</label><title>Sperm and blastocyst methylomes</title><p>We used a modified reduced representation bisulfite sequencing (RRBS) method for global methylome analysis on six young (average age: 28.5 ± 2.1 years) and six APA (average age: 54.5 ± 3.9 years) normozoospermic sperm samples, achieving an average of 32 million read pairs and 46% mapping efficiency. In total, we captured approximately 10 million unique CpG sites at 8X coverage, consistent among genomic features: gene body (85%), promoter (73%‐76%), and CpG islands (77%‐81%) (Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). An increase in global methylation was observed in the APA sperm epigenome and confirmed by <italic>LINE1</italic> bisulfite pyrosequencing (Figure <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S1</xref>).</p><p>Due to the extremely limited starting material, a whole‐genome bisulfite sequencing (WGBS) approach was required for global methylome analysis of six young (average paternal age: 32.3 ± 1.5 years) and six APA (average paternal age: 53.5 ± 3.4 years) blastocyst samples. An average of 480 million read pairs and 62% mapping efficiency was achieved, capturing 34 million unique CpGs at 25X coverage. With limited starting input, genomic coverage was moderately adversely affected; gene body coverage was high and consistent (85%‐89%), followed by promoters (69%‐84%) and CpG islands (56%‐78%) with variable coverage across samples (Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). At 5X coverage, 654,665 CpGs were common to both sperm and blastocysts.</p><p>We identified 49,722 and 106,995 CpGs as statistically significant between the young and APA groups for sperm and blastocysts, respectively (<italic>p</italic> ≤ .05). The use of a sliding window analysis reduced the risk of false positives by calling differentially methylated regions (DMRs) with ≥ 3 significantly altered CpGs in the same direction within a 1KB region. This analysis resulted in 3,405 sperm DMRs and 3,997 blastocyst DMRs (Table <xref rid=\"acel13178-tbl-0001\" ref-type=\"table\">1</xref>). Unsupervised hierarchical clustering analysis of significant DMR‐associated CpGs differentiated between the young and APA groups in both sperm and blastocysts, demonstrating distinct sample branches and unique DMR methylation patterns (Figure <xref rid=\"acel13178-fig-0001\" ref-type=\"fig\">1a‐b</xref>). Hypomethylated DMRs in APA sperm were significantly enriched at CpG islands (<italic>p</italic> = 1.80 × 10<sup>‐22</sup>), shelves (<italic>p</italic> = 1.16 × 10<sup>‐175</sup>), and shores (<italic>p</italic> = 1.76 × 10<sup>‐26</sup>), with a similar trend observed preferentially for hypomethylated DMRs in APA blastocysts, although not to significance (Figure <xref rid=\"acel13178-fig-0001\" ref-type=\"fig\">1c‐d</xref>).</p><table-wrap id=\"acel13178-tbl-0001\" xml:lang=\"en\" content-type=\"Table\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>APA sperm and blastocyst DMRs</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\"/><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"top\" rowspan=\"1\">\n<p>APA sperm (<italic>n</italic> = 3,405 DMRs)</p>\n<p>Paternal age:</p>\n<p>Young (28.5 ± 2.1 years)</p>\n<p>APA (54.5 ± 3.9 years)</p>\n</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"top\" rowspan=\"1\">\n<p>APA blastocyst</p>\n<p>(<italic>n</italic> = 3,997 DMRs)</p>\n<p>Paternal age:</p>\n<p>Young (32.7 ± 1.4 years)</p>\n<p>APA (52.7 ± 3.0 years)</p>\n</th></tr><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hypermethylated</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hypomethylated</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hypermethylated</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hypomethylated</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Total DMRs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1929</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,476</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1729</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2,268</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Significant CpGs in DMRs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8,729</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7,262</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6,217</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8,244</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Average CpGs per DMR</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.5 (range 3–35)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.9 (range 3–40)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.6 (range 3–25)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.6 (range 3–14)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Average DMR window width</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">211 bp (range 3–4069)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">370 bp (range 3–3792)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">550 bp (range 3–5432)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">544 bp (range 4–4033)</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Total associated genes</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">612</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">810</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,087</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,457</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Average genes per DMR</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.32</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.55</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.63</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">0.64</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Total unique associated genes</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">568</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">766</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">803</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,173</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">DMRs not associated with a gene</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1,291 (67%)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">641 (43%)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">428 (25%)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">497 (22%)</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13178-fig-0001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Significant DMR‐associated CpG sites and the fraction of DMRs localized to CpG islands, shelves, and shores. (a–b) Heat map representation of the hierarchical clustering of significant (<italic>p</italic> ≤ .05) DMR‐associated CpG sites in a) sperm (<italic>n</italic> = 15,991 CpGs) and b) blastocyst (<italic>n</italic> = 14,461 CpGs), where a positive z‐score corresponds to hypermethylation and a negative z‐score corresponds to hypomethylation relative to the mean. Samples in both sperm and blastocyst cluster into two distinct groups by young and APA samples. (c–d) The proportion of DMRs associated with CpG islands, shelves, and shores in c) sperm and d) blastocyst was tallied for hypermethylated and hypomethylated DMRs in APA relative to young. Hypomethylated CpG regions were significantly enriched in sperm but not blastocyst</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13178-g001\"/></fig><p>A statistical comparison of the sperm and blastocyst DMR‐associated genes demonstrated a highly significant enrichment of genes between the two methylomes upon paternal aging (<italic>p</italic> = 3.46 × 10<sup>‐55</sup>; odds ratio [OR] 3.19). We identified 218 genes with significant and directional DMRs, 167 genes were hypomethylated, and 61 genes were hypermethylated in both sources, with 10 genes exhibiting both hypo‐ and hypermethylated DMRs (Table <xref rid=\"acel13178-tbl-0002\" ref-type=\"table\">2</xref>).</p><table-wrap id=\"acel13178-tbl-0002\" xml:lang=\"en\" content-type=\"Table\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Overlapping DMRs</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" rowspan=\"2\" valign=\"top\" colspan=\"1\"/><th align=\"left\" colspan=\"3\" style=\"border-bottom:solid 1px #000000\" valign=\"top\" rowspan=\"1\">APA sperm‐blastocyst overlapping DMRs</th></tr><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hypermethylated</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hypomethylated</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Any</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Overlapping DMR‐associated genes</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">61</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">167</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">323</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Fold enrichment (odds ratio)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.36</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.31</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.19</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">OR 95% confidence interval</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.77–3.10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.58–5.17</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.79–3.65</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>‐value</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.25E−08</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.14E−45</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.46E−55</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>Methylation validation was achieved in an independent cohort of twenty‐four sperm samples from additional young (<italic>n</italic> = 12; average age: 30.2 ± 1.5 years) and APA (<italic>n</italic> = 12; average age: 55.3 ± 3.7 years) normozoospermic men (<italic>p</italic> < .05; Figure <xref rid=\"acel13178-fig-0002\" ref-type=\"fig\">2</xref>). Genes were selected for methylation validation for various reasons, including altered DMRs from both sperm and blastocyst methylomes, genes identified in significant pathways, genes implicated in neurodevelopmental disorders, genes localized to significant cytobands, imprinted genes, or genes that correspond with published literature on aging sperm (Jenkins et al., <xref rid=\"acel13178-bib-0017\" ref-type=\"ref\">2014</xref>) (Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S3</xref>). We generated linear regression models using the APA sperm results to further characterize methylation changes with respect to aging. As paternal age increased from 50 years to > 60 years, hypomethylation was observed to be significantly exacerbated (Figure <xref rid=\"acel13178-fig-0003\" ref-type=\"fig\">3</xref>). DMRs from two genes significantly associated with neurodevelopmental disorders were selected for blastocyst methylome validation. For this, we used a cohort of twelve blastocysts derived from young paternal age fathers (average paternal age: 31.1 ± 2.2 years) and twelve APA blastocysts (average paternal age: 55.3 ± 3.1 years). Pyrosequencing data confirmed significant hypomethylation along the amplified <italic>CACNA1H</italic> DMR region, with 40% average methylation for the young paternal age blastocyst group significantly decreased to 25% in the APA blastocyst group (<italic>p</italic> < .05), while validation of the <italic>SHANK2</italic> blastocyst DMR showed a trend toward hypomethylation (young: 75%, APA: 56%; <italic>p</italic> < .1) (Figure <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S2</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13178-fig-0002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Sperm DNA methylation validation results. Each plot represents pyrosequencing results for selected validation genes at distinct CpG sites within a significant DMR. CpG sites on the x‐axis are followed by the average percent methylation across all CpGs for young (gray) and APA (red) individuals. Each line represents results for individual sperm samples (<italic>n</italic> = 18 young and <italic>n</italic> = 18 APA sperm samples). Significant hypomethylation in APA relative to young is demonstrated in plots (A‐J), and significant hypermethylation in APA relative to young is demonstrated in plots (K‐M); *<italic>p</italic> ≤ .01, **<italic>p</italic> ≤ .001</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13178-g002\"/></fig><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13178-fig-0003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Sperm DNA methylation change by paternal age. Linear regression models demonstrate a significant negative association between sperm DNA methylation and paternal age in APA fathers (≥50 years; <italic>n</italic> = 18 APA sperm samples). Models were fitted using the lm() function in R with default arguments, and those with <italic>p</italic> ≤ .05 were considered significant. R‐squared, slope, and p‐values are displayed above each plot, and shaded gray areas around each red regression line represent a 95% confidence interval</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13178-g003\"/></fig></sec><sec id=\"acel13178-sec-0005\"><label>2.3</label><title>Localization of DMRs</title><p>To determine whether specific chromosomal regions were more susceptible to age‐related methylation alterations, we analyzed chromosomal enrichment for DMR‐associated gene density at individual cytobands (<italic>p</italic> < .05, <italic>q</italic> < 0.05). We found that methylation alterations were not randomly distributed across the genome, but appear clustered at certain chromosomal locations, with chromosome 19 having the greatest significance between young and APA samples (sperm: <italic>p</italic> = 5.51 × 10<sup>‐7</sup>, <italic>q</italic> = 6.62 × 10<sup>‐6</sup>, blastocyst: <italic>p</italic> = 9.01 × 10<sup>‐13</sup>, <italic>q</italic> = 1.08 × 10<sup>‐11</sup>, and overlapping: <italic>p</italic> = 7.28 × 10<sup>‐6</sup>, <italic>q</italic> = 1.75 × 10<sup>‐4</sup>, OR: 2.21) (Figure <xref rid=\"acel13178-fig-0004\" ref-type=\"fig\">4a</xref>). A significant enrichment was identified at 5 cytobands in APA sperm. Four of the five cytobands were independently enriched in the APA blastocyst methylome, and three cytobands were significant in the overlapping APA sperm and blastocyst gene list (Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S4</xref>). The greatest enrichment for all three datasets was chr19p13.3 (sperm: <italic>p</italic> = 4.32 × 10<sup>‐19</sup>, blastocyst: <italic>p</italic> = 3.76 × 10<sup>‐22</sup>, and overlapping: <italic>p</italic> = 5.62 × 10<sup>‐16</sup>, OR: 9.89) (Figure <xref rid=\"acel13178-fig-0004\" ref-type=\"fig\">4b‐d</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13178-fig-0004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>DMR localization on Chromosome 19. (a) DMRs on chromosome 19 were localized for sperm (left panel) and blastocyst (right panel) according to hypermethylated DMRs (blue) and hypomethylated DMRs (red) in APA relative to young. Increased DMR clustering is observed for cytoband 19p13.3. (b–d) All genes located on chromosome 19 (gray lines) were compared with DMR‐associated genes (red lines) in (b) sperm, (c) blastocyst, and (d) overlapping sperm blastocyst using kernel density estimates. Shaded areas around the lines signify the 95% confidence interval. Cytobands where the density of genes was significantly different between DMR‐associated genes and the genome are highlighted with gray boxes and include cytoband 19p13.3 for all comparisons</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13178-g004\"/></fig><p>We subsequently analyzed the colocalization of DMR‐associated genes with known regions of nucleosome retention in sperm, using previously published ChIP data by Hammoud and colleagues (Hammoud et al., <xref rid=\"acel13178-bib-0013\" ref-type=\"ref\">2009</xref>). For APA sperm DMRs, a statistically significant enrichment was identified with mononucleosomes (<italic>p</italic> = 9.36 × 10<sup>‐29</sup>; OR: 1.89) and with the presence of the repressive histone mark H3K27me3 (<italic>p</italic> = 7.21 × 10<sup>‐6</sup>; OR: 1.38). The colocalization with mononucleosomes was enhanced for the directionally overlapping DMRs identified in both sperm and blastocyst datasets (<italic>p</italic> = 2.52 × 10<sup>‐18</sup>; OR: 3.32) (Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S5</xref>).</p></sec><sec id=\"acel13178-sec-0006\"><label>2.4</label><title>Pathway analysis and disease associations</title><p>Several neurological signaling pathways were highly represented among regions of the genome that were differentially methylated in our APA samples. In particular, the opioid signaling pathway was the top canonical pathway for the directionally overlapping genes found in both APA sperm and blastocyst datasets (<italic>p</italic> = 2.14 × 10<sup>‐3</sup>). This pathway was also the most significant pathway identified independently for the APA blastocyst methylome (<italic>p</italic> = 2.48 × 10<sup>‐7</sup>), and the fourth pathway identified for the APA sperm methylome (<italic>p</italic> = 1.24 × 10<sup>‐3</sup>). Multiple genes from the opioid signaling pathway were successfully validated in APA sperm (<italic>CACNA1H</italic>, <italic>GRIN1</italic>, and <italic>PRKCZ</italic>) and APA blastocysts (<italic>CACNA1H</italic>). The pathway involving nNOS signaling in neurons was highlighted as the top canonical pathway impacted in the APA sperm methylome (<italic>p</italic> = 2.08 × 10<sup>‐4</sup>), which also comprises similar successfully validated genes (<italic>GRIN1</italic> and <italic>PRKCZ</italic>). Additional pathways impacted by advanced paternal age can be found in Table <xref rid=\"acel13178-tbl-0003\" ref-type=\"table\">3</xref>.</p><table-wrap id=\"acel13178-tbl-0003\" xml:lang=\"en\" content-type=\"Table\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Top canonical pathways</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Rank</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Pathway</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>‐value</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\" rowspan=\"1\">APA sperm DMRs</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">nNOS signaling in neurons</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.08E−04</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Sumoylation pathway</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.50E−04</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Glutamate receptor signaling</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.54E−04</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Opioid signaling pathway</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.24E−03</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Dopamine‐DARPP32 feedback in cAMP signaling</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.17E−03</td></tr><tr><td align=\"left\" colspan=\"3\" rowspan=\"1\">APA blastocyst DMRs</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Opioid signaling pathway</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.45E−07</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Netrin signaling</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.58E−07</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GPCR‐mediated nutrient sensing in enteroendocrine cells</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.69E−06</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CREB signaling in neurons</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.28E−06</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">PPARα/RXRα Activation</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.77E−06</td></tr><tr><td align=\"left\" colspan=\"3\" rowspan=\"1\">APA sperm‐blastocyst directional overlapping DMRs</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Opioid signaling pathway</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.14E−03</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Tight junction signaling</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.84E−03</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Wnt/β‐catenin signaling</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.42E−03</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Role of Wnt/GSK−3β signaling in the pathogenesis of influenza</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.71E−03</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Phosphatidylethanolamine biosynthesis III</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9.26E−03</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>To determine whether paternal aging alters methylation at genes linked to neurodevelopmental disorders, we compared publicly available gene lists to our DMRs. We identified a highly significant enrichment for genes implicated in three neurodevelopmental disorders for the directionally overlapping DMRs, specifically for autism spectrum disorder (<italic>p</italic> = 1.94 × 10<sup>‐4</sup>; OR: 4.34), schizophrenia (<italic>p</italic> = 5.55 × 10<sup>‐4</sup>; OR: 5.30), and bipolar disorder (<italic>p</italic> = 4.73 × 10<sup>‐5</sup>; OR: 3.30). This enrichment was also identified independently in the APA sperm DMRs and APA blastocyst DMRs (Table <xref rid=\"acel13178-tbl-0004\" ref-type=\"table\">4</xref>). Several genes were validated in APA sperm that were associated with autism spectrum disorder (<italic>CACNA1H</italic>, <italic>CNTNAP2</italic>, <italic>GRIN1</italic>, <italic>SHANK2</italic>, <italic>SHANK3</italic>, <italic>ZNF804A</italic>), schizophrenia (<italic>TCF3</italic>, <italic>ZNF804A</italic>), and bipolar disorder (<italic>COMT</italic>, <italic>DRD4</italic>, <italic>GRIN1</italic>, <italic>MBP</italic>, <italic>PRKCZ</italic>, <italic>SHANK2</italic>, <italic>TRPM2</italic>, <italic>ZNF804A</italic>), and in APA blastocysts (<italic>CACNA1H</italic>). Notably, the opioid signaling pathway is shared by all three candidate neurodevelopmental disease gene lists (Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S6</xref>).</p><table-wrap id=\"acel13178-tbl-0004\" xml:lang=\"en\" content-type=\"Table\" orientation=\"portrait\" position=\"float\"><label>Table 4</label><caption><p>Neurodevelopmental disorder associations</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Disease</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Overlapping genes</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Fold enrichment (odds ratio)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">OR 95% confidence interval</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>‐value</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"5\" rowspan=\"1\">APA sperm DMRs</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Autism spectrum disorder</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">35</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.48</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1.68–3.57</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7.76E−06</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Schizophrenia</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.31</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1.35–3.77</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.57E−03</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Bipolar disorder</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">63</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1.93</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1.45–2.53</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">8.24E−06</td></tr><tr><td align=\"left\" colspan=\"5\" rowspan=\"1\">APA blastocyst DMRs</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Autism spectrum disorder</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">60</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.80</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.06–3.76</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.88E−10</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Schizophrenia</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">3.40</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.30–4.94</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.42E−09</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Bipolar disorder</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">91</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1.71</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1.35–2.16</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.17E−05</td></tr><tr><td align=\"left\" colspan=\"5\" rowspan=\"1\">APA sperm‐blastocyst directional overlapping DMRs</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Autism spectrum disorder</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">4.34</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.03–8.28</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.94E−04</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Schizophrenia</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">7</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">5.30</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2.07–11.38</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.55E−04</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Bipolar disorder</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">3.30</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1.87–5.47</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.73E−05</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>To further characterize our findings with respect to neurodevelopmental disorders, we utilized the publicly available methylation profiling array described by Nardone and colleagues (Nardone et al., <xref rid=\"acel13178-bib-0027\" ref-type=\"ref\">2014</xref>). We compared two brain tissues between autistic and nonautistic samples and identified 141 and 62 DMRs, respectively (Table <xref rid=\"acel13178-tbl-0005\" ref-type=\"table\">5</xref>). We found that the genes within these DMRs significantly overlapped our APA sperm (frontal cortex <italic>p</italic> = 5.78x10<sup>‐9</sup>, cingulate cortex <italic>p</italic> = 1.62x10<sup>‐4</sup>) and APA blastocyst (frontal cortex <italic>p</italic> = 1.94 × 10<sup>‐16</sup>, cingulate cortex <italic>p</italic> = 3.24 × 10<sup>‐5</sup>) DMR‐associated gene lists. Using the genes associated with these DMRs, we performed pathway analysis and found that the frontal cortex was also significantly enriched for opioid signaling (<italic>p</italic> = 2.14 × 10<sup>‐3</sup>; Table <xref rid=\"acel13178-tbl-0006\" ref-type=\"table\">6</xref>).</p><table-wrap id=\"acel13178-tbl-0005\" xml:lang=\"en\" content-type=\"Table\" orientation=\"portrait\" position=\"float\"><label>Table 5</label><caption><p>DMR‐associated genes in autistic brains compared with APA sperm and APA blastocyst</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Nardone et al., <xref rid=\"acel13178-bib-0027\" ref-type=\"ref\">2014</xref>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">DMRs</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Genes in DMRs</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">APA sperm gene overlap</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>‐value</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">APA blastocyst gene overlap</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic>‐value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Autistic frontal cortex</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">141</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">86</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.78E−09</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.94E−16</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Autistic cingulate cortex</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">62</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">42</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.62E−04</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.24E−05</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><table-wrap id=\"acel13178-tbl-0006\" xml:lang=\"en\" content-type=\"Table\" orientation=\"portrait\" position=\"float\"><label>Table 6</label><caption><p>Pathway analysis for DMR‐associated genes in autistic brains compared with APA sperm and APA blastocyst</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Canonical pathway (<italic>p</italic> ≤ .01)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Autistic frontal cortex (Nardone et al., <xref rid=\"acel13178-bib-0027\" ref-type=\"ref\">2014</xref>)<sup>23</sup>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">APA sperm</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">APA blastocyst</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Opioid signaling Pathway</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.14E−03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.23E−03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.45E−07</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Melatonin signaling</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.24E−03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6.17E−03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3.72E−04</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Dopamine‐DARPP32 feedback in cAMP signaling</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">2.88E−03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4.17E−03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">1.10E−05</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Neuregulin signaling</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.13E−03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5.25E−03</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N</italic>.S.</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>Given that genomic imprints are established in the germline and can persist through fertilization, along with their critical functions in the developing brain and various overlapping characteristics with neurodevelopmental disorders, it is reasonable to consider their involvement in mediating APA effects. The APA datasets were therefore compared to the current list of known and putative human‐imprinted genes. We found that APA sperm had 19 hypomethylated and 7 hypermethylated significant DMRs that were associated with imprinted genes. Blastocysts derived from APA fathers revealed 22 hypomethylated and 10 hypermethylated significant DMRs associated with imprinted genes, and 6 imprinted genes overlapped in both datasets (<italic>p</italic> ≤ .05; Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S7</xref>).</p></sec><sec id=\"acel13178-sec-0007\"><label>2.5</label><title>Sperm miRNA profiles</title><p>The same six young (average age: 28.5 ± 2.1 years) and six APA (average age: 54.5 ± 3.9 years) normozoospermic sperm samples used for global methylome analysis were also used for small RNA sequencing, identifying 278 expressed miRNAs with > 5 counts per million. However, the expression was consistent between the two groups with no differentially expressed miRNAs following FDR adjustment (<italic>p</italic> ≤ .05, q ≤ 0.05; Figure <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S3</xref>). Sixteen DMRs were situated near genes that encode for miRNAs, with no significant impact to miRNA expression (Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S8</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13178-sec-0008\"><label>3</label><title>DISCUSSION</title><p>Risks of offspring neurodevelopmental disorders are increased with advancing paternal age (Croen, Najjar, Fireman, & Grether, <xref rid=\"acel13178-bib-0005\" ref-type=\"ref\">2007</xref>; Frans et al., <xref rid=\"acel13178-bib-0009\" ref-type=\"ref\">2008</xref>; Malaspina et al., <xref rid=\"acel13178-bib-0024\" ref-type=\"ref\">2002</xref>). One potential mechanism involves epimutations arising over time in the sperm, resulting in a phenotype that then manifests later in offspring development (Milekic et al., <xref rid=\"acel13178-bib-0025\" ref-type=\"ref\">2015</xref>). We are the first to report an observed generational inheritance of epigenetic dysregulation from human sperm to the preimplantation embryo. Our results demonstrate a mechanism for the paternal age effect from sperm to offspring, with confirmed significant susceptibility at neurodevelopmental genes associated with autism spectrum disorder, schizophrenia, and bipolar disorder. Furthermore, the genes identified in our study significantly overlapped those that were also differentially methylated in the frontal cortex and cingulate cortex of autistic brains (Nardone et al., <xref rid=\"acel13178-bib-0027\" ref-type=\"ref\">2014</xref>).</p><p>In accordance with the literature on aging sperm methylation (Jenkins, Aston, Cairns, & Carrell, <xref rid=\"acel13178-bib-0016\" ref-type=\"ref\">2013</xref>; Jenkins et al., <xref rid=\"acel13178-bib-0017\" ref-type=\"ref\">2014</xref>), an increase in global methylation was observed in the APA sperm epigenome; however, our DMR analysis identified a comparable number of hypermethylated and hypomethylated regions. It was striking that, among individuals, these methylation changes occurred with tight consistency at each CpG site of validated genes, seen in both the original and independent sperm cohorts. Additionally, linear regression analysis clearly illustrated significant progressive methylation alterations as paternal age increased. Interestingly, while considerable DNA methylation changes were identified between the young and APA sperm samples, differential expression of small RNAs was not observed, suggesting that miRNA regulation is not an epigenetic mechanism affected by advancing paternal age.</p><p>There was a highly significant overlap of DMR‐associated genes between the sperm and blastocyst methylomes upon paternal aging, suggesting that the same genomic regions are affected by methylation dysregulation. Methylation alterations in our datasets were not randomly distributed across the genome, but appear clustered at certain chromosomal locations. Subtelomeric regions were highly enriched for methylation alterations, particularly cytoband 19p13.3, for both sperm and blastocyst. This cytoband harbors a large number of genes (second only to cytoband 16p13.3, also significant in all three groups). Access to these genes may require a looser chromatin conformation, generating vulnerability to epigenetic alterations. It has also been proposed that methylation in subtelomeric regions like these may escape the large‐scale epigenetic reprogramming events and therefore have potential susceptibility to disruption and transmission to offspring (Guibert, Forne, & Weber, <xref rid=\"acel13178-bib-0011\" ref-type=\"ref\">2012</xref>; Hajkova et al., <xref rid=\"acel13178-bib-0012\" ref-type=\"ref\">2002</xref>; Popp et al., <xref rid=\"acel13178-bib-0031\" ref-type=\"ref\">2010</xref>), and age‐associated effects have previously been localized to these regions in sperm (Jenkins et al., <xref rid=\"acel13178-bib-0017\" ref-type=\"ref\">2014</xref>).</p><p>This observation that methylation alterations were not randomly distributed leads to the hypothesis that particular features of chromatin packaging in APA sperm are vulnerable to epigenetic errors, giving rise to aberrant reprogramming within the same regions in blastocysts. As such, we postulated that the DMRs identified in APA sperm colocalized with regions of retained histones. During spermiogenesis, the majority of histones are exchanged for protamines, but as many as 15% of nucleosomes are retained in regions of high CpG density and enriched at loci of developmental importance, including developmental gene promoters, imprinted loci, and genes encoding miRNAs (de Vries et al., <xref rid=\"acel13178-bib-0007\" ref-type=\"ref\">2013</xref>; Hammoud et al., <xref rid=\"acel13178-bib-0013\" ref-type=\"ref\">2009</xref>). By comparing our findings to those putatively bound to retained histones, previously identified by ChIP data from Hammoud et al., <xref rid=\"acel13178-bib-0013\" ref-type=\"ref\">2009</xref>, a statistically significant enrichment of APA sperm DMR‐associated genes correlated with nucleosome retention. A comparable enrichment was observed for the directional overlapping DMRs identified in both sperm and blastocyst datasets. This suggests that paternal germline methylation alterations induced by advancing age is transmitted in a chromatin context. In particular, hypomethylated DMRs were more likely to be associated with a gene, to be associated with CpG islands and flanking regions, and more likely to colocalize with mononucleosomes. Interestingly, while our DMR‐associated genes significantly colocalized with retained histones, only a handful of DMRs were situated near genes that encode miRNAs, with no significant impact to miRNA expression in APA sperm.</p><p>Both APA sperm and blastocyst DMRs exhibited significant enrichment for neurodevelopmental genes associated with autism spectrum disorder, schizophrenia, and bipolar disorder. The incidence of these neuropsychiatric conditions is known to increase progressively with increasing paternal age (de Kluiver et al., <xref rid=\"acel13178-bib-0006\" ref-type=\"ref\">2017</xref>), likewise DNA methylation alterations have been associated with these disorders (Rutten & Mill, <xref rid=\"acel13178-bib-0034\" ref-type=\"ref\">2009</xref>; Wockner et al., <xref rid=\"acel13178-bib-0043\" ref-type=\"ref\">2014</xref>; Wong et al., <xref rid=\"acel13178-bib-0044\" ref-type=\"ref\">2014</xref>). Genomic imprinting is an epigenetic phenomenon that utilizes DNA methylation to restrict gene expression to only one inherited allele. Imprinted genes play a critical role in brain development and therefore may contribute to the etiologies of neurodevelopmental conditions (Crespi, <xref rid=\"acel13178-bib-0004\" ref-type=\"ref\">2008</xref>). Many imprinting disorders present with autistic‐like characteristics, including Beckwith–Wiedemann syndrome (chr11p15.5), Angelman syndrome, and Prader–Willi syndrome (chr15q11‐13) (Crespi, <xref rid=\"acel13178-bib-0004\" ref-type=\"ref\">2008</xref>). Given that imprinting is established in the germline and persists through offspring by escaping widespread epigenetic reprogramming, it is reasonable to consider its involvement in mediating APA effects. In fact, advanced paternal age was associated with methylation differences in brain‐expressed imprinted loci in a mouse model, with concurrent behavioral changes (Smith et al., <xref rid=\"acel13178-bib-0038\" ref-type=\"ref\">2013</xref>). Both APA datasets comprised of various human‐imprinted genes. <italic>DLGAP2</italic>, which has been implicated in the development of autism (Nardone et al., <xref rid=\"acel13178-bib-0027\" ref-type=\"ref\">2014</xref>; Rasmussen, Rasmussen, & Silahtaroglu, <xref rid=\"acel13178-bib-0032\" ref-type=\"ref\">2017</xref>; Soler et al., <xref rid=\"acel13178-bib-0039\" ref-type=\"ref\">2018</xref>), was significantly hypomethylated in both our APA sperm and blastocyst groups. In addition, the cytoband classically responsible for Beckwith–Wiedemann syndrome (chr11p15.5) was significantly impacted in the APA sperm as well as the overlapping group of DMR‐associated genes. Hypomethylation at <italic>KCNQ1</italic> was statistically significant in APA sperm and blastocysts and was validated in our APA sperm samples, in accordance with previously observed hypomethylation in aged human sperm (Jenkins et al., <xref rid=\"acel13178-bib-0017\" ref-type=\"ref\">2014</xref>). Future studies will further examine the relationship between advanced paternal age and genomic imprinting.</p><p>Neurodevelopmental disorders in offspring may also manifest as epigenetic errors at genes associated with neurological development and function, including the opioid signaling pathway, in APA sperm. Opioid signaling was identified as a primary pathway affected in APA sperm and blastocysts, as well as in the combined directional overlapping DMRs, and shared by all three candidate gene lists for autism spectrum disorder, schizophrenia, and bipolar disorder. The frontal cortex in autistic brains was also significantly enriched for genes in the opioid signaling pathway and significantly overlapped our APA sperm and blastocyst DMR‐associated gene lists (Nardone et al., <xref rid=\"acel13178-bib-0027\" ref-type=\"ref\">2014</xref>). The opioid signaling pathway in the brain is a neurotransmitter system involved in mood regulation, and abnormal brain opioid activity likely plays a role in the genesis of neurodevelopmental disorders. Increased opioid receptors are reported in autism and schizophrenia cases (Pellissier, Gandia, Laboute, Becker, & Le Merrer, <xref rid=\"acel13178-bib-0029\" ref-type=\"ref\">2018</xref>; Volk, Radchenkova, Walker, Sengupta, & Lewis, <xref rid=\"acel13178-bib-0042\" ref-type=\"ref\">2012</xref>), and there is a strong similarity between symptoms of opiate addiction and autistic symptoms (Kalat, <xref rid=\"acel13178-bib-0018\" ref-type=\"ref\">1978</xref>). Conversely, autistic‐like symptoms have shown to be attenuated by opioid blocking in the severely mentally disabled (Sandman et al., <xref rid=\"acel13178-bib-0035\" ref-type=\"ref\">1983</xref>). These contrary reactions suggest that a delicate balance is required in the opioid signaling pathway, where either excess or deficiency may produce autistic‐like symptoms (Pellissier et al., <xref rid=\"acel13178-bib-0029\" ref-type=\"ref\">2018</xref>).</p><p>\n<italic>CACNA1H</italic> is one gene identified in the opioid signaling pathway. It encodes CaV3.2, a T‐type calcium channel abundantly expressed in the brain and implicated in neuronal function and brain development. It interacts with opioid receptors in the opioid signaling pathway to mediate analgesia (Altier & Zamponi, <xref rid=\"acel13178-bib-0001\" ref-type=\"ref\">2008</xref>), and missense mutations were identified in individuals with autism spectrum disorder (Splawski et al., <xref rid=\"acel13178-bib-0040\" ref-type=\"ref\">2006</xref>). Hypomethylation at <italic>CACNA1H</italic> was previously observed in aged human sperm compared with the same individual when he was young (Jenkins et al., <xref rid=\"acel13178-bib-0017\" ref-type=\"ref\">2014</xref>). Not only was aberrant DMR hypomethylation confirmed for this same gene in our APA sperm methylome and validation data, we also reveal DMR hypomethylation in the preimplantation blastocyst methylome, which we validated in embryos derived from APA fathers. Evidently some level of methylation retention is required for <italic>CACNA1H</italic> in blastocysts, which is then lost in those derived from APA fathers. Altered CaV3.2 calcium channel activity could ultimately lead to affected neuronal function and brain development in these offspring.</p><p>DNA methylation during spermatogenesis is susceptible to errors that can be propagated to the subsequent generation. It is important to note that while a large proportion of the sperm ejaculate can be analyzed for epigenetic alterations, only one methylation pattern from a single sperm utilized in fertilization has potential inheritance into the blastocyst. Current technologies do not facilitate analysis of DNA methylation in this individual sperm that requires viability. Additional limitations include the minute amount of starting material in blastocysts, the complex epigenetic reprogramming events occurring in preimplantation embryos comprising of global DNA demethylation and variable timing of remethylation based on blastocyst developmental stage and tissue lineage, and the presence of the maternal contribution from the oocyte that complicates the interpretation of epigenetic origins.</p><p>APA is a subtle and varying effect on the human population, and likewise, neurodevelopmental disorders exist on a spectrum due to substantial ranges of symptom severity. Not every child that is born to an APA father is autistic. As such, it would be unreasonable to expect a drastic alteration, similar to a gene mutation or knockout, in sperm DNA methylation in all APA fathers or derived APA blastocysts. We therefore conclude that a threshold for cumulative risk may exist in terms of aberrant epigenetic alterations in sperm that, if surpassed, culminates in a predisposition to disease and ultimately an observed phenotype in offspring. Our data substantiate an increasingly compromised DNA methylation profile as sperm ages. For the first time, we also demonstrate a generational correlation in sperm and embryo of an altered human methylation landscape associated with APA, with significant susceptibility at genes associated with neurodevelopmental disorders. Ongoing investigation into the critical window of sperm epigenetic reprogramming will further our understanding of the role of paternal age in the etiology of these inherited neurological conditions.</p></sec><sec id=\"acel13178-sec-0009\"><label>4</label><title>Experimental Procedures</title><sec id=\"acel13178-sec-0010\"><label>4.1</label><title>Patient selection</title><p>Advanced paternal age (APA) was defined as male age ≥ 50 years, and young control paternal age (young) was defined as male age ≤ 35 years. Sperm and blastocysts were divided into these two groups based on paternal age at the time of oocyte retrieval. Patient demographics and cycle outcomes can be found in Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S1</xref>. Surplus sperm donated with patient consent (<italic>n</italic> = 36) met normozoospermic standards based on semen parameter analysis including concentration, motility, and morphology (Kruger strict), DNA fragmentation and were primarily nonsmokers. Samples were prepared by a two‐layer (90%–45%) PureSperm density‐gradient centrifugation (Nidacon) with subsequent swim‐up technique to collect the viable progressively motile portion. A somatic cell lysis step (0.1% SDS, 0.5% Triton X‐100 in H<sub>2</sub>O) removed round cell contamination, and clean motile sperm samples were diluted to 5 × 10<sup>6</sup> sperm and stored at − 80°C in lysis buffer (Norgen Biotek).</p><p>Surplus cryopreserved blastocysts (<italic>n</italic> = 36) were donated from couples (<italic>n</italic> = 19) undergoing IVF treatment, with informed consent and Institutional Review Board approval. Only young fertile donor oocyte IVF cycles with normozoospermic male partners and a successful live birth were included. Blastocysts included in the study were scored based on developmental stage, inner cell mass, and trophectoderm appearance (Gardner & Schoolcraft, <xref rid=\"acel13178-bib-0010\" ref-type=\"ref\">1999</xref>), and were all morphologically graded as high, transferrable quality (grade ≥ 3BB). Ovarian stimulation, oocyte retrieval, intracytoplasmic sperm injection (ICSI), embryo culture, vitrification, and warming procedures were routinely performed as previously reported (Schoolcraft & Katz‐Jaffe, <xref rid=\"acel13178-bib-0037\" ref-type=\"ref\">2013</xref>).</p></sec><sec id=\"acel13178-sec-0011\"><label>4.2</label><title>Global bisulfite sequencing (WGBS or RRBS)</title><p>Individual sperm samples (<italic>n</italic> = 12) underwent RRBS with the use of the Methyl Mini‐Seq platform (Zymo Research). Samples underwent digestion with 60 units of TaqαI and 30 units of MspI (New England Biolabs) sequentially, and addition of adapter sequences was followed by bisulfite treatment using the EZ DNA Methylation‐Lightning Kit (Zymo Research) and purification (DNA Clean & Concentrator‐5; Zymo Research). Individual blastocysts (<italic>n</italic> = 12) underwent WGBS with the use of the Methyl Maxi‐Seq platform (Zymo Research). Bisulfite conversion was performed using the EZ DNA Methylation‐Direct Kit (Zymo Research) with subsequent amplification steps to add adapter and barcode sequences and PCR purification (DNA Clean & Concentrator‐5; Zymo Research). All samples underwent sequencing on the Illumina HiSeq 2,500 platform. Sequence reads were identified with the use of standard Illumina base‐calling software with a 5x minimum read count filter and theoretical resolution for detection at 20%. Bisulfite sequence data alignments were performed using the alignment software Bismark between APA and young groups. Index files were mapped to the human genome (hg19). Methylation percentage was calculated as the number of reads reporting a C, divided by the total number of reads reporting a C or T. Statistical significance of the methylation difference was determined using the Student's <italic>t</italic> test (sperm) or Fisher's exact test (blastocyst), where <italic>p</italic> < .05 was deemed to be significant. Promoter and gene body annotations were included where available. CpG island annotations were downloaded from the UCSC genome browser hg19. CpG shores were defined as those areas within 2kb on either side of an island, and shelves were defined as areas within 2kb of either side of a shore.</p></sec><sec id=\"acel13178-sec-0012\"><label>4.3</label><title>Differentially methylated region (DMR) analysis</title><p>Differentially methylated regions were identified in R (v3.5.1) using DMRcate (v1.18.0) (Peters et al., <xref rid=\"acel13178-bib-0030\" ref-type=\"ref\">2015</xref>) and significantly differentially methylated CpGs (<italic>p</italic> ≤ .05). DMRs were called using lambda = 1,000 and kernel adjustment C = 75. Due to the interdependence of the significance of p‐values in a sliding window analysis, additional filtering parameters were implemented following window identification; DMRs were required to have ≥ 3 significant CpGs and a mean |%methdiff| ≥10%. DMRs with a difference in methylation ≥ 10% in the APA samples were considered hypermethylated and ≤‐10% were considered hypomethylated. Directional DMR‐associated genes had at least one DMR in a shared methylation direction between sperm and blastocyst datasets. For unsupervised hierarchical clustering, significant DMR‐associated CpGs (<italic>p</italic> ≤ .05) were clustered using Pearson's rank correlation and average linkage using the “hclust” function with the pheatmap package in R.</p></sec><sec id=\"acel13178-sec-0013\"><label>4.4</label><title>Targeted bisulfite pyrosequencing for methylation validation</title><p>Individual sperm (<italic>n</italic> = 36) and blastocysts (<italic>n</italic> = 24) underwent targeted bisulfite pyrosequencing using the PyroMark Q24 Advanced system (Qiagen). Genomic DNA was isolated using the Norgen RNA/DNA/Protein Kit (Norgen) for sperm and the QIAamp DNA Micro Kit (Qiagen) for blastocysts. Bisulfite conversion was performed using the EZ DNA Methylation‐Direct Kit (Zymo Research), followed by nested PCR amplification with the use of AmpliTaq Gold (Applied Biosystems) and a universal reverse biotinylated primer in the second round. Primers for methylation analysis (CpG) were designed with the use of PyroMark Assay Design Software v.2.0.1.15 (Qiagen) and overlapped ≥ 2 significant CpGs from the originally identified DMR. Pyrosequencing reactions were prepared using the PyroMark Q24 Advanced CpG Kit (Qiagen), and the DNA methylation level was calculated as a ratio of the C to T peaks at a given CpG site using PyroMark Q24 Advanced Software v.3.0.0. (Qiagen). Student's <italic>t</italic> test and Mann–Whitney U test were used for methylation differences between young and APA cohorts, where <italic>p</italic> ≤ .05 was considered to be statistically significant. Simple linear regression was used to calculate the association between APA and methylation as age increased. Models with <italic>p</italic> ≤ .05 were considered significant. Pyrosequencing primers designed in‐house are outlined in Table <xref rid=\"acel13178-sup-0001\" ref-type=\"supplementary-material\">S9</xref>.</p></sec><sec id=\"acel13178-sec-0014\"><label>4.5</label><title>Kernel density estimate</title><p>All genes from hg19 RefSeq annotations were localized by promoter start site. All genes and DMR‐associated genes were individually parsed by chromosome. For density analysis, start sites per chromosome were smoothed using a Gaussian kernel and a 0.2 bandwidth adjustment from the “density” function in R. A 95% confidence interval was generated by bootstrapping. Random sampling with replacement of the original data points followed by density estimate was performed 10,000 times to calculate a bootstrap variance. For cytoband enrichment, start sites were mapped to hg19 cytoband locations downloaded from the UCSC genome browser. Enrichment was tested using a Fisher exact test where p‐values were adjusted for multiple comparison using the Benjamini–Hochberg method and <italic>p</italic> ≤ .05 were considered significant.</p></sec><sec id=\"acel13178-sec-0015\"><label>4.6</label><title>Pathway analysis</title><p>DMR‐associated genes were used as input for core analysis in Ingenuity Pathway Analysis (IPA; version 1–13) (Qiagen). Gene enrichment analyses were performed in R using Fisher's exact test and a <italic>p</italic> ≤ .05 to define significance.</p></sec><sec id=\"acel13178-sec-0016\"><label>4.7</label><title>Public datasets</title><p>Autism spectrum disorder (ASD) genes were downloaded from SFARI Gene (<ext-link ext-link-type=\"uri\" xlink:href=\"https://gene.sfari.org/\">https://gene.sfari.org/</ext-link>, accessed 10/11/18), a database of genes implicated in autism susceptibility. Genes were filtered for an enrichment score ≤ 3. Schizophrenia (SZ) genes were downloaded from a database (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.szdb.org/download.php\">http://www.szdb.org/download.php</ext-link>, accessed 8/13/19) collated from two genome‐wide association studies (Pardinas et al., <xref rid=\"acel13178-bib-0028\" ref-type=\"ref\">2018</xref>; Schizophrenia Working Group of the Psychiatric Genomics, <xref rid=\"acel13178-bib-0036\" ref-type=\"ref\">2014</xref>) and from a database of differentially methylated genes (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.szdb.org/methy.php\">http://www.szdb.org/methy.php</ext-link>, accessed 11/20/18). Bipolar disorder (BD) genes were downloaded from a database reported in the literature (<ext-link ext-link-type=\"uri\" xlink:href=\"http://bdgene.psych.ac.cn/directSearch.do?type=gene%26keyword\">http://bdgene.psych.ac.cn/directSearch.do?type=gene&keyword</ext-link>=, accessed 10/15/18) and were filtered for genes positively associated with BD in ≥ 1 study.</p><p>Processed microarray β‐values for GSE53924 (Nardone et al., <xref rid=\"acel13178-bib-0027\" ref-type=\"ref\">2014</xref>) were downloaded from the NCBI GEO website. This dataset consists of Illumina HumanMethylation450 BeadChip methylation profiles for autistic and nonautistic individual brain samples from the frontal cortex and the cingulate cortex. A <italic>t</italic> test was used to calculate significant differences between groups. Probes with <italic>p</italic> ≤ .05 were processed through our DMR pipeline to test for regional enrichment with the only difference that DMRs were required to have ≥ 2 significant CpGs. A threshold of <italic>p</italic> ≤ .01 was used to compare significant pathways.</p><p>The current list of known and putative human‐imprinted genes was downloaded from the Genomic Imprinting Website (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.geneimprint.com\">http://www.geneimprint.com</ext-link>, accessed 10/03/2019) and used to determine overlap of our APA sperm and blastocyst DMR‐associated genes with imprinted genes.</p></sec><sec id=\"acel13178-sec-0017\"><label>4.8</label><title>Small RNA sequencing and analysis</title><p>Individual sperm samples (<italic>n</italic> = 12) underwent small RNA sequencing to assess the miRNA expression profiles between young and APA. Paired‐end libraries were constructed with the Small RNA‐Seq Library Prep Kit (Lexogen) and sequenced 150 cycles on an Illumina NovaSEQ6000. Reads were trimmed for adapters, quality, and minimum length of 15 bases using bbduk.sh (BBMap v38.75). Reads ≤ 31 bases were retained by Cutadapt v1.9.1. Reads were aligned to GRCh38 (GSNAP v2019‐09–12), filtered (SAMtools v1.9) and counted (Subread featureCounts v1.6.2 with miRBase v22.1 mature miRNAs). Differential expression was calculated in edgeR v3.24.3 using Fisher's exact test. Adjusted p‐values (Benjamini–Hochberg) with a false discovery rate (FDR) ≤5% were considered significant.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13178-sec-0018\"><title>CONFLICTS OF INTEREST</title><p>The authors have declared that no conflict of interest exists.</p></sec><sec id=\"acel13178-sec-0019\"><title>AUTHOR CONTRIBUTIONS</title><p>M.M.D. contributed to study design, executed critical analyses, performed methylation validation experiments, and took the lead in manuscript preparation. M.E.H. executed critical analyses, bioinformatics, and pathway analyses, and participated in manuscript preparation. J.C.P collected and processed samples for all experiments, and participated in manuscript preparation. W.B.S. provided financial support, sample contribution, and clinical input. M.G.K‐J designed and supervised the completion of the study, and participated in critical discussion and in manuscript preparation.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13178-sup-0001\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13178-s001.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><sec sec-type=\"data-availability\" id=\"acel13178-sec-0021\"><title>Data Availability Statement</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13178-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13178-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13178-cit-0001\">\n<string-name>\n<surname>Altier</surname>, <given-names>C.</given-names>\n</string-name>, & <string-name>\n<surname>Zamponi</surname>, <given-names>G. 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id=\"acel13181-cr-0006\" contrib-type=\"author\"><name><surname>El Hajj</surname><given-names>Nady</given-names></name><xref ref-type=\"aff\" rid=\"acel13181-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13181-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13181-cr-0007\" contrib-type=\"author\"><name><surname>Hahn</surname><given-names>Thomas</given-names></name><xref ref-type=\"aff\" rid=\"acel13181-aff-0006\">\n<sup>6</sup>\n</xref></contrib><contrib id=\"acel13181-cr-0008\" contrib-type=\"author\"><name><surname>Drummer</surname><given-names>Charis</given-names></name><xref ref-type=\"aff\" rid=\"acel13181-aff-0007\">\n<sup>7</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13181-aff-0008\">\n<sup>8</sup>\n</xref></contrib><contrib id=\"acel13181-cr-0009\" contrib-type=\"author\"><name><surname>Behr</surname><given-names>Rüdiger</given-names></name><xref ref-type=\"aff\" rid=\"acel13181-aff-0007\">\n<sup>7</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13181-aff-0008\">\n<sup>8</sup>\n</xref></contrib><contrib id=\"acel13181-cr-0010\" contrib-type=\"author\"><name><surname>Lucas‐Hahn</surname><given-names>Andrea</given-names></name><xref ref-type=\"aff\" rid=\"acel13181-aff-0009\">\n<sup>9</sup>\n</xref></contrib><contrib id=\"acel13181-cr-0011\" contrib-type=\"author\"><name><surname>Niemann</surname><given-names>Heiner</given-names></name><xref ref-type=\"aff\" rid=\"acel13181-aff-0010\">\n<sup>10</sup>\n</xref></contrib><contrib id=\"acel13181-cr-0012\" contrib-type=\"author\"><name><surname>Schorsch</surname><given-names>Martin</given-names></name><xref ref-type=\"aff\" rid=\"acel13181-aff-0006\">\n<sup>6</sup>\n</xref></contrib><contrib id=\"acel13181-cr-0013\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Haaf</surname><given-names>Thomas</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-0737-0763</contrib-id><xref ref-type=\"aff\" rid=\"acel13181-aff-0001\">\n<sup>1</sup>\n</xref><address><email>thomas.haaf@uni-wuerzburg.de</email></address></contrib></contrib-group><aff id=\"acel13181-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Institute of Human Genetics</named-content>\n<institution>Julius Maximilians University</institution>\n<city>Würzburg</city>\n<country country=\"DE\">Germany</country>\n</aff><aff id=\"acel13181-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Malopolska Centre of Biotechnology</named-content>\n<institution>Jagiellonian University</institution>\n<city>Krakow</city>\n<country country=\"PL\">Poland</country>\n</aff><aff id=\"acel13181-aff-0003\">\n<label><sup>3</sup></label>\n<institution>Percuros B.V.</institution>\n<city>Leiden</city>\n<country country=\"NL\">The Netherlands</country>\n</aff><aff id=\"acel13181-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Department of Bioinformatics</named-content>\n<institution>Julius Maximilians University</institution>\n<city>Würzburg</city>\n<country country=\"DE\">Germany</country>\n</aff><aff id=\"acel13181-aff-0005\">\n<label><sup>5</sup></label>\n<named-content content-type=\"organisation-division\">College of Health and Life Sciences</named-content>\n<institution>Hamad Bin Khalifa University</institution>\n<city>Doha</city>\n<country country=\"QA\">Qatar</country>\n</aff><aff id=\"acel13181-aff-0006\">\n<label><sup>6</sup></label>\n<institution>Fertility Center</institution>\n<city>Wiesbaden</city>\n<country country=\"DE\">Germany</country>\n</aff><aff id=\"acel13181-aff-0007\">\n<label><sup>7</sup></label>\n<named-content content-type=\"organisation-division\">Platform Degenerative Diseases</named-content>\n<institution>Leibniz Institute for Primate Research</institution>\n<city>Göttingen</city>\n<country country=\"DE\">Germany</country>\n</aff><aff id=\"acel13181-aff-0008\">\n<label><sup>8</sup></label>\n<institution>German Center for Cardiovascular Research, Partner Site Göttingen</institution>\n<city>Göttingen</city>\n<country country=\"DE\">Germany</country>\n</aff><aff id=\"acel13181-aff-0009\">\n<label><sup>9</sup></label>\n<named-content content-type=\"organisation-division\">Institute of Farm Animal Genetics</named-content>\n<institution>Friedrich‐Loeffler‐Institute</institution>\n<city>Mariensee/Neustadt</city>\n<country country=\"DE\">Germany</country>\n</aff><aff id=\"acel13181-aff-0010\">\n<label><sup>10</sup></label>\n<named-content content-type=\"organisation-division\">Clinic for Gastroenterology, Hepatology and Endocrinology</named-content>\n<institution>Medical University Hannover</institution>\n<city>Hannover</city>\n<country country=\"DE\">Germany</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nThomas Haaf, Institute of Human Genetics, Biozentrum, Am Hubland, Julius Maximilians University, Würzburg 97074, Germany.<break/>\nEmail: <email>thomas.haaf@uni-wuerzburg.de</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>01</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13181</elocation-id><history><date date-type=\"received\"><day>05</day><month>2</month><year>2020</year></date><date date-type=\"rev-recd\"><day>06</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by Anatomical Society and John Wiley & Sons Ltd</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13181.pdf\"/><abstract id=\"acel13181-abs-0001\"><title>Abstract</title><p>In somatic cells/tissues, methylation of ribosomal DNA (rDNA) increases with age and age‐related pathologies, which has a direct impact on the regulation of nucleolar activity and cellular metabolism. Here, we used bisulfite pyrosequencing and show that methylation of the rDNA transcription unit including upstream control element (UCE), core promoter, 18S rDNA, and 28S rDNA in human sperm also significantly increases with donor's age. This positive correlation between sperm rDNA methylation and biological age is evolutionarily conserved among mammals with widely different life spans such as humans, marmoset, bovine, and mouse. Similar to the tandemly repeated rDNA, methylation of human α‐satellite and interspersed LINE1 repeats, marmoset α‐satellite, bovine alpha‐ and testis satellite I, mouse minor and major satellite, and LINE1‐T repeats increases in the aging male germline, probably related to their sperm histone packaging. Deep bisulfite sequencing of single rDNA molecules in human sperm revealed that methylation does not only depend on donor's age, but also depend on the region and sequence context (A vs. G alleles). Both average rDNA methylation of all analyzed DNA molecules and the number of fully (>50%) methylated alleles, which are thought to be epigenetically silenced, increase with donor's age. All analyzed CpGs in the sperm rDNA transcription unit show comparable age‐related methylation changes. Unlike other epigenetic aging markers, the rDNA clock appears to operate in similar ways in germline and soma in different mammalian species. We propose that sperm rDNA methylation, directly or indirectly, influences nucleolar formation and developmental potential in the early embryo.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13181-abs-0002\"><p>Sperm methylation of the rDNA transcription unit increases with donor's age in mammals with widely different life spans. Scatter plots show positive correlations between donor's age (<italic>x</italic>‐axis in percentage of life span) and mean methylation (<italic>y</italic>‐axis in %) of 18S rDNA in 80 mouse (purple dots), 36 bull (yellow), 16 marmoset (red), and 295 human (green) sperm samples. An orthologous region covering 8 CpGs was analyzed by bisulfite pyrosequencing. The black regression line applies to all 427 analyzed samples.\n<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13181-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13181-g007.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13181-kwd-0001\">embryo developmental potential</kwd><kwd id=\"acel13181-kwd-0002\">epigenetic clock</kwd><kwd id=\"acel13181-kwd-0003\">germline aging</kwd><kwd id=\"acel13181-kwd-0004\">repetitive DNA elements</kwd><kwd id=\"acel13181-kwd-0005\">ribosomal DNA</kwd><kwd id=\"acel13181-kwd-0006\">sperm DNA methylation</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source>National Science Centre of Poland</funding-source><award-id>2015/19/D/NZ4/03696</award-id></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>Deutsche Forschungsgemeinschaft </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100001659</institution-id></institution-wrap></funding-source><award-id>HA1374/19‐1</award-id></award-group><award-group id=\"funding-0003\"><funding-source><institution-wrap><institution>Horizon 2020 Framework Programme </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100010661</institution-id></institution-wrap></funding-source><award-id>692185</award-id></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"2\"/><page-count count=\"14\"/><word-count count=\"8467\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13181-cit-1001\">\n<string-name>\n<surname>Potabattula</surname>\n<given-names>R</given-names>\n</string-name>, <string-name>\n<surname>Zacchini</surname>\n<given-names>F</given-names>\n</string-name>, <string-name>\n<surname>Ptak</surname>\n<given-names>GE</given-names>\n</string-name>, et al. <article-title>Increasing methylation of sperm rDNA and other repetitive elements in the aging male mammalian germline</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13181</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13181</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13181-body-0001\"><sec id=\"acel13181-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Older men have a much lower chance of achieving a pregnancy by natural conception and assisted reproduction as well (Dain, Auslander, & Dirnfeld, <xref rid=\"acel13181-bib-0008\" ref-type=\"ref\">2011</xref>). Genome‐wide sequencing studies revealed an increasing rate of de novo genetic mutations in the offspring of older males (Kong et al., <xref rid=\"acel13181-bib-0025\" ref-type=\"ref\">2012</xref>), implying an elevated risk for rare monogenic (Crow, <xref rid=\"acel13181-bib-0007\" ref-type=\"ref\">2000</xref>) and neurodevelopmental disorders (Grether, Anderson, Croen, Smith, & Windham, <xref rid=\"acel13181-bib-0016\" ref-type=\"ref\">2009</xref>). The number of spermatogonial cell divisions prior to spermatogenesis increases from 35 times at puberty to >800 times at 50 years (Crow, <xref rid=\"acel13181-bib-0007\" ref-type=\"ref\">2000</xref>). During each replication cycle, not only the DNA sequence itself but also the epigenetic make‐up must be copied to the daughter cells. Since the error rate of this copying process is estimated to be 10–100 times higher for epigenetic than for genetic information (Bennett‐Baker, Wilkowski, & Burke, <xref rid=\"acel13181-bib-0003\" ref-type=\"ref\">2003</xref>), it can be assumed that the accumulation of epigenetic changes is critical for paternal aging. In the mouse model, age‐dependent sperm DNA methylation changes have been associated with changes in gene methylation and expression in the brain and abnormal behavior in the offspring of older males (Milekic et al., <xref rid=\"acel13181-bib-0028\" ref-type=\"ref\">2015</xref>). Age‐related sperm DNA methylation changes and their transmission to the next generation were also found in humans (Atsem et al., <xref rid=\"acel13181-bib-0001\" ref-type=\"ref\">2016</xref>; Jenkins, Aston, Pflueger, Cairns, & Carrell, <xref rid=\"acel13181-bib-0022\" ref-type=\"ref\">2014</xref>; Potabattula et al., <xref rid=\"acel13181-bib-0033\" ref-type=\"ref\">2018</xref>).</p><p>The association between aging and DNA methylation changes is very well documented across a broad spectrum of somatic tissues (Hannum et al., <xref rid=\"acel13181-bib-0018\" ref-type=\"ref\">2013</xref>; Horvath, <xref rid=\"acel13181-bib-0019\" ref-type=\"ref\">2013</xref>). In humans, epigenetic clocks which are built on methylation levels of highly selected CpGs can indicate (with error rates <5 years) the chronological age of the donor. DNA methylation age can also serve as a biomarker to predict life span and age‐related conditions (Field et al., <xref rid=\"acel13181-bib-0013\" ref-type=\"ref\">2018</xref>). The epigenetic signatures of the donor are erased and replaced by germline‐specific epigenomes in germ cells (Reik, Dean, & Walter, <xref rid=\"acel13181-bib-0034\" ref-type=\"ref\">2001</xref>). Because DNA methylation patterns largely differ between sperm and somatic cells (Molaro et al., <xref rid=\"acel13181-bib-0029\" ref-type=\"ref\">2011</xref>), epigenetic clocks that have been trained on somatic tissues cannot be used to predict the sperm donor's age. Overall, there is little overlap between the target CpGs of different epigenetic clocks which were all derived by linear regression algorithms on different data sets. The relationship (cause, consequence, or mere bystander) of clock CpGs to the aging process remains unclear (Field et al., <xref rid=\"acel13181-bib-0013\" ref-type=\"ref\">2018</xref>). The calculated DNA methylation age may be a surrogate marker that tracks the cumulative work done by an epigenetic maintenance system (Horvath, <xref rid=\"acel13181-bib-0019\" ref-type=\"ref\">2013</xref>) and an age‐dependent decay of the methylation landscape (Field et al., <xref rid=\"acel13181-bib-0013\" ref-type=\"ref\">2018</xref>).</p><p>Methylation‐dependent transcriptional regulation of ribosomal DNA (rDNA) is essential for ribosome biogenesis, protein synthesis, and each cellular process (Santoro & Grummt, <xref rid=\"acel13181-bib-0036\" ref-type=\"ref\">2001</xref>). Impaired ribosome biogenesis causes nucleolar stress that is involved in aging and pathogenesis of many age‐related diseases (Wang et al., <xref rid=\"acel13181-bib-0043\" ref-type=\"ref\">2015</xref>). In the human genome, several hundred rDNA transcription units are tandemly arrayed on the acrocentric short arms. Increased rDNA methylation with age has been observed in different rodent tissues (D'Aquila et al., <xref rid=\"acel13181-bib-0009\" ref-type=\"ref\">2017</xref>; Oakes, Smiraglia, Plass, Trasler, & Robaire, <xref rid=\"acel13181-bib-0031\" ref-type=\"ref\">2003</xref>) as well as during in vitro aging of human fibroblasts (Flunkert et al., <xref rid=\"acel13181-bib-0015\" ref-type=\"ref\">2018</xref>). Recently, an evolutionarily conserved epigenetic clock based on rDNA methylation has been developed on mouse blood methylation data sets (Wang & Lemos, <xref rid=\"acel13181-bib-0042\" ref-type=\"ref\">2019</xref>).</p><p>Apart from the tandemly arrayed rDNA transcription units, the human genome contains approximately 600,000 LINE‐1 and more than 1,000,000 ALU retrotransposons, comprising 17% and 11% of total genomic DNA, respectively (De Koning, Gu, Castoe, Batzer, & Pollock, <xref rid=\"acel13181-bib-0010\" ref-type=\"ref\">2011</xref>). Up to several megabases of α‐satellite DNA are present in the centromeric region of human chromosomes. Progressive loss of methylation in repetitive elements in somatic cells has been associated with aging and aging‐related diseases (Flunkert et al., <xref rid=\"acel13181-bib-0015\" ref-type=\"ref\">2018</xref>; Jones, Goodman, & Kobor, <xref rid=\"acel13181-bib-0023\" ref-type=\"ref\">2015</xref>).</p></sec><sec sec-type=\"results\" id=\"acel13181-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13181-sec-0003\"><label>2.1</label><title>Male aging effects on sperm repetitive DNA methylation</title><p>Bisulfite pyrosequencing (BPS) was used to determine rDNA methylation levels of four target regions, including the upstream control element (UCE), core promoter, 18S rDNA, and 28S rDNA in human sperm samples of two independent cohorts, 1 (<italic>N</italic> = 186) and 2 (<italic>N</italic> = 109). Both cohorts displayed a wide range of donor's age (25–66 years), body mass index (BMI) (17–40 kg/m<sup>2</sup>), and sperm concentration (0.2–200 million/ml) (Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S1</xref>). The number of analyzed CpGs ranged from 8 in the 18S rDNA to 26 in the UCE amplicon (Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). Each of the 53 analyzed CpG sites in the rDNA transcription unit displayed a highly significant positive correlation with donor's age in both sperm cohorts (Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S3</xref>). Consequently, the mean sperm methylation of all four analyzed rDNA regions significantly (<italic>p</italic> < 0.0001) increased with donor's age (Figure <xref rid=\"acel13181-fig-0001\" ref-type=\"fig\">1</xref>). Pearson's partial correlations were applied to adjust mean methylation values for potential confounding factors such as sperm concentration and donor's BMI (Table <xref rid=\"acel13181-tbl-0001\" ref-type=\"table\">1</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13181-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Methylation of the rDNA transcription unit in human sperm increases with donor's age. Scatter plots show significant (****<italic>p</italic> < 0.0001) positive correlations between donor's age (<italic>x</italic>‐axis in years) and mean methylation (<italic>y</italic>‐axis in %) of the UCE (26 CpGs), core promoter (9 CpGs), 18S rDNA (8 CpGs), and 28S rDNA (10 CpGs). One hundred and eighty‐six sperm samples of cohort 1 (a) and 109 samples of cohort 2 (b) were analyzed by bisulfite pyrosequencing. Pearson's partial correlations were used to adjust for confounding factors</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13181-g001\"/></fig><table-wrap id=\"acel13181-tbl-0001\" xml:lang=\"en\" content-type=\"TABLE\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>Pearson's partial correlations between donor's age and mean repeat methylation in human sperm cohorts 1 and 2</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"bottom\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" rowspan=\"2\" valign=\"bottom\" colspan=\"1\">Region</th><th align=\"left\" rowspan=\"2\" valign=\"bottom\" colspan=\"1\">Number of CpGs</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">Cohort 1 (<italic>N</italic> = 186)</th><th align=\"left\" colspan=\"2\" style=\"border-bottom:solid 1px #000000\" valign=\"bottom\" rowspan=\"1\">Cohort 2 (<italic>N</italic> = 109)</th></tr><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Correlation coefficient</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic> Value</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Correlation coefficient</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic>p</italic> Value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">rDNA upstream control element</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.55</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.56</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">rDNA promoter</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">9</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.61</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.59</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">18S rDNA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.37</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.39</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">28S rDNA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">10</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.45</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.42</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">α‐satellite DNA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.30</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.28</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.007</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">LINE1</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.28</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"><0.0001</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">+0.30</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.008</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">ALU</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">−0.05</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.47</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">−0.07</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">0.48</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>To test whether BPS also works reliably with very low sperm counts, eight genomic DNA samples were diluted down to aliquots equivalent to 10 sperm cells and analyzed by semi‐nested PCRs. Samples were pre‐selected to cover a maximum age range with 5‐year gaps. For most samples, the differences between three technical replicates were in the order of 2–3 percentage points. After adjusting for sperm concentration and donor's BMI, we observed significant Pearson's partial correlations between donor's age and rDNA (UCE and promoter) methylation as well (Figure <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S1</xref>).</p><p>In addition, BPS on sperm genomic DNA samples was used to determine methylation of other repetitive DNA families, including centromeric α‐satellite DNA and interspersed LINE1 and ALU repeats. For α‐satellite DNA and LINE1, there were highly significant positive correlations (both at the single CpG and the regional level) between sperm methylation and donor's age (Table <xref rid=\"acel13181-tbl-0001\" ref-type=\"table\">1</xref> and Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S3</xref>).</p></sec><sec id=\"acel13181-sec-0004\"><label>2.2</label><title>Sequence‐ and age‐dependent rDNA methylation and epimutation rates</title><p>The haploid sperm genome is endowed with several hundred copies of the rDNA transcription unit. Different alleles can be distinguished on the basis of single nucleotide polymorphisms (Babaian, <xref rid=\"acel13181-bib-0002\" ref-type=\"ref\">2017</xref>). We have developed deep bisulfite sequencing (DBS) assays for two regions of the rDNA transcription unit, each containing an A/G variant with a heterozygosity rate of around 35% (Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S4</xref>). Region 1 targets the external transcribed spacer (ETS) and region 2 the UCE and core promoter. For each variant, we selected 46 informative sperm samples, 23 from young donors (26–36 years) and 23 from old donors (43–60 years). DBS was used to determine variant‐specific methylation at single DNA molecule resolution. For both regions, we observed an increased average methylation level of both the A and the G allele in sperm samples of older donors (Figure <xref rid=\"acel13181-fig-0002\" ref-type=\"fig\">2</xref>; Table <xref rid=\"acel13181-tbl-0002\" ref-type=\"table\">2</xref>). Heat maps (Figure <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S2</xref>) showed an age‐dependent increase in methylation at each of the 38 analyzed CpGs in rDNA region 1 and the 25 CpGs in region 2. Interestingly, in region 2 the A allele was considerably higher methylated than the G allele in almost all analyzed sperm samples from both young and old donors (Figure <xref rid=\"acel13181-fig-0002\" ref-type=\"fig\">2</xref>). In region 1, there were comparable differences between allele‐specific methylation levels; however in a given sample, either the A or the G allele could be hypermethylated.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13181-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>Allele‐specific methylation of rDNA region 1 (comprising 38 CpGs in the external transcribed spacer) and region 2 (25 CpGs in the upstream control element and core promoter) in sperm samples from young and old human donors. Twenty‐three informative samples from each age class were analyzed by deep bisulfite sequencing. Alleles with an A variant are indicated by a blue and alleles with a G variant by a green diamond symbol. Methylation of both alleles is increased in older males. Region 2 also shows a higher methylation of the A, compared to the G allele</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13181-g002\"/></fig><table-wrap id=\"acel13181-tbl-0002\" xml:lang=\"en\" content-type=\"TABLE\" orientation=\"portrait\" position=\"float\"><label>TABLE 2</label><caption><p>Methylation and epimutation rates in sperm of young and old human donors, determined by deep bisulfite sequencing</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Donor's age class</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sample size (<italic>N</italic>)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Methylation (%)</p>\n<p>Mean ± <italic>SD</italic> [range]</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Epimutation rate (%)</p>\n<p>Mean ± <italic>SD</italic> [range]</p>\n</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\" colspan=\"1\">\n<p>Region 1</p>\n<p>A variant</p>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Young</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">7.92 ± 1.24 [5.20–11.10]</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">0.32 ± 0.75 [0.00–3.58]</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Old</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">14.75 ± 2.53 [9.40–21.40]</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">1.36 ± 1.45 [0.00–4.78]</td></tr><tr><td align=\"left\" rowspan=\"2\" colspan=\"1\">\n<p>Region 1</p>\n<p>G variant</p>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Young</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">7.92 ± 1.22 [6.20–10.20]</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">0.23 ± 0.49 [0.00–1.78]</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Old</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">14.80 ± 2.70 [11.40–19.70]</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">1.00 ± 1.83 [0.00–6.94]</td></tr><tr><td align=\"left\" rowspan=\"2\" colspan=\"1\">\n<p>Region 2</p>\n<p>A variant</p>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Young</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">11.47 ± 2.95 [7.40–18.10]</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">1.61 ± 1.96 [0.00–6.13]</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Old</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">22</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">23.70 ± 9.38 [14.80–53.50]</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">9.52 ± 14.90 [0.16–62.98]</td></tr><tr><td align=\"left\" rowspan=\"2\" colspan=\"1\">\n<p>Region 2</p>\n<p>G variant</p>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Young</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">9.30 ± 1.19 [6.10–11.30]</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">0.42 ± 0.66 [0.02–3.02]</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Old</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">18.24 ± 2.52 [12.80–24.50]</td><td align=\"char\" char=\"±\" rowspan=\"1\" colspan=\"1\">2.30 ± 1.77 [0.14–5.88]</td></tr></tbody></table><table-wrap-foot id=\"acel13181-ntgp-0001\"><fn id=\"acel13181-note-0001\"><p>Abbreviation: <italic>SD</italic> = standard deviation.</p></fn></table-wrap-foot><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>Bisulfite pyrosequencing measures the average methylation of millions of DNA molecules in a genomic DNA sample. The observed methylation changes can be due to a gain of methylation at single CpG positions in a large number of different rDNA transcription units or to a few transcription units, where all or most CpGs become methylated. Unlike BPS, DBS can determine the methylation profiles of many thousand individual DNA molecules each in multiple samples in a single experiment. Because it is usually the density of CpG methylation in a cis‐regulatory region rather than individual CpGs that turns a gene “on” or “off,” most single CpG methylation errors represent stochastic aberrant methylation of one or a few CpGs in a target region with a much larger number of unmethylated CpGs and remain without functional consequences. In contrast, individual transcription units with the majority of CpGs being methylated are thought to be epigenetically silenced (Santoro & Grummt, <xref rid=\"acel13181-bib-0036\" ref-type=\"ref\">2001</xref>). In this light, allele methylation errors that are DBS reads with >50% methylated CpGs were considered as functionally relevant epimutations. Epimutation rates (ERs) were calculated by dividing the number of hypermethylated (>50%) alleles by the total number of reads.</p><p>For both variants and both analyzed regions of the rDNA transcription unit, sperm samples of the older donors displayed 4–6 times (<italic>p</italic> = 0.0001–0.005) higher ERs than those of younger males (Figure <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S3</xref>; Table <xref rid=\"acel13181-tbl-0002\" ref-type=\"table\">2</xref>). Sperm samples from old donors exhibited a higher range in rDNA methylation and ER variation than younger males (Figure <xref rid=\"acel13181-fig-0002\" ref-type=\"fig\">2</xref> and Figure <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S3</xref>), indicating epigenetic drift in aging sperm. Apart from age, ERs also depended on sequence context. In both young and older donors, the ER of rDNA region 2 (UCE and core promoter) was 4–5 times (<italic>p</italic> = 0.001–0.002) higher than that of region 1 (ETS) (Figure <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S3</xref>). Consistent with the overall higher methylation of the A alleles, compared to the G alleles in region 2, we also observed a 4 times higher ER of A alleles (Table <xref rid=\"acel13181-tbl-0002\" ref-type=\"table\">2</xref>).</p></sec><sec id=\"acel13181-sec-0005\"><label>2.3</label><title>rDNA methylation in cord blood and peripheral blood</title><p>We have shown previously that paternal age can influence average methylation of particular genes in fetal cord blood, which represents a mixture of paternal and maternal alleles (Atsem et al., <xref rid=\"acel13181-bib-0001\" ref-type=\"ref\">2016</xref>). To study the possible transmission of sperm epigenetic signatures to the next generation, we performed BPS of the rDNA UCE and promoter in cord bloods of 121 children conceived by IVF/ICSI with sperm samples of cohort 2 (Figure <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S4</xref>). We observed a slight increase of rDNA methylation with age of the father at conception; however, the results were not significant (Pearson's <italic>r</italic> = 0.01; <italic>p</italic> = 0.92 for the UCE and <italic>r</italic> = 0.06; <italic>p</italic> = 0.53 for the promoter).</p><p>The effect of paternal age on offspring's blood methylation, if any, is concealed by a much larger effect of aging of the individual. To demonstrate this, we determined the methylation levels of rDNA promoter and α‐satellite DNA in peripheral blood samples of 94 males and 94 females, ranging from 1 to 70 years in age. There was a highly significant positive correlation (Pearson's <italic>r</italic> = 0.27; <italic>p</italic> < 0.0001) between blood rDNA methylation and donor's age across all 188 samples. Similar age effects were seen in male (Pearson's <italic>r</italic> = 0.25; <italic>p</italic> = 0.016) and female (<italic>r</italic> = 0.29; <italic>p</italic> = 0.004) samples (Figure <xref rid=\"acel13181-fig-0003\" ref-type=\"fig\">3</xref>). Consistent with earlier studies (Flunkert et al., <xref rid=\"acel13181-bib-0015\" ref-type=\"ref\">2018</xref>), blood α‐satellite DNA methylation decreased with age (Pearson's <italic>r</italic> = −0.21; <italic>p</italic> = 0.046 in males and <italic>r</italic> = −0.17; <italic>p</italic> = 0.098 in females).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13181-fig-0003\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>Methylation of the rDNA core promoter and α‐satellite DNA in human peripheral blood, determined by bisulfite pyrosequencing. rDNA (9 CpGs) methylation increases, whereas α‐satellite DNA (4 CpGs) methylation decreases with donor's age. Scatter plots show significant (*<italic>p</italic> < 0.05 and **<italic>p</italic> < 0.01) correlations in 94 samples each from males (green dots) and females (red dots). Pearson's correlations were used for statistical analysis</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13181-g003\"/></fig></sec><sec id=\"acel13181-sec-0006\"><label>2.4</label><title>Sperm rDNA clock</title><p>Specific CpGs in the rDNA transcription unit can be used to predict the chronological age of the donor from sperm methylation. As detailed in the methods section, we fitted an ElasticNet regression model using methylation values of the 53 analyzed CpG sites in four different BPS amplicons. The formula estimates the methylation age from the ElasticNet regression model based on rDNA methylation data. The estimated age is essentially a weighted average of the methylation β values measured at distinct CpG sites including a baseline (intercept) offset. Using tenfold cross‐validation, a model with 15 CpGs including three in the rDNA core promoter, 10 in the UCE, and two in 28S rDNA, but none in 18S rDNA, was selected. In the training data set with 278 samples from sperm cohorts 1 and 2, we observed a Pearson's correlation coefficient between DNA methylation age (predicted age) and chronological age of 0.72 and a median absolute difference (MAD) between predicted and chronological age of 2.78 (Figure <xref rid=\"acel13181-fig-0004\" ref-type=\"fig\">4</xref>). In an independent test data set with 154 samples from cohort 3, the predictive error was only slightly lower (<italic>r</italic> = 0.67, MAD = 2.91), meaning that in over half of the cases the methylation age differs less than three years from the chronological age (Figure <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S5</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13181-fig-0004\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>Building the rDNA methylation clock. Scatter plots showing the chronological age (<italic>y</italic>‐axis in years) versus rDNA methylation age (<italic>x</italic>‐axis in years) in the training (<italic>N</italic> = 278) and testing cohort (<italic>N</italic> = 154). Model performance on training cohort: mean squared error (<italic>MSE</italic>) = 16.30, median absolute difference (MAD) = 2.78, Pearson's <italic>r</italic> = 0.72; and on test cohort: <italic>MSE</italic> = 16.96, MAD = 2.91, <italic>r</italic> = 0.67</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13181-g004\"/></fig></sec><sec id=\"acel13181-sec-0007\"><label>2.5</label><title>Evolutionary conservation of age‐related sperm rDNA methylation</title><p>To uncover whether the paternal age effect is evolutionarily conserved, sperm DNA methylation was analyzed in mouse, bovine, and marmoset, a small new‐world primate. Eighty sperm samples were collected from 3‐ to 12‐month‐old mice (8 samples each per month). At most two animals were taken from the same litter. There was a significant increase in methylation of rDNA (spacer and gene promoter, 18S and 28S), (peri)centromeric minor and major satellite DNA, and interspersed LINE1 T repeats with donor's age (Figure <xref rid=\"acel13181-fig-0005\" ref-type=\"fig\">5</xref>; Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S5</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13181-fig-0005\" orientation=\"portrait\" position=\"float\"><label>FIGURE 5</label><caption><p>Repeat methylation in mouse sperm increases with donor's age. Scatter plots show significant (*<italic>p</italic> < 0.05, ***<italic>p</italic> < 0.001, and ****<italic>p</italic> < 0.0001) Pearson's correlations between donor's age (<italic>x</italic>‐axis in years) and mean methylation (<italic>y</italic>‐axis in %) of the rDNA spacer promoter (14 CpGs), rDNA gene promoter (7 CpGs), 18S rDNA (8 CpGs), 28S rDNA (10 CpGs), minor satellite DNA (2 CpGs), major satellite DNA (3 CpGs), and LINE1‐T repeats (4 CpGs). Eighty sperm samples from 3‐ to 12‐month‐old mice were analyzed by bisulfite pyrosequencing</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13181-g005\"/></fig><p>Similarly, 36 sperm samples from 15 different bulls were analyzed by BPS. From nine bulls, we had samples at young (<3 years), middle (3–6 years), and old age (>6 years), and from three bulls at two different ages. Both 18S and 28S rDNA as well as bovine alpha‐ and testis satellite I DNA showed a significant correlation between methylation and donor's age (Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S5</xref>). For each of the 12 bulls with samples at different ages and each amplicon analyzed, DNA methylation increased from young, to middle, to old age (Figure <xref rid=\"acel13181-fig-0006\" ref-type=\"fig\">6</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13181-fig-0006\" orientation=\"portrait\" position=\"float\"><label>FIGURE 6</label><caption><p>Sperm methylation of 18S rDNA (8 CpGs), 28S rDNA (10 CpGs), bovine alpha‐satellite (12 CpGs), and bovine testis satellite I (9 CpGs) increases with donor's age. The color‐coded regression lines show the age‐related increase in DNA methylation of sperm samples from 12 bulls, analyzed by BPS at 2–3 different ages</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13181-g006\"/></fig><p>We also analyzed 16 sperm samples from 1‐ to 12‐year‐old marmosets. There was a slight increase in 18S and 28S rDNA methylation with donor's age but due to the small sample size, the correlations were not significant. There was a highly significant positive correlation between centromeric α‐satellite DNA methylation and donor's age (Figure <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S6</xref>, Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S5</xref>).</p><p>Since the primers for 18S and 28S rDNA are conserved across all analyzed species, it was possible to compare the methylation changes of orthologous regions in the rDNA transcription unit. To this end, methylation of a given sample was adjusted to the life span, which is largely different between mouse (28 months), bull (20 years), marmoset (12 years), and humans (80 years). Although average sperm rDNA methylation and methylation increase during life span differed to some extent between species (Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S6</xref>), combined analysis of 80 murine, 36 bovine, 16 marmoset, and 295 human sperm samples was consistent with an evolutionarily conserved age effect on 18S and 28S rDNA methylation (Figure <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S7</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13181-sec-0008\"><label>3</label><title>DISCUSSION</title><sec id=\"acel13181-sec-0009\"><label>3.1</label><title>Age‐dependent methylation of rDNA and other repetitive elements in sperm</title><p>More than half of the CpG sites in the human genome reside in repetitive DNA elements. The DNA methylation levels differ between various repeat types and in response to a wide range of external and internal factors. Methylation of ALU and LINE1 elements prevents retrotransposon activity and genome instability (Yoder, Walsh, & Bestor, <xref rid=\"acel13181-bib-0045\" ref-type=\"ref\">1997</xref>). Retrotransposons can modulate the local epigenetic landscape through changes in DNA methylation and, thus, confer phenotypic plasticity upon environmental exposures (Sharif, Shinkai, & Koseki, <xref rid=\"acel13181-bib-0038\" ref-type=\"ref\">2013</xref>).</p><p>Sperm methylation of rDNA, α‐satellite DNA, and LINE1 methylation significantly increased with donor's age. Our results are consistent with an earlier study demonstrating LINE1 hypermethylation in aging sperm (Jenkins et al., <xref rid=\"acel13181-bib-0022\" ref-type=\"ref\">2014</xref>). One explanation for the susceptibility of various repeat classes to paternal age is chromatin remodeling during spermiogenesis. Although most DNA in mature sperm is packaged by protamines into an almost crystalline structure, rDNA, centromere repeats, LINE, and SINE retrotransposons appear to preserve nucleosomes and a more open chromatin configuration (Samans et al., <xref rid=\"acel13181-bib-0035\" ref-type=\"ref\">2014</xref>; Sillaste et al., <xref rid=\"acel13181-bib-0039\" ref-type=\"ref\">2017</xref>). Paternally derived nucleosomes are generally retained at gene promoters and genomic loci that play an important role during embryogenesis (Hammoud et al., <xref rid=\"acel13181-bib-0017\" ref-type=\"ref\">2009</xref>; Samans et al., <xref rid=\"acel13181-bib-0035\" ref-type=\"ref\">2014</xref>), including many genes for RNA processing factors.</p><p>Previously, we have shown that rDNA methylation increased, whereas LINE1 and α‐satellite DNA methylation decreased during in vitro aging of human fibroblasts (Flunkert et al., <xref rid=\"acel13181-bib-0015\" ref-type=\"ref\">2018</xref>). This is also true for blood samples from aging individuals. Thus, the rDNA behaves similarly in sperm and somatic cells/tissues, whereas α‐satellite DNA and LINE1 show opposite correlations in germline and soma with age. This argues in favor of the notion that repeat methylation is not a mere side effect of aging process, but the product of specific mechanisms, which can distinguish different repeat classes in germ cells and soma.</p></sec><sec id=\"acel13181-sec-0010\"><label>3.2</label><title>Mechanisms underlying age‐associated hypermethylation of sperm repetitive DNA</title><p>Male germline‐specific DNA methylation patterns are established after prenatal mitotic arrest of spermatogonia and completed postnatally during meiosis (Boyano, Andollo, Zalduendo, & Arechaga, <xref rid=\"acel13181-bib-0005\" ref-type=\"ref\">2008</xref>). By immunolocalization, it was shown that the DNA methyltransferases DNMT1, DNMT3A, and DNMT3B are present in spermatogonia and spermatocytes. <italic>DNMT1</italic> and <italic>DNMT3A</italic> mRNAs are also detected in mature sperm (Marques et al., <xref rid=\"acel13181-bib-0027\" ref-type=\"ref\">2011</xref>). In somatic cells, methylation of rDNA critically depends on DNMT1 and DNMT3B activity (Schmitz, Mayer, Postepska, & Grummt, <xref rid=\"acel13181-bib-0037\" ref-type=\"ref\">2010</xref>), that of LINE1 on DNMT3B (Choi et al., <xref rid=\"acel13181-bib-0006\" ref-type=\"ref\">2011</xref>), that of α‐satellite DNA on DNMT3B and to a lesser extent on DNMT3A (Choi et al., <xref rid=\"acel13181-bib-0006\" ref-type=\"ref\">2011</xref>). In the germline, de novo methylation of interspersed repeats also requires the catalytically inactive DNMT3L. The demethylating enzymes TET2, TET1, and TET3 are expressed successively in spermatocytes and spermatids. Decreased <italic>TET1‐3</italic> mRNAs are found in sperm with low concentration and fertilization rates (Ni et al., <xref rid=\"acel13181-bib-0030\" ref-type=\"ref\">2016</xref>). Increased DNMT (most likely DNMT3B and DNMT3L) and/or decreased TET activities during spermatogenesis of old males could explain the age‐related gain of repeat methylation in sperm.</p><p>We observed increasing sperm methylation with age for all analyzed CpGs (53 by BPS and 63 by DBS) covering different regions (ETS, UCE, core promoter, 18S rDNA, and 28S rDNA) of the human rDNA transcription unit. Single‐molecule sequencing suggests that the gain in methylation is at least partially due to an increasing rate of hypermethylated transcription units, which negatively influence rRNA expression (Santoro & Grummt, <xref rid=\"acel13181-bib-0036\" ref-type=\"ref\">2001</xref>). Methylation of the upstream non‐transcribed spacer may facilitate 5′ to 3′ methylation spreading during aging or age‐related diseases (Turker, <xref rid=\"acel13181-bib-0041\" ref-type=\"ref\">2002</xref>).</p></sec><sec id=\"acel13181-sec-0011\"><label>3.3</label><title>Functional implications of age‐related sperm repeat DNA methylation for the next generation</title><p>The rDNA transcription unit contains more than 1,500 CpG sites whose methylation is involved in epigenetic rDNA silencing (Santoro & Grummt, <xref rid=\"acel13181-bib-0036\" ref-type=\"ref\">2001</xref>; Wang & Lemos, <xref rid=\"acel13181-bib-0042\" ref-type=\"ref\">2019</xref>). Our results demonstrate that sperm rDNA is subject to the same age‐related increase in DNA methylation as somatic tissues. In somatic cells, rDNA methylation reflects changes in nucleolar biology during aging and in age‐related conditions (Wang et al., <xref rid=\"acel13181-bib-0043\" ref-type=\"ref\">2015</xref>). Therefore, rDNA methylation is a primary candidate when looking for age‐related sperm epigenetic signatures which might have an impact on the next generation. By BPS, we did not observe a correlation between paternal age and rDNA methylation in cord blood of children. Although detection of a paternal age effect is hampered by the presence of maternal rDNA, we can largely exclude effect sizes similar to those observed in sperm. On the other hand, the most obvious consequences of increased sperm rDNA methylation can be expected shortly after fertilization, when restoration of totipotency and epigenetic genome reprogramming occur (Reik et al., <xref rid=\"acel13181-bib-0034\" ref-type=\"ref\">2001</xref>).</p><p>Activation of RNA polymerase I, rDNA synthesis, and formation of the nucleolus mark activation of the embryonic genome, which occurs at the two‐cell stage in mouse and at the eight‐cell stage in humans (Eckersley‐Maslin, Alda‐Catalinas, & Reik, <xref rid=\"acel13181-bib-0011\" ref-type=\"ref\">2018</xref>). The early embryo is the fastest dividing tissue and, therefore, highly dependent on efficient ribosome biogenesis and protein synthesis. The nucleolus is the most prominent and, arguably, the most important cellular machinery in the early embryo. A higher methylation and, by extrapolation, lower transcription of the paternal rDNA component during early embryogenesis may have immediate functional consequences and primarily affect embryo metabolism. We propose that increased rDNA methylation in sperm of old males decreases the developmental potential of the resulting embryos, contributing to age‐related fertility problems. Disorganization or delayed formation of the nucleolus is an indicator of aberrant nuclear reprogramming in cloned embryos (Fléchon, <xref rid=\"acel13181-bib-0014\" ref-type=\"ref\">2006</xref>).</p><p>Most LINE1 elements in the human genome are truncated; only about 100 full‐length copies are still active. Intragenic LINE1 elements can act as cis‐regulatory elements. LINE1 expression and retrotransposition occur in the germline, during embryogenesis, and to some extent in somatic tissues (Kano et al., <xref rid=\"acel13181-bib-0024\" ref-type=\"ref\">2009</xref>). The decreasing LINE1 methylation with age is thought to slowly erode genome integrity in the soma, contributing to biological aging (St. Laurent, Hammell, & McCaffrey, <xref rid=\"acel13181-bib-0040\" ref-type=\"ref\">2010</xref>). Sperm LINE1 methylation (mean 70%, range 20%–80%) was comparable to that of somatic cells. Notably, approximately 5% of sperm samples displayed low (20%–50%) LINE1 methylation, which may represent a threat to genome stability. In this respect, an age‐dependent increase in sperm LINE1 methylation may be advantageous. Due to drastic demethylation of the paternal genome after fertilization (Reik et al., <xref rid=\"acel13181-bib-0034\" ref-type=\"ref\">2001</xref>), the highest level of LINE1 activity is observed in the mouse 2‐cell embryo, which then decreases up to the 8‐cell stage. Intuitively, this could be considered as an undesirable side effect of epigenetic reprogramming. However, accumulating evidence (Jachowicz et al., <xref rid=\"acel13181-bib-0020\" ref-type=\"ref\">2017</xref>; Peaston et al., <xref rid=\"acel13181-bib-0032\" ref-type=\"ref\">2004</xref>) suggests that expression of specific classes of repetitive elements in early embryogenesis regulates global chromatin accessibility and is essential for the highly coordinated activation of developmental programs. The increasing methylation of sperm LINE1 elements with paternal age could interfere with their timely reactivation after fertilization, decreasing developmental rates (Jachowicz et al., <xref rid=\"acel13181-bib-0020\" ref-type=\"ref\">2017</xref>).</p><p>In vitro aging and irradiation of human fibroblasts are associated with decreasing α‐satellite methylation (Flunkert et al., <xref rid=\"acel13181-bib-0015\" ref-type=\"ref\">2018</xref>). Reduced methylation of centromeric satellite DNAs may contribute to chromosomal instability in cancer cells (Bollati et al., <xref rid=\"acel13181-bib-0004\" ref-type=\"ref\">2009</xref>). In contrast to somatic cells/tissues, sperm aging is associated with a gain of α‐satellite methylation. However, in this context, it is worth emphasizing that sperm α‐satellite shows considerable undermethylation (mean 35%), compared to somatic tissues such as embryonal fibroblasts (78%–85%; Flunkert et al., <xref rid=\"acel13181-bib-0015\" ref-type=\"ref\">2018</xref>) and blood (83%–91%). If hypomethylation of centromeric satellite DNAs is an epigenetic mark to discriminate germ cells from somatic cells (Yamagata et al., <xref rid=\"acel13181-bib-0044\" ref-type=\"ref\">2007</xref>), increasing methylation levels may affect the reprogramming of the sperm epigenome after fertilization.</p></sec><sec id=\"acel13181-sec-0012\"><label>3.4</label><title>Human sperm rDNA clock</title><p>To date, two epigenetic clocks for human sperm DNA have been developed, based on the analysis of Illumina 450K methylation array data. Lee et al. (<xref rid=\"acel13181-bib-0026\" ref-type=\"ref\">2015</xref>) presented an age‐predictive linear regression model trained on 3 CpGs. This comparatively small model achieved a MAD of 5.4 years on a validation cohort. Jenkins, Aston, Cairns, Smith, and Carrell (<xref rid=\"acel13181-bib-0021\" ref-type=\"ref\">2018</xref>) focused on a pre‐selected set of CpGs aggregated in 51 regions. They used a similar approach (ElasticNet regression) as ours to build a model, yielding a mean absolute error of 2.37 years on an independent cohort, compared to 2.04 years in the training data set. Because clock CpGs were selected from a much larger number of array CpGs, their model gives higher correlations (<italic>r</italic>\n<sup>2</sup> = 0.76 in the test and 0.89 in the training cohort) than ours (<italic>r</italic>\n<sup>2</sup> = 0.45 and 0.52, respectively), which is based on 15 of 53 CpGs in four rDNA amplicons.</p><p>While the role of methylation changes in the highly selected array CpGs for biological aging processes remains largely unclear, our sperm rDNA clock may reflect changes in nucleolar activity which are functionally related to biological aging (Wang et al., <xref rid=\"acel13181-bib-0043\" ref-type=\"ref\">2015</xref>). Wang and Lemos (<xref rid=\"acel13181-bib-0042\" ref-type=\"ref\">2019</xref>) suggested that rDNA methylation can serve as a universal predictor of age in different somatic tissues and species. They developed rDNA models for mouse (and other species) based on reduced representation and whole‐genome bisulfite sequencing data. A mouse model built exclusively on mouse–human homologous CpGs was applied to skin samples of 6 human adults and yielded a strong positive correlation between rDNA methylation age and chronological age. A systematic evaluation of the model was not performed, precluding a direct comparison to our model, but their data corroborate the conservation of the age‐related methylation. Here, we demonstrate an rDNA clock which works in human sperm. Both sperm, which has undergone germline reprogramming, and peripheral blood, which represents somatic tissue, showed an increase of rDNA methylation with donor's age. Notably, the paternal age effect on sperm (0.33% increments per year) was much larger than that on blood (0.06%). One important advantage of our BPS‐based rDNA clock is that reliable measurements can be obtained with very low amounts of DNA equivalent to 10 sperm cells. This is essential for forensic analyses of semen traces, when array or NGS‐based methylation tests fail.</p></sec></sec><sec sec-type=\"conclusions\" id=\"acel13181-sec-0013\"><label>4</label><title>CONCLUSIONS</title><p>Paternal age is associated with increased sperm methylation of rDNA and other repetitive DNA families, which preserve nucleosomes and a more open chromatin organization in mature sperm. The paternal age effect on sperm rDNA and repeat methylation has been conserved in different mammalian species, which is generally considered as a good indicator of functional significance. We propose that increased methylation of paternal rDNA transcription units may interfere with nucleolar structure and function in the early embryo and, directly or indirectly, with developmental potential.</p><p>We have developed a human sperm rDNA clock which works with very low amounts of DNA, however, for clinical applications the performance must be improved, that is, by the analysis of all 1,500 CpGs in the rDNA transcription unit. Although the correlations described here are relatively low, increasing methylation with paternal age was observed for all analyzed CpGs (by two different techniques) in humans and other species. One important point is considerable sperm rDNA methylation variation (10%–15%) among individuals of comparable age within the same species. This biological variation may be due to genetic variants, stochastic factors during germline reprogramming, environmental exposures, and lifestyle. Technical variation is in the order of only 1%–2%. Overall, the effect size of paternal age may be small, compared to other factors, and/or paternal age may be only one of many factors shaping the sperm epigenome.</p><p>The DNA methylation age of germ cells can deviate considerably from chronological age, which is at least partially due to biological variation. Preliminary evidence suggests that somatic DNA methylation age‐based predictors of human morbidity and mortality can be reversed by pharmacological aging intervention (Fahy et al., <xref rid=\"acel13181-bib-0012\" ref-type=\"ref\">2019</xref>). It is interesting to speculate whether sperm with low rDNA methylation show a higher developmental potential of the resulting embryos than sperm with high DNA methylation age. If so, it may be possible to decrease a given donor's sperm methylation and increase his fertility by certain diets or drugs.</p></sec><sec sec-type=\"materials-and-methods\" id=\"acel13181-sec-0014\"><label>5</label><title>MATERIALS AND METHODS</title><sec id=\"acel13181-sec-0015\"><label>5.1</label><title>Study samples</title><p>The study on human sperm and blood samples was approved by the ethics committee at the medical faculty of the University of Würzburg (no. 117/11 and 212/15). After in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) at the Fertility Center Wiesbaden, the left‐over swim‐up sperm fraction (excess material) was collected, pseudonymized, and frozen at −80°C. After thawing, the swim‐up sperm samples were purified further by density gradients PureSperm 80 and 40 (Nidacon, Mölndal, Sweden). Fetal cord bloods from newborn singletons conceived through IVF/ICSI were collected by collaborating obstetric clinics throughout Germany. For sperm cohort 1 (186 samples, Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S1</xref>), the outcome of fertility treatment was unknown. For sperm cohort 2 (109 samples, Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S1</xref>), cord bloods of the resulting children were available. Peripheral blood DNAs from 94 male and 94 female mutation‐negative individuals with an age range from 1 to 71 years were anonymized excess materials from predictive genetic diagnostics.</p><p>Mouse (<italic>Mus musculus</italic>) sperm samples were isolated from 3‐ to 12‐month‐old mice after cervical dislocation. The vas deferens and caudal epididymis were dissected and placed separately into 500 µl GMOPS with 10 mg/ml human serum albumin at 37°C. After repeated washing, the final fraction was resuspended in 500 µl 1xPBS and stored at −80°C.</p><p>Sperm samples of 15 high‐performance breeding bulls (<italic>Bos taurus</italic>) were obtained from Masterrind, Verden, Germany. Two or three samples were available from 12 bulls at different ages. Bull sperm samples were purified by BoviPure and BoviDilute (Nidacon) following the manufacturer's protocol.</p><p>Sperm samples from common marmosets (<italic>Callithrix jacchus</italic>) were obtained by penile vibrostimulation of male animals housed at the German Primate Center in Göttingen. Swim‐up purification of sperm was performed after density gradient purification of fresh sperm samples. Animal experiments were approved by the Niedersächsisches Landesamt für Verbraucherschutz und Lebensmittelsicherheit (no. 42502‐04‐17/2496).</p><p>For DNA isolation, the purified sperm cells were resuspended in 300 µl buffer (5 ml of 5 M NaCl, 5 ml of 1 M Tris‐HCl; pH 8, 5 ml of 10% SDS; pH 7.2, 1 ml of 0.5 M EDTA; pH 8, 1 ml of 100% β‐mercaptoethanol, and 33 ml H<sub>2</sub>O) and 100 µl (20 mg/ml; 600 mAU/ml) proteinase K (Qiagen, Hilden, Germany), and incubated for 2 hr at 56°C. Sperm DNA was isolated using the DNeasy Blood and Tissue kit (Qiagen), and blood DNA by the classical salting‐out method. DNA concentration and purity were measured by NanoDrop 2000c spectrophotometer (Thermo Scientific, MA, USA). Bisulfite conversion of DNA was performed using the EpiTect Fast 96 Bisulfite Kit (Qiagen).</p></sec><sec id=\"acel13181-sec-0016\"><label>5.2</label><title>Bisulfite pyrosequencing</title><p>PCR and sequencing primers (Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S2</xref>) for human rDNA (UCE, core promoter, 18S rDNA, and 28S rDNA), α‐satellite DNA, ALU, and LINE1, marmoset α‐satellite DNA, mouse rDNA (spacer and gene promoter), (peri)centromeric minor and major satellite DNA, and interspersed LINE1‐T, bovine alpha‐satellite and testis satellite I DNA were designed using the PyroMark Assay Design 2.0 software (Qiagen).</p><p>Fully methylated and unmethylated DNA standards (Qiagen) were used as controls in each run. PCRs were carried out using ~25 ng bisulfite‐converted DNA and FastStart Taq DNA polymerase (Roche Diagnostics, Mannheim, Germany). Amplifications were performed with an initial denaturation at 95°C for 5 min, 35 cycles of 95°C for 30 s, primer‐specific annealing temperature (Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S2</xref>) for 30 s, and 72°C for 45 s, and a final extension step at 72°C for 10 min. Pyro Q‐CpG software (Qiagen) and PyroMark Gold Q96 CDT reagent kit were used to perform pyrosequencing on the PyroMark Q96 MD system.</p></sec><sec id=\"acel13181-sec-0017\"><label>5.3</label><title>Deep bisulfite sequencing</title><p>DBS primers (Table <xref rid=\"acel13181-sup-0001\" ref-type=\"supplementary-material\">S4</xref>) were designed for human rDNA region 1 (ETS) and region 2 (UCE and core promoter). First‐round PCRs were carried out using ~50 ng bisulfite‐converted DNA and FastStart Taq DNA polymerase. Artificially 0%, 50%, and 100% methylated DNAs (Qiagen) served as controls. PCR products were cleaned with Agencourt AMPure XP Beads (Beckmann Coulter, Krefeld, Germany), quantified using Qubit dsDNA BR Assay system kit (Invitrogen, Karlsruhe, Germany), and diluted to a concentration of 0.2 ng/µl. In a second PCR, the samples from different assays were pooled together and barcoded using 48 multiplex identifiers. NEBNext Multiplex Oligos (Dual Index Primer Set 1) for Illumina (New England BioLabs, Frankfurt, Germany) were used for adaptor ligation. The purified and quantified PCR pools were diluted to a concentration of 4 nM, and 3 µl of this dilution from each of the 48 MIDs were pooled together into one final pool for next‐generation sequencing.</p><p>Paired‐end (250 bp) sequencing was performed using the Illumina MiSeq and Reagent Kit v2 (500 cycles) cartridge (Illumina, CA, USA) according to the manufacturer's instructions. After the run, the sequencing reads were processed by Illumina Genome Analyzer. FASTQ files were analyzed further using the Amplikyzer2 software (<ext-link ext-link-type=\"uri\" xlink:href=\"https://bitbucket.org/svenrahmann/amplikyzer/wiki/Installation\">https://bitbucket.org/svenrahmann/amplikyzer/wiki/Installation</ext-link>) and in‐house R scripts. To determine allele‐specific methylation rates, all analyzed amplicon sequences were aligned to the reference genomic sequence for each assay, and allele splitting was done based on the genetic variant within the target region. Only reads with an overall bisulfite conversion rate of >95% were considered.</p></sec><sec id=\"acel13181-sec-0018\"><label>5.4</label><title>Statistical analyses</title><p>Statistical analysis was performed using the software R (version 3.2.2) and the packages of the Bioconductor project (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.bioconductor.org/\">https://www.bioconductor.org/</ext-link>). Male donor's age was correlated with the sperm DNA methylation level of the corresponding amplicon. For human samples, Pearson's partial correlations were applied to adjust for possible confounding factors. For animal models, either Pearson's or Spearman's correlations were used depending on data distribution. Parametric <italic>t</italic> tests or non‐parametric Mann–Whitney <italic>U</italic> tests were performed for group comparisons.</p><p>Building of the rDNA clock model was performed with a training cohort of 289 samples and validated on an independent test cohort of 154 samples. The β values of 53 CpG sites in the four analyzed rDNA gene regions were used as features. Samples with 10 or more missing values in the CpG measurements were removed, yielding a final training cohort of 278 samples. For the remaining samples, missing CpG values were imputed using the K‐nearest neighbor approach of the impute.knn function in the impute package (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.bioconductor.org/packages/3.2/bioc/html/impute.html\">https://www.bioconductor.org/packages/3.2/bioc/html/impute.html</ext-link>) with default settings. An ElasticNet model was fitted as implemented in the glmnet package (<ext-link ext-link-type=\"uri\" xlink:href=\"https://cran.r-project.org/web/packages/glmnet/index.html\">https://cran.r‐project.org/web/packages/glmnet/index.html</ext-link>) with default parameter settings. The parameter α which selects between lasso and ridge penalty was set to α = 0.5 as in previous studies (Horvath, <xref rid=\"acel13181-bib-0019\" ref-type=\"ref\">2013</xref>). Selection of the penalizing parameter lambda was performed based on tenfold cross‐validation on the training set, using the mean square error (<italic>MSE</italic>) loss function. A lambda yielding an <italic>MSE</italic> of one standard error above the minimal <italic>MSE</italic> was chosen for the final model (λ = 1.15). This model contained 15 features in total. To obtain an estimate of the prediction error, the final model was validated on an independent cohort.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13181-sec-0020\"><title>CONFLICT OF INTEREST</title><p>None declared.</p></sec><sec id=\"acel13181-sec-0021\"><title>AUTHOR CONTRIBUTIONS</title><p>T.Hf. designed the study. T.Hf. and R.P. wrote and revised the manuscript. R.P. performed all the experiments and analyzed the data. N.E.H. supervised the experiments. M.D. and T.M. designed the epigenetic clock. M.S. and T.Hn. contributed human study samples. F.Z. and G.E.P. contributed mouse study samples. A.L‐H. and H.N. contributed bovine study samples. C.D. and R.B. contributed marmoset study samples. All authors reviewed and approved the final manuscript.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13181-sup-0001\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13181-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13181-sec-0019\"><title>ACKNOWLEDGEMENTS</title><p>We thank all the couples and obstetric clinics participating in the study. We thank Drs. J. Pott and S. Akabanes from Masterrind, Verden, for providing the bovine semen samples. This study was supported by the German Research Foundation (grant no. HA1374/19‐1), the National Science Centre of Poland (grant no. 2015/19/D/NZ4/03696), and the EU Horizon 2020 Research and Innovation Programme (grant no. 692185, acronym ERAofART).</p></ack><sec sec-type=\"data-availability\" id=\"acel13181-sec-0023\"><title>DATA AVAILABILITY STATEMENT</title><p>The DBS data set is deposited in NCBI's Sequence Read Archive (number SUB6706443). 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">33073926</article-id><article-id pub-id-type=\"pmc\">PMC7431826</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13192</article-id><article-id pub-id-type=\"publisher-id\">ACEL13192</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Short Take</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Short Take</subject></subj-group></article-categories><title-group><article-title>The aged hematopoietic system promotes hippocampal‐dependent cognitive decline</article-title><alt-title alt-title-type=\"left-running-head\">SMITH et al.</alt-title></title-group><contrib-group><contrib id=\"acel13192-cr-0001\" contrib-type=\"author\"><name><surname>Smith</surname><given-names>Lucas K.</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-3452-6177</contrib-id><address><email>lucas.smith@ucsf.edu</email></address><xref ref-type=\"aff\" rid=\"acel13192-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13192-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13192-cr-0002\" contrib-type=\"author\"><name><surname>Verovskaya</surname><given-names>Evgenia</given-names></name><address><email>evgenia.verovskaya@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13192-aff-0003\">\n<sup>3</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13192-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13192-cr-0003\" contrib-type=\"author\"><name><surname>Bieri</surname><given-names>Gregor</given-names></name><address><email>Gregor.Bieri@ucsf.edu</email></address><xref ref-type=\"aff\" rid=\"acel13192-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13192-cr-0004\" contrib-type=\"author\"><name><surname>Horowitz</surname><given-names>Alana M.</given-names></name><address><email>alana.horowitz@ucsf.edu</email></address><xref ref-type=\"aff\" rid=\"acel13192-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13192-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13192-cr-0005\" contrib-type=\"author\"><name><surname>von Ungern‐Sternberg</surname><given-names>Saskia N. I.</given-names></name><address><email>Saskia.Ungern-Sternberg@med.uni-tuebingen.de</email></address><xref ref-type=\"aff\" rid=\"acel13192-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13192-cr-0006\" contrib-type=\"author\"><name><surname>Lin</surname><given-names>Karin</given-names></name><address><email>karinlin28@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13192-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13192-aff-0006\">\n<sup>6</sup>\n</xref></contrib><contrib id=\"acel13192-cr-0007\" contrib-type=\"author\"><name><surname>Seizer</surname><given-names>Peter</given-names></name><address><email>Peter.Seizer@med.uni-tuebingen.de</email></address><xref ref-type=\"aff\" rid=\"acel13192-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13192-cr-0008\" contrib-type=\"author\"><name><surname>Passegué</surname><given-names>Emmanuelle</given-names></name><address><email>ep2828@cumc.columbia.edu</email></address><xref ref-type=\"aff\" rid=\"acel13192-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13192-cr-0009\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Villeda</surname><given-names>Saul A.</given-names></name><xref ref-type=\"aff\" rid=\"acel13192-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13192-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13192-aff-0003\">\n<sup>3</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13192-aff-0006\">\n<sup>6</sup>\n</xref><address><email>Saul.villeda@ucsf.edu</email></address></contrib></contrib-group><aff id=\"acel13192-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Department of Anatomy</named-content>\n<institution>University of California San Francisco</institution>\n<city>San Francisco</city>\n<named-content content-type=\"country-part\">CA</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13192-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Biomedical Sciences Graduate Program</named-content>\n<institution>University of California San Francisco</institution>\n<city>San Francisco</city>\n<named-content content-type=\"country-part\">CA</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13192-aff-0003\">\n<label><sup>3</sup></label>\n<institution>The Eli and Edyth Broad Center for Regenerative Medicine and Stem Cell Research</institution>\n<city>San Francisco</city>\n<named-content content-type=\"country-part\">CA</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13192-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Columbia Stem Cell Initiative</named-content>\n<named-content content-type=\"organisation-division\">Department of Genetics and Development</named-content>\n<institution>Columbia University Irving Medical Center</institution>\n<city>New York</city>\n<named-content content-type=\"country-part\">NY</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13192-aff-0005\">\n<label><sup>5</sup></label>\n<named-content content-type=\"organisation-division\">Department of Cardiology and Cardiovascular Medicine</named-content>\n<institution>University of Tübingen</institution>\n<city>Tübingen</city>\n<country country=\"DE\">Germany</country>\n</aff><aff id=\"acel13192-aff-0006\">\n<label><sup>6</sup></label>\n<named-content content-type=\"organisation-division\">Neuroscience Graduate Program</named-content>\n<institution>University of California San Francisco</institution>\n<city>San Francisco</city>\n<named-content content-type=\"country-part\">CA</named-content>\n<country country=\"US\">USA</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label>\nCorrespondence<break/>\nSaul A. Villeda, Department of Anatomy, University of California San Francisco, 513 Parnassus Ave, Box 0452, San Francisco, CA 94143.<break/>\nEmail: <email>saul.villeda@ucsf.edu</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>21</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13192</elocation-id><history><date date-type=\"received\"><day>20</day><month>11</month><year>2019</year></date><date date-type=\"rev-recd\"><day>15</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by Anatomical Society and John Wiley & Sons Ltd</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13192.pdf\"/><abstract id=\"acel13192-abs-0001\"><title>Abstract</title><p>The aged systemic milieu promotes cellular and cognitive impairments in the hippocampus. Here, we report that aging of the hematopoietic system directly contributes to the pro‐aging effects of old blood on cognition. Using a heterochronic hematopoietic stem cell (HSC) transplantation model (in which the blood of young mice is reconstituted with old HSCs), we find that exposure to an old hematopoietic system inhibits hippocampal neurogenesis, decreases synaptic marker expression, and impairs cognition. We identify a number of factors elevated in the blood of young mice reconstituted with old HSCs, of which cyclophilin A (CyPA) acts as a pro‐aging factor. Increased systemic levels of CyPA impair cognition in young mice, while inhibition of CyPA in aged mice improves cognition. Together, these data identify age‐related changes in the hematopoietic system as drivers of hippocampal aging.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13192-abs-0002\"><p>The aged systemic milieu promotes cellular and cognitive impairments in the hippocampus. Here, we report that heterochronic hematopoietic stem cell (HSC) transplantation impairs hippocampal neurogenesis, decreases synaptic marker expression, and elicits age‐related cognitive impairments in young mice. We identify a number of factors that are elevated in the blood of young mice reconstituted with old HSCs, of which cyclophilin A (CyPA) acts as a pro‐aging factor. Increased systemic levels of CyPA impair cognition in young mice, while inhibition of CyPA in aged mice improves cognition.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13192-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13192-g003.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13192-kwd-0001\">aging</kwd><kwd id=\"acel13192-kwd-0002\">cognition</kwd><kwd id=\"acel13192-kwd-0003\">cyclophilin A</kwd><kwd id=\"acel13192-kwd-0004\">hematopoietic system</kwd><kwd id=\"acel13192-kwd-0005\">hippocampus</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>National Institute on Aging </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100000049</institution-id></institution-wrap></funding-source><award-id>AG053382</award-id><award-id>AG055797</award-id></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>ARCS Foundation </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100008227</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0003\"><funding-source>Hillblom Foundation Predoctoral</funding-source></award-group><award-group id=\"funding-0004\"><funding-source>Netherlands Organization for Scientific Research Rubicon Fellowship</funding-source></award-group><award-group id=\"funding-0005\"><funding-source>BD Biosciences Stem Cell Grant</funding-source></award-group><award-group id=\"funding-0006\"><funding-source>Marc and Lynne Benioff</funding-source></award-group></funding-group><counts><fig-count count=\"2\"/><table-count count=\"0\"/><page-count count=\"10\"/><word-count count=\"6802\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13192-cit-1001\">\n<string-name>\n<surname>Smith</surname>\n<given-names>LK</given-names>\n</string-name>, <string-name>\n<surname>Verovskaya</surname>\n<given-names>E</given-names>\n</string-name>, <string-name>\n<surname>Bieri</surname>\n<given-names>G</given-names>\n</string-name>, et al. <article-title>The aged hematopoietic system promotes hippocampal‐dependent cognitive decline</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13192</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13192</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13192-body-0001\"><sec id=\"acel13192-sec-0001\"><label>1</label><title>INTRODUCTION, RESULTS, DISCUSSION</title><p>Exposure to the aging systemic milieu, through models such as heterochronic parabiosis, promotes cellular, molecular, and structural changes in the brain that lead to cognitive decline (Katsimpardi et al., <xref rid=\"acel13192-bib-0007\" ref-type=\"ref\">2014</xref>; Smith et al., <xref rid=\"acel13192-bib-0018\" ref-type=\"ref\">2015</xref>; Villeda et al., <xref rid=\"acel13192-bib-0022\" ref-type=\"ref\">2011</xref>; Yousef et al., <xref rid=\"acel13192-bib-0028\" ref-type=\"ref\">2019</xref>). To date, studies have focused on the role of circulating factors, such as CCL11, B2M, and VCAM‐1, as mediators of the pro‐aging effects of old blood on the brain (Das et al., <xref rid=\"acel13192-bib-0003\" ref-type=\"ref\">2019</xref>; Smith et al., <xref rid=\"acel13192-bib-0018\" ref-type=\"ref\">2015</xref>; Villeda et al., <xref rid=\"acel13192-bib-0022\" ref-type=\"ref\">2011</xref>; Yousef et al., <xref rid=\"acel13192-bib-0028\" ref-type=\"ref\">2019</xref>). Despite the fact that the pro‐aging factors identified to date are immune‐related molecules (Smith, White, & Villeda, <xref rid=\"acel13192-bib-0019\" ref-type=\"ref\">2018</xref>), whether the aging hematopoietic system promotes hippocampal aging has not yet been investigated.</p><p>In mice and humans, the hematopoietic system undergoes many functional and structural changes during aging, characterized by myeloid expansion, decreased immunity, and chronic low‐grade inflammation (Van Zant & Liang, <xref rid=\"acel13192-bib-0021\" ref-type=\"ref\">2012</xref>). We hypothesized that these cellular changes contribute to hippocampal aging through the accumulation of pro‐aging immune factors in old blood. Many of the age‐related changes observed in old blood have roots in hematopoietic stem cell (HSC) aging (Kim, Moon, & Spangrude, <xref rid=\"acel13192-bib-0008\" ref-type=\"ref\">2003</xref>; Pang et al., <xref rid=\"acel13192-bib-0013\" ref-type=\"ref\">2011</xref>). Therefore, we employed a heterochronic HSC transplantation model to test how exposure to an aged hematopoietic system contributes to hippocampal aging (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1a</xref>). Young (2 months) recipient mice were sublethally irradiated (9 Gy) and transplanted with HSCs isolated from young (2 months) or old (24 months) donors, generating isochronic (Iso) and heterochronic (Het) HSC‐reconstituted young mice. Blood chimerism was assessed by measuring the proportion of CD45.2 donor cells in CD45.1 recipient mouse blood by flow cytometry (Figure <xref rid=\"acel13192-sup-0001\" ref-type=\"supplementary-material\">S1</xref>a,b). Blood derived from old HSCs exhibited characteristic age‐related myeloid bias 4.5 months post‐transplantation (Figure <xref rid=\"acel13192-sup-0001\" ref-type=\"supplementary-material\">S1</xref>c,d). Animals showed no signs of illness or weight loss regardless of treatment (Figure <xref rid=\"acel13192-sup-0001\" ref-type=\"supplementary-material\">S1</xref>e).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13192-fig-0001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>The aged hematopoietic system promotes cellular and cognitive hallmarks of hippocampal aging. (a) Schematic showing isochronic (Iso) and heterochronic (Het) HSC transplantation paradigm in young mice. (b, c) Hippocampal‐ and amygdala‐dependent memory was assessed in Iso and Het mice by contextual (b) or cued (c) fear conditioning (FC), respectively (<italic>n</italic> = 17‐19/group). (d, e) Representative fields (d; scale bar = 100 μm) and quantification (e) of Sox2+/GFAP+ (<italic>n</italic> = 5‐8/group), DCX+ (<italic>n</italic> = 7‐8/group), and NeuN+/BrdU+ (<italic>n</italic> = 10‐13/group) cells in Iso and Het HSC‐reconstituted mice. (f, g) Representative Western blot (f) and quantification (g) of hippocampal lysates from Iso and Het mice, probed with anti‐pCreb, AMPAR, NR2B, and GAPDH (<italic>n</italic> = 4‐5/group). (h, i) Representative Golgi stain (h; scale bar = 5 μm) and quantification (i) of dendritic spine density on tertiary branches (<italic>n</italic> = 4‐5/group). (j) Heat map of proteins differentially expressed (<italic>p</italic> < 0.05) between plasma of Iso and Het HSC‐reconstituted young mice, as determined by label‐free mass spectrometry (<italic>n</italic> = 6‐8/group). (k) CyPA expression across mouse tissues during aging by qPCR (<italic>n</italic> = 5–6/group). (l) Aging coefficients for CyPA across human tissues from RNA‐seq dataset (<italic>n</italic> = 83–156 subjects/tissue). (m) Correlation of plasma CyPA levels assessed by Western blot analysis, with percent time freezing in contextual FC in Iso and Het HSC‐reconstituted young mice (<italic>n</italic> = 5–6/group). All data shown as the mean + <italic>SEM</italic>. <italic>*p</italic> < 0.05. <italic>t</italic> test (b, c, e, g, i–k). Pearson's correlation (m).</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13192-g001\"/></fig><p>To investigate whether exposure to an old hematopoietic system elicits age‐related cognitive impairments, we assessed hippocampal‐dependent associative fear memory and spatial memory using contextual fear conditioning (FC) and radial arm water maze (RAWM) behavioral paradigms, respectively. During contextual FC, Het HSC‐reconstituted young mice exhibited decreased freezing during testing compared with Iso controls (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1b</xref>), indicating impaired hippocampal‐dependent memory. No differences were observed in baseline freezing during the training portion of the task (Figure <xref rid=\"acel13192-sup-0001\" ref-type=\"supplementary-material\">S1</xref>f), or in the amygdala‐dependent cued FC paradigm (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1c</xref>). During RAWM, all mice showed similar swim speeds (Figure <xref rid=\"acel13192-sup-0001\" ref-type=\"supplementary-material\">S1</xref>g) and learning capacity during the training portion of the task (Figure <xref rid=\"acel13192-sup-0001\" ref-type=\"supplementary-material\">S1</xref>h). Moreover, exposure to an aged hematopoietic system did not result in robust impairments in spatial memory during the testing portion of the task (Figure <xref rid=\"acel13192-sup-0001\" ref-type=\"supplementary-material\">S1</xref>h). These data indicate that the aged hematopoietic system drives impairments in hippocampal‐dependent associative fear memory.</p><p>We next sought to assess cellular changes in the hippocampi of Het HSC‐reconstituted young mice that might contribute to cognitive decline. Hippocampal neurogenesis has been shown to decline with aging and after exposure to an aged systemic milieu (Villeda et al., <xref rid=\"acel13192-bib-0022\" ref-type=\"ref\">2011</xref>). Therefore, we tested whether exposure to an old hematopoietic system impairs neurogenesis by immunohistochemical analysis 4.5 months post‐HSC transplantation (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1d,e</xref>). Although no difference was observed between groups in the number of Sox2+/GFAP+neural stem cells in the dentate gyrus (DG), young mice exposed to an old hematopoietic system had decreased numbers of doublecortin (DCX)‐positive immature neurons compared with controls (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1d,e</xref>). To assess neuronal differentiation and survival, we employed a long‐term bromodeoxyuridine (BrdU) incorporation paradigm, in which mature neurons derived from proliferating stem cells retain BrdU labeling (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1a</xref>). We observed decreased numbers of BrdU+cells co‐labeled with the mature neuronal marker NeuN in the DG of Het HSC‐reconstituted young mice compared with Iso controls (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1d,e</xref>). These data indicate that exposure to an old hematopoietic system impairs hippocampal neurogenesis.</p><p>We next investigated whether exposure to an aged hematopoietic system elicits synaptic changes in the hippocampus at a molecular and structural level. Activation of the transcription factor, CREB, via phosphorylation, has been implicated in age‐related cognitive decline and rejuvenation (Villeda et al., <xref rid=\"acel13192-bib-0023\" ref-type=\"ref\">2014</xref>; Yu, Curlik, Oh, Yin, & Disterhoft, <xref rid=\"acel13192-bib-0029\" ref-type=\"ref\">2017</xref>). Correspondingly, we assessed the levels of CREB phosphorylation (pCreb) and observed a decrease in Het HSC‐reconstituted young mice compared with Iso controls (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1f</xref>,g). Moreover, we observed decreased expression of the synaptic markers AMPAR and the NMDA receptor subunit NR2B (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1f</xref>,g), both of which have previously been shown to decline with hippocampal aging (Shi et al., <xref rid=\"acel13192-bib-0017\" ref-type=\"ref\">2007</xref>; Wheatley et al., <xref rid=\"acel13192-bib-0025\" ref-type=\"ref\">2019</xref>). At a structural level, we examined dendritic spine density in granule cell neurons and observed a decrease in Het HSC‐reconstituted young mice (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1h,i</xref>). Together, these data indicate that the old hematopoietic system leads to an age‐related decrease in synaptic density in the hippocampus.</p><p>To gain mechanistic insight into how the old hematopoietic system exerts its deleterious effects on cognition, we assessed peripheral immune cell infiltration into the hippocampus. Immunohistochemical identification of CD45.2+ hematopoietic cells in the DG of CD45.1 recipient mice revealed low and equivalent levels of immune cell infiltration in Het and Iso HSC‐reconstituted young mice (Figure <xref rid=\"acel13192-sup-0002\" ref-type=\"supplementary-material\">S2</xref>a,b). While we cannot exclude the possible contribution of these small numbers of peripheral immune cells, we hypothesized that the pro‐aging effects of the old hematopoietic system are predominantly mediated through peripheral changes in circulating blood factors.</p><p>We performed unbiased proteomic analysis on blood plasma collected from Het and Iso HSC‐reconstituted young mice 4.5 months post‐transplantation. Using label‐free mass spectrometry, we identified 22 factors that were differentially expressed between Het and Iso HSC‐reconstituted young mice (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1j</xref>). To identify potential pro‐aging factors, we focused analysis on proteins that were upregulated ≥1.5‐fold in the plasma of Het HSC‐reconstituted young mice (dotted line, Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1j</xref>). Of these, the most significantly upregulated cytokine was cyclophilin A (CyPA, encoded by <italic>Ppia</italic>)—an intracellular protein‐containing peptidyl‐prolyl cis‐trans isomerase activity that is secreted in response to inflammatory stimuli (Nigro, Pompilio, & Capogrossi, <xref rid=\"acel13192-bib-0011\" ref-type=\"ref\">2013</xref>). Elevated CyPA plasma levels were confirmed in Het HSC‐reconstituted mice by Western blot analysis (Figure <xref rid=\"acel13192-sup-0002\" ref-type=\"supplementary-material\">S3</xref>a,b). In an independent cohort of naïve mice, we detected an age‐related increase in CyPA expression in blood cells by qPCR (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1k</xref>). No age‐related changes in CyPA expression were observed in other tissues (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1k</xref>). Examination of a previously published human RNA‐seq dataset from the GTEx project (Yang et al., <xref rid=\"acel13192-bib-0027\" ref-type=\"ref\">2015</xref>) demonstrated a similar increase in CyPA in human blood cells during aging (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1l</xref>). We next analyzed the relationship between CyPA plasma levels and contextual FC performance in Iso and Het HSC‐reconstituted mice. We found an inverse correlation between CyPA levels and cognitive performance (Figure <xref rid=\"acel13192-fig-0001\" ref-type=\"fig\">1m</xref>) positing CyPA as a potential pro‐aging factor with relevance to cognition.</p><p>To test whether increasing systemic CyPA levels are sufficient to elicit age‐related cognitive or cellular impairments, we utilized a hydrodynamic tail vein injection (HDTVI) in vivo transfection approach, wherein young (2 months) mice were intravenously injected with overexpression constructs encoding either CyPA or GFP control (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2a</xref>). The HDTVI‐mediated increase in plasma CyPA levels was confirmed using a HiBiT‐tagged CyPA and luminescent detection approach (Figure <xref rid=\"acel13192-sup-0002\" ref-type=\"supplementary-material\">S3</xref>c). Animals showed no signs of illness or weight loss regardless of treatment (Figure <xref rid=\"acel13192-sup-0002\" ref-type=\"supplementary-material\">S4</xref>a). One month after HDTVI, we assessed hippocampal‐dependent object recognition and associative fear memory using novel object recognition (NOR) and contextual FC behavioral paradigms, respectively (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2b,c</xref>). In the testing portion of the NOR task, young control mice exhibited a significant preference for the novel object over the familiar object, consistent with proper cognitive function; however, this preference was lost in young mice overexpressing CyPA (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2b</xref>). In contextual FC, we observed decreased freezing in young mice overexpressing CyPA mice compared with controls (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2c</xref>). No differences were observed in baseline freezing (Figure <xref rid=\"acel13192-sup-0002\" ref-type=\"supplementary-material\">S4</xref>b) or in cued FC (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2d</xref>). At the cellular level, we examined hippocampal neurogenesis and observed no differences in the number of DCX‐positive immature neurons between treatment groups (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2e,f</xref>). At the molecular level, we assessed the expression of synaptic markers in the hippocampus. Increased systemic CyPA resulted in lower levels of NMDA receptor subunit NR2B and the presynaptic markers synapsin‐1 and synaptophysin compared with control conditions (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2g</xref>,h). These data indicate that increasing systemic CyPA promotes age‐related cognitive decline and decreased levels of key proteins important for synapse function.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13192-fig-0002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Cyclophilin A regulates cellular and cognitive hallmarks of hippocampal aging. (a) Schematic showing hydrodynamic tail vein injection (HDTVI)‐mediated CyPA overexpression (OE) paradigm in young mice. (b–d) Young CyPA‐OE or GFP control mice were tested in novel object recognition (b; NOR; <italic>n</italic> = 8–12/group), contextual fear conditioning (c; FC; <italic>n</italic> = 15–16), and cued FC (d; <italic>n</italic> = 15–16). (e, f) Representative fields (e; scale bar = 100 μm) and quantification (f) of DCX+ cells (<italic>n</italic> = 11/group), in DG of young CyPA‐OE and GFP control mice. (g, h) Representative Western blot (g) and quantification (h) of hippocampal lysates from young CyPA‐OE and GFP control mice, probed with anti‐NR2B, synapsin‐1 (Syn‐1), synaptophysin (Syp), and GAPDH (<italic>n</italic> = 5/group). (i) Schematic showing CyPA inhibition paradigm in aged mice. (j–l) Aged mice treated with an anti‐CyPA antibody or IgG isotype control (ctrl) were tested in NOR (j; <italic>n</italic> = 10–14/group), contextual FC (k; <italic>n</italic> = 10–14/group), and cued FC (l; <italic>n</italic> = 10–14/group). (m, n) Representative fields (m; scale bar = 100 μm) and quantification (n) of DCX+ (<italic>n</italic> = 8–10/group), in anti‐CyPA or IgG ctrl‐treated old mice. (o, p) Representative Western blot (o) and quantification (p) of hippocampal lysates from anti‐CyPA or IgG ctrl‐treated old mice, probed with anti NR2A, Syn‐1, Syp, and GAPDH (<italic>n</italic> = 5/group). All data shown as the mean + <italic>SEM</italic>. <italic>*p</italic> < 0.05. <italic>t</italic> test (c, d, f, h, k, l, n, p). One‐sample <italic>t</italic> test vs. hypothetical mean of 50 (b, j).</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13192-g002\"/></fig><p>Last, we explored whether inhibition of systemic CyPA in aged animals could ameliorate cognitive and cellular hallmarks of hippocampal aging. Aged (19 months) mice were administered with an anti‐CyPA neutralizing antibody (von Ungern‐Sternberg et al., <xref rid=\"acel13192-bib-0024\" ref-type=\"ref\">2017</xref>) or IgG isotype control, 9 times over 1 month, via intraperitoneal injection (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2i</xref>). No differences in weight were observed between groups (Figure <xref rid=\"acel13192-sup-0002\" ref-type=\"supplementary-material\">S4</xref>c). Hippocampal‐dependent cognition was examined by NOR and contextual FC behavioral paradigms (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2j</xref>,k). In the testing portion of the NOR task, aged control mice failed to differentiate the novel from familiar object (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2j</xref>), consistent with impaired cognitive function. However, aged mice treated with an anti‐CyPA antibody exhibited a significant preference for the novel object (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2j</xref>), indicating improved hippocampal‐dependent memory. No differences in freezing were observed in FC (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2k</xref>,l; Figure <xref rid=\"acel13192-sup-0001\" ref-type=\"supplementary-material\">S4</xref>d). At the cellular and molecular levels, inhibition of systemic CyPA in aged mice increased the number of DCX‐positive immature neurons (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2m</xref>,n) and increased expression of the NMDA receptor subunit NR2A and the presynaptic markers synapsin‐1 and synaptophysin (Figure <xref rid=\"acel13192-fig-0002\" ref-type=\"fig\">2o</xref>,p). These data indicate that targeting extracellular CyPA at old age is sufficient to improve object recognition memory, promote neurogenesis, and increase the levels of key proteins important for synapse function.</p><p>Cumulatively, our data demonstrate that age‐related changes in the hematopoietic system promote molecular, cellular, and cognitive hallmarks of hippocampal aging. We identify CyPA as a pro‐aging factor whose expression is elevated in the blood of het HSC‐reconstituted young mice. While the cellular source of CyPA in het HSC‐reconstituted young mice remains to be elucidated, increasing CyPA in the blood of young mice, by HDTVI‐mediated overexpression of CyPA, impaired hippocampal‐dependent cognition. Moreover, we found that the systemic inhibition of CyPA improved cognition in aged mice. Notably, inhibiting CyPA has been demonstrated to be neuroprotective in a mouse model of amyotrophic lateral sclerosis (Pasetto et al., <xref rid=\"acel13192-bib-0014\" ref-type=\"ref\">2017</xref>). In humans, elevated cerebrospinal fluid CyPA levels have recently been associated with cognitive impairments in Alzheimer's disease patients expressing apolipoprotein E4 (Montagne et al., <xref rid=\"acel13192-bib-0010\" ref-type=\"ref\">2020</xref>). Moreover, in humans elevated CyPA plasma levels accompany a number of inflammatory age‐related diseases, including diabetes (Ramachandran et al., <xref rid=\"acel13192-bib-0015\" ref-type=\"ref\">2014</xref>), and cardiovascular disease (Ohtsuki et al., <xref rid=\"acel13192-bib-0012\" ref-type=\"ref\">2017</xref>; Satoh et al., <xref rid=\"acel13192-bib-0016\" ref-type=\"ref\">2013</xref>). In these studies, CyPA plasma levels were also found to be elevated with aging (Ohtsuki et al., <xref rid=\"acel13192-bib-0012\" ref-type=\"ref\">2017</xref>; Ramachandran et al., <xref rid=\"acel13192-bib-0015\" ref-type=\"ref\">2014</xref>; Satoh et al., <xref rid=\"acel13192-bib-0016\" ref-type=\"ref\">2013</xref>). While little is known about the role of CyPA in aging, recent proteomic analysis using mass spectrometry has identified CyPA as part of the senescence‐associated secretory phenotype (SASP) (Wiley et al., <xref rid=\"acel13192-bib-0026\" ref-type=\"ref\">2019</xref>). Ultimately, our data identify the aged hematopoietic system, and downstream circulating immune factors, as potential therapeutic targets to restore cognitive function in the elderly.</p></sec><sec sec-type=\"materials-and-methods\" id=\"acel13192-sec-0002\"><label>2</label><title>MATERIALS AND METHODS</title><sec id=\"acel13192-sec-0003\"><label>2.1</label><title>Mice</title><p>Young (2 months) and old (19–24 months) wild‐type C57BL/6J and B6.SJL/BoyJ mice were obtained from the Jackson Laboratory and subsequently maintained in‐house under specific pathogen‐free conditions under a 12‐hr light–dark cycle. All studies were done in male mice. The numbers of mice used to result in statistically significant differences were calculated using standard power calculations with <italic>α</italic> = 0.05 and a power of 0.8. We used an online tool (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.stat.uiowa.edu/%7Erlenth/Power/index.html\">http://www.stat.uiowa.edu/~rlenth/Power/index.html</ext-link>) to calculate power and sample size on the basis of experience with the respective tests, variability of the assays, and interindividual differences within groups. All animal handling and use were in accordance with institutional guidelines approved by the University of California, San Francisco (UCSF) Institutional Animal Care and Use Committee (IACUC).</p></sec><sec id=\"acel13192-sec-0004\"><label>2.2</label><title>Irradiation and HSC transplantation</title><p>For transplantation studies, 2‐month‐old CD45.1 C57Bl/6‐Boy/J recipient mice were sublethally irradiated (9 Gy, administered in split doses, 3 hr apart) using a <sup>137</sup>Cs source (J.L. Shepherd). 2000 purified CD45.2 C57BL/6J HSCs were transplanted by retro‐orbital injection to recipient mice, within 24 hr of irradiation. Recipient mice were maintained on antibiotic‐containing water for 4 weeks post‐transplantation. Blood chimerism was assessed every 5 weeks by retro‐orbital bleeding and flow cytometry. Mice with <80% blood chimerism, 3 months post‐transplantation, were excluded from further analysis.</p></sec><sec id=\"acel13192-sec-0005\"><label>2.3</label><title>Flow cytometry</title><p>HSCs were isolated as previously described (Ho et al., <xref rid=\"acel13192-bib-0006\" ref-type=\"ref\">2017</xref>). Bone marrow was obtained from leg, arm, and pelvic bones, isolated in Hanks' buffered saline solution (HBSS) supplemented with 2% heat‐inactivated FBS. Erythrocytes were lysed in ACK (150 mM NH4Cl/10 mM kHCO3/10 mM EDTA). Bone marrow cells were further purified using a Ficoll gradient (Histopaque 119, Sigma‐Aldrich). c‐Kit‐positive cells were enriched for with c‐Kit microbeads (Miltenyi Biotec) and MACS separation LS columns (Miltenyi Biotec). Bone marrow cells were then incubated with purified rat anti‐mouse antibodies for CD3 (BioLegend), B220 (eBioscience), CD4 (eBioscience), CD5 (eBioscience), CD8 (eBioscience), CD11b (eBioscience), GR‐1 (eBioscience), Ter119 (eBioscience), followed by goat anti‐rat PE‐Cy5 (Invitrogen). Cells were blocked with purified rat IgG (Sigma‐Aldrich) and stained with c‐Kit‐APC‐eFluor780 (eBioscience), Sca‐1‐PB (BioLegend), CD48‐A647 (BioLegend), CD150‐PE (BioLegend), and Flk2‐bio (eBioscience), followed by SA‐PE‐Cy7 (eBioscience). Stained bone marrow cells were resuspended in 1 μg/ml propidium iodide (PI) to exclude dead cells. HSCs were isolated by double sorting with a FACSAria II (BD Biosciences). For blood chimerism analysis, erythrocytes were lysed in ACK, and cells were maintained in HBSS +2% heat‐inactivated FBS. Blood was stained with CD11b‐PE‐Cy7 (eBioscience), Gr1‐PB (eBioscience), B220 APC‐Cy7 (eBioscience), CD3 APC (eBioscience), and TER119‐PE‐Cy5 (eBioscience), CD45.1‐PE (eBioscience), and CD45.2‐FITC (eBioscience). Stained blood cells were resuspended in 1 μg/ml PI (Thermo Fisher) to exclude dead cells. Chimerism was analyzed with a FACS LSR II using DIVA software (BD Biosciences).</p></sec><sec id=\"acel13192-sec-0006\"><label>2.4</label><title>BrdU administration</title><p>For long‐term BrdU labeling studies, 50 mg/kg of BrdU (Sigma‐Aldrich) was injected intraperitoneally into mice daily for 5 days. Mice were euthanized 33 days after first BrdU injection.</p></sec><sec id=\"acel13192-sec-0007\"><label>2.5</label><title>Immunohistochemistry</title><p>Tissue processing and immunohistochemistry were performed on free‐floating sections according to standard published techniques (Smith et al., <xref rid=\"acel13192-bib-0018\" ref-type=\"ref\">2015</xref>). Mice were anesthetized with 87.5 mg/kg ketamine and 12.5 mg/kg xylazine and transcardially perfused with 1× phosphate‐buffered saline (PBS). Brains were removed and fixed in phosphate‐buffered 4% paraformaldehyde, pH 7.4, at 4°C for 48 hr before cryoprotection in 30% sucrose. Brains were sectioned coronally, at 40 μM with a cryomicrotome (Leica Camera, Inc.), and stored in cryoprotective medium. Primary antibodies used were as follows: goat anti‐DCX (1:7000; Santa Cruz), rat anti‐BrdU (1:1000; Abcam), mouse anti‐NeuN (1:1000; Millipore), goat anti‐Sox2 (1:200, Santa Cruz), rabbit anti‐GFAP (1:1000, Dako), and anti‐CD45.2‐Alexa Fluor 647 (BioLegend). After overnight incubation, unconjugated primary antibody staining was revealed using fluorescence‐conjugated secondary antibodies (Life Technologies). For BrdU labeling, brain sections were pre‐treated with 2 N HCl at 37°C for 30 min and washed three times with Tris‐buffered saline with Tween (TBST) before incubation with primary antibody. All cells were counted in the DG of every sixth coronal hemibrain section through the hippocampus and analyzed by confocal or epifluorescent microscopy using a Zeiss Axio Observer Z1 or Axio Imager M2 microscope, respectively. CD45.2 cells were imaged as a Z‐stack and counted in a standardized box centered on the DG and covered part of the hilus, granular cell layer, and molecular layer. CD45.2 counts were normalized to volume.</p></sec><sec id=\"acel13192-sec-0008\"><label>2.6</label><title>Golgi staining and analysis</title><p>Mice were anesthetized with 87.5 mg/kg ketamine and 12.5 mg/kg xylazine, and brains were removed without perfusion. Golgi staining was performed using FD Rapid GolgiStain Kit (FD Neurotechnologies, Inc.; PK401), per manufacturer's instructions. Hemibrains were rinsed in Milli‐Q water and immersed in the impregnation solution (FD Solutions A + B) for 2 weeks (replacing impregnation solution one time, 24 hr after immersion). Samples were transferred into FD Solution C and incubated for 1 week (replacing FD Solution C one time, 24 hr after immersion). 200‐μm sections were cut using a cryomicrotome (Leica Camera, Inc.), and sections were mounted on gelatin‐coated slides (FD NeuroTechnologies) in a drop of FD Solution C. After drying, slides were rinsed in Milli‐Q H2O 2× for 4 min. Slides were transferred to developing solution (FD Solution D + E) for 10 min. Slides were then rinsed 2× in Milli‐Q water, 4 min each, and dehydrated in 50%, 75%, 95% (1 × 4 min each), and 100% ETOH (4× 4 min). Slides were then incubated in xylene 3×, 4 min each, and coverslipped with Permount (Fisher). Tertiary dendritic spines on granule cell neurons were imaged at 63×. Spines were counted on 2‐3 tertiary dendrites/neuron and 5–11 neurons/mouse.</p></sec><sec id=\"acel13192-sec-0009\"><label>2.7</label><title>Western blot analysis</title><p>Mouse hippocampi were dissected after perfusion of animals, snap‐frozen, and lysed in RIPA lysis buffer (500 nM Tris, pH 7.4, 150 mM NaCl, 0.5% sodium deoxycholate, 1% NP‐40, 0.1% SDS, and complete protease inhibitors; Roche). For analysis of synaptic genes, in the Iso and Het HSC‐reconstituted cohort and GFP or CyPA overexpression cohort, mice with >55% contextual freezing in the control group and <55% contextual freezing in the treatment group were used. For analysis of plasma CyPA levels in Iso and Het mice, samples were depleted of the most abundant proteins using Proteome Purify 2 Mouse Serum Protein Immunodepletion Resin (R&D Systems) according to the manufacturer's protocol. For analysis of HiBiT‐tagged CyPA in plasma of HDTVI mice, equal volumes of blood plasma were used. Samples were mixed with 4× NuPAGE LDS loading buffer (Invitrogen) and loaded on a 4%‐12% SDS‐polyacrylamide gradient gel (Invitrogen) and subsequently transferred onto a nitrocellulose membrane. The blots were blocked in 5% milk in TBST and incubated with rabbit anti‐pCREB (1:1000; Cell Signaling), rabbit anti‐AMPAR (1:3000; Abcam), mouse anti‐synaptophysin (1:1000; Millipore), rabbit anti‐NR2B (1:2000; Millipore), rabbit anti‐NR2A (1:4000; Millipore), rabbit anti‐synapsin‐1 (1:5000; Abcam), mouse anti‐GAPDH (1:5000; Abcam), and rabbit anti‐CyPA (1:200; Enzo Life Sciences). Horseradish peroxidase‐conjugated secondary antibodies and enhanced chemiluminescence (ECL) kit (GE Healthcare) were used to detect protein signals. Membranes were imaged using a ChemiDoc system (Bio‐Rad) and quantified using ImageJ software (Version 1.52a). GAPDH bands were used for normalization for hippocampal lysates. Equal loading of plasma was confirmed using Ponceau S solution (Sigma‐Aldrich). HiBiT was detected using the Nano‐Glo HiBit Lytic Detection System (Promega), and luminescence was recorded with ChemiDoc (Bio‐Rad).</p></sec><sec id=\"acel13192-sec-0010\"><label>2.8</label><title>qPCR</title><p>Tissue was dissected, snap‐frozen, and total RNA was extracted using TRIzol reagent (Invitrogen). cDNA was subsequently synthesized and mixed using the High‐Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Cat# 4374966) and mixed SYBR FAST mix (Kapa Biosystems), and our primers of interest. Quantitative RT‐PCR was carried out in a CFX384 Real Time System (Bio‐Rad). The following primers were used: GAPDH (F‐AGGTCGGTGTGAACGGATTTG; R‐TGTAGACCATGTAGTTGAGGTCA) and PPIA (F‐GGCAAATGCTGGACCAAAC; R‐CATTCCTGGACCCAAAACG).</p></sec><sec id=\"acel13192-sec-0011\"><label>2.9</label><title>Tissue expression of CyPA (human)</title><p>Yang et al. (<xref rid=\"acel13192-bib-0027\" ref-type=\"ref\">2015</xref>) identified age‐related gene expression changes in human RNA‐seq data collected by the Genotype‐Tissue Expression (GTEx) project (Ardlie et al., <xref rid=\"acel13192-bib-0002\" ref-type=\"ref\">2015</xref>). They examined tissue expression changes in nine tissues: subcutaneous adipose, tibial artery, left ventricle heart, lung, skeletal muscle, tibial nerve, skin, thyroid, and whole blood (n/tissue = 83–156 subjects, ages 20–70 years). Using a linear regression model, Yang et al. generated an aging coefficient for each gene detected in each tissue. Aging coefficients that are significantly >0 after false discovery rate adjustments (<italic>q</italic> < 0.05) are upregulated with age. We examined how CyPA expression changes with age in their dataset by graphing the aging coefficient of CyPA against the –log10 adjusted <italic>q</italic>‐value.</p><sec id=\"acel13192-sec-0012\"><label>2.9.1</label><title>Plasmid generation</title><p>RNA was isolated from adult mouse spleen tissue using TRIzol reagent (Thermo Fisher Scientific) and PureLink™ RNA Mini Kit following the manufacturer's instructions. The RNA concentration was determined via NanoDrop, and RNA was reverse‐transcribed using the High‐Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) and oligodT primers (Promega). The following primers were used for PCR amplification of the <italic>PPIA</italic>‐coding sequences and partial 3′ and 5′ untranslated regions (UTRs) from cDNA: CACCATGGTCAACCCCACCGTGTT (forward primer); GGTAAAATGCCCGCAAGTCA (reverse primer). The <italic>PPIA</italic> PCR product was purified, cloned into the pENTR D‐TOPO vector (Thermo Fisher Scientific), and sequence‐verified using standard M13F, M13R primers. The coding sequence was further amplified and cloned into a bicistronic mammalian expression plasmid using the restriction sites NheI and EcoRI. A canonical Kozak sequence (GCCACC) was included in the forward primer. For the C‐terminal HiBiT‐tagged PPIA, the nucleotide sequence coding for the 11‐amino acid HiBiT tag was included in the reverse primer. The resulting bicistronic plasmid vectors expressed untagged or HiBiT‐tagged Ppia and an IRES eGFP reporter using a CMV promoter. An empty IRES eGFP construct, based on the same plasmid, was used as a control for in vivo experiments. All coding sequences of the plasmids were verified by Sanger sequencing. Endotoxin‐free plasmid kits were used for plasmid preparation prior to in vivo use.</p></sec><sec id=\"acel13192-sec-0013\"><label>2.9.2</label><title>Hydrodynamic tail vein injection</title><p>The hydrodynamic tail vein injection protocol was adapted from a previously described protocol (Kovacsics & Raper, <xref rid=\"acel13192-bib-0009\" ref-type=\"ref\">2014</xref>). Endotoxin‐free plasmids were prepared using the Qiagen Maxi‐Prep Plus Kit (VWR). CyPA, CyPA‐HiBiT, or GFP plasmid DNA (50 μg) was suspended in 10% body weight saline and injected in the tail vein in 5–7 s in young mice. For detection of HiBiT‐tagged CyPA, blood was drawn 24 hr post‐HDTVI. Following a similar timeline as previous pro‐aging studies (Smith et al., <xref rid=\"acel13192-bib-0018\" ref-type=\"ref\">2015</xref>), behavioral testing was started 33 days post‐HDTVI.</p></sec><sec id=\"acel13192-sec-0014\"><label>2.9.3</label><title>CyPA neutralizing antibody administration</title><p>The anti‐CyPA antibody, 8H7 (Von Ungern‐Sternberg et al., <xref rid=\"acel13192-bib-0024\" ref-type=\"ref\">2017</xref>), or a monoclonal IgG2A isotype control (R&D Systems) was intraperitoneally administered to aged (19 months) mice (20 μg/kg). Prior to behavioral testing, antibodies were administered 7 times every third day, with two additional injections administered between behavioral testing.</p></sec><sec id=\"acel13192-sec-0015\"><label>2.9.4</label><title>Radial arm water maze</title><p>Spatial learning and memory were assessed using the radial arm water maze (RAWM) paradigm according to established protocol (Alamed, Wilcock, Diamond, Gordon, & Morgan, <xref rid=\"acel13192-bib-0001\" ref-type=\"ref\">2006</xref>). In this task, the location of the goal arm, which contains a platform, remains constant throughout the training and testing phase, while the start arm is changed during each trial. On day 1 during the training phase, mice are trained for 15 trails, with trials alternating between a visible and hidden platforms. On day 2 during the testing phase, mice are tested for 15 trials with a hidden platform. Entry into an incorrect arm is scored as an error, and errors are averaged over training blocks (three consecutive trials).</p></sec><sec id=\"acel13192-sec-0016\"><label>2.9.5</label><title>Contextual fear conditioning</title><p>In this task, mice learned to associate the environmental context (fear‐conditioning chamber) with an aversive stimulus (mild foot shock; unconditioned stimulus, US) enabling testing for hippocampal‐dependent contextual fear conditioning. As contextual fear conditioning is hippocampal‐ and amygdala‐dependent, the mild foot shock was paired with a light and tone cue (conditioned stimulus, CS) in order to also assess amygdala‐dependent cued fear conditioning. Conditioned fear was displayed as freezing behavior. Specific training parameters are as follows: Tone duration is 30 s; level is 70 dB, 2 kHz; shock duration is 2 s; and intensity is 0.6 mA. This intensity is not painful and can easily be tolerated but will generate an unpleasant feeling. On day 1, each mouse was placed in a fear‐conditioning chamber and allowed to explore for 2 min before delivery of a 30‐s tone (70 dB) ending with a 2‐s foot shock (0.6 mA). Two minutes later, a second CS‐US pair was delivered. On day 2, each mouse was first placed in the fear‐conditioning chamber containing the same exact context, but with no CS or foot shock. Freezing was analyzed for 2 min. One hour later, the mice were placed in a new context containing a different odor, cleaning solution, floor texture, chamber walls, and shape. Animals were allowed to explore for 2 min before being re‐exposed to the CS. Freezing was analyzed for 30 s after re‐exposure to CS. Freezing was measured using a FreezeScan video tracking system and software (Clever Sys, Inc) or EthoVision XT 11.5 tracking software (Noldus) and a Ugo Basile FC system.</p></sec><sec id=\"acel13192-sec-0017\"><label>2.9.6</label><title>Novel object recognition</title><p>The novel object recognition task was adapted from a previously described protocol (Dubal et al., <xref rid=\"acel13192-bib-0004\" ref-type=\"ref\">2015</xref>). During the habituation phase (day 1), mice could freely explore an empty arena for 10 min. During the training phase (day 2), mice were exposed to two identical objects (either two striped scintillation vials or two lego constructions). Mice were allowed to explore the objects for 5 min. Mice that did not explore the familiar objects for more than 3 s during the training phase were excluded from analysis. During testing (day 3), one familiar object was replaced with a novel object (vial replaced with lego or lego replaced with vial), and mice could explore for 5 min. Time spent exploring each object was quantified using the Smart Video Tracking Software (Panlab; Harvard Apparatus).</p></sec><sec id=\"acel13192-sec-0018\"><label>2.9.7</label><title>Plasma collection</title><p>At time of euthanasia, mouse blood was collected via intracardial bleeds into EDTA‐coated tubes. Plasma was generated by centrifugation of freshly (<30 min) isolated blood at 1,000 g. Aliquots were stored at −80°C until use.</p></sec><sec id=\"acel13192-sec-0019\"><label>2.9.8</label><title>Label‐free mass spectrometry</title><p>For proteomic analysis of old hematopoietic system associated factors, plasma was collected from young Iso and Het HSC‐reconstituted that had exhibited >60% or <50% freezing in contextual FC, respectively. Plasma was collected from 8 individual mice per condition for a total of 16 samples. Samples were depleted of the most abundant proteins using Proteome Purify 2 Mouse Serum Protein Immunodepletion Resin (R&D Systems) according to the manufacturer's protocol. Depleted samples were buffer exchanged into water on a Corning Spin X 5 kD molecular weight cut off spin column and quantified by Qubit fluorometer (Life Technologies). Plasma proteins (50 μg) were reduced with dithiothreitol, alkylated with iodoacetamide, and digested with trypsin (Promega). Individual digested samples were processed by solid‐phase extraction using an Empore C18 (3 M) plate under vacuum. Briefly, columns were activated (400 μl 95% acetonitrile/0.1% TFA X2) and equilibrated (400 μl 0.1% TFA X4). Next, acidified samples were loaded and columns were washed (400 μl 0.1% TFA X2). Finally, peptides were eluted (200 μl 70% acetonitrile/0.1% TFA X2) and lyophilized for downstream processing. For mass spectrometry analysis, 2 μg of protein per sample was analyzed by nano LC‐MS/MS with a Waters NanoACQUITY interfaced to a Thermo Fisher Fusion Lumos Mass Spectrometer. Peptides were loaded on a trapping column and eluted over a 75 μm × 50 cm analytical column (Thermo Fisher P/N ES‐803) at 300 nl/min using a 3‐hr reverse‐phase gradient. The mass spectrometer was operated in data‐dependent mode, with the Orbitrap operating at 60,000 FWHM and 15,000 FWHM for MS and MS/MS, respectively. The instrument was run with a 3‐s cycle for MS and MS/MS, and APD was enabled. The data were processed with MaxQuant (version 1.6.0.13; Max Planck Institute for Biochemistry; (Tyanova, Temu, & Cox, <xref rid=\"acel13192-bib-0020\" ref-type=\"ref\">2016</xref>), which incorporates the Andromeda search engine. Using this program, the MS data were recalibrated, protein/peptide identification was made using the Andromeda database search engine, the database search results were filtered at the 1% protein and peptide FDR, and protein levels were quantified. The resulting MaxQuant output was further processed using Perseus (V 1.6.0.7; Max Planck Institute for Biochemistry). Two samples exhibiting clotting changes as described in Geyer et al. (<xref rid=\"acel13192-bib-0005\" ref-type=\"ref\">2016</xref>) were excluded from analysis. Differentially expressed genes were identified by comparing LFQ intensities for each protein detected in Heterochronic HSC‐reconstituted mice compared with isochronic HSC‐reconstituted controls.</p></sec><sec id=\"acel13192-sec-0020\"><label>2.9.9</label><title>Data and statistical analysis</title><p>Mice were randomized prior to treatment. Researchers were blinded throughout histological, biochemical, and behavioral assessments. Groups were un‐blinded at the end of each experiment upon statistical analysis. Data are expressed as mean + <italic>SEM</italic>. The distribution of data in each set of experiments was tested for normality using the D'Agostino–Pearson omnibus test or Shapiro–Wilk test. No significant differences in variance between groups were detected using an <italic>F</italic> test. Statistical analysis was performed with Prism 6.0 software (GraphPad Software). Means between two groups were compared with two‐tailed, unpaired Student's <italic>t</italic> test. Comparisons of means from multiple groups with each other or against one control group were analyzed with one‐way ANOVA followed by appropriate post hoc tests (indicated in figure legends). The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13192-sec-0022\"><title>CONFLICT OF INTEREST</title><p>The authors declare that they have no competing financial interests.</p></sec><sec id=\"acel13192-sec-0023\"><title>AUTHOR CONTRIBUTIONS</title><p>L.KS. and S.A.V. developed concept and designed experiments. L.K.S. collected and analyzed data. E.V. assisted with HSC transplantation studies and analysis. G.B. and A.M.H. assisted with CyPA‐OE studies. S.U‐S. and P.S. generated and provided the antibody for CyPA inhibition studies. K.L. assisted with cognitive analysis. E.P. provided reagents and conceptual advice. L.K.S. and S.A.V. wrote the manuscript. S.A.V. supervised all aspects of this project. All authors had the opportunity to discuss results and comment on the manuscript.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13192-sup-0001\"><caption><p>Fig S1</p></caption><media xlink:href=\"ACEL-19-e13192-s002.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13192-sup-0002\"><caption><p>Fig S1‐S4</p></caption><media xlink:href=\"ACEL-19-e13192-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13192-sup-0003\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13192-s003.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13192-sec-0021\"><title>ACKNOWLEDGMENTS</title><p>This work was funded by the ARCS Foundation (L.K.S), Hillblom Foundation Predoctoral (A.M.H., K.L.), Netherlands Organization for Scientific Research Rubicon Fellowship (E.V.), BD Biosciences Stem Cell Grant (E.V.), NIA AG053382 (S.A.V.), AG055797 (S.A.V.), and gift from Marc and Lynne Benioff (S.A.V). We acknowledge the Parnassus Flow Cytometry Core, supported in part by Grant NIH P30 DK063720 and by the NIH S10 Instrumentation Grant S10 1S10OD021822‐01.</p></ack><sec sec-type=\"data-availability\" id=\"acel13192-sec-0025\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13192-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13192-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13192-cit-0001\">\n<string-name>\n<surname>Alamed</surname>, <given-names>J.</given-names>\n</string-name>, <string-name>\n<surname>Wilcock</surname>, <given-names>D. M.</given-names>\n</string-name>, <string-name>\n<surname>Diamond</surname>, <given-names>D. M.</given-names>\n</string-name>, <string-name>\n<surname>Gordon</surname>, <given-names>M. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32666684</article-id><article-id pub-id-type=\"pmc\">PMC7431827</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13186</article-id><article-id pub-id-type=\"publisher-id\">ACEL13186</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Aging aggravated liver ischemia and reperfusion injury by promoting STING‐mediated NLRP3 activation in macrophages</article-title><alt-title alt-title-type=\"left-running-head\">ZHONG et al.</alt-title></title-group><contrib-group><contrib id=\"acel13186-cr-0001\" contrib-type=\"author\"><name><surname>Zhong</surname><given-names>Weizhe</given-names></name><address><email>phil@njmu.edu.cn</email></address><xref ref-type=\"aff\" rid=\"acel13186-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13186-cr-0002\" contrib-type=\"author\"><name><surname>Rao</surname><given-names>Zhuqing</given-names></name><address><email>zhuqingrao@njmu.edu.cn</email></address><xref ref-type=\"aff\" rid=\"acel13186-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13186-cr-0003\" contrib-type=\"author\"><name><surname>Rao</surname><given-names>Jianhua</given-names></name><address><email>raojh@njmu.edu.cn</email></address><xref ref-type=\"aff\" rid=\"acel13186-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13186-cr-0004\" contrib-type=\"author\"><name><surname>Han</surname><given-names>Guoyong</given-names></name><xref ref-type=\"aff\" rid=\"acel13186-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13186-cr-0005\" contrib-type=\"author\"><name><surname>Wang</surname><given-names>Ping</given-names></name><xref ref-type=\"aff\" rid=\"acel13186-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13186-cr-0006\" contrib-type=\"author\"><name><surname>Jiang</surname><given-names>Tao</given-names></name><xref ref-type=\"aff\" rid=\"acel13186-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13186-cr-0007\" contrib-type=\"author\"><name><surname>Pan</surname><given-names>Xiongxiong</given-names></name><xref ref-type=\"aff\" rid=\"acel13186-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13186-cr-0008\" contrib-type=\"author\"><name><surname>Zhou</surname><given-names>Shun</given-names></name><xref ref-type=\"aff\" rid=\"acel13186-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13186-cr-0009\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Zhou</surname><given-names>Haoming</given-names></name><xref ref-type=\"aff\" rid=\"acel13186-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0003\">\n<sup>3</sup>\n</xref><address><email>hmzhou@njmu.edu.cn</email></address></contrib><contrib id=\"acel13186-cr-0010\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Wang</surname><given-names>Xuehao</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-5849-0098</contrib-id><xref ref-type=\"aff\" rid=\"acel13186-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13186-aff-0003\">\n<sup>3</sup>\n</xref><address><email>wangxh@njmu.edu.cn</email></address></contrib></contrib-group><aff id=\"acel13186-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Hepatobiliary/Liver Transplantation Center</named-content>\n<institution>The First Affiliated Hospital with Nanjing Medical University</institution>\n<city>Nanjing</city>\n<country country=\"CN\">China</country>\n</aff><aff id=\"acel13186-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Research Unit of Liver Transplantation and Transplant Immunology</named-content>\n<institution>Chinese Academy of Medical Sciences</institution>\n<city>Nanjing</city>\n<country country=\"CN\">China</country>\n</aff><aff id=\"acel13186-aff-0003\">\n<label><sup>3</sup></label>\n<named-content content-type=\"organisation-division\">Key Laboratory of Liver Transplantation</named-content>\n<institution>Chinese Academy of Medical Sciences</institution>\n<city>Nanjing</city>\n<country country=\"CN\">China</country>\n</aff><aff id=\"acel13186-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Department of Anesthesiology</named-content>\n<institution>The First Affiliated Hospital with Nanjing Medical University</institution>\n<city>Nanjing</city>\n<country country=\"CN\">China</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label>\nCorrespondence<break/>\nHaoming Zhou and Xuehao Wang, Hepatobiliary/Liver Transplantation Center, First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China.<break/>\nEmails: <email>hmzhou@njmu.edu.cn</email> (H.Z.); <email>wangxh@njmu.edu.cn</email> (X.W.)<break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>14</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13186</elocation-id><history><date date-type=\"received\"><day>10</day><month>12</month><year>2019</year></date><date date-type=\"rev-recd\"><day>29</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>06</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13186.pdf\"/><abstract id=\"acel13186-abs-0001\"><title>Abstract</title><p>Although aggravated liver injury has been reported in aged livers post‐ischemia and reperfusion (IR), the underlying mechanism of innate immune activation of aged macrophages is not well understood. Here, we investigated whether and how Stimulator of interferon genes (STING) signaling regulated macrophage proinflammatory activation and liver IR injury. Mice were subjected to hepatic IR in vivo. Macrophages isolated from IR‐stressed livers and bone marrow‐derived macrophages (BMDMs) from young and aged mice were used for in vitro studies. Enhanced nucleotide‐binding domain and leucine‐rich repeat containing protein 3 (NLRP3) activation was found in both livers and macrophages of aged mice post‐IR. NLRP3 knockdown in macrophages inhibited intrahepatic inflammation and liver injury in both young and aged mice. Interestingly, enhanced activation of the STING/ TANK‐binding kinase 1 (TBK1) signaling pathway was observed in aged macrophages post‐IR and mitochondria DNA (mtDNA) stimulation. STING suppression blocked over‐activation of NLRP3 signaling and excessive secretion of proinflammatory cytokines/chemokines in the mtDNA‐stimulated BMDMs from aged mice. More importantly, STING knockdown in macrophages abrogated the detrimental role of aging in aggravating liver IR injury and intrahepatic inflammation. Finally, peripheral blood from the recipients undergoing liver transplantation was collected and analyzed. The results showed that the elderly recipients had much higher levels of TNF‐α, IL‐6, IL‐1β, and IL‐18 post‐transplantation, indicating increased NLRP3 activation in lR‐stressed livers of elderly recipients. In summary, our study demonstrated that the STING‐NLRP3 axis was critical for the proinflammatory response of aged macrophages and would be a novel therapeutic target to reduce IR injury in elderly patients.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13186-abs-0002\"><p>Liver IR triggered mtDNA release from stressed hepatocytes. Aging promoted STING over‐activation, leading to enhanced NLRP3 inflammasome activation and increased proinflammatory cytokines/chemokines production of macrophages, which ultimately aggravated IR injury.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13186-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13186-g007.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13186-kwd-0001\">aging</kwd><kwd id=\"acel13186-kwd-0002\">leucine‐rich repeat containing protein 3</kwd><kwd id=\"acel13186-kwd-0003\">liver ischemia</kwd><kwd id=\"acel13186-kwd-0004\">and reperfusion injury</kwd><kwd id=\"acel13186-kwd-0005\">macrophage immune response</kwd><kwd id=\"acel13186-kwd-0006\">nucleotide‐binding domain</kwd><kwd id=\"acel13186-kwd-0007\">stimulator of interferon genes</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source>Xuehao Wang</funding-source><award-id>2019‐I2M‐5‐035</award-id><award-id>81870448</award-id><award-id>31930020</award-id></award-group><award-group id=\"funding-0002\"><funding-source>Haoming Zhou</funding-source><award-id>2019‐I2M‐5‐035</award-id><award-id>81600450</award-id><award-id>BK20191490</award-id><award-id>2018‐WSN‐011</award-id><award-id>DG000D4007</award-id></award-group><award-group id=\"funding-0003\"><funding-source>Zhuqing Rao</funding-source><award-id>81901628</award-id></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"0\"/><page-count count=\"15\"/><word-count count=\"7835\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13186-cit-1001\">\n<string-name>\n<surname>Zhong</surname>\n<given-names>W</given-names>\n</string-name>, <string-name>\n<surname>Rao</surname>\n<given-names>Z</given-names>\n</string-name>, <string-name>\n<surname>Rao</surname>\n<given-names>J</given-names>\n</string-name>, et al. <article-title>Aging aggravated liver ischemia and reperfusion injury by promoting STING‐mediated NLRP3 activation in macrophages</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13186</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13186</pub-id>\n</mixed-citation>\n</p><fn-group id=\"acel13186-ntgp-0001\"><fn id=\"acel13186-note-0001\"><p>Weizhe Zhong, Zhuqing Rao, and Jianhua Rao contributed equally to this work.</p></fn></fn-group></notes></front><body id=\"acel13186-body-0001\"><sec id=\"acel13186-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>With an increasingly aging population, more elderly patients are likely to develop hepatic malignancies that are amenable to liver surgeries. IR injury is a multifactorial process that affects liver function post–liver partial resection and transplantation (Zhai, Petrowsky, Hong, Busuttil, & Kupiec‐Weglinski, <xref rid=\"acel13186-bib-0038\" ref-type=\"ref\">2013</xref>). Increased sensitivity of the aged liver to IR injury has been reported (Chun et al., <xref rid=\"acel13186-bib-0004\" ref-type=\"ref\">2018</xref>). However, protective strategies are still lacking and need to be further studied.</p><p>The inflammatory response is an important factor that contributes to the aging progress as well as hepatic IR injury (Kan, Ungelenk, Lupp, Dirsch, & Dahmen, <xref rid=\"acel13186-bib-0011\" ref-type=\"ref\">2018</xref>). Macrophages play a critical role in the pathogenesis of liver IR injury (Lu et al., <xref rid=\"acel13186-bib-0018\" ref-type=\"ref\">2016</xref>). Activation of macrophages in response to pathogen‐associated molecular patterns (PAMPs) or damage‐associated molecular patterns (DAMPs) enhances the recruitment and activation of other innate and adaptive immune cells to amplify the intrahepatic inflammation.</p><p>NLRP3 is a well‐studied inflammasome that induces strong proinflammatory responses upon activation. NLRP3 activation in macrophages has been shown to promote inflammation and hepatocellular injury in livers post‐IR (Mohamadi et al., <xref rid=\"acel13186-bib-0020\" ref-type=\"ref\">2018</xref>). Endogenous extracellular histones activate the NLRP3 inflammasome in Kupffer cells (KCs) induced sterile inflammatory live IR injury (Huang et al., <xref rid=\"acel13186-bib-0009\" ref-type=\"ref\">2013</xref>). Reactive oxygen species (ROS)‐mediated activation of the NLRP3 and absent in melanoma 2 (AIM2) inflammasomes in KCs were also found to promote IR‐induced inflammatory responses (Kim, Kim, & Lee, <xref rid=\"acel13186-bib-0012\" ref-type=\"ref\">2015</xref>). Additionally, autophagy blockade led to the accumulation of ROS‐generating mitochondria, which further activated the NLRP3 inflammasome (Zhou, Yazdi, Menu, & Tschopp, <xref rid=\"acel13186-bib-0041\" ref-type=\"ref\">2011</xref>). In aged mice, spontaneously elevated systemic levels of TNF activated the NLRP3 inflammasome in liver and adipose tissues (Bauernfeind, Niepmann, Knolle, & Hornung, <xref rid=\"acel13186-bib-0003\" ref-type=\"ref\">2016</xref>). However, little is known about the role of NLRP3 inflammasome activation during IR in aged mice.</p><p>STING is a universal receptor that recognizes released DNA and triggers innate immune activation, which has important functions in infection, inflammation and cancer (Barber, <xref rid=\"acel13186-bib-0002\" ref-type=\"ref\">2015</xref>). In liver, STING‐mediated inflammation in macrophages contributed to the progression of non‐alcoholic steatohepatitis in both humans and mice (Yu et al., <xref rid=\"acel13186-bib-0036\" ref-type=\"ref\">2019</xref>; Luo et al., <xref rid=\"acel13186-bib-0019\" ref-type=\"ref\">2018</xref>). Lack of immunological DNA sensing in hepatocytes facilitated hepatitis B virus (HBV) infection, and introduction of STING expression specifically in hepatocytes reconstituted the DNA‐sensing pathway, leading to improved control of HBV infection (Thomsen et al., <xref rid=\"acel13186-bib-0031\" ref-type=\"ref\">2016</xref>). Few data were available about the role of STING signaling in liver IR injury. A recent study showed that there was no significant difference in liver IR injury between WT and STING‐deficient mice (Lei et al., <xref rid=\"acel13186-bib-0014\" ref-type=\"ref\">2018</xref>). Interplay of STING and NLRP3 has been recently revealed. In an LPS‐induced cardiac injury model, STING activation by LPS stimulation triggered ROS‐dependent NLRP3 activation, and NLRP3 overexpression by adenovirus abrogated the protective effects of STING knockdown in LPS‐induced cardiomyocytes (Li, Zhou, et al., <xref rid=\"acel13186-bib-0016\" ref-type=\"ref\">2019</xref>). However, whether STING signaling affected NLRP3 inflammasome activation and liver injury in aged mice post‐IR remains unclear.</p><p>In the present study, we investigated whether and how STING signaling regulated liver IR injury in aged mice. We demonstrated that liver IR triggered over‐activation of the NLRP3 inflammasome in macrophages in a STING‐dependent manner, which contributed to the increased intrahepatic inflammation and liver injury in the aged mice.</p></sec><sec sec-type=\"results\" id=\"acel13186-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13186-sec-0003\"><label>2.1</label><title>Aging aggravated hepatocellular injury and intrahepatic inflammation in IR‐stressed livers</title><p>First, we sought to determine whether aging increased liver IR injury. The young and aged mice were subjected to IR or the sham procedure. After 6 hr of reperfusion, the extent of the liver injury and intrahepatic inflammation was compared between the groups. Compared with the levels observed in the young group, the aged group showed significantly higher levels of serum ALT and AST (Figure <xref rid=\"acel13186-fig-0001\" ref-type=\"fig\">1a</xref>), fewer preserved liver architectures, higher Suzuki scores (Figure <xref rid=\"acel13186-fig-0001\" ref-type=\"fig\">1b</xref>) and more TUNEL‐positive stained hepatocytes (Figure <xref rid=\"acel13186-fig-0001\" ref-type=\"fig\">1c</xref>), which indicated exacerbated liver injury.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13186-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Aging aggravated hepatocellular injury and intrahepatic inflammation in IR‐stressed livers. Young and aged mice were subjected to liver partial warm ischemia for 1.5 hr followed by 6 hr of reperfusion. (a) Average levels of serum ALT and AST in mice. (b) H&E‐stained tissue sections of livers; Suzuki scores were based on liver H&E‐stained sections. (c) TUNEL‐stained sections of liver tissues; Positive cell percentage was evaluated by ImageJ software. (d) Inflammation‐related gene expressions (NLRP3, TNF‐α, IL‐6, IL‐1β, IL‐18, MCP‐1, CXCL‐10, and IFN‐β) were measured by qRT‐PCR; Average target gene/GAPDH ratios of the different experimental groups were presented. (e) IL‐1β/IL‐18/MCP‐1/CXCL‐10 in mice serum measured by ELISA. (f) Protein lysates were prepared from liver tissues and subjected to Western blot analysis, which was used to determine the levels of NLRP3, Cleaved caspase‐1, and β‐actin expressions. Relative intensity was analyzed by ImageJ software. n = 6 mice/group. All results were representative of at least two independent experiments. Values were presented as the mean ± <italic>SD</italic>. Significance (<italic>p</italic>‐value) was determined by <italic>t</italic> test, *<italic>p</italic> < 0.05.</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13186-g001\"/></fig><p>NLRP3, a danger signal sensor, is essential for the initiation of profound, sterile inflammation during liver IR injury (Li, Jin, et al., <xref rid=\"acel13186-bib-0015\" ref-type=\"ref\">2019</xref>; Xu et al., <xref rid=\"acel13186-bib-0034\" ref-type=\"ref\">2018</xref>). Thus, we evaluated NLRP3 activation and intrahepatic inflammation in the livers post‐IR. Indeed, the aged group had enhanced expressions of NLRP3, TNF‐α, IL‐6, IL‐1β, IL‐18, MCP‐1, and CXCL‐10 (Figure <xref rid=\"acel13186-fig-0001\" ref-type=\"fig\">1d</xref>), accompanied by higher levels of serum IL‐1β, IL‐18, MCP‐1, and CXCL‐10 (Figure <xref rid=\"acel13186-fig-0001\" ref-type=\"fig\">1e</xref>). IFN‐β was elevated in both young and aged livers post‐IR. However, no significant differences were observed between the young and aged groups (Figure <xref rid=\"acel13186-fig-0001\" ref-type=\"fig\">1e</xref>). Enhanced NLRP3 and Cleaved caspase‐1 activation was observed in the results of the Western blot analysis of the aged livers post‐IR (Figure <xref rid=\"acel13186-fig-0001\" ref-type=\"fig\">1f</xref>). These results demonstrated that aging enhanced intrahepatic NLRP3 activation and aggravated liver IR injury.</p></sec><sec id=\"acel13186-sec-0004\"><label>2.2</label><title>Aging increased liver IR injury by promoting NLRP3 activation in macrophages</title><p>NLRP3 activation in macrophages has been implicated as important in the pathogenesis of liver IR injury (Lu et al., <xref rid=\"acel13186-bib-0018\" ref-type=\"ref\">2016</xref>). Therefore, we investigated the role of NLRP3 activation in macrophages that regulate IR injury in the aged livers. As shown in Figure <xref rid=\"acel13186-fig-0002\" ref-type=\"fig\">2a</xref>, there was no significant difference in the number of infiltrated macrophages in the livers post‐IR between the young and aged mice. However, macrophages isolated from the aged livers post‐IR showed increased levels of NLRP3 and IL‐1β, IL‐18, MCP‐1, and CXCL‐10 (Figure <xref rid=\"acel13186-fig-0002\" ref-type=\"fig\">2b</xref>). IFN‐β was elevated in both young and aged macrophages post‐IR, with no significant differences between the young and aged groups (Figure <xref rid=\"acel13186-fig-0002\" ref-type=\"fig\">2b</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13186-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>Aging increased liver IR injury by promoting NLRP3 activation in macrophages. Young and aged mice were subjected to liver partial warm ischemia for 1.5 hr followed by 6 hr of reperfusion. KCs were isolated from the livers of each group after operations. (a) IHC F4/80‐stained liver tissue sections; positive cell percentage was measured using ImageJ software. (b) Inflammation‐related gene expressions (NLRP3, IL‐1β, IL‐18, MCP‐1, CXCL‐10, and IFN‐β) of KCs were measured by qRT‐PCR, and the average target gene/GAPDH ratios of different experimental groups were presented. Mice were pretreated with NLRP3 siRNA (5 mg/kg) or non‐specific siRNA (Control) 3 hr before liver partial warm IR. Mice were sacrificed 6 hr after reperfusion. KCs were isolated from the livers after operations. (c) Western blot analysis results of NLRP3, Cleaved caspase‐1, and β‐actin expression of KCs. Relative intensity was analyzed by ImageJ software. (d) Average levels of serum ALT and AST in the mice. (e) H&E‐stained section of livers; average Suzuki scores were based on the H&E‐stained liver sections from different groups of mice. (f) TUNEL assay (green) was used to detect DNA fragmentation in the livers of the young and aged mice, and DAPI (blue) was used as a counterstain. The value of the integrated density/cell was calculated with ImageJ software. (g) NLRP3, IL‐1β, IL‐18, MCP‐1, and CXCL‐10 gene expressions of livers were measured by qRT‐PCR. Average target gene/GAPDH ratios of different experimental groups were shown. n = 6 mice/group. All results were representative of at least two independent experiments. Values were presented as the mean ± <italic>SD</italic>. Significance (<italic>p</italic>‐value) was determined by <italic>t</italic> test, *<italic>p</italic> < 0.05.</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13186-g002\"/></fig><p>To further determine the role of NLRP3 activation in macrophages in regulating IR injury in aged livers, we used mannose‐conjugated polymers to deliver NLRP3 siRNA or a scramble non‐specific siRNA (Control) specifically to phagocytes in vivo. NLRP3 induction and the subsequent activation of Cleaved caspase‐1 in macrophages in both the young and aged mice post‐IR was effectively inhibited by NLRP3 siRNA but not by the non‐specific siRNA (Figure <xref rid=\"acel13186-fig-0002\" ref-type=\"fig\">2c</xref>). Moreover, NLRP3 siRNA administration protected livers against IR injury in both young and aged mice, as shown by the reduced levels of serum ALT and AST, better preserved liver architecture with lower Suzuki scores and fewer TUNEL‐positive stained hepatocytes (Figure <xref rid=\"acel13186-fig-0002\" ref-type=\"fig\">2d‐f</xref>, IR: Young NLRP3 siRNA vs. Young Control; Aged NLRP3 siRNA vs. Aged Control). NL RP3 inhibition also decreased proinflammatory gene induction of NLRP3, IL‐1β, and IL‐18 in young and aged mice post‐IR (Figure <xref rid=\"acel13186-fig-0002\" ref-type=\"fig\">2g</xref>, IR: Young NLRP3 siRNA vs. Young Control siRNA; Aged NLRP3 siRNA vs. Aged Control). In addition, NLRP3 knockdown reduced MCP‐1 and CXCL‐10 expressions in aged mice, but not in young mice (Figure <xref rid=\"acel13186-fig-0002\" ref-type=\"fig\">2g</xref>, IR: Aged NLRP3 siRNA vs. Aged Control; Young NLRP3 siRNA vs. Young Control). Notably, compared with the effects found in the young mice, the protective effects of the NLRP3 blockade on liver IR injury were more significant in the aged mice, leading to comparable liver IR injury and intrahepatic inflammation in both the young and aged mice post‐IR (Figure <xref rid=\"acel13186-fig-0002\" ref-type=\"fig\">2d</xref>‐g, IR: Young NLRP3 siRNA vs. Aged NLRP3 siRNA). Furthermore, NLRP3 inhibition in the macrophages decreased neutrophil infiltration and promoted Tregs activation in the both young and aged mice post‐liver IR (Figure <xref rid=\"acel13186-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). Thus, enhanced NLRP3 activation in macrophages was essential for promoting intrahepatic inflammation and exacerbating liver injury in the aged mice post‐IR.</p></sec><sec id=\"acel13186-sec-0005\"><label>2.3</label><title>Aging promoted NLRP3 activation in macrophages in a STING‐dependent manner</title><p>STING is a signaling molecule that elicits a powerful type I interferon response and innate immune activation upon stimulation (Motwani, Pesiridis, & Fitzgerald, <xref rid=\"acel13186-bib-0021\" ref-type=\"ref\">2019</xref>; Ablasser & Chen, <xref rid=\"acel13186-bib-0001\" ref-type=\"ref\">2019</xref>). Recent studies have also revealed the role of STING in regulating macrophage activation in various liver diseases (Zhang et al., <xref rid=\"acel13186-bib-0039\" ref-type=\"ref\">2019</xref>; Luo et al., <xref rid=\"acel13186-bib-0019\" ref-type=\"ref\">2018</xref>; Yu et al., <xref rid=\"acel13186-bib-0036\" ref-type=\"ref\">2019</xref>). Next, we examined whether aging affected STING activation during IR. mtDNA has been recognized as an important endogenous DAMPs, which can be detected by STING‐dependent sensors (White et al., <xref rid=\"acel13186-bib-0033\" ref-type=\"ref\">2014</xref>). We measured mtDNA release from IR‐stressed hepatocytes in young and aged mice. Elevated mtDNA release was observed in the aged hepatocytes post‐IR (Figure <xref rid=\"acel13186-sup-0001\" ref-type=\"supplementary-material\">S1</xref>). As shown in Figure <xref rid=\"acel13186-fig-0003\" ref-type=\"fig\">3a</xref> (IR Young vs. Sham Young), liver IR induction slightly upregulated STING signaling, as shown by the slightly increased expression of phosphorylated STING Ser365 (P‐STING) and phosphorylated TANK‐binding kinase 1 Ser172 (P‐TBK1) in the macrophages post‐IR. In contrast, the macrophages from aged livers demonstrated significantly increased protein levels of P‐STING and P‐TBK1 post‐IR (Figure <xref rid=\"acel13186-fig-0003\" ref-type=\"fig\">3a</xref>, IR Aged vs. Sham Aged).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13186-fig-0003\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>Aging aggravated mtDNA induced‐inflammation through STING activation in macrophages. Mice were subjected to liver partial warm ischemia for 1.5 hr followed by 6 hr of reperfusion. KCs were isolated from the livers of each group. (a) Western blot analysis of P‐STING, P‐TBK1, and β‐actin expression in KCs; relative intensity levels were analyzed by ImageJ software. BMDMs were co‐cultured with HR‐stressed hepatocytes, the supernatant or mtDNA (100 ng/ml) from HR‐stressed primary hepatocytes. BMDMs were harvested 6 hr later, and the cellular proteins and RNA were prepared for further study. (b) NLRP3, IL‐1β, IL‐18, MCP‐1, CXCL‐10, and IFN‐β gene expressions in BMDMs were measured by qRT‐PCR. Average target gene/GAPDH ratios of different experimental groups were presented. (c) Western blot analysis of P‐STING, P‐TBK1, NLRP3, Cleaved caspase‐1, and β‐actin expression in BMDMs; relative intensity levels were evaluated by ImageJ software. (d) Staining of P‐STING (red), NLRP3 (green), and nuclei (DAPI) in BMDMs; the integrated density/cell values were determined by ImageJ software. n = 6 mice/group. All results were representative of at least two independent experiments. Values were presented as the mean ± <italic>SD</italic>. Significance (<italic>p</italic>‐value) was determined by <italic>t</italic> test, *<italic>p</italic> < 0.05.</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13186-g003\"/></fig><p>To further study the role of STING signaling in regulating macrophage activation, BMDMs were isolated from young and aged mice and co‐cultured with hypoxia and reoxygenation (HR)‐stressed primary hepatocytes (Cell) or its supernatant (Sup) or with mtDNA isolated from primary hepatocytes post‐HR (mtDNA). Interestingly, all three treatments triggered STING and NLRP3 activation in the BMDMs from both the young and aged mice, as shown by the results of the Western blot analysis of P‐STING, P‐TBK1, NLRP3, and Cleaved caspase‐1 (Figure <xref rid=\"acel13186-fig-0003\" ref-type=\"fig\">3c</xref>), as well as proinflammation‐related gene expressions (Figure <xref rid=\"acel13186-fig-0003\" ref-type=\"fig\">3b</xref>). Moreover, compared with those from the young group, the stimulated BMDMs from aged mice showed much higher protein levels of P‐STING, P‐TBK1, NLRP3, and Cleaved caspase‐1 as measured by Western blot analysis (Figure <xref rid=\"acel13186-fig-0003\" ref-type=\"fig\">3c</xref>) and immunofluorescence assay (Figure <xref rid=\"acel13186-fig-0003\" ref-type=\"fig\">3d</xref>).</p><p>To determine the importance of STING in NLRP3 regulation by aging in macrophages, C‐176, a specific inhibitor of STING, and STING siRNA were used to block STING activation. The BMDMs from the young and aged mice were pretreated with C‐176 or STING siRNA followed by stimulation with mtDNA. The C‐176 treatment effectively inhibited STING activation in both young and aged BMDMs post‐mtDNA stimulation, as shown by the decreased protein levels of P‐STING and P‐TBK1 (Figure <xref rid=\"acel13186-fig-0004\" ref-type=\"fig\">4a</xref>). More importantly, STING inhibition by C‐176 blocked the over‐activation of NLRP3 signaling in the BMDMs from the young and aged mice post‐mtDNA stimulation, as shown by decreased levels of NLRP3 and Cleaved caspase‐1 expression (Figure <xref rid=\"acel13186-fig-0004\" ref-type=\"fig\">4a</xref>), findings that were confirmed by immunofluorescent staining of STING and NLRP3 (Figure <xref rid=\"acel13186-fig-0004\" ref-type=\"fig\">4b</xref>). Functionally, the increased level of proinflammatory cytokines/chemokines secretion were abrogated by STING inhibition with C‐176 in the mtDNA‐stimulated BMDMs of young and aged mice, as shown by significantly decreased levels of IL‐1β, IL‐18, MCP‐1, and CXCL‐10 in supernatant medium (Figure <xref rid=\"acel13186-fig-0004\" ref-type=\"fig\">4c</xref>). To avoid the non‐specific and off‐target effects of the small‐molecule inhibitor, STING siRNA was also used for STING inhibition. Similar results were observed (Figure <xref rid=\"acel13186-fig-0004\" ref-type=\"fig\">4d‐f</xref>). These results suggested that STING was required for NLRP3 over‐activation in the macrophages from aged mice.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13186-fig-0004\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>Aging aggravated mtDNA induced‐inflammation through STING‐dependent NLRP3 activation in macrophages. BMDMs were pretreated with C‐176 (20 µM) or a vehicle control 3 hr before mtDNA stimulation (100 ng/ml). The cells and culture supernatant were collected 6 hr after stimulation. (a) Treated BMDMs were analyzed for P‐STING, P‐TBK1, NLRP3, and Cleaved caspase‐1 levels by Western blotting; relative levels of protein expression were evaluated by ImageJ software. (b) Staining of P‐STING (red), NLRP3 (green), and nuclei (DAPI) in BMDMs; the integrated density/cell values were determined by ImageJ software. (c) The levels of cytokines/chemokines (IL‐1β, IL‐18, MCP‐1, and CXCL‐10) in the cell culture supernatant were measured by ELISA. BMDMs were pretreated with STING siRNA (10 µM) or non‐specific siRNA (Control) 48 hr before mtDNA stimulation (100 ng/ml). The cells and culture supernatant were collected 6 hr after stimulation. (d) Treated BMDMs were analyzed for the levels of P‐STING, P‐TBK1, NLRP3, and Cleaved caspase‐1 by Western blotting; relative levels of protein expressions were evaluated by ImageJ software. (e) Staining of P‐STING (red), NLRP3 (green), and nuclei (DAPI) in BMDMs; the value of integrated density/cell determined by ImageJ software. (f). The levels of cytokines/chemokines (IL‐1β, IL‐18, MCP‐1, and CXCL‐10) in the cell culture supernatant were measured by ELISA. All results were representative of at least two independent experiments. Values were presented as the mean ± <italic>SD</italic>. Significance (<italic>p</italic>‐value) was determined by <italic>t</italic> test, *<italic>p</italic> < 0.05.</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13186-g004\"/></fig></sec><sec id=\"acel13186-sec-0006\"><label>2.4</label><title>Aging aggravated IR injury by promoting STING‐dependent NLRP3 activation in macrophages</title><p>To further dissect the effects of STING‐dependent NLRP3 activation of macrophages during IR injury in aged mice, mannose‐conjugated polymers with STING siRNA were used to knock down STING activation in macrophages in vivo (Figure <xref rid=\"acel13186-fig-0005\" ref-type=\"fig\">5a</xref>). STING siRNA administration protected the livers against IR injury in both the young and aged mice, as shown by the reduced levels of serum ALT and AST (Figure <xref rid=\"acel13186-fig-0005\" ref-type=\"fig\">5b</xref>), better preserved liver architecture with lower Suzuki scores (Figure <xref rid=\"acel13186-fig-0005\" ref-type=\"fig\">5c</xref>), fewer TUNEL‐positive stained hepatocytes (Figure <xref rid=\"acel13186-fig-0005\" ref-type=\"fig\">5d</xref>) and decreased induction of intrahepatic proinflammation‐related genes (Figure <xref rid=\"acel13186-fig-0005\" ref-type=\"fig\">5e</xref>). STING inhibition in the macrophages abrogated the detrimental role of aging in aggravating liver injury and intrahepatic inflammation in the livers post‐IR (Figure <xref rid=\"acel13186-fig-0005\" ref-type=\"fig\">5a‐e</xref>, IR: Young STING siRNA vs. Aged STING siRNA). Interestingly, STING inhibition also reduced mtDNA release (Figure <xref rid=\"acel13186-sup-0001\" ref-type=\"supplementary-material\">S1</xref>), decreased neutrophil infiltration, and promoted Treg activation in aged mice post‐IR (Figure <xref rid=\"acel13186-sup-0001\" ref-type=\"supplementary-material\">S2</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13186-fig-0005\" orientation=\"portrait\" position=\"float\"><label>FIGURE 5</label><caption><p>Aging aggravated IR injury by promoting STING‐dependent NLRP3 activation in macrophages. Mice were pretreated with STING siRNA (5 mg/kg) or non‐specific siRNA (Control) 3 hr before liver partial warm IR. Mice were sacrificed 6 hr after reperfusion. KCs were isolated from livers of each group. (a) Western blot analysis of P‐STING, P‐TBK1, and β‐actin expression of KCs. Relative intensity analyzed by ImageJ software. (b) Average serum ALT and AST levels in mice. (c) H&E‐stained liver tissue sections from each group; average Suzuki scores were based on the H&E‐stained liver tissue sections. (d) TUNEL staining of liver tissue sections; positive cell percentage was measured by ImageJ software. (e) NLRP3, IL‐1β, IL‐18, MCP‐1, and CXCL‐10 gene expressions in livers were measured by qRT‐PCR. Average target gene/GAPDH ratios of different experimental groups were presented. n = 6 mice/group. All results were representative of at least two independent experiments. Values were presented as the mean ± <italic>SD</italic>. Significance (<italic>p</italic>‐value) was determined by <italic>t</italic> test, *<italic>p</italic> < 0.05.</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13186-g005\"/></fig></sec><sec id=\"acel13186-sec-0007\"><label>2.5</label><title>Aging promoted NLRP3 activation in humans post‐IR</title><p>Finally, to evaluate the clinical relevance of NLRP3 signaling regulated by aging during liver ischemia, we collected human peripheral blood from young and elderly patients undergoing liver transplantation. The levels of NLRP3 activation and inflammation were analyzed by ELISA. As shown in Figure <xref rid=\"acel13186-fig-0006\" ref-type=\"fig\">6</xref> (Post‐operation vs. Pre‐operation, Young/Elderly), significantly increased levels of serum TNF‐α, IL‐6, IL‐1β, and IL‐18 were found in the patients post‐IR stress by transplantation. Moreover, the elderly patients showed much higher levels of these inflammatory cytokines and chemokines post‐transplantation (Figure <xref rid=\"acel13186-fig-0006\" ref-type=\"fig\">6</xref>, post‐operation: Elderly vs. Young). All patients recovered well, no significant difference was observed in regarding the time of post‐operative hospital stay, and none patients occurred liver failure or acute rejection. These findings confirmed that aging promoted excessive inflammation and NLRP3 over‐activation during liver IR injury.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13186-fig-0006\" orientation=\"portrait\" position=\"float\"><label>FIGURE 6</label><caption><p>Aging promoted NLRP3 activation in humans post‐IR. Peripheral blood was collected respectively before transplantation and 12 hr post‐operation for study. TNF‐α, IL‐6, IL‐1β, and IL‐18 levels in patients' serum were measured by ELISA. All results were representative of at least two independent experiments. Values were presented as the mean ± <italic>SD</italic>. Significance (<italic>p</italic>‐value) was determined by <italic>t</italic> test, *<italic>p</italic> < 0.05.</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13186-g006\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"acel13186-sec-0008\"><label>3</label><title>DISCUSSION</title><p>Although we found liver IR injury to be aggravated in aged mice in our previous study (Jiang et al., <xref rid=\"acel13186-bib-0010\" ref-type=\"ref\">2019</xref>), which was consistent with findings from other studies (Okaya et al., <xref rid=\"acel13186-bib-0024\" ref-type=\"ref\">2005</xref>; Selzner et al., <xref rid=\"acel13186-bib-0027\" ref-type=\"ref\">2009</xref>), the underlying mechanism remains to be determined. In the present study, we demonstrated that aging aggravated IR injury by promoting STING‐dependent NLRP3 activation in macrophages, which provided a novel regulatory mechanism of macrophage innate immune activation in aged mice during IR injury.</p><p>Multiple alterations at the cellular and molecular levels contributed to the increased liver injury post‐IR in the aged mice, among which the enhanced inflammatory response has been shown to be an important factor (Kan et al., <xref rid=\"acel13186-bib-0011\" ref-type=\"ref\">2018</xref>). Age‐dependent loss of induced regulatory T‐cell function has also been shown to exacerbate liver IR injury in a recent study (Liu et al., <xref rid=\"acel13186-bib-0017\" ref-type=\"ref\">2018</xref>). However, controversial results have been found regarding the proinflammatory response of macrophages from young and aged subjects. Fagiolo U measured in vitro cytokines production of peripheral mononuclear cells from healthy young and elderly people and found significantly increased levels of IL‐6, TNF‐α, and IL‐1β but similar levels of IFN‐γ in the mitogen‐stimulated cultured cells from the elderly donors (Fagiolo et al., <xref rid=\"acel13186-bib-0007\" ref-type=\"ref\">1993</xref>). Sadeghi, Schnelle, Thoma, Nishanian, and Fahey (<xref rid=\"acel13186-bib-0025\" ref-type=\"ref\">1999</xref>) found that monocyte‐derived macrophages from elderly persons produced higher levels of IL‐1β and IL‐6 at a steady state but lower levels of IL‐1β and higher levels of IL‐6 and IL‐10 secretion upon stimulation. In another study, monocyte‐derived macrophages from aged and young individuals had similar levels of TNF‐α, IL‐6, IL‐1β, and MCP‐1 release in vitro at a steady state and upon LPS stimulation (Seidler, Zimmermann, Bartneck, Trautwein, & Tacke, <xref rid=\"acel13186-bib-0026\" ref-type=\"ref\">2010</xref>). In the present study, we found that macrophages from the livers of the aged mice secreted higher levels of IL‐1β, IL‐18, MCP‐1, and CXCL‐10 post‐IR in vivo and post‐mtDNA stimulation in vitro.</p><p>Critical roles for NLRP3 have been found in the regulation of liver IR injury (Xu et al., <xref rid=\"acel13186-bib-0034\" ref-type=\"ref\">2018</xref>). Proinflammatory mediators such as ROS and high mobility group box 1 (HMGB1) produced during IR injury to the liver could activate NLRP3. Gene silencing of NLRP3 protected livers against IR injury in mice (Zhu et al., <xref rid=\"acel13186-bib-0042\" ref-type=\"ref\">2011</xref>). Furthermore, Kim HY et al. found that depletion of KCs markedly decreased NLRP3 and AIM2 inflammasome activation, indicating that activation of NLRP3 and AIM2 inflammasomes in KCs contributed to the pathogenesis of hepatic IR injury (Kim et al., <xref rid=\"acel13186-bib-0012\" ref-type=\"ref\">2015</xref>). Endogenous extracellular histones activated the NLRP3 inflammasome in macrophages through TLR9, which triggered sterile inflammation during liver IR injury (Huang et al., <xref rid=\"acel13186-bib-0009\" ref-type=\"ref\">2013</xref>). A recent study also reported that NLRP3 activation in macrophages was controlled by the HSF1‐β‐catenin axis and promoted liver IR injury in mice (Yue et al., <xref rid=\"acel13186-bib-0037\" ref-type=\"ref\">2016</xref>).</p><p>Emerging evidence of aging‐related NLRP3 activation has been reported. Spontaneously elevated TNF levels were observed in aged mice and were found to be critical for increased NLRP3 expression and caspase‐1 activity in adipose and liver tissues (Bauernfeind et al., <xref rid=\"acel13186-bib-0003\" ref-type=\"ref\">2016</xref>). Ablation of the NLRP3 inflammasome protected mice from aging‐related increases in innate immune activation and systemic low‐grade aging‐related sterile inflammation (Youm et al., <xref rid=\"acel13186-bib-0035\" ref-type=\"ref\">2013</xref>). Bone marrow‐derived and alveolar macrophages from aged mice had higher levels of NLRP3 inflammasome activation and caspase‐1–dependent IL‐1β and IL‐18 production, which contributed to the development of experimental pulmonary fibrosis (Stout‐Delgado et al., <xref rid=\"acel13186-bib-0029\" ref-type=\"ref\">2016</xref>). In the present study, we found that NLRP3 was activated in both young and aged mice and that NLRP3 activation was enhanced in aged mice post‐IR. Furthermore, inhibition of NLRP3 abrogated the increase in liver IR injury in the aged mice compared with increase in the young mice. Thus, enhanced NLRP3 activation in macrophages may contribute to the development of aggravated liver IR injury in aged mice.</p><p>STING, a protein with 379 amino acids, is expressed in various cell types and has been shown to play multiple critical roles in regulating infection and inflammation (Barber, <xref rid=\"acel13186-bib-0002\" ref-type=\"ref\">2015</xref>). Early studies revealed that STING was essential for the immune response to bacteria and virus invasion. Recent studies have also found that STING signaling could also be activated by self‐DNA in necrotic cells, which subsequently initiated autoinflammatory diseases. Specifically, cytosolic DNA species could bind to cyclic GMP–AMP synthase (cGAS), leading to the production of a type of cyclic dinucleotide (CDN). After binding to these CDNs, STING forms a complex with TBK1 to induce signaling transduction and ultimately to IRF3 and NF‐kB activation.</p><p>Increasing evidence has been reported regarding the regulatory role of STING signaling in various liver diseases. Due to the lack of STING expression, human and murine hepatocytes did not produce type I IFN in response to HBV infection. However, introduction of STING expression in these hepatocytes reconstituted the STING signaling pathway, leading to improved HBV control (Thomsen et al., <xref rid=\"acel13186-bib-0031\" ref-type=\"ref\">2016</xref>). Blocking STING signaling has been identified as an important mechanism for HCV evasion of host innate immunity (Ding et al., <xref rid=\"acel13186-bib-0005\" ref-type=\"ref\">2013</xref>). Luo et al. (<xref rid=\"acel13186-bib-0019\" ref-type=\"ref\">2018</xref>) found that liver tissues from patients with non‐alcoholic fatty liver disease and mice with HFD‐induced steatosis expressed higher levels of STING, while STING inhibition in macrophages decreased the inflammation and the severity of the liver fibrosis. The mtDNA from hepatocytes of HFD‐fed mice induced TNF‐α and IL‐6 expression in KCs, which was inhibited when STING was inhibited (Yu et al., <xref rid=\"acel13186-bib-0036\" ref-type=\"ref\">2019</xref>).</p><p>Few studies have shown the role of STING signaling in mediating inflammation in aging‐related conditions. Cells from older subjects harbored higher levels of extranuclear DNA than cells from younger subjects, which triggered innate immune responses through the DNA‐sensing cGAS‐STING pathway (Lan et al., <xref rid=\"acel13186-bib-0013\" ref-type=\"ref\">2019</xref>). Lutz Hamann et al. investigated the influence of a STING mutant, which led to known impaired function and found that STING SNP R293Q was associated with a decreased risk of aging‐related diseases (Hamann et al., <xref rid=\"acel13186-bib-0008\" ref-type=\"ref\">2019</xref>). Here, we demonstrated that elevated mtDNA release from the aged hepatocytes post‐IR, which was suppressed by STING inhibition in macrophages. These findings indicated that the elevated mtDNA in aged mice post‐IR may be at least partially caused by enhanced macrophage proinflammatory activation. Other factors such as the energy metabolism and autophagy may directly affect the hepatocellular cell injury and mtDNA release as well (Niazi, Schneekloth, & Taner, <xref rid=\"acel13186-bib-0023\" ref-type=\"ref\">2017</xref>).</p><p>Recent studies have shown that mtDNA released in the cytoplasm played a key role in promoting NLPR3 inflammasome activation (Shimada et al., <xref rid=\"acel13186-bib-0028\" ref-type=\"ref\">2012</xref>; Nakahira et al., <xref rid=\"acel13186-bib-0022\" ref-type=\"ref\">2011</xref>). Li, Zhou, et al. (<xref rid=\"acel13186-bib-0016\" ref-type=\"ref\">2019</xref>) found that LPS stimulation triggered perinuclear STING translocation and interferon regulatory Factor 3 (IRF3) phosphorylation, leading to subsequent NLRP3 activation, which contributed to cardiac dysfunction and inflammation. In the present study, we found that mtDNA stimulation triggered STING and NLRP3 activation in macrophages and that inhibition of STING signaling decreased NLRP3 expression in the macrophages of aged mice.</p><p>A major limitation of our study is the lack of STING deficiency mice. Although the siRNA or inhibitor was able to effectively inhibit STING activation, it may have off‐target and cytotoxic side effects. Thus, the use of STING deficiency mice would provide more powerful evidence to support our conclusion and will be critical for our future studies.</p></sec><sec sec-type=\"summary\" id=\"acel13186-sec-0009\"><label>4</label><title>SUMMARY</title><p>In summary, this is the first study to suggest an important role for the STING‐NLRP3 pathway in regulating macrophage innate immune activation and enhanced liver IR injury in aged mice. Therefore, targeting STING to inhibit macrophage excessive proinflammatory activation in macrophages would be a viable therapeutic or preventive approach for the management of aggravated liver IR injury in aged patients.</p></sec><sec id=\"acel13186-sec-0010\"><label>5</label><title>EXPERIMENTAL PROCEDURES</title><sec id=\"acel13186-sec-0011\"><label>5.1</label><title>Animals</title><p>Young (8 weeks) and aged (100 weeks) male C57/BL6 mice were purchased from GemPharmatech Co., Ltd. The mice were housed and maintained under a 12 hr light/dark cycle with ad libitum access to water and standard chow with supplements under specific pathogen‐free conditions. All animal work was performed according to the “Guide for the Care and Use of Laboratory Animals” published by the National Research Council.</p></sec><sec id=\"acel13186-sec-0012\"><label>5.2</label><title>Liver IR injury model</title><p>A model of partial hepatic warm IR injury was used as described previously (Zhou et al., <xref rid=\"acel13186-bib-0040\" ref-type=\"ref\">2018</xref>). In brief, after successful anesthesia with 2.5% isoflurane, the mice were injected intraperitoneally with heparin (100 mg/kg). An atraumatic clip was used to interrupt the arterial and portal venous blood supply to the cephalad lobes of the liver. After 90 min of ischemia, the clip was removed, initiating hepatic reperfusion. Sham controls underwent the same procedure but without vascular occlusion. The mice were sacrificed after 6 hr of reperfusion.</p></sec><sec id=\"acel13186-sec-0013\"><label>5.3</label><title>Serum biochemical measurements and liver histopathology</title><p>Serum ALT and AST levels were measured with an AU680 clinical chemistry analyser (Beckman Coulter). Some liver specimens were fixed in 4% paraformaldehyde and embedded in paraffin. Liver sections were stained with H&E or F4/80. The severity of liver the ischemia/reperfusion injury was graded using Suzuki score. Tissues without necrosis or congestion/centrilobular ballooning were given a score of 0, whereas those presenting with severe congestion and/or >60% lobular necrosis were given a score of 4.</p></sec><sec id=\"acel13186-sec-0014\"><label>5.4</label><title>Isolation and treatment of liver cells</title><p>Livers were perfused in situ via the portal vein with Hanks balanced salt solution (HBSS; Gibco) supplemented with 5% heat‐inactivated FBS, followed by 0.3% collagenase IV (Sigma‐Aldrich). Perfused livers were dissected and teased through 70 µm nylon mesh cell strainers (Corning). Liver cells were suspended and centrifuged at 50 <italic>g</italic> for 2 min for 3 times.\n<list list-type=\"order\" id=\"acel13186-list-0001\"><list-item><p>The supernatant was collected by centrifugation at 800 <italic>g</italic> for 5 min. Thereafter, cells were suspended and allowed to attach to cell culture plates for 15 min at 37 C, and the attached cells were KCs.</p></list-item><list-item><p>Primary hepatocytes were pelleted after centrifugation at 50 <italic>g</italic> for 2 min. Cells were resuspended in 20 ml of 40% cold Percoll solution (Sigma‐Aldrich) and centrifuged at 150 <italic>g</italic> for 7 min. The pelleted hepatocytes were suspended in plating medium (Williams E medium with hepatocyte thawing and plating supplement pack; Gibco) and plated in collagen type I‐coated plates for 3 hr. Maintenance medium (Williams E medium with hepatocyte maintenance supplement pack; Gibco) was used for cultures overnight or longer.</p></list-item></list>\n</p><p>Hepatocytes culture HR patterns were imposed following a method described previously (Strey et al., <xref rid=\"acel13186-bib-0030\" ref-type=\"ref\">2010</xref>).</p></sec><sec id=\"acel13186-sec-0015\"><label>5.5</label><title>Culture of BMDMs</title><p>BMDMs were generated as previously described (Zhou et al., <xref rid=\"acel13186-bib-0040\" ref-type=\"ref\">2018</xref>). In brief, bone marrow cells were isolated from femurs and tibias of young and aged mice. The cells were cultured in DMEM supplemented with 10% fetal bovine serum and 20% L929‐conditioned medium for 7 days. The BMDMs were replated and cultured overnight for further experiments.</p><p>BMDM stimulation and activation studies: the hepatocytes were subjected to the HR model for 12 hr, the hepatocytes and supernatant were collected, and the mtDNA was extracted from the HR‐stressed hepatocytes using a mitochondrial DNA isolation kit following the instructions (ab65321; Abcam). After incubation with the above hepatocytes (BMDM/hepatocyte at a ratio of 2:1), supernatant or mtDNA (100 ng/ml) for 6 hr, the BMDMs and supernatant were harvested for further analysis.</p></sec><sec id=\"acel13186-sec-0016\"><label>5.6</label><title>NLRP3 and STING signaling inhibition</title><p>In vivo studies, NLRP3 siRNA or STING siRNA was mixed with mannose‐conjugated polymers (Polyplus Transfection) in a ratio specified by the manufacturer and administered intraperitoneally (siRNA 5 mg/kg; Santa Cruz Biotechnology) 3 hr before the onset of liver ischemia.</p><p>In vitro studies, the BMDMs were treated with STING inhibitor C‐176 (20 μM; MedChemExpress, Monmouth Junction, New Jersey, USA)/vehicle control for 3 hr or transiently transfected with STING siRNA (10 μM; Santa Cruz Biotechnology)/non‐specific siRNA using Lipofectamine 3000 (Thermo Fisher Scientific) for 48 hr before mtDNA (100 ng/ml) stimulation. Culture supernatant was collected 6 hr after stimulation to measure cytokines/chemokines levels. The cells were collected 6 hr after stimulation and used for Western blot or qRT‐PCR analysis.</p></sec><sec id=\"acel13186-sec-0017\"><label>5.7</label><title>Quantitative reverse transcription PCR</title><p>Total RNA (2.0 mg) was reverse transcribed into complementary DNA using an RR047A PrimeScript RT reagent kit with gDNA Eraser (TaKaRa). qRT‐PCR was performed with a StepOnePlus Real‐Time PCR system (Thermo Fisher Scientific, Waltham, Massachusetts, USA) in a final reaction volume of 20 μl, containing 1× TB Green Premix (TaKaRa), complementary DNA, and each primer at 0.125 μM. The amplification conditions were as follows: 50°C for 2 min, 95°C for 10 min followed by 40 cycles of 95°C for 15 s, and 60°C for 1 min.</p></sec><sec id=\"acel13186-sec-0018\"><label>5.8</label><title>Western blotting</title><p>Tissues and cellular proteins were extracted with ice‐cold RIPA lysis buffer (Beyotime, Shanghai, China) supplemented with protease and phosphatase inhibitors (Beyotime, Shanghai, China). Protein concentrations were determined by a Bradford BCA assay (Beyotime, Shanghai, China). Proteins (30 μg) were subjected to 10% SDS‐PAGE electrophoresis and transferred to a Polyvinylidene Fluoride (PVDF) nitrocellulose membrane. Antibodies against P‐STING, P‐TBK1, NLRP3, Cleaved caspase‐1, and β‐actin (Cell Signaling Technology) were used and incubated overnight at 4 °C. After 2 hr of incubation with the appropriate HRP‐conjugated secondary antibody, bands were detected with Immobilon ECL Ultra Western HRP substrate (Millipore), and images were taken using a Tanon chemiluminescent imaging system (Tanon). Densitometry to determine changes in protein expression was measured using ImageJ software.</p></sec><sec id=\"acel13186-sec-0019\"><label>5.9</label><title>ELISA</title><p>The secretion of cytokines/chemokines (TNF‐α, IL‐1β, IL‐6, IL‐18, MCP‐1, CXCL‐10) was measured by ELISA, according to the manufacturer's protocols (Thermo Fisher Scientific).</p></sec><sec id=\"acel13186-sec-0020\"><label>5.10</label><title>Detection of mtDNA in cytosolic extracts</title><p>The method of mtDNA detection in cytosolic extracts followed the protocol described previously (West et al., <xref rid=\"acel13186-bib-0032\" ref-type=\"ref\">2015</xref>). In brief, primary hepatocytes were subjected to HR. The whole cell extracts served normalization controls for total mtDNA. Cytosolic fractions were isolated by centrifugation as described previously (West et al., <xref rid=\"acel13186-bib-0032\" ref-type=\"ref\">2015</xref>). DNA was then isolated from whole cell extracts and cytosolic fractions using Qiaquick nucleotide removal columns (Qiagen). qRT‐PCR was performed on both whole cell extracts and cytosolic fractions.</p></sec><sec id=\"acel13186-sec-0021\"><label>5.11</label><title>Confocal microscopy</title><p>The samples for confocal immunofluorescent staining were stored at −80°C for frozen sectioning. The frozen sections were cut into 4 μm slices, blocked and permeated with 3% BSA‐0.5% Triton for 30 min at room temperature, and incubated with primary antibody to detect P‐STING or NLRP3 (Abcam, Cambridge, England) at 4°C overnight. Donkey anti‐rabbit IgG H&L (Alexa Fluor 647; Abcam) or donkey anti‐goat IgG H&L (Alexa Fluor 488; Abcam) was used to visualize the primary antibody. Nuclei were stained with 4′, 6‐diamidino‐2‐phenylindole dihydrochloride (DAPI, Invitrogen) Images were captured and analyzed with a confocal microscope (Carl Zeiss).</p></sec><sec id=\"acel13186-sec-0022\"><label>5.12</label><title>Patients and specimens</title><p>A total of 12 patients (6 young and 6 elderly men) with hepatocellular carcinoma and undergoing liver transplantation were included in the current study. Patients aged over 65 were considered as elderly group (Eufrasio et al., <xref rid=\"acel13186-bib-0006\" ref-type=\"ref\">2011</xref>{Niazi, 2017 #39)}. The mean age (±<italic>SD</italic>) of the young group was 33 ± 4.76 years, while that elderly group was 70.17 ± 2.92 years. Peripheral blood was collected respectively before transplantation and 12 hr post‐operation for study. The study protocol was approved by the Institutional Review Board of The First Affiliated Hospital with Nanjing Medical University (Institutional Review Board approval number 2017SRFA‐138). Informed consent was obtained from each patient.</p></sec><sec id=\"acel13186-sec-0023\"><label>5.13</label><title>Data analysis</title><p>All results were representative of at least two independent experiments. Results were shown as the mean ± standard deviation (<italic>SD</italic>). Multiple group comparisons were performed by one‐way analysis of variance followed by Bonferroni's post hoc test. All analyses were performed with Graphpad8.0. <italic>p</italic>‐value < 0.05 (two‐tailed) was considered statistically significant.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13186-sec-0025\"><title>CONFLICT OF INTEREST</title><p>The authors disclosed no conflicts of interest.</p></sec><sec id=\"acel13186-sec-0026\"><title>AUTHOR CONTRIBUTIONS</title><p>WZ, HZ, and XW conceived the project, designed experimental strategies, and drafted and revised the manuscript for publication, WZ, ZR, and JR performed the experiments and did data analysis. GH, PW, TJ, XP, and SZ collected clinical samples and assisted analysis. XW, HZ, and ZR provided funding support and supervised the study.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13186-sup-0001\"><caption><p>Figures S1‐S2</p></caption><media xlink:href=\"ACEL-19-e13186-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13186-sec-0024\"><title>ACKNOWLEDGEMENTS</title><p>This work was supported by grants from National Nature Science Foundation of China (81870448, 31930020, 81600450, 81901628), the National Science Foundation of Jiangsu Province (BK20191490), CAMS Innovation Fund for Medical Sciences (No.2019‐I2M‐5‐035), Six Talent Peaks Project in Jiangsu Province (No. 2018‐WSN‐011), Jiangsu Science and Technology Association Young Science and Technology Talents Lifting Project (No. DG000D4007), and A Project Funded by the PAPD.</p></ack><sec sec-type=\"data-availability\" id=\"acel13186-sec-0028\"><title>DATA AVAILABILITY STATEMENT</title><p>All data supporting the findings of this study are available within the paper.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13186-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13186-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13186-cit-0001\">\n<string-name>\n<surname>Ablasser</surname>, <given-names>A.</given-names>\n</string-name>, & <string-name>\n<surname>Chen</surname>, <given-names>Z. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32599664</article-id><article-id pub-id-type=\"pmc\">PMC7431828</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13161</article-id><article-id pub-id-type=\"publisher-id\">ACEL13161</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Responsiveness of dentate neurons generated throughout adult life is associated with resilience to cognitive aging</article-title><alt-title alt-title-type=\"left-running-head\">MONTARON et al.</alt-title></title-group><contrib-group><contrib id=\"acel13161-cr-0001\" contrib-type=\"author\"><name><surname>Montaron</surname><given-names>Marie‐Françoise</given-names></name><xref ref-type=\"aff\" rid=\"acel13161-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13161-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13161-cr-0002\" contrib-type=\"author\"><name><surname>Charrier</surname><given-names>Vanessa</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-0239-7374</contrib-id><xref ref-type=\"aff\" rid=\"acel13161-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13161-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13161-cr-0003\" contrib-type=\"author\"><name><surname>Blin</surname><given-names>Nicolas</given-names></name><xref ref-type=\"aff\" rid=\"acel13161-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13161-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13161-cr-0004\" contrib-type=\"author\"><name><surname>Garcia</surname><given-names>Pierre</given-names></name><xref ref-type=\"aff\" rid=\"acel13161-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13161-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13161-cr-0005\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Abrous</surname><given-names>Djoher Nora</given-names></name><xref ref-type=\"aff\" rid=\"acel13161-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13161-aff-0002\">\n<sup>2</sup>\n</xref><address><email>nora.abrous@inserm.fr</email></address></contrib></contrib-group><aff id=\"acel13161-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">INSERM UMR 1215, Magendie Neurocenter</named-content>\n<institution>Neurogenesis and Pathophysiology Group</institution>\n<city>Bordeaux</city>\n<country country=\"FR\">France</country>\n</aff><aff id=\"acel13161-aff-0002\">\n<label><sup>2</sup></label>\n<institution>Université de Bordeaux</institution>\n<city>Bordeaux</city>\n<country country=\"FR\">France</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nDjoher Nora Abrous, INSERM UMR 1215, Neurocentre Magendie, Neurogenesis and Physiopathologie Group, 146 Rue Léo Saignat, 33077 Bordeaux Cedex, France.<break/>\nEmail: <email>nora.abrous@inserm.fr</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>6</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13161</elocation-id><history><date date-type=\"received\"><day>11</day><month>2</month><year>2020</year></date><date date-type=\"rev-recd\"><day>09</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>4</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13161.pdf\"/><abstract id=\"acel13161-abs-0001\"><title>Abstract</title><p>During aging, some individuals are resilient to the decline of cognitive functions whereas others are vulnerable. These inter‐individual differences in memory abilities have been associated with differences in the rate of hippocampal neurogenesis measured in elderlies. Whether the maintenance of the functionality of neurons generated throughout adult life is linked to resilience to cognitive aging remains completely unexplored. Using the immediate early gene Zif268, we analyzed the activation of dentate granule neurons born in adult (3‐month‐old), middle‐aged (12‐month‐old), or senescent (18‐month‐old) rats (<italic>n</italic> = 96) in response to learning when animals reached 21 months of age. The activation of neurons born during the developmental period was also examined. We show that adult‐born neurons can survive up to 19 months and that neurons generated 4, 10, or 19 months <italic>before</italic> learning, but not developmentally born neurons, are activated in senescent rats with good learning abilities. In contrast, aged rats with bad learning abilities do not exhibit activity‐dependent regulation of newborn cells, whatever their birthdate. In conclusion, we propose that resilience to cognitive aging is associated with responsiveness of neurons born during adult life. These data add to our current knowledge by showing that the aging of memory abilities stems not only from the number but also from the responsiveness of adult‐born neurons.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13161-abs-0002\"><p>By imaging learning‐induced activation of dentate granule neurons born in adult, middle‐aged and old rats using a marker of neuronal activity Zif268, it was found that responsiveness of new neurons was preserved in old rats resilient to cognitive aging and not in vulnerable ones.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13161-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13161-g005.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13161-kwd-0001\">adult neurogenesis</kwd><kwd id=\"acel13161-kwd-0002\">aging cell</kwd><kwd id=\"acel13161-kwd-0003\">hippocampus</kwd><kwd id=\"acel13161-kwd-0004\">resilience</kwd><kwd id=\"acel13161-kwd-0005\">spatial memory</kwd><kwd id=\"acel13161-kwd-0006\">successful aging</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>Agence Nationale de la Recherche </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100001665</institution-id></institution-wrap></funding-source><award-id>MemoNeuro ANR2010‐BLAN‐1408‐01</award-id></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>Institut National de la Santé et de la Recherche Médicale </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100001677</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0003\"><funding-source>Centre National de la Recherche Scientifique (MFM)</funding-source></award-group><award-group id=\"funding-0004\"><funding-source>Région Aquitaine and Agence Nationale pour la Recherche</funding-source><award-id>ANR2010‐BLAN‐1408‐01</award-id></award-group><award-group id=\"funding-0005\"><funding-source>Rachel Azjen and Leon Iagolnitzer Scientific Prize</funding-source></award-group><award-group id=\"funding-0006\"><funding-source>LabEX BRAIN ANR‐10‐LABX‐43</funding-source></award-group><award-group id=\"funding-0007\"><funding-source><institution-wrap><institution>Inserm </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100001677</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0008\"><funding-source>LabEX BRAIN ANR‐10‐LABX‐43</funding-source></award-group></funding-group><counts><fig-count count=\"4\"/><table-count count=\"1\"/><page-count count=\"12\"/><word-count count=\"8603\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13161-cit-1001\">\n<string-name>\n<surname>Montaron</surname>\n<given-names>M‐F</given-names>\n</string-name>, <string-name>\n<surname>Charrier</surname>\n<given-names>V</given-names>\n</string-name>, <string-name>\n<surname>Blin</surname>\n<given-names>N</given-names>\n</string-name>, <string-name>\n<surname>Garcia</surname>\n<given-names>P</given-names>\n</string-name>, <string-name>\n<surname>Abrous</surname>\n<given-names>DN</given-names>\n</string-name>. <article-title>Responsiveness of dentate neurons generated throughout adult life is associated with resilience to cognitive aging</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13161</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13161</pub-id>\n</mixed-citation>\n</p><fn-group id=\"acel13161-ntgp-0001\"><fn fn-type=\"equal\" id=\"acel13161-note-0001\"><p>Marie‐Françoise Montaron and Vanessa Charrier equally contributed to the work.</p></fn><fn fn-type=\"funding\" id=\"acel13161-note-0100\"><p>Funding information</p><p>Institut National de la Santé et de la Recherche Médicale (to DNA), Centre National de la Recherche Scientifique (MFM), Région Aquitaine and Agence Nationale pour la Recherche (to DNA, MemoNeuro ANR2010‐BLAN‐1408‐01) and by Rachel Azjen and Leon Iagolnitzer Scientific Prize (to DNA). This work benefited from the support of the Biochemistry and Biophysics Facility of the Bordeaux Neurocampus funded by the LabEX BRAIN ANR‐10‐LABX‐43 and the Animal Housing facility funded by Inserm and LabEX BRAIN ANR‐10‐LABX‐43.</p></fn></fn-group></notes></front><body id=\"acel13161-body-0001\"><sec id=\"acel13161-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Brain and cognition change with age, and although patterns of decline are evident at the population level, the rates of change differ among individuals as well as across brain regions and cognitive domains (Gray & Barnes, <xref rid=\"acel13161-bib-0021\" ref-type=\"ref\">2015</xref>; Nyberg, Lovden, Riklund, Lindenberger, & Backman, <xref rid=\"acel13161-bib-0043\" ref-type=\"ref\">2012</xref>). Indeed, some old individuals exhibit cognitive abilities similar to those of younger ones (optimal/successful aging) whereas others show a clear and substantial cognitive decline without signs of pathologies (suboptimal/accelerated aging). Episodic memory is particularly sensitive to aging, and investigations conducted so far both in humans and in animal models have revealed that the preservation of episodic memory abilities is correlated with the structural and functional integrity of the hippocampal formation (Gonzalez‐Escamilla, Muthuraman, Chirumamilla, Vogt, & Groppa, <xref rid=\"acel13161-bib-0020\" ref-type=\"ref\">2018</xref>). Several models and theories (maintenance, reserve, compensation) emerged in an effort to account for variability in cognitive outcome across old subjects, and high level of neural plasticity has been proposed for brain reserve and resilience to cognitive aging (Nyberg et al., <xref rid=\"acel13161-bib-0043\" ref-type=\"ref\">2012</xref>).</p><p>The ability of the adult brain, and in particular the dentate gyrus (DG) of the hippocampus, to create new neurons is a peculiar form of plasticity to protect the aging brain. Briefly, new dentate granule neurons (DGNs) generated throughout the entire life of an individual (Aimone et al., <xref rid=\"acel13161-bib-0003\" ref-type=\"ref\">2014</xref>), humans included (Moreno‐Jiménez et al., <xref rid=\"acel13161-bib-0040\" ref-type=\"ref\">2019</xref>), are integrated into functional circuits and play a crucial role in complex forms of learning and memory (Clelland et al., <xref rid=\"acel13161-bib-0007\" ref-type=\"ref\">2009</xref>; Dupret et al., <xref rid=\"acel13161-bib-0013\" ref-type=\"ref\">2008</xref>; Tronel et al., <xref rid=\"acel13161-bib-0056\" ref-type=\"ref\">2012</xref>). In particular, both the addition and the elimination of new neurons <italic>before, during,</italic> or <italic>after</italic> learning were found to be important for learning and forgetting in young adult rodents (Akers et al., <xref rid=\"acel13161-bib-0004\" ref-type=\"ref\">2014</xref>; Dupret et al., <xref rid=\"acel13161-bib-0012\" ref-type=\"ref\">2007</xref>).</p><p>During aging, the rate of cell proliferation (and thus neurogenesis) decreases (Drapeau & Abrous, <xref rid=\"acel13161-bib-0009\" ref-type=\"ref\">2008</xref>), a process associated with the progressive loss or phenotypic and functional changes of neural stem cells (NSCs), (Martin‐Suarez, Valero, Muro‐Garcia, & Encinas, <xref rid=\"acel13161-bib-0033\" ref-type=\"ref\">2019</xref>; Schouten et al., <xref rid=\"acel13161-bib-0051\" ref-type=\"ref\">2019</xref>), or their niche (Diaz‐Moreno et al., <xref rid=\"acel13161-bib-0008\" ref-type=\"ref\">2018</xref>). Inter‐individual differences in the rate of adult neurogenesis (ANg) have been linked to variability in spatial memory abilities of senescent animals: Preserved memory functions are associated with the maintenance of a relatively high neurogenesis level measured <italic>after</italic> learning, whereas memory deficits are linked to exhaustion of neurogenesis (Drapeau et al., <xref rid=\"acel13161-bib-0010\" ref-type=\"ref\">2003</xref>). Moreover, we have found that corticosterone dampening from middle age on has a beneficial effect on both the rate of neurogenesis and spatial memory measured once animals have reached senescence (Montaron et al., <xref rid=\"acel13161-bib-0038\" ref-type=\"ref\">2006</xref>). Altogether this dataset raises the fascinating hypothesis that neurons generated throughout adult life could constitute a mechanism that promotes resilience to cognitive aging.</p><p>To tackle this question, we examined whether the activation of DGNs generated throughout adult life was related to the maintenance of memory function by imaging them when animals reached senescence. DGNs born in adult (3‐month‐old), middle‐aged (12‐month‐old), or senescent (18‐month‐old) rats were labeled with analogs of thymidine, and their activation in response to spatial learning was measured using Zif268, an immediate early gene (IEG) (Tronel, Lemaire, Charrier, Montaron, & Abrous, <xref rid=\"acel13161-bib-0058\" ref-type=\"ref\">2015</xref>), when animals have reached senescence. The activation of DGNs born during development was also examined.</p></sec><sec sec-type=\"results\" id=\"acel13161-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13161-sec-0003\"><label>2.1</label><title>Fate of dentate granule neurons born in old rats</title><p>In a first step, we sought to determine whether new neurons born during <italic>senescence</italic> are recruited by spatial learning. To do so, 18‐month‐old rats were injected with BrdU according to a previously described protocol (Table <xref rid=\"acel13161-tbl-0001\" ref-type=\"table\">1</xref>) and were trained 4 months later in the water maze using a reference memory protocol (Drapeau et al., <xref rid=\"acel13161-bib-0010\" ref-type=\"ref\">2003</xref>). Animals were trained for 11 days (Figure <xref rid=\"acel13161-sup-0001\" ref-type=\"supplementary-material\">S1</xref>a,b) until the aged‐unimpaired rats (AU) learned the task (day effect on Latency: <italic>F</italic>\n<sub>11,66</sub> = 2.35, <italic>p</italic> = .016; day effect on Distance: <italic>F</italic>\n<sub>11,66</sub> = 2.76, <italic>p</italic> = .005) and reached asymptotic levels of performances (with no statistical significant differences over the last 3 days). In contrast, the aged‐impaired (AI) rats did not learn the task although they were searching and finding the platform most of the time (2 or 3 trials out of 4) (day effect on Latency: <italic>F</italic>\n<sub>11,66</sub> = 1.25, <italic>p</italic> = 1.25; day effect on Distance: <italic>F</italic>\n<sub>11,66</sub> = 0.96, <italic>p</italic> = .48). Ninety minutes after the last trial, animals (and their age‐matched control group) were sacrificed for immunohistochemistry. At the time of sacrifice, BrdU‐IR cells were 4 months old and their majority was located within the granule cell layer (GCL) (Figure <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1a</xref>).</p><table-wrap id=\"acel13161-tbl-0001\" xml:lang=\"en\" content-type=\"Table\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Summary of the procedures</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Batch</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Experiment</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">XdU</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Rats’ age at XdU injections</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Rats’ age at time of sacrifice</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Neurons’ age at time of sacrifice</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Group size</th></tr></thead><tbody><tr><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Recruitment of 4‐month‐old Adu‐DGNs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">BrdU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">18 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">4 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<p>C = 5</p>\n<p>AI = 7</p>\n<p>AU = 7</p>\n</td></tr><tr><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Recruitment of 10‐month‐old Adu‐DGNs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">IdU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">12 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">10 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<p>C = 10</p>\n<p>AI = 11</p>\n<p>AU = 11</p>\n</td></tr><tr><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" rowspan=\"1\" colspan=\"1\">Recruitment of 19‐month‐old Adu‐DGNs</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CldU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">19 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<p>C = 10</p>\n<p>AI = 11</p>\n<p>AU = 11</p>\n</td></tr><tr><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">3</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Recruitment of DGNs born in adolescent rats</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CldU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">PN28</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">22 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">21 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<p>AI = 5</p>\n<p>AU = 5</p>\n</td></tr><tr><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Recruitment of DGNs born in adolescent rats</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CldU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">PN28</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">14 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<p>AI = 5</p>\n<p>AU = 5</p>\n</td></tr><tr><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Recruitment of DGNs born in embryos</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CldU</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Ed18.5</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">15 months</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<p>AI = 6</p>\n<p>AU = 7</p>\n</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13161-fig-0001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Granule neurons in the DG of aged rats. (a) Illustration of 4‐month‐old BrdU‐IR neurons in an animal with preserved memory. (b) Confocal photomicrographs of 4‐month‐old BrdU‐IR cells (blue) expressing NeuN (green). Confocal photomicrographs of (c) neurons (NeuN, green) expressing Zif268 (blue) and of (d) 4‐month‐old BrdU‐IR cells (red) expressing Zif268 (green). (e) Illustration of 10‐month‐old IdU‐IR neurons. Confocal photomicrographs of IdU‐IR cells (red) expressing (f) calbindin (green) or (g) Zif268 (green). Illustration of 19‐month‐old CldU‐IR neurons. (h) Confocal photomicrographs of CldU‐IR cells (red) expressing (i) calbindin (green) or (j) Zif268 (green). (k) Illustration of CldU‐IR neurons born in adolescent rats (PN28). (l) Illustration of CldU‐IR neurons born in embryons (ED18.5). Bar scale for DAB = 20 µm. Bar scale for confocal illustration = 10 µm</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13161-g001\"/></fig><p>These cells were more numerous in the GCL of animals with good learning abilities (AU) compared to animals with memory deficits (AI) (Figure <xref rid=\"acel13161-fig-0002\" ref-type=\"fig\">2a</xref>, <italic>F</italic>\n<sub>2,16</sub> = 7.64, <italic>p</italic> = .05 with C = AI<AU at <italic>p</italic> < .01). This finding is consistent with our previous study showing that the number of neurons generated 1 month <italic>after</italic> learning is higher in AU compared to AI (Drapeau et al., <xref rid=\"acel13161-bib-0010\" ref-type=\"ref\">2003</xref>) senescent rats. More than fifty percent of BrdU‐IR cells in the GCL expressed NeuN (Figure <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1b</xref>), and neuronal differentiation was not different among groups (Figure <xref rid=\"acel13161-fig-0002\" ref-type=\"fig\">2b</xref>, <italic>F</italic>\n<sub>2,16</sub> = 2.07, <italic>p</italic> = .15).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13161-fig-0002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Neurons produced during old age are activated by spatial learning. Top: Experimental design. (a) The number of BrdU‐IR cells is higher in the aged rats that learned the task (AU) compared to those with spatial memory deficits (AI) or to control animals (c). (b) The percentage of cells differentiating into neurons (BrdU‐IR cells expressing NeuN) is similar in the three groups. (c) The expression of Zif268 in BrdU‐IR cells generated in senescent DG is increased in AU compared to AI rats and C rats. (d) The number of neurons expressing Zif268 is similar in the three groups. *<italic>p</italic> < .05, **<italic>p</italic> < .01 compared to AU</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13161-g002\"/></fig><p>To determine whether newborn neurons are recruited by learning, we used Zif268 since this IEG is still expressed in the old DG (Gheidi, Azzopardi, Adams, & Marrone, <xref rid=\"acel13161-bib-0018\" ref-type=\"ref\">2013</xref>). Given that a substantial fraction of cells generated during senescence did not express NeuN, we verified in trained animals that Zif268‐expressing cells were expressing NeuN (Figure <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1c</xref>). We found that the vast majority of activated cells (Zif268) were neurons (NeuN) and that this ratio was similar in good and bad learners (AI: 96.4 ± 0.5; AU: 96 ± 1.3, <italic>p</italic> > .05). Then, we examined the activation of adult‐born cells, meant to be neurons, in response to learning (Figure <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1d</xref>). We found that the percentage of BrdU‐IR cells expressing Zif268‐IR in aged animals with good learning abilities was greater than that of aged animals with memory deficits and that of the untrained control group (Figure <xref rid=\"acel13161-fig-0002\" ref-type=\"fig\">2c</xref>, <italic>F</italic>\n<sub>2,16</sub> = 3.70, <italic>p</italic> = .05 with C = AI<AU at <italic>p</italic> < .05). In contrast, the total number of Zif268‐IR nuclei did not differ between groups (Figure <xref rid=\"acel13161-fig-0002\" ref-type=\"fig\">2d</xref>, <italic>F</italic>\n<sub>2,16</sub> = 0.25, <italic>p</italic> = .78). These results show that neuronal cells in the senescent DG are recruited by spatial learning and not by nonspecific effects of training (swimming, stress) as revealed by the lowest level of recruitment of 4‐month‐old cells in aged‐impaired and control animals.</p></sec><sec id=\"acel13161-sec-0004\"><label>2.2</label><title>Fate of dentate granule neurons born in middle‐aged and young rats</title><p>Then, we asked whether neurons born earlier, that is, in middle age or young adulthood, are also recruited by learning during aging. For this purpose, animals were injected with CldU when 3 months old and with IdU when middle aged (12 months old; Table <xref rid=\"acel13161-tbl-0001\" ref-type=\"table\">1</xref>). Animals were trained 10 months later for 11 days until the AU learned the task (day effect on Latency: <italic>F</italic>\n<sub>10,100</sub> = 22.08, <italic>p</italic> < .001; day effect on Distance: <italic>F</italic>\n<sub>10,100</sub> = 18.77, <italic>p</italic> < .001) and reached 3 days of stable performances (Figure <xref rid=\"acel13161-sup-0001\" ref-type=\"supplementary-material\">S1</xref>c,d). In this batch, the AI showed a dramatic improvement of their performances on the last training day (day effect on Latency: <italic>F</italic>\n<sub>10,100</sub> = 6.67, <italic>p</italic> < .001; day effect on Distance: <italic>F</italic>\n<sub>10,100</sub> = 22.08, <italic>p</italic> < .001). Trained animals (and their age‐matched control group) were sacrificed 90 min after the last trial. At the time of sacrifice, IdU cells were 10 months old (Figure <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1d</xref>). Their number was not influenced by training or by the cognitive status of the animals (Figure <xref rid=\"acel13161-fig-0003\" ref-type=\"fig\">3a</xref>, <italic>F</italic>\n<sub>2,29</sub> = 0.87, <italic>p</italic> = .43). More than eighty percent of IdU cells expressed the neuronal marker calbindin (Figures <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1f</xref> and <xref rid=\"acel13161-fig-0003\" ref-type=\"fig\">3b</xref>, <italic>F</italic>\n<sub>2,28</sub> = 4.21, <italic>p</italic> = .02 with C = AI<AU at <italic>p</italic> = .02). The percentage of neurons born during middle age and expressing Zif268 was greater in the AU group than that measured in AI and C groups (Figures <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1g</xref> and <xref rid=\"acel13161-fig-0003\" ref-type=\"fig\">3c</xref>, <italic>F</italic>\n<sub>2,29</sub> = 4.87, <italic>p</italic> = .02 with C = AI<AU at <italic>p</italic> < .01 and <italic>p</italic> < .05, respectively).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13161-fig-0003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Neurons produced during middle age are activated by spatial learning in aged good learners. Top: Experimental design. (a) The number of IdU‐IR cells generated at mid‐age is independent of the memory abilities measured when rats reached senescence. (b) The percentage of cells differentiating into neurons (IdU‐IR cells expressing calbindin) is slightly increased in AI (compared to C and AU). (c) The expression of Zif268 in IdU‐IR cells is increased in AU rats compared to AI rats and C rats. *<italic>p</italic> < .05, ** < .01 compared to AU. °<italic>p</italic> < .05 compared to c</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13161-g003\"/></fig><p>Nineteen‐month‐old CldU‐IR cells were examined in the same animals (Figure <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1h</xref>). Their number was not influenced by training or the cognitive status of the animal (Figure <xref rid=\"acel13161-fig-0004\" ref-type=\"fig\">4a</xref>, <italic>F</italic>\n<sub>2,29</sub> = 0.52, <italic>p</italic> = .6). Their phenotypic analysis revealed that exposure to the water maze slightly increased neuronal differentiation (CldU‐calbindin co‐expressing cells; Figures <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1j</xref> and <xref rid=\"acel13161-fig-0004\" ref-type=\"fig\">4b,c</xref>: 82.8 ± 0.1% AI: 86.9 ± 0.8%; AU: 86.2 ± 0.7%; <italic>F</italic>\n<sub>2,29</sub> = 6.54, <italic>p</italic> < .01 with C < AI = AU at <italic>p</italic> = .01). Again, we found that the percentage of CldU‐IR cells expressing Zif268 was greater in the AU group than that measured in AI and C groups (Figures <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1j</xref> and <xref rid=\"acel13161-fig-0004\" ref-type=\"fig\">4c</xref>, <italic>F</italic>\n<sub>2,29</sub> = 6.96, <italic>p</italic> = .004 with C = AI<AU at <italic>p</italic> < .01 and <italic>p</italic> < .05, respectively). The total number of cells expressing Zif268‐IR (C: 29,270.02 ± 2,360.54: AI: 26,068.94 ± 2,366.78; AU: 28,739.22 ± 3,095.74, <italic>F</italic>\n<sub>2,29</sub> = 0.42, <italic>p</italic> = .65) did not differ between groups.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13161-fig-0004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Neurons produced during young adulthood are activated by spatial learning in good leaners. Top: Experimental design. (a) The numbers of CldU‐IR cells generated when animals are young adult are independent of the memory abilities measured when rats reached senescence. (b) The percentage of CldU‐IR cells expressing calbindin is increased by training. (c) The expression of Zif268 in CldU‐IR cells generated in young adult DG is increased in AU compared to AI rats and C rats. °<italic>p</italic> < .05 compared to AU, +<italic>p</italic> < .05 compared to AI. *<italic>p</italic> < .05, **<italic>p</italic> < .01 compared to AU</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13161-g004\"/></fig></sec><sec id=\"acel13161-sec-0005\"><label>2.3</label><title>Fate of dentate granule neurons born during development</title><p>Finally, we explored the role of dentate granule born during development of the DG by tagging neurons born in adolescent rats (PN28, Figure <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1k</xref>) and neurons born in embryos (E18.5, Figure <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1l</xref>) with CldU. Animals were sacrificed when 22 or 15 months old, and for both groups, the number of CldU‐IR cells and the percentage of CldU‐IR cells expressing Zif268 were similar in AU and AI rats (Table <xref rid=\"acel13161-sup-0002\" ref-type=\"supplementary-material\">S1</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13161-sec-0006\"><label>3</label><title>DISCUSSION</title><p>To determine whether neurons generated during adult life participate in learning abilities in old age, the expression of the IEG Zif268 was assessed in new neurons. We found that cells generated during young adulthood, middle age, and senescence survive for a long period of time and are functionally integrated into the dentate network. When taking into account individual differences in memory abilities, we highlight that although the number of new cells generated in 12‐month‐old animals (IdU‐IR cells) is decreased 10‐fold compared to 3‐month‐old rats (CldU‐IR cells), the total number of CldU‐IR or IdU‐IR cells measured when animals reached senescence is similar in AU and AI and not different from untrained control animals.</p><p>These conclusions have been obtained using the hippocampal‐dependent version of the Morris water maze (measuring reference memory with variable starting points), one of the most widely used behavioral tests to study normal aging in rats; this task does not involve food restriction or the administration of shock to encourage participation of old animals and is very sensitive to the aging process (Kennard & Woodruff‐Pak, <xref rid=\"acel13161-bib-0025\" ref-type=\"ref\">2011</xref>; Lubec et al., <xref rid=\"acel13161-bib-0028\" ref-type=\"ref\">2019</xref>; Mota et al., <xref rid=\"acel13161-bib-0041\" ref-type=\"ref\">2019</xref>). Although inter‐individual differences in memory in aged rats have been demonstrated in other type of mazes (Barrett, Bennie, Trieu, Ping, & Tsafoulis, <xref rid=\"acel13161-bib-0005\" ref-type=\"ref\">2009</xref>; Temido‐Ferreira et al., <xref rid=\"acel13161-bib-0053\" ref-type=\"ref\">2018</xref>), in object location memory tasks (Lux, Masseck, Herlitze, & Sauvage, <xref rid=\"acel13161-bib-0030\" ref-type=\"ref\">2017</xref>), and in the hole‐board task (Lubec et al., <xref rid=\"acel13161-bib-0028\" ref-type=\"ref\">2019</xref>), we chose to use the water maze because it is the most widely used task to study the implication of adult‐born neurons in aged rats (our own work, (Bizon & Gallagher, <xref rid=\"acel13161-bib-0006\" ref-type=\"ref\">2003</xref>; Marrone, Ramirez‐Amaya, & Barnes, <xref rid=\"acel13161-bib-0032\" ref-type=\"ref\">2012</xref>)) whereas their role remains largely unexplored using the other tasks.</p><p>To map neuronal activity, Zif268 was preferred over many other markers such as c‐Fos or Arc that have been frequently used in the memory field (Gallo, Katche, Morici, Medina, & Weisstaub, <xref rid=\"acel13161-bib-0016\" ref-type=\"ref\">2018</xref>). Zif 268 presents the advantage to have a high “basal” expression which increases the probability to map new neurons, the number of which decreases with aging. In addition, we have previously shown using Zif 268 and c‐Fos that new neurons are recruited during both spatial memory acquisition and retrieval (Tronel, Charrier, et al., <xref rid=\"acel13161-bib-0057\" ref-type=\"ref\">2015</xref>; Tronel, Charrier, et al., <xref rid=\"acel13161-bib-0057\" ref-type=\"ref\">2015</xref>), indicating that either one or the other IEG can be used to analyze adult‐born neurons’ activation in response to learning.</p><p>To study adult‐born neuronal activity, we used immunohistochemistry, the only method available to study their role without impairing their activity. This technical approach is dependent on multiple factors that can bias sensitivity/detection of the antigens, and “old age” is a critical factor as it is accompanied with many changes—among which lipofuscin accumulation—that can interfere with antigen visualization (Moreno‐Jiménez et al., <xref rid=\"acel13161-bib-0040\" ref-type=\"ref\">2019</xref>) for discussion (Lucassen, Fitzsimons, Salta, & Maletic‐Savatic, <xref rid=\"acel13161-bib-0029\" ref-type=\"ref\">2020</xref>). Although it cannot be excluded that signal detection could be improved (Moreno‐Jiménez et al., <xref rid=\"acel13161-bib-0040\" ref-type=\"ref\">2019</xref>), given that all old brains within a batch were processed together from perfusion to labeling, we tend to exclude a technical bias. However, visualizing the activity of new neurons during learning using calcium imaging is now the best available technic to confirm our conclusions in behaving rats in the course of aging.</p><p>Our conclusion that the responsiveness of dentate neurons generated throughout adult life is associated with resilience to cognitive aging has been obtained using two different cohorts of rats. The first one was utilized to study Adu‐DGNs generated in senescent DG and the second one to study Adu‐DGNs generated in young adult and middle‐aged DG. When comparing the behavior of the two batches of rats, it appears that deficits in the aged‐impaired rats were much more pronounced in the first batch of animals. This cohort effect, a well‐known phenomenon in aging research (Schaie & Willis, <xref rid=\"acel13161-bib-0050\" ref-type=\"ref\">2015</xref>), could be related to housing conditions. Indeed, the first batch was raised in the vendor facilities until 16 months of age whereas the second one was raised in‐house. Supporting this, in our previous experiments performed in rats not aged in‐house, the difference between AU and AI was more pronounced than that observed in the second experiment (Drapeau, Montaron, Aguerre, & Abrous, <xref rid=\"acel13161-bib-0011\" ref-type=\"ref\">2007</xref>). However, independently of the cohort of rats the same profile of activation was observed (C = AI <AU).</p><p>While the process of neurogenesis has been well characterized in young adult rodents (Aimone et al., <xref rid=\"acel13161-bib-0003\" ref-type=\"ref\">2014</xref>), information about their aging and their function is less abundant (Drapeau & Abrous, <xref rid=\"acel13161-bib-0009\" ref-type=\"ref\">2008</xref>; Encinas & Fitzsimons, <xref rid=\"acel13161-bib-0014\" ref-type=\"ref\">2017</xref>; McAvoy & Sahay, <xref rid=\"acel13161-bib-0036\" ref-type=\"ref\">2017</xref>). The number of stem cells, their rate of proliferation, and neuronal differentiation dramatically decrease with age, and the number of immature neurons (<4 weeks old) is thus significantly decreased. Recently, the development and functional integration of these cells have been described to be delayed by age. Indeed, 3‐week‐old neurons generated in middle‐aged mice (10–14 months) displayed shorter and simpler dendrites and a dramatic reduction in spine number compared to those generated in 2‐month‐old mice and exhibited immature neuronal electrophysiological properties as revealed by the lack of functional glutamatergic synaptic inputs (Trinchero et al., <xref rid=\"acel13161-bib-0054\" ref-type=\"ref\">2017</xref>). In fact, their overall mature excitability and maximal glutamatergic connectivity are delayed compared to neurons born in younger animals (Trinchero, Herrero, Monzon‐Salinas, & Schinder, <xref rid=\"acel13161-bib-0055\" ref-type=\"ref\">2019</xref>). The long‐term destiny of adult‐born neurons generated in young adult animals has not been explored in depth. We and others have shown that contrary to what was initially hypothesized, new neurons survive for several months (Tronel, Charrier, et al., <xref rid=\"acel13161-bib-0057\" ref-type=\"ref\">2015</xref>) and even years in the DG (present results) and do not show signs of decline in excitability when they age: 5‐month‐old neurons are as excitable as 1‐month‐old‐cells; they can even exhibit high levels of excitability following either enriched environment exposure or induction of LTP (Ohline et al., <xref rid=\"acel13161-bib-0044\" ref-type=\"ref\">2018</xref>). This latest very exciting result supports our hypothesis that even when several months old, Adu‐DGNs are still plastic, they do not retire and participate in memory functions (Abrous & Wojtowicz, <xref rid=\"acel13161-bib-0001\" ref-type=\"ref\">2015</xref>), and even more so their persistence is not passive, but a result of their activity.</p><p>Here, we found that between middle age and senescence, the number of cells is further decreased, but then a difference between the AU and AI groups appears. Based on previous data, it is likely that the emergence of such a difference results from a difference in cell proliferation (Drapeau et al., <xref rid=\"acel13161-bib-0010\" ref-type=\"ref\">2003</xref>), neuronal differentiation (Drapeau et al., <xref rid=\"acel13161-bib-0010\" ref-type=\"ref\">2003</xref>; Qiao et al., <xref rid=\"acel13161-bib-0046\" ref-type=\"ref\">2019</xref>), cellular senescence (Hernandez‐Segura, Nehme, & Demaria, <xref rid=\"acel13161-bib-0023\" ref-type=\"ref\">2018</xref>; Micheli et al., <xref rid=\"acel13161-bib-0037\" ref-type=\"ref\">2019</xref>), or changes in the neurogenic niche and/or to the systemic milieu (Mahmoudi, Xu, & Brunet, <xref rid=\"acel13161-bib-0031\" ref-type=\"ref\">2019</xref>; Villeda et al., <xref rid=\"acel13161-bib-0060\" ref-type=\"ref\">2011</xref>). Interestingly, we have shown that the senescent neurogenic niche is capable to rejuvenate upon removal of corticosterone (Montaron et al., <xref rid=\"acel13161-bib-0039\" ref-type=\"ref\">1999</xref>) or addition of pregnenolone sulfate (Mayo et al., <xref rid=\"acel13161-bib-0035\" ref-type=\"ref\">2005</xref>), indicating that neural stem cells are not depleted and keep their abilities to divide.</p><p>The main finding of our study is that there is a strong link between the ability for newborn cells to be recruited by learning and memory abilities in aged rats. Indeed, the percentage of adult‐born cells expressing Zif268 was higher in animals that learned the task compared to animals that did it to a lesser extent. This finding is in accordance with our previous data showing that (a) when compared to control rats (naïve rats or rats trained to find a visible platform), adults required to use an hippocampal‐dependent strategy in the water maze (or the dry maze) exhibit an increased percentage of mature adult‐born neurons expressing Zif268 (Tronel, Charrier, et al., <xref rid=\"acel13161-bib-0057\" ref-type=\"ref\">2015</xref>), and (b) ablating mature adult‐born neurons generated 4 months before training (when animals were 3 months old) delays the ability of rats to learn such a task (Lemaire et al., <xref rid=\"acel13161-bib-0027\" ref-type=\"ref\">2012</xref>). In the present experiment, the percentage of adult‐born cells expressing Zif268 in each experimental group was similar in the three neuronal populations studied. It was thus independent of the age of the animals at the time of labeling (3, 12, and 18 months) and of the age of the cells at the time of training (4, 10, and 19 months). It was also independent of the total number of XdU cells. Note that even if in the first batch of animals, fifty percent of BrdU‐IR cells differentiated into neurons, 96% of Zif268 cells were neurons suggesting that all BrdU‐Zif268 cells were meant to be activated new neurons.</p><p>It could be argued that neurons born during development, which represent a major part of the DG, are also involved in differences in spatial memory abilities in old age. However, three arguments seem to rule out this hypothesis. First, the total number of granule cells is similar in AU and AI groups (Drapeau et al., <xref rid=\"acel13161-bib-0010\" ref-type=\"ref\">2003</xref>, <xref rid=\"acel13161-bib-0011\" ref-type=\"ref\">2007</xref>; Rapp & Gallagher, <xref rid=\"acel13161-bib-0047\" ref-type=\"ref\">1996</xref>). Second, if neurons generated during development (prenatal and postnatal periods) were activated by spatial learning, given their high numbers, differences in the total number of Zif268 cells should have emerged as a function of the cognitive status. Third, we have shown that neurons born in neonates are not activated by spatial learning when they are mature compared to neurons of the same age born in adults. Indeed, the former are not recruited by spatial learning in the water maze when animals are tested at 7 months (Tronel, Charrier, et al., <xref rid=\"acel13161-bib-0057\" ref-type=\"ref\">2015</xref>). Here, we extended this observation showing that neurons born during the juvenile (PN28) or the embryonic (E18.5/19.5) period are not differentially recruited in good and bad learners. Recently, we began to explore the reasons for which developmentally and adult‐generated neurons do not respond in the same way. By comparing the dendritic arbor of neurons born at different ontogenetic stages (embryonic, neonatal, adolescence, adulthood), we found that they display distinct morphological features and also different location (Kerloch, Clavreul, Goron, Abrous, & Pacary, <xref rid=\"acel13161-bib-0026\" ref-type=\"ref\">2019</xref>; Mathews et al., <xref rid=\"acel13161-bib-0034\" ref-type=\"ref\">2010</xref>) that may underlie different inputs and functions.</p><p>One question that we did not address is whether the three neuronal populations studied participate to the same extent to learning. To address this point, sophisticated models that allow to selectively tag new neurons generated within a defined period of time (adulthood, middle age, or senescence) and to ablate them during training performed at senescence are required. One possibility would be to take advantage of the recently developed pharmacogenetic or optogenetic approaches or in order to tag specifically neurons born in young adult rats and manipulate them when animals have reached senescence.</p><p>A previous study has shown that 4‐month‐old neurons generated in old rats exhibiting spatial memory deficits are recruited in response to spatial exploration behavior with the same probability than 4‐month‐old neurons generated in aged good learners or in young adult rats (Marrone et al., <xref rid=\"acel13161-bib-0032\" ref-type=\"ref\">2012</xref>). From this dataset, it was concluded that disrupted information processing at old age may be linked to a reduced number of adult‐generated granule neurons and not to a deficit in their functionality. However, in this study the activation of adult‐generated neurons was evaluated in response to a simple form a learning (spatial exploration). Taking the present data into consideration, we rather suggest that adult‐born neurons in AU are sufficiently connected to integrate simple stimulations generated during simple forms of learning but insufficiently integrated to process the complex stimulations generated during spatial navigation.</p><p>Zif268 is known to be regulated in an activity‐dependent manner by learning (for review, see Gallo et al., <xref rid=\"acel13161-bib-0016\" ref-type=\"ref\">2018</xref>; Veyrac, Besnard, Caboche, Davis, & Laroche, <xref rid=\"acel13161-bib-0059\" ref-type=\"ref\">2014</xref>). It is overexpressed in response to different types of learning in distinct structures and circuits that are processing the ongoing information, and several arguments indicate that it is required for the stabilization (and not acquisition) of long‐lasting memories. Although the mechanisms are not fully understood, the activation of Zif268 may strengthen/stabilize the memory trace. It can be hypothesized that during learning the activation of Zif268 in adult‐born neurons may be involved in the formation, stabilization, and reactivation of place cells in the hippocampal network, events known to support spatial learning.</p><p>Here, we hypothesize that adult‐born neurons that do not exhibited activity‐dependent regulation of Zif268 become functionally silent in the course of aging, leading to memory deficits. Although the firing patterns that are sufficient to induce Zif268 in adult‐born neurons in “behaving” animals are so far unknown, adult‐born neurons silencing may have several origins. It may result from a loss of synaptic inputs (Geinisman, Toledo‐Morrell, & Morrell, <xref rid=\"acel13161-bib-0017\" ref-type=\"ref\">1986</xref>) altering the ability to fire properly (Ahlenius, Visan, Kokaia, Lindvall, & Kokaia, <xref rid=\"acel13161-bib-0002\" ref-type=\"ref\">2009</xref>); these synaptic alterations of Adu‐DGNs could be linked to the acceleration of senescence through epigenetic changes (Penner, Roth, Barnes, & Sweatt, <xref rid=\"acel13161-bib-0045\" ref-type=\"ref\">2010</xref>), decreased autophagy activity (Glatigny et al., <xref rid=\"acel13161-bib-0019\" ref-type=\"ref\">2019</xref>), or changes of the local and systemic milieu (Villeda et al., <xref rid=\"acel13161-bib-0060\" ref-type=\"ref\">2011</xref>).</p><p>The HPA axis (and corticosterone) deserves a special attention in relation to its role in age‐related memory disorders (Sapolsky, <xref rid=\"acel13161-bib-0049\" ref-type=\"ref\">1992</xref>). In his pioneer work, Issa and colleagues showed that AU rats were characterized by high level of corticosterone in basal condition and in response to a restraint stress (Issa, Rowe, Gauthier, & Meaney, <xref rid=\"acel13161-bib-0024\" ref-type=\"ref\">1990</xref>). We replicated and extended this finding by showing that animals with the heaviest adrenal glands, indicative of chronic HPA axis hyperactivity, exhibited the worst memory performance and the lowest number of proliferating cells or 3‐week‐old surviving cells in comparison with AU rats (Drapeau et al., <xref rid=\"acel13161-bib-0010\" ref-type=\"ref\">2003</xref>). Although some reports failed to support a relationship between cell birth and corticosterone levels during aging (using different rat strain (Heine, Maslam, Joels, & Lucassen, <xref rid=\"acel13161-bib-0022\" ref-type=\"ref\">2004</xref>), we can hypothesize that in the present experiment AU and AI rats exhibit different HPA axis activity both in basal condition and in response to training. This raises the possibility that differences in the reactivity of adult‐born neurons in these animals are related to differences in the activity of the HPA axis. The influence of learning in the water maze on HPA axis activity is largely unknown at the exception of one study showing that corticosterone secretion is increased (in adult rats) immediately after the first training session and that corticosterone has pro‐mnesic effect on learning (Sandi, Loscertales, & Guaza, <xref rid=\"acel13161-bib-0048\" ref-type=\"ref\">1997</xref>). Whether or not learning still increases corticosterone at the end of training when animals get habituated to the task is not known. But independently of this, AI exhibit a hyperactive HPA axis and it cannot be excluded that excessive levels of corticosterone may impair adult‐born neurons’ functioning and memory processing. As a consequence, blocking/removing glucocorticoid receptors expressed by adult‐born neurons generated early in life at the time of training may increase their responsiveness to learning and promote memory. In this context, we have previously shown that blocking age‐related increased in HPA axis activity form middle‐age onward rejuvenates memory and the neurogenic niche in senescent subjects (Montaron et al., <xref rid=\"acel13161-bib-0038\" ref-type=\"ref\">2006</xref>).</p><p>Other strategies are also promising to prevent the development of memory disorders during the course of aging. To name a few, transfusion of aging individuals with young plasma, using metabolic drugs (rapamycin/metformin) or intracellular metabolic cascade (mTor), increasing mitochondrial fitness, ablating senescent cells (with senolytics), cellular reprogramming through epigenomic remodeling, transplanting cells, or other noninvasive environmental approaches may promote successful brain aging (Mahmoudi et al., <xref rid=\"acel13161-bib-0031\" ref-type=\"ref\">2019</xref>; Villeda et al., <xref rid=\"acel13161-bib-0060\" ref-type=\"ref\">2011</xref>) (Fakouri, Hansen, Desler, Anugula, & Rasmussen, <xref rid=\"acel13161-bib-0015\" ref-type=\"ref\">2019</xref>; Munoz‐Espin & Serrano, <xref rid=\"acel13161-bib-0042\" ref-type=\"ref\">2014</xref>; Shetty, Kodali, Upadhya, & Madhu, <xref rid=\"acel13161-bib-0052\" ref-type=\"ref\">2018</xref>).</p><p>In conclusion, our results highlight the importance of neurons born throughout adult life in providing resilience to age‐related memory disorders. They reveal a novel perspective for developing therapies to promote resilience to age‐related memory disorders or to rejuvenate the DG by acting throughout adult life on adult‐born dentate neurons.</p></sec><sec id=\"acel13161-sec-0007\"><label>4</label><title>EXPERIMENTAL PROCEDURES</title><sec id=\"acel13161-sec-0008\"><label>4.1</label><title>Animal</title><p>For these experiments, a total of 96 male Sprague Dawley rats (OFA, Janvier, France) were used. Animals were housed collectively until behavioral testing under a 12‐hr:12‐hr light/dark cycle with ad libitum access to food and water. Temperature (22°C) and humidity (60%) were kept constant.</p><p>\n<italic>In the first experiment</italic>, male rats (<italic>n</italic> = 19) were 16 months old on delivery. <italic>In the second experiment</italic>, rats (<italic>n</italic> = 32) were 2 months old on delivery. <italic>In the third and fourth experiments</italic>, rats (<italic>n</italic> = 25) were 21 days old on delivery. In the fifth experiment, pregnant Sprague Dawley female rats (<italic>n</italic> = 4) were individually housed in transparent cages. After delivery, litters were raised by their biological mothers until weaning (21 days after birth). After weaning, only the male progeny (<italic>n</italic> = 20) was kept. Rats were individually housed before the beginning of behavioral training. Animals with a bad general health status or tumors were excluded. Experimental procedures have been planned respecting the European directive of the parliament and the conceal of September 22, 2010 (2010/63/UE, 5012006A).</p></sec><sec id=\"acel13161-sec-0009\"><label>4.2</label><title>Thymidine analog injections</title><p>Newly born cells were labeled by the incorporation of synthetic thymidine analogs (XdU, Sigma‐Aldrich, Table <xref rid=\"acel13161-tbl-0001\" ref-type=\"table\">1</xref>). <italic>In the first experiment</italic>, rats were injected with 5‐bromo‐2′‐deoxyuridine (BrdU) according to a previously described protocol (Drapeau et al., <xref rid=\"acel13161-bib-0010\" ref-type=\"ref\">2003</xref>, <xref rid=\"acel13161-bib-0011\" ref-type=\"ref\">2007</xref>). These animals received one daily BrdU injection (50 mg kg<sup>‒1</sup> day<sup>‒1</sup>; ip) for 5 days when 18 months old, that is, 4 months before training. <italic>In the second experiment</italic>, rats received five injections of 5‐chloro‐2′‐deoxyuridine (CldU) when 3 months old and five injections of 5‐iodo‐2′‐deoxyuridine (IdU) when 12 months old (Dupret et al., <xref rid=\"acel13161-bib-0012\" ref-type=\"ref\">2007</xref>), both at equimolar doses of 50 mg BrdU/kg. <italic>In the third and fourth experiments,</italic> animals received one injection of CldU when 28 days old (equimolar dose of 50 mg BrdU/kg). <italic>In the fifth experiment,</italic> pregnant female rats received two injections of 5‐chloro‐2'‐deoxyuridine (CldU, equimolar dose of 50 mg BrdU/kg 50 mg/kg) at E18.5 and E19.5.</p></sec><sec id=\"acel13161-sec-0010\"><label>4.3</label><title>Water‐maze training</title><p>Rats were tested in the water maze when 22 months old (experiments 1, 2, and 4) or 15 months old (experiments 3 and 5). The apparatus consisted of a circular plastic swimming pool (180 cm diameter, 60 cm height) that was filled with water (20 ± 1°C) rendered opaque by the addition of a white cosmetic adjuvant. Before the start of training, animals were habituated to the pool for 2 days for 1 min per day. During training, the <italic>Learning</italic> group (L) was composed of animals that were required to locate the submerged platform, which was hidden 1.5 cm under the surface of the water in a fixed location, using spatial cues available within the room. Rats were all trained for four trials per day (90 s with an inter‐trial interval of 30 s and released from 3 different starting points that varied randomly each day). If an animal failed to locate the platform, it was placed on it at the end of the trial. The time necessary to reach the platform was recorded using a video camera that was secured to the ceiling of the room and connected to a computerized tracking system (Videotrack, Viewpoint). Daily results were analyzed in order to rank animals according to their behavioral score calculated over the last 3 days of training (when performances reached an asymptotic level). The behavioral scores calculated over the whole training duration of aged‐unimpaired (AU) rats were below the median of the group, whereas those of aged‐impaired (AI) animals were above the median of the group. Control groups consisted of animals that were transferred to the testing room at the same time and with the same procedures as trained animals but that were not exposed to the water maze.</p></sec><sec id=\"acel13161-sec-0011\"><label>4.4</label><title>Immunohistochemistry</title><p>Animals were sacrificed 90 min after the last trial (Table <xref rid=\"acel13161-tbl-0001\" ref-type=\"table\">1</xref>). The different age‐matched control groups were sacrificed within the same period. Free‐floating sections (50 µm) were processed using a standard immunohistochemical procedure to visualize thymidine analogs (BrdU, CldU, IdU) on alternate one‐in‐ten sections using different anti‐BrdU antibodies from different vendors (for BrdU: 1/200, Dako; CldU: 1/500, Accurate Chemical; IdU: 1/200, BD Biosciences) and Zif268 (1:500, Santa Cruz Biotechnology). The number of XdU‐immunoreactive (IR) cells in the granule and subgranular layers (GCL) of the DG was estimated on a systematic random sampling of every tenth section along the septo‐temporal axis of the hippocampal formation using a modified version of the optical fractionator method. Indeed, all XdU‐IR cells were counted on each section and the resulting numbers were tallied and multiplied by the inverse of the section sampling fraction (1/ssf = 10 for BrdU and IdU cells that were counted in both sides of the DG and 1/ssf = 20 for CldU‐IR cells that were counted in the left side). The number of Zif268‐IR cells (left side) was determined using a 100× lens, and a 60 × 60 µm frame at evenly spaced x‐y intervals of 350 µm by 350 µm with a Stereo Investigator software (Microbrightfield).</p></sec><sec id=\"acel13161-sec-0012\"><label>4.5</label><title>Activation of new cells</title><p>The activation of adult‐born cells was examined using immunohistofluorescence. To visualize cells that incorporated thymidine analogs, one‐in‐ten sections were incubated with different anti‐BrdU antibodies (BrdU and CldU, rat primary antibodies at 1/200 Accurate Chemical; IdU, mouse primary antibodies at 1/200, BD Biosciences). Sections were also incubated with Zif268 (rabbit, 1:500, Santa Cruz Biotechnology). Bound antibodies were visualized, respectively, with Cy3‐goat anti‐rat (1:1,000, Jackson) or Cy3‐goat anti‐mouse (1:1,000, Jackson) and Alexa 488 goat anti‐rabbit antibodies (1:1,000, Jackson). CldU‐Zif268 and IdU‐Zif268 labeling were analyzed on different sections because of some cross‐reactivity between secondary antibodies made in mice or rat (Figure <xref rid=\"acel13161-fig-0001\" ref-type=\"fig\">1</xref> in Tronel, Charrier, et al., <xref rid=\"acel13161-bib-0057\" ref-type=\"ref\">2015</xref>). All BrdU<sup>‒</sup>, CldU<sup>‒</sup>, or IdU‐labeled cells expressing Zif268 (one side) were analyzed using a confocal microscope with HeNe and Arg lasers (Leica, DMR TCSSP2AOBS), with a plane apochromatic 63X oil lens (numerical aperture 1.4; Leica). The percentage of BrdU<sup>‒</sup>, CldU<sup>‒</sup>, or IdU‐labeled cells that expressed Zif268 was calculated as follow: (Nb of Xd <sup>+</sup>/IEG<sup>+</sup> cells)/[(Nb of XdU<sup>+</sup>/IEG<sup>‐</sup> cells) + (Nb of XdU<sup>+</sup>/IEG<sup>+</sup> cells)] × 100. All sections were optically sliced in the Z plane using 1‐µm interval, and cells were rotated in orthogonal planes to verify double labeling.</p></sec><sec id=\"acel13161-sec-0013\"><label>4.6</label><title>Analysis of phenotype</title><p>One‐of‐ten series was incubated with a rat monoclonal anti‐BrdU antibody (1/200, Accurate Chemical) and with a mouse monoclonal anti‐NeuN antibody (1:500, Millipore). Bound anti‐BrdU and anti‐NeuN antibodies were visualized with a Cy3‐goat anti‐rat (1:1,000, Jackson) and an Alexa 488 goat anti‐mouse IgG antibody (1:1,000, Jackson). The phenotype of IdU‐IR cells and CldU‐IR cells was determined using rabbit anti‐calbindin antibodies (1/200, Millipore) that were revealed with Alexa 488 goat anti‐rabbit IgG antibodies (1/500, Jackson). We also analyzed the phenotype of Zif268 cells by incubating one‐in‐ten sections with a rabbit anti‐Zif268 antibody (1:500, Santa Cruz Biotechnology) and a mouse monoclonal anti‐NeuN antibody (1:500, Millipore). Bound anti‐Zif268 and anti‐NeuN antibodies were visualized with a Cy3‐goat anti‐rabbit (1:1,000, Jackson) and an Alexa 488 goat anti‐mouse IgG antibody (1:1,000, Jackson).</p></sec><sec id=\"acel13161-sec-0014\"><label>4.7</label><title>Statistical analysis</title><p>All data are expressed as mean ± <italic>SEM</italic>. Data were analyzed using an ANOVA or Student's <italic>t</italic> test (2 tails) when necessary.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13161-sec-0016\"><title>CONFLICT OF INTEREST</title><p>The authors declare no conflict of interest.</p></sec><sec id=\"acel13161-sec-0017\"><title>AUTHOR CONTRIBUTIONS</title><p>MFM performed experiments. VC analyzed the recruitment of DGNs of rats from batches 4 and 5 and revised the paper. NB analyzed the recruitment of DGNs of animals from batch 3. PG performed experiment from batch 1. DNA conceived experiments, analyzed data and wrote the paper.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13161-sup-0001\"><caption><p>Fig S1</p></caption><media xlink:href=\"ACEL-19-e13161-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13161-sup-0002\"><caption><p>Table S1</p></caption><media xlink:href=\"ACEL-19-e13161-s002.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13161-sec-0015\"><title>ACKNOWLEDGMENTS</title><p>The authors thank Dr M Koehl (Inserm, UMR 1215) and Dr A Schinder (Leloir Institute, BA, Argentina) for useful discussion. We greatly acknowledge C Dupuy for animal care and A Mangin for his technical help. This work was supported by Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique (MFM), Région Aquitaine and Agence Nationale pour la Recherche (to DNA, MemoNeuro ANR2010‐BLAN‐1408‐01) and by Rachel Azjen and Leon Iagolnitzer Scientific Prize (to DNA). This work benefited from the support of the Biochemistry and Biophysics Facility of the Bordeaux Neurocampus funded by the LabEX BRAIN ANR‐10‐LABX‐43 and the Animal Housing facility funded by Inserm and LabEX BRAIN ANR‐10‐LABX‐43. The confocal analysis was done in the Bordeaux Imaging Center (BIC), a service unit of the CNRS‐INSERM and Bordeaux University, member of the national infrastructure France BioImaging supported by the French National Research Agency (ANR‐10‐INBS‐04).</p></ack><sec sec-type=\"data-availability\" id=\"acel13161-sec-0019\"><title>DATA AVAILABILITY STATEMENT</title><p>The authors elect to not share data.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13161-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13161-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13161-cit-0001\">\n<string-name>\n<surname>Abrous</surname>, <given-names>D. N.</given-names>\n</string-name>, & <string-name>\n<surname>Wojtowicz</surname>, <given-names>J. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32691484</article-id><article-id pub-id-type=\"pmc\">PMC7431829</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13196</article-id><article-id pub-id-type=\"publisher-id\">ACEL13196</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Vascular dysfunction in aged mice contributes to persistent lung fibrosis</article-title><alt-title alt-title-type=\"left-running-head\">CAPORARELLO et al.</alt-title></title-group><contrib-group><contrib id=\"acel13196-cr-0001\" contrib-type=\"author\"><name><surname>Caporarello</surname><given-names>Nunzia</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-3183-2868</contrib-id><xref ref-type=\"aff\" rid=\"acel13196-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0002\" contrib-type=\"author\"><name><surname>Meridew</surname><given-names>Jeffrey A.</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0003\" contrib-type=\"author\"><name><surname>Aravamudhan</surname><given-names>Aja</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0004\" contrib-type=\"author\"><name><surname>Jones</surname><given-names>Dakota L.</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0005\" contrib-type=\"author\"><name><surname>Austin</surname><given-names>Susan A.</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0006\" contrib-type=\"author\"><name><surname>Pham</surname><given-names>Tho X.</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0007\" contrib-type=\"author\"><name><surname>Haak</surname><given-names>Andrew J.</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0008\" contrib-type=\"author\"><name><surname>Moo Choi</surname><given-names>Kyoung</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0009\" contrib-type=\"author\"><name><surname>Tan</surname><given-names>Qi</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0010\" contrib-type=\"author\"><name><surname>Haresi</surname><given-names>Adil</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0011\" contrib-type=\"author\"><name><surname>Huang</surname><given-names>Steven K.</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0012\" contrib-type=\"author\"><name><surname>Katusic</surname><given-names>Zvonimir S.</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0013\" contrib-type=\"author\"><name><surname>Tschumperlin</surname><given-names>Daniel J.</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-5115-9025</contrib-id><xref ref-type=\"aff\" rid=\"acel13196-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13196-cr-0014\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Ligresti</surname><given-names>Giovanni</given-names></name><xref ref-type=\"aff\" rid=\"acel13196-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13196-aff-0002\">\n<sup>2</sup>\n</xref><address><email>ligresti@bu.edu</email></address></contrib></contrib-group><aff id=\"acel13196-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Department of Medicine</named-content>\n<institution>Boston University School of Medicine</institution>\n<city>Boston</city>\n<named-content content-type=\"country-part\">MA</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13196-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Department of Physiology & Biomedical Engineering</named-content>\n<institution>Mayo Clinic</institution>\n<city>Rochester</city>\n<named-content content-type=\"country-part\">MN</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13196-aff-0003\">\n<label><sup>3</sup></label>\n<named-content content-type=\"organisation-division\">Department of Anesthesiology and Molecular Pharmacology and Experimental Therapeutics</named-content>\n<institution>Mayo Clinic</institution>\n<city>Rochester</city>\n<named-content content-type=\"country-part\">MN</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13196-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Department of Internal Medicine</named-content>\n<institution>University of Michigan Medical School</institution>\n<city>Ann Arbor</city>\n<named-content content-type=\"country-part\">MI</named-content>\n<country country=\"US\">USA</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label>\nCorrespondence<break/>\nGiovanni Ligresti, Department of Medicine, 72 East Concord Street, Boston University School of Medicine, Boston, MA.<break/>\nEmail: <email>ligresti@bu.edu</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>21</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13196</elocation-id><history><date date-type=\"received\"><day>10</day><month>2</month><year>2020</year></date><date date-type=\"rev-recd\"><day>28</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by Anatomical Society and John Wiley & Sons Ltd</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13196.pdf\"/><abstract id=\"acel13196-abs-0001\"><title>Abstract</title><p>Idiopathic pulmonary fibrosis (IPF) is a progressive disease thought to result from impaired lung repair following injury and is strongly associated with aging. While vascular alterations have been associated with IPF previously, the contribution of lung vasculature during injury resolution and fibrosis is not well understood. To compare the role of endothelial cells (ECs) in resolving and non‐resolving models of lung fibrosis, we applied bleomycin intratracheally to young and aged mice. We found that injury in aged mice elicited capillary rarefaction, while injury in young mice resulted in increased capillary density. ECs from the lungs of injured aged mice relative to young mice demonstrated elevated pro‐fibrotic and reduced vascular homeostasis gene expression. Among the latter, <italic>Nos3</italic> (encoding the enzyme endothelial nitric oxide synthase, eNOS) was transiently upregulated in lung ECs from young but not aged mice following injury. Young mice deficient in eNOS recapitulated the non‐resolving lung fibrosis observed in aged animals following injury, suggesting that eNOS directly participates in lung fibrosis resolution. Activation of the NO receptor soluble guanylate cyclase in human lung fibroblasts reduced TGFβ‐induced pro‐fibrotic gene and protein expression. Additionally, loss of eNOS in human lung ECs reduced the suppression of TGFβ‐induced lung fibroblast activation in 2D and 3D co‐cultures. Altogether, our results demonstrate that persistent lung fibrosis in aged mice is accompanied by capillary rarefaction, loss of EC identity, and impaired eNOS expression. Targeting vascular function may thus be critical to promote lung repair and fibrosis resolution in aging and IPF.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13196-abs-0002\"><p>Bleomycin‐induced lung injury promotes transient fibrosis accompanied by increased capillary density in young mice. In contrast, persistent fibrosis, capillary rarefaction, loss of endothelial cell identity, and reduction of Nos3 are observed in aged mice. eNOS/NO signal is an important driver of fibroblast quiescence and fibrosis resolution, that is lost with aging. Lung vascular bed plays a critical role during lung repair and fibrosis resolution.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13196-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13196-g006.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13196-kwd-0001\">aging</kwd><kwd id=\"acel13196-kwd-0002\">eNOS</kwd><kwd id=\"acel13196-kwd-0003\">fibroblast activation</kwd><kwd id=\"acel13196-kwd-0004\">lung fibrosis</kwd><kwd id=\"acel13196-kwd-0005\">vascular dysfunction</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>National Heart, Lung, and Blood Institute </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100000050</institution-id></institution-wrap></funding-source><award-id>HL092961</award-id><award-id>HL137366 </award-id><award-id>HL142596</award-id></award-group></funding-group><counts><fig-count count=\"5\"/><table-count count=\"1\"/><page-count count=\"14\"/><word-count count=\"9891\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13196-cit-1001\">\n<string-name>\n<surname>Caporarello</surname>\n<given-names>N</given-names>\n</string-name>, <string-name>\n<surname>Meridew</surname>\n<given-names>JA</given-names>\n</string-name>, <string-name>\n<surname>Aravamudhan</surname>\n<given-names>A</given-names>\n</string-name>, et al. <article-title>Vascular dysfunction in aged mice contributes to persistent lung fibrosis</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13196</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13196</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13196-body-0001\"><sec id=\"acel13196-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial pneumonia, and its incidence and prevalence greatly increase with age (Lederer & Martinez, <xref rid=\"acel13196-bib-0039\" ref-type=\"ref\">2018</xref>). IPF prognosis is poor with a median survival of 2‐3 years after diagnosis and a mortality rate higher than most common cancers (Mora, Rojas, Pardo, & Selman, <xref rid=\"acel13196-bib-0047\" ref-type=\"ref\">2017</xref>). Abnormal wound healing, excessive scarring, and loss of gas exchange function are cardinal features of IPF, and current therapeutic options are limited and not able to fully reverse established disease (Somogyi et al., <xref rid=\"acel13196-bib-0060\" ref-type=\"ref\">2019</xref>). It has been hypothesized that the limited regenerative capacity of the aged lung may greatly influence lung repair and ultimately fibrosis resolution (Meiners, Eickelberg, & Konigshoff, <xref rid=\"acel13196-bib-0043\" ref-type=\"ref\">2015</xref>), but the role of the lung vasculature in fibrosis and repair of the aging lung is not well studied.</p><p>Pulmonary microvasculature is abundant in mature lung and plays a critical role in mediating gas exchange (Aird, <xref rid=\"acel13196-bib-0002\" ref-type=\"ref\">2007</xref>). In addition to carrying blood, endothelial cells actively release angiocrine factors that have been shown to regulate both alveogenesis during mouse lung development as well as lung regeneration in adult animals (Ding et al., <xref rid=\"acel13196-bib-0017\" ref-type=\"ref\">2011</xref>), making these cells important regulators of lung homeostasis. Intriguingly, endothelial dysfunction increases with advancing aging and multiple lines of research have established that vascular aging is a critical step in the development of numerous chronic disorders including chronic lung diseases (Polverino, Celli, & Owen, <xref rid=\"acel13196-bib-0056\" ref-type=\"ref\">2018</xref>; Seals, Jablonski, & Donato, <xref rid=\"acel13196-bib-0059\" ref-type=\"ref\">2011</xref>). These findings suggest that targeting specific aging‐associated endothelial alterations may represent a therapeutic strategy to promote lung repair and halt disease progression.</p><p>Aberrant vascular remodeling is a previously noted feature in the pathogenesis of IPF, and increased capillary irregularities including vessel dilatation and loss of microvasculature have been observed in IPF lungs (Barratt & Millar, <xref rid=\"acel13196-bib-0005\" ref-type=\"ref\">2014</xref>; Mlika, Bacha, Braham, & El Mezni, <xref rid=\"acel13196-bib-0045\" ref-type=\"ref\">2016</xref>; Renzoni, <xref rid=\"acel13196-bib-0057\" ref-type=\"ref\">2004</xref>). Although these vascular abnormalities have been well documented, whether they are important drivers of disease progression still remains debated. Thus, understanding the roles lung vascular remodeling plays in lung repair and fibrosis in young and aged animal models may provide important insights in the pathogenesis of IPF.</p><p>Prior work with mouse models have showed that lung microvasculature undergoes extensive remodeling following bleomycin‐induced lung injury (Kato et al., <xref rid=\"acel13196-bib-0029\" ref-type=\"ref\">2018</xref>). Inhibiting key angiogenic pathways in endothelial cells (ECs) during the early injury and inflammatory phase post‐bleomycin delivery limits fibroblast activation and reduces collagen deposition (Dang et al., <xref rid=\"acel13196-bib-0013\" ref-type=\"ref\">2017</xref>; Wan et al., <xref rid=\"acel13196-bib-0069\" ref-type=\"ref\">2013</xref>). While these studies suggest that ECs positively support fibrogenic responses during the early phase post‐injury, they do not provide specific insights on the role of the vasculature during later phases of lung repair and fibrosis resolution. Given the important role of vascular ECs in homeostasis and regeneration of multiple organs, including lungs (DeLisser et al., <xref rid=\"acel13196-bib-0014\" ref-type=\"ref\">2006</xref>; Ding et al., <xref rid=\"acel13196-bib-0016\" ref-type=\"ref\">2010</xref>; Maeda et al., <xref rid=\"acel13196-bib-0042\" ref-type=\"ref\">2002</xref>; Nolan et al., <xref rid=\"acel13196-bib-0052\" ref-type=\"ref\">2013</xref>), we reasoned that ECs from the lung vascular bed may play an important role during fibrosis resolution by limiting fibrogenic milieus and reestablishing a functional alveolar niche.</p><p>We have recently demonstrated that lung fibroblast activation following bleomycin challenge is transient in young mice but more persistent in aged ones (Caporarello et al., <xref rid=\"acel13196-bib-0009\" ref-type=\"ref\">2019</xref>). Similarly, other groups have shown that fibrosis is persistent in aged mice and resolution is markedly impaired relative to young mice (Hecker et al., <xref rid=\"acel13196-bib-0025\" ref-type=\"ref\">2014</xref>; Podolsky et al., <xref rid=\"acel13196-bib-0055\" ref-type=\"ref\">2020</xref>; Torres‐Gonzalez et al., <xref rid=\"acel13196-bib-0066\" ref-type=\"ref\">2012</xref>; Xu et al., <xref rid=\"acel13196-bib-0072\" ref-type=\"ref\">2009</xref>). Thus, in this work, we have taken advantage of the resolving and non‐resolving nature of fibrosis in young and aged mice to compare vascular remodeling and lung endothelial cell behavior associated with these divergent outcomes. We have found that injured lungs from young and aged mice displayed divergent vascular remodeling, with dramatic capillary rarefaction observed in aged mice following bleomycin injury. Gene expression analysis of freshly isolated lung ECs identified profound transcriptional changes in these cells after injury in young and aged mice, including a marked reduction of endothelial cell markers and an increased expression of pro‐fibrotic and inflammatory markers in the ECs from aged mice. In addition, we identified endothelial nitric oxide synthase (eNOS) as an important player during the resolution phase of lung fibrosis that fails to increase in aged mice. Co‐culture experiments demonstrated that lung ECs restrained fibroblast activation and loss of eNOS in vascular cells failed to promote fibroblast deactivation.</p><p>Hence, our data shed new light on the pulmonary vasculature as an important regulator of lung fibrosis resolution and identified altered transcriptional responses in lung endothelial cells during aging that may be targeted to promote fibrosis resolution.</p></sec><sec sec-type=\"results\" id=\"acel13196-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13196-sec-0003\"><label>2.1</label><title>Vascular rarefaction accompanies persistent fibrosis in aged mice</title><p>Numerous studies have shown that bleomycin‐induced lung fibrosis in young mice resolves over time (Tashiro et al., <xref rid=\"acel13196-bib-0064\" ref-type=\"ref\">2017</xref>). Using Col1α1‐GFP transgenic mice in combination with fluorescence‐activated cell sorting (FACS) analysis, we have previously shown a transient but reversible expansion of high GFP+ lung fibroblasts from young mice following delivery of a single dose of bleomycin to the lung, whereas fibroblasts isolated from the lungs of aged mice exhibited persistent expansion of the high GFP+ population (Caporarello et al., <xref rid=\"acel13196-bib-0009\" ref-type=\"ref\">2019</xref>).</p><p>Here, we expanded on this approach to compare lung fibroblast collagen gene expression at timepoints associated with fibrosis resolution in young mice (30 and 75 days post‐bleomycin) and compared responses in young (2 months) and aged mice (18 months). Analysis of body weight and survival curves showed no significant differences for these parameters between the two groups (Figure <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>). As shown in Figure <xref rid=\"acel13196-fig-0001\" ref-type=\"fig\">1b</xref>, lung fibroblasts isolated from both young and aged mice showed comparable elevation of <italic>Col1a1</italic> transcripts at day 30 following bleomycin treatment. However, while <italic>Col1a1</italic> expression trended downwards in lung fibroblasts from young mice at 75 days post‐bleomycin, its expression remained elevated in lung fibroblasts from aged animals at the same timepoint. These results concur with our prior observation that bleomycin‐induced lung fibrosis in young mice resolves over time with hydroxyproline content peaking at 11 days and returning to baseline at 75 days post‐bleomycin (Caporarello et al., <xref rid=\"acel13196-bib-0009\" ref-type=\"ref\">2019</xref>). In contrast, here we show that in aged mice, lung fibrosis continues to increase out to day 75 following a single dose of bleomycin (Figure <xref rid=\"acel13196-fig-0001\" ref-type=\"fig\">1d</xref>). Histological analysis confirmed increased collagen deposition in the lungs of aged mice compared to those from young animals at 75 days post‐bleomycin (Figure <xref rid=\"acel13196-fig-0001\" ref-type=\"fig\">1e,f</xref>). These data confirm that lungs from young animals spontaneously resolve from bleomycin‐induced fibrosis while lungs from aged animals maintained elevated collagen levels.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13196-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Delayed fibrosis resolution in aged mice following bleomycin challenge. (a) Young and aged mice were exposed to bleomycin and sacrificed after 30 and 75 days. Lungs were harvested and prepared for FACS sorting. (b) <italic>Col1a1</italic> transcriptional analysis of FACS‐sorted GFP+/CD31−/CD45−/EpCAM− lung fibroblasts isolated from young and aged animals after bleomycin‐induced injury (young sham, <italic>N</italic> = 5; young 30 days, <italic>N</italic> = 8; young 75 days, <italic>N</italic> = 5; aged sham, <italic>N</italic> = 7; aged 30 days, <italic>N</italic> = 9; aged 75 days, <italic>N</italic> = 5). (c) Hydroxyproline assay was used to evaluate collagen deposition in the lungs (young sham, <italic>N</italic> = 8; young 14 days, <italic>N</italic> = 7; young 30 days, <italic>N</italic> = 9; young 75 days, <italic>N</italic> = 7). Data passed Kolmogorov–Smirnov normality test, are expressed as mean ± <italic>SD</italic>, and analyzed using one‐way analysis of variance (followed by Tukey's post hoc test). (d) Hydroxyproline assay was used to evaluate collagen deposition in the lungs (aged sham, <italic>N</italic> = 8; aged 14 days, <italic>N</italic> = 5; aged 30 days, <italic>N</italic> = 11; aged 75 days, <italic>N</italic> = 8). Data passed Kolmogorov–Smirnov normality test, are expressed as mean ± <italic>SD</italic>, and analyzed using one‐way analysis of variance (followed by Tukey's post hoc test). (e, f). Representative immunohistochemistry images and quantification of Collagen I by automated image analysis (young 75 days, <italic>N</italic> = 4; aged 75 days, <italic>N</italic> = 4). Data are non‐normally distributed, are expressed as median and IQR, and analyzed using non‐parametric Mann–Whitney test (*<italic>p</italic> < 0.05; **<italic>p</italic> < 0.01).</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13196-g001\"/></fig><p>Because aging‐induced functional and structural alterations of the microcirculation contribute to the pathogenesis of a range of age‐related diseases (Scioli, Bielli, Arcuri, Ferlosio, & Orlandi, <xref rid=\"acel13196-bib-0058\" ref-type=\"ref\">2014</xref>), we sought to investigate changes to the pulmonary vasculature in aged mice following lung injury. To evaluate vascular changes that are associated with sustained fibrosis, we immunostained bleomycin‐treated lung tissues from young and aged mice with an antibody against the endothelial cell marker PECAM‐1 followed by an automated vascular density analysis. While we did not observe significant differences in the vascular density between young and aged animals in the absence of injury, lungs from aged mice exhibited significant reduction of vessel density at 30 and 75 days post‐bleomycin relative to injured young mice (Figure <xref rid=\"acel13196-fig-0002\" ref-type=\"fig\">2a,b</xref>). These observations resemble the vascular regression we observed within the fibroblastic foci (FF) of human lung tissue from IPF patients (Figure <xref rid=\"acel13196-fig-0002\" ref-type=\"fig\">2c</xref>) and are in agreement with previous analyses of IPF fibroblastic foci described in literature (Cosgrove et al., <xref rid=\"acel13196-bib-0012\" ref-type=\"ref\">2004</xref>). In contrast, lungs of young mice showed a significant but transient increase in vessel density at the same timepoints (Figure <xref rid=\"acel13196-fig-0002\" ref-type=\"fig\">2a,b</xref>). These data demonstrated that persistent lung fibrosis in aged mice is accompanied by a reduced vascular density, suggesting that aging may alter lung endothelial cell responses that contribute to the divergent lung remodeling observed in young and aged mice.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13196-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>Vascular rarefaction accompanies persistent fibrosis in aged mice challenged with bleomycin. (a) Quantification of vascular density by automated image analysis (young sham, <italic>N</italic> = 4; young 30 days, <italic>N</italic> = 8; young 75 days, <italic>N</italic> = 9; aged sham, <italic>N</italic> = 4; aged 30 days, <italic>N</italic> = 6; aged 75 days, <italic>N</italic> = 11). Data passed Shapiro–Wilk normality test, are expressed as mean ± <italic>SD</italic>, and analyzed using one‐way analysis of variance (followed by Tukey's post hoc test). (b) Representative IF images of mouse lung tissue stained with PECAM‐1 antibody. Scale bars: 100 μm. (c) Immunostaining of human tissue derived from normal or IPF lung for PECAM‐1 counterstained with hematoxylin. Magnifications: upper row, 4X, scale bars: 250 μm, lower row, 10X, scale bars: 100 μm. FF = fibroblastic foci. Arrows show areas occupied by microvessels in regions bordering FF. (d) Schematic for ex vivo lung tissue culture. Pieces of lungs from young and aged mice were embedded in collagen for 7 days in presence of 20 ng/ml VEGFA. (e) Collagen gel culture of lung explants derived from young and aged mice. (f) Vessel counts demonstrate reduction of sprouting outgrowth in aged mice. Data are non‐normally distributed, are expressed as median and IQR, and analyzed using non‐parametric Mann–Whitney test (*<italic>p</italic> < 0.05; ***<italic>p</italic> < 0.001).</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13196-g002\"/></fig></sec><sec id=\"acel13196-sec-0004\"><label>2.2</label><title>Aged mice lungs showed impaired angiogenic capacity <italic>ex vivo</italic>\n</title><p>Angiogenesis, the growth of new blood vessels from pre‐existing ones, plays a critical role in tissue injury responses, and abnormal/limited angiogenesis has been shown to contribute to the pathogenesis of numerous chronic disorders (Carmeliet, <xref rid=\"acel13196-bib-0010\" ref-type=\"ref\">2003</xref>). Thus, we reasoned that the reduced vascular density we observed in aged mice may be the consequence of an altered angiogenic response in lung ECs from these mice. In order to evaluate the angiogenic capacity of lung ECs from young and aged mice, we generated lung explant cultures by embedding freshly cut lung tissues in collagen matrices. In this model, angiogenesis is triggered both as a consequence of the injury associated with the dissection procedure (Ligresti, Aplin, Zorzi, Morishita, & Nicosia, <xref rid=\"acel13196-bib-0040\" ref-type=\"ref\">2011</xref>) as well as in response to the presence of exogenous vascular endothelial growth factor A (VEGFA) (Dang et al., <xref rid=\"acel13196-bib-0013\" ref-type=\"ref\">2017</xref>). The growth of vessel sprouts from the lung explants was monitored for 7 days and then quantified by counting the number of sprouts in each lung explant under the microscope. As shown in Figure <xref rid=\"acel13196-fig-0002\" ref-type=\"fig\">2e,f</xref>, while lung explants from young mice showed numerous vessel sprouts, those harvested from aged animals exhibited a reduced number of vessel sprouts. These results demonstrate that lungs from aged mice have a limited angiogenic capacity compared to young animals, and suggest that aged ECs may differ in their capacity to respond to injury and angiogenic stimuli.</p></sec><sec id=\"acel13196-sec-0005\"><label>2.3</label><title>Loss of endothelial cell identity in the lungs of aged mice following bleomycin challenge</title><p>The limited angiogenic capacity of aged lung and the reduced vascular density observed in the injured lungs of these animals prompted us to further investigate potential mechanisms responsible for this altered vascular behavior. In order to do that, and to capture gene expression signatures that lead to resolution that may be lost at later timepoints, we freshly isolated lung ECs using FACS from both young (<italic>N</italic> = 4) and aged mice (<italic>N</italic> = 4) at 30 days following bleomycin challenge. Next, we measured expression of endothelial genes by using a commercially available qPCR endothelial biology array (Figure <xref rid=\"acel13196-fig-0003\" ref-type=\"fig\">3a,b</xref>). We found that lung ECs isolated from aged mice following injury lose their identity, exhibiting a widespread reduction of endothelial cell markers relative to ECs from young mice, including genes known to play role in angiogenesis. In contrast, lung ECs isolated from aged mice following injury exhibited an increased expression of inflammatory and pro‐fibrotic genes, including those encoding for cytokines highly expressed in fibrotic lung, such as IL6 and IL11 (Le et al., <xref rid=\"acel13196-bib-0038\" ref-type=\"ref\">2014</xref>; Ng et al., <xref rid=\"acel13196-bib-0050\" ref-type=\"ref\">2019</xref>). Gene expression changes of selected genes from the array were further confirmed by qPCR on a larger number of samples as shown in Figure <xref rid=\"acel13196-fig-0003\" ref-type=\"fig\">3c</xref>.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13196-fig-0003\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>Loss of endothelial cell identity in the lungs of aged mice following bleomycin challenge. (a) Young and aged mice were exposed to bleomycin and sacrificed after 30 and 75 days. Lungs were harvested and prepared for FACS sorting. (b) FACS‐sorted CD31+/GFP−/CD45−/EpCAM− lung ECs from young and aged mice (30 days) were analyzed by using an endothelial cell biology profiler PCR Array (<italic>N</italic> = 4 mice). The heatmap was generated by averaging <italic>n</italic> = 4 mice for each condition. Sham animals were harvested at the same time of bleomycin‐treated animals. The data represent fold changes relative to the young sham and normalized to the housekeeping gene <italic>Actb</italic>. (c) Transcriptional analysis of FACS‐sorted CD31+/GFP−/CD45−/EpCAM− lung ECs isolated from young and aged mice after bleomycin challenge (young sham, <italic>N</italic> = 5, young 30 days, <italic>N</italic> = 10; aged sham, <italic>N</italic> = 4; aged 30 days, <italic>N</italic> = 9). Data passed D’Agostino and Pearson omnibus or Kolmogorov–Smirnov normality test, are expressed as mean ± <italic>SD</italic>, and analyzed using one‐way analysis of variance (followed by Tukey's post hoc test) (*<italic>p</italic> < 0.05; **<italic>p</italic> < 0.01; ***<italic>p</italic> < 0.001).</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13196-g003\"/></fig><p>All together, these data demonstrated a striking divergence in the endothelial gene expression program that emerges following injury in young and aged mice, with lung ECs from aged mice acquiring an altered transcriptional state reminiscent of that observed during the endothelial/mesenchymal transition (EndMT) (Piera‐Velazquez & Jimenez, <xref rid=\"acel13196-bib-0054\" ref-type=\"ref\">2019</xref>). These observations suggest that endothelial injury responses during aging may be a critical driver of progressive lung fibrosis and that reestablishing normal endothelial homeostatic and repair programs may provide a therapeutic strategy to promote fibrosis resolution.</p></sec><sec id=\"acel13196-sec-0006\"><label>2.4</label><title>Loss of eNOS leads to sustained lung fibrosis in young animals following bleomycin challenge</title><p>Our endothelial gene expression analysis of injured young and aged lungs revealed that several genes involved in vascular remodeling and angiogenesis were significantly reduced in lung ECs from aged mice. Among these was nitric oxide synthase 3 (<italic>Nos3</italic>, encoding endothelial NOS, eNOS), eNOS is predominantly expressed in ECs and belongs to a family of enzymes catalyzing the production of nitric oxide (NO), a gaseous molecule that binds and activates the receptor soluble guanylate cyclase (sGC) in multiple cell types including fibroblasts (Lambers et al., <xref rid=\"acel13196-bib-0036\" ref-type=\"ref\">2019</xref>). Previous studies have reported that stimulation of sGC pathway is beneficial in experimental models of organ fibrosis including liver, kidney, heart, and skin (Geschka et al., <xref rid=\"acel13196-bib-0021\" ref-type=\"ref\">2011</xref>; Knorr et al., <xref rid=\"acel13196-bib-0033\" ref-type=\"ref\">2008</xref>; Stasch, Schlossmann, & Hocher, <xref rid=\"acel13196-bib-0061\" ref-type=\"ref\">2015</xref>; Wang et al., <xref rid=\"acel13196-bib-0070\" ref-type=\"ref\">2006</xref>). Thus, we hypothesized that absence of enhanced eNOS expression in lung ECs from aged mice may contribute to the sustained fibroblast activation and reduced fibrosis resolution we observed previously (Figure <xref rid=\"acel13196-fig-0001\" ref-type=\"fig\">1</xref>). To shed new light on the role of vascular eNOS during lung fibrosis resolution, we first confirmed lower <italic>Nos3</italic> gene expression in freshly isolated lung ECs from a larger cohort of young and aged mice after bleomycin injury. In young mice, we observed that <italic>Nos3</italic> gene expression was significantly elevated at 30 days and returned to baseline at 75 days post‐bleomycin (Figure <xref rid=\"acel13196-fig-0004\" ref-type=\"fig\">4a</xref>). In contrast, ECs from aged mice showed no increase in <italic>Nos3</italic> transcript level at the same timepoints. To directly test the role of eNOS during lung fibrosis resolution, we induced lung injury with bleomycin in young eNOS<sup>−/−</sup> and WT mice and evaluated lung fibrosis by measuring hydroxyproline content and pro‐fibrotic gene expression. Body weight and survival curves showed a modest but significant reduction of weight loss and, although not significant, an increased survival in eNOS<sup>−/−</sup> relative to WT mice (Figure <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>). As shown in Figure <xref rid=\"acel13196-fig-0004\" ref-type=\"fig\">4c</xref>, lung hydroxyproline content was comparable in WT and eNOS<sup>−/−</sup> mice at day 11 post‐bleomycin. However, WT and eNOS<sup>−/−</sup> mice exhibited divergent resolution responses at later timepoints, with lung hydroxyproline content returning to baseline at day 60 in WT mice but remaining significantly elevated in lungs from eNOS<sup>−/−</sup> mice at the same timepoint. Lung histological examination confirmed increased collagen in the lungs of bleomycin‐treated eNOS<sup>−/−</sup> mice at day 60 (Figure <xref rid=\"acel13196-fig-0004\" ref-type=\"fig\">4d</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13196-fig-0004\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>Loss of eNOS leads to sustained lung fibrosis in young animals following bleomycin challenge. (a) <italic>Nos3</italic> transcriptional analysis of FACS‐sorted CD31+/GFP−/CD45−/EpCAM− lung ECs isolated from young and aged mice after bleomycin‐induced lung injury (young sham, <italic>N</italic> = 6; young 30 days, <italic>N</italic> = 10; young 75 days, <italic>N</italic> = 8; aged sham, <italic>N</italic> = 7; aged 30 days, <italic>N</italic> = 9; aged 75 days, <italic>N</italic> = 7). Data passed Kolmogorov–Smirnov normality test, are expressed as mean ± <italic>SD</italic>, and analyzed using one‐way analysis of variance (followed by Tukey's post hoc test). (b) Lung homogenates from WT and eNOS<sup>−/−</sup> mice were analyzed via Western blot using anti eNOS and anti GAPDH antibodies. (c) Hydroxyproline assay was used to evaluate collagen deposition in the lungs (WT sham, <italic>N</italic> = 7; WT 11 days, <italic>N</italic> = 3; WT 60 days, <italic>N</italic> = 10; eNOS<sup>−/−</sup> sham, <italic>N</italic> = 7; eNOS<sup>−/−</sup> 11 days, <italic>N</italic> = 3; eNOS<sup>−/−</sup> 60 days, <italic>N</italic> = 14). Data passed Shapiro–Wilk normality test, are expressed as mean ± <italic>SD</italic>, and analyzed using one‐way analysis of variance (followed by Tukey's post hoc test). (d) Masson's trichrome assay was used to stain lung tissue. (e) Transcriptional analysis of whole lung homogenates obtained from WT and eNOS<sup>−/−</sup> mice (WT 60 days, <italic>N</italic> = 7; eNOS<sup>−/−</sup> 60 days, <italic>N</italic> = 4). The reference group throughout all the genes analyzed in this panel is WT 60 days after bleomycin. Data passed Kolmogorov–Smirnov normality test, are expressed as mean ± <italic>SD</italic>, and analyzed using Student's <italic>t</italic> test (*<italic>p</italic> < 0.05; **<italic>p</italic> < 0.01).</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13196-g004\"/></fig><p>To evaluate whether the lack of vascular eNOS altered fibrogenic gene expression in the lung following bleomycin challenge, we measured the expression of a panel of fibrotic and anti‐fibrotic genes, including the mitochondrial regulator <italic>Ppargc1a</italic> whose repression is critical during the transition of fibroblasts from a quiescent to an activated state (Caporarello et al., <xref rid=\"acel13196-bib-0009\" ref-type=\"ref\">2019</xref>). qPCR analysis of whole lung post‐bleomycin revealed increased pro‐fibrotic gene expression, including <italic>Col1a1</italic>,<italic> Fn1</italic>,<italic> and Acta2</italic>, and a reduction of the anti‐fibrotic gene <italic>Ppargc1a</italic> in eNOS<sup>−/−</sup> mice compared to WT animals (Figure <xref rid=\"acel13196-fig-0004\" ref-type=\"fig\">4e</xref>). Interestingly, injured lungs from eNOS<sup>−/−</sup> mice also showed reduced expression of the endothelial cell markers <italic>Flk1</italic>,<italic> Vwf</italic>,<italic> Cdh5</italic>,<italic> and Erg</italic> relative to lungs from WT animals (Figure <xref rid=\"acel13196-fig-0004\" ref-type=\"fig\">4e</xref>), recapitulating the altered transcriptional responses observed in lung ECs from aged mice post‐bleomycin treatment. All together, these results demonstrate that vascular eNOS plays a critical role during the resolution of bleomycin‐induced lung fibrosis.</p></sec><sec id=\"acel13196-sec-0007\"><label>2.5</label><title>eNOS promotes lung fibroblast deactivation through the engagement of the NO/sGC pathway</title><p>NO released from vascular ECs can activate sGC in other cell types, including smooth muscle cells, in a paracrine manner (Kollau et al., <xref rid=\"acel13196-bib-0035\" ref-type=\"ref\">2018</xref>). Activation of sGC has been shown to be beneficial in multiple pathological conditions and small molecule activators/stimulators of this pathway are currently being used in the clinic to treat patients with pulmonary hypertension (Ghofrani & Grimminger, <xref rid=\"acel13196-bib-0022\" ref-type=\"ref\">2009</xref>). To investigate the contribution of sGC activation in inhibiting pro‐fibrotic gene expression, we treated human lung fibroblasts (HLFs) with TGFβ for 48 hr in the presence or absence of the sGC stimulator BAY 41‐2272 or the sGC activator BAY 60‐2770. As shown in Figure <xref rid=\"acel13196-fig-0005\" ref-type=\"fig\">5a,b</xref>, both compounds significantly reduced TGFβ‐induced <italic>ACTA2</italic>, <italic>COL1A1</italic>, and <italic>FN1</italic> expression at RNA and protein levels. Figure <xref rid=\"acel13196-fig-0005\" ref-type=\"fig\">5a,b</xref> compares to HLFs treated with TGFβ alone.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13196-fig-0005\" orientation=\"portrait\" position=\"float\"><label>FIGURE 5</label><caption><p>eNOS promotes lung fibroblast deactivation through activation of the NO/sGC pathway. (a, b) Pro‐fibrotic gene and protein analysis of HLFs treated with TGFβ (2 ng/ml) and BAY 41‐2272 (5 μM) or TGFβ (2 ng/ml) and BAY 60‐2770 (1 μM) for 48 hr. <italic>N</italic> = 5 independent experiments. Data passed Kolmogorov–Smirnov normality test, are expressed as mean ± <italic>SD</italic>, and analyzed using one‐way analysis of variance (followed by Tukey's post hoc test). (c) Schematic of 2D co‐culture system. TGFβ‐primed HLFs and control‐ or <italic>NOS3</italic>‐silenced HLMECs were seeded for co‐cultures in μ‐Slide 2 well Co‐culture. (d, e) Immunofluorescence images (10x objective magnification) of HLFs primed 24 hr with 2 ng/ml TGFβ and then co‐cultured with control or <italic>NOS3</italic> siRNA transfected HLMECs (72 hr). Scale bars: 1000 μm. αSMA intensity was determined using automated imaging software. <italic>N</italic> = 4 independent experiments. Data are non‐normally distributed, are expressed as median and IQR, and analyzed using non‐parametric Mann–Whitney test. (f) Control‐ and <italic>NOS3</italic>‐silenced HLMECs (72 hr) were analyzed via Western blot using anti eNOS and anti GAPDH antibodies. (g) Transcriptional analysis of control and <italic>NOS3</italic>‐silenced HLMECs (72 hr). <italic>N</italic> = 4 independent experiments. Data passed Kolmogorov–Smirnov normality test, are expressed as mean ± SD, and analyzed using Student's <italic>t</italic> test. (h, i) Schematic of 3D co‐cultures generation. Visualization of DiI‐labeled HLMECs (red) and Col1α1‐GFP mouse fibroblasts (green) within an endothelial cell fibroblast 3D co‐culture. Scale bar: 500 μm. (j) Gene expression analysis of fibroblasts transcripts from mouse fibroblasts alone versus co‐cultures with control‐ and <italic>NOS3</italic> siRNA transfected HLMECs at day 3. <italic>N</italic> = 3 independent experiments. Data passed Shapiro–Wilk normality test, are expressed as mean ± <italic>SD</italic>, and analyzed using one‐way analysis of variance (followed by Tukey's post hoc test). (**<italic>p</italic> < 0.01; ***<italic>p</italic> < 0.001).</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13196-g005\"/></fig><p>To test the ability of ECs to deactivate fibroblasts through the eNOS pathway, we co‐cultured TGFβ‐primed HLFs with human lung microvascular endothelial cells (HLMECs) that have been previously treated with a siRNA targeting eNOS. As shown in Figure <xref rid=\"acel13196-fig-0005\" ref-type=\"fig\">5d,e</xref>, TGFβ–primed HLFs co‐cultured for 3 days with eNOS‐silenced HLMECs showed a trend upwards in αSMA intensity compared to HLFs that were co‐cultured with HLMECs transfected with a control siRNA. Analysis of inflammatory/fibrotic (<italic>IL6</italic> and <italic>IL11</italic>) and endothelial (<italic>TEK</italic> and <italic>VWF</italic>) transcripts showed no differences in <italic>NOS3</italic>‐silenced HLMECs compared to control cells (Figure <xref rid=\"acel13196-fig-0005\" ref-type=\"fig\">5g</xref>). Because NO disperse rapidly and has a very short half‐life (Thomas, Liu, Kantrow, & Lancaster, <xref rid=\"acel13196-bib-0065\" ref-type=\"ref\">2001</xref>), its ability to deactivate fibroblasts may be limited by the distance between cells. To overcome this limitation and to further confirm the capacity of NO to influence pro‐fibrotic gene expression in lung fibroblasts, we adapted a 3D culture system in which cells are mixed within the same matrix (Tan, Choi, Sicard, & Tschumperlin, <xref rid=\"acel13196-bib-0063\" ref-type=\"ref\">2017</xref>). FACS‐sorted Col1α1‐GFP mouse lung fibroblasts were primed with TGFβ for 24 hr and subsequently co‐cultured with eNOS‐silenced HLMECs or control silenced cells in a 3D matrigel gel for additional 72 hr. To identify fibroblast‐specific transcriptional changes in our 3D co‐culture system, we designed primers that recognized mouse‐specific sequences in transcripts for analysis by qPCR (Table <xref rid=\"acel13196-tbl-0001\" ref-type=\"table\">1</xref>). As shown in Figure <xref rid=\"acel13196-fig-0005\" ref-type=\"fig\">5j</xref>, mouse lung fibroblasts cultured with eNOS‐silenced HLMECs showed a significant increase in <italic>Acta2</italic> and <italic>Col1a1</italic> gene expression compared to those cultured with control HLMECs. All together, these findings demonstrated that ECs have the capacity to alter fibroblast activation and promote their deactivation in an eNOS‐dependent manner, suggesting that the absence of induced <italic>NOS3</italic> expression in the lungs of aged mice following injury may directly contribute to the perpetuation of fibroblast activation and fibrosis progression observed in the aging lung.</p><table-wrap id=\"acel13196-tbl-0001\" xml:lang=\"en\" content-type=\"Table\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Mouse and human primer sequences for qPCR analysis</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Primers</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Forward</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Reverse</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Gapdh</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GTGGAGTCATACTGGAACATGTAG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">AATGGTGAAGGTCGGTGTG</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Col1a1</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CCA GCG AAG AAC TCA TAC AGC</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGA CAC CCC TTC TAC GTT GT</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Acta2</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GAGAAGCCCAGCCAGTCG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CTCTTGCTCTGGGCTTCA</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Il6</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TAGTCCTTCCTACCCCAATTTCC</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TTGGTCCTTAGCCACTCCTTC</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Fn1</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TGTCAGTCAAAGCAAGCCCG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TTAGGACGCTCATAAGTGTCACCC</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Il11</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">AAATTCCCAGCTGACGGAGATCAC</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TACATGCCGGAGGTAGGACATCAA</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Erg</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CCGGATACTGTGGGGATGAG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TCTGCGCTCATTTGTGGTCA</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Vwf</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TGTTCATCAAATGGTGGGCAGC</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">ACAGACGCCATCTCCAGATTCA</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Flk1</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CAAACCTCAATGTGTCTCTTTGC</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">AGAGTAAAGCCTATCTCGCTGT</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Cdh5</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GTCGATGCTAACACAGGGAATG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">AATACCTGGTGCGAAAACACA</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Nos3</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGCTGGGTTTAGGGCTGTG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CTGAGGGTGTCGTAGGTGATG</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Ppargc1a</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CCCTGCCATTGTTAAGAC</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TGCTGCTGTTCCTGTTTT</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>GAPDH</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGAAGGGCTCATGACCACAG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">ACA GTC TTC TGG GTG GCA GTG</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>ACTA2</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GTGAAGAAGAGGACAGCACTG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CCCATTCCCACCATCACC</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>COL1A1</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">AAGGGACACAGAGGTTTCAGTGG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CAGCACCAGTAGCACCATCATTTC</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>FN1</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TGTCAGTCAAAGCAAGCCCG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TTAGGACGCTCATAAGTGTCACCC</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap></sec></sec><sec sec-type=\"discussion\" id=\"acel13196-sec-0008\"><label>3</label><title>DISCUSSION</title><p>IPF is characterized by the excessive accumulation of extracellular matrix that leads to distortion of lung architecture and loss of organ function. Myofibroblasts are the main source of ECM in IPF lungs and their sustained pathological activation is primarily responsible for the progressive worsening of the disease (Moore & Herzog, <xref rid=\"acel13196-bib-0046\" ref-type=\"ref\">2013</xref>). Dysregulated epithelium/mesenchyme cross talk, unresolved inflammation, and limited lung regenerative capacity are among the causes thought to lead to the sustained myofibroblast activation and consequently unresolved fibrosis (Wolters, Collard, & Jones, <xref rid=\"acel13196-bib-0071\" ref-type=\"ref\">2014</xref>). In this regard, little is known about the possible link between aberrant fibrosis resolution and vascular remodeling, and elucidation of this interaction is essential to better understand lung repair and regeneration in the context of sustained lung fibrogenesis. Moreover, since aging is associated with endothelial dysfunction (Jane‐Wit & Chun, <xref rid=\"acel13196-bib-0027\" ref-type=\"ref\">2012</xref>) and increased risk and severity of fibrotic diseases (Meiners et al., <xref rid=\"acel13196-bib-0043\" ref-type=\"ref\">2015</xref>) understanding how vascular alterations during aging contribute to persistent lung fibrosis may lead to novel therapeutic approaches.</p><p>In order to identify altered molecular pathways in the pulmonary vasculature that may contribute to sustained fibrosis, we evaluated the responses of lung ECs and fibroblasts to bleomycin‐induced lung injury and fibrosis in young and aged mice. By combining FACS sorting, gene expression, and imaging analysis on injured lungs from both young and aged mice, we demonstrated that lung fibroblast activation in young mice post‐bleomycin is transient and is accompanied by a significant increase in vessel density. On the contrary, fibroblasts isolated from aged lungs post‐bleomycin exhibited a persistent fibrogenic behavior characterized by the sustained elevation of <italic>Col1a1</italic> expression. The lungs of aged mice were characterized by persistently elevated levels of hydroxyproline and significant reductions in lung vascular density. Interestingly, vascular rarefaction has been reported in multiple other mouse models of fibrosis in young mice, including scleroderma and kidney fibrosis (Loganathan et al., <xref rid=\"acel13196-bib-0041\" ref-type=\"ref\">2018</xref>; Trojanowska, <xref rid=\"acel13196-bib-0067\" ref-type=\"ref\">2010</xref>). Additionally, it has been shown that the loss of capillaries observed in these fibrosis models leads to secondary events including tissue hypoxia and oxidative stress which further exacerbate fibrosis progression by promoting proliferation and activation of tissue resident fibroblasts (Basile et al., <xref rid=\"acel13196-bib-0006\" ref-type=\"ref\">2011</xref>; Fleming et al., <xref rid=\"acel13196-bib-0020\" ref-type=\"ref\">2008</xref>). Our findings are in part consistent with these previous observations, but only in aged mice. In contrast, bleomycin‐treated lungs from young animals showed a significant increase in vessel density at 30 days, suggesting a potential active role for the lung vascular bed during fibrosis resolution. Intriguingly, the capillary rarefaction we observed in the injured lungs from aged mice is consistent with the non‐resolving nature of this animal model of fibrosis, strongly suggesting that aging may lead to a progressive decline of lung endothelial cell functions thereby influencing fibrosis resolution following lung injury.</p><p>Moreover, our ex vivo results from mouse lung explants are consistent with the in vivo observations and demonstrated that while lung explants from young mice responded to angiogenic stimuli and generated numerous vessel sprouts, lung explants isolated from aged mice are refractory to angiogenesis and showed a limited number of microvessels upon angiogenic stimulation. These observations support the notion that lung ECs from aged mice have an impaired angiogenic capacity compared to those from young animals, potentially impacting lung fibrosis resolution following injury. Because fibrotic lungs are characterized by the accumulation of extracellular matrix and by an increased stiffness, a limited angiogenic potential due to an impaired endothelial cell migration through a stiff microenvironment may be responsible for the altered vascular remodeling in injured aged lungs.</p><p>In addition, we found that lung ECs isolated from aged lungs after injury have a significant reduction in endothelial markers and an enrichment in inflammatory and fibrotic markers, which is consistent with the transcriptional switch observed during EndMT, a process observed in several chronic conditions including atherosclerosis, pulmonary hypertension, and organ fibrosis (Gong, Lyu, Wang, Hu, & Zhang, <xref rid=\"acel13196-bib-0023\" ref-type=\"ref\">2017</xref>; Kitao et al., <xref rid=\"acel13196-bib-0032\" ref-type=\"ref\">2009</xref>). During this process, endothelial‐specific markers are lost while mesenchymal and inflammatory markers are acquired, together with alterations in cellular morphology and functions, including loss of the ability of endothelial cells to organize in vessel‐like structures (Piera‐Velazquez & Jimenez, <xref rid=\"acel13196-bib-0054\" ref-type=\"ref\">2019</xref>). Moreover, TGFβ, which is highly abundant in fibrotic lungs (Yue, Shan, & Lasky, <xref rid=\"acel13196-bib-0074\" ref-type=\"ref\">2010</xref>), plays an important role during EndMT (Piera‐Velazquez & Jimenez, <xref rid=\"acel13196-bib-0054\" ref-type=\"ref\">2019</xref>), and <italic>in vitro</italic> evidence indicates that VEGF, whose levels are reduced in IPF patients (Barratt, Flower, Pauling, & Millar, <xref rid=\"acel13196-bib-0004\" ref-type=\"ref\">2018</xref>), can block this effect (Yang, Wylie‐Sears, & Bischoff, <xref rid=\"acel13196-bib-0073\" ref-type=\"ref\">2008</xref>). Tissue hypoxia is also a critical driver of EndMT (Zhang et al., <xref rid=\"acel13196-bib-0076\" ref-type=\"ref\">2018</xref>) and the vascular rarefaction we observed in injured aged lungs could certainly lead to the formation of a hypoxic microenvironment which can influence both endothelial cell fate and consequently fibroblast activation. Interestingly, in line with our results, altered endothelial cell activation and abnormal vascular remodeling also occurs in an experimental model of persistent lung fibrosis which is triggered by repetitive doses of bleomycin (Cao et al., <xref rid=\"acel13196-bib-0008\" ref-type=\"ref\">2016</xref>). Hence, our findings suggested that aging leads to the formation of an unfavorable lung microenvironment that promotes vascular rarefaction and endothelial dysfunction thereby influencing disease progression.</p><p>Nitric oxide plays an important role in numerous biological processes, including regulation of vascular tone, endothelial cell barrier preservation, endothelial cell survival, and apoptosis (Dimmeler & Zeiher, <xref rid=\"acel13196-bib-0015\" ref-type=\"ref\">1999</xref>). Interestingly, it has been reported that endogenous NO plays a protective role in a murine model of experimental lung fibrosis in young mice (Noguchi et al., <xref rid=\"acel13196-bib-0051\" ref-type=\"ref\">2014</xref>). In addition, alterations of eNOS pathway have been reported during aging (Cau, Carneiro, & Tostes, <xref rid=\"acel13196-bib-0011\" ref-type=\"ref\">2012</xref>). Our transcriptional analysis on freshly isolated lung ECs demonstrated upregulation of Nos3 in young mice at 30 days following bleomycin delivery. In contrast, endothelial cells from aged mice failed to upregulate Nos3 at the same timepoint, strongly suggesting that eNOS may be required for promoting lung fibrosis resolution post‐injury and failure to activate this pathway in the lungs of aged mice contributes to sustained fibrogenesis. Interestingly, in line with our results in the absence of injury, a recent study using single‐cell RNA sequencing reported no differences in lung endothelial <italic>Nos3</italic> expression in aged <italic>vs</italic> young mice (Angelidis et al., <xref rid=\"acel13196-bib-0003\" ref-type=\"ref\">2019</xref>). However, single‐cell RNA sequencing data for <italic>NOS3</italic> expression in endothelial cells of IPF versus normal lungs were more variable. In agreement with our results, <italic>NOS3</italic> expression in arterial endothelial cells of IPF lung was reduced; however, an elevation or no differences in other vascular cell types were found (Adams et al., <xref rid=\"acel13196-bib-0001\" ref-type=\"ref\">2019</xref>), suggesting that regulation of <italic>NOS3</italic> during disease may occur differently across the endothelial cell types. Together, our observations shed light on the active role of eNOS pathway in promoting lung fibrosis resolution in young mice that is lost with aging. These observations are in line with other studies reporting a protective role for eNOS in experimental models of cardiac and renal fibrosis (Kazakov et al., <xref rid=\"acel13196-bib-0030\" ref-type=\"ref\">2013</xref>; Nakayama et al., <xref rid=\"acel13196-bib-0049\" ref-type=\"ref\">2009</xref>).</p><p>The increased capillary loss we observed in the lungs of aged mice and the lack of transcriptional activation of <italic>Nos3</italic> gene in aged ECs leads us to hypothesize that eNOS/NO pathway may play a critical role in promoting endothelial cell survival following lung injury. Interestingly, previous studies have reported that NO can protect ECs from apoptosis through multiple mechanisms, including blocking caspase activation (Kim, Kwon, Chung, & Kim, <xref rid=\"acel13196-bib-0031\" ref-type=\"ref\">2002</xref>) and by promoting survival signaling (Dimmeler & Zeiher, <xref rid=\"acel13196-bib-0015\" ref-type=\"ref\">1999</xref>). Our observations together with previous reports suggest that the reduced NO availability in the lungs of aged mice during the resolution phase of bleomycin‐induced lung injury may limit endothelial cell survival thereby contributing to the sustained lung fibrosis in these animals. Another intriguing aspect related to the beneficial effect of NO is its capability to suppress the nucleotide‐binding domain and leucine‐rich repeat containing family, pyrin domain containing 3 (NLRP3) inflammasome in lungs (Mishra et al., <xref rid=\"acel13196-bib-0044\" ref-type=\"ref\">2013</xref>). NLRP3 inflammasome activation is implicated in the pathogenesis of IPF (Lasithiotaki et al., <xref rid=\"acel13196-bib-0037\" ref-type=\"ref\">2016</xref>) and contributes to the development of experimental lung fibrosis in aged mice, with bleomycin‐treated aged NLRP3<sup>−/−</sup> mice showing reduced lung fibrosis compared to their WT age‐matched counterparts (Stout‐Delgado et al., <xref rid=\"acel13196-bib-0062\" ref-type=\"ref\">2016</xref>). Thus, we speculate that NO‐NLRP3 cross talk may be another anti‐fibrotic mechanism in lung that is lost with aging.</p><p>While the role of NO/sGC in regulating vascular tone by promoting smooth muscle cells (SMCs) relaxation has been investigated in great depth (Park et al., <xref rid=\"acel13196-bib-0053\" ref-type=\"ref\">2019</xref>; Tsai & Kass, <xref rid=\"acel13196-bib-0068\" ref-type=\"ref\">2009</xref>), its contribution to lung fibroblast biology and ECM remodeling has just begun to emerge (Dunkern, Feurstein, Rossi, Sabatini, & Hatzelmann, <xref rid=\"acel13196-bib-0018\" ref-type=\"ref\">2007</xref>; Lambers et al., <xref rid=\"acel13196-bib-0036\" ref-type=\"ref\">2019</xref>). Our findings demonstrate that in addition to inhibiting αSMA expression, activation of sGC signaling pathways in lung fibroblasts greatly reduces TGFβ responses, including collagen synthesis. Given that ECs can influence the lung microenvironment by producing and releasing angiocrine factors, in this study, we wanted to determine whether lung ECs can directly alter fibroblast activation. Our in vitro results strongly support a functional role for ECs in facilitating the return of activated fibroblasts to a less activated state. While additional work is needed to fully understand endothelial/mesenchymal interactions during lung fibrosis, our work highlights the concept that the lung vascular bed plays an active role during lung fibrosis resolution through the release of NO and the paracrine activation of sGC pathway in neighboring fibroblasts.</p><p>A limitation of our study is that we have not measured endogenous NO production or evaluated regulation of eNOS phosphorylation in the lung of young relative to aged mice. Moreover, our results raised several questions not addressed in this study. Investigations aimed at understanding whether restoring <italic>Nos3</italic> expression in aged animals may have beneficial effects in reestablishing endothelial homeostasis and limiting lung fibrosis may be an interesting future subject of study. While our study highlighted the important role of eNOS/NO/sGC during lung fibrosis resolution, a clinical trial conducted in IPF patients showed that sildenafil was not effective for improving pulmonary fibrosis (Kolb et al., <xref rid=\"acel13196-bib-0034\" ref-type=\"ref\">2018</xref>). However, sildenafil is a phosphodiesterase‐5 inhibitor that prevents the degradation of cGMP. Importantly, such a drug relies on a sufficient source of endogenous NO to be effective. In contrast, targeting the same pathway to generate cGMP through the use of sGC modulators could be a more suitable therapeutic intervention for treatment of IPF.</p><p>Our observations set the stage for future investigations aimed at identifying additional endothelial‐derived signals lost with aging that can influence fibroblast activation as well as the integrity of other neighboring cells, for instance epithelial cells. In fact, recent observations highlighted an important role for ECs in creating a niche for epithelial cells that protect from cell injury and promote repair (Cao et al., <xref rid=\"acel13196-bib-0008\" ref-type=\"ref\">2016</xref>; Murray et al., <xref rid=\"acel13196-bib-0048\" ref-type=\"ref\">2017</xref>) and aging could contribute to the loss of these pro‐regenerative signals, exacerbating the disease.</p><p>In conclusion, our study provides compelling evidence of the direct contribution of the pulmonary vascular bed during lung repair and fibrosis resolution that is lost with aging, suggesting that preserving or rescuing the normal vascular repair and homeostasis responses could represent a new therapeutic strategy to limit lung fibrosis progression.</p></sec><sec id=\"acel13196-sec-0009\"><label>4</label><title>EXPERIMENTAL PROCEDURES</title><p>Detailed methods are provided in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>.</p><sec id=\"acel13196-sec-0010\"><label>4.1</label><title>Mice</title><p>Female and male Col1α1‐GFP transgenic mice (FVB strain) were provided by Dr. Derek Radisky. Female and male wild‐type (C57BL6) and eNOS<sup>−/−</sup> (Nos3<sup>tm1Unc</sup>/J) mice were provided by Dr. Zvonimir S. Katusic.</p></sec><sec id=\"acel13196-sec-0011\"><label>4.2</label><title>Cell culture</title><p>Normal primary human lung fibroblasts, HLFs (Walkersville, MD, USA) were used between passages 3 and 7. Normal human lung microvascular endothelial cells, HLMECs (Lonza) were used within passage 4. In experiments involving siRNA, serum was reduced to 0.1%.</p></sec><sec id=\"acel13196-sec-0012\"><label>4.3</label><title>Mouse model of bleomycin‐induced lung injury</title><p>All animal experiments were carried out conforming to the ARRIVE guidelines. Mice were anesthetized with ketamine/xylazine solution and bleomycin (1 U/kg) or PBS was intratracheally delivered on day 0 as described in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13196-sec-0013\"><label>4.4</label><title>FACS sorting</title><p>Mice were anesthetized with ketamine/xylazine solution and perfused via left ventricle with cold PBS. The lungs were immediately harvested and the single cell suspension was obtained as detailed in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>. The single cell suspension was incubated with anti‐CD45:PerCp‐Cy5.5 (1:200, Biolegend, Cat# 103132), anti‐CD31:PE (1:200, Biolegend, Cat#102408), anti‐EpCAM:APC (1:200, Biolegend, Cat#118214) antibodies, and DAPI (1:1000, Biolegend, Cat#422801) for 30 min on ice. After incubation, cells were washed with ice‐cold FACS buffer and resuspended in 1 ml of FACS buffer. FACS sorting was conducted using a BD FACS Aria II (BD Biosciences) as described in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>. Total mRNA was isolated using RNeasy micro kit, followed by Nanodrop concentration and purity analysis. cDNA was synthesized using SuperScript VILO (Thermo Fisher Scientific); RT–PCR was performed using FastStart Essential DNA Green Master (Roche Diagnostics) and analyzed using a LightCycler 96 (Roche Diagnostics).</p></sec><sec id=\"acel13196-sec-0014\"><label>4.5</label><title>Fibrosis evaluation</title><p>Hydroxyproline content was measured using a hydroxyproline assay kit (Biovision), comparing the samples to a hydroxyproline standard curve as described in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13196-sec-0015\"><label>4.6</label><title>Immunohistochemistry</title><p>Lung tissue from patients with IPF and from non‐fibrotic healthy controls was obtained from Dr. Steven Huang at the University of Michigan. Diagnoses of patients with IPF were established by clinic‐pathologic criteria and confirmed by multidisciplinary consensus conference. All IPF tissues were derived from explanted lungs obtained at the time of transplantation. Normal control lungs were obtained from deceased donors (Gift of Life, Michigan) whose lungs were deemed unsuitable for transplant. All patient samples were obtained with informed consent and were approved by the University of Michigan IRB (IRB #: HUM00105694). Mouse and human lung tissues were stained with Collagen 1α1 (dilution: 1:100; Novus biologicals, Centennial, CO, Cat# NB600‐408) or PECAM‐1 (dilution 1:2000; Abcam, Cat# ab28364) antibodies as described in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13196-sec-0016\"><label>4.7</label><title>RNA interference</title><p>RNA interference was performed with siGENOME Non‐Targeting Control siRNA Pool #1 (D‐001206‐13‐05) or siGENOME Human <italic>NOS3</italic> siRNA (M‐006490‐00‐0005) by using Lipofectamine RNAiMAX reagent (Thermo Fisher Scientific, Cat# 13778075) as described in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13196-sec-0017\"><label>4.8</label><title>\n<bold>2D co‐culture and</bold> α<bold>SMA staining</bold>\n</title><p>Co‐cultivation of HLFs and HLMECs and αSMA staining were performed by using μ‐Slide 2 well Co‐culture (ibidi, Lochhamer, Germany). HLFs were primed with TGFβ (2 ng/ml) for 24 hr and then transferred into the inner minor well of the μ‐Slide. HLMECs were transfected with Non‐Targeting or <italic>NOS3</italic> siRNA. Six hours after transfection, the cells were lifted and plated into the outer minor wells of the μ‐Slide. After 72 hr, HLFs were processed and stained as described in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13196-sec-0018\"><label>4.9</label><title>3D co‐culture generation and analysis</title><p>A mixture of FACS‐sorted Col1α1‐GFP mice fibroblasts and HLMECs was plated in a 1:1 solution of Matrigel Matrix (Corning, Cat# 354248) and endothelial cell growth basal medium (Lonza, Cat# 00190860) as detailed in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>. After 3 days, cells were removed from Matrigel with Corning Cell Recovery Solution (Corning, Cat#35425), and total RNA, cDNA synthesis, and qPCR analysis were performed.</p></sec><sec id=\"acel13196-sec-0019\"><label>4.10</label><title>Immunofluorescence staining</title><p>Mouse lung tissue sections (7 μm) were permeabilized, blocked, and stained with PECAM‐1 antibody (dilution 1:100; BD Biosciences, Cat# 550274), and nuclei were counterstained with DAPI (dilution: 1:1000; Biolegend, Cat#422801) as described in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13196-sec-0020\"><label>4.11</label><title>Ex vivo lung tissue culture</title><p>Each well of a 48‐well culture plate was coated with 250 μl of rat tail collagen I (2 mg/ml; Thermo Fisher Scientific, Cat# A1048301). Fresh lung explants were embedded into the collagen I layers, cultured for 7 days in presence of VEGFA (20 ng/ml) and analyzed as described in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13196-sec-0021\"><label>4.12</label><title>Western blotting</title><p>Western blotting analysis of whole lung tissue or cell lysates was performed using eNOS (Cell Signaling, Cat#32027) and GAPDH (Cell Signaling, Cat#14C10) antibodies, as described in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13196-sec-0022\"><label>4.13</label><title>Statistical analysis</title><p>Individual data points are shown in all plots and represent data from independent mice or biological replicates from cell culture experiments. Statistical analysis was performed using Student's <italic>t</italic> test, one‐way analysis of variance (followed by Tukey's <italic>post hoc</italic> test), or non‐parametric Mann–Whitney test as detailed in Appendix <xref rid=\"acel13196-sup-0001\" ref-type=\"supplementary-material\">S1</xref>. All analyses and plots were generated using GraphPad Prism 8.0 (La Jolla, CA, USA) with statistical significance defined as <italic>p</italic> < 0.05.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13196-sec-0024\"><title>CONFLICT OF INTEREST</title><p>None declared.</p></sec><sec id=\"acel13196-sec-0025\"><title>AUTHOR CONTRIBUTIONS</title><p>N.C., D.J.T., and G.L. designed the study. N.C., J.A.M., A.A., D.L.J., S.A.A., T. X. P., A.J.H., K.M.C., Q.T., and A.H. performed experiments. N.C. and G.L. analyzed data. The manuscript was drafted by N.C., D.J.T., and G.L. and revised by N.C., S.K.H., Z.S.K., D.J.T., and G.L. All authors participated in manuscript preparation and provided final approval of the submitted work.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13196-sup-0001\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13196-s001.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13196-sec-0023\"><title>ACKNOWLEDGMENTS</title><p>Funding support was provided by the National Institutes of Health (NIH) grants HL142596 (G.L.), HL092961 (D.J.T.), and HL137366 (D.J.T.). We thank Mr Brandon Nelson for his assistance with quantification of immunofluorescence staining. The authors have declared that no conflict of interest exists.</p></ack><sec sec-type=\"data-availability\" id=\"acel13196-sec-0027\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available from the corresponding author upon request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13196-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13196-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13196-cit-0001\">\n<string-name>\n<surname>Adams</surname>, <given-names>T. S.</given-names>\n</string-name>, <string-name>\n<surname>Schupp</surname>, <given-names>J. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32627932</article-id><article-id pub-id-type=\"pmc\">PMC7431830</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13179</article-id><article-id pub-id-type=\"publisher-id\">ACEL13179</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Lifespan regulation in α/β posterior neurons of the fly mushroom bodies by Rab27</article-title><alt-title alt-title-type=\"left-running-head\">LIEN et al.</alt-title></title-group><contrib-group><contrib id=\"acel13179-cr-0001\" contrib-type=\"author\"><name><surname>Lien</surname><given-names>Wen‐Yu</given-names></name><xref ref-type=\"aff\" rid=\"acel13179-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0002\" contrib-type=\"author\"><name><surname>Chen</surname><given-names>Yu‐Ting</given-names></name><xref ref-type=\"aff\" rid=\"acel13179-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0003\" contrib-type=\"author\"><name><surname>Li</surname><given-names>Yi‐Jhan</given-names></name><xref ref-type=\"aff\" rid=\"acel13179-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0004\" contrib-type=\"author\"><name><surname>Wu</surname><given-names>Jie‐Kai</given-names></name><xref ref-type=\"aff\" rid=\"acel13179-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0005\" contrib-type=\"author\"><name><surname>Huang</surname><given-names>Kuan‐Lin</given-names></name><xref ref-type=\"aff\" rid=\"acel13179-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0006\" contrib-type=\"author\"><name><surname>Lin</surname><given-names>Jian‐Rong</given-names></name><xref ref-type=\"aff\" rid=\"acel13179-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0007\" contrib-type=\"author\"><name><surname>Lin</surname><given-names>Shih‐Ching</given-names></name><xref ref-type=\"aff\" rid=\"acel13179-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0008\" contrib-type=\"author\"><name><surname>Hou</surname><given-names>Chia‐Chun</given-names></name><xref ref-type=\"aff\" rid=\"acel13179-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0009\" contrib-type=\"author\"><name><surname>Wang</surname><given-names>Horng‐Dar</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-9570-3611</contrib-id><xref ref-type=\"aff\" rid=\"acel13179-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0010\" contrib-type=\"author\"><name><surname>Wu</surname><given-names>Chia‐Lin</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-7906-2364</contrib-id><xref ref-type=\"aff\" rid=\"acel13179-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13179-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0011\" contrib-type=\"author\"><name><surname>Huang</surname><given-names>Shu‐Yi</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-3522-0500</contrib-id><xref ref-type=\"aff\" rid=\"acel13179-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13179-cr-0012\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Chan</surname><given-names>Chih‐Chiang</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-2626-3805</contrib-id><xref ref-type=\"aff\" rid=\"acel13179-aff-0001\">\n<sup>1</sup>\n</xref><address><email>chancc1@ntu.edu.tw</email></address></contrib></contrib-group><aff id=\"acel13179-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Graduate Institute of Physiology</named-content>\n<named-content content-type=\"organisation-division\">College of Medicine</named-content>\n<institution>National Taiwan University</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13179-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Department of Biochemistry and Graduate Institute of Biomedical Sciences</named-content>\n<named-content content-type=\"organisation-division\">College of Medicine</named-content>\n<institution>Chang Gung University</institution>\n<city>Taoyuan</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13179-aff-0003\">\n<label><sup>3</sup></label>\n<named-content content-type=\"organisation-division\">Institute of Biotechnology</named-content>\n<institution>National Tsing Hua University</institution>\n<city>Hsinchu</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13179-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Department of Neurology</named-content>\n<institution>Linkou Chang Gung Memorial Hospital</institution>\n<city>Taoyuan</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13179-aff-0005\">\n<label><sup>5</sup></label>\n<named-content content-type=\"organisation-division\">Department of Medical Research</named-content>\n<institution>National Taiwan University Hospital</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nChih‐Chiang Chan, Graduate Institute of Physiology, College of Medicine, National Taiwan University, Taipei, Taiwan.<break/>\nEmail: <email>chancc1@ntu.edu.tw</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>06</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13179</elocation-id><history><date date-type=\"received\"><day>31</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>5</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13179.pdf\"/><abstract id=\"acel13179-abs-0001\"><title>Abstract</title><p>Brain function has been implicated to control the aging process and modulate lifespan. However, continuous efforts remain for the identification of the minimal sufficient brain region and the underlying mechanism for neuronal regulation of longevity. Here, we show that the <italic>Drosophila</italic> lifespan is modulated by <italic>rab27</italic> functioning in a small subset of neurons of the mushroom bodies (MB), a brain structure that shares analogous functions with mammalian hippocampus and hypothalamus. Depleting <italic>rab27</italic> in the α/βp neurons of the MB is sufficient to extend lifespan, enhance systemic stress responses, and alter energy homeostasis, all without trade‐offs in major life functions. Within the α/βp neurons, <italic>rab27KO</italic> causes the mislocalization of phosphorylated S6K thus attenuates TOR signaling, resulting in decreased protein synthesis and reduced neuronal activity. Consistently, expression of dominant‐negative S6K in the α/βp neurons increases lifespan. Furthermore, the expression of phospho‐mimetic S6 in α/βp neurons of <italic>rab27KO</italic> rescued local protein synthesis and reversed lifespan extension. These findings demonstrate that inhibiting TOR‐mediated protein synthesis in α/βp neurons is sufficient to promote longevity.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13179-abs-0002\"><p>The Drosophila lifespan is modulated by <italic>rab27</italic> functioning in α/βp neurons of the mushroom bodies, a brain structure functionally analogous to mammalian hippocampus and hypothalamus. Depleting <italic>rab27</italic> in these neurons is sufficient to extend lifespan and enhance systemic stress resistance without trade‐offs in major life functions.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13179-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13179-g007.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13179-kwd-0001\">Drosophila</kwd><kwd id=\"acel13179-kwd-0002\">lifespan extension</kwd><kwd id=\"acel13179-kwd-0003\">mushroom body</kwd><kwd id=\"acel13179-kwd-0004\">Rab27</kwd><kwd id=\"acel13179-kwd-0005\">S6K</kwd><kwd id=\"acel13179-kwd-0006\">TOR</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>Ministry of Science and Technology, Taiwan </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100004663</institution-id></institution-wrap></funding-source><award-id>104‐2321‐B‐002‐069</award-id><award-id>105‐2321‐B‐002‐023</award-id><award-id>107‐2311‐B‐002‐008</award-id></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>National Taiwan University </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100006477</institution-id></institution-wrap></funding-source><award-id>107C101‐81</award-id><award-id>107L880303</award-id><award-id>109L104308</award-id><award-id>109L893603</award-id></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"0\"/><page-count count=\"15\"/><word-count count=\"9112\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13179-cit-1001\">\n<string-name>\n<surname>Lien</surname>\n<given-names>W‐Y</given-names>\n</string-name>, <string-name>\n<surname>Chen</surname>\n<given-names>Y‐T</given-names>\n</string-name>, <string-name>\n<surname>Li</surname>\n<given-names>Y‐J</given-names>\n</string-name>, et al. <article-title>Lifespan regulation in α/β posterior neurons of the fly mushroom bodies by Rab27</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13179</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13179</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13179-body-0001\"><sec id=\"acel13179-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>The desire for lifespan extension is a never‐ending quest throughout human history. While extreme lifespans are likely limited genetically, environmental factors and acquired characteristics do affect the speed of degeneration and health span. As a result, lifespan is determined as the collective effect of internal and external factors, including the challenges of oxidative stress, the homeostasis of circulating metabolites, intake of energy and nutrients, and the allocation of resources among major life functions. Restriction of caloric intake is by far the most effective approach to extend lifespan in all species examined from yeast to non‐human primates. Although its long‐term effect is hard to evaluate in humans, caloric restriction has been shown to induce physiological changes that are similar to those observed in animal models. However, when energy intake is restricted, limited resources need to be relocated from growth and reproduction to maintain life‐sustaining functions, resulting in trade‐offs between longevity and the size/weight of an individual as well as the reproductive success (Maklakov & Immler, <xref rid=\"acel13179-bib-0031\" ref-type=\"ref\">2016</xref>).</p><p>Animals need to sense, integrate, and adapt to changes from diverse physiological and environmental cues. In the mammalian hypothalamus, different clusters of neurons integrate internal and external inputs to regulate important life functions including appetite, body temperature, and sleep. For example, the murine arcuate nucleus integrates hormonal signals from the periphery including ghrelin and leptin to regulate food intake for energy homeostasis. Two other hypothalamic nuclei, the preoptic area, and the dorsomedial hypothalamus integrate peripheral inputs to maintain body temperature. This evidence illustrates the importance of the central nervous system (CNS) for the summation of peripheral signals to regulate systemic homeostasis. Whether the CNS also functions as a control hub for lifespan regulation; however, remains debatable.</p><p>Several recent studies suggest that the hypothalamus plays a pivotal role in longevity and it may be a regulator of systemic aging (Zhang et al., <xref rid=\"acel13179-bib-0050\" ref-type=\"ref\">2013</xref>; Zhao et al., <xref rid=\"acel13179-bib-0053\" ref-type=\"ref\">2017</xref>). In the fruit fly <italic>Drosophila</italic>, the functions of mammalian hypothalamus are divided among the mushroom bodies (MB) and the pars intercerebralis (Bang et al., <xref rid=\"acel13179-bib-0004\" ref-type=\"ref\">2011</xref>; Dus et al., <xref rid=\"acel13179-bib-0012\" ref-type=\"ref\">2015</xref>). Within the pars intercerebralis, the median neurosecretory cluster (mNSC) has been shown to regulate lifespan, as the ablation of mNSC results in altered systemic energy metabolism, enhanced stress tolerance, and lifespan extension (Broughton et al., <xref rid=\"acel13179-bib-0007\" ref-type=\"ref\">2005</xref>). The MB consists of ~ 2000 Kenyon cells that can be classified according to the axon innervation patterns into three major groups: the α/β, α’/β’, and γ lobes. Although the MB is important for learning and memory similar to the hippocampus, the MB also shows functional analogy to the mammalian hypothalamus in regulating food‐seeking behavior, courtship behavior, sleep, and temperature preference (Bang et al., <xref rid=\"acel13179-bib-0004\" ref-type=\"ref\">2011</xref>; Joiner, Crocker, White, & Sehgal, <xref rid=\"acel13179-bib-0022\" ref-type=\"ref\">2006</xref>; McBride et al., <xref rid=\"acel13179-bib-0035\" ref-type=\"ref\">2005</xref>). For example, the <italic>Drosophila</italic> MB integrates hunger and satiety signals to regulate feeding behavior to meet organismal needs (Tsao, Chen, Lin, Yang, & Lin, <xref rid=\"acel13179-bib-0045\" ref-type=\"ref\">2018</xref>). Also, reduction of insulin signaling in the MB decreases food consumption (Zhao & Campos, <xref rid=\"acel13179-bib-0052\" ref-type=\"ref\">2012</xref>). However, whether the MB contributes to the regulation of lifespan remains unknown.</p><p>Rab27 is a highly conserved small Rab GTPase widely expressed in various secretory cells, including endocrine, cancer, and immune cells, and is well recognized for its role in exosome secretion. We have reported that the <italic>Drosophila</italic> Rab27 is exclusively expressed in neuronal tissues predominantly in specific brain regions including the MB (Chan et al., <xref rid=\"acel13179-bib-0009\" ref-type=\"ref\">2011</xref>; Jin et al., <xref rid=\"acel13179-bib-0021\" ref-type=\"ref\">2012</xref>). At present, the neuronal functions of Rab27 remain poorly understood. Rab27 regulates the docking of dense‐core vesicles in PC12 neuroendocrine cells (Tsuboi & Fukuda, <xref rid=\"acel13179-bib-0046\" ref-type=\"ref\">2006</xref>), whereas inhibition of Rab27 is found to impair synaptic transmission in <italic>C. elegans</italic> and giant squid (Mahoney et al., <xref rid=\"acel13179-bib-0030\" ref-type=\"ref\">2006</xref>; Yu et al., <xref rid=\"acel13179-bib-0049\" ref-type=\"ref\">2008</xref>). We have previously characterized <italic>rab27KO</italic> flies as viable and fertile, without apparent developmental defects (Chan et al., <xref rid=\"acel13179-bib-0009\" ref-type=\"ref\">2011</xref>). Here, we describe our investigation of how Rab27 functions to control lifespan in a small subset of neurons in the <italic>Drosophila</italic> MB and the underlying molecular mechanism.</p></sec><sec sec-type=\"results\" id=\"acel13179-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13179-sec-0003\"><label>2.1</label><title>Loss of <italic>rab27</italic> in adult neurons is sufficient to prolong lifespan</title><p>We have shown that <italic>rab27<sup>Gal4‐KO</sup></italic> homozygotes are viable (this strain was used for most experiments unless otherwise noted and is herein referred to as <italic>rab27KO</italic>) (Chan et al., <xref rid=\"acel13179-bib-0009\" ref-type=\"ref\">2011</xref>). Characterizing these flies in detail, we found that the homozygous <italic>rab27KO</italic> flies showed a pronounced lifespan extension compared with wild‐type (WT) controls (average survival + 46.6%) and <italic>rab27</italic> heterozygotes also had an intermediate yet significant lifespan extension in the population‐based longevity assay (Figure <xref rid=\"acel13179-fig-0001\" ref-type=\"fig\">1a,b</xref>, average survival + 20.3%; (Flatt et al., <xref rid=\"acel13179-bib-0013\" ref-type=\"ref\">2008</xref>); all of the lifespan statistics of the female were shown in the following figures and summarized in Table <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S1</xref>, whereas those of the male were summarized in Table <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). We generated another independent null allele of <italic>rab27</italic> using CRISPR/cas9 (<italic>rab27<sup>Crispr‐KO</sup></italic>, Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S1</xref>a,b) which showed the same effect on lifespan (Figure <xref rid=\"acel13179-fig-0001\" ref-type=\"fig\">1c,d</xref>, average survival + 53.5%). These two <italic>rab27</italic> knockout strains were further validated by the single‐sex vial assays (Bjedov et al., <xref rid=\"acel13179-bib-0005\" ref-type=\"ref\">2010</xref>), and they exhibited similar trends of lifespan extension (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S1</xref>c). The effect of <italic>rab27KO</italic> on lifespan was reversed by the expression of <italic>rab27</italic> cDNA, confirming that <italic>rab27</italic> regulates longevity (Figure <xref rid=\"acel13179-fig-0001\" ref-type=\"fig\">1c,d</xref>). Thus, the global loss of <italic>rab27</italic> prolongs lifespan in <italic>Drosophila</italic>.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13179-fig-0001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Loss of <italic>rab27</italic> in adult neurons is sufficient to prolong lifespan. (a) Survival of females from <italic>w<sup>1118</sup></italic> control (black), <italic>rab27</italic> heterozygote <italic>rab27KO/+</italic> (blue), and homozygous <italic>rab27KO</italic> (red), <italic>p < </italic>.001. (b) Relative abundance of <italic>rab27</italic> mRNA in strains in (a) determined by qRT‐PCR in fly heads; mean ± <italic>SEM</italic> of three independent experiments. **<italic>p</italic> < .01, ***<italic>p</italic> < .001, one‐way ANOVA. (c) Survival of females from two independent <italic>rab27</italic> nulls, <italic>rab27<sup>Crispr‐KO</sup></italic> (green), and <italic>rab27KO</italic> (red), comparing with <italic>w<sup>1118</sup></italic> control (black) and <italic>rab27KO</italic>;UAS‐<italic>rab27</italic> (purple), <italic>p < </italic>.001. (d) Relative abundance of <italic>rab27</italic> mRNA in strains in (c) in fly heads. *<italic>p</italic> < .05, ***<italic>p</italic> < .001, one‐way ANOVA. (e) Body length measured at 24, 48, and 72 hr after egg collection, <italic>n</italic> = 3 independent experiments. (f) Developmental time from egg to pupa in <italic>w<sup>1118</sup></italic> and <italic>rab27KO</italic>, <italic>n</italic> = 3 independent experiments. (g) Relative abundance of <italic>rab27</italic> mRNA in the heads of <italic>elav</italic>‐GS‐Gal4 > UAS‐<italic>rab27</italic>‐RNAi flies fed with RU486 normalized to the solvent‐fed control (EtOH). <italic>p</italic> < .01, significance determined by one‐way ANOVA, also for Gal4 or UAS, controls is shown in Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S2</xref>e. (h) Survival of females from RU486‐induced <italic>elav</italic>‐GS‐Gal4 > UAS‐<italic>rab27</italic>‐RNAi (red) compared with the solvent‐fed control (black), <italic>p</italic> < .001. All survival data were analyzed by log‐rank tests. Please see Tables <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S1</xref> and <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S2</xref> for detailed information including mean lifespan and statistical comparisons</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13179-g001\"/></fig><p>We have shown that Rab27 is expressed throughout life (Chan et al., <xref rid=\"acel13179-bib-0009\" ref-type=\"ref\">2011</xref>; Jin et al., <xref rid=\"acel13179-bib-0021\" ref-type=\"ref\">2012</xref>). We observed no difference in larval body length and pupation timing between WT and <italic>rab27KO</italic> (Figure <xref rid=\"acel13179-fig-0001\" ref-type=\"fig\">1e,f</xref>), suggesting that the lifespan extension is less likely due to developmental defects. To determine whether depleting <italic>rab27</italic> after the flies reach the adult stages still promotes longevity, we utilized an RU486‐inducible system for pan‐neuronal <italic>rab27</italic> RNAi after eclosion. Feeding of RU486 to adult flies leads to effective pan‐neuronal activation of <italic>elav</italic>‐GS‐Gal4 (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S2</xref>a‐d’’), lowered <italic>rab27</italic> mRNA abundance in the head (Figure <xref rid=\"acel13179-fig-0001\" ref-type=\"fig\">1g</xref> and Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S2</xref>e), and significantly prolonged lifespan compared with the non‐fed control (Figure <xref rid=\"acel13179-fig-0001\" ref-type=\"fig\">1h</xref>, average survival + 17.1% and Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S2</xref>f). Thus, reducing <italic>rab27</italic> mRNA in the adult brain is sufficient to extend lifespan.</p></sec><sec id=\"acel13179-sec-0004\"><label>2.2</label><title>\n<italic>rab27</italic> controls systematic stress response and energy metabolism</title><p>Many long‐lived mutants are reported to be highly resistant to oxidative stress and starvation. Indeed, we found that <italic>rab27KO</italic> flies lived longer than WT even under starvation or paraquat‐induced oxidative stress (Figure <xref rid=\"acel13179-fig-0002\" ref-type=\"fig\">2a,b</xref>). Also, we observed a similar increase in the lifespan of <italic>rab27KO</italic> under starvation in an isogenized Canton‐S background (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S3</xref>a), suggesting that the extension effect is independent of genetic backgrounds. Moreover, the levels of <italic>Wolbachia</italic> DNA in our experimental strains were negligible (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S3</xref>b) and the longevity of <italic>rab27KO</italic> flies remained after 3 generations of tetracycline treatment (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S3</xref>c), demonstrating that the effect of <italic>rab27KO</italic> on longevity was not related to <italic>Wolbachia</italic> infection, which has been shown to reduce the lifespan in <italic>Drosophila</italic> (Min & Benzer, <xref rid=\"acel13179-bib-0036\" ref-type=\"ref\">1997</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13179-fig-0002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>\n<italic>rab27KO</italic> flies lived longer under stress and had altered metabolic homeostasis. (a–b) Survival of females from <italic>w<sup>1118</sup></italic> and <italic>rab27KO</italic> under (a) starvation (<italic>p</italic> < .001) or (b) paraquat‐induced reactive oxygen species stress (<italic>p</italic> < .001). (c–d’’) Representative confocal fluorescence images of the subcellular distribution of endogenous dFOXO (green) in the abdominal fat body of WT (c–c’’) or <italic>rab27KO</italic> (d–d’’). Underlined numbers at the upper right corner (c”, d”) indicate the ratio of cytoplasmic‐to‐nuclear dFOXO signal (<italic>n</italic> = 3; <italic>p</italic> < .05). Nuclei are labeled by DAPI (blue). Scale bars: 25 µm. (e) Levels of circulating glucose in females normalized to <italic>w<sup>1118</sup></italic>, *<italic>p</italic> < .05, ***<italic>p</italic> < .001, <italic>n</italic> = 3 independent experiments. (f) Levels of TAG in whole female animals, normalized to both the body weight and the level in <italic>w<sup>1118</sup></italic>. *<italic>p</italic> < .05, ***<italic>p</italic> < .001, <italic>n</italic> = 3 independent experiments. (g) Levels of food intake normalized to <italic>w<sup>1118</sup></italic>. <italic>p</italic> < .01, <italic>n</italic> = 10 independent experiments. (h) Body weight of 21‐day‐old female <italic>w<sup>1118</sup></italic> and <italic>rab27KO</italic> flies, <italic>n</italic> = 70 flies per condition. (i) Quantification of female fecundity shown by accumulated eggs laid from day 7 to day 42 post‐eclosion. <italic>n</italic> = 20 flies per condition. (j) The performance index of an olfactory associative learning assay testing 7‐day‐old flies of the indicated genotypes. <italic>n</italic> = 3 independent experiments</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13179-g002\"/></fig><p>The nuclear translocation of Forkhead box class O (dFOXO) in the peripheral tissues of <italic>Drosophila</italic> is associated with an extended lifespan, increased stress resistance, and altered lipid metabolism (Hwangbo, Gershman, Tu, Palmer, & Tatar, <xref rid=\"acel13179-bib-0020\" ref-type=\"ref\">2004</xref>). The activation of dFOXO in the fat body, as indicated by its nuclear localization, is a common feature linked to extended lifespan (Martins, Lithgow, & Link, <xref rid=\"acel13179-bib-0032\" ref-type=\"ref\">2016</xref>). We thus examined the subcellular dFOXO localization in adult fat bodies, a tissue that does not express <italic>rab27</italic>. In the <italic>rab27KO</italic> fat bodies, we found a reduced ratio of cytoplasmic‐to‐nuclear dFOXO compared with that of WT control (Figure <xref rid=\"acel13179-fig-0002\" ref-type=\"fig\">2c‐d</xref>’’), suggesting a cell non‐autonomous effect of <italic>rab27KO</italic>. In <italic>rab27KO</italic> adults, the levels of circulating glucose, TAG, and food intake were significantly higher than those in WT controls, and the increases in metabolites could be reversed by <italic>rab27</italic> expression (Figure <xref rid=\"acel13179-fig-0002\" ref-type=\"fig\">2e</xref>‐g). Notably, while many long‐lived mutants are known to prolong lifespan at the expense of body size/weight and fecundity (Bai, Post, Kang, & Tatar, <xref rid=\"acel13179-bib-0003\" ref-type=\"ref\">2015</xref>; Bjedov et al., <xref rid=\"acel13179-bib-0005\" ref-type=\"ref\">2010</xref>; Gronke, Clarke, Broughton, Andrews, & Partridge, <xref rid=\"acel13179-bib-0017\" ref-type=\"ref\">2010</xref>), <italic>rab27KO</italic> flies exhibited comparable weight and fecundity with WT (Figure <xref rid=\"acel13179-fig-0002\" ref-type=\"fig\">2h,i</xref>). Also, the olfactory memory of <italic>rab27KO</italic> flies was equivalent to that of WT (Figure <xref rid=\"acel13179-fig-0002\" ref-type=\"fig\">2j</xref>). We concluded that removing <italic>rab27</italic> increases resistance to starvation and ROS stress and affects specific aspects of energy metabolism.</p></sec><sec id=\"acel13179-sec-0005\"><label>2.3</label><title>Reduction of <italic>rab27</italic> in the α/β posterior (α/βp) neurons of the MB increases longevity</title><p>Rab27 protein is only detected in neurons (Jin et al., <xref rid=\"acel13179-bib-0021\" ref-type=\"ref\">2012</xref>). We verified this finding by examining <italic>rab27</italic> expression in the whole body and identified specific enrichment in the MB, mNSC, and subesophageal ganglion (SOG) neurons of the brain (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S4</xref>a‐d and Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S5</xref>a). To determine which subset of neurons requires <italic>rab27</italic> to mediate lifespan regulation, we performed <italic>rab27</italic> RNAi with a series of Gal4 lines that drives expression in distinct brain regions (the Gal4 expression patterns are shown in Figure <xref rid=\"acel13179-fig-0003\" ref-type=\"fig\">3a‐d</xref> and Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S5</xref>a‐d; summarized in Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S4</xref>e). Reduction of <italic>rab27</italic> mRNA in all <italic>rab27</italic>‐expressing cells (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S5</xref>a’, average survival + 12.7%) or the MB neurons (Figure <xref rid=\"acel13179-fig-0003\" ref-type=\"fig\">3a</xref>’, average survival + 23.3%), but not the mNSC or the SOG (Figure <xref rid=\"acel13179-fig-0003\" ref-type=\"fig\">3b</xref>’and Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S5</xref>b’), extended lifespan compared with the controls. Within the MB, knocking down <italic>rab27</italic> in the α/β lobes leads to a modest but significant increase of lifespan (Figure <xref rid=\"acel13179-fig-0003\" ref-type=\"fig\">3c</xref>’, average survival + 17.5%), whereas reducing <italic>rab27</italic> in the α’/β’ lobes or γ lobe did not (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S5</xref>c’,d’). Interestingly, lowering <italic>rab27</italic> mRNA in the α/βp region, which contains only ~ 73 neurons (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S6</xref>a,b; (Aso et al., <xref rid=\"acel13179-bib-0001\" ref-type=\"ref\">2009</xref>)), was sufficient to extend lifespan (Figure <xref rid=\"acel13179-fig-0003\" ref-type=\"fig\">3d</xref>’, average survival + 13%). We examined the expression of an endogenously tagged Rab27<sup>EYFP</sup> (Dunst et al., <xref rid=\"acel13179-bib-0011\" ref-type=\"ref\">2015</xref>) and confirmed that Rab27 was indeed detected in the axons and dendrites but not the cell bodies of the α/βp neurons (Figure <xref rid=\"acel13179-fig-0003\" ref-type=\"fig\">3e‐e</xref>’” and Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S6</xref>c‐d’’). Importantly, expressing <italic>rab27</italic> cDNA in the α/βp neurons significantly reversed the lifespan extension of <italic>rab27<sup>Crispr‐KO</sup></italic> (Figure <xref rid=\"acel13179-fig-0003\" ref-type=\"fig\">3f</xref>). In sum, our data indicate that the α/βp neurons of MB regulate lifespan via a <italic>rab27</italic>‐dependent mechanism.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13179-fig-0003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Reducing <italic>rab27</italic> in the α/β posterior (α/βp) neurons of the mushroom bodies (MB) increases longevity. (a–d) Expression patterns of Gal4 lines in the brain visualized by UAS‐<italic>mCD8‐GFP</italic> (green). The brain was stained with anti‐Disks large (DLG) to label general neuropils (red). Scale bars: 75 µm. (a’–d’) Survival of females from <italic>rab27</italic> knockdown strains (red) compared with the corresponding Gal4 (black) and UAS (blue) lines, including <italic>rab27</italic> knockdown in (a’) the MB (<italic>238Y</italic> > <italic>rab27</italic>‐RNAi), <italic>p</italic> < .001; (b’) the mNSCs (<italic>dilp2</italic> > <italic>rab27</italic>‐RNAi),<italic> p</italic> < .01; (c’) α/β lobes of the MB (<italic>VT49246</italic> > <italic>rab27</italic>‐RNAi), <italic>p</italic> < .001; and (d’) α/βp neurons of the MB (<italic>VT14429</italic> > <italic>rab27</italic>‐RNAi),<italic> p</italic> < .001. (e–e’’’) Comparison of Expression of Rab27<sup>EYFP</sup> (green) from the endogenous locus versus. <italic>VT14429</italic>‐Gal4‐driving UAS‐<italic>mCD8‐RFP</italic> (red). Anti‐DLG labels the post‐synaptic dendritic termini (gray). Scale bars: 10 µm. (f) Survival of males from <italic>VT14429</italic> > <italic>mCD8‐GFP</italic> (black), <italic>VT14429</italic> > <italic>rab27</italic> (green, solid), <italic>rab27<sup>Crispr‐KO</sup></italic>;<italic>VT14429</italic> > <italic>mCD8‐GFP</italic> (red), <italic>rab27<sup>Crispr‐KO</sup></italic>;<italic>VT14429</italic> > <italic>rab27</italic> (blue), UAS‐<italic>rab27</italic> (green, dashed). <italic>p < </italic>.001. All survival data were analyzed by log‐rank tests</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13179-g003\"/></fig></sec><sec id=\"acel13179-sec-0006\"><label>2.4</label><title>The α/βp neurons are required for lifespan maintenance</title><p>We next examined the requirement and mechanism of the α/βp neurons in lifespan regulation. Expression of the pro‐apoptotic gene <italic>reaper</italic> in the α/βp neurons in the WT background shortened the lifespan (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S7</xref>a), indicating that the α/βp neurons are essential to lifespan control. Genetic ablation of these neurons with the cytotoxic protein Ricin by 3 independent Gal4 lines led to larval lethality (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S7</xref>b), possibly due to a general toxic effect during development. In contrast, ablation of γ lobe wherein <italic>rab27</italic> expresses does not affect viability. To better elucidate whether the α/βp neurons are required to regulate lifespan, we bypassed larval development by applying Gal80<sup>ts</sup> to inhibit <italic>ricin</italic> expression at 18℃ until eclosion. We shifted adult flies to 29℃ to allow Ricin expression in the α/βp neurons, and the lifespan was significantly shortened (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S7</xref>c). Thus, the α/βp neurons are required for the maintenance of lifespan.</p><p>Rab27 participates in exocytosis in worms and mammalian cells (Mahoney et al., <xref rid=\"acel13179-bib-0030\" ref-type=\"ref\">2006</xref>; Tsuboi & Fukuda, <xref rid=\"acel13179-bib-0046\" ref-type=\"ref\">2006</xref>); therefore, we asked whether neurosecretion mediates the effect of the α/βp neurons on lifespan. We forced membrane depolarization thus enhanced neurosecretion by activating the α/βp neurons with overexpression of sodium channel (Geminard, Rulifson, & Leopold, <xref rid=\"acel13179-bib-0015\" ref-type=\"ref\">2009</xref>) and found a slight but significant decrease in lifespan (Figure <xref rid=\"acel13179-fig-0004\" ref-type=\"fig\">4a</xref>). To reveal whether the neuronal activity of the α/βp neurons is affected by <italic>rab27</italic>, we utilized the CaLexA (calcium‐dependent nuclear entry of LexA) system, wherein GFP intensity correlates with depolarization‐induced calcium influx (Masuyama, Zhang, Rao, & Wang, <xref rid=\"acel13179-bib-0033\" ref-type=\"ref\">2012</xref>). Comparing to WT, the GFP intensity was lower in the α/βp neurons of <italic>rab27KO</italic> flies after either 1.5 days of starvation (Figure <xref rid=\"acel13179-fig-0004\" ref-type=\"fig\">4b‐f</xref>”) or 3 weeks of aging (Figure <xref rid=\"acel13179-fig-0004\" ref-type=\"fig\">4b‐d</xref>’’ and Figure <xref rid=\"acel13179-fig-0004\" ref-type=\"fig\">4g</xref>‐h”), indicating a reduction in neuronal activity when <italic>rab27KO</italic> animals were stressed or aged.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13179-fig-0004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Reduced α/βp neuronal activity in <italic>rab27KO</italic> flies corresponds with lifespan extension. (a) Survival of female flies overexpressing the sodium channels (<italic>VT14429</italic> > <italic>NaCh</italic>) compared with controls (UAS<italic>‐NaCh</italic>/+ and <italic>VT14429</italic>‐Gal4/+), <italic>*p</italic> < .05, <italic>***p</italic> < .001, log‐rank tests. (b) Quantification of GFP fluorescence intensity in the α/βp neurons of male flies. Data are represented as mean ± <italic>SEM</italic> measured in at least five brains, <italic>p</italic> < .05. (c–h’’) Representative confocal fluorescence images of the α/βp neurons of flies bearing the <italic>VT14429</italic>‐Gal4 > <italic>LexAop‐CD2‐GFP</italic> and UAS‐<italic>mLexA‐VP16‐NFAT</italic>, <italic>LexAop‐CD2‐GFP</italic> transgenes. (c–d”) control group; (e–f”) under 1.5 days of starvation; and (g–h’’) at day 21 post‐eclosion. The area labeled with anti‐DLG showed the post‐synaptic, dendritic termini (gray). Scale bar: 10 µm</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13179-g004\"/></fig></sec><sec id=\"acel13179-sec-0007\"><label>2.5</label><title>\n<italic>rab27</italic> deactivates TOR signaling in the α/βp neurons to extend longevity</title><p>A wealth of literature has shown that TOR signaling mediates neuronal activity. <italic>rab27</italic> knockout phenocopies flies of reduced TOR activity in several aspects, including lifespan extension (Figure <xref rid=\"acel13179-fig-0001\" ref-type=\"fig\">1</xref> versus. (Kapahi et al., <xref rid=\"acel13179-bib-0024\" ref-type=\"ref\">2004</xref>)), stress resistance (Figure <xref rid=\"acel13179-fig-0002\" ref-type=\"fig\">2a, b</xref> versus. (Bjedov et al., <xref rid=\"acel13179-bib-0005\" ref-type=\"ref\">2010</xref>)), and TAG accumulation (Figure <xref rid=\"acel13179-fig-0002\" ref-type=\"fig\">2f</xref> versus. (Bjedov et al., <xref rid=\"acel13179-bib-0005\" ref-type=\"ref\">2010</xref>)). We then examined the genetic interplays between <italic>rab27</italic> and components of the TOR pathway. The lifespan of <italic>rab27KO</italic> flies was not further extended when we inhibited TOR signaling by Rapamycin feeding (Figure <xref rid=\"acel13179-fig-0005\" ref-type=\"fig\">5a</xref>) or <italic>tsc2</italic> overexpression (Figure <xref rid=\"acel13179-fig-0005\" ref-type=\"fig\">5b</xref>). Thus, <italic>rab27</italic> likely functions through the TOR pathway. The phosphorylation of ribosomal protein S6 (p‐S6) by S6 kinase is an important downstream readout of TOR activity. We then deactivated TOR signaling by expressing a dominant‐negative form of the S6 kinase (<italic>s6k<sup>DN</sup></italic>) specifically in <italic>rab27</italic>‐expressing cells or in the α/βp neurons. Interestingly, suppressing TOR activity in these neurons was sufficient to increase longevity (Figure <xref rid=\"acel13179-fig-0005\" ref-type=\"fig\">5c,d</xref>, average survival + 5.2% and + 14.2%, respectively), supporting that the effect of <italic>rab27KO</italic> on lifespan is mainly mediated via the TOR pathway. Besides S6K, the <italic>Drosophila</italic> 4E‐BP (<italic>thor</italic>) is another target downstream of the TOR signaling pathway. Overexpression of <italic>thor</italic> in muscle has been shown to result in lifespan extension in flies (Demontis & Perrimon, <xref rid=\"acel13179-bib-0010\" ref-type=\"ref\">2010</xref>). Here, we showed that <italic>thor</italic> overexpression in the α/βp neurons did not extend lifespan (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S8</xref>). Altogether, <italic>rab27</italic> deactivates TOR signaling for lifespan extension likely through an <italic>s6k</italic>‐specific but <italic>thor</italic>‐independent pathway.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13179-fig-0005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>\n<italic>rab27</italic> interacts with TOR signaling to modulate lifespan. (a) Survival of females from <italic>rab27KO</italic> fed with Rapamycin (Rapa) compared with controls EtOH‐fed <italic>rab27KO</italic>, Rapa‐fed <italic>w<sup>1118</sup></italic>, and EtOH‐fed <italic>w<sup>1118</sup></italic>, <italic>p < </italic>.001. Notably, EtOH feeding causes significant shortening in lifespan. (b) Survival of females from <italic>rab27KO</italic> expressing <italic>tsc2</italic> compared with controls (<italic>w<sup>1118</sup></italic> and <italic>rab27KO</italic>), <italic>p < </italic>.001. (c) Survival of females from <italic>rab27‐</italic>Gal4 > UAS‐<italic>s6k<sup>DN</sup></italic> with controls (UAS<italic>‐s6k<sup>DN</sup></italic>/+ and <italic>rab27‐</italic>Gal4/+), <italic>p < </italic>.001. (d) Survival of females from <italic>VT14429‐</italic>Gal4 > UAS‐<italic>s6k<sup>DN</sup></italic> compared with controls (UAS<italic>‐s6k<sup>DN</sup></italic>/+ and <italic>VT14429‐</italic>Gal4/+), <italic>p < </italic>.001. All survival data were analyzed by log‐rank tests. (e) Subcellular fractionation of <italic>w<sup>1118</sup></italic> and <italic>rab27KO</italic> head lysates immunoblotted with p‐S6K, Rab7 (late endosomal marker), and ATP5A (mitochondrial marker). The quantification blots represent the means of 5 individual experiments. (f) Immunoprecipitation of head lysates with the expression of YFP‐tagged Rab27<sup>WT</sup>, constitutively activated Rab27 (Rab27<sup>CA</sup>), or constitutively activated Rab6 (Rab6<sup>CA</sup>) with an anti‐GFP antibody. The resulting samples were analyzed by immunoblotting with anti‐p‐S6K antibody. The number indicates the ratio of p‐S6K to GFP/YFP in the IP fraction. (g) Quantification of co‐IP of p‐S6K with YFP‐Rabs. *<italic>p</italic> < .05, **<italic>p</italic> < .01, one‐way ANOVA. (h) The in vivo association of Rab27<sup>EYFP</sup> and p‐S6K determined by the Proximity Ligation Assay (PLA). The brains were stained with anti‐GFP antibodies to detect Rab27<sup>EYFP</sup> (green) and anti‐p‐S6K antibodies (red), followed by the addition of PLA‐specific probes to detect proximity ligation events (gray). White circles mark the post‐synaptic, dendritic area of the α/βp neurons. Scale bars: 7.5 µm</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13179-g005\"/></fig></sec><sec id=\"acel13179-sec-0008\"><label>2.6</label><title>Rab27 binds with p‐S6K and determines its subcellular localization</title><p>Because many Rab proteins are known regulators of vesicle trafficking, we performed sucrose gradient centrifugation of fly head extracts to determine whether Rab27 regulates the localization of TOR pathway components. In the head extracts of Rab27<sup>EYFP</sup> flies, Rab27 co‐fractionated with phosphorylated S6K (p‐S6K) proteins (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S9</xref>). Compared with the WT control, the distribution of p‐S6K changed in <italic>rab27KO</italic> (Figure <xref rid=\"acel13179-fig-0005\" ref-type=\"fig\">5e</xref>), whereas two control proteins, Rab7 and ATP5A, showed no differences, suggesting that <italic>rab27KO</italic> did not abolish global protein distribution but specifically affected the subcellular localization of p‐S6K. Also, we found that p‐S6K co‐immunoprecipitated with Rab27 (Figure <xref rid=\"acel13179-fig-0005\" ref-type=\"fig\">5f</xref>,g) and confirmed the interaction in vivo by a Proximity Ligation Assay (PLA) (Mosca, Luginbuhl, Wang, & Luo, <xref rid=\"acel13179-bib-0037\" ref-type=\"ref\">2017</xref>) (Figure <xref rid=\"acel13179-fig-0005\" ref-type=\"fig\">5h</xref>), demonstrating a direct association between Rab27 and p‐S6K in the α/βp neurons. Altogether, these results indicate that Rab27 may regulate the subcellular localization of p‐S6K in <italic>Drosophila</italic> brains.</p></sec><sec id=\"acel13179-sec-0009\"><label>2.7</label><title>Rab27 anchors activated S6K to the periphery of α/βp neurons for de novo protein synthesis</title><p>We then investigated the subcellular localization of Rab27 to further understand its function in the α/βp neurons. Since Rab27 has long been recognized to function in cargo docking during exocytosis (Fukuda, <xref rid=\"acel13179-bib-0014\" ref-type=\"ref\">2006</xref>; Kasai et al., <xref rid=\"acel13179-bib-0026\" ref-type=\"ref\">2005</xref>), we speculated that Rab27 may serve as an anchor to mediate the transport of S6K. While Rab27 was only detected in the axons and dendrites but not the cell bodies of the α/βp neurons (Figure <xref rid=\"acel13179-fig-0003\" ref-type=\"fig\">3e‐e</xref>’” and Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S6</xref>c‐d’’), the levels of p‐S6K in the dendrites and axons were both reduced in <italic>rab27KO</italic> (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S10</xref>). Also, the level of p‐S6 was decreased in the dendrites in <italic>rab27<sup>Crispr‐KO</sup></italic> (Figure <xref rid=\"acel13179-fig-0006\" ref-type=\"fig\">6a‐b</xref>’” and Figure <xref rid=\"acel13179-fig-0006\" ref-type=\"fig\">6l</xref>). Also, we detected a significant increase in p‐S6 level by <italic>rab27</italic> expression specifically in the α/βp neurons of <italic>rab27KO</italic> (Figure <xref rid=\"acel13179-fig-0006\" ref-type=\"fig\">6c‐c</xref>’” and Figure <xref rid=\"acel13179-fig-0006\" ref-type=\"fig\">6l</xref>). The effect of <italic>rab27</italic> on de novo protein synthesis in α/βp dendrites was monitored by Kaede, a photoconvertible fluorescent protein that irreversibly turns from green to red upon UV irradiation. Loss of <italic>rab27</italic> does not affect Kaede maturation, as shown by the baseline green fluorescence compared with WT control (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S11</xref>). We then UV‐converted the majority of existing Kaede to red and then examined the presence of newly synthesized Kaede green protein in the α/βp neurons 1 hr post‐UV irradiation. In the α/βp neurons of <italic>rab27KO</italic> animals, the amount of de novo Kaede synthesis was significantly lower compared with that in WT controls (Figure <xref rid=\"acel13179-fig-0006\" ref-type=\"fig\">6d</xref>‐g’ and Figure <xref rid=\"acel13179-fig-0006\" ref-type=\"fig\">6m</xref>), and the reduction was rescued by <italic>s6</italic>‐S5D expression (Figure <xref rid=\"acel13179-fig-0006\" ref-type=\"fig\">6h</xref>‐k’ and Figure <xref rid=\"acel13179-fig-0006\" ref-type=\"fig\">6m</xref>). The extended longevity of <italic>rab27KO</italic> was not affected by the expression of a constitutively activated S6K (<italic>s6k<sup>CA</sup></italic>) (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S12</xref>a). In contrast, the lifespan was significantly reversed by the expression of phospho‐mimetic <italic>s6</italic>‐S5D specifically in the α/βp neurons of <italic>rab27KO</italic> (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S12</xref>b). As a control, overexpression of <italic>rab27</italic> or phospho‐mimetic <italic>s6</italic>‐S5D in the WT α/βp neurons did not reduce lifespan (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S12</xref>c). Therefore, loss of <italic>rab27</italic> reduced protein synthesis in the α/βp neurons, likely as the consequence of mislocalized p‐S6K and reduced phosphorylation of S6.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13179-fig-0006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Rab27 anchors p‐S6K to the periphery of the α/βp neurons for de novo protein synthesis. (a–c’’’) Representative confocal fluorescence images of the dendrites of the α/βp neurons expressing UAS‐<italic>mCD8‐GFP</italic> (green) stained with anti‐p‐S6 (red) and DLG (post‐synaptic marker, gray) in <italic>VT14429‐</italic>Gal4 > UAS‐<italic>mCD8‐GFP</italic> (a‐a’’’), <italic>rab27 <sup>Crispr‐KO</sup></italic>;<italic>VT14429</italic>‐Gal4 > UAS‐<italic>mCD8‐GFP</italic> (b–b’’’) or <italic>rab27<sup>Crispr‐KO</sup></italic>;<italic>VT14429</italic>‐Gal4 > UAS‐<italic>rab27</italic>, UAS‐<italic>mCD8‐GFP</italic> (c–c’’’). Scale bars: 10 µm. (d–i’) Tracing de novo protein synthesis in the α/βp neurons with photoconvertible Kaede protein. Adult <italic>VT14429‐</italic>Gal4 > UAS‐<italic>Kaede</italic> or <italic>rab27<sup>Crispr‐KO</sup></italic>;<italic>VT14429</italic>‐Gal4 > UAS‐<italic>Kaede</italic> animals were exposed to ultraviolet light (UV) for 6 hr (d,f) to irreversibly convert all existing Kaede to red fluorescence (d’,f’). Newly synthesized green Kaede protein was examined 1 hr after UV exposure in <italic>VT14429‐</italic>Gal4 > UAS‐<italic>Kaede</italic> (e‐e’) or <italic>rab27<sup>Crispr‐KO</sup></italic>;<italic>VT14429</italic>‐Gal4 > UAS‐<italic>Kaede</italic> (g–g’). The strain for rescue experiments: <italic>rab27<sup>Crispr‐KO</sup></italic>;<italic>VT14429</italic>‐Gal4 > UAS‐<italic>s6</italic>‐S5D, UAS‐<italic>mCD8‐GFP</italic> (h–i’). Scale bars: 25 µm. (j) Quantification of p‐S6 intensity by marking DLG positive areas and measured the red signal. The fluorescence intensity was normalized to the control levels. Data are represented as mean ± <italic>SEM</italic> measured in at least three brains, *<italic>p</italic> < .05, **<italic>p</italic> < .01. (k) Quantification of de novo protein synthesis expressed as the difference in green Kaede protein levels (ΔKaede) immediately after photoconversion and 1‐hr post‐UV. The ΔKaede level in <italic>rab27<sup>Crispr‐KO</sup></italic> was normalized to the WT levels. Data are represented as mean ± <italic>SEM</italic> measured in at least three brains, **<italic>p</italic> < .01</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13179-g006\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"acel13179-sec-0010\"><label>3</label><title>DISCUSSION</title><p>Our data show that depleting <italic>rab27</italic> in the α/βp neurons of the <italic>Drosophila</italic> MB results in significant lifespan extension. Knockdown of <italic>rab27</italic> in these ~ 73 neurons was sufficient to cause systematic effects including altered homeostasis of key metabolites and the nuclear translocation of dFOXO in the fat body. Not only are <italic>rab27KO</italic> flies more resistant to starvation or oxidative stress, but they also show no detectable trade‐offs in weight, fecundity. Of note, adult <italic>rab27KO</italic> flies are not defective in olfactory memory at 1 week, although we could not rule out the possible acceleration in decline at older age. <italic>rab27</italic> modulates lifespan through the TOR pathway because 1) p‐S6K was mislocalized in <italic>rab27KO</italic> neurons resulting in reduced protein synthesis in α/βp neurons and diminished the neuronal activity; and 2) like <italic>rab27KO</italic>, expressing <italic>s6k<sup>DN</sup></italic> in the α/βp neurons was also sufficient to extend lifespan. Together, our findings suggest that Rab27 functions upstream of S6 for protein synthesis in a specific small group of brain neurons to control longevity in <italic>Drosophila</italic>.</p><p>We observed the nuclear translocation of dFOXO in the fat body, wherein <italic>rab27</italic> is not expressed. The nuclear localization of dFOXO in the fat body is a common feature of lifespan extension, as it alters the downstream gene transcription, likely through the transcriptional regulation of longevity pathways such as stress resistance (Cathy Slack, Giannakou, Foley, Goss, & Partridge, <xref rid=\"acel13179-bib-0043\" ref-type=\"ref\">2011</xref>). Hwangbo <italic>et al</italic>. have shown that overexpression of dFOXO in adult fat bodies increased its nuclear localization and result in lifespan extension (Hwangbo et al., <xref rid=\"acel13179-bib-0020\" ref-type=\"ref\">2004</xref>). We show that <italic>rab27KO</italic> alleles exert non‐cell autonomous effects on dFOXO nuclear localization in the fat body, suggesting that <italic>rab27</italic> may act in a brain–fat body axis to elicit lifespan extension, thus highlighting the systemic effect of <italic>rab27KO</italic> on longevity. However, the molecular identity through which the <italic>rab27</italic>‐expressing neurons exert the cell non‐autonomous regulation on lifespan remains an open question.</p><p>The evolutionary fitness of an individual organism is best defined by the balance between survival and other major life functions such as reproduction. From an organismal perspective, a shift in energy expenditure from reproduction is often required to extend lifespan. Indeed, many studies have suggested trade‐offs between lifespan and reproduction, and many long‐lived mutants, such as single‐gene mutants of the TOR or insulin pathways, produce less offspring (Hansen, Flatt, & Aguilaniu, <xref rid=\"acel13179-bib-0018\" ref-type=\"ref\">2013</xref>; Partridge, Gems, & Withers, <xref rid=\"acel13179-bib-0038\" ref-type=\"ref\">2005</xref>). Growth is another life function that is highly associated with longevity from flies to monkeys (Mattison et al., <xref rid=\"acel13179-bib-0034\" ref-type=\"ref\">2017</xref>). For example, mutants of <italic>chico</italic>, which encodes the insulin receptor substrate in <italic>Drosophila</italic>, are long‐lived but have a metabolic imbalance and small body size (Bohni et al., <xref rid=\"acel13179-bib-0006\" ref-type=\"ref\">1999</xref>). However, the link between lifespan and body weight seems to be more complicated, as Slack et al. (<xref rid=\"acel13179-bib-0042\" ref-type=\"ref\">2015</xref>) have shown that a <italic>chico</italic> mutation that disrupts its interaction with Grb2/Drk extends lifespan without affecting body weight (Slack et al., <xref rid=\"acel13179-bib-0042\" ref-type=\"ref\">2015</xref>). Moreover, a mutation in the odorant receptor <italic>Or83b<sup>2</sup></italic> increases TAG level, enhances starvation tolerance, and extends lifespan with no effect on body weight or female fecundity (Libert et al., <xref rid=\"acel13179-bib-0029\" ref-type=\"ref\">2007</xref>). These reports and our findings all point to the possibility to uncouple lifespan control from the performance in essential life functions measured in this study. We noticed that the aforementioned phenotypes, such as glucose level, TAG level, food intake, and stress tolerance, are detected in aged flies but not young ones. There are two possibilities. For one, Rab27 may be only required at old age so that r<italic>ab27KO</italic> only causes physiological declines that are significant in old flies. Alternatively, the minute phenotypical differences that are below detectable levels at younger ages may accumulate over time and become obvious upon aging.</p><p>Most studies have attempted to modulate longevity at the level of individual animals. For example, systematic inhibition of the TOR signaling has been shown to extend lifespan from yeast to mammals. However, since the TOR pathway also regulates cell growth, ribosome biogenesis, and the process of translation, systemic inhibition of TOR signaling may come with a wide range of complications such as stomatitis, diabetes, and nephrotoxicity (Kaplan, Qazi, & Wellen, <xref rid=\"acel13179-bib-0025\" ref-type=\"ref\">2014</xref>). Instead of systematic manipulation, one plausible strategy to avoid potential complications is to identify the minimal region required to promote longevity. The brain serves as a good candidate since it is the integration center of input and output signals and can regulate a wide range of physiological functions. Previous reports have indicated that, in the <italic>Drosophila</italic> brain, the mNSC regulates systemic insulin levels by producing Dilps to mediate organism growth and lifespan (Broughton et al., <xref rid=\"acel13179-bib-0007\" ref-type=\"ref\">2005</xref>; Rulifson, Kim, & Nusse, <xref rid=\"acel13179-bib-0040\" ref-type=\"ref\">2002</xref>). Ablating mNSC leads to lifespan extension (Broughton et al., <xref rid=\"acel13179-bib-0007\" ref-type=\"ref\">2005</xref>), and limiting the secretion of Dilps from within the mNSC is also sufficient to increase longevity (Bai, Kang, & Tatar, <xref rid=\"acel13179-bib-0002\" ref-type=\"ref\">2012</xref>). In this study, we find that the α/βp neurons of the MB regulate systemic metabolism and lifespan. The <italic>Drosophila</italic> MB has been compared with the human hippocampus and hypothalamus based on functional analogy. Both the reduction of ROS levels in the hippocampus and expression of Sirt1 in the hypothalamus are linked to increased longevity in mice (Hu et al., <xref rid=\"acel13179-bib-0019\" ref-type=\"ref\">2007</xref>; Satoh et al., <xref rid=\"acel13179-bib-0041\" ref-type=\"ref\">2013</xref>). Also, Yang <italic>et al</italic>. showed that TOR activity is elevated in the hypothalamic pro‐opiomelanocortin (POMC) neurons in old mice and is linked to altered protein expression and changes in body weight (S. B. Yang et al., <xref rid=\"acel13179-bib-0048\" ref-type=\"ref\">2012</xref>), but the effect on lifespan remains unknown. However, how the neurons of the mammalian hippocampus or hypothalamus affect lifespan remains to be investigated. In this study, we show that <italic>rab27KO</italic> reduced the activity of a subset of MB neurons in aged flies, implying that manipulation of neuronal activity in a small number of neurons may have a profound effect on lifespan.</p><p>Here, we have shown that activating the α/βp neurons reduces the lifespan (Figure <xref rid=\"acel13179-fig-0004\" ref-type=\"fig\">4a</xref>), while the loss of <italic>rab27</italic> decreases neuronal activity (Figure <xref rid=\"acel13179-fig-0004\" ref-type=\"fig\">4b</xref>) and extends lifespan. We also provide evidence that <italic>rab27KO</italic> causes the mislocalization of S6K thus suppresses the phosphorylation of S6 in the dendrites of α/βp neurons of adult flies. Importantly, <italic>s6</italic>‐S5D expression in these neurons fully rescued the extended lifespan and reduced protein synthesis in <italic>rab27KO</italic>, suggesting that restoring protein synthesis suppresses α/βp‐mediated longevity. What is the mechanism linking S6K‐dependent protein synthesis to the neuron activity of α/βp and ultimately affecting lifespan? Reduction in localized protein translation has been shown to modulate neuronal function/activity. For example, localized protein synthesis at hippocampal synapses regulated by the neurotrophic factor BDNF is important for synaptic plasticity (Leal, Comprido, & Duarte, <xref rid=\"acel13179-bib-0028\" ref-type=\"ref\">2014</xref>). Regarding lifespan regulation, Zullo <italic>et al</italic>. recently found in <italic>C. elegans</italic> that inhibiting the excitability of glutamatergic or cholinergic neurons can increase longevity (Zullo et al., <xref rid=\"acel13179-bib-0054\" ref-type=\"ref\">2019</xref>). Also, Zhang <italic>et al</italic>. showed that the lifespan extension of <italic>s6k</italic> null mutant in <italic>C. elegans</italic> is suppressed by neuronal expression of <italic>s6k</italic> (Y. Zhang et al., <xref rid=\"acel13179-bib-0051\" ref-type=\"ref\">2019</xref>). While the nervous system of <italic>C. elegans</italic> is not organized into a brain with higher‐order structures and domains, our findings show that lifespan regulation by a small number of brain neurons may be an evolutionarily conserved phenomenon in the <italic>Drosophila</italic> brain, which shares functional analogous organization with higher organisms. In conjunction with these findings, we conclude that lifespan can be modulated by <italic>rab27</italic> in TOR‐mediated protein homeostasis in a small group of neurons. The consequence of reduced protein synthesis in the α/βp neurons of <italic>rab27KO</italic> remains to be investigated.</p></sec><sec id=\"acel13179-sec-0011\"><label>4</label><title>EXPERIMENTAL PROCEDURES</title><sec id=\"acel13179-sec-0012\"><label>4.1</label><title>Key resources table</title><p>\n<table-wrap id=\"nlm-table-wrap-1\" xml:lang=\"en\" orientation=\"portrait\" position=\"anchor\"><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Reagent or Resource</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Source</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Identifier</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\" rowspan=\"1\">Antibodies</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Rabbit anti‐dFOXO</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cosmo Bio Co</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat# CAC‐THU‐A‐DFOXO; RRID: AB_10705391</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Mouse anti‐Drosophila discs large</td><td align=\"left\" rowspan=\"3\" colspan=\"1\">Developmental Studies Hybridoma Bank (DSHB)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat# 4F3; RRID: AB_528203</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Mouse anti‐Drosophila Bruchpilot (nc82)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat# nc82; RRID: AB_2314866</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Mouse anti‐GFP</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat# 12A6; RRID: AB_2617417</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Alexa 568‐conjugated phalloidin antibody</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Thermo Fisher</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat# 12,380</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Rabbit anti‐GFP antibody</td><td align=\"left\" rowspan=\"2\" colspan=\"1\">Abcam</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat# ab290; RRID: AB_303395</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Mouse anti‐ATP5A antibody</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat# ab14748; RRID: AB_301447</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Rabbit anti‐Phospho‐S6 Kinase Ribosomal Protein (Thr398)</td><td align=\"left\" rowspan=\"2\" colspan=\"1\">Cell Signaling Technology</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat# 9,209; RRID: AB_2269804</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Mouse anti‐Phospho‐S6 Kinase Ribosomal Protein (Thr389)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat# 9,206; RRID: AB_2285392</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Rabbit anti‐phosphorylated ribosomal protein S6 antibody</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">(Kim, Jang, Yang, & Chung, <xref rid=\"acel13179-bib-0027\" ref-type=\"ref\">2017</xref>)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr><td align=\"left\" style=\"padding-left:10%\" rowspan=\"1\" colspan=\"1\">Rabbit anti‐Rab7</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">(Jung et al., <xref rid=\"acel13179-bib-0023\" ref-type=\"ref\">2017</xref>)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>Drosophila</italic> Strains</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Source</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">Cat#</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>w<sup>1118</sup></italic>\n</td><td align=\"left\" rowspan=\"28\" colspan=\"1\">Bloomington stock center (BDSC)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">3,605</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Rab27<sup>EYFP</sup>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">62,556</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>elav</italic>‐GS‐Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">43,642</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>238Y</italic>‐Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">81,009</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>dilp2‐</italic>Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">37,516</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>SOG</italic>‐Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">37,295</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>G0050</italic>‐Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>R16A06</italic>‐Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">48,709</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>c708a</italic>‐Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">50,743</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>N‐syb‐</italic>Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">51,635</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>tub</italic>‐Gal80<sup>ts</sup>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">7,108</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐YFP‐rab27</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9,810</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐rab27‐RNAi</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">31,887</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐mCD8‐GFP</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5,137</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐mCD8‐RFP</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">27,398</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS‐<italic>reaper</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">5,824</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐NaCh</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9,469</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS‐<italic>NFAT</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">66,542</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS‐<italic>ricin/CyO</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">28,998</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐tsc2</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">80,576</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐s6k<sup>DN</sup></italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6,911</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐s6k<sup>CA</sup></italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">6,914</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐YFP‐rab27<sup>CA</sup></italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">23,266</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐YFP‐rab6<sup>CA</sup></italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9,776</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS‐<italic>Denmark</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33,062</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS‐<italic>thor</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">9,147</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS<italic>‐kaede</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">26,161</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS‐<italic>Denmark,syt‐eGFP</italic>\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">33,065</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>VT49246</italic>‐Gal4</td><td align=\"left\" rowspan=\"3\" colspan=\"1\">Vienna Drosophila RNAi Center‐ Vienna Tile (VDRC)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">205,379</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>VT14429</italic>‐Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">204,199</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>VT24615</italic>‐Gal4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">203,429</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>rab27<sup>Gal4‐KO</sup></italic>\n</td><td align=\"left\" rowspan=\"2\" colspan=\"1\">(Chan et al., <xref rid=\"acel13179-bib-0009\" ref-type=\"ref\">2011</xref>)</td><td align=\"left\" rowspan=\"2\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>rab27</italic>‐Gal4</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n<italic>rab27<sup>Crispr‐KO</sup></italic>\n</td><td align=\"left\" rowspan=\"2\" colspan=\"1\">This study</td><td align=\"left\" rowspan=\"2\" colspan=\"1\"/></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">UAS‐<italic>s6</italic>‐S5D</td></tr></tbody></table></table-wrap>\n</p></sec><sec id=\"acel13179-sec-0013\"><label>4.2</label><title>Fly husbandry and stocks</title><p>Flies were maintained with standard cornmeal medium at 25℃, 60% humidity in a 12:12 hr light/dark (LD) cycle. <italic>w<sup>1118</sup></italic> was used as the WT control for all experiments unless otherwise stated. All of the experimental flies were backcrossed 10 times to the isogenic <italic>w<sup>1118</sup></italic> to remove possible background mutations, except for (reaper (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S6</xref>a) and ricin (Figure <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S6</xref>c), both of which have been isogenized for 3 generations. To determine the effect of different genetic backgrounds on lifespan, we outcrossed <italic>rab27KO</italic> flies to Canton‐S wild‐type strain and measured lifespan under starvation. To activate GeneSwitch‐Gal4, 100 µl of 200 µM RU486 (mifepristone, Tokyo Chemical Industry #84371‐65–3) in ethanol was added to the surface of the food. Rapamycin (LC laboratories #R‐5000) was dissolved in ethanol and added to the food to inhibit TOR signaling at a final concentration of 200 µM.</p></sec><sec id=\"acel13179-sec-0014\"><label>4.3</label><title>Generation of <italic>rab27</italic> knockout flies using CRISPR/Cas9 system</title><p>The <italic>rab27</italic> knockout flies (<italic>rab27<sup>Crispr‐KO</sup>)</italic> were generated utilizing the CRISPR/Cas9 system as described in (Jung et al., <xref rid=\"acel13179-bib-0023\" ref-type=\"ref\">2017</xref>) with modifications. One pair of gRNAs was designed to target the start codon and 3’ UTR of <italic>rab27</italic> locus for the removal of the entire coding region. Further details are provided in Supporting Information.</p></sec><sec id=\"acel13179-sec-0015\"><label>4.4</label><title>Lifespan analysis and antibiotic treatment of <italic>Wolbachia</italic> infection</title><p>Lifespan was measured with two independent methods. The “cage” method was modified from (Bai et al., <xref rid=\"acel13179-bib-0003\" ref-type=\"ref\">2015</xref>). Briefly, flies were raised at a density of approximately 200 larvae per bottle. Newly eclosed flies were allowed to mate within 48 hr and then transferred to experimental cages at a density of 100 males and 100 females in a 1‐L cage with good ventilation. Fresh food was provided, and deaths were scored every 2–3 days. For the “vial” method, 10 newly hatched flies of the same sex were reared in a standard fly vial. Flies were transferred to fresh vials and dead flies were removed and scored every 2–3 days. To create starvation‐induced stress, 21‐day‐old adult flies were transferred to vials containing 1% agar. Flies were separated by sex into 10 flies per vial, and dead flies were counted every 6 hr. To assay oxidative stress resistance, flies were exposed to filter paper soaked with 5 mM paraquat (PQ) dissolved in 6.5% sucrose solution at the bottom of vials. The effects on survival were analyzed by the log‐rank test. The results of all lifespan experiments were summarized in Tables <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S1</xref> and <xref rid=\"acel13179-sup-0001\" ref-type=\"supplementary-material\">S2</xref>. For the antibiotic treatment, the flies were reared for three generations with 50 µg/ml tetracycline (Omics Bio) added to the food (Rottschaefer & Lazzaro, <xref rid=\"acel13179-bib-0039\" ref-type=\"ref\">2012</xref>). The infection status for <italic>Wolbachia</italic> was then verified via qRT‐PCR with the <italic>wspB</italic> primers. Further details are provided in Supporting Information.</p></sec><sec id=\"acel13179-sec-0016\"><label>4.5</label><title>Quantitative Real‐Time PCR (qRT‐PCR)</title><p>qRT‐PCR was performed following the guideline of (Bustin et al., <xref rid=\"acel13179-bib-0008\" ref-type=\"ref\">2019</xref>). The procedures were as previously described in (Yang et al., <xref rid=\"acel13179-bib-0047\" ref-type=\"ref\">2017</xref>). Further details are provided in Supporting Information.</p></sec><sec id=\"acel13179-sec-0017\"><label>4.6</label><title>Measurements of larval length and pupation timing</title><p>Body lengths were measured at 24, 48, and 72 hr after egg collection. Larvae were fixed in 4% paraformaldehyde and then washed with phosphate‐buffered saline (PBS). Images of larvae were taken with a Canon EOS‐700D digital camera on a Leica S8APO microscope, and the body lengths were measured with ImageJ. To measure pupation time, eggs were collected for 24 hr and the number of pupae was recorded every 24 hr.</p></sec><sec id=\"acel13179-sec-0018\"><label>4.7</label><title>Immunohistochemistry and confocal imaging</title><p>Adult fly brains were dissected, immunostained, and imaged following (Jung et al., <xref rid=\"acel13179-bib-0023\" ref-type=\"ref\">2017</xref>) with modifications. Further details are provided in Supporting Information.</p></sec><sec id=\"acel13179-sec-0019\"><label>4.8</label><title>Quantification of dFOXO staining</title><p>A region of interest (ROI) was drawn around the DAPI positive nuclei, and an equally sized circle was drawn to mark the ROI in the cytoplasm in the same image. The green signal (dFOXO staining) was measured in these areas from individual confocal images using ImageJ. The ratio of cytoplasmic‐to‐+nuclear dFOXO was calculated by dividing the mean fluorescent intensity of cytoplasmic dFOXO to the mean fluorescent intensity of nuclear dFOXO from the same image. We then plotted all data in boxplots and used an unpaired Student's <italic>t</italic> test for significant differences.</p></sec><sec id=\"acel13179-sec-0020\"><label>4.9</label><title>Triglycerides (TAG) and glucose measurement</title><p>TAG measurement was performed according to (Slack et al., <xref rid=\"acel13179-bib-0044\" ref-type=\"ref\">2010</xref>) with modifications. Further details are provided in Supporting Information.</p></sec><sec id=\"acel13179-sec-0021\"><label>4.10</label><title>Feeding assay</title><p>21‐day‐old adult flies were starved for 6 hr. Subsequently, ten single‐sex flies were transferred into a new vial containing standard cornmeal food and blue dye (0.0375 mg/ml, Sigma‐Aldrich #3844‐45–9) for 3 hr. Flies were homogenized with 400 µl PBS. After centrifugation, the amount of food ingested was determined by absorbance at wavelength 620 nm.</p></sec><sec id=\"acel13179-sec-0022\"><label>4.11</label><title>Body weight measurement</title><p>Ten 21‐day‐old flies were weighed on a microbalance (Denver Instrument TB‐124). Body weight measurements were performed in triplicates for each sex of each strain.</p></sec><sec id=\"acel13179-sec-0023\"><label>4.12</label><title>Female fecundity</title><p>Eggs laid by mated female flies were counted daily from flies that were 7‐day post‐eclosion for 35 consecutive days. Fresh standard food was changed daily.</p></sec><sec id=\"acel13179-sec-0024\"><label>4.13</label><title>Olfactory aversive memory</title><p>Conditioned odor avoidance was performed as previously described (Yang et al., <xref rid=\"acel13179-bib-0047\" ref-type=\"ref\">2017</xref>). Further details are provided in Supporting Information.</p></sec><sec id=\"acel13179-sec-0025\"><label>4.14</label><title>Western blot and co‐immunoprecipitation (co‐IP)</title><p>Western blot was performed as previously described (Yang et al., <xref rid=\"acel13179-bib-0047\" ref-type=\"ref\">2017</xref>). For co‐IP experiments, 200 adult heads were collected through a small sieve. The head lysate was incubated with protein G agarose beads to minimize nonspecific binding. And the remaining lysate was incubated with GFP antibody‐bound Mag beads (GE healthcare #28‐9670–70) overnight, and then the beads were washed and boiled. Further details are provided in Supporting Information.</p></sec><sec id=\"acel13179-sec-0026\"><label>4.15</label><title>Sucrose density gradient fractions</title><p>500 adult flies were flash‐frozen in liquid nitrogen, vortexed, and passed through a small sieve to collect fly heads. Adult heads were homogenized using a pestle in lysis buffer (50 mM Tris pH8.0, 150 mM NaCl, 2 mM EDTA, 1% igepal, 0.5% sodium deoxycholate) with protease inhibitor cocktail (Roche). The lysate supernatant was layered on 20%–55% sucrose gradient. The gradient was centrifuged for 16 hr at 35,000 rpm at 4℃ in an SW‐41 or SW‐55 Ti rotor (Beckman). Serial fractions (1 ml each) were collected from the top of the tube and analyzed by Western Blotting. The band intensity was quantified with ImageJ software.</p></sec><sec id=\"acel13179-sec-0027\"><label>4.16</label><title>Proximity ligation assay (PLA)</title><p>The PLA assay was performed according to (Mosca et al., <xref rid=\"acel13179-bib-0037\" ref-type=\"ref\">2017</xref>). Further details are provided in Supporting Information.</p></sec><sec id=\"acel13179-sec-0028\"><label>4.17</label><title>Kaede measurement</title><p>To measure the amount of newly synthesized Kaede proteins, pre‐existing Kaede was first photoconverted into red fluorescent proteins by UV irradiation. After 6‐hr of UV irradiation, the flies were kept at 25℃, 60% humidity for 60 min. Next, the brains were dissected in PBS and fixed in 4% paraformaldehyde in PBS at room temperature for 45 min. The brains were then washed in 0.5% PBST three times for 20 min and mounted with Vectashield. The images were quantified and measured as described.</p></sec><sec id=\"acel13179-sec-0029\"><label>4.18</label><title>Quantification and statistical analysis</title><p>Each experiment was performed at least three biological replicates in all graphs. For fluorescence images, the intensity was quantified double‐blindly and measured using Adobe Photoshop CS6 and ImageJ. The band intensity of Western blotting or co‐IP was quantified with ImageJ. All data were expressed as mean ± <italic>SEM</italic> and were compared using ANOVA followed by a Tukey test (for experimental groups ≥ 3) or an unpaired Student's <italic>t</italic> test (for experimental groups = 2). Survival data were analyzed by log‐rank tests (Gronke et al., <xref rid=\"acel13179-bib-0017\" ref-type=\"ref\">2010</xref>). All statistical analysis was carried out using GraphPad Prism 5 software. A <italic>p</italic> < .05 was considered statistically significant: * indicates<italic> p</italic> < .05; ** indicates <italic>p</italic> < .01; *** indicates <italic>p</italic> < .001. All images were processed in Adobe Photoshop and assembled with Adobe Illustrator.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13179-sec-0031\"><title>CONFLICT OF INTERESTS</title><p>The authors declare no competing interests.</p></sec><sec id=\"acel13179-sec-0032\"><title>AUTHOR CONTRIBUTIONS</title><p>W.‐Y.L. and C.‐C.C. conceptualized the study; W.‐Y.L., Y.‐T.C., J.‐R.L., and J.‐K.W. involved in methodology; W.‐Y.L., Y.‐T.C., Y.‐J.L., J.‐K.W., K.‐L.H., J.‐R.L., S.‐C.L., C.‐C.H., H.‐D.W., C.‐L.W., S.‐Y.H., and C.‐C.C. investigated the study; W.Y.L., S.‐Y.H., and C.‐C.C. wrote the original draft; W.‐Y.L., Y.‐T.C., Y.‐J.L., J.‐K.W., K.‐L.H., J.‐R.L., S.‐C.L., C.‐C.H., H.‐D.W., C.‐L.W., S.‐Y.H., and C.‐C.C. wrote, reviewed, and edited the article; C.‐C.C. involved in funding acquisition; C.‐C.C. performed supervision.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13179-sup-0001\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13179-s001.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13179-sec-0030\"><title>ACKNOWLEDGEMENTS</title><p>We would like to thank Drs. Jongkyeong Chung, Marc Tatar, Ann‐Shyn Chiang, Pei‐Yu Wang, Ya‐Wen Chen, and the Blooming Stock Center, Vienna Drosophila RNAi Center, and the Developmental Studies Hybridoma Bank for reagents. We thank Drs. Robin Hiesinger, Shuwei Lin, Jennifer Jin, Friederike Kohrs, and all members of the Chan laboratory for critical comments on this manuscript. We further thank WellGenetics, Inc. for the generation of <italic>rab27<sup>Crispr‐KO</sup></italic>, and the Imaging Core of the First Core Lab at NTU College of Medicine for technical assistance in confocal microscopy. This work was supported by grants from the Ministry of Science and Technology of Taiwan (104‐2321‐B‐002‐069, 105‐2321‐B‐002‐023, 107‐2311‐B‐002‐008) and National Taiwan University (107L880303, 107C101‐81) to C.‐C.C.</p></ack><sec sec-type=\"data-availability\" id=\"acel13179-sec-0034\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13179-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13179-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13179-cit-0001\">\n<string-name>\n<surname>Aso</surname>, <given-names>Y.</given-names>\n</string-name>, <string-name>\n<surname>Grubel</surname>, <given-names>K.</given-names>\n</string-name>, <string-name>\n<surname>Busch</surname>, <given-names>S.</given-names>\n</string-name>, <string-name>\n<surname>Friedrich</surname>, <given-names>A. 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pub-id-type=\"pmc\">PMC7431831</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13152</article-id><article-id pub-id-type=\"publisher-id\">ACEL13152</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Cytoskeleton stiffness regulates cellular senescence and innate immune response in Hutchinson–Gilford Progeria Syndrome</article-title><alt-title alt-title-type=\"left-running-head\">MU et al.</alt-title></title-group><contrib-group><contrib id=\"acel13152-cr-0001\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Mu</surname><given-names>Xiaodong</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-0362-0083</contrib-id><xref ref-type=\"aff\" rid=\"acel13152-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13152-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13152-aff-0003\">\n<sup>3</sup>\n</xref><address><email>Xiaodong_m@yahoo.com</email></address></contrib><contrib id=\"acel13152-cr-0002\" contrib-type=\"author\"><name><surname>Tseng</surname><given-names>Chieh</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0003\" contrib-type=\"author\"><name><surname>Hambright</surname><given-names>William S.</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0004\" contrib-type=\"author\"><name><surname>Matre</surname><given-names>Polina</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0005\" contrib-type=\"author\"><name><surname>Lin</surname><given-names>Chih‐Yi</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0006\" contrib-type=\"author\"><name><surname>Chanda</surname><given-names>Palas</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0007\" contrib-type=\"author\"><name><surname>Chen</surname><given-names>Wanqun</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13152-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0008\" contrib-type=\"author\"><name><surname>Gu</surname><given-names>Jianhua</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0006\">\n<sup>6</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0009\" contrib-type=\"author\"><name><surname>Ravuri</surname><given-names>Sudheer</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0010\" contrib-type=\"author\"><name><surname>Cui</surname><given-names>Yan</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0011\" contrib-type=\"author\"><name><surname>Zhong</surname><given-names>Ling</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0012\" contrib-type=\"author\"><name><surname>Cooke</surname><given-names>John P.</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0013\" contrib-type=\"author\"><name><surname>Niedernhofer</surname><given-names>Laura J.</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-1074-1385</contrib-id><xref ref-type=\"aff\" rid=\"acel13152-aff-0007\">\n<sup>7</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0014\" contrib-type=\"author\"><name><surname>Robbins</surname><given-names>Paul D.</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0007\">\n<sup>7</sup>\n</xref></contrib><contrib id=\"acel13152-cr-0015\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Huard</surname><given-names>Johnny</given-names></name><xref ref-type=\"aff\" rid=\"acel13152-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13152-aff-0004\">\n<sup>4</sup>\n</xref><address><email>jhuard@sprivail.org</email></address></contrib></contrib-group><aff id=\"acel13152-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Department of Molecular Physiology and Biophysics</named-content>\n<institution>Baylor College of Medicine</institution>\n<city>Houston</city>\n<named-content content-type=\"country-part\">Texas</named-content>\n</aff><aff id=\"acel13152-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Department of Orthopaedic Surgery</named-content>\n<named-content content-type=\"organisation-division\">McGovern Medical School</named-content>\n<institution>University of Texas Health Science Center at Houston</institution>\n<city>Houston</city>\n<named-content content-type=\"country-part\">Texas</named-content>\n</aff><aff id=\"acel13152-aff-0003\">\n<label><sup>3</sup></label>\n<institution>Shandong First Medical University & Shandong Academy of Medical Sciences</institution>\n<city>Ji'nan</city>\n<country country=\"CN\">China</country>\n</aff><aff id=\"acel13152-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Center for Regenerative Sports Medicine</named-content>\n<institution>Steadman Philippon Research Institute</institution>\n<city>Vail</city>\n<named-content content-type=\"country-part\">Colorado</named-content>\n</aff><aff id=\"acel13152-aff-0005\">\n<label><sup>5</sup></label>\n<named-content content-type=\"organisation-division\">Department of Cardiovascular Sciences</named-content>\n<institution>Houston Methodist Research Institute</institution>\n<city>Houston</city>\n<named-content content-type=\"country-part\">Texas</named-content>\n</aff><aff id=\"acel13152-aff-0006\">\n<label><sup>6</sup></label>\n<named-content content-type=\"organisation-division\">Electron Microscopy Core</named-content>\n<institution>Houston Methodist Research Institute</institution>\n<city>Houston</city>\n<named-content content-type=\"country-part\">Texas</named-content>\n</aff><aff id=\"acel13152-aff-0007\">\n<label><sup>7</sup></label>\n<named-content content-type=\"organisation-division\">Institute on the Biology of Aging and Metabolism and Department of Biochemistry, Molecular Biology and Biophysics</named-content>\n<institution>University of Minnesota</institution>\n<city>Minneapolis</city>\n<named-content content-type=\"country-part\">Minnesota</named-content>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nXiaodong Mu, Department of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, TX, USA.<break/>\nEmail: <email>Xiaodong_m@yahoo.com</email><break/>\nJohnny Huard, Center for Regenerative Sports Medicine, Steadman Philippon Research Institute, Houston, TX, USA.<break/>\nEmail: <email>jhuard@sprivail.org</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>25</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13152</elocation-id><history><date date-type=\"received\"><day>18</day><month>11</month><year>2019</year></date><date date-type=\"rev-recd\"><day>10</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>3</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13152.pdf\"/><abstract id=\"acel13152-abs-0001\"><title>Abstract</title><p>Hutchinson–Gilford progeria syndrome (HGPS) is caused by the accumulation of mutant prelamin A (progerin) in the nuclear lamina, resulting in increased nuclear stiffness and abnormal nuclear architecture. Nuclear mechanics are tightly coupled to cytoskeletal mechanics via lamin A/C. However, the role of cytoskeletal/nuclear mechanical properties in mediating cellular senescence and the relationship between cytoskeletal stiffness, nuclear abnormalities, and senescent phenotypes remain largely unknown. Here, using muscle‐derived mesenchymal stromal/stem cells (MSCs) from the <italic>Zmpste24</italic>\n<sup>−/−</sup> (<italic>Z24</italic>\n<sup>−/−</sup>) mouse (a model for HGPS) and human HGPS fibroblasts, we investigated the mechanical mechanism of progerin‐induced cellular senescence, involving the role and interaction of mechanical sensors RhoA and Sun1/2 in regulating F‐actin cytoskeleton stiffness, nuclear blebbing, micronuclei formation, and the innate immune response. We observed that increased cytoskeletal stiffness and RhoA activation in progeria cells were directly coupled with increased nuclear blebbing, Sun2 expression, and micronuclei‐induced cGAS‐Sting activation, part of the innate immune response. Expression of constitutively active RhoA promoted, while the inhibition of RhoA/ROCK reduced cytoskeletal stiffness, Sun2 expression, the innate immune response, and cellular senescence. Silencing of Sun2 expression by siRNA also repressed RhoA activation, cytoskeletal stiffness and cellular senescence. Treatment of <italic>Zmpste24<sup>−</sup></italic>\n<sup>/</sup>\n<italic><sup>−</sup></italic> mice with a RhoA inhibitor repressed cellular senescence and improved muscle regeneration. These results reveal novel mechanical roles and correlation of cytoskeletal/nuclear stiffness, RhoA, Sun2, and the innate immune response in promoting aging and cellular senescence in HGPS progeria.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13152-abs-0002\"><p>There is increased RhoA activation and cytoskeleton stiffness in progeria cells, which mediates increased nuclear blebbing, micronuclei formation, innate immune response, and cellular senescence. RhoA activation is coupled with elevated Sun2 expression, but not Sun1, especially in nucleus with blebbing and micronuclei. Repression of RhoA and Sun2 activation was able to reduce cytoskeleton stiffness, nuclear blebbing, and micronuclei formation and delay the progression of cellular senescence.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13152-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13152-g007.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13152-kwd-0001\">accelerated aging</kwd><kwd id=\"acel13152-kwd-0002\">cell nucleus</kwd><kwd id=\"acel13152-kwd-0003\">cellular senescence</kwd><kwd id=\"acel13152-kwd-0004\">skeletal muscle</kwd><kwd id=\"acel13152-kwd-0005\">stem cells</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>NIH </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100000002</institution-id></institution-wrap></funding-source><award-id>PO1AG043376</award-id><award-id>RO1AR065445</award-id><award-id>P01AG062412</award-id><award-id>U19AG056278</award-id><award-id>R01HL133254</award-id></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>Progeria Research Foundation </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100002287</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0003\"><funding-source>University of Texas Health Science Center at Houston</funding-source></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"0\"/><page-count count=\"16\"/><word-count count=\"11412\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13152-cit-1001\">\n<string-name>\n<surname>Mu</surname>\n<given-names>X</given-names>\n</string-name>, <string-name>\n<surname>Tseng</surname>\n<given-names>C</given-names>\n</string-name>, <string-name>\n<surname>Hambright</surname>\n<given-names>WS</given-names>\n</string-name>, et al. <article-title>Cytoskeleton stiffness regulates cellular senescence and innate immune response in Hutchinson–Gilford Progeria Syndrome</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13152</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13152</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13152-body-0001\"><sec id=\"acel13152-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Hutchinson–Gilford progeria syndrome (HGPS) is a rare, fatal genetic disorder caused by the mutation of <italic>LMNA</italic> (lamin A) gene (Schreiber & Kennedy, <xref rid=\"acel13152-bib-0037\" ref-type=\"ref\">2013</xref>). As a nucleoskeletal protein at nuclear lamina, lamin A is essential for mechanical support of the nucleus and is required for the structural link between the nucleoskeleton and cytoskeleton (Phillip, Aifuwa, Walston, & Wirtz, <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>). <italic>LMNA</italic> gene mutation in HGPS leads to the production of mutant prelamin A (progerin), which accumulates at the nuclear envelope (NE) and causes dramatic changes in the nuclear architecture, including thickening of the nuclear lamina, increased nuclear stiffness and nuclear irregularity (nuclear blebbing, a hallmark of HGPS cells), and impaired nucleus deformation capacity (Cao et al., <xref rid=\"acel13152-bib-0009\" ref-type=\"ref\">2011</xref>; Dahl et al., <xref rid=\"acel13152-bib-0014\" ref-type=\"ref\">2006</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>; Verstraeten, Ji, Cummings, Lee, & Lammerding, <xref rid=\"acel13152-bib-0043\" ref-type=\"ref\">2008</xref>; Young, Fong, & Michaelis, <xref rid=\"acel13152-bib-0045\" ref-type=\"ref\">2005</xref>). This abnormal nuclear architecture confers additional adverse cellular processes, such as disruption of chromatin anchoring on the laminar structure, inappropriate reorganization of chromatin, telomere dislocation, dysfunction and erosion, delayed response in DNA damage repair and thereby increased DNA damage, and accelerated senescence (Cao et al., <xref rid=\"acel13152-bib-0009\" ref-type=\"ref\">2011</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>). Progerin accumulation can also cause natural age‐associated increase in nuclear stiffness, abnormal histone modification patterns, global changes in gene expression, and impaired cell function (Cao et al., <xref rid=\"acel13152-bib-0009\" ref-type=\"ref\">2011</xref>; Pacheco et al., <xref rid=\"acel13152-bib-0034\" ref-type=\"ref\">2014</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>). In contrast to HGPS cells, lamin A knockout (<italic>LMNA</italic>\n<sup>−/−</sup>) cells do not express lamin A and were reported to develop decreased mechanical <italic>stiffness</italic> in both the nucleus/nucleoskeleton and cytoskeleton (Broers et al., <xref rid=\"acel13152-bib-0007\" ref-type=\"ref\">2004</xref>; Kim et al., <xref rid=\"acel13152-bib-0026\" ref-type=\"ref\">2017</xref>; Schreiber & Kennedy, <xref rid=\"acel13152-bib-0037\" ref-type=\"ref\">2013</xref>). Although the increased nuclear stiffness in HGPS cells had been previously observed (Booth, Spagnol, Alcoser, & Dahl, <xref rid=\"acel13152-bib-0006\" ref-type=\"ref\">2015</xref>; Dahl et al., <xref rid=\"acel13152-bib-0014\" ref-type=\"ref\">2006</xref>), changes in cytoskeletal stiffness in progerin‐expressing cells and the potential correlation of cytoskeletal stiffness with nuclear abnormalities and progeria phenotypes have not been examined.</p><p>The cell nucleus is tightly integrated into the structural network of the cytoplasm through linker of the nucleoskeleton and cytoskeleton (LINC) complexes, which contain Sun1/2 proteins (Isermann & Lammerding, <xref rid=\"acel13152-bib-0023\" ref-type=\"ref\">2013</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>). Lamin A/C is required for this structural connection of nucleus and cytoskeleton (Isermann & Lammerding, <xref rid=\"acel13152-bib-0023\" ref-type=\"ref\">2013</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>), which is essential for a broad range of cellular functions. Mechanical stimuli to the cells can be transmitted from the extracellular matrix (ECM) to the nucleus via the cytoskeleton (Isermann & Lammerding, <xref rid=\"acel13152-bib-0023\" ref-type=\"ref\">2013</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>). The mechanical properties of the ECM, especially the stiffness of the local environment, can have a direct and profound impact on the expression of mechano‐responsive genes and the organization of cytoskeletal and nucleoskeletal proteins (Isermann & Lammerding, <xref rid=\"acel13152-bib-0023\" ref-type=\"ref\">2013</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>).</p><p>Filamentous actin (F‐actin), a key component of the cytoskeleton, is a major determinant of a cell's mechanical properties and is critical to stress response. F‐actin plays a crucial role in mediating ECM‐nuclear mechanical coupling. The stiffness of local environment influences cellular functions via regulating cytoskeletal structure, and cells on stiffer substrates exhibited increased F‐actin polymerization and stress fiber formation with an order of orientation. Lamin A/C is connected to F‐actin cytoskeleton via Sun proteins (Sun1/2), which can mediate the formation of the perinuclear apical actin cap to regulate the nuclear structural integrity (Hoffman et al., <xref rid=\"acel13152-bib-0022\" ref-type=\"ref\">2020</xref>; Kim et al., <xref rid=\"acel13152-bib-0026\" ref-type=\"ref\">2017</xref>; Lei et al., <xref rid=\"acel13152-bib-0027\" ref-type=\"ref\">2009</xref>). Normal actin cytoskeletal dynamics can protect cells from stress by modulating the stress response and cell death (Baird et al., <xref rid=\"acel13152-bib-0004\" ref-type=\"ref\">2014</xref>; Gourlay, Carpp, Timpson, Winder, & Ayscough, <xref rid=\"acel13152-bib-0017\" ref-type=\"ref\">2004</xref>). Actin cytoskeletal dynamics is closely regulated by the activity of Rho GTPases, particularly RhoA (Sit & Manser, <xref rid=\"acel13152-bib-0040\" ref-type=\"ref\">2011</xref>). RhoA is crucial for regulating cell morphology, migration, adhesion, autophagosome formation and function, and many more events associated with F‐actin dynamics (Li et al., <xref rid=\"acel13152-bib-0028\" ref-type=\"ref\">2011</xref>). The interaction of RhoA with key mechano‐sensing factors located at the cell membrane, cytoplasm/cytoskeleton, or nuclear membrane has been well documented (Li et al., <xref rid=\"acel13152-bib-0028\" ref-type=\"ref\">2011</xref>). In particular, our recent study demonstrated the co‐activation of RhoA with pro‐inflammatory, pro‐fibrogenic, and pro‐osteogenic signaling factors in skeletal muscles of muscular dystrophic mice (Mu et al., <xref rid=\"acel13152-bib-0032\" ref-type=\"ref\">2013</xref>).</p><p>Moreover, RhoA also plays a role in determining the commitment of stem cell fate, by regulating cytoskeletal mechanics and interacting with mechanical signaling pathways involved in stem cell differentiation (Li et al., <xref rid=\"acel13152-bib-0028\" ref-type=\"ref\">2011</xref>). Cell shape, mechanical cues, and cytoskeletal tension were found to regulate the switch in lineage commitment of MSCs by modulating RhoA activity (Li et al., <xref rid=\"acel13152-bib-0028\" ref-type=\"ref\">2011</xref>; McBeath, Pirone, Nelson, Bhadriraju, & Chen, <xref rid=\"acel13152-bib-0029\" ref-type=\"ref\">2004</xref>). Abnormal RhoA regulation can lead to the dysregulated adipo‐osteogenic balance, which has been linked to various pathological conditions, such as aging, obesity, osteopenia, osteopetrosis, and osteoporosis (Li et al., <xref rid=\"acel13152-bib-0028\" ref-type=\"ref\">2011</xref>). Indeed, dysregulated adipo‐osteogenic balance also has been reported in HGPS MSCs with increased osteogenic, but decreased adipogenic potential (Meshorer & Gruenbaum, <xref rid=\"acel13152-bib-0031\" ref-type=\"ref\">2008</xref>). Thus, it is possible that RhoA signaling may play a role in conferring the dysregulated adipo‐osteogenic balance in HGPS cells.</p><p>Based on these previous findings, we hypothesized that there is increased cytoskeletal stiffness and RhoA activation in HGPS cells, possibly as a response to increased nuclear stiffness, DNA damage, and/or production of reactive oxygen species (ROS). We also hypothesized that increased cytoskeletal stiffness and RhoA activation are associated with accelerated cellular senescence. Thus, we examined cytoskeletal stiffness and RhoA activation in cells from HGPS patients and from a mouse model of HGPS due to a deficiency in Zmpste24 (Z24), a zinc metalloproteinase involved in the formation of mature lamin A. <italic>Z24</italic>\n<sup>−/−</sup> mice have premature onset of aging‐related musculoskeletal changes, similar to those observed in HGPS (Bergo et al., <xref rid=\"acel13152-bib-0005\" ref-type=\"ref\">2002</xref>; Fong et al., <xref rid=\"acel13152-bib-0016\" ref-type=\"ref\">2004</xref>; Yang et al., <xref rid=\"acel13152-bib-0044\" ref-type=\"ref\">2006</xref>). Through a gain and loss of RhoA function, we examined whether changes in cytoskeletal stiffness and RhoA activation in progeria cells correlate with nuclear blebbing, Sun1/2 expression, and micronuclei‐induced cGAS‐Sting activation, part of the innate immune response that has also been linked to senescence. Because the LINC complex containing Sun2 was found to promote focal adhesion assembly by activating RhoA (Thakar, May, Rogers, & Carroll, <xref rid=\"acel13152-bib-0042\" ref-type=\"ref\">2017</xref>), we also examined the effect of siRNA‐mediated Sun2 inhibition on cytoskeleton stiffness, perinuclear actin cap formation, nuclear blebbing, and cellular senescence.</p></sec><sec sec-type=\"results\" id=\"acel13152-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13152-sec-0003\"><label>2.1</label><title>\n<bold><italic>MSCs from muscle of Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> mice have elevated expression of senescence‐associated secretory phenotype (SASP) factors that negatively impacts muscle stem cell function</italic></bold>\n</title><p>Mesenchymal stromal/stem cells in skeletal muscle are nonmyogenic and express platelet‐derived growth factor receptors (PDGFRs). These PDGFR‐α<sup>+</sup> cells often function as fibro‐adipogenic progenitors (FAPs) and have been found as the main cell type that contributes to pathological fibrosis and fat infiltration in diseased and injured muscles (Contreras, Rebolledo, Oyarzun, Olguin, & Brandan, <xref rid=\"acel13152-bib-0013\" ref-type=\"ref\">2016</xref>). Analysis of skeletal muscle from <italic>Z24</italic>\n<sup>−/−</sup> mice (5‐month‐old) showed there was increased activation of PDGFR‐α<sup>+</sup> cells and CD68<sup>+</sup> cells (macrophages) and decreased activation of Pax7<sup>+</sup> cells (Figure <xref rid=\"acel13152-fig-0001\" ref-type=\"fig\">1a–d</xref>) compared to age‐matched WT controls. qPCR analysis of PDGFR‐α in muscle tissues further verified the increased expression of PDGFR‐α in <italic>Z24</italic>\n<sup>−/−</sup> muscle (Figure <xref rid=\"acel13152-fig-0001\" ref-type=\"fig\">1e</xref>). Consistently, the skeletal muscle of <italic>Z24</italic>\n<sup>−/−</sup> mice had increased fibrosis and cellular senescence and impaired muscle regeneration capacity (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S1</xref>). In both WT and <italic>Z24</italic>\n<sup>−/−</sup> muscle, PDGFR‐α<sup>+</sup> cells localize adjacent to Pax7<sup>+</sup> muscle stem cells at the muscle stem cell niche among myofibers (Figure <xref rid=\"acel13152-fig-0001\" ref-type=\"fig\">1f</xref>), suggesting that PDGFR‐α<sup>+</sup> cells could impact Pax7<sup>+</sup> cells through the release of soluble factors or through direct cell–cell interaction. Therefore, we examined the effect of mesenchymal stromal/stem cells (MSCs) isolated from the skeletal muscles of WT and <italic>Z24</italic>\n<sup>−</sup>\n<italic><sup>/</sup></italic>\n<sup>−</sup> mice on WT muscle progenitor cells (MPCs).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13152-fig-0001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Increased expression of SASP factors in <italic>Z24</italic>\n<sup>−/−</sup> MSCs negatively impacts muscle stem cell function. (a) Gastrocnemius (GM) skeletal muscle tissues were harvested from 5‐month‐old <italic>Z24</italic>\n<sup>−/−</sup> and wild‐type (WT) mice. Immunostaining analysis of PDGFR‐α and CD68 showed increased number of PDGFR‐α<sup>+</sup> MSCs and CD68<sup>+</sup> macrophages. Scale bar = 100 µm. (b) Quantification of CD68<sup>+</sup> cells is shown. (c) Immunostaining analysis of dystrophin and Pax7 showed a decreased number of Pax7<sup>+</sup> muscle stem cell in <italic>Z24</italic>\n<sup>−/−</sup> mice. (d) Quantification of the ratio of the PDGFR‐α<sup>+</sup> cells to Pax7<sup>+</sup> cells is shown. (d) Immunostaining analysis of PDGFR‐α and Pax7 demonstrating a close interaction between these two types of cells in stem cell niche. White arrow indicates a Pax7<sup>+</sup> cell; orange arrow indicates a PDGFR‐α<sup>+</sup> cell. Scale bar = 50 µm. (e) Quantification of mRNA level of PDGFR‐α in muscles is shown. (f) Immunostaining analysis of PDGFR‐α and Pax7 to show their relative localization at stem cell niche. Scale bar = 10µm. (g) Immunostaining analysis of PDGFR‐α in mesenchymal stem/stromal cells (MSCs) from WT mice and <italic>Z24</italic>\n<sup>−/−</sup> mice. Scale bar = 100 µm. (h) qPCR results of mRNA from WT MSC and <italic>Z24</italic>\n<sup>−/−</sup> MSCs. (i) Treatment of WT muscle progenitor cells (MPCs) with conditioned medium from WT or <italic>Z24</italic>\n<sup>−/−</sup> MSCs to check the impact on myogenesis potential [the formation of fast‐myosin heavy chain (f‐MHC)‐positive myotubes], and level of DNA damage (γ‐H2AX). Quantitation of cells positive with f‐MHC or γ‐H2AX is shown. Scale bar = 100 µm. (j) qPCR results of mRNA from WT MPCs treated with conditioned medium from WT or <italic>Z24</italic>\n<sup>−/−</sup> MSCs. Data are shown as mean ± standard error. <italic>N </italic>≥ 6. * indicates <italic>p</italic> < .05</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13152-g001\"/></fig><p>Immunostaining of MSCs demonstrated that both <italic>Z24</italic>\n<sup>−/−</sup> and WT MSCs were predominantly PDGFR‐α positive (Figure <xref rid=\"acel13152-fig-0001\" ref-type=\"fig\">1g</xref>). qPCR analysis further demonstrated that <italic>Z24</italic>\n<sup>−</sup>\n<italic><sup>/</sup></italic>\n<sup>−</sup> MSCs specifically expressed higher level of senescence and SASP markers (i.e., p16<sup>INK4a</sup>, CXCL1, MCP1, IL‐1α, IL‐1β, IL‐6, and TNFR1) and the pro‐fibrotic factor TGF‐β1 (Figure <xref rid=\"acel13152-fig-0001\" ref-type=\"fig\">1h</xref>), whereas the expression of anti‐inflammatory factors (i.e., IL‐10 and Klotho) was down‐regulated. In addition, conditioned medium (CM) from <italic>Z24</italic>\n<sup>−</sup>\n<italic><sup>/</sup></italic>\n<sup>−</sup> MSCs was able to confer repression of the myogenic potential of WT MPCs (Figure <xref rid=\"acel13152-fig-0001\" ref-type=\"fig\">1i</xref>) and increase the percentage of cells with damaged DNA (γ‐H2AX<sup>+</sup>) (Figure <xref rid=\"acel13152-fig-0001\" ref-type=\"fig\">1i</xref>). The expression of pro‐inflammatory factors (i.e., IL‐6, and TNF‐α) and pro‐fibrogenic factors (i.e., TGF‐β1, Collagen I, PDGFR‐α, PDGFR‐β) was up‐regulated in WT MPCs, whereas the expression of anti‐inflammation factors (i.e., IL‐10, and Klotho) was down‐regulated by cultivating the WT MPCs with CM from <italic>Z24</italic>\n<sup>−</sup>\n<italic><sup>/</sup></italic>\n<sup>−</sup> MSCs (Figure <xref rid=\"acel13152-fig-0001\" ref-type=\"fig\">1j</xref>).</p></sec><sec id=\"acel13152-sec-0004\"><label>2.2</label><title>\n<bold><italic>MSCs from muscle of Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> mice exhibit increased nuclear abnormalities and cytoskeletal stiffness</italic></bold>\n</title><p>\n<italic>Z24</italic>\n<sup>−</sup>\n<italic><sup>/</sup></italic>\n<sup>−</sup> MSCs developed increased DNA damage (γ‐H2AX<sup>+</sup>) and cellular senescence (SA‐β‐Gal<sup>+</sup>) (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2a</xref>), which are consistent to the <italic>Z24</italic>\n<sup>−</sup>\n<italic><sup>/</sup></italic>\n<sup>−</sup> mice progeria phenotypes. p21<sup>Cip1</sup>, a protein associated with cellular senescence (Calio et al., <xref rid=\"acel13152-bib-0008\" ref-type=\"ref\">2015</xref>), was increased in <italic>Z24</italic>\n<sup>−</sup>\n<italic><sup>/</sup></italic>\n<sup>−</sup> MSCs (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2a</xref>). Abnormal nuclear morphology and protrusions termed “blebs” are diagnostic markers for aging and progeria and are key features of the HGPS cell nucleus (Capell et al., <xref rid=\"acel13152-bib-0010\" ref-type=\"ref\">2005</xref>). Immunostaining of Lamin A/C proteins was performed to examine nuclear morphology, revealing increased nuclear blebbing and nuclear irregularity in <italic>Z24</italic>\n<sup>−</sup>\n<italic><sup>/</sup></italic>\n<sup>−</sup> MSCs compared to WT MSCs (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2a</xref>; Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S2</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13152-fig-0002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>\n<italic>Z24</italic>\n<sup>−/−</sup> MSCs display increased senescent phenotypes, and enhanced F‐actin polymerization and cytoskeletal stiffness is directly associated with increased and nuclear blebbing. MSCs isolated from the skeletal muscle of WT and <italic>Z24</italic>\n<sup>−/−</sup> mice were compared. (a) Immunostaining analysis of γ‐H2AX, p21<sup>Cip1</sup>, and lamin A/C was performed, as well as SA‐β‐Gal staining for senescence. Quantitation of γ‐H2AX<sup>+</sup> cells, SA‐β‐Gal<sup>+</sup> cells, p21<sup>+</sup> cells, and cells with nuclear blebbing is shown. Scale bar = 30µm. (b) Immunostaining analysis and quantification of H3K9me3. The increased level of H3K9me3 (red) in the micronuclei in contrast to nucleus indicates the loss of heterochromatin from nucleus to micronuclei (arrows). Scale bar = 2.5 µm. (c) Staining of F‐actin with Alexa Fluor 488 Phalloidin and quantification of F‐actin polymerization. Scale bar = 20 µm. (d–g). Testing of cytoplasm stiffness using a Bruker AFM probe. H. The cytoplasm stiffness (kPa) calculated by NanoScope analysis. (i) Immunostaining analysis of lamin A/C and F‐actin in WT and <italic>Z24</italic>\n<sup>−/−</sup> MSCs, showing higher level of F‐actin and nuclear blebbing in same <italic>Z24</italic>\n<sup>−/−</sup> cell (arrow). Scale bar = 50 µm. (j) Immunostaining analysis of lamin A/C and F‐actin in <italic>Z24</italic>\n<sup>−/−</sup> MSCs. Quantitation of nuclear blebbing is shown. The number of cells with nuclear blebbing was compared between cells with top 30% of F‐actin intensity (Actin‐high) and cells with bottom 30% of F‐actin intensity (Actin‐low). Scale bar = 30 µm. (k) Immunostaining analysis of lamin A/C and F‐actin to observe the effect of treatment of <italic>Z24</italic>\n<sup>−/−</sup> MSCs with F‐actin stabilizing JPK (200 nM) or F‐actin depolymerizing CyD (100 ng/ml) for 48 hr. Quantitation of nuclear blebbing is shown. Scale bar = 15 µm. Arrows: nuclear blebbing. <italic>N </italic>≥ 6. “*” at bar charts indicates <italic>p</italic> < .05</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13152-g002\"/></fig><p>Recently, cytoplasmic chromatin fragments (CCF) present in the micronuclei derived from the main nucleus were shown to trigger cGAS‐Sting innate immune signaling and cellular senescence (Dou et al., <xref rid=\"acel13152-bib-0015\" ref-type=\"ref\">2017</xref>). Progerin and telomere dysfunction both can trigger cellular senescence by inducing chromatin‐carrying micronuclei (Cao et al., <xref rid=\"acel13152-bib-0009\" ref-type=\"ref\">2011</xref>). We observed the presence of cytoplasmic chromatin fragments [H3K9me3‐positive heterochromatin (Shumaker et al., <xref rid=\"acel13152-bib-0039\" ref-type=\"ref\">2006</xref>)] in the micronuclei of <italic>Z24</italic>\n<sup>−/−</sup> MSCs (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2b</xref>). We also observed intensive condensation of telomeres and translocation of telomeres into micronuclei (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S3</xref>), which confirms the cytoplasmic localization of chromatin fragments in <italic>Z24</italic>\n<sup>−/−</sup> MSCs. Importantly, using phalloidin staining of F‐actin, we also observed increased F‐actin polymerization in <italic>Z24</italic>\n<sup>−/−</sup> MSCs (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2c</xref>; Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S4</xref>a,b), suggesting a potentially increased cytoskeleton stiffness in these cells. In addition, the level of total actin was not significantly different between WT MSCs and <italic>Z24</italic>\n<sup>−/−</sup> MSCs (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S4</xref>c,d), suggesting that the dynamic F‐actin formation from G‐actin in <italic>Z24</italic>\n<sup>−/−</sup> MSCs is responsible for the increased F‐actin polymerization, rather than the increased production of new actin protein.</p><p>To further confirm this observation, cell stiffness was tested by the atomic force microscopy (AFM) system (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2d</xref>,h). The Young modulus force of <italic>Z24</italic>\n<sup>−/−</sup> MSCs was significantly higher than WT MSCs (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2h</xref>), especially in cells with nuclear blebbing, demonstrating a positive correlation of cell stiffness and nuclear blebbing.</p></sec><sec id=\"acel13152-sec-0005\"><label>2.3</label><title>\n<bold><italic>F‐actin polymerization and nuclear abnormalities are closely coupled in Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> MSCs</italic></bold>\n</title><p>We further validated that <italic>Z24</italic>\n<sup>−/−</sup> MSCs exhibited both increased F‐actin polymerization and higher rate of nuclear blebbing by co‐staining F‐actin and lamin A/C (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2i</xref>). Within <italic>Z24</italic>\n<sup>−/−</sup> MSCs, the increased F‐actin polymerization in individual cells was directly coupled with a higher rate of nuclear blebbing (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2j</xref>). In order to verify the direct effect of F‐actin polymerization on nuclear blebbing, cytoskeletal stiffness was modified by stabilizing or destabilizing F‐actin in <italic>Z24</italic>\n<sup>−/−</sup> MSCs. <italic>Z24</italic>\n<sup>−/−</sup> MSCs were treated with jasplakinolide (JPK) to induce F‐actin stabilization or with cytochalasin D (CyD) to induce F‐actin depolymerization. An increase in nuclear blebbing in <italic>Z24</italic>\n<sup>−/−</sup> MSCs was observed upon JPK treatment, whereas CyD treatment decreased nuclear blebbing (Figure <xref rid=\"acel13152-fig-0002\" ref-type=\"fig\">2k</xref>). This result further suggests that the increased cytoskeletal stiffness developed by sustained polymerization of F‐actin can contribute to nuclear blebbing.</p><p>In order to further determine whether F‐actin polymerization can induce a senescent phenotype in progeria cells and whether releasing the mechanical tension of F‐actin cytoskeleton can rescue the senescent phenotypes, we treated <italic>Z24</italic>\n<sup>−/−</sup> MSCs with JPK or CyD and examined the expression of genes associated with cellular senescence. JPK treatment increased expression of p16<sup>INK4a</sup>, p21<sup>Cip1</sup>, IL1‐β, and TNFR1. In contrast, expression of p16<sup>INK4a</sup>, p21<sup>Cip1</sup>, IL1‐β, and TNFR1 was down‐regulated after CyD treatment of <italic>Z24</italic>\n<sup>−/−</sup> MSCs, whereas expression of IL‐10 was up‐regulated (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S5</xref>).</p></sec><sec id=\"acel13152-sec-0006\"><label>2.4</label><title>\n<bold><italic>RhoA is activated in Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> MSCs and is coupled with increased nuclear blebbing and cellular senescence</italic></bold>\n</title><p>F‐actin polymerization requires the activation of RhoA, and RhoA can be activated by DNA damage (Aghajanian, Wittchen, Campbell, & Burridge, <xref rid=\"acel13152-bib-0001\" ref-type=\"ref\">2009</xref>). Since there is both increased F‐actin polymerization and DNA damage in <italic>Z24</italic>\n<sup>−/−</sup> MSCs, RhoA signaling was examined in <italic>Z24</italic>\n<sup>−/−</sup> MSCs. Immunostaining analysis showed that the ratio of RhoA<sup>+</sup> cells was higher in <italic>Z24</italic>\n<sup>−/−</sup> MSCs than WT MSCs and increased RhoA in the <italic>Z24</italic>\n<sup>−/−</sup> MSCs is coupled with increased F‐actin polymerization (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3a,b</xref>). Consistently, there was higher RhoA activity in <italic>Z24</italic>\n<sup>−/−</sup> MSCs in contrast to WT MSCs (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3c</xref>). There was also an increased ratio of nuclear blebbing in RhoA<sup>+</sup> cells (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3d,e</xref>). Western blot analysis also demonstrated a higher level of RhoA in <italic>Z24</italic>\n<sup>−/−</sup> MSCs (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3f</xref>,g). Furthermore, there was increased activation of RhoA<sup>+</sup> cells in the skeletal muscle of <italic>Z24</italic>\n<sup>−/−</sup> mice as analyzed by both immunohistochemistry and Western blot analysis (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3h</xref>‐i).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13152-fig-0003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Increased RhoA activation in <italic>Z24</italic>\n<sup>−/−</sup> MSCs, and effect of RhoA over‐expression on Sun2 and nuclear blebbing in WT MSCs. (a) Immunostaining analysis of RhoA and F‐actin in WT and <italic>Z24</italic>\n<sup>−/−</sup> MSCs. Scale bar = 30 µm. (b) Quantification of RhoA<sup>+</sup> cells is shown. (c) Quantification of RhoA activity is shown. (d) Immunostaining analysis of RhoA and lamin A/C in <italic>Z24</italic>\n<sup>−/−</sup> MSCs. Arrows: cells with higher RhoA expression and nuclear blebbing. Scale bar = 5 µm. (e) Quantification of nuclear blebbing in RhoA<sup>+</sup> and RhoA‐ <italic>Z24</italic>\n<sup>−/−</sup> MSCs is shown. (f, g) Western blot analysis and quantification of RhoA in WT and <italic>Z24</italic>\n<sup>−/−</sup> MSCs, with GAPDH as loading control. (h) Immunostaining analysis of RhoA<sup>+</sup> cells and CD68<sup>+</sup> inflammatory cells in skeletal muscle of <italic>Z24</italic>\n<sup>−/−</sup> mice. (i, j) Western blot analysis and quantification of RhoA in muscle tissues from WT and <italic>Z24</italic>\n<sup>−/−</sup> mice, with GAPDH as loading control. (k) WT MSCs were transfected with a plasmid carrying constitutively active RhoA‐GFP and stained for F‐actin. Scale bar = 5 µm. (l) Immunostaining analysis of Sun2 to check Sun2 and nuclear blebbing in RhoA‐GFP transfected WT MSCs. Yellow arrows: cells with RhoA‐GFP; red arrows: cells without RhoA‐GFP. Scale bar = 5 µm. (m) Quantification nuclear blebbing (RhoA‐GFP‐ V.S. RhoA‐GFP<sup>+</sup> cells) is shown. (n) Immunostaining analysis of Sun2 and lamin A/C in <italic>Z24</italic>\n<sup>−/−</sup> MSCs. Scale bar = 10µm. (o) Quantification of Sun2 in <italic>Z24</italic>\n<sup>−/−</sup> MSCs without or without nuclear blebbing is shown. (p) Quantification of Sun2 in WT and <italic>Z24</italic>\n<sup>−/−</sup> MSCs is shown. <italic>N </italic>≥ 6. “*” at bar charts indicates <italic>p</italic> < .05</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13152-g003\"/></fig></sec><sec id=\"acel13152-sec-0007\"><label>2.5</label><title>Constitutive activation of RhoA signaling promotes nuclear abnormalities and cellular senescence</title><p>In order to verify further the role of RhoA activation in modulating nuclear abnormalities and cellular senescence, the effect of RhoA activation in WT MSCs was examined. Treatment of WT MSCs with the Rho activator II (Cytoskeleton Inc.), which increases the level of GTP‐bound RhoA (Schmidt et al., <xref rid=\"acel13152-bib-0036\" ref-type=\"ref\">1997</xref>), increased F‐actin polymerization and nuclear blebbing (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S6</xref>). Similarly, WT MSCs transfected with a plasmid carrying constitutively active RhoA‐GFP developed increased F‐actin polymerization in GFP‐positive cells (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3k</xref>). The level of nuclear blebbing also was found to be higher in RhoA‐GFP<sup>+</sup> cells than RhoA‐GFP‐ cells (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3l</xref>,m). Sun proteins (i.e., Sun1 and Sun2) at the nuclear envelope transduce mechanical stress from the ECM and cytoskeleton into the nucleus (Lei et al., <xref rid=\"acel13152-bib-0027\" ref-type=\"ref\">2009</xref>). LINC complexes that contain Sun2, but not Sun1, were found to promote focal adhesion assembly by activating RhoA (Thakar et al., <xref rid=\"acel13152-bib-0042\" ref-type=\"ref\">2017</xref>). Thus, we examined whether Sun2 may mediate the effect of RhoA activation in promoting nuclear blebbing in <italic>Z24</italic>\n<sup>−/−</sup> MSCs. We observed that the expression of constitutively active RhoA‐GFP in WT MSCs elevated Sun2 protein level (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3l</xref>). Meanwhile, we observed that Sun2 was not expressed uniformly in <italic>Z24</italic>\n<sup>−/−</sup> MSCs and appeared to be more prominently expressed in cells with nuclear blebbing (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3n</xref>,o; Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S7</xref>) and increased F‐actin polymerization (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S7</xref>a). Consistently, the level of Sun2 was generally higher in <italic>Z24</italic>\n<sup>−/−</sup> MSCs than WT MSCs (Figure <xref rid=\"acel13152-fig-0003\" ref-type=\"fig\">3p</xref>).</p><p>In WT MSCs, the level of Sun2 also was increased by JPK treatment (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S8</xref>a), further suggesting that Sun2 is responsive to the changes in F‐actin cytoskeleton stiffness and nuclear stiffness. Western blot analysis of Sun1 and Sun2 in WT MSCs, with or without treatment with Rho activator II or JPK, and <italic>Z24</italic>\n<sup>−/−</sup> MSCs showed that the level of Sun2, but not Sun1, was elevated by RhoA activation and F‐actin stabilization (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S8</xref>b). Similarly, we observed increased number of cells with higher Sun2 expression in the skeletal muscle tissues of <italic>Z24</italic>\n<sup>−/−</sup> mice (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S9</xref>).</p></sec><sec id=\"acel13152-sec-0008\"><label>2.6</label><title>\n<bold><italic>Inhibition of RhoA signaling in Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> MSCs reduced F‐actin polymerization, nuclear blebbing, DNA damage, and cellular senescence</italic></bold>\n</title><p>Based on the results above, we hypothesized that inhibition of RhoA signaling could suppress senescent phenotypes in progeria cells by reducing polymerized F‐actin and relaxing the cytoskeleton stiffness. <italic>Z24</italic>\n<sup>−/−</sup> MSCs treated with RhoA/ROCK inhibitor Y‐27632 showed a decrease in F‐actin polymerization and nuclear blebbing in contrast to <italic>Z24</italic>\n<sup>−/−</sup> MSCs and <italic>Z24</italic>\n<sup>−/−</sup> MSCs treated with Rho activator II (Figure <xref rid=\"acel13152-fig-0004\" ref-type=\"fig\">4a</xref>). Also, Y‐27632 treatment reduced the level of the DNA damage marker γ‐H2AX<sup>+</sup> and the number of senescent cells (SA‐β‐Gal+ or p21<sup>Cip1</sup>+) (Figure <xref rid=\"acel13152-fig-0004\" ref-type=\"fig\">4b</xref>; Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S10</xref>). Y‐27632 treatment of <italic>Z24</italic>\n<sup>−/−</sup> MSCs also reduced the translocation of telomeres into micronuclei and the condensation of telomere (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S10</xref>b). The specificity of Y‐27632 in inhibiting RhoA/ROCK signaling in <italic>Z24</italic>\n<sup>−/−</sup> MSCs was further verified by measuring the activity of RhoA and ROCK in <italic>Z24</italic>\n<sup>−/−</sup> MSCs with or without Y‐27632 treatment (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S10</xref>c). Importantly, Y‐27632 treatment of <italic>Z24</italic>\n<sup>−/−</sup> MSCs also reduced the level of Sun2 protein, which is correlated with reduced nuclear blebbing and F‐actin polymerization (Figure <xref rid=\"acel13152-fig-0004\" ref-type=\"fig\">4c</xref>; Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S11</xref>a). C3 transferase, a specific inhibitor targeting RhoA (Gutekunst, Tung, McDougal, & Gross, <xref rid=\"acel13152-bib-0018\" ref-type=\"ref\">2016</xref>), also reduced F‐actin polymerization and Sun2 expression in <italic>Z24</italic>\n<sup>−/−</sup> MSCs (Figure <xref rid=\"acel13152-fig-0004\" ref-type=\"fig\">4c</xref>). Moreover, the testing of cell stiffness with AFM system showed that Y‐27632 treatment reduced the nuclear and cytoskeletal stiffness of <italic>Z24</italic>\n<sup>−/−</sup> MSCs (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S14</xref>a–c). These results suggest that increased sun2 expression in <italic>Z24</italic>\n<sup>−/−</sup> MSCs plays a crucial role in regulating cytoskeleton stiffness.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13152-fig-0004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Effect of inhibition of RhoA/ROCK signaling or Sun2 expression in <italic>Z24</italic>\n<sup>−/−</sup> MSCs. (a) Immunostaining analysis of lamin A/C and F‐actin in <italic>Z24</italic>\n<sup>−/−</sup> MSCs treated with Rho activator II or RhoA/ROCK inhibitor Y‐27632. Quantification of nuclear blebbing and F‐actin is shown. Scale bar = 5 µm. (b) Immunostaining analysis and quantification of γ‐H2AX and SA‐β‐Gal staining in <italic>Z24</italic>\n<sup>−/−</sup> MSCs treated with Y‐27632. Quantification of γ‐H2AX<sup>+</sup> or SA‐β‐Gal<sup>+</sup> cells is shown. Scale bar = 100 µm. (c) Immunostaining analysis of Sun2 and F‐actin in <italic>Z24</italic>\n<sup>−/−</sup> MSCs treated with Y‐27632 or C3 transferase (C3). Quantification of Sun2 with or without RhoA inhibition is shown. Scale bar = 3 µm. (d) Immunostaining analysis of Sun1 and Sun2 in nuclear and micronuclei of <italic>Z24</italic>\n<sup>−/−</sup> MSCs. Scale bar = 3 µm. (e) Quantitation of Sun1 and Sun2 protein level in micronuclei of <italic>Z24</italic>\n<sup>−/−</sup> MSCs is shown. (f) Demonstration of perinuclear actin cap stress fiber. (g) Immunostaining analysis of Sun2 and F‐actin in <italic>Z24</italic>\n<sup>−/−</sup> MSCs with or without Sun2 SiRNA treatment. Scale bar = 3 µm. (h) Quantitation of Sun2 and nuclear blebbing is shown. (i) Quantification of F‐actin level is shown. (j) Western blot analysis of Sun1 and Sun2 in <italic>Z24</italic>\n<sup>−/−</sup> MSCs and <italic>Z24</italic>\n<sup>−/−</sup> MSCs treated with Y‐27632 or Sun2 SiRNA. (k) Quantitation of Sun1 and Sun2 in western blot result is shown. <italic>N </italic>≥ 6. “*” at bar charts indicates <italic>p</italic> < .05</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13152-g004\"/></fig></sec><sec id=\"acel13152-sec-0009\"><label>2.7</label><title>\n<bold><italic>Effect of repressing Sun2 expression with Sun2 SiRNA in Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> MSCs</italic></bold>\n</title><p>We observed higher Sun2 expression in micronuclei of <italic>Z24</italic>\n<sup>−/−</sup> MSCs than Sun1 (Figure <xref rid=\"acel13152-fig-0004\" ref-type=\"fig\">4d,e</xref>; Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S12</xref>). In order to examine the role of Sun2 further, <italic>Z24</italic>\n<sup>−/−</sup> MSCs were transfected with Sun2 siRNA to repress Sun2 expression. Reduced Sun2 expression disrupted the aligned structure of the perinuclear actin cap stress fiber (Figure <xref rid=\"acel13152-fig-0004\" ref-type=\"fig\">4f</xref>,g) and reduce the F‐actin level at the perinuclear actin cap (Khatau, Kim, Hale, Bloom, & Wirtz, <xref rid=\"acel13152-bib-0025\" ref-type=\"ref\">2010</xref>) (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S13</xref>). Also, reduced Sun2 expression decreased nuclear blebbing in <italic>Z24</italic>\n<sup>−/−</sup> MSCs (Figure <xref rid=\"acel13152-fig-0004\" ref-type=\"fig\">4g</xref>,h) and the level of total F‐actin (Figure <xref rid=\"acel13152-fig-0004\" ref-type=\"fig\">4i</xref>). Moreover, the testing of cell stiffness with AFM system showed that the Young modulus force of <italic>Z24</italic>\n<sup>−/−</sup> MSCs treated with Sun2 SiRNA or Y‐27632 was reduced, at both the nuclear/cytoskeletal and cytoskeletal locations (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S14</xref>a–c). In addition, treatment of <italic>Z24</italic>\n<sup>−/−</sup> MSCs treated with a Sun2 SiRNA reduced RhoA activity (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S14</xref>d) and down‐regulated expression of senescence‐associated genes (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S14</xref>e). In addition, treatment of <italic>Z24</italic>\n<sup>−/−</sup> MSCs with either Sun2 SiRNA or Y‐27632 reduced the level of Sun2 protein, but not Sun1 protein (Figure <xref rid=\"acel13152-fig-0004\" ref-type=\"fig\">4j</xref>,k).</p></sec><sec id=\"acel13152-sec-0010\"><label>2.8</label><title>\n<bold><italic>Inhibition of RhoA/ROCK repressed cGAS/Sting signaling in Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> MSCs</italic></bold>\n</title><p>Micronuclei formation is a danger signal to the cells, leading to activation of the cGAS‐Sting signaling pathway, a component of the innate immune system, to promote cellular senescence (Dou et al., <xref rid=\"acel13152-bib-0015\" ref-type=\"ref\">2017</xref>). The micronuclei formed in <italic>Z24</italic>\n<sup>−/−</sup> cells are positive for cGAS protein by immunostaining assay (Figure <xref rid=\"acel13152-fig-0005\" ref-type=\"fig\">5a</xref>). In addition, treatment of the <italic>Z24</italic>\n<sup>−/−</sup> cells with Y‐27632 was able to reduce the protein level of cGAS, phospho‐p65/RelA, and phopho‐TBK1 in <italic>Z24</italic>\n<sup>−/−</sup> cells (Figure <xref rid=\"acel13152-fig-0005\" ref-type=\"fig\">5b</xref>). Also, Y‐27632 treatment down‐regulated the expression of the type 1 interferon‐β (IFN‐1β), a marker of activation of cGAS‐Sting innate immune signaling (Figure <xref rid=\"acel13152-fig-0005\" ref-type=\"fig\">5c</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13152-fig-0005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>RhoA inhibition in <italic>Z24</italic>\n<sup>−/−</sup> MSCs represses micronuclei/cytoplasmic DNA‐induced innate immune response, reduces SASP expression, and rescues senescent phenotypes. (a) Immunostaining analysis of lamin A/C and cGAS showed that there is positive cGAS deposition at the micronuclei formed in <italic>Z24</italic>\n<sup>−/−</sup> MSCs (arrows). Scale bar = 3 µm. (b) Western blot analysis and quantification of proteins related to the cGAS‐Sting signaling (cGAS, phosphor‐p65, phosphor‐TBK1) in WT MSCs,<italic> Z24</italic>\n<sup>−/−</sup> MSCs, and <italic>Z24</italic>\n<sup>−/−</sup> MSCs treated with Y‐27632. (c) qPCR analysis of interferon‐1β (IFN‐1β) expression. (d) qPCR analysis of the expression of SASP and senescent‐associated genes in <italic>Z24</italic>\n<sup>−/−</sup> MSCs with or without Y‐27632 treatment. (e) Osteogenesis assay and adipogenesis assay of <italic>Z24</italic>\n<sup>−/−</sup> MSCs with or without Y‐27632 treatment. Osteogenic potential was examined with ALP staining of osteogenic cells, and adipogenic potential was examined with AdipoRed staining of lipid in adipogenic cells. Scale bar = 30 µm. (f) Quantification of ALP or AdipoRed is shown. (g) Immunostaining analysis of lamin A/C in <italic>Z24</italic>\n<sup>−/−</sup> MPCs treated with conditioned medium from <italic>Z24</italic>\n<sup>−/−</sup> MSCs with or without Y‐27632 pretreatment. Arrows indicate cells with nuclear blebbing. Scale bar = 50 µm. (h) Quantification of myotube number and nuclear blebbing is shown. <italic>N </italic>≥ 6. “*” at bar charts indicates <italic>p</italic> < .05</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13152-g005\"/></fig></sec><sec id=\"acel13152-sec-0011\"><label>2.9</label><title>\n<bold><italic>Inhibition of RhoA/ROCK signaling in Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> MSCs repressed SASP expression, modified the differentiation potential, and rescued the deleterious effect of Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> MSCs on MPCs</italic></bold>\n</title><p>Hutchinson–Gilford progeria syndrome MSCs have decreased adipogenic potential and increased osteogenic potential (Meshorer & Gruenbaum, <xref rid=\"acel13152-bib-0031\" ref-type=\"ref\">2008</xref>). qPCR analysis revealed that the expression of SASP factors (i.e., IL‐1β, IL‐6, and TNF‐α), p16<sup>INK4a</sup>, and TGF‐β1 was down‐regulated upon Y‐27632 treatment of <italic>Z24</italic>\n<sup>−/−</sup> MSCs, whereas the expression of the anti‐inflammation factors IL‐10 and Klotho was up‐regulated (Figure <xref rid=\"acel13152-fig-0005\" ref-type=\"fig\">5d</xref>). Also, Y‐27632 treatment of <italic>Z24</italic>\n<sup>−/−</sup> MSCs was able to restore the adipo‐osteogenic balance by promoting adipogenesis and repressing osteogenesis (Figure <xref rid=\"acel13152-fig-0005\" ref-type=\"fig\">5e,f</xref>). In addition, when the <italic>Z24</italic>\n<sup>−/−</sup> MPCs were treated with conditioned medium from <italic>Z24</italic>\n<sup>−/−</sup> MSC pretreated with Y‐27632, the myogenic potential of <italic>Z24</italic>\n<sup>−/−</sup> MPCs was increased and the number of cells with nuclear blebbing was reduced, compared to <italic>Z24</italic>\n<sup>−/−</sup> MPCs treated with control conditioned medium from <italic>Z24</italic>\n<sup>−/−</sup> MSCs without Y‐27632 pretreatment (Figure <xref rid=\"acel13152-fig-0005\" ref-type=\"fig\">5g</xref>,h).</p></sec><sec id=\"acel13152-sec-0012\"><label>2.10</label><title>\n<bold><italic>Inhibition of RhoA/ROCK promotes epigenetic changes in chromatin of Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> MSCs</italic></bold>\n</title><p>HGPS cells have profound alterations in epigenetics and histone modification with reduced heterochromatin markers H3K9me3 and H3K27me3 (Arancio, Pizzolanti, Genovese, Pitrone, & Giordano, <xref rid=\"acel13152-bib-0002\" ref-type=\"ref\">2014</xref>). We examined whether the inhibition of RhoA/ROCK activity or Sun2 expression affected the epigenetic state of chromatin in <italic>Z24</italic>\n<sup>−/−</sup> MSCs. H3K9me3 and H3K27me3 were higher in cells without obvious nuclear blebbing in contrast to cells with nuclear blebbing (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S15</xref>a). Also, both Y‐27632 and Sun2 siRNA were effective in increasing the level of H3K9me3 or H3K27me3 in <italic>Z24</italic>\n<sup>−/−</sup> MSCs (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S15</xref>b–d).</p></sec><sec id=\"acel13152-sec-0013\"><label>2.11</label><title>\n<bold><italic>Systemic inhibition of RhoA in Z24</italic></bold>\n<sup>−/−</sup>\n<bold><italic> mice extended the healthspan and rescued the defective phenotypes in skeletal muscle</italic></bold>\n</title><p>To examine the effect of RhoA inhibition in vivo, 10‐week‐old <italic>Z24</italic>\n<sup>−/−</sup> mice were treated with Y‐27632 (10 mg/kg) by i.p. injection 3 times a week for 12 weeks. Following this treatment regimen, the number of myogenic Pax7<sup>+</sup> cells was increased in the skeletal muscles of <italic>Z24</italic>\n<sup>−/−</sup> mice (Figure <xref rid=\"acel13152-fig-0006\" ref-type=\"fig\">6a,b</xref>), whereas the number of SA‐β‐Gal<sup>+</sup> cells, pro‐inflammatory CD68<sup>+</sup> cells, and PDGFR‐α<sup>+</sup> cells was reduced (Figure <xref rid=\"acel13152-fig-0006\" ref-type=\"fig\">6b</xref>). Also, RhoA inhibition improved the muscle regeneration potential of <italic>Z24</italic>\n<sup>−/−</sup> mice following cardiotoxin‐induced injury (7 days after injury) (Figure <xref rid=\"acel13152-fig-0006\" ref-type=\"fig\">6a,c</xref>). This suggests that RhoA inhibition improves the function of muscle stem cells and muscle regeneration both by increasing the number of muscle stem cells and repressing the deleterious impact of senescent cells on muscle stem cells. Based on these results, we propose a potential cellular and molecular mechanism of muscle stem cell dysfunction in <italic>Z24</italic>\n<sup>−/−</sup> mice, involving the over‐activation of RhoA signaling, and pro‐inflammatory, and pro‐fibrogenic factors in MSCs and inflammatory cells (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S16</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13152-fig-0006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Effect of RhoA inhibition in <italic>Z24</italic>\n<sup>−/−</sup> mice in vivo, and effect of RhoA inhibition in HGPS fibroblasts. (a) Systemic inhibition of RhoA/ROCK signaling in <italic>Z24</italic>\n<sup>−/−</sup> mice was performed by i.p. injection of Y‐27632. Immunostaining analysis of Pax7 and RhoA and SA‐β‐Gal staining of senescent cells was performed with muscle tissues from WT and <italic>Z24</italic>\n<sup>−/−</sup> mice. Trichrome staining was performed with regenerating muscle tissues (7 days after cardiotoxin‐induced muscle injury). Scale bars = 100 µm. (b) Quantitation of the changes in CD68<sup>+</sup>, PDGFR‐α<sup>+</sup>, Pax7<sup>+</sup>, and SA‐β‐Gal<sup>+</sup> cells in muscles of <italic>Z24</italic>\n<sup>−/−</sup> mice with or without Y‐27632 injection are shown. (c) Quantification of the size of regenerating neo‐myofibers (positive with centrally located nuclei). (d) Immunostaining analysis of lamin A/C and F‐actin in normal human fibroblasts, HGPS fibroblasts, and HGPS fibroblasts treated with Y‐27632 or farnesyltransferase inhibitor (FTI). Scale bar = 5µm. (e) Quantitation of F‐actin is shown. (f) Quantitation of nuclear blebbing is shown. (g) Quantitation of Sun2 is shown. (h) The proposed model for the potential mechanism of RhoA/Sun2‐mediated increase of cytoskeletal stiffness in progeria cells. The ECM, cytoskeleton and nucleoskeleton are physically connected. Our results suggest that both increased ECM stiffness and nuclear stiffness of progeria cells contribute to increased RhoA activation and cytoskeleton stiffness. Also, elevated ROS production, inflammatory signaling (NF‐κB), and DNA damages in progeria cells can promote RhoA activation, resulting in further increased cytoskeleton stiffness. Increased RhoA and Sun2 expression, nuclear blebbing, and cytoplasmic DNA fragments in micronuclei, can all mediate accelerated cellular senescence. <italic>N </italic>≥ 6. “*” at bar charts indicates <italic>p</italic> < .05</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13152-g006\"/></fig></sec><sec id=\"acel13152-sec-0014\"><label>2.12</label><title>Human HGPS fibroblasts have increased F‐actin polymerization, which was reduced by RhoA/ROCK inhibitor and farnesyltransferase inhibitor (FTI)</title><p>To extend the results from the <italic>Z24</italic>\n<sup>−/−</sup> mouse model of HGPS, fibroblasts from HGPS patients also were examined. Similar to murine <italic>Z24</italic>\n<sup>−/−</sup> cells, there was increased F‐actin polymerization and nuclear blebbing (Figure <xref rid=\"acel13152-fig-0006\" ref-type=\"fig\">6d</xref>) in human HGPS fibroblasts compared to normal fibroblasts. Treatment of HGPS fibroblasts with Y‐27632 reduced F‐actin polymerization, nuclear blebbing, and Sun2 expression (Figure <xref rid=\"acel13152-fig-0006\" ref-type=\"fig\">6d</xref>–g). Furthermore, both H3K9me3 and H3K27me3 were generally less prevalent in HGPS fibroblasts with nuclear blebbing (Figure <xref rid=\"acel13152-fig-0006\" ref-type=\"fig\">6h</xref>), whereas treatment of HGPS cells with Y‐27632 resulted in increased levels of H3K9me3 and H3K27me3 (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S17</xref>). In addition, AFM probe testing of cell stiffness showed that HGPS fibroblasts seemed to have a higher cytoskeletal stiffness than normal fibroblasts (Figure <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S18</xref>).</p><p>The farnesyltransferase inhibitor (FTI) lonafarnib currently is the only approved medication for HGPS patients, effective in reversing the dramatic nuclear structure abnormalities and to alleviate some disease symptoms. FTIs act by inhibiting the farnesylation of prelamin A (Mehta, Eskiw, Arican, Kill, & Bridger, <xref rid=\"acel13152-bib-0030\" ref-type=\"ref\">2011</xref>). Intriguingly, the activity of RhoA protein also is regulated by farnesylation. This common farnesylation‐dependent regulatory mechanism of both prelamin A and RhoA proteins suggests that RhoA might serve as a promising target for therapeutic treatment of HGPS. We observed that treatment of HGPS fibroblasts with lonafarnib efficiently reduced F‐actin polymerization, nuclear blebbing, and Sun2 expression (Figure <xref rid=\"acel13152-fig-0006\" ref-type=\"fig\">6d</xref>–g).</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13152-sec-0015\"><label>3</label><title>DISCUSSION</title><p>The <italic>LMNA</italic> mutation in HGPS disease causes nuclear abnormalities and cellular fragility in response to mechanical stress; however, correlations between genotypes and mechanical phenotypes in the cells remain unclear. Also, the role of cytoskeletal stiffness and its potential association with nuclear abnormality in progeria cells also remains to be investigated. Progerin production occurs not only in progeria cells, but also in naturally aged human cells (Booth et al., <xref rid=\"acel13152-bib-0006\" ref-type=\"ref\">2015</xref>; Cao et al., <xref rid=\"acel13152-bib-0009\" ref-type=\"ref\">2011</xref>; Pacheco et al., <xref rid=\"acel13152-bib-0034\" ref-type=\"ref\">2014</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>). The role of cytoskeletal stiffness in mediating the deleterious effect of progerin in promoting cellular senescence and aging is poorly understood. Therefore, we examined the mechanism of cytoskeletal stiffness‐mediated nuclear variations in HGPS cells and in a HGPS mouse model. Here, we demonstrate the crucial role of cytoskeletal stiffness in mediating nuclear abnormalities in cells from HGPS patients and a progeria disease mouse model as well as the mechanical roles of RhoA and Sun2 in modulating cytoskeletal stiffness, nuclear blebbing, and senescence.</p><p>Our results are consistent with previous reports of increased cytoskeletal stiffness in certain types of senescent cells, which also suggests that cytoskeletal stiffness is coupled with nucleoskeletal stiffness (Nishio, Inoue, Qiao, Kondo, & Mimura, <xref rid=\"acel13152-bib-0033\" ref-type=\"ref\">2001</xref>; Schulze et al., <xref rid=\"acel13152-bib-0038\" ref-type=\"ref\">2010</xref>). Our results further indicate that activation of the RhoA signaling and Sun2 protein is crucial for mediating the accelerated cellular senescence of MSCs and aging‐associated pathologies in the muscle of HGPS patients. Interestingly, a recent chemical screening study identified ROCK as a target for recovering mitochondrial function in HGPS cells. Here, Y‐27632 treatment of HGPS cells induced the recovery of mitochondrial function and reduction of abnormal nuclear morphology (Kang et al., <xref rid=\"acel13152-bib-0024\" ref-type=\"ref\">2017</xref>), which is consistent with our results. However, our study further revealed the biomechanical mechanism of RhoA/ROCK activation in mediating cellular senescence, including the direct connection of RhoA/ROCK activation with cell stiffness and nuclear blebbing, the interaction of RhoA/ROCK and Sun2 in response to cytoskeleton stiffness and nuclear blebbing, and the correlation among RhoA/ROCK activation, Sun2 expression, micronuclei formation, and the innate (cGAS/Sting) immune response.</p><p>There have been previous reports describing the normal cytoskeletal stiffness in HGPS cells (Verstraeten et al., <xref rid=\"acel13152-bib-0043\" ref-type=\"ref\">2008</xref>) and reduced cytoskeletal stiffness in LMNA mutant (<italic>LmnaL530P</italic>/<italic>L530P</italic>) mouse cells (Hale et al., <xref rid=\"acel13152-bib-0019\" ref-type=\"ref\">2008</xref>). These different results in cytoskeletal stiffness compared to our studies may be partially attributed to the different cell lineages, cell culture conditions, or stiffness testing methods. <italic>LmnaL530P</italic>/<italic>L530P</italic> cells do not produce progerin, and there is a decreased level of lamin A in nuclear lamina (Hale et al., <xref rid=\"acel13152-bib-0019\" ref-type=\"ref\">2008</xref>). The weaker/softer nuclear lamina in <italic>LmnaL530P</italic>/<italic>L530P</italic> cells could be responsible for the reduced cytoskeleton stiffness observed. However, LMNA mutation in HGPS cells leads to progerin accumulation and trapping of lamin A/C in the nuclear lamina, forming orientationally ordered microdomains and stiffer lamina network (Dahl et al., <xref rid=\"acel13152-bib-0014\" ref-type=\"ref\">2006</xref>).</p><p>Our results reveal the mechanical mechanism of how RhoA/ROCK activation promotes cellular senescence by modulating F‐actin polymerization in progeria cells. The increased F‐actin polymerization and cytoskeletal stiffness in senescent cells could be caused by sustained RhoA activation, which can impair proper actin dynamics and normal cellular function, and further enhance DNA damage, ROS production, nuclear blebbing, cytoplasmic DNA/cGAS‐Sting‐mediated innate immune responses, chromatin dysfunction, and other progeria phenotypes. Thus, inhibition of RhoA activation potentially rescues progeria phenotypes by relieving cytoskeletal stiffness and consequently nucleoskeletal stiffness. Our results also suggest that the accelerated DNA damage and cellular senescence in progeria cells can be delayed by simply reducing cytoskeletal stiffness. In addition, systemic inhibition of RhoA/ROCK signaling in progeria mice was able to rescue the progeria phenotypes in skeletal muscle.</p><p>We propose that RhoA activation is an indirect outcome of the <italic>LMNA</italic> mutation in progeria cells through two possible mechanisms. The first mechanism involves the elevated cytoskeletal mechanical stress induced by mutation of <italic>LMNA</italic>. This leads to progerin accumulation in nuclear lamina, which increases the stiffness of lamina network (Dahl et al., <xref rid=\"acel13152-bib-0014\" ref-type=\"ref\">2006</xref>). This mechanical stress from lamina network can be transmitted to cytoskeleton via the LINC complex, and mechanical sensing factors in the cytosol can mobilize RhoA activation to promote F‐actin polymerization. The second possibly mechanism involves the increased Sun2 expression induced by <italic>LMNA</italic> mutation. Recent studies indicates that Sun2 is mechanical responsive (Hoffman et al., <xref rid=\"acel13152-bib-0022\" ref-type=\"ref\">2020</xref>), and the expression of Sun2 in LINC complex can be modulated by varied mechanical features of lamina network. Sun2 was found to promote RhoA activation (Thakar et al., <xref rid=\"acel13152-bib-0042\" ref-type=\"ref\">2017</xref>), and thus, the <italic>LMNA</italic> mutation may cause RhoA activation by increasing Sun2 expression in HGPS cells.</p><p>Previous observations have demonstrated the role of Sun1 in responding to progerin accumulation in nuclear lamina (Haque et al., <xref rid=\"acel13152-bib-0020\" ref-type=\"ref\">2010</xref>; Lei et al., <xref rid=\"acel13152-bib-0027\" ref-type=\"ref\">2009</xref>). It was shown that the reduction of Sun1 accumulation in LMNA mutant cells (i.e., <italic>Lmna<sup>−/−</sup></italic> and <italic>Lmna</italic>Δ9 fibroblasts) corrected nuclear defects and cellular senescence (Chen et al., <xref rid=\"acel13152-bib-0011\" ref-type=\"ref\">2012</xref>). We believe that this beneficial effect of Sun1 reduction in progeria cells may be partially due to the regulation of mechanical features of nucleus or cytoskeleton since nuclear lamina network is connected to cytoskeleton by both Sun1 and Sun2 in the LINC complexes. However, we did not observe the accumulation of Sun1 in our cells, potentially because of the different cell culture condition and genotypes of cell lineages, especially the potential difference in progerin generation in cells. Recent studies have suggested a potential role for Sun2 in mechano‐sensing and involvement in mechanical stimulation‐induced changes in actin cytoskeleton (Hoffman et al., <xref rid=\"acel13152-bib-0022\" ref-type=\"ref\">2020</xref>). Our results have confirmed further the mechanical role of Sun2 in mediating increased nuclear stiffness, nuclear abnormalities, and cellular senescence, as well as a potential mechanical feedback loop of Sun2 with RhoA.</p><p>In aged tissues, the extracellular matrix (ECM) usually becomes stiffer because of increased collagen deposition, varied composition of collagen subtypes, and the crosslinking structure of collagen fiber. Cells adapt to this increased ECM stiffness by modifying the expression of mechano‐responsive genes. Mechanical stimuli from the ECM can be transmitted to the nucleus via the cytoskeleton and LINC complex, and directly contribute to the triggering of mechano‐responsive genes to adapt to the mechanical environment (Isermann & Lammerding, <xref rid=\"acel13152-bib-0023\" ref-type=\"ref\">2013</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>). Both lamin and Sun proteins are required for this structural connection between the nucleus and cytoskeleton (Isermann & Lammerding, <xref rid=\"acel13152-bib-0023\" ref-type=\"ref\">2013</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>) and for transducing mechanical stresses from ECM and cytoskeleton into nucleus, which is essential for a broad range of cellular functions. Furthermore, the expression of both lamin A and Sun2 was found to be mechano‐responsive and scales with ECM/substrate stiffness or elasticity. A stiff substrate is associated with increased lamin A and Sun2 expression and strengthened nuclear envelope, whereas a soft substrate for cells is associated with reduced lamin A and Sun2 expression. In aged and progeroid cells, progerin accumulation in the nuclear lamina causes increased nuclear stiffness (Booth et al., <xref rid=\"acel13152-bib-0006\" ref-type=\"ref\">2015</xref>; Cao et al., <xref rid=\"acel13152-bib-0009\" ref-type=\"ref\">2011</xref>; Dahl et al., <xref rid=\"acel13152-bib-0014\" ref-type=\"ref\">2006</xref>; Phillip et al., <xref rid=\"acel13152-bib-0035\" ref-type=\"ref\">2015</xref>; Verstraeten et al., <xref rid=\"acel13152-bib-0043\" ref-type=\"ref\">2008</xref>; Young et al., <xref rid=\"acel13152-bib-0045\" ref-type=\"ref\">2005</xref>). Our current results with <italic>Z24</italic>\n<sup>−/−</sup> MSCs and HGPS fibroblasts demonstrate the increased accumulation of Sun2 protein in the nuclear envelope, especially in cells with abnormal nuclear architecture (nuclear blebbing). The increased activation of Sun2 in these in vitro cultured progeria cells is not caused by stiffer substrate/ECM, but rather is a result of an intrinsic change in the mechanical properties of the cell nucleus. We propose that Sun2 accumulation in progeria cells is a result of progerin accumulation because there is direct physical interaction between the Sun2 and lamin A proteins. Mechanical stimuli resulting from increased nuclear stiffness could be transmitted to the cytoskeleton via LINC, leading to increased F‐actin polymerization. The Sun2 protein also can activate RhoA signaling directly (Thakar et al., <xref rid=\"acel13152-bib-0042\" ref-type=\"ref\">2017</xref>), which may further enhance F‐actin polymerization and cytoskeletal stiffness. Therefore, increased cytoskeletal stiffness in progeria cells could actually be the results of mechanical stress from both ECM stiffness (Hernandez et al., <xref rid=\"acel13152-bib-0021\" ref-type=\"ref\">2010</xref>) and nuclear stiffness (Figure <xref rid=\"acel13152-fig-0006\" ref-type=\"fig\">6c</xref>). We have proposed the potential mechanisms of cytoskeletal stiffening in mediating nuclear blebbing, micronuclei formation, and cellular senescence, the involvement of the RhoA and Sun2 factors, and how increased ECM, nucleoskeletal, and cytoskeletal stiffness may be coupled in progeria cells.</p><p>Our results also demonstrate that there is sustained activation of RhoA signaling in progeria cells, which promotes F‐actin cytoskeletal stiffness and impairs proper F‐actin dynamics. RhoA activation in progeria cells could be a result of Sun2 activation as well as increased pro‐inflammatory signaling, pro‐fibrotic signaling, DNA damage, ROS production, and activated mechano‐sensing signaling (Li et al., <xref rid=\"acel13152-bib-0028\" ref-type=\"ref\">2011</xref>). Lamin A or Zmpste24‐deficiency has been shown to cause dysregulated differentiation capacity of stem cells, including increased osteogenic potential and repressed adipogenic potential. Our results strongly suggest that increased cytoskeletal stiffness and RhoA activation in progeria cells are determinant for this disrupted adipo‐osteogenic balance of cell fate.</p><p>Aging is associated with senescence and depletion of functional stem cells in various tissues, leading to impeded tissue homeostasis and regeneration capacity. Senescence and depletion of stem cells can be caused by both cell autonomous mechanisms and noncell autonomous mechanisms (Baar, Perdiguero, Munoz‐Canoves, & de Keizer, <xref rid=\"acel13152-bib-0003\" ref-type=\"ref\">2018</xref>). Senescent cells have a senescence‐associated secretory phenotype (SASP), which alters the microenvironment and impacts the function of nonsenescent cells (Baar et al., <xref rid=\"acel13152-bib-0003\" ref-type=\"ref\">2018</xref>). The SASP can affect the stem cell niche, impacting stem cell function via a noncell autonomous mechanism. In skeletal muscle, muscle stem cells (satellite cells) reside in niches at the basal lamina of myofibers and are responsible for the regeneration of skeletal muscle. Muscle stem cell behaviors are regulated by multiple microenvironmental cues/factors in and around the niches, such as myofibers, b<italic>asal lamina,</italic> extracellular matrix, neighboring nonmyogenic cells, and soluble factors secreted by other cells. Muscle stem cells are negatively impacted by pro‐inflammatory cytokines and pro‐fibrotic growth factors released by inflammatory cells, fibroblasts, and PDGFR‐α<sup>+</sup> FAPs (or MSCs in the current study). Our previous studies of severely dystrophic muscle in mouse models have revealed that macrophage and MSC proliferation and function increased during disease progression in dystrophic muscles, whereas MPC proliferation and function declined (Mu et al., <xref rid=\"acel13152-bib-0032\" ref-type=\"ref\">2013</xref>; Sohn, Lu, Tang, Wang, & Huard, <xref rid=\"acel13152-bib-0041\" ref-type=\"ref\">2015</xref>). Similarly, homeostasis and regeneration of aged skeletal muscle can be impaired by macrophage‐mediated chronic inflammation and fibrogenic cell‐mediated fibrosis formation. SASP factors from senescent cells also can chronically impair muscle stem cell function and attenuate tissue regeneration in muscle. Therefore, the defective muscle regenerative capacity of muscle stem cells depends intimately on the microenvironment modulated by various types of senescent cells, and senescent <italic>Z24</italic>\n<sup>−/−</sup> MSCs could contribute to the impaired function of muscle stem cells in <italic>Z24</italic>\n<sup>−/−</sup> muscle. It is notable that increased intrinsic defects including nuclear abnormalities, DNA damage, and cellular senescence also can be induced by <italic>LMNA</italic> mutation in Pax7<sup>+</sup> muscle stem cells, and the paracrine effect by MSCs is not the only causal factor of muscle stem cell defects. Importantly, treating Z24<sup>−/−</sup> mice with a RhoA inhibitor reduced senescence, suggesting that RhoA inhibitors represent a novel type of senotherapeutic.</p><p>The increased micronuclei formation and cytoplasmic DNA induced by DNA damage can activate cGAS‐Sting innate immune signaling and contribute to cellular senescence (Dou et al., <xref rid=\"acel13152-bib-0015\" ref-type=\"ref\">2017</xref>). The potential correlation of cGAS‐Sting signaling with mechanical properties of a cell has not been addressed before our study. Current results suggest that increased RhoA activation, cytoskeletal stiffness, and Sun2 expression in progeria cells are associated with increased generation of nuclear blebs/micronuclei and cytoplasmic DNA fragments, thus mediating the activation of cGAS‐Sting signaling and cellular senescence progression.</p><p>Taken together, our novel results demonstrate that increased cytoskeletal stiffness and RhoA/Sun2 signaling represents a novel mechanism for promoting aging and cellular senescence and therefore represents a novel therapeutic target for intervention in progeria diseases and for extending healthspan with natural aging. These results extend our understanding regarding how mutations in lamin A, a structural and mechano‐responsive protein in nuclear lamina network, changes the mechanical properties of nuclear and cytoskeleton and contributes to driving cellular senescence and thus aging and age‐related diseases.</p></sec><sec sec-type=\"methods\" id=\"acel13152-sec-0016\"><label>4</label><title>METHODS</title><sec id=\"acel13152-sec-0017\"><label>4.1</label><title>Animal models</title><p>\n<italic>Zmpste24</italic>\n<sup>−/−</sup> (<italic>Z24</italic>\n<sup>−/−</sup>\n<italic>)</italic> mice (B6.129SZmpste24tm1Sgy/Mmucd) were studied as an established model for HGPS. The aged‐matched littermates (<italic>Zmpste24<sup>+/+</sup></italic>) mice born from same <italic>Zmpste24 ± </italic>parents were used as wild‐type (WT) controls. Both male and female mice were used for this study since both genders are susceptible to HGPS disease. All mice were housed and maintained in the Center for Laboratory Animal Medicine and Care (CLAMC) at UTHealth in accordance with established guidelines and protocols approved by the UTHealth Animal Welfare Committee.</p></sec><sec id=\"acel13152-sec-0018\"><label>4.2</label><title>Muscle cell isolation and culturing</title><p>Muscle‐derived mesenchymal stem/stromal cells (MSCs) and muscle stem/progenitor cells (MPCs) were isolated from the skeletal muscle of <italic>Z24</italic>\n<sup>−/−</sup> mice and WT mice (~5 months old, male and female) using the modified preplate technique, based on their adhering capacity to collagen‐coated surface/substrate. MSCs adhere quickly in hours, whereas MPCs continue floating in medium and only attached and start to grow days later. Cells were cultured in proliferation medium (DMEM supplemented with 20% fetal bovine serum (FBS]). WT and <italic>Z24</italic>\n<sup>−/−</sup> MSCs were generally always cultured in collagen type I (0.1%)‐coated plastic flasks or plates for all the experiments in this study.</p></sec><sec id=\"acel13152-sec-0019\"><label>4.3</label><title>Human fibroblasts from HGPS and normal patients</title><p>The fibroblasts from HGPS patients and normal humans were kindly shared from the Coriell Institute and were cultured in DMEM medium supplemented with 15% fetal bovine serum (FBS), 1X glutamax, and 1% penicillin–streptomycin. The HGPS fibroblasts cell strains include AG03513, AG06917, and AG08466, and normal fibroblasts include AG08468, AG08469, and AG08470. Living cells gotten from Coriell Institute were cultured to passage 3 to be compared in our studies.</p></sec><sec id=\"acel13152-sec-0020\"><label>4.4</label><title>Atomic force microscopy (AFM) testing of cell stiffness</title><p>Cells were fixed 4% paraformaldehyde, and the cytoplasm or nuclear stiffness was measured with AFM system at room temperature (Collum et al., <xref rid=\"acel13152-bib-0012\" ref-type=\"ref\">2017</xref>). The force curves measurements were performed with a Catalyst Bioscope System (Bruker Corporation, Billerica, MA). The AFM was equipped with an inverted light microscope (Olympus IX81) to track the position of cell and AFM probe. The AFM probe has a material of nonconductive silicon nitride, with the spring constant values of approximately 0.05 N/m (Bruker. Model: MLCT; cantilever: T:0.55 µm). The cantilever sensitivity was calibrated with the NanoScope software by measuring a force curve on a clean silicon wafer. Force curves were acquired at a ramp size of 10 µm and ramp rate of 1.03 Hz. Young's modulus, E, was calculated from obtained force curves based on the active curve of “extend” and fit mode of Sneddon (conical) using NanoScope analysis program from the Bruker Corporation. The <italic>F</italic> = 2π<italic>E</italic>1 − υ2tanαδ2 where <italic>F</italic> = force, E = Young's modulus, ν = Poisson's ratio, α = half‐angle of the indenter, and δ = indentation depth (Collum et al., <xref rid=\"acel13152-bib-0012\" ref-type=\"ref\">2017</xref>).</p></sec><sec id=\"acel13152-sec-0021\"><label>4.5</label><title>Telomere staining</title><p>Telomeres in the interphase nuclei were detected using a Cy3‐conjugated peptide nucleic acid (PNA) probe provided with the Telomere PNA FISH Kit (Agilent). Fluorescence in situ hybridization of telomere was performed according to the manufacturer's instruction. Briefly, the cells were heated at 80°C for 5 min to denature DNA in the presence of the Cy3‐conjugated PNA probe followed by hybridization in the dark at room temperature (RT) for 30 min. The hybridization was followed by a brief rinse with a Rinse Solution, and a posthybridization wash with a Wash Solution at 65°C for 5 min. DAPI was used to counterstain DNA.</p></sec><sec id=\"acel13152-sec-0022\"><label>4.6</label><title>Treatment of <italic>Z24</italic>\n<sup>−/−</sup> MSCs with F‐actin stabilizing and destabilizing effectors</title><p>\n<italic>Z24</italic>\n<sup>−/−</sup> MSCs cultured in collagen‐coated plastic plates were treated with growth medium supplemented with Jasplakinolide (JPK) (200 nM) to induce F‐actin stabilization or with Cytochalasin D (CyD) (100 ng/ml) to induce F‐actin depolymerization for 48 hr. The effect of F‐actin modification on nuclear blebbing was compared by immune‐staining of lamin A/C or Sun2.</p></sec><sec id=\"acel13152-sec-0023\"><label>4.7</label><title>In vitro RhoA inhibition assays</title><p>\n<italic>Z24</italic>\n<sup>−/−</sup> MSCs or HGPS fibroblasts cultured in collagen‐coated plastic plates were treated with a specific RhoA/Rho kinase (ROCK) inhibitor Y‐27632 (EMD Millipore, Billerica, MA) (10 μM) for 48 hr in cell proliferation medium, and the potential changes of F‐actin polymerization, nuclear blebbing, lamin A/C, Sun2, γ‐H2AX, and Histone methylation/acetylation markers were observed. RhoA‐specific inhibition also was performed with C3 transferase (5 µg/ml) treatment for 48 hr to verify the effect of RhoA inhibition on F‐actin polymerization and nuclear blebbing.</p></sec><sec id=\"acel13152-sec-0024\"><label>4.8</label><title>Measurement of cellular senescence</title><p>The percent of senescent muscle cells cultured in vitro (10% FBS in DMEM for 4 days) and in skeletal muscle tissue was measured using the senescence‐associated β‐Galactosidase (SA‐β‐gal) Staining Kit (Cell Signaling Technology) following the manufacturer's protocol. The number of cells positive for β‐gal activity at pH 6, a known characteristic of senescent cells, was determined.</p></sec><sec id=\"acel13152-sec-0025\"><label>4.9</label><title>Preparation of conditioned medium (CM)</title><p>The WT MSCs and <italic>Z24</italic>\n<sup>−/−</sup> MSCs were plated and grown to ~90% confluence in proliferation medium, and the culture medium was changed to low‐serum medium (DMEM with 2% FBS) to initiate the conditioning of culture medium. The cells were continued to be cultured for 48 hr to collect the CM by filtering through 0.4‐µm filter. The cell number of WT MSCs and <italic>Z24</italic>\n<sup>−/−</sup> MSCs was similar when CM was initiated and collected. The CM was mixed with same volume of fresh medium (1:1) when being used for cell treatment.</p></sec><sec id=\"acel13152-sec-0026\"><label>4.10</label><title>In vitro myogenic, osteogenic, and adipogenic differentiation assays</title><p>MPCs from 5‐month‐old WT or Z24<sup>−/−</sup> mice (30 000 cells per well of 12‐well collage type I‐coated plastic plate), maintained in DM, 2% horse serum in DMEM (Invitrogen), were cultured with different conditioned medium (CM) from <italic>Z24</italic>\n<sup>−/−</sup> MSCs with or without Y‐27632 pretreatment. The <italic>Z24</italic>\n<sup>−/−</sup> MSCs were pretreated with or without Y‐27632 for 48 hr and continued to be cultured for another 48 hr to harvest the CM. Z24<sup>−/−</sup> MPCs were then treated with the CM for 4 days to observe the progression of myogenic differentiation. The number of myotubes was determined by immunocytochemical staining with a fast‐type myosin heavy chain (f‐MHC) antibody (Sigma‐Aldrich). Osteogenic differentiation was performed by culturing the Z24<sup>−/−</sup> MSCs in 12‐well plate in osteogenic differentiation medium which contains DMEM, 10% FBS, supplemented with dexamethasone (0.1 µm, Sigma‐Aldrich), ascorbic‐acid‐2‐phosphate (50 µg/ml, Sigma‐Aldrich), and 10 mm β‐glycerophosphate (Sigma‐Aldrich) and BMP2 (100 ng/ml, Medtronic). Cells were cultured in osteogenic differentiation medium for 4 days, and osteogenesis was assessed using a Fast Blue Alkaline Phosphatase (ALP) Kit (Sigma‐Aldrich). Adipogenic differentiation assay was performed with <italic>Z24</italic>\n<sup>−/−</sup> MSCs in adipogenic differentiation medium (Lonza). When the cells reached 100% confluence, three cycles of induction/maintenance medium were applied to the cells to induce optimal adipogenic differentiation. Adipogenesis was assessed using AdipoRed reagent (30 µl/ml, Thermo Fisher) and 4′,6‐diamidino‐2‐phenylindole (DAPI).</p></sec><sec id=\"acel13152-sec-0027\"><label>4.11</label><title>Constant activation of RhoA in MSCs</title><p>Rho activator II (Cytoskeleton Inc.), which can robustly increase the level of GTP‐bound RhoA and result in constitutively active Rho (Schmidt et al., <xref rid=\"acel13152-bib-0036\" ref-type=\"ref\">1997</xref>), was added to WT MSCs cultured on collagen‐coated plates at 200 ng/ml for 24 hr to observe increased RhoA protein and F‐actin polymerization. Expression of constitutively active RhoA‐GFP was also performed with amplified plasmid of pcDNA3‐EGFP‐RhoA‐Q63L (Addgene, plasmid #12968). Expression of a dominantly negative RhoA‐GFP with amplified plasmid of pcDNA3‐EGFP‐RhoA‐T19N (Addgene, plasmid #12967) served as a negative control. Lipofectamine 3,000 (Thermo Fisher) was applied to facilitate the plasmid transfection into WT MSCs.</p></sec><sec id=\"acel13152-sec-0028\"><label>4.12</label><title>Repression of Sun2 expression in Z24<sup>−/−</sup> MSCs with siRNA</title><p>Z24<sup>−/−</sup> MSCs cultured in plates were transfected with siRNA of Sun2 gene (Ambion siRNA from Thermo Fisher; AM16708, Sun2 SiRNA/mouse) (sense sequence: GCAUCACCAAGACUCGGAATT; anti‐sense sequence: UUCCGAGUCUUGGUGAUGCTC) to repress Sun2 expression, and cells were fixed for observation 48 hr posttransfection. Cells transfected with a control SiRNA served as control cells.</p></sec><sec id=\"acel13152-sec-0029\"><label>4.13</label><title>Epigenetics assay</title><p>The heterochromatin markers histone H3K9me3 and H3K27me3 were immunostained in <italic>Z24</italic>\n<sup>−/−</sup> MSCs with and without Y‐27632 treatment (10 μM for 48 hr) or Sun2 siRNA treatment, and HGPS cells with and without Y‐27632 treatment. The anti‐H3K9me3 and H3K27me3 antibodies were from Abcam.</p></sec><sec id=\"acel13152-sec-0030\"><label>4.14</label><title>RhoA and ROCK activity assays</title><p>Relative RhoA activity was measured in WT MSCs, <italic>Z24</italic>\n<sup>−/−</sup> MSCs, Y‐27632‐treated <italic>Z24</italic>\n<sup>−/−</sup> MSCs, and Sun2 siRNA‐treated <italic>Z24</italic>\n<sup>−/−</sup> MSCs to verify the activation state of RhoA, with a RhoA G‐LISA Activation Assay kit (Cytoskeleton Inc.), according to the manufacturer's instructions. Briefly, cells were lysed and snap‐frozen in liquid nitrogen. Approximately 30–40 μg total protein was used for each sample. The active GTP‐bound form of RhoA was detected with a specific anti‐RhoA antibody. Absorbance readings were obtained by measuring optical density (<italic>OD</italic>) at 490 nm. Relative ROCK activity was measured in <italic>Z24</italic>\n<sup>−/−</sup> MSCs and Y‐27632‐treated <italic>Z24</italic>\n<sup>−/−</sup> MSCs to verify the specificity of RhoA/ROCK inhibition, with a ROCK Activity Assay kit (Cell Biolabs, <italic>San Diego</italic>, CA), according to the manufacturer's instructions. The level of ROCK <italic>activation</italic> was determined by measuring <italic>OD</italic> at 450 nm.</p></sec><sec id=\"acel13152-sec-0031\"><label>4.15</label><title>qRT‐PCR</title><p>Total RNA was obtained from muscle cells or muscle tissues using the RNeasy Mini Kit (Qiagen, Inc.). Reverse transcription was performed using an iScript cDNA Synthesis Kit (Bio‐Rad Laboratories, Inc.). The sequences of primers were given in Table <xref rid=\"acel13152-sup-0001\" ref-type=\"supplementary-material\">S1</xref> for SASP genes (IL‐1α, IL‐1β, IL‐6, TNF‐α, TNFR1, MCP1, and CxCl2), IL‐10, Klotho, PDGFRs, GAPDH (glyceraldehyde 3‐phosphate dehydrogenase), etc.. PCR reactions were performed using an iCycler thermal cycler (Bio‐Rad Laboratories, Inc.). The cycling parameters used for all primers were as follows: 95°C for 10 min; PCR, 40 cycles of 30 s at 95°C for denaturation, 1 min at 54–58°C for annealing, and 30 s at 72°C for extension. Products were separated by size and visualized on a 1.5% agarose gel stained with ethidium bromide. All data were normalized to the expression of GAPDH.</p></sec><sec id=\"acel13152-sec-0032\"><label>4.16</label><title>In vivo RhoA inhibition with Y‐27632</title><p>Systemic inhibition of RhoA/ROCK signaling was conducted by i.p. (Intraperitoneal) injection of 10 mg/kg Y‐27632 (5 mM in phosphate‐buffered saline [PBS]) into <italic>Z24</italic>\n<sup>−/−</sup> mice, starting at 10 weeks of age with PBS used as the vehicle control. The i.p. injections of Y‐27632 into <italic>Z24</italic>\n<sup>−/−</sup> mice were conducted three times a week for 12 weeks.</p></sec><sec id=\"acel13152-sec-0033\"><label>4.17</label><title>Cardiotoxin‐induced muscle injury</title><p>In order to compare the muscle regeneration potential in WT mice, <italic>Z24</italic>\n<sup>−/−</sup> mice, and <italic>Z24</italic>\n<sup>−/−</sup> mice treated with Y‐27632, cardiotoxin (Sigma) (10 μM in 40 μl of PBS) was injected intramuscularly into the GM muscles of mice (~20‐week old) to induce muscle injury. Muscle tissues were collected at 7 days after the injection of cardiotoxin to compare fibrosis formation, muscle regeneration, and number of senescent cells.</p></sec><sec id=\"acel13152-sec-0034\"><label>4.18</label><title>Histology and immunofluorescent staining</title><p>Frozen tissue sections were fixed with 10% formalin, and cultured cells were fixed with 4% paraformaldehyde. All of the primary antibodies—lamin A/C (Santa Cruz), Pax7 (DHSB), CD68 (Abcam), PDGFR‐α (Abcam), fast‐type myosin heavy chain (f‐MHC) (Abcam), Sun1 (Novus Biologicals), Sun2 (Abcam), p21 (Cell signaling), γ‐H2AX (Cell Signaling), RhoA (Santa Cruz and Abcam), H3K9me3 (Abcam), and H3K27me3 (Abcam)—were used at a 1:100 to 1:300 dilution. All slides were analyzed via fluorescence microscopy (Nikon Instruments Inc.) and photographed at 4–40 × magnification. F‐actin was stained with Alexa Fluor 488 Phalloidin or Alexa Fluor 594 Phalloidin (Thermo Fisher). G‐actin was detected with an antibody to DNase I which specifically bind to G‐actin (Abcam). The cell nuclei were stained with DAPI. Fibrosis formation in muscle tissues was visualized by Masson trichrome staining with the Trichrome Stain (Masson) Kit (Sigma‐Aldrich). Sections were incubated in Weigert's iron hematoxylin working solution for 10 min and rinsed under running water for 10 min. Slides were transferred to Biebrich scarlet‐acid fuchsin solution for 15 min before incubation in aniline blue solution for another 5 min. Slides were then rinsed, dehydrated, and mounted as earlier. The ratio of the area of fibrotic collagen (blue) to the area of normal muscle (red) was quantified to measure fibrosis formation.</p></sec><sec id=\"acel13152-sec-0035\"><label>4.19</label><title>Measurements of results and statistical analysis</title><p>Image analysis was performed using Nikon <italic>NIS‐Elements</italic> (Nikon Instruments Inc.) and Image J software (National Institutes of Health). Data from at least three samples were pooled for statistical analysis. Results are given as the mean ± standard deviation (<italic>SD</italic>). The statistical significance of any difference was calculated using Student's <italic>t</italic> test or one‐way ANOVA test. <italic>P</italic> values < .05 were considered statistically significant.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13152-sec-0037\"><title>CONFLICT OF INTEREST</title><p>Johnny Huard discloses the fact that he receives royalties from Cook Myosite, Inc. for muscle stem cell technologies.</p></sec><sec id=\"acel13152-sec-0038\"><title>AUTHOR CONTRIBUTIONS</title><p>XM, PR, and JH designed research. XM, CT, WH, CL, PC, PM, WC, KS, JG, PG, SR, EM, YC, and LZ performed the experiments. PC, PR, LN, and JC contributed drugs, protocols, and critique. XM, CT, WH, WC, JG, and LZ analyzed the data. XM, PR, LN, and JH wrote the manuscript.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13152-sup-0001\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13152-s001.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13152-sec-0036\"><title>ACKNOWLEDGMENTS</title><p>This research was supported in part by grants from the NIH (PO1AG043376 and RO1AR065445 to JH, PDR, and LJN, P01AG062412 and U19AG056278 to PDR and LJN and R01HL133254 to JPC); by funding from the Progeria Research Foundation (to JPC) and by funding from the University of Texas Health Science Center at Houston. We also thank Dr. Mary Hall and Dr. Lavanya Rajagopalan for editorial assistance while completing this manuscript.</p></ack><sec sec-type=\"data-availability\" id=\"acel13152-sec-0040\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13152-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13152-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13152-cit-0001\">\n<string-name>\n<surname>Aghajanian</surname>, <given-names>A.</given-names>\n</string-name>, <string-name>\n<surname>Wittchen</surname>, <given-names>E. S.</given-names>\n</string-name>, <string-name>\n<surname>Campbell</surname>, <given-names>S. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32627317</article-id><article-id pub-id-type=\"pmc\">PMC7431832</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13187</article-id><article-id pub-id-type=\"publisher-id\">ACEL13187</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Paper</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Aging is associated with a decline in Atg9b‐mediated autophagosome formation and appearance of enlarged mitochondria in the heart</article-title><alt-title alt-title-type=\"left-running-head\">LIANG et al.</alt-title></title-group><contrib-group><contrib id=\"acel13187-cr-0001\" contrib-type=\"author\"><name><surname>Liang</surname><given-names>Wenjing</given-names></name><xref ref-type=\"aff\" rid=\"acel13187-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13187-cr-0002\" contrib-type=\"author\"><name><surname>Moyzis</surname><given-names>Alexandra G.</given-names></name><xref ref-type=\"aff\" rid=\"acel13187-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13187-cr-0003\" contrib-type=\"author\"><name><surname>Lampert</surname><given-names>Mark A.</given-names></name><xref ref-type=\"aff\" rid=\"acel13187-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13187-cr-0004\" contrib-type=\"author\"><name><surname>Diao</surname><given-names>Rachel Y.</given-names></name><xref ref-type=\"aff\" rid=\"acel13187-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13187-cr-0005\" contrib-type=\"author\"><name><surname>Najor</surname><given-names>Rita H.</given-names></name><xref ref-type=\"aff\" rid=\"acel13187-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13187-cr-0006\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Gustafsson</surname><given-names>Åsa B.</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-6347-8210</contrib-id><xref ref-type=\"aff\" rid=\"acel13187-aff-0001\">\n<sup>1</sup>\n</xref><address><email>abgustafsson@ucsd.edu</email></address></contrib></contrib-group><aff id=\"acel13187-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Department of Pharmacology</named-content>\n<named-content content-type=\"organisation-division\">Skaggs School of Pharmacy and Pharmaceutical Sciences</named-content>\n<institution>University of California, San Diego</institution>\n<city>La Jolla</city>\n<named-content content-type=\"country-part\">California</named-content>\n<country country=\"US\">USA</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nÅsa B. Gustafsson, 9500 Gilman Drive #0751, La Jolla, CA 92093‐0751.<break/>\nEmail: <email>abgustafsson@ucsd.edu</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>06</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13187</elocation-id><history><date date-type=\"received\"><day>10</day><month>4</month><year>2020</year></date><date date-type=\"rev-recd\"><day>25</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>06</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13187.pdf\"/><abstract id=\"acel13187-abs-0001\"><title>Abstract</title><p>Advancing age is a major risk factor for developing heart disease, and the biological processes contributing to aging are currently under intense investigation. Autophagy is an important cellular quality control mechanism that is reduced in tissues with age but the molecular mechanisms underlying the age‐associated defects in autophagy remain poorly characterized. Here, we have investigated how the autophagic process is altered in aged mouse hearts. We report that autophagic activity is reduced in aged hearts due to a reduction in autophagosome formation. Gene expression profile analysis to evaluate changes in autophagy regulators uncovered a reduction in Atg9b transcript and protein levels. Atg9 proteins are critical in delivering membrane to the growing autophagosome, and siRNA knockdown of Atg9b in cells confirmed a reduction in autophagosome formation. Autophagy is also the main pathway involved in eliminating dysfunctional mitochondria via a process known as mitophagy. The E3 ubiquitin ligase Parkin plays a key role in labeling mitochondria for mitophagy. We also found increased levels of Parkin‐positive mitochondria in the aged hearts, an indication that they have been labeled for mitophagy. In contrast, Nrf1, a major transcriptional regulator of mitochondrial biogenesis, was significantly reduced in aged hearts. Additionally, our data showed reduced Drp1‐mediated mitochondrial fission and formation of enlarged mitochondria in the aged heart. Overall, our findings suggest that cardiac aging is associated with reduced autophagosome number, decreased mitochondrial turnover, and formation of megamitochondria.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13187-abs-0002\"><p>Here, we report that autophagic activity is reduced in aged hearts due to decreased expression of the autophagy‐related protein Atg9b. This protein is involved in delivering membrane to form and expand the autophagosome. Our findings also demonstrate that there is an imbalance in the labelling and degradation steps in the aged myocardium due to reduced formation of autophagosomes. The decline in mitochondrial clearance also coincides with formation of megamitochondria in aged hearts.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13187-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13187-g007.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13187-kwd-0001\">aging</kwd><kwd id=\"acel13187-kwd-0002\">Atg9</kwd><kwd id=\"acel13187-kwd-0003\">autophagy</kwd><kwd id=\"acel13187-kwd-0004\">heart</kwd><kwd id=\"acel13187-kwd-0005\">mitochondria</kwd><kwd id=\"acel13187-kwd-0006\">mitophagy</kwd><kwd id=\"acel13187-kwd-0007\">Parkin</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>NIH </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100000002</institution-id></institution-wrap></funding-source><award-id>R21AG052280</award-id><award-id>R01HL138560</award-id><award-id>R01HL132300</award-id></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>TRDRP </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100005188</institution-id></institution-wrap></funding-source><award-id>T30FT0846</award-id></award-group><award-group id=\"funding-0003\"><funding-source>National Institutes of Health Predoctoral Fellowship</funding-source><award-id>F31HL136228</award-id></award-group><award-group id=\"funding-0004\"><funding-source>UCSD Graduate Training Program in Cellular and Molecular Pharmacology</funding-source><award-id>T32GM007752</award-id></award-group><award-group id=\"funding-0005\"><funding-source>National Institutes of Health Predoctoral Fellowship</funding-source><award-id>F31HL145973</award-id></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"0\"/><page-count count=\"14\"/><word-count count=\"8086\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13187-cit-1001\">\n<string-name>\n<surname>Liang</surname>\n<given-names>W</given-names>\n</string-name>, <string-name>\n<surname>Moyzis</surname>\n<given-names>AG</given-names>\n</string-name>, <string-name>\n<surname>Lampert</surname>\n<given-names>MA</given-names>\n</string-name>, <string-name>\n<surname>Diao</surname>\n<given-names>RY</given-names>\n</string-name>, <string-name>\n<surname>Najor</surname>\n<given-names>RH</given-names>\n</string-name>, <string-name>\n<surname>Gustafsson</surname>\n<given-names>ÅB</given-names>\n</string-name>. <article-title>Aging is associated with a decline in Atg9b‐mediated autophagosome formation and appearance of enlarged mitochondria in the heart</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13187</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13187</pub-id>\n</mixed-citation>\n</p><fn-group id=\"acel13187-ntgp-0001\"><fn fn-type=\"equal\" id=\"acel13187-note-0001\"><p>Wenjing J. Liang and Alexandra G. Moyzis contributed equally to this manuscript.</p></fn></fn-group></notes></front><body id=\"acel13187-body-0001\"><sec id=\"acel13187-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Aging is associated with a gradual decline in tissue functions, which can lead to age‐related disorders. Advancing age is considered a major risk factor for heart disease, and intrinsic alterations in aging cardiac myocytes likely contribute to the underlying pathogenesis (Dai & Rabinovitch, <xref rid=\"acel13187-bib-0004\" ref-type=\"ref\">2009</xref>). Autophagy is an important cellular quality control mechanism, and defects in this process lead to reduced removal of cytotoxic protein aggregates and damaged organelles. Several studies have demonstrated that enhancing autophagy increases life span in various organisms ranging from worms to mice (Eisenberg et al., <xref rid=\"acel13187-bib-0005\" ref-type=\"ref\">2016</xref>; Fernandez et al., <xref rid=\"acel13187-bib-0007\" ref-type=\"ref\">2018</xref>; Nakamura et al., <xref rid=\"acel13187-bib-0015\" ref-type=\"ref\">2019</xref>; Pyo et al., <xref rid=\"acel13187-bib-0020\" ref-type=\"ref\">2013</xref>; Simonsen et al., <xref rid=\"acel13187-bib-0024\" ref-type=\"ref\">2008</xref>; Toth et al., <xref rid=\"acel13187-bib-0030\" ref-type=\"ref\">2008</xref>). There is also evidence that autophagy decreases with age in tissues, including the heart (Inuzuka et al., <xref rid=\"acel13187-bib-0011\" ref-type=\"ref\">2009</xref>; Nakamura et al., <xref rid=\"acel13187-bib-0015\" ref-type=\"ref\">2019</xref>; Ren et al., <xref rid=\"acel13187-bib-0022\" ref-type=\"ref\">2017</xref>; Taneike et al., <xref rid=\"acel13187-bib-0028\" ref-type=\"ref\">2010</xref>; Zhou et al., <xref rid=\"acel13187-bib-0036\" ref-type=\"ref\">2017</xref>). However, the mechanisms underlying the age‐related decline in autophagy remain unclear.</p><p>Autophagy is also the main pathway involved in eliminating mitochondria in a process known as mitophagy. This is a highly regulated process involving two coordinated steps to ensure the selective removal of dysfunctional mitochondria. The first step involves activation of the E3 ubiquitin ligase Parkin which is responsible for labeling damaged mitochondria for degradation (Narendra, Tanaka, Suen, & Youle, <xref rid=\"acel13187-bib-0016\" ref-type=\"ref\">2008</xref>). Parkin is cytosolic under basal conditions but is recruited to dysfunctional mitochondria by PINK1, where it then proceeds to ubiquitinate various proteins in the outer membrane (Narendra et al., <xref rid=\"acel13187-bib-0016\" ref-type=\"ref\">2008</xref>; Suen, Narendra, Tanaka, Manfredi, & Youle, <xref rid=\"acel13187-bib-0026\" ref-type=\"ref\">2010</xref>). The ubiquitinated proteins serve as a signal for an autophagosome to sequester the mitochondrion (Geisler et al., <xref rid=\"acel13187-bib-0008\" ref-type=\"ref\">2010</xref>). The second event in mitophagy is the concurrent formation of autophagosomes, which are responsible for engulfing and delivering the ubiquitinated mitochondria to lysosomes for degradation. The importance of Parkin in clearing damaged mitochondria in myocytes during acute stress is well established (Hoshino et al., <xref rid=\"acel13187-bib-0009\" ref-type=\"ref\">2013</xref>; Kubli, Quinsay, & Gustafsson, <xref rid=\"acel13187-bib-0013\" ref-type=\"ref\">2013</xref>). Also, overexpression of Parkin leads to increased life span in <italic>Drosophila</italic> (Rana, Rera, & Walker, <xref rid=\"acel13187-bib-0021\" ref-type=\"ref\">2013</xref>). While Parkin<sup>−/−</sup> mice accumulate abnormal mitochondria in the heart with age (Hoshino et al., <xref rid=\"acel13187-bib-0009\" ref-type=\"ref\">2013</xref>; Kubli et al., <xref rid=\"acel13187-bib-0013\" ref-type=\"ref\">2013</xref>), overexpression of Parkin preserves mitochondrial function in aging mouse hearts (Hoshino et al., <xref rid=\"acel13187-bib-0009\" ref-type=\"ref\">2013</xref>). However, cardiac Parkin overexpression or systemic deficiency has little effect on the accelerated cardiac aging phenotype in mtDNA mutator mice, suggesting a limited role for Parkin‐mediated mitophagy in this aging mouse model (Woodall et al., <xref rid=\"acel13187-bib-0033\" ref-type=\"ref\">2019</xref>). These studies indicate a role for Parkin preventing the aging process, but whether Parkin‐mediated mitophagy is altered in the aged heart is currently unclear.</p><p>To date, most mechanistic studies on autophagy and mitophagy in aging have been restricted to lower organisms and how these processes are affected at the molecular level are still lacking in mammalian systems. In this study, we demonstrate that aging is associated with decreased expression of the autophagy‐related protein Atg9b which correlates with reduced formation of autophagosomes in the aged heart. Our data also show that there is an increase in mitochondria that have been labeled for mitophagy, indicating a potential imbalance in the mitophagy process in aged hearts. Finally, we found that decreased autophagy also coincided with reduced Drp1‐mediated fission and formation of enlarged mitochondria.</p></sec><sec sec-type=\"results\" id=\"acel13187-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13187-sec-0003\"><label>2.1</label><title>Characterization of aged mice</title><p>To examine the effect of aging on the heart, we evaluated cardiac structure and function in 4 (young)‐ and 24 (aged)‐month‐old male mice. We found no significant differences in ejection fraction (EF) or fractional shortening (FS), left ventricular internal end‐diastolic and systolic dimensions (LVID;d and LVID;s) between young and old mice (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S1</xref>a). However, aging is associated with diastolic dysfunction and using the pulse Doppler wave mode to assess the E/A ratio revealed a significant decrease in E/A ratio in aged hearts (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S1</xref>b). We also found that aged mice had a significant increase in heart weight/body weight (HW/BW) and heart weight/tibia length (HW/TL) ratios compared to young mice (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S1</xref>c). Additionally, we found significantly elevated levels of β myosin heavy chain (<italic>Myh7</italic>), while transcript levels of inflammatory markers, interleukin‐6 <italic>(IL</italic>‐<italic>6)</italic> and tumor necrosis factor alpha (<italic>Tnfα</italic>), trended higher in the aged hearts (Figure <xref rid=\"acel13187-fig-0001\" ref-type=\"fig\">1d</xref>). Haematoxylin and eosin (H&E) staining showed similar structure in young and aged heart sections, while Masson's trichrome staining revealed increased perivascular, but not interstitial, fibrosis in the aged hearts (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S1</xref>e). Despite the increase in perivascular fibrosis, transforming growth factor beta (<italic>Tgfβ)</italic>, type I collagen (<italic>Col1)</italic>, and type III collagen (<italic>Col3)</italic> transcript levels in the whole heart did not increase (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S1</xref>f). Overall, these data confirm the cardiac aging phenotype characterized by diastolic dysfunction, mild cardiac hypertrophy, early stages of inflammation, and fibrosis.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13187-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Assessment of general autophagy in hearts from young and aged mice. (a) Representative Western blot and quantitation of LC3 levels from heart lysates of young and aged mice (<italic>n</italic> = 9–12). (b) Representative Western blot and quantitation of p62 levels in young and aged hearts (<italic>n</italic> = 11–12). (c) Representative images for LC3 immunostaining in heart sections (<italic>n</italic> = 3 for each). (d) Representative Western blot and quantitation of protein ubiquitination in young and aged hearts using a polyclonal ubiquitin antibody (<italic>n</italic> = 10). (e) Representative Western blot and quantitation of protein ubiquitination in young and aged hearts using a monoclonal ubiquitin antibody (<italic>n</italic> = 10–12). (f) Representative Western blot and quantitation of Parkin protein and transcript levels in young and aged hearts (<italic>n</italic> = 10–12). Scale bars are 20 μm. Data represent the mean ± SEM (*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ****<italic>p</italic> < 0.0001, ns = not significant)</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13187-g001\"/></fig></sec><sec id=\"acel13187-sec-0004\"><label>2.2</label><title>Autophagy is reduced in the aged heart</title><p>It has been previously reported that autophagy is decreased with age in various tissues. Therefore, we investigated whether autophagy was reduced in the aged heart by assessing levels of LC3 and the adaptor protein p62 by Western blot analysis. We used Gapdh as a loading control in the Western blot experiments after confirming that Gapdh protein levels were similar in young and aged heart tissues (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S2</xref>). LC3II is associated with the autophagosome membrane, and p62 is an adaptor protein that is degraded with autophagic cargo (Kabeya et al., <xref rid=\"acel13187-bib-0012\" ref-type=\"ref\">2000</xref>; Pankiv et al., <xref rid=\"acel13187-bib-0018\" ref-type=\"ref\">2007</xref>). We found that LC3II levels were significantly decreased in aged mouse hearts (Figure <xref rid=\"acel13187-fig-0001\" ref-type=\"fig\">1a</xref> + Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S3</xref>), while p62 levels were significantly increased (Figure <xref rid=\"acel13187-fig-0001\" ref-type=\"fig\">1b</xref> + Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S3</xref>). Moreover, immunohistochemical detection of LC3 in heart sections from young and aged mice confirmed the presence of more LC3‐positive puncta in young myocytes compared to aged myocytes (Figure <xref rid=\"acel13187-fig-0001\" ref-type=\"fig\">1c</xref>). Autophagy is a cellular degradation pathway involved in delivering ubiquitinated cargo to the lysosome for degradation. Therefore, we tested whether a decrease in autophagy coincided with increased levels of ubiquitinated proteins in aged hearts. Because different anti‐ubiquitin antibodies do not have equal affinities for all ubiquitin linkage types (Emmerich & Cohen, <xref rid=\"acel13187-bib-0006\" ref-type=\"ref\">2015</xref>), we used two different ubiquitin antibodies (polyclonal and monoclonal) in these experiments. We found that the levels of ubiquitinated proteins were elevated in the aged heart using either antibody (Figure <xref rid=\"acel13187-fig-0001\" ref-type=\"fig\">1d,e</xref> + Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S3</xref>). Interestingly, the polyclonal antibody detected mostly ubiquitinated proteins with heavier mass in whole heart lysates, while the monoclonal anti‐ubiquitin preferentially detected ubiquitinated proteins with lower molecular weights. We were also intrigued by the strong broadband detected above the 50 kDa marker in the ubiquitin blot in aged mice using the monoclonal antibody (Figure <xref rid=\"acel13187-fig-0001\" ref-type=\"fig\">1e</xref>) because it suggests that many of the ubiquitinated proteins in aged hearts are ~50–65 kDa in size. We performed mass spectrometry analysis of the bands and identified the most abundant proteins as ATP synthase (alpha and beta) and Hsp60, as well as pyruvate kinase and tubulins (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S4</xref> and Tables <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S1–S6</xref>). The top 100 proteins with a minimum of 40% peptide coverage identified in young (<italic>n</italic> = 3) and aged hearts (<italic>n</italic> = 3) are listed in Tables <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S1–S6</xref>. The proteasome is also responsible for protein degradation in cells and is another important cellular quality control pathway. Although this pathway can compensate for a decrease in autophagy, we found that proteasomal activities were similar in young and aged hearts (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S5</xref>). This suggests that the reduced elimination of ubiquitinated cargo is primarily due to decreased autophagic activity. The E3 ubiquitin ligase Parkin is a regulator of mitophagy, and we found that Parkin protein levels were significantly increased in the aged hearts (Figure <xref rid=\"acel13187-fig-0001\" ref-type=\"fig\">1f</xref> + Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S3</xref>). Given that transcript levels of Parkin were unaltered (Figure <xref rid=\"acel13187-fig-0001\" ref-type=\"fig\">1f</xref>), this suggests reduced degradation of Parkin.</p><p>Next, we investigated whether elevated levels of Parkin alone could contribute to the aging process. Upon evaluation of wild‐type and cardiac‐specific Parkin transgenic (TG) mice, we found elevated levels of perivascular, but not interstitial, fibrosis, in hearts overexpressing Parkin at 16 months of age (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S6</xref>a). However, we found no differences in Myh7, IL‐6, and Tnf‐α transcript levels between WT and Parkin TG hearts at this age (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S6</xref>b). Interestingly, Parkin TG mice had increased autophagic flux and lacked accumulation of ubiquitinated proteins at this age (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S6</xref>c–e). This suggests that chronically elevated levels of Parkin lead to a corresponding increase in autophagic activity to balance the labeling and degradation.</p><p>Finally, we investigated whether autophagic activity was also altered with age in liver and brain tissues. Interestingly, we found that Parkin levels and protein ubiquitination were increased in both liver and brain tissues from aged mice (Figures <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S7 and S8</xref>). However, p62 levels were only increased in liver tissue and remained unchanged in brain tissue. There were no differences in LC3I or LC3II levels between young and aged mice in either tissue. This suggests that the reduced autophagic activity is mostly restricted to the heart at 24 months of age in these mice.</p></sec><sec id=\"acel13187-sec-0005\"><label>2.3</label><title>Autophagosome formation is reduced in the aged myocardium</title><p>The decreased LC3II levels indicated a reduction in autophagosome formation in the aged heart, which would contribute to reduced autophagic activity. To assess whether formation of autophagosomes was altered in the aged hearts, we monitored levels of LC3II after injecting mice with rapamycin, an inhibitor of the mammalian target of rapamycin (mTOR). mTOR functions as a negative regulator of autophagy and activated mTOR suppresses formation of autophagosomes. We found that rapamycin treatment led to an increase in LC3II in hearts of young mice, indicating increased formation of autophagosomes (Figure <xref rid=\"acel13187-fig-0002\" ref-type=\"fig\">2a</xref>). In contrast, the rapamycin treatment failed to increase LC3II levels in the hearts of 24‐month‐old mice. To determine whether the reduced formation of autophagosomes in the aged hearts was due to increased activation of mTOR, we examined the phosphorylation status of mTOR at Ser2448 and its downstream targets, p70S6 kinase and Ulk1 as indicators of mTOR kinase activity and signaling. We found no differences in phosphorylated mTOR (phospho‐S2448) and its substrates p70S6K (phospho‐S371 and phospho‐Thr389) or in Ulk1 (phospho‐Ser757) in young and aged hearts (Figure <xref rid=\"acel13187-fig-0002\" ref-type=\"fig\">2b,c</xref>). Overall, these results suggest that increased activation of mTOR is not responsible for the reduced autophagosome formation in the aged hearts.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13187-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>Reduced formation of autophagosomes in aged mice. (a) Representative Western blot and quantitation of LC3II levels in young and aged hearts following administration of vehicle or rapamycin (<italic>n</italic> = 5–6). (b) Assessment of mTOR activity in young and aged hearts. Representative Western blots of p‐mTOR(Ser2448), mTOR, p‐pS6K(Ser371 and Thr389), p70S6K, p‐Ulk1(Ser757), and Ulk1. (c) Quantitation of protein levels (<italic>n</italic> = 10). Data are mean ± SEM (*<italic>p</italic> < 0.05, ns = not significant)</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13187-g002\"/></fig><p>To further explore the underlying mechanisms of the reduced autophagosome formation in aged hearts, we performed a gene expression profile analysis of key genes involved in autophagy. The mRNA profiles from young and aged hearts were screened using an autophagy PCR array containing key autophagy genes. Out of the 84 key genes involved in autophagy, we found that 3 Atg proteins involved in autophagosome formation were reduced in the aged hearts (Figure <xref rid=\"acel13187-fig-0003\" ref-type=\"fig\">3a,b</xref>). We confirmed that Atg9b was significantly decreased in the aged hearts both at the mRNA and protein levels compared to young hearts (Figure <xref rid=\"acel13187-fig-0003\" ref-type=\"fig\">3c,d</xref>). We were unable to confirm reduced transcript levels of Atg10 and Atg12 by traditional qPCR (Figure <xref rid=\"acel13187-fig-0003\" ref-type=\"fig\">3c</xref>). Atg9b is involved in delivering membrane to the expanding phagophore (Yamamoto et al., <xref rid=\"acel13187-bib-0035\" ref-type=\"ref\">2012</xref>). It has previously been reported that Atg9 is critical for autophagy (Yamamoto et al., <xref rid=\"acel13187-bib-0035\" ref-type=\"ref\">2012</xref>) and knockdown of <italic>ATG9B</italic> in HeLa cells using siRNA confirmed that reduced ATG9B protein leads to reduced autophagy (Figure <xref rid=\"acel13187-fig-0003\" ref-type=\"fig\">3e</xref>). Overall, these data suggest that the compromised autophagy in aged hearts is due to decreased levels of proteins involved in autophagosome formation/expansion.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13187-fig-0003\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>Atg9b transcript and protein levels are reduced in aged hearts. (a) Analysis of autophagy gene expression in young and aged hearts using the RT<sup>2</sup> Profiler PCR autophagy gene array (<italic>n</italic> = 5). (b) Scatter plot for RT<sup>2</sup> array analysis of genes with differential expression in aged compared to young mice. (c) Analysis of Atg9b, Atg10, and Atg12 mRNA levels by qPCR in young and aged hearts (<italic>n</italic> = 10). (d) Representative Western blot and quantitation of Atg9b protein levels in young and aged hearts (<italic>n</italic> = 10). (e) Representative Western blot and quantitation of ATG9B and LC3 levels following knockdown of <italic>ATG9B</italic> using siRNA in HeLa cells (<italic>n</italic> = 8). Data are shown as mean ± SEM (*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001, ns = not significant)</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13187-g003\"/></fig></sec><sec id=\"acel13187-sec-0006\"><label>2.4</label><title>Reduced autophagy coincides with an increase in mitochondria that have been labeled for mitophagy</title><p>Given that aged mice had a decrease in general autophagy and increased Parkin levels in aged hearts, we investigated whether the aged mitochondria had been labeled for mitophagy. We found significant increases in Parkin and p62 levels in the mitochondrial fraction from aged hearts (Figure <xref rid=\"acel13187-fig-0004\" ref-type=\"fig\">4a</xref>). Western blotting using a polyclonal ubiquitin antibody showed a small but significant increase in ubiquitinated mitochondrial proteins (Figure <xref rid=\"acel13187-fig-0004\" ref-type=\"fig\">4b</xref>). Ubiquitination of proteins in the mitochondrial fraction trended higher but did not reach significance when using a monoclonal anti‐ubiquitin (Figure <xref rid=\"acel13187-sup-0001\" ref-type=\"supplementary-material\">S9</xref>). The elevated Parkin, p62, and ubiquitin levels indicate that there is an increase in mitochondria that have been labeled for degradation. Consistent with reduced formation of autophagosomes, LC3II levels did not increase in the aged mitochondrial fraction (Figure <xref rid=\"acel13187-fig-0004\" ref-type=\"fig\">4a</xref>). These findings suggest that the rate of mitochondrial turnover is reduced due to decreased autophagosome formation.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13187-fig-0004\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>Reduced mitochondrial turnover in aged hearts. Representative Western blots and quantitation of (a) Parkin, LC3, p62, and (b) ubiquitin levels in the mitochondrial fraction from young and aged hearts (<italic>n</italic> = 9). Ubiquitin was detected using a polyclonal antibody. (c) Mitochondrial DNA (mtDNA) content in young and aged hearts (<italic>n</italic> = 11). (d) Representative Western blots and quantitation of proteins involved in mitochondrial oxidative phosphorylation: COX I = complex I subunit NDUFB8; COX II = complex II subunit 30 kDa; COX III = complex III subunit core 2; COX IV = complex IV subunit II; ATP synthase = ATP synthase subunit α (<italic>n</italic> = 9–10). (e) Representative Western blots and quantitation of mitochondrial proteins Tim23 and Tom20 (<italic>n</italic> = 10). (f) Analysis of PGC‐1α, Tfam, and Nrf1 mRNA levels by qPCR in young and aged hearts (<italic>n</italic> = 8–10). Data are shown as mean ± SEM (*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001, ****<italic>p</italic> < 0.0001 ns = not significant)</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13187-g004\"/></fig><p>Therefore, we investigated whether the reduced autophagy had an effect on mitochondrial content in the aged hearts. Changes in mitochondrial DNA (mtDNA) content, measured as mitochondrial genome‐to‐nuclear genome ratio (mtDNA/nDNA) using real‐time quantitative PCR, often correlates with changes in mitochondrial mass. Interestingly, we found that mtDNA content was significantly reduced in the aged hearts (Figure <xref rid=\"acel13187-fig-0004\" ref-type=\"fig\">4c</xref>), suggesting a potential reduction in mitochondrial number. However, Western blotting for various mitochondrial proteins involved in oxidative phosphorylation (ATP synthase, subunits in complexes IV, III, II, and I) and protein import (Tom20, Tim23), showed similar levels of these proteins in young and aged hearts (Figure <xref rid=\"acel13187-fig-0004\" ref-type=\"fig\">4d,e</xref>). This suggests that there is no change in mitochondrial content but that the mitochondrial genome is unstable in the aged hearts. To ensure a stable mitochondrial mass, mitophagy is balanced by mitochondrial biogenesis to replace degraded mitochondria. Pgc‐1α, Tfam, and Nrf1 are regulators of mitochondrial biogenesis (Li, Hou, & Hao, <xref rid=\"acel13187-bib-0014\" ref-type=\"ref\">2017</xref>), and we found that Pgc‐1α and Tfam transcript levels were similar in young and aged hearts (Figure <xref rid=\"acel13187-fig-0004\" ref-type=\"fig\">4f</xref>). In contrast, mRNA levels of Nrf1, a major transcriptional regulator of mitochondrial biogenesis, were significantly reduced in the aged hearts. A simultaneous decrease in mitophagy and mitochondrial biogenesis prevents a change in overall mitochondrial content.</p><p>To further assess changes in the myocardium at the ultrastructural level, we evaluated heart sections prepared from young and aged mice using transmission electron microscopy (TEM). We focused on identifying changes in aged myocytes since they are postmitotic and cannot be replaced when lost. Interestingly, we found that many of the mitochondria were enlarged in the aged myocytes and that the average volume of mitochondria was significantly increased compared to young myocytes (Figure <xref rid=\"acel13187-fig-0005\" ref-type=\"fig\">5a,b</xref>). Since we observed increased Parkin at the mitochondria in the aged hearts when analyzing a mix of normal and enlarged mitochondria, we wanted to determine whether there was a difference in the labeling of normal/small versus large mitochondria in the aged hearts. Using differential centrifugation, we separated the enlarged and small/normal mitochondria from the aged hearts and then analyzed the protein content in two different mitochondrial fractions by Western blotting. Analysis of mitophagy markers showed that the small mitochondria contained increased levels of Parkin, LC3II, and ubiquitination when compared to enlarged mitochondria (Figure <xref rid=\"acel13187-fig-0005\" ref-type=\"fig\">5c,d</xref>), suggesting that smaller fragmented mitochondria are selectively targeted for degradation. Levels of other mitochondrial proteins, such as Tom20 and Tim23, were found to be similar in enlarged and small mitochondria (Figure <xref rid=\"acel13187-fig-0005\" ref-type=\"fig\">5c,d</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13187-fig-0005\" orientation=\"portrait\" position=\"float\"><label>FIGURE 5</label><caption><p>Megamitochondria are present in aged hearts. (a) Representative transmission electron micrographs of young and aged heart tissues reveal the presence of enlarged mitochondria in the hearts of aged mice. Scale bars are 500 nm. (b) Analysis of mean mitochondrial area (μm<sup>2</sup>) in young and aged myocytes. One hundred mitochondria per heart were scored (<italic>n</italic> = 3). Representative Western blots and quantitation of (c) Parkin, LC3II, and (d) ubiquitin levels in large and small mitochondria from aged hearts separated by differential centrifugation (<italic>n</italic> = 5). A monoclonal antibody was used to detect ubiquitin levels. (*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ****<italic>p</italic> < 0.0001).</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13187-g005\"/></fig></sec><sec id=\"acel13187-sec-0007\"><label>2.5</label><title>Mitochondrial fission is reduced in the aged hearts</title><p>A change in mitochondria morphology is a highly regulated process, and the balance between fission and fusion dictates the overall morphology of the mitochondria. Based on the presence of enlarged mitochondria in the aged hearts, we examined whether there were differences in key regulators of mitochondrial fission and fusion. Mitofusin 1 and 2 (Mfn1/2) are involved in fusion of the outer mitochondrial membrane, while Opa1 regulates fusion of the inner membranes. However, we found similar levels of Mfn1, Mfn2, and Opa1 in young and aged hearts (Figure <xref rid=\"acel13187-fig-0006\" ref-type=\"fig\">6a</xref>). Drp1 and Fis1 regulate mitochondrial fission, and we found a significant decrease in Drp1 in aged hearts (Figure <xref rid=\"acel13187-fig-0006\" ref-type=\"fig\">6b</xref>). Fis1 levels were unchanged. Drp1 activity is regulated by phosphorylation on at least two distinct residues. Phosphorylation at Ser637 inhibits Drp1 activity, while phosphorylation at Ser616 activates Drp1. Interestingly, we found that the phosphorylation status of Drp1 was also altered in the aged hearts. While Drp1 was dephosphorylated at Ser‐637 in aged hearts, there was no increase in the phosphorylation of Ser616 (Figure <xref rid=\"acel13187-fig-0006\" ref-type=\"fig\">6c</xref>). Overall, these results suggest that a decrease in Drp1 levels combined with a potential imbalance in the phosphorylation status of Drp1 prevents its activation and shifts the mitochondrial morphology toward a fused state.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13187-fig-0006\" orientation=\"portrait\" position=\"float\"><label>FIGURE 6</label><caption><p>Mitochondrial fission is reduced in aged hearts. (a) Representative Western blots and quantitation of mitochondrial fusion proteins Mfn1, Mfn2, and Opa1 in whole heart lysates from young and aged mice (<italic>n</italic> = 5–10). (b) Representative Western blots and quantitation of mitochondrial fission proteins Drp1 and Fis1 in whole heart lysates from young and aged mice (<italic>n</italic> = 5–10). (c) Representative Western blots and quantitation of the phosphorylation status of Drp1 at Ser637 and Ser616 in young and aged hearts (<italic>n</italic> = 9–10). Data are mean ± SEM (*<italic>p</italic> < 0.05, ns = not significant)</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13187-g006\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"acel13187-sec-0008\"><label>3</label><title>DISCUSSION</title><p>The molecular mechanisms underlying the defective autophagy with age remain poorly characterized, especially in the heart. The findings in this study provide important new insights into the mechanism underlying the insufficient autophagy in aged hearts and the potential downstream consequences on mitochondrial turnover and morphology. Our findings suggest that the suppressed autophagic activity is, at least in part, due to decreased levels of key autophagy‐related proteins such as Atg9b, which leads to reduced ability to form autophagosomes. We also discovered a concurrent accumulation of the E3 ubiquitin ligase Parkin and ubiquitinated proteins in the aged heart. Finally, our data demonstrate reduced Drp‐1‐mediated fission and formation of enlarged mitochondria in the aged heart. Overall, our findings suggest that there is an imbalance in the labeling and degradation steps in the aged myocardium due to reduced formation of autophagosomes.</p><p>In our screen for changes in autophagy regulators, we discovered that Atg9b transcript and protein levels were significantly reduced in the aged heart. Atg9 is a transmembrane protein and is required for autophagy. Atg9‐positive vesicles supply the growing autophagosome with membrane and other required components (Imai et al., <xref rid=\"acel13187-bib-0010\" ref-type=\"ref\">2016</xref>; Yamamoto et al., <xref rid=\"acel13187-bib-0035\" ref-type=\"ref\">2012</xref>). There are two different isoforms of Atg9 in vertebrates: Atg9a and Atg9b. The differences between these two distinct isoforms are currently unclear, but there is evidence that they have different tissue‐specific functions. For instance, Atg9b has been linked to various pathologies, including hepatocellular carcinoma (HCC), and a combination of suppressed Atg9b expression and decreased autophagy correlates with poor prognosis for patients with HCC (Wang, Tan, Li, & Feng, <xref rid=\"acel13187-bib-0032\" ref-type=\"ref\">2017</xref>). A genetic screen in flies for autophagy proteins involved in regulating health and life spans revealed that RNAi knockdown of Atg9 led to reduced survival (Xu et al., <xref rid=\"acel13187-bib-0034\" ref-type=\"ref\">2019</xref>). Specific knockdown of Atg9 in fly hearts led to accelerated age‐dependent loss of cardiac function and development of hypertrophy (Xu et al., <xref rid=\"acel13187-bib-0034\" ref-type=\"ref\">2019</xref>), confirming the importance of Atg9 in the heart and preventing the aging process in flies.</p><p>Previous studies have demonstrated that reduced autophagy leads to accumulation of cargo, which includes protein aggregates and dysfunctional mitochondria (Pattison, Osinska, & Robbins, <xref rid=\"acel13187-bib-0019\" ref-type=\"ref\">2011</xref>; Pyo et al., <xref rid=\"acel13187-bib-0020\" ref-type=\"ref\">2013</xref>). Consistent with this, we observed an increase in the levels of ubiquitinated proteins in the aged hearts, which is likely due to their reduced clearance via autophagy as proteasomal activity was unaltered. Interestingly, we also found increased levels of Parkin protein, but not transcripts, in aged hearts indicating that the increased Parkin protein in the aged hearts might be due to reduced degradation. Our data also suggest that elevated levels of Parkin might not be beneficial unless there is a corresponding increase in autophagy to clear ubiquitinated substrates. Despite enhanced autophagic activity in Parkin TG hearts, there was still increased perivascular fibrosis in these hearts at 16 months of age. Hoshino et al. previously reported that transgenic mice with overexpression of Parkin in the heart lead to preserved mitochondrial function in the aged heart (Hoshino et al., <xref rid=\"acel13187-bib-0009\" ref-type=\"ref\">2013</xref>). The underlying reasons for these different findings are likely due to the different genetic backgrounds of the transgenic mice (C57BL/6 vs. C57BL/6J) and the level of Parkin overexpression. The mice used in our study have much higher levels of Parkin, thus creating a greater imbalance between labeling of cargo and degradation. This could potentially overwhelm both the UPS and the autophagy degradation pathways in some cells in the aging heart. Importantly, our findings demonstrate that without a corresponding increase in autophagosome formation, elevating Parkin levels alone will not be beneficial and could potentially accelerate the aging process when the imbalance (labeling vs. degradation) reaches a certain threshold.</p><p>A decline in mitochondrial function in myocytes is also thought to be a contributor to cardiac aging (Dai & Rabinovitch, <xref rid=\"acel13187-bib-0004\" ref-type=\"ref\">2009</xref>). Here, we found that reduced autophagy coincided with formation of elongated mitochondria in cells. The presence of enlarged mitochondria has previously been noted in various tissues with age, including cardiac myocytes (Coleman, Silbermann, Gershon, & Reznick, <xref rid=\"acel13187-bib-0003\" ref-type=\"ref\">1987</xref>; Tandler, Dunlap, Hoppel, & Hassan, <xref rid=\"acel13187-bib-0027\" ref-type=\"ref\">2002</xref>), skeletal muscle (Beregi & Regius, <xref rid=\"acel13187-bib-0001\" ref-type=\"ref\">1987</xref>), and neurons (Vanneste & van den Bosch de Aguilar, <xref rid=\"acel13187-bib-0031\" ref-type=\"ref\">1981</xref>). It remains unknown whether these enlarged mitochondria are beneficial for the heart or whether they contribute to the development of cardiac aging and disease. It is possible that the enlarged mitochondria are formed as an adaptive response to the reduced autophagy in the aged hearts, but this still needs to be investigated in more detail. A recent study reported that flies with heart‐specific Atg9 knockdown had reduced autophagy, which led to formation of elongated mitochondria with age in the heart (Xu et al., <xref rid=\"acel13187-bib-0034\" ref-type=\"ref\">2019</xref>), confirming a link between reduced autophagy and increased mitochondrial fusion. Fusion between damaged and healthy mitochondria leads to mixing of damaged components and dilution of the damage (Chen et al., <xref rid=\"acel13187-bib-0002\" ref-type=\"ref\">2010</xref>). Under normal conditions, damaged mitochondria undergo asymmetrical fission where the dysfunctional mitochondrial fragments are then eliminated by mitophagy. However, if these mitochondria fail to be eliminated by autophagosomes, then they can fuse with healthy mitochondria instead (Song, Mihara, Chen, Scorrano, & Dorn, <xref rid=\"acel13187-bib-0025\" ref-type=\"ref\">2015</xref>). Although the increased mitochondrial fusion might function to dilute mitochondrial damage, eventually the damage will lead to excessive contamination of the mitochondrial pool. As aging progresses, an increasing proportion of mitochondria will reach a threshold for damage where they will no longer function properly. Thus, it will be important for future studies to dissect the relationship between reduced autophagy and formation of enlarged mitochondria in the aging heart.</p><p>A limitation of the current study is that we only assessed autophagy markers and mitochondrial proteins in the intact heart. The heart is composed of many cell types, including cardiac myocytes, fibroblasts, endothelial cells, and perivascular cells. The immunostaining of heart sections confirmed a reduced number of for LC3‐positive autophagosomes in aged cardiac myocytes. Similarly, TEM analysis confirmed changes in mitochondrial morphology in aged myocytes. Whether autophagic activity and/or mitochondrial function are altered in non‐myocyte cells still needs to be investigated. Myocytes occupy ~70%–85% of the volume of the mammalian heart (Zhou & Pu, <xref rid=\"acel13187-bib-0037\" ref-type=\"ref\">2016</xref>), but it is clear that the non‐myocytes are also important contributors to myocyte contraction and cardiac homeostasis. Thus, it is likely that changes in autophagy and mitochondrial function in both myocytes and non‐myocytes contribute to the cardiac aging process. Future studies need to focus on examining the effect of aging on autophagy and mitochondria in the various cell types in the heart.</p><p>In summary, our study has uncovered that the formation of autophagosomes is reduced in aged hearts, which results in an imbalance between labeling and degradation of cargo such as mitochondria. The reduction in autophagosome formation is, in part, due to decreased levels of the autophagy‐related protein Atg9b. Thus, our findings highlight the potential feasibility of targeting specific autophagy regulators (i.e., Atg9b levels), directly or indirectly, to correct the defect and consequently restore normal autophagic activity in aging tissues. Future studies need to focus on why Atg9b is reduced with age and whether this can be prevented or restored.</p></sec><sec id=\"acel13187-sec-0009\"><label>4</label><title>EXPERIMENTAL PROCEDURES</title><sec id=\"acel13187-sec-0010\"><label>4.1</label><title>Animals</title><p>All animal experiments were performed in accordance with the National Institutes of Health Guidelines on the Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee of the University of California, San Diego. Four‐month‐old (young) and 24‐month‐old (aged) male C57BL/6 mice (a total of 60 in each group) were obtained from the National Institute of Health (NIH) Institute of Aging colony (Charles River). The cardiomyocyte‐specific Parkin transgenic (TG) mice were generated on a C57BL/6 background and have been previously described (Woodall et al., <xref rid=\"acel13187-bib-0033\" ref-type=\"ref\">2019</xref>). Mice were housed under standard conditions and given free access to chow and water with a 12/12‐hr dark–light cycle. Echocardiography was performed on young and aged mice using a Vevo770 In Vivo Micro‐Imaging System with an RMV707B 15–45 MHz imaging transducer (VisualSonics Inc.) as previously described (Thomas et al., <xref rid=\"acel13187-bib-0029\" ref-type=\"ref\">2013</xref>). M‐mode, B‐mode, and pulsed wave Doppler views were acquired. Mice were kept on a recirculating water warming pad and maintained under light anesthesia (0.5%–1% isoflurane, 98%–99.5% O<sub>2</sub>), while measurements were obtained. The VisualSonics software was used for analysis and quantification. Tissues were collected from mice following anesthesia with pentobarbital (100 mg/kg) and exsanguination. For the in vivo rapamycin autophagy experiments, 3 mg/kg rapamycin (LC Laboratories) was administered to young and aged mice by intraperitoneal injection, and then, a second dose was administered 12 hr later. The control mice were injected with 10% DMSO in saline as the vehicle. Hearts were harvested 24 hr following the initial injection. After harvest, the atria were removed and the ventricles were rinsed in ice‐cold sterile PBS to remove remaining blood. The ventricles were snap‐frozen at −80°C and saved for qPCR analysis and Western blotting.</p></sec><sec id=\"acel13187-sec-0011\"><label>4.2</label><title>Histology and transmission electron microscopy</title><p>Hearts were arrested in diastole with 200 mM KCl, fixed in 10% neutral buffered formalin for 24 hr, and then dehydrated in 70% alcohol for 24 hr before being processed in a tissue processor (Thermo Scientific STP 120). Hearts were embedded in paraffin and cut into 6‐μm sections using a microtome (Leica Biosystems). Deparaffinized and rehydrated sections were stained with haematoxylin and eosin (H&E) or Masson's trichrome (MilliporeSigma). Alternatively, deparaffinized and rehydrated heart sections were subjected to antigen retrieval in sodium citrate buffer (10 mM sodium citrate, 0.05% Tween‐20, pH 6.6), blocked with normal goat serum, incubated with anti‐LC3 (Cell Signaling, #4108) overnight at 4°C, followed by incubation with Alexa Fluor 488 secondary antibodies (Life Technologies) for 90 min at room temperature. The sections were mounted with VECTASHIELD HardSet Mounting Media with DAPI (Vector Laboratories). For each experiment, all images were collected with the same exposure time using a Nikon Eclipse microscope equipped with DS‐Fi3 camera (for color images) or a DS‐Qi2 (for fluorescence images). The Masson's trichrome blue stain and total tissue area were quantified in ImageJ. Percent positive signal was determined for each image (<italic>n</italic> = 4–6 images per heart using the 20× objective), and mean % positive signal was calculated per heart.</p><p>Transmission electron microscopy was performed on heart sections from 3 young and 3 aged mice as previously described (Orogo et al., <xref rid=\"acel13187-bib-0017\" ref-type=\"ref\">2015</xref>). Hearts were subjected to fixation with 2.5% glutaraldehyde in 0.1 M cacodylate buffer, followed by post‐fixation in 1% osmium tetroxide. Hearts were then treated with 0.5% tannic acid and 1% sodium sulfate and cleared in 2‐hydroxypropyl methacrylate. Finally, hearts were embedded in LX112 (Ladd Research). A Philips CM100 electron microscope (FEI) was used to examine sections after they were mounted on copper slot grids coated with Parlodion and stained with uranyl acetate and lead citrate. Mean mitochondrial area (μm<sup>2</sup>) was quantified from measurements of 100 mitochondria per heart.</p></sec><sec id=\"acel13187-sec-0012\"><label>4.3</label><title>Real‐time quantitative PCR</title><p>For gene expression assays, the RNeasy Fibrous Tissue Mini Kit (Qiagen) was used to extract RNA from tissue. The QuantiTect Reverse Transcription Kit (Qiagen) or RT<sup>2</sup> First‐Strand Kit (Qiagen) was used for the synthesis of cDNA. TaqMan primers were obtained from Life Technologies/Thermo Fisher Scientific for <italic>Park2</italic>, <italic>Myh7</italic>,<italic> Pgc</italic>‐<italic>1α</italic>,<italic> IL</italic>‐<italic>6</italic>,<italic> Tnfα</italic>,<italic> Atg9b</italic>,<italic> Atg10</italic>,<italic> Atg12</italic>,<italic> Tfam</italic>,<italic> Nrf1</italic>,<italic> Tgfβ</italic>,<italic> Collagen I</italic>,<italic> Collagen III</italic>, and <italic>Rn18s</italic>. The TaqMan Universal Master Mix II was purchased from Applied Biosystems/Life Technologies. Autophagy RT2 Profile PCR Arrays were obtained from Qiagen. A CFX96 Real‐Time PCR Detection System (Bio‐Rad) was used to perform qPCR. To calculate fold change in gene expression, relative amounts of mRNA were normalized to <italic>Rn18s</italic> and the 2<sup>(−ΔΔCt)</sup> method was employed. To assess mitochondrial DNA copy number, genomic DNA was extracted from young and aged hearts using the GenElute Mammalian Genomic DNA Miniprep Kit (Sigma) and PCR‐amplified with TaqMan Universal Master Mix II. 18S rRNA was used as a control for nuclear DNA content, and D‐loop was used for mtDNA quantitation as previously described (Woodall et al., <xref rid=\"acel13187-bib-0033\" ref-type=\"ref\">2019</xref>).</p></sec><sec id=\"acel13187-sec-0013\"><label>4.4</label><title>Western blot</title><p>Ventricles were minced and then homogenized in lysis buffer composed of 50 mM Tris‐HCl, 150 mM NaCl, 1 mM EGTA, 1 mM EDTA, complete protease inhibitor cocktail (Roche), PhosSTOP (Roche), and n‐ethylmaleimide (Sigma). After the addition of Triton X‐100 (1% final concentration), the homogenates were incubated on ice for 45 min and then cleared by centrifugation at 20,000 <italic>g</italic> for 20 min. To isolate mitochondrial fractions, ventricles were minced in buffer containing 250 mM sucrose, 5 mM KH<sub>2</sub>PO<sub>4</sub>, 2 mM MgCl<sub>2</sub>, 10 mM MOPS, pH 7.4, 1 mM EGTA, 0.1% fatty acid‐free BSA, protease inhibitor cocktail (Roche), PhosSTOP (Roche), and n‐ethylmaleimide (Sigma). The tissue was briefly homogenized by polytron followed by 3–4 strokes using the Potter–Elvehjem Teflon tissue grinder. Unbroken cells, debris, and nuclei were removed by centrifugation at 600 <italic>g</italic> for 3 × 5 min at 4°C. To obtain the mitochondrial fraction, the supernatant was centrifuged at 6,000 <italic>g</italic> for 10 min at 4°C. For separation of large vs. small mitochondria in aged hearts, large mitochondria were collected by centrifugation at 2,000 <italic>g</italic>, while the remaining small/normal mitochondria were spun down at 6,000 <italic>g</italic>. The final mitochondrial pellets were resuspended in 50 mM Tris‐HCl, 150 mM NaCl, 1 mM EGTA, 1 mM EDTA, 1% Triton X‐100, protease inhibitor cocktail (Roche), PhosSTOP (Roche), and n‐ethylmaleimide (Sigma). All protein concentrations were determined by Bradford assay.</p><p>The following antibodies were purchased to probe the membranes: Actin (GeneTex, GTX109638), Atg9b (Abcam, ab117591 or Novus NBP1‐77169), Drp1 (BD Biosciences, #611113), Fis1, (Abcam, ab96764), GAPDH (GeneTex, GTX627408), LC3 (Cell Signaling, #4108), Mfn1 (Santa Cruz Biotechnology, sc‐50330), Mfn2 (MilliporeSigma, M6319), MitoProfile Total OXPHOS Rodent WB Antibody Cocktail (Abcam/MitoSciences, MS604), mTOR (Cell Signaling, #2983), Opa1 (BD Biosciences, #612607), p62 (Abcam, ab56416), p70S6 Kinase (Cell Signaling, #49D7), Parkin (Prk8) Monoclonal antibody (Cell Signaling, #4211), Parkin Polyclonal antibody (Cell Signaling, #2132S), Phospho‐Drp1 S616 (Cell Signaling, #3455), Phospho‐Drp1 S637 (Cell signaling, #6319), Phospho‐mTOR Ser2448 (Cell Signaling, #5536), Phospho‐p70 S6 Kinase Ser371 (Cell Signaling, #9208), Phospho‐p70 S6 Kinase Thr389 (Cell Signaling, #9234), Phospho‐Ulk1 Ser757 (Cell Signaling, #6888), Tim23 (BD Biosciences, #611222), Tom20 (Santa Cruz, sc‐11415), Tubulin (MilliporeSigma, T6074), Ubiquitin Monoclonal antibody (Santa Cruz Biotechnology, sc‐8017), Ubiquitin Polyclonal antibody (Cell Signaling, #3933), and Ulk1 (Cell Signaling, #8054). Blots were developed with the SuperSignal West Dura Extended Duration Substrate (Thermo Fisher, #34076) and images captured using a ChemiDoc XRS+ System (Bio‐Rad). To quantify protein bands, grayscale images of blots were either analyzed using Image‐Lab software (Bio‐Rad) or imported into ImageJ (NIH). A frame was drawn around each band in the same row to select the region of interest. The same frame was used for each protein band across one row. The pixel density for each band was normalized to the pixel density of the corresponding loading control.</p></sec><sec id=\"acel13187-sec-0014\"><label>4.5</label><title>Proteasomal activity assay</title><p>Ventricles were homogenized by polytron in assay buffer containing 50 mM HEPES, 10 mM NaCl, 1.5 mM MgCl<sub>2</sub>, 1 mM EDTA, 1 mM EGTA, 250 mM sucrose, 2 mM ATP, and 5 mM DTT, pH 7.8. The homogenates were centrifuged at 16,000 <italic>g</italic> for 10 min, and the resulting supernatant was collected and used to assay proteasomal activities. Ten μg of protein/well was added to a clear‐bottomed/black‐walled 96‐well plate. One hundred ninety‐five μl assay buffer and 5 μl of fluorescent substrate (chymotrypsin‐like, trypsin‐like, or caspase‐like, Enzo) were also added to each well. Plates were incubated at 37°C for 60 min, and then, fluorescence was measured at A<sub>360</sub>ex/A<sub>460</sub>em on a fluorescent plate reader. Each sample was measured in triplicate.</p></sec><sec id=\"acel13187-sec-0015\"><label>4.6</label><title>Cell culture and siRNA knockdown experiments</title><p>HeLa cells were cultured in media consisting of DMEM (Life Technologies) supplemented with 10% fetal bovine serum (Life Technologies), 100 U/ml penicillin (Gemini), and 100 μg/ml streptomycin (Gemini) and cultured at 37°C in a 5% CO<sub>2</sub> atmosphere. ATG9B knockdown was performed by transfecting HeLa cells with 100 nM MISSION small interfering RNA (siRNA) Universal Negative Control #1 (Sigma, SIC001) or ATG9B siRNA (Sigma, SASI_Hs02_00368624) using Lipofectamine RNAiMax Transfection Reagent (Invitrogen) according to the manufacturer's instructions. After 48 hr, cells were harvested for Western blot analysis.</p></sec><sec id=\"acel13187-sec-0016\"><label>4.7</label><title>Proteomics analysis</title><p>The proteomics analysis was done in collaboration with the Biomolecular/Proteomics Mass Spectrometry Facility at UCSD as described (Shevchenko, Wilm, Vorm, & Mann, <xref rid=\"acel13187-bib-0023\" ref-type=\"ref\">1996</xref>). Briefly, proteins were subjected to in‐gel tryptic digestion and the peptides were analyzed by ultra‐high‐pressure liquid chromatography (UPLC) coupled with tandem mass spectroscopy (LC‐MS/MS) using nanospray ionization. The nanospray ionization experiments were performed using an Orbitrap fusion Lumos hybrid mass spectrometer (Thermo) interfaced with nanoscale reversed‐phase UPLC (Thermo Dionex UltiMate™ 3000 RSLC nano System). Protein identification and label‐free quantification were carried out using PEAKS Studio 8.5 (Bioinformatics Solutions Inc.)</p></sec><sec id=\"acel13187-sec-0017\"><label>4.8</label><title>Statistical analyses</title><p>Data are expressed as mean ± standard error of mean (SEM). Because the two groups (young and aged) in this study were all males from same genetic background and environment, we used Student's <italic>t</italic> test or Mann–Whitney <italic>U</italic> test to evaluate differences between the means (GraphPad Software, Inc., USA). Data that passed the normality test were compared using an unpaired Student's <italic>t</italic> test. A nonparametric Mann–Whitney <italic>U</italic> test was used for data that were not normally distributed. A <italic>p</italic>‐value < 0.05 was considered significant.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13187-sec-0019\"><title>CONFLICT OF INTEREST</title><p>Authors declare that there is no conflict of interest in this project.</p></sec><sec id=\"acel13187-sec-0020\"><title>AUTHORS' CONTRIBUTION</title><p>ÅBG, WJL, and AGM designed the study, analyzed the experiments, and wrote the paper. WJL and AGM designed, performed, and analyzed the majority of the experiments. MAL performed the echocardiography on the mice. RYD and RHN assisted with Western blotting and qPCR experiments. All authors reviewed the results and approved the manuscript.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13187-sup-0001\"><caption><p>Appendix S1</p></caption><media xlink:href=\"ACEL-19-e13187-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13187-sec-0018\"><title>ACKNOWLEDGMENTS</title><p>ÅBG is supported by NIH grants R21AG052280, R01HL138560, and R01HL132300. WJL is supported by TRDRP grant T30FT0846. AGM is supported by the National Institutes of Health Predoctoral Fellowship F31HL136228. MAL is supported by UCSD Graduate Training Program in Cellular and Molecular Pharmacology grant T32GM007752 and National Institutes of Health Predoctoral Fellowship F31HL145973.</p></ack><sec sec-type=\"data-availability\" id=\"acel13187-sec-0022\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13187-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13187-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13187-cit-0001\">\n<string-name>\n<surname>Beregi</surname>, <given-names>E.</given-names>\n</string-name>, & <string-name>\n<surname>Regius</surname>, <given-names>O.</given-names>\n</string-name> (<year>1987</year>). <article-title>Comparative morphological study of age related mitochondrial changes of the lymphocytes and skeletal muscle cells</article-title>. <source xml:lang=\"en\">Acta Morphologica Hungarica</source>, <volume>35</volume>(<issue>3–4</issue>), <fpage>219</fpage>–<lpage>224</lpage>.<pub-id pub-id-type=\"pmid\">3137786</pub-id></mixed-citation></ref><ref id=\"acel13187-bib-0002\"><mixed-citation publication-type=\"journal\" id=\"acel13187-cit-0002\">\n<string-name>\n<surname>Chen</surname>, <given-names>H.</given-names>\n</string-name>, <string-name>\n<surname>Vermulst</surname>, <given-names>M.</given-names>\n</string-name>, <string-name>\n<surname>Wang</surname>, <given-names>Y. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32700357</article-id><article-id pub-id-type=\"pmc\">PMC7431833</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13194</article-id><article-id pub-id-type=\"publisher-id\">ACEL13194</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>RTN4B‐mediated suppression of Sirtuin 2 activity ameliorates β‐amyloid pathology and cognitive impairment in Alzheimer's disease mouse model</article-title><alt-title alt-title-type=\"left-running-head\">WANG et al.</alt-title></title-group><contrib-group><contrib id=\"acel13194-cr-0001\" contrib-type=\"author\"><name><surname>Wang</surname><given-names>Yan</given-names></name><address><email>jiaodawy@stu.xjtu.edu.cn</email></address><xref ref-type=\"aff\" rid=\"acel13194-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13194-cr-0002\" contrib-type=\"author\"><name><surname>Yang</surname><given-names>Jing‐Qi</given-names></name><address><email>57411531@qq.com</email></address><xref ref-type=\"aff\" rid=\"acel13194-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13194-cr-0003\" contrib-type=\"author\"><name><surname>Hong</surname><given-names>Ting-Ting</given-names></name><address><email>347056464@qq.com</email></address><xref ref-type=\"aff\" rid=\"acel13194-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13194-cr-0004\" contrib-type=\"author\"><name><surname>Sun</surname><given-names>Yuan‐Hong</given-names></name><address><email>704868334@qq.com</email></address><xref ref-type=\"aff\" rid=\"acel13194-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13194-cr-0005\" contrib-type=\"author\"><name><surname>Huang</surname><given-names>Hai‐Li</given-names></name><address><email>793869691@qq.com</email></address><xref ref-type=\"aff\" rid=\"acel13194-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13194-cr-0006\" contrib-type=\"author\"><name><surname>Chen</surname><given-names>Feng</given-names></name><address><email>1209253646@qq.com</email></address><xref ref-type=\"aff\" rid=\"acel13194-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13194-cr-0007\" contrib-type=\"author\"><name><surname>Chen</surname><given-names>Xiong‐Jin</given-names></name><address><email>791598233@qq.com</email></address><xref ref-type=\"aff\" rid=\"acel13194-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13194-cr-0008\" contrib-type=\"author\"><name><surname>Chen</surname><given-names>Hui‐Yi</given-names></name><address><email>401163206@qq.com</email></address><xref ref-type=\"aff\" rid=\"acel13194-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13194-cr-0009\" contrib-type=\"author\"><name><surname>Dong</surname><given-names>Shan‐Shan</given-names></name><address><email>dongss@xjtu.edu.cn</email></address><xref ref-type=\"aff\" rid=\"acel13194-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13194-cr-0010\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Cui</surname><given-names>Li‐Li</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-0273-2531</contrib-id><xref ref-type=\"aff\" rid=\"acel13194-aff-0002\">\n<sup>2</sup>\n</xref><address><email>cuilili@gdmu.edu.cn</email></address></contrib><contrib id=\"acel13194-cr-0011\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Yang</surname><given-names>Tie‐Lin</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">http://orcid.org/0000-0001-7062-3025</contrib-id><xref ref-type=\"aff\" rid=\"acel13194-aff-0001\">\n<sup>1</sup>\n</xref><address><email>yangtielin@xjtu.edu.cn</email></address></contrib></contrib-group><aff id=\"acel13194-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Key Laboratory of Biomedical Information Engineering of Ministry of Education</named-content>\n<named-content content-type=\"organisation-division\">Biomedical Informatics & Genomics Center</named-content>\n<named-content content-type=\"organisation-division\">School of Life Science and Technology</named-content>\n<institution>Xi'an Jiaotong University</institution>\n<city>Xi'an</city>\n<country country=\"CN\">China</country>\n</aff><aff id=\"acel13194-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Guangdong Key Laboratory of Age‐Related Cardiac and Cerebral Diseases</named-content>\n<institution>Affiliated Hospital of Guangdong Medical University</institution>\n<city>Zhanjiang</city>\n<country country=\"CN\">China</country>\n</aff><aff id=\"acel13194-aff-0003\">\n<label><sup>3</sup></label>\n<named-content content-type=\"organisation-division\">Department of Pharmacology and Neuroscience</named-content>\n<institution>University of North Texas Health Science Center</institution>\n<city>Fort Worth</city>\n<named-content content-type=\"country-part\">TX</named-content>\n<country country=\"US\">USA</country>\n</aff><aff id=\"acel13194-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Institute of Plastic Surgery</named-content>\n<institution>Affiliated Hospital of Guangdong Medical University</institution>\n<city>Zhanjiang</city>\n<country country=\"CN\">China</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nLi‐Li Cui, Guangdong Key Laboratory of Age‐Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.<break/>\nEmail: <email>cuilili@gdmu.edu.cn</email><break/>\nTie‐Lin Yang, Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.<break/>\nEmail: <email>yangtielin@xjtu.edu.cn</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>23</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13194</elocation-id><history><date date-type=\"received\"><day>03</day><month>3</month><year>2020</year></date><date date-type=\"rev-recd\"><day>31</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by Anatomical Society and John Wiley & Sons Ltd</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13194.pdf\"/><abstract id=\"acel13194-abs-0001\"><title>Abstract</title><p>Sirtuin 2 (SIRT2) is an NAD+ dependent deacetylase that is the most abundant sirtuin protein in the brain. Accumulating evidence revealed the role of SIRT2 in a wide range of biological processes and age‐related diseases. However, the pivotal mechanism of SIRT2 played in Alzheimer's disease (AD) remains unknown. Here, we report that pharmacological inactivation of SIRT2 has a beneficial effect in AD. The deacetylase inhibitor of SIRT2 rescued the cognitive impairment in amyloid precursor protein/presenilin 1 transgenic mouse (<italic>APP</italic>/<italic>PS1</italic> mouse), and the BACE1 cleavage was weakened to reduce the β‐amyloid (Aβ) production in the hippocampus. Moreover, we firstly identified that Reticulon 4B (RTN4B) played a crucial role between SIRT2/BACE1 regulation in AD. RTN4B, as a deacetylation substrate for SIRT2, the deacetylation by SIRT2 drived the ubiquitination and degradation of RTN4B and then the disturbed RTN4B interacted with and influenced the expression of BACE1. When we overexpressed RTN4B in neurons of the hippocampus in the AD mouse model, the abnormal Aβ accumulation and cognitive impairment were ameliorated, consistent with the results of SIRT2 inhibition in vivo. Moreover, we showed that the regulatory effect of SIRT2 on BACE1 is dependent on RTN4B. When RTN4B was knocked down, the effects of SIRT2 inhibition on the BACE1 level, Aβ pathology, and AD‐liked behaviors were also blocked. Collectively, we provide evidence that SIRT2 may be a potential target for AD; the new found SIRT2/RTN4B/BACE1 pathological pathway is one of the critical mechanisms for the improvement of SIRT2 on AD.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13194-abs-0002\"><p>The repression of SIRT2 deacetylase activity could ameliorate Aβ pathology and cognitive deficits in the Alzheimer's Disease (AD) mouse model. As for the mechanism, SIRT2 influences the β‐secretase 1 (BACE1) by directly deacetylates reticulon 4B protein (RTN4B), thus affect the production of Aβ, finally contribute to the AD progress. These data marked that SIRT2/RTN4B/BACE1 is a new critical pathological pathway in recusing AD.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13194-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13194-g007.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13194-kwd-0001\">Alzheimer's disease</kwd><kwd id=\"acel13194-kwd-0002\"><italic>APP</italic>/<italic>PS1</italic> transgenic mice</kwd><kwd id=\"acel13194-kwd-0003\">Aβ</kwd><kwd id=\"acel13194-kwd-0004\">BACE1</kwd><kwd id=\"acel13194-kwd-0005\">RTN4B</kwd><kwd id=\"acel13194-kwd-0006\">Sirtuin 2</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source>Guangdong Province: Special Support Plan for High‐Level Talents</funding-source><award-id>2016</award-id></award-group><award-group id=\"funding-0002\"><funding-source>Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme</funding-source><award-id>2017</award-id></award-group><award-group id=\"funding-0003\"><funding-source><institution-wrap><institution>National Natural Science Foundation of China </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100001809</institution-id></institution-wrap></funding-source><award-id>81671181</award-id></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"0\"/><page-count count=\"13\"/><word-count count=\"8055\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13194-cit-1001\">\n<string-name>\n<surname>Wang</surname>\n<given-names>Y</given-names>\n</string-name>, <string-name>\n<surname>Yang</surname>\n<given-names>J‐Q</given-names>\n</string-name>, <string-name>\n<surname>Hong</surname>\n<given-names>T.‐T.</given-names>\n</string-name>, et al. <article-title>RTN4B‐mediated suppression of Sirtuin 2 activity ameliorates β‐amyloid pathology and cognitive impairment in Alzheimer's disease mouse model</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13194</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13194</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13194-body-0001\"><sec id=\"acel13194-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Alzheimer's disease (AD) is the most common neurodegenerative disorder and the most prevalent cause of dementia, representing a worldwide epidemic problem in contemporary health care (World Health Organization, <xref rid=\"acel13194-bib-0035\" ref-type=\"ref\">2015</xref>). At present, AD could only take measures with symptomatic, lacking effective methods of early diagnosis and treatment; the underlying mechanisms of this disease remain incompletely defined (Alzheimer’s Association, <xref rid=\"acel13194-bib-0001\" ref-type=\"ref\">2019</xref>; Long & Holtzman, <xref rid=\"acel13194-bib-0021\" ref-type=\"ref\">2019</xref>). The primary neuropathological signs of AD, including the extracellular deposition of amyloid‐β (Aβ) peptides and intracellular neurofibrillary tau tangles, are closely associated with synapse and neuron loss, ultimately memory impairment in AD (De Strooper & Karran, <xref rid=\"acel13194-bib-0006\" ref-type=\"ref\">2016</xref>; Palop & Mucke, <xref rid=\"acel13194-bib-0024\" ref-type=\"ref\">2016</xref>; Zott, Busche, Sperling, & Konnerth, <xref rid=\"acel13194-bib-0037\" ref-type=\"ref\">2018</xref>). Although Aβ theory has been challenged due to the setback of clinical experiments with Aβ as the target, a great many convincing pieces of evidence support that Aβ is the most critical target of AD, and its pathogenesis in AD still acquired a greater depth of understanding (Rao, Asch, Carr, & Yamada, <xref rid=\"acel13194-bib-0027\" ref-type=\"ref\">2020</xref>).</p><p>The sirtuin family of proteins, which includes seven mammalian members, are famous protein deacetylases that participate in considerable biological and pathological processes, highlighting their crucial physiological functions (Finkel, Deng, & Mostoslavsky, <xref rid=\"acel13194-bib-0010\" ref-type=\"ref\">2009</xref>; Gomes, Leal, Mendes, Reis, & Cavadas, <xref rid=\"acel13194-bib-0011\" ref-type=\"ref\">2019</xref>; Lin et al., <xref rid=\"acel13194-bib-0019\" ref-type=\"ref\">2018</xref>). Among them, SIRT2 is the only sirtuin mainly located in the cytoplasm and abundantly expressed in the brain (Jayasena et al., <xref rid=\"acel13194-bib-0016\" ref-type=\"ref\">2016</xref>). Moreover, SIRT2 also accumulates in the aging central nervous system (CNS), marking its potential role in aging or related neurological diseases (Maxwell et al., <xref rid=\"acel13194-bib-0022\" ref-type=\"ref\">2011</xref>). Recently, a <italic>SIRT2</italic> polymorphism was associated with AD risk in different populations, providing evidence for the relationship between SIRT2 and AD from the perspective of genetics (Polito et al., <xref rid=\"acel13194-bib-0025\" ref-type=\"ref\">2013</xref>; Porcelli et al., <xref rid=\"acel13194-bib-0026\" ref-type=\"ref\">2013</xref>). Furthermore, <italic>SIRT2</italic> mRNA levels increased in the peripheral blood of patients with AD (Wongchitrat et al., <xref rid=\"acel13194-bib-0034\" ref-type=\"ref\">2019</xref>), and SIRT2 levels escalated alongside the decreased acetylation of its recognized substrate α‐tubulin in the AD brain (Silva, Esteves, Oliveira, & Cardoso, <xref rid=\"acel13194-bib-0031\" ref-type=\"ref\">2016</xref>). Moreover, recent studies showed that the interference of SIRT2 mitigated AD‐like recognition deficits in both the mouse model of neurodegenerative diseases and the aged‐accelerated mouse model (Biella et al., <xref rid=\"acel13194-bib-0002\" ref-type=\"ref\">2016</xref>; Diaz‐Perdigon et al., <xref rid=\"acel13194-bib-0008\" ref-type=\"ref\">2020</xref>). These above pieces of evidence suggested that SIRT2 might play a significant role in CNS and represent a potential drug target for AD. However, the molecular details underpinning the effects of SIRT2 in AD remain elusive.</p><p>In the present study, we aim to explore the potential effect of SIRT2 on the AD process and the hidden mechanism. We report that the repression of SIRT2 deacetylase activity ameliorates Aβ pathology and cognitive deficits in the AD mouse model. Furthermore, we provide detail mechanisms that SIRT2 deacetylates reticulon 4B protein (RTN4B) and then influences the β‐secretase 1 (BACE1) , ultimately contribute to the Aβ pathology. Our data demonstrate that targeting SIRT2 could be a rational strategy for AD, and RTN4B is the critical regulator of the SIRT2‐mediated Aβ metabolism for modifying the AD progression.</p></sec><sec sec-type=\"results\" id=\"acel13194-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13194-sec-0003\"><label>2.1</label><title>Inhibition of SIRT2 deacetylation activity has beneficial effects in the AD mouse model</title><p>To confirm whether interfering with the function of SIRT2 deacetylation could slow down the progression of AD‐like changes and behavior, we inhibited SIRT2 function using the selective, brain‐permeable inhibitor AK‐7. Our in vitro results first confirmed that as the increased AK‐7 concentration, the acetylation level of α‐tubulin also increased (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>). Next, to determine whether inhibition of SIRT2 may improve AD‐related behavioral deficits, the Morris water maze (MWM) test was carried out in the <italic>APP</italic>/<italic>PS1</italic> mouse. Our results showed that the 3‐week administration of AK‐7 (100 mg/kg, twice/d, intraperitoneally [i.p.,]) ameliorated the cognitive functional defect in 7‐month‐old <italic>APP</italic>/<italic>PS1</italic> mice compared to vehicle‐treated mice (Figure <xref rid=\"acel13194-fig-0001\" ref-type=\"fig\">1a–f</xref>). Meanwhile, the bodyweight not changed with the AK‐7 administration (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S2</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13194-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Inhibition of SIRT2 activity ameliorates AD‐associated pathology in <italic>APP</italic>/<italic>PS1</italic> mouse. (a) Illustration of timeline of the experimental flow. Seven‐month‐old <italic>APP</italic>/<italic>PS</italic> mice were treated with AK‐7 or vehicle, and age‐matched WT mice treated with vehicle were used as controls. (b) In the hidden platform test, the escape latency to find the platform was plotted against the days of training (<italic>n</italic> = 6 per group). (c–e) In the probe trial, the time, distance, and crossing times in the target quadrant where the platform was removed were recorded (<italic>n</italic> = 6 per group). (f) Representative swim trajectory of mice in the MWM test, the green circle indicates the hidden platform. The red dot indicates the start site, and the blue indicates the stop site. (g) Representative image of Aβ staining in the brain of an <italic>APP</italic>/<italic>PS1</italic> mouse treated with AK‐7 or vehicle (scale bar, 250 μm). (h) Representative western blot images of Aβ in the brain of an <italic>APP</italic>/<italic>PS1</italic> mouse treated with AK‐7 or vehicle. (i) ELISA was used to measure soluble and insoluble Aβ42 levels in the <italic>APP</italic>/<italic>PS1</italic> mouse brain (<italic>n</italic> = 8 per group). (j) Representative western blot images of SIRT2, APP, ADAM10, BACE1, acetylated‐α‐tubulin, and α‐tubulin. (k–n) Relative expression of the above proteins was analyzed (<italic>n</italic> = 8 per group). Data are presented as mean ± standard error of the mean (<italic>SEM</italic>). *<italic>p</italic> < 0.05, **<italic> p</italic> < 0.01, ***<italic> p</italic> < 0.001, ****<italic> p</italic> < 0.0001. Non‐paired Student's <italic>t</italic> test for (i); one‐way ANOVA with Tukey's post hoc test for (c–e, k–n); two‐way ANOVA for (b)</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13194-g001\"/></fig><p>To further investigate how SIRT2 influenced the cognitive behavior and its mechanism of AD, we assessed the hippocampal Aβ deposition of <italic>APP</italic>/<italic>PS1</italic> mouse with the effect of SIRT2 inactivation. As shown in Figure <xref rid=\"acel13194-fig-0001\" ref-type=\"fig\">1g,h</xref>, the SIRT2 inhibition reduced the Aβ load in the hippocampus of <italic>APP</italic>/<italic>PS1</italic> mice and reduced the aggregated form of Aβ. Moreover, repression of SIRT2 significantly reduced the insoluble form of Aβ42 (Figure <xref rid=\"acel13194-fig-0001\" ref-type=\"fig\">1i</xref>). These results above suggest that SIRT2 inhibition reduces Aβ42 abundance and aggregation. To further explore the mechanism underlying SIRT2 inhibition on Aβ pathology, we assayed the key secretases involved in APP processing. The results showed that SIRT2 inhibition reduced the expression of BACE1, which increased in <italic>APP</italic>/<italic>PS1</italic> mice compared to WT mice. Meanwhile, no significant change in APP and A disintegrin and metalloproteinase domain‐containing protein 10 (ADAM10) abundance was observed (Figure <xref rid=\"acel13194-fig-0001\" ref-type=\"fig\">1j</xref>–m). The activity inhibition effect of AK‐7 in vivo was confirmed by the increased acetylation level of α‐tubulin and unchanged SIRT2 expression (Figure <xref rid=\"acel13194-fig-0001\" ref-type=\"fig\">1n</xref>, Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S3</xref>). We further generated <italic>SIRT2</italic> knockout mice and found that depletion of <italic>SIRT2</italic> significantly reduced BACE1 levels in both the hippocampus and cortex in 15‐month‐old mice (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S4</xref>). Collectively, these results suggest that the inhibition of SIRT2 deacetylation activity may be effective against the cognitive impairment in AD mice, via a reduction of Aβ production by influencing BACE1.</p></sec><sec id=\"acel13194-sec-0004\"><label>2.2</label><title>Characterization of SIRT2 interactions in vitro</title><p>To identify how SIRT2 regulated BACE1 in AD progress, we performed co‐immunoprecipitation (Co‐IP) in the SIRT2‐overexpressing SY5Y cell line and conducted mass spectrometry (MS) analysis of the SIRT2 trapped proteins to screening the potential substrate of SIRT2 in the neural cell line. This proteomic approach finally identified 285 candidate SIRT2‐binding partners and some of which have been previously reported to interact with SIRT2 (Lin et al., <xref rid=\"acel13194-bib-0018\" ref-type=\"ref\">2013</xref>; Wang et al., <xref rid=\"acel13194-bib-0033\" ref-type=\"ref\">2014</xref>). We further focused on identifying the protein candidates with the Alzheimer's disease‐related and finally identified five proteins in the KEGG pathway analysis. Among them, RTN4 was identified that both have a direct relationship with BACE1 and act as the potential substrate of SIRT2 according to the results of informatics analysis (Figure <xref rid=\"acel13194-fig-0002\" ref-type=\"fig\">2a</xref>, Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S5</xref>, Table <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13194-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>SIRT2 interacted with RTN4B and regulated its level through deacetylase activity. (a) Experimental flow chart of RTN4B discovery by mass spectrometry. (b–c) HA‐SIRT2 and Flag‐RTN4B plasmids were transiently transfected into 293T cells. Total proteins were IPed with HA or Flag antibodies and subsequently by western blot with HA or Flag antibodies. (d) Endogenous SIRT2 and RTN4B were stained using SIRT2 and RTN4B antibodies in 293T cells and hippocampal tissue from WT mouse brain (scale bar, 50 μm). (e and f) Representative western blot images and relative expression of RTN4B in the hippocampus (Hpp) and cortex (Ctr) from <italic>APP</italic>/<italic>PS1</italic> and WT mice at 10 and 20 months old (<italic>n</italic> = 3 per group). (g, h) SY5Y and H4 cells were transiently transfected with HA‐SIRT2 plasmids, and the RTN4B expression was detected by western blot. (i, j) SY5Y and H4 cells were infected with lentivirus‐mediated shSIRT2. RTN4B levels were determined by western blot. (k) Flag‐RTN4B and HA‐SIRT2 or SIRT2H187Y were transiently transfected into 293T cells, in the presence of a non‐sirtuin HDAC (histone deacetylase) inhibitor TSA. Total proteins were IPed with Flag and subsequently western blot with Ac and Flag antibodies. (l) HA or HA‐SIRT2 were transiently transfected into 293T cells, and cells were then treated with CHX at the indicated time points. RTN4B levels were determined by western blot analysis. (m) HA‐Ub and Flag‐RTN4B were transiently transfected into 293T cells. Total proteins were IPed with Flag and subsequently by western blot using HA and Flag antibodies. Data are presented as mean ± <italic>SEM</italic>. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01. Non‐paired Student's <italic>t</italic> test for (f, h); one‐way ANOVA with Tukey's post hoc test for (j). Data in (b‐d, g‐m) are representative of 3–4 independent experiments</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13194-g002\"/></fig></sec><sec id=\"acel13194-sec-0005\"><label>2.3</label><title>SIRT2 interacts with and regulates RTN4B</title><p>Considering RTN4 has three isoforms, we firstly evaluated which RTN4 isoform is the major targets of SIRT2, and the Co‐IP results showed that only RNT4B has the significant interaction with SIRT2 (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S7</xref>). Then, reciprocal Co‐IP was further performed to confirm the interaction between them and the results showed that SIRT2 Co‐IPed with RTN4B, and vice versa, indicating a direct interaction with each other (Figure <xref rid=\"acel13194-fig-0002\" ref-type=\"fig\">2b,c</xref>). Moreover, SIRT2 and RTN4B also showed co‐location in both 293T cells and the hippocampus of WT mice (Figure <xref rid=\"acel13194-fig-0002\" ref-type=\"fig\">2d</xref>). Next, we also determined whether the protein level of RTN4B changed in AD. Our informatics analysis results show that RTN4 was reduced in the brain of AD patients (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S6</xref>), and our results showed that the protein level of RTN4B also significantly reduced in <italic>APP</italic>/<italic>PS1</italic> mice compared to the WT control mice group, especially in the hippocampus of both 10‐month‐old and 20‐month‐old mice (Figure <xref rid=\"acel13194-fig-0002\" ref-type=\"fig\">2e,f</xref>).</p><p>Next, we investigated the regulatory relationship between SIRT2 and RTN4B. Two overexpressed and knockdown SIRT2 cell lines were established in SH‐SY5Y and H4 cells. The efficacy of the transfections was determined by visualization of green fluorescent protein (GFP) (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S8</xref>). The results showed that overexpression of SIRT2 reduced the abundance of RTN4B in the two neural cell lines (Figure <xref rid=\"acel13194-fig-0002\" ref-type=\"fig\">2g</xref>,h), and when the level of SIRT2 decreased, RTN4B has up‐regulated accordingly (Figure <xref rid=\"acel13194-fig-0002\" ref-type=\"fig\">2i</xref>,j). This negative association was further supported in vivo that we observed the increased RTN4B expression in the hippocampus and cortex of the <italic>SIRT2</italic> knockout mice compared to the WT mice (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S9</xref>). Moreover, we further evaluated the possible association between the protein expression of SIRT2 and RTN4 in the brain of AD patients based on the published data and found that the change direction of RTN4 and SIRT2 is opposite in some brain regions (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S6</xref>). Collectively, these results suggest that SIRT2 can interact with and negatively regulate the protein level of RTN4B.</p></sec><sec id=\"acel13194-sec-0006\"><label>2.4</label><title>SIRT2 influences the ubiquitination and degradation of RTN4B by deacetylation</title><p>Since SIRT2 is a classical cytoplasmic deacetylase, and RTN4B has also been identified as a protein that has lysine acetylation site in a sizeable proteomic study (Choudhary et al., <xref rid=\"acel13194-bib-0005\" ref-type=\"ref\">2009</xref>). So we further evaluate whether SIRT2 regulated the RTN4B expression by deacetylation activity. We transfected 293T cells with Flag‐RTN4B and HA‐SIRT2 WT or deacetylation‐null mutants HA‐SIRT2 H187Y, and found that the acetylation statue of RTN4B can be decreased by SIRT2 but not the H187Y mutation of SIRT2 in the presence a non‐sirtuin HDAC inhibitor TSA (Figure <xref rid=\"acel13194-fig-0002\" ref-type=\"fig\">2k</xref>). These results indicate that RTN4B is a legitimate deacetylation substrate of SIRT2.</p><p>To further determine whether SIRT2 regulates the expression of RTN4B by post‐translational level, cycloheximide (CHX, 100 μg/ml) was used to prevent new protein synthesis, and the results show that the RTN4B expression decreased when cells were treated with CHX. Moreover, this decrease tended to be more noticeable when SIRT2 overexpressed suggested that SIRT2 plays a post‐translational regulatory role in RTN4B (Figure <xref rid=\"acel13194-fig-0002\" ref-type=\"fig\">2l</xref>). Acetylation and ubiquitination are commonly competing for post‐translational modifications that target the lysine residues of proteins. Then, we evaluated whether the acetylation of RTN4B could influence its ubiquitination, therefore influence its degradation as well. To prove this hypothesis, we immunoprecipitated RTN4B in 293T cells co‐transfected with HA‐Ub and Flag‐RTN4B, and the result showed that RTN4B can be ubiquitinated and that the inhibition of SIRT2 by AGK2 (10 μM, 12 hr) reduced the ubiquitination of RTN4B (Figure <xref rid=\"acel13194-fig-0002\" ref-type=\"fig\">2m</xref>).</p></sec><sec id=\"acel13194-sec-0007\"><label>2.5</label><title>SIRT2 regulates BACE1 via RTN4B in vitro</title><p>Some common domains of the RTN family have direct interaction with BACE1 (He et al., <xref rid=\"acel13194-bib-0012\" ref-type=\"ref\">2004</xref>), which prompted us to consider whether SIRT2 inhibition reduces BACE1 levels by increasing RTN4B expression. First, we confirmed that BACE1 interacts with RTN4B in 293T cells by reciprocal Co‐IP (Figure <xref rid=\"acel13194-fig-0003\" ref-type=\"fig\">3a,b</xref>). Subsequently, the assay of fluorescence colocalization in SY5Y cells shows that BACE1 and RTN4B colocalize mainly in the cytoplasm (Figure <xref rid=\"acel13194-fig-0003\" ref-type=\"fig\">3c</xref>). To determine whether SIRT2 regulates RTN4B and then influences the expression of BACE1, we overexpressed SIRT2 in SY5Y cells and evaluated RTN4B and BACE1 expression levels. As shown in Figure <xref rid=\"acel13194-fig-0003\" ref-type=\"fig\">3d</xref>, the increased SIRT2 expression can reduce RTN4B protein levels and increase BACE1 protein levels accordingly. Alternatively, the reduction of SIRT2 can increase RTN4B and decrease BACE1 levels (Figure <xref rid=\"acel13194-fig-0003\" ref-type=\"fig\">3e</xref>). We also assayed the mRNA level of <italic>RTN4B</italic> and <italic>BACE1</italic> and conformed that this regulation of SIRT2 only occurred at the protein level (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S10</xref>). To ascertain whether overexpression of RTN4B could have a similar effect to SIRT2 inhibition, we overexpressed RTN4B in SY5Y cells and then examined BACE1 levels. The results showed that RTN4B overexpression reduced BACE1 levels (Figure <xref rid=\"acel13194-fig-0003\" ref-type=\"fig\">3f</xref>). We subsequently examined whether SIRT2 regulation of BACE1 is RTN4B‐dependent, <italic>RTN4B</italic> was first knocked down by small interfering RNA (siRNA) in SY5Y cell lines, and then the effect of SIRT2 inhibition by AGK2 (10 μM, 12 hr) on BACE1 abundance was assessed. The results showed that the influence of SIRT2 inhibition on BACE1 was abrogated on the premise of RTN4B reduction in SY5Y cells (Figure <xref rid=\"acel13194-fig-0003\" ref-type=\"fig\">3g</xref>). In brief, the above results suggest that SIRT2/RTN4B/BACE1 is a potential pathway by which SIRT2 could alleviate AD‐like pathology.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13194-fig-0003\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>SIRT2 regulates the RTN4B/BACE1 axis in vitro. (a, b) HA‐BACE1 and Flag‐RTN4B plasmids were transiently transfected into 293T cells. Total proteins were IPed with HA and Flag antibodies and subsequently by western blot analysis using HA and Flag antibodies. (c) Endogenous BACE1 and RTN4B were stained using BACE1 and RTN4B antibodies in SY5Y cells (scale bar, 5 μm). (d, e) HA‐SIRT2 or shSIRT2 were transiently transfected into SY5Y cells, and RTN4B and BACE1 protein levels were determined by western blot. (f) Flag‐RTN4B plasmids were transiently transfected into 293T cells, and BACE1 protein levels were determined by western blot. (g) siRTN4B or negative control was transfected into SY5Y cells, then treat cells with AGK2 or DMSO, and RTN4B and BACE1 protein levels were determined by western blot. All panels are representative of 3–4 independent experiments</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13194-g003\"/></fig></sec><sec id=\"acel13194-sec-0008\"><label>2.6</label><title>Overexpressed RTN4B in hippocampal neurons alleviates cognitive impairments in the AD mouse model</title><p>Next, to directly investigate the contribution of RTN4B to the AD‐like pathology in AD, we overexpressed RTN4B in the hippocampal neurons of <italic>APP</italic>/<italic>PS1</italic> mice by bilateral injection of an AAV2/9 virus equipped with a ZsGreen tag driven by neuron‐specific promoters (Figure <xref rid=\"acel13194-fig-0004\" ref-type=\"fig\">4a</xref>). First, we verified the effect of the virus using immunofluorescence analysis of brain sections 3 weeks post‐injection. As shown in Figure <xref rid=\"acel13194-fig-0004\" ref-type=\"fig\">4b</xref>, green fluorescence was co‐located with NeuN, suggesting that hippocampal neurons specifically expressed the virus. To evaluate the influence of RTN4B on hippocampal‐dependent learning and memory, we conducted the MWM test after 3 weeks of administration. The results show that the overexpressing RTN4B <italic>APP</italic>/<italic>PS1</italic> mice took less time to find the platform in the training trial. In the probe trial, RTN4B overexpressed groups increased the time and the distance in the target quadrant and the times crossing the platform, respectively (Figure <xref rid=\"acel13194-fig-0004\" ref-type=\"fig\">4c</xref>–g). As expected, the hippocampal RTN4B expression increased after virus injection compared to the control vector injection, and the protein level of BACE1 was suppressed in the hippocampus under the RTN4B overexpression (Figure <xref rid=\"acel13194-fig-0004\" ref-type=\"fig\">4h</xref>). To further assess the progression of Aβ pathology, we performed immunofluorescence and western blot analyses in the hippocampus, and the results showed that RTN4B overexpression reduced the number of Aβ plaques and aggregation in <italic>APP</italic>/<italic>PS1</italic> mice (Figure <xref rid=\"acel13194-fig-0004\" ref-type=\"fig\">4i</xref>,j). In parallel with the immunofluorescence result, the insoluble Aβ42 was also reduced by RTN4B overexpression using ELISA assay (Figure <xref rid=\"acel13194-fig-0004\" ref-type=\"fig\">4k</xref>). These results suggest that RTN4B overexpression rescued the cognitive impairments in <italic>APP</italic>/<italic>PS1</italic> mouse, suppressed BACE1 protein level, and ultimately reduced the production of Aβ.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13194-fig-0004\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>Overexpression of RTN4B rescued AD‐associated pathology in <italic>APP</italic>/<italic>PS1</italic> mice. (a) Illustration of bilateral stereotactic injection of AAV and timeline of the experimental flow. Seven‐month‐old <italic>APP</italic>/<italic>PS</italic> mice were injected with AAV in hippocampus. (b) The effect of the injection was assessed by immunofluorescence staining for NeuN cells (scale bar, upper 500 μm lower 50 μm). (c) In the hidden platform test, the escape latency to find the platform was plotted against the days of training (<italic>n</italic> = 9 per group). (d–f) In the probe trial, time, distance, and crossing times in the target quadrant where the platform was removed were recorded (<italic>n</italic> = 9 per group). (g) Representative swim trajectory of mice in the MWM test. The green circle indicates the hidden platform. The red dot indicates the start site, and the blue indicates the stop site. (h) Representative western blot images of RTN4B and BACE1 and relative expression of BACE1 (<italic>n</italic> = 9 per group). (i, j) Representative images of immunofluorescence staining and western blot of hippocampal Aβ (scale bar, 250 μm). (k) ELISA was used to measure soluble and insoluble Aβ42 levels in hippocampal tissue (<italic>n</italic> = 6 per group). Data are presented as mean ± <italic>SEM</italic>. *<italic>p</italic> < 0.05, **<italic> p</italic> < 0.01, ***<italic> p</italic> < 0.001. Non‐paired Student's <italic>t</italic> test for (d–f, h, k); two‐way ANOVA for (b)</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13194-g004\"/></fig></sec><sec id=\"acel13194-sec-0009\"><label>2.7</label><title>The beneficial effects of SIRT2 inhibition in the AD mouse model are RTN4B dependent</title><p>From the in vitro and in vivo experiments above, we concluded that inhibition of SIRT2 could reduce BACE1 by increasing RTN4B. To further determine whether the beneficial effect of SIRT2 inhibition in the AD mouse model is RTN4B‐dependent, we knocked down the RTN4B and then examined whether the effect of SIRT2 inhibition is still effective in vivo (Figure <xref rid=\"acel13194-fig-0005\" ref-type=\"fig\">5a</xref>). The stereotactic injection of an AAV2/9 virus that contained shRTN4B sequence was conducted bilaterally in the <italic>APP</italic>/<italic>PS1</italic> mouse hippocampus to knockdown RTN4B, a virus containing a scrambled sequence was used as the control, and GFP was visualized to confirm the effect of the injection (Figure <xref rid=\"acel13194-fig-0005\" ref-type=\"fig\">5b</xref>). Then, MWM and novel object recognition test (NOR) were employed to verify the behavior cognition changes after intervention. As shown in Figure <xref rid=\"acel13194-fig-0005\" ref-type=\"fig\">5c–f</xref>, AK‐7 administration reduced the time spent finding the platform and increase the time and distance in the target quadrant. However, when RTN4B was knocked down, all of these improvements in behavior cognition were attenuated. With regard to the NOR test, a similar result was observed. AK‐7 administration increased the recognition index and discrimination index of <italic>APP</italic>/<italic>PS1</italic> mice compared with the placebo groups. However, the absence of RTN4B abrogated this behavior improvement (Figure <xref rid=\"acel13194-fig-0005\" ref-type=\"fig\">5g</xref>,h).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13194-fig-0005\" orientation=\"portrait\" position=\"float\"><label>FIGURE 5</label><caption><p>RTN4B‐dependent SIRT2 inhibition ameliorates AD‐associated pathology and improves cognition. (a) Illustration of the experimental timeline. 7‐month‐old <italic>APP</italic>/<italic>PS1</italic> mice were used. (b) Illustration of bilateral stereotactic injection of AAV and the efficiency of the viral stereotactic injection was assessed by immunofluorescence. (c) In the hidden platform test, the escape latency to find the platform was plotted against the days of training (<italic>n</italic> = 8 per group). (d–f) In the probe trial, time, distance, and crossing times in the target quadrant where the platform removed were recorded (<italic>n</italic> = 8 per group). (g, h) In the object recognition test, the recognition index and the discrimination index were recorded (<italic>n</italic> = 8‐10 per group). (i) Representative western blot images of RTN4B and BACE1 and Relative expression of the above proteins (<italic>n</italic> = 9 per group). (j, k) Representative images of immunofluorescence staining and western blot of Aβ in the hippocampus (scale bar, 250 μm). (l) ELISA was used to measure soluble and insoluble Aβ42 levels in hippocampal tissue (<italic>n</italic> = 5‐6 per group). Data are presented as mean ± <italic>SEM</italic>. *<italic>p</italic> < 0.05, **<italic> p</italic> < 0.01, ***<italic> p</italic> < 0.001. One‐way ANOVA with Tukey's post hoc test for (d–h, i, l); two‐way ANOVA for (c)</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13194-g005\"/></fig><p>Furthermore, we analyzed the expression of RTN4B and BACE1 by western blot, and as expected, AK‐7 administration increased RTN4B and reduced BACE1. Nevertheless, when RTN4B was knocked down, AK‐7 barely increased RTN4B or decreased BACE1 (Figure <xref rid=\"acel13194-fig-0005\" ref-type=\"fig\">5i</xref>) in the hippocampus of <italic>APP</italic>/<italic>PS1</italic> mice, which is consistent with our in vitro results (Figure <xref rid=\"acel13194-fig-0003\" ref-type=\"fig\">3g</xref>). Besides, the assay for the Aβ level showed a consistent effect. When RTN4B was knocked down, the effect of reduced Aβ by AK7 administration was dampened (Figure <xref rid=\"acel13194-fig-0005\" ref-type=\"fig\">5j</xref>,k). The results from Elisa assay also confirmed that AK‐7 prominently reduced soluble and insoluble Aβ42 levels in the hippocampus, and the RTN4B knockdown increased Aβ42 to a level similar to that in the untreated group (Figure <xref rid=\"acel13194-fig-0005\" ref-type=\"fig\">5l</xref>). In summary, these in vitro and in vivo results suggested that this freshly identified SIRT2/RTN4B/BACE1 pathological pathway is one of the critical mechanisms for the improvement of SIRT2 on AD.</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13194-sec-0010\"><label>3</label><title>DISCUSSION</title><p>Here, we report that the inhibition of SIRT2 deacetylase activity has a beneficial effect in the AD mouse model, the reduction in Aβ pathology via a decrease in BACE1 protein level contributed to the improvement of cognitive behavior. As for the mechanism, deacetylation RTN4B by SIRT2 contributed to a reduction in RTN4B and therefore leading to the disturbance of BACE1. Our report suggest that SIRT2/RTN4B/BACE1 is a pathological pathway in AD, and SIRT2 is a promising therapeutic target for AD treatment.</p><p>To evaluate the role of SIRT2 in cognitive impairment in AD, we first considered that reducing SIRT2 activity rather than its expression levels might represent a more promising therapeutic target for AD. Although AD is a neurodegenerative disease, non‐invasive treatment with blood‐brain barrier permeable drugs is still the optimal choice. Our results were consistent with previous reports that AK‐7 administration can improve cognition in two other AD mice models (Biella et al., <xref rid=\"acel13194-bib-0002\" ref-type=\"ref\">2016</xref>) and that another SIRT2 inhibitor can improve cognition in a senescence‐accelerated mouse model (Diaz‐Perdigon et al., <xref rid=\"acel13194-bib-0008\" ref-type=\"ref\">2020</xref>). Furthermore, we confirmed that SIRT2 inhibition reduces the Aβ burden, mainly by curtailing APP processing, via the regulation of BACE1 in vivo, which was also supported by another study that showed SIRT2 inhibition decreased Aβ levels in vitro (Biella et al., <xref rid=\"acel13194-bib-0002\" ref-type=\"ref\">2016</xref>). Considering SIRT2 and SIRT1 have been reported showed an opposite effect on neurodegeneration (Donmez & Outeiro, <xref rid=\"acel13194-bib-0009\" ref-type=\"ref\">2013</xref>), for eliminating the possible feedback increase of SIRT1 caused by SIRT2 inhibition, we also assayed the expression level of SIRT1 in the context of SIRT2 pharmacological or genetic inhibition. The result showed that the SIRT2 repress does not significantly disturb SIRT1 (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S11</xref>). Recently, the setback in the development of drugs that target Aβ and BACE1 challenged the classical Aβ hypothesis of AD. However, the resurrection of Aducanumab (Biogen), which targets Aβ, combining with the exclusive genetic evidence, has reignited the hope for Aβ‐targeted therapy (Schneider, <xref rid=\"acel13194-bib-0028\" ref-type=\"ref\">2020</xref>; Selkoe, <xref rid=\"acel13194-bib-0029\" ref-type=\"ref\">2019</xref>). In this study, we found that SIRT2 regulated both Aβ40 and Aβ42 production (Figure <xref rid=\"acel13194-fig-0005\" ref-type=\"fig\">5l</xref>, Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figures <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S12</xref> and <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S11</xref>), and rescued the death of neuron in the hippocampus of AD mouse model (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S13</xref>). We further found that SIRT2 influenced Aβ production by regulated BACE1, which is one of the compelling new pathological pathways for AD. BACE1 has acetylation sites, which may be deacetylated (Ko & Puglielli, <xref rid=\"acel13194-bib-0017\" ref-type=\"ref\">2009</xref>), so we tried to verify whether SIRT2 has direct interactions with BACE1. However, our Co‐IP‐MS database did not identify the BACE1 protein. Nevertheless, we did further identify the candidate protein RTN4B through the informatics analysis, which may link the SIRT2 on BACE1 regulation (Figure <xref rid=\"acel13194-fig-0006\" ref-type=\"fig\">6</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13194-fig-0006\" orientation=\"portrait\" position=\"float\"><label>FIGURE 6</label><caption><p>Schematic diagram depicting the possible mechanisms. In AD pathology, the imbalance of the APP metabolic pathway caused the abnormally accumulation and aggregation of Aβ in the brain, and the abnormal increase in the activity and expression of BACE1 is one of the most important causes for the Aβ accumulation in AD. Our study found that the inhibition of SIRT2 induces ubiquitination and degradation of RTN4B by deacetylating RTN4B, then the upregulation of RTN4B leading to the reduction of BACE1, suppress Aβ production, ultimately alleviates the cognitive decline of AD.</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13194-g006\"/></fig><p>The interaction between SIRT2 and RTN4B was firstly confirmed in our study. Our MS results identified 285 certain proteins that may be candidate targets of SIRT2 in neuronal cell lines (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Table <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>). Then, we further locked five candidates that may serve as SIRT2 targets in AD progression by informatics analysis and finally identified RTN4 as the most impressive candidate for that it may interact with BACE1 (He et al., <xref rid=\"acel13194-bib-0012\" ref-type=\"ref\">2004</xref>; Murayama et al., <xref rid=\"acel13194-bib-0023\" ref-type=\"ref\">2006</xref>). Besides, a previous study that using metabolic labeling quantitative MS also suggested that RTN4 may transiently target SIRT2 (Budayeva & Cristea, <xref rid=\"acel13194-bib-0003\" ref-type=\"ref\">2016</xref>), and this possible interaction was also identified in other proteomic studies (Huttlin et al., <xref rid=\"acel13194-bib-0015\" ref-type=\"ref\">2015</xref>), supporting our results. RTN4, also known as Nogo, is one member of the RTN family and has mainly three isoforms (RTN4A, RTN4B, RTN4C), which are abundant in the nervous system (Chen et al., <xref rid=\"acel13194-bib-0004\" ref-type=\"ref\">2000</xref>). Although our screening results and previous reports suggest that SIRT2 may interact with RTN4, it is unclear which subtype of RTN4 is involved in this interaction. We found that SIRT2 strongly interacts with RTN4B and slightly interacts with RTN4A, but we did not observe the interaction between SIRT2 and RTN4C in our Co‐IP assay (Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Figure <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S7</xref>). So, we focused on RTN4B and further confirmed that RTN4B interacts with SIRT2.</p><p>Concerning BACE1 and RTN4B, several studies suggested that RTN family members are the binding partners of BACE1 (Murayama et al., <xref rid=\"acel13194-bib-0023\" ref-type=\"ref\">2006</xref>). We provide further evidence that RTN4B could interact with BACE1, and further influence its protein level, and contribute to Aβ production. It is worth noting that BACE1 protein levels, but not mRNA levels, remarkably increased in AD pathology (Holsinger, McLean, Beyreuther, Masters, & Evin, <xref rid=\"acel13194-bib-0013\" ref-type=\"ref\">2002</xref>; Yang et al., <xref rid=\"acel13194-bib-0036\" ref-type=\"ref\">2003</xref>), and BACE1 is also predominantly present in and plays roles in the endoplasmic reticulum, plasma membrane, and endosomes (Huse, Pijak, Leslie, Lee, & Doms, <xref rid=\"acel13194-bib-0014\" ref-type=\"ref\">2000</xref>). It is consistent with the subcellular localization of RTN4B, which supports the conclusion that RTN4B interacts with and disturbs BACE1 in the cytoplasm. RTN3 negatively regulates BACE1 abundance in vitro and reduces the localization of BACE1 in axons (Deng et al., <xref rid=\"acel13194-bib-0007\" ref-type=\"ref\">2013</xref>). As the author speculated, BACE1 reduction in axons led to more BACE1 retention in some where resident ubiquitin‐lysosome can degrade it (Shi et al., <xref rid=\"acel13194-bib-0030\" ref-type=\"ref\">2014</xref>). Considering RTN4B not only share the same C‐terminal domain with RTN3 which interacts with BACE1 but also has a similar feature with RTN3 as an endoplasmic reticulum protein, we speculate that RTN4B may regulate BACE1 negatively by the same pattern with RTN3. SIRT2 interacts with and deacetylates RTN4B which, in turn, increases the ubiquitination and degradation.</p><p>Some limitations of this study should be addressed. In this study, we reduced RTN4B protein levels in vivo by knocking down RTN4B using shRNA, and when RTN4B was decreased, the effect of SIRT2 inhibition on AD progression was not abrogated completely, merely dampened. Two possible reasons may account for this result. First, it may be caused by the incomplete inhibition of RTN4B. Second, many substrates of SIRT2 have been identified; SIRT2 inhibition may also occur in other pathways at the same time. For example, the other four candidate proteins, from our proteomic screen, are all mitochondrial proteins, and several reports suggested that SIRT2 could influence neurodegeneration via mitochondrial function or autophagy (Liu et al., <xref rid=\"acel13194-bib-0020\" ref-type=\"ref\">2017</xref>). Thus, we cannot rule out the possibility that other pathways were impacted by SIRT2 inhibition in AD. Third, we did not determine the interaction between SIRT2 and RTN4B in vivo and identified the certain acetylation sites of RTN4B in this report, and this part will be clarified in future research to strengthen the conclusion.</p><p>In conclusion, data from our study indicate that inhibition of SIRT2 deacetylase activity could be an attractive target to mitigate neurodegeneration in AD. SIRT2 regulates BACE1 by deacetylating RTN4B, which, in turn, influences Aβ production and aggregation, ultimately alleviates the cognitive decline of AD. Considering the abundance of SIRT2 in the brain and it mainly plays a regulatory role in physiological processes, (Wang, Yang, Hong, Chen, & Cui, <xref rid=\"acel13194-bib-0032\" ref-type=\"ref\">2019</xref>) strongly suggest that it may represent a promising target for the development of new treatments for AD. In the future, a deeper understanding of the role SIRT2 plays in AD will be needed to provide confidence in developing it as a therapeutic target for AD.</p></sec><sec id=\"acel13194-sec-0011\"><label>4</label><title>EXPERIMENTAL PROCEDURES</title><sec id=\"acel13194-sec-0012\"><label>4.1</label><title>Plasmids, viruses, chemicals, and antibodies</title><p>The plasmids encoding Flag‐SIRT2, HA‐SIRT2, HA‐SIRT2H187Y, Flag‐RTN4A, Flag‐RTN4B, Flag‐RTN4C, and HA‐BACE1 were constructed by Longqian Biotech (China). HA‐Ub was purchased from Addgene (USA). AAV2/9‐hSyn‐Rtn4b‐3×flag and AAV2/9‐Rtn4b shRNA were constructed and packaged by Hanbio Biotechnology Co., Ltd. LV‐SIRT2‐shRNA1 and LV‐SIRT2‐shRNA2 were constructed and packaged by Cyagen Biosciences (China). CHX was purchased from Abcam (UK). AGK2 was purchased from Sigma (USA), and AK‐7 was purchased from MCE (USA). TSA was purchased from MCE (USA). The antibodies employed in this study are listed in Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Table <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S2</xref>.</p></sec><sec id=\"acel13194-sec-0013\"><label>4.2</label><title>Cell culture, transfection, and drug treatment</title><p>The human embryonic kidney 293T (HEK293T), human neuroblastoma SY5Y, human neuroglioma H4, and mouse hippocampal neuron HT22 cell lines were used in this study, and details of culture and treatments methods were described in Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13194-sec-0014\"><label>4.3</label><title>Animals</title><p>Male <italic>APP</italic>/<italic>PS1</italic> double‐transgenic mouse lines, which harbor human <italic>APP</italic>swe (Swedish mutations K595N/M596L) and <italic>PS1</italic> with an exon 9 deletion (PS1dE9) under the control of the mouse prion promoter, were purchased from the Model Animal Research Center of Nanjing University. <italic>Sirt2 </italic>knockout (<italic>Sirt2</italic>\n<sup>−/−</sup>) mice were generated using a gene‐trapping method with a C57BL/6 background and purchased from Cyagen Biosciences. All mice had free access to food and water and were housed in a pathogen‐free environment in a reversed day/night cycle. Bodyweight, food and water intake, and overall general health were assessed every week. 7‐month‐old <italic>APP</italic>/<italic>PS1</italic> and age‐matched C57BL/6 mice were used in this study. 15‐month‐old <italic>Sirt2 </italic>knockout mice and WT mice were used. <italic>APP</italic>/<italic>PS1</italic> and C57BL/6 mice were injected intraperitoneally with 10 mg/kg AK‐7, which was confirmed to specifically inhibit the activity of SIRT2 and effectively penetrate the blood‐brain barrier to slow the disease progression in mouse models of neurodegeneration or vehicle (10% DMSO, 90% saline) twice a day for 3 weeks.</p></sec><sec id=\"acel13194-sec-0015\"><label>4.4</label><title>Brain tissue preparation</title><p>Mice were deeply anesthetized with chloral hydrate and transcardially perfused with saline. Brains were removed rapidly and dissected into two hemispheres. One hemisphere was embedded in OCT compound immediately before freezing and cutting and stored at −80°C. The other hemisphere was immediately submerged in ice‐cold phosphate‐buffered saline (PBS), and the cortex and hippocampus were carefully dissected. The dissected tissues were snap‐frozen in liquid nitrogen and stored at −80°C until use in the biochemical analyses.</p></sec><sec id=\"acel13194-sec-0016\"><label>4.5</label><title>Immunoprecipitation and western blot</title><p>Cells or tissues were lysed with lysis buffer containing protease inhibitor cocktail (Sigma) and phenylmethylsulfonyl fluoride (PMSF) on ice and then subjected to a BCA assay (Pierce). For IP, whole‐cell lysates were incubated with specific antibodies overnight at 4 ℃, followed by incubation with protein A/G agarose beads (GE) for 3 hr at 4°C and washing with lysis buffer. Thereafter, the beads were boiled with 2× sample buffer at 95°C for 3 min before western blot. For western blot, details were described in Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13194-sec-0017\"><label>4.6</label><title>ELISA assay</title><p>Aβ from <italic>APP</italic>/<italic>PS</italic> mouse brain was measured by ELISA. See Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref> for details.</p></sec><sec id=\"acel13194-sec-0018\"><label>4.7</label><title>Real‐time quantitative PCR</title><p>Total RNA was extracted using Trizol reagent (Invitrogen), and 800 ng RNA was used as a template to convert to complementary DNA with the PrimeScriptTM RT reagent kit (Takara). Quantitative real‐time PCR was performed using SYBR Green by LightCycler96 (Roche). Relative abundance was calculated using the ΔΔCt method normalized to the housekeeping gene. The primers are listed in Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>, Table <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S3</xref>.</p></sec><sec id=\"acel13194-sec-0019\"><label>4.8</label><title>Stereotaxic injection of the virus</title><p>Mice were anesthetized with 5% chloral hydrate (8 μl/g). Bilateral intracerebral injection of AAV Vector was performed stereotactically using the following coordinates: anterior‐posterior, −2.3 mm; medial‐lateral, ±1.8 mm; and dorsal‐ventral, −2.2 mm relative to bregma. A volume of 1 μl of virus (virus titer: 10<sup>12</sup> vg/ml) was injected into each point at a rate of 0.1 μl/min using 1 μl syringes with a fixed needle. The needle was kept in place for 5 min before it was removed slowly. The mice were placed on a heating pad until they began to recover from the surgery. All animal experiments were approved by the Animal Care and Use Committee of Guangdong Medical University and performed in compliance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.</p></sec><sec id=\"acel13194-sec-0020\"><label>4.9</label><title>Morris water maze test (MWM)</title><p>Spatial learning and memory abilities were evaluated with the MWM with some modifications, details were described in Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>.</p></sec><sec id=\"acel13194-sec-0021\"><label>4.10</label><title>Novel object recognition</title><p>The NOR test is based on the innate tendency of rodents to explore a novel object more often than a familiar one, details were described in Appendix <xref rid=\"acel13194-sup-0002\" ref-type=\"supplementary-material\">S1</xref>. The recognition index = time spent exploring the novel object/ the time spent exploring both objects. The discrimination index = time (spent on the novel object‐ time spent on the old object)/ the time spent exploring both objects.</p></sec><sec id=\"acel13194-sec-0022\"><label>4.11</label><title>Immunofluorescence and laser scanning confocal microscopy</title><p>Coronal sections were prepared from fresh‐frozen sections of mouse brain hemispheres. Sections (10 μm) were fixed with acetone/methanol for 10 min at room temperature and blocked with 0.1% Triton X‐100/10% goat serum for 30 min at room temperature. Sections were then incubated with primary antibody at 4 °C overnight and stained with fluorescently conjugated secondary antibodies at room temperature for 1 hr. A final counterstaining with DAPI was performed for 3 min. All sections were washed with PBS, and images were acquired using a confocal microscope (FV3000, Olympus).</p></sec><sec id=\"acel13194-sec-0023\"><label>4.12</label><title>Detection of apoptosis by TdT‐mediated dUTP nick‐end labeling (TUNEL) assay</title><p>Coronal sections were prepared from fresh‐frozen sections of mouse brain hemispheres. Sections (10 μm) were fixed with 4% paraformaldehyde for 45 min at room temperature and then measured using kits (Beyotime) according to the manufacturer's instructions. Images were acquired using a confocal microscope (FV3000, Olympus).</p></sec><sec id=\"acel13194-sec-0024\"><label>4.13</label><title>Mass spectrometry analysis of protein mixtures</title><p>Whole‐cell lysates of SY5Y cells transfected with Flag‐SIRT2 or vector were IPed by Flag‐conjugated beads (Sigma) and eluted using a 3× Flag peptide. The immunoprecipitate was digested overnight at 37°C with trypsin and then lyophilized to dryness. After separation using a C18 column, the peptides were identified by MS (Thermo Scientific Q Exactive). Raw data from the MS analysis were extracted and subjected to a search against the UniProt Swiss‐Prot sequence database (HUMAN_2017.10.29_UniProt.fasta).</p></sec><sec id=\"acel13194-sec-0025\"><label>4.14</label><title>Statistical analysis</title><p>Data are presented as mean ± standard error of the mean (<italic>SEM</italic>). Statistical analyses were performed by Student's <italic>t</italic> test for the comparison of two groups; by one‐way ANOVA with Tukey's post hoc test for the comparison of three groups with one independent variable; and by two‐way ANOVA for groups with two independent variables. Statistical analysis was performed using GraphPad Prism 6 (GraphPad Software). ImageJ software was used to quantify the expression of the protein.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13194-sec-0027\"><title>CONFLICT OF INTEREST</title><p>The authors declare no conflict of interest.</p></sec><sec id=\"acel13194-sec-0028\"><title>AUTHOR CONTRIBUTIONS</title><p>Y.W. participated in experiments designed, involved in cell culture, analyzed and interpreted data, prepared figures, and wrote the manuscript; J.Y. performed mice experiments and ELISA assays; T.H. assisted in the mice experiments and performed the immunostaining experiments and acquired images; Y.S. performed the western blot experiments and quantitative PCR studies; H. H. and F.C. provided helpful discussions; X.C. performed mice management; H.C. helped draw the schematic diagrams; S.D. performed the bioinformatics analysis; L.C. and T.Y. provided guidance, helped design the experiment, supervised the overall project, and edited the manuscript.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13194-sup-0002\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13194-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13194-sec-0026\"><title>ACKNOWLEDGMENTS</title><p>This work was supported by the National Natural Science Foundation of China (81671181), Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2017), and Guangdong Province: Special Support Plan for High‐Level Talents.</p></ack><sec sec-type=\"data-availability\" id=\"acel13194-sec-0030\"><title>DATA AVAILABILITY STATEMENT</title><p>The authors declare that the authors provide all data included in this study upon request when there is a reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13194-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13194-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13194-cit-0001\">\n<collab collab-type=\"authors\">Alzheimer's Association</collab>\n, (<year>2019</year>). <article-title>2019 Alzheimer’s disease facts and figures</article-title>. <source xml:lang=\"en\">Alzheimer's & Dementia</source>, <volume>15</volume>(<issue>3</issue>), <fpage>321</fpage>–<lpage>387</lpage>. <pub-id pub-id-type=\"doi\">10.1016/j.jalz.2019.01.010</pub-id>\n</mixed-citation></ref><ref id=\"acel13194-bib-0002\"><mixed-citation publication-type=\"journal\" id=\"acel13194-cit-0002\">\n<string-name>\n<surname>Biella</surname>, <given-names>G.</given-names>\n</string-name>, <string-name>\n<surname>Fusco</surname>, <given-names>F.</given-names>\n</string-name>, <string-name>\n<surname>Nardo</surname>, <given-names>E.</given-names>\n</string-name>, <string-name>\n<surname>Bernocchi</surname>, <given-names>O.</given-names>\n</string-name>, <string-name>\n<surname>Colombo</surname>, <given-names>A.</given-names>\n</string-name>, <string-name>\n<surname>Lichtenthaler</surname>, <given-names>S. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32666649</article-id><article-id pub-id-type=\"pmc\">PMC7431834</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13191</article-id><article-id pub-id-type=\"publisher-id\">ACEL13191</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Aging shifts mitochondrial dynamics toward fission to promote germline stem cell loss</article-title><alt-title alt-title-type=\"left-running-head\">AMARTUVSHIN et al.</alt-title></title-group><contrib-group><contrib id=\"acel13191-cr-0001\" contrib-type=\"author\"><name><surname>Amartuvshin</surname><given-names>Oyundari</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-0626-8350</contrib-id><address><email>oyundariamar@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0002\" contrib-type=\"author\"><name><surname>Lin</surname><given-names>Chi‐Hung</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-5511-3286</contrib-id><address><email>victorianlin@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0003\" contrib-type=\"author\"><name><surname>Hsu</surname><given-names>Shao‐Chun</given-names></name><address><email>peggyschsu@gate.sinica.edu.tw</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0004\" contrib-type=\"author\"><name><surname>Kao</surname><given-names>Shih‐Han</given-names></name><address><email>qrs458@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-curr-0001\">\n<sup>8</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0005\" contrib-type=\"author\"><name><surname>Chen</surname><given-names>Alvin</given-names></name><address><email>asdfg31724@smail.nchu.edu.tw</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0006\" contrib-type=\"author\"><name><surname>Tang</surname><given-names>Wei‐Chun</given-names></name><address><email>aibillton@gate.sinica.edu.tw</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0007\" contrib-type=\"author\"><name><surname>Chou</surname><given-names>Han‐Lin</given-names></name><address><email>d992050005@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0008\" contrib-type=\"author\"><name><surname>Chang</surname><given-names>Dong‐Lin</given-names></name><address><email>710127@gs.hs.ntnu.edu.tw</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-aff-0006\">\n<sup>6</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0009\" contrib-type=\"author\"><name><surname>Hsu</surname><given-names>Yen‐Yang</given-names></name><address><email>youngyoungthebest@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-aff-0006\">\n<sup>6</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0010\" contrib-type=\"author\"><name><surname>Hsiao</surname><given-names>Bai‐Shiou</given-names></name><address><email>hsiaobs@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-aff-0006\">\n<sup>6</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0011\" contrib-type=\"author\"><name><surname>Rastegari</surname><given-names>Elham</given-names></name><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0012\" contrib-type=\"author\"><name><surname>Lin</surname><given-names>Kun‐Yang</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-6197-3995</contrib-id><address><email>kylin0407@outlook.com</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0013\" contrib-type=\"author\"><name><surname>Wang</surname><given-names>Yu‐Ting</given-names></name><address><email>yutingwang1977@gmail.com</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0014\" contrib-type=\"author\"><name><surname>Yao</surname><given-names>Chi‐Kuang</given-names></name><address><email>ckyao@gate.sinica.edu.tw</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0007\">\n<sup>7</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0015\" contrib-type=\"author\"><name><surname>Chen</surname><given-names>Guang‐Chao</given-names></name><address><email>gcchen@gate.sinica.edu.tw</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0007\">\n<sup>7</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0016\" contrib-type=\"author\"><name><surname>Chen</surname><given-names>Bi‐Chang</given-names></name><address><email>chenb10@gate.sinica.edu.tw</email></address><xref ref-type=\"aff\" rid=\"acel13191-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13191-cr-0017\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Hsu</surname><given-names>Hwei‐Jan</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-7892-2310</contrib-id><xref ref-type=\"aff\" rid=\"acel13191-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13191-aff-0003\">\n<sup>3</sup>\n</xref><address><email>cohsu@gate.sinica.edu.tw</email></address></contrib></contrib-group><aff id=\"acel13191-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Molecular and Cell Biology</named-content>\n<named-content content-type=\"organisation-division\">Taiwan International Graduate Program</named-content>\n<institution>Academia Sinica</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13191-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Graduate Institute of Life Science</named-content>\n<institution>National Defense Medical Center</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13191-aff-0003\">\n<label><sup>3</sup></label>\n<institution>Institute of Cellular and Organismic Biology</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13191-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Imaging Core Facility at the Institute of Cellular and Organismic Biology</named-content>\n<institution>Academia Sinica</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13191-aff-0005\">\n<label><sup>5</sup></label>\n<named-content content-type=\"organisation-division\">Research Center for Applied Science</named-content>\n<institution>Academia Sinica</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13191-aff-0006\">\n<label><sup>6</sup></label>\n<institution>The Affiliated Senior High School of National Taiwan Normal University</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13191-aff-0007\">\n<label><sup>7</sup></label>\n<named-content content-type=\"organisation-division\">Institute of Biological Chemistry</named-content>\n<institution>Academia Sinica</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><aff id=\"acel13191-curr-0001\"><label><sup>8</sup></label>Present address:\n<named-content content-type=\"organisation-division\">Institute of Chemistry</named-content>\n<institution>Academia Sinica</institution>\n<city>Taipei</city>\n<country country=\"TW\">Taiwan</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label>\nCorrespondence<break/>\nHwei‐Jan Hsu, Molecular and Cell Biology, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan.<break/>\nEmail: <email>cohsu@gate.sinica.edu.tw</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>14</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13191</elocation-id><history><date date-type=\"received\"><day>26</day><month>2</month><year>2020</year></date><date date-type=\"rev-recd\"><day>20</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13191.pdf\"/><abstract id=\"acel13191-abs-0001\"><title>Abstract</title><p>Changes in mitochondrial dynamics (fusion and fission) are known to occur during stem cell differentiation; however, the role of this phenomenon in tissue aging remains unclear. Here, we report that mitochondrial dynamics are shifted toward fission during aging of <italic>Drosophila</italic> ovarian germline stem cells (GSCs), and this shift contributes to aging‐related GSC loss. We found that as GSCs age, mitochondrial fragmentation and expression of the mitochondrial fission regulator, Dynamin‐related protein (Drp1), are both increased, while mitochondrial membrane potential is reduced. Moreover, preventing mitochondrial fusion in GSCs results in highly fragmented depolarized mitochondria, decreased BMP stemness signaling, impaired fatty acid metabolism, and GSC loss. Conversely, forcing mitochondrial elongation promotes GSC attachment to the niche. Importantly, maintenance of aging GSCs can be enhanced by suppressing Drp1 expression to prevent mitochondrial fission or treating with rapamycin, which is known to promote autophagy via TOR inhibition. Overall, our results show that mitochondrial dynamics are altered during physiological aging, affecting stem cell homeostasis via coordinated changes in stemness signaling, niche contact, and cellular metabolism. Such effects may also be highly relevant to other stem cell types and aging‐induced tissue degeneration.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13191-abs-0002\"><p>Aging shifts mitochondrial balance toward fission; fragmented mitochondria with low membrane potential (△Ψ), and ROS levels, along with decreased BMP signaling causing GSC loss. Marf depletion induces highly fragmented mitochondria with low fatty acid (FA) oxidation, causing oil droplet (LD) accumulation, and attenuated BMP signaling that cause GSC loss. Drp1 depletion generates elongated mitochondria and increased E‐cadherin expression to strengthen GSC competitiveness for niche occupancy. <boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13191-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13191-g007.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13191-kwd-0001\">BMP</kwd><kwd id=\"acel13191-kwd-0002\">Drp1</kwd><kwd id=\"acel13191-kwd-0003\">GSC</kwd><kwd id=\"acel13191-kwd-0004\">Marf</kwd><kwd id=\"acel13191-kwd-0005\">mitochondrial fission</kwd><kwd id=\"acel13191-kwd-0006\">mitochondrial fusion</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>Academia Sinica </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100001869</institution-id></institution-wrap></funding-source></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"0\"/><page-count count=\"19\"/><word-count count=\"13276\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13191-cit-1001\">\n<string-name>\n<surname>Amartuvshin</surname>\n<given-names>O</given-names>\n</string-name>, <string-name>\n<surname>Lin</surname>\n<given-names>C‐H</given-names>\n</string-name>, <string-name>\n<surname>Hsu</surname>\n<given-names>S‐C</given-names>\n</string-name>, et al. <article-title>Aging shifts mitochondrial dynamics toward fission to promote germline stem cell loss</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13191</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13190</pub-id>\n</mixed-citation>\n</p><fn-group id=\"acel13191-ntgp-0001\"><fn id=\"acel13191-note-0001\"><p>Dong‐Lin Chang, Yen‐Yang Hsu, Bai‐Shiou Hsiao and Elham Rastegari <bold>contributed equally in this study.</bold>\n</p></fn></fn-group></notes></front><body id=\"acel13191-body-0001\"><sec id=\"acel13191-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Stem cells reside in a specialized microenvironment called the niche, which provides both physical contact and stemness factors that ensure and maintain the stem cell fate (Morrison & Spradling, <xref rid=\"acel13191-bib-0044\" ref-type=\"ref\">2008</xref>). While stem cells promote tissue longevity by continually producing differentiated cells, the maintenance and/or function of stem cells often decrease with age, leading to aging‐dependent tissue degeneration (Ahmed, Sheng, Wasnik, Baylink, & Lau, <xref rid=\"acel13191-bib-0001\" ref-type=\"ref\">2017</xref>; Schultz & Sinclair, <xref rid=\"acel13191-bib-0054\" ref-type=\"ref\">2016</xref>). However, the mechanisms by which aging affects stem cells are only partially understood.</p><p>Mitochondria frequently undergo coordinated cycles of fusion and fission (known as mitochondrial dynamics) to properly adjust the shape, size, and cellular distribution of the organelle to meet specific cellular requirements (Hoppins, Lackner, & Nunnari, <xref rid=\"acel13191-bib-0024\" ref-type=\"ref\">2007</xref>; McQuibban, Lee, Zheng, Juusola, & Freeman, <xref rid=\"acel13191-bib-0040\" ref-type=\"ref\">2006</xref>). Fusion produces elongated mitochondria by respectively joining the outer and inner membranes of two mitochondria. The closely related Dynamin‐related GTPases, Mitofusin (Mfn) 1 and Mfn2, mediate outer membrane fusion, while optic atrophy (Opa1) is integral for fusion of the inner membrane (Pernas & Scorrano, <xref rid=\"acel13191-bib-0049\" ref-type=\"ref\">2016</xref>). On the other hand, excessive mitochondrial fission produces fragmented mitochondria and is mediated by another Dynamin‐related GTPase, called Dynamin‐related protein (Drp1) (Pernas & Scorrano, <xref rid=\"acel13191-bib-0049\" ref-type=\"ref\">2016</xref>). Drp1 is recruited by its receptors on the outer membrane and oligomerizes along the mitochondrial constriction site to constrict the organelle and induce scission (Pernas & Scorrano, <xref rid=\"acel13191-bib-0049\" ref-type=\"ref\">2016</xref>). The two <italic>Drosophila</italic> homologues of Mfn1/2 are Fuzzy onion (Fzo) and Mitochondrial assembly regulatory factor (Marf) (Hales & Fuller, <xref rid=\"acel13191-bib-0022\" ref-type=\"ref\">1997</xref>; Hwa, Hiller, Fuller, & Santel, <xref rid=\"acel13191-bib-0026\" ref-type=\"ref\">2002</xref>). Fzo is exclusively expressed in the testes, while Marf is expressed in the germline and somatic cells (Hwa et al., <xref rid=\"acel13191-bib-0026\" ref-type=\"ref\">2002</xref>). <italic>Drosophila</italic> also has single homologues of Opa1 and Drp1, which have the same names as their mammalian counterparts (Verstreken et al., <xref rid=\"acel13191-bib-0065\" ref-type=\"ref\">2005</xref>; Yarosh et al., <xref rid=\"acel13191-bib-0072\" ref-type=\"ref\">2008</xref>).</p><p>Mitochondrial dynamics are known to influence several mitochondria‐dependent biological processes, such as lipid homeostasis, calcium homeostasis, and ATP production (Tilokani, Nagashima, Paupe, & Prudent, <xref rid=\"acel13191-bib-0062\" ref-type=\"ref\">2018</xref>). Recent studies have also proposed a role for mitochondrial fusion and fission in regulating stem cell fate (Fu, Liu, & Yin, <xref rid=\"acel13191-bib-0020\" ref-type=\"ref\">2019</xref>; Seo, Yoon, & Do, <xref rid=\"acel13191-bib-0057\" ref-type=\"ref\">2018</xref>). In one interesting example, murine neural stem cells were shown to exhibit elongated mitochondria, and depletion of Mfn1 or Opa1 impaired their self‐renewal (Khacho et al., <xref rid=\"acel13191-bib-0029\" ref-type=\"ref\">2016</xref>). Despite tantalizing observations such as these, the overall impact of mitochondrial dynamics in aging stem cells and the mechanisms by which mitochondrial dynamics might affect stem cell function remain unclear.</p><p>We used the <italic>Drosophila</italic> ovary to address the question of how mitochondrial dynamics affect and are affected by stem cell aging, taking advantage of the short lifespan of <italic>Drosophila</italic> and its amenability to powerful genetic methods. Most importantly, the <italic>Drosophila</italic> ovary houses well‐characterized germline stem cells (GSCs) (Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1a</xref>) (Kirilly, Spana, Perrimon, Padgett, & Xie, <xref rid=\"acel13191-bib-0031\" ref-type=\"ref\">2005</xref>), which gradually escape the niche and become differentiated during aging (Kao et al., <xref rid=\"acel13191-bib-0028\" ref-type=\"ref\">2015</xref>). A <italic>Drosophila</italic> ovary contains 16–20 egg‐producing functional units, which are called ovarioles (Spradling, <xref rid=\"acel13191-bib-0061\" ref-type=\"ref\">1993</xref>). The germarium is the anterior‐most structure of the ovariole, and it houses two to three GSCs at its anterior tip. The terminal filament, cap cells, and anterior escort cells are also located in the anterior tip of the germarium and form the GSC niche (Losick, Morris, Fox, & Spradling, <xref rid=\"acel13191-bib-0036\" ref-type=\"ref\">2011</xref>). GSCs directly contact niche cap cells (the major niche component)(Song & Xie, <xref rid=\"acel13191-bib-0060\" ref-type=\"ref\">2002</xref>), and each of GSC contains a fusome, an organelle with a membranous‐like structure that is juxtaposed to the GSC‐cap cell interface (Xie & Spradling, <xref rid=\"acel13191-bib-0068\" ref-type=\"ref\">2000</xref>). As a single asymmetric GSC division gives rise to a cystoblast (CB), the fusome changes morphology according to the stage of the cell cycle (Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1b</xref>). During G2/M phase, the GSC fusome is round. Then, at G1 and S phases, it grows and fuses with a newly formed fusome destined for the daughter CB, generating an elongated fusome. This elongated fusome is pinched off when the GSC and CB begin to separate during early G2 phase, leading it to regain its round shape in the GSC until the end of M phase (de Cuevas & Spradling, <xref rid=\"acel13191-bib-0015\" ref-type=\"ref\">1998</xref>; Kao et al., <xref rid=\"acel13191-bib-0028\" ref-type=\"ref\">2015</xref>). After M phase, the daughter CB undergoes four rounds of incomplete division to form a 16‐cell cyst; each germ cell within the cyst is interconnected by a branched fusome (Spradling, <xref rid=\"acel13191-bib-0061\" ref-type=\"ref\">1993</xref>). Next, the 16‐cell cyst is surrounded by a layer of follicle cells, and the whole structure buds off from the germarium, finally developing into a mature egg (Spradling, <xref rid=\"acel13191-bib-0061\" ref-type=\"ref\">1993</xref>). Mitochondria are generally found in a big cluster located near the fusome in GSCs. In contrast, highly fragmented mitochondria are located far from the fusome in 4‐ and 8‐cell cysts, while elongated mitochondria are observed in close proximity to the fusome in 16‐cell cysts (see Figure <xref rid=\"acel13191-fig-0004\" ref-type=\"fig\">4b</xref>) (Cox & Spradling, <xref rid=\"acel13191-bib-0013\" ref-type=\"ref\">2003</xref>).</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13191-fig-0001\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Aged GSCs exhibit increased numbers of fragmented mitochondria. (a) An illustration of the anterior part of the <italic>Drosophila</italic> germarium. Terminal filament (TF) and cap cells form the GSC niche to house GSCs. Each GSC carries a fusome. The cystoblast (CB), the immediate daughter cell of the GSC, undergoes four rounds of incomplete division to form a 16‐cell cyst. Each cyst carries a branched fusome that interconnects germ cells within the cyst. (b) Fusome morphology changes according to the GSC cell cycle phase. During S phase, GSCs display a “plug,” “elongated,” or “bar” fusome morphology, as a nascent fusome (or plug) is assembled and then fused to the original fusome, thereby connecting the GSC and the cystoblast. During early G2, GSCs exhibit “exclamation point” fusome morphology, as the connection between GSCs and the cystoblast is severed. During late G2 and M, GSC fusomes display a “round” fusome. (c and d) One‐ (c) and 8‐week‐old germaria (d) with LamC (red, TF and cap cell nuclear envelopes), 1B1 (red, fusomes), Vasa (blue, germ cells), and ATP5ase (gray, mitochondria). Inserts are higher magnification of GSCs marked by yellow asterisks in E and F, with ATP5ase shown in green. (c′ and d′) are same GSCs shown in the inserts, but with less layers and the surface model of mitochondria from Imaris shown. Mitochondria (mito) forming networks are shown in green; fragmented mitochondria (fragm. mito) are shown in yellow. Asterisks indicate GSC(s) in the germarium; dashed circles outline GSCs. Yellow dashed lines outline the anterior edge of the germarium. Scale bars in c and d are 5 μm, in the insert of c and d are 2 μm, and in c′ and d′ are 1 μm. (e) Percentage (%) of mitochondria with indicated volume in 1‐ and 8‐week‐old GSCs. (e′) Number (no) of fragmented mitochondria in 1‐ and 8‐week‐old GSCs at S or G2/M phases. (e″) Percentage of mitochondrial content per GSC in 1‐ and 8‐week‐day‐old GSCs. Numbers of analyzed GSCs are shown above each bar. Error bars, <italic>SEM</italic>. **<italic>p </italic>< 0.01; ***<italic>p </italic>< 0.001. (f and g) Representative electron micrographs of the anterior regions of 1‐ (f) and 8‐week (W)‐old germaria (g). f′ and g′ are enlarged views from the areas indicated by squares in f and g. N, Nucleus. CpC, cap cells. Scale bars in e and g are 2 μm, and bars in f′ and g′ are 0.5 μm. (h) The area (μm<sup>2</sup>) and the width to height (W/H) ratio of individual mitochondria in 1‐ (red) and 8‐week‐old GSCs (blue). Mitochondria distributed in blue, green, and pink areas are elongated, medium, and fragmented mitochondria, respectively. Solid and dashed lines represent the mean of W/H ratio and area of mitochondria in 1‐week‐old GSCs, respectively. n, number of analyzed mitochondria. Percentages of mitochondria in each group (1‐ versus 8‐week‐old GSCs) showed significant differences (<italic>p </italic>< 0.001, Chi‐squared test). Representative germaria are shown in 3D‐reconstructed images; genotype of flies is <italic>yw</italic>.</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13191-g001\"/></fig><p>In this study, we used fluorescence and transmission electron microscopy (TEM) to show that fragmented mitochondria are increased in aged GSCs in fixed ovarian tissues. Our live‐imaging data further show that mitochondrial fission is increased in aged GSCs, suggesting a source for the accumulated fragmented mitochondria. GSCs with mitochondrial fragmentation forced by a mutation of <italic>marf</italic> mimic aged GSCs, which divide slowly, exhibit low Dpp (BMP orthologue) stemness signaling and have a tendency to leave the niche and differentiate (Kao et al., <xref rid=\"acel13191-bib-0028\" ref-type=\"ref\">2015</xref>). Furthermore, the fragmented mitochondria exhibit reduced membrane potential and defective fatty acid metabolism, as revealed by cytoplasmic oil droplet accumulation. On the other hand, stimulating mitochondrial elongation in GSCs by <italic>drp1</italic> mutation increases GSC<bold>–</bold>niche occupancy, at least in part, through increased expression of E‐cadherin. Notably, cytoplasmic oil droplet accumulation is not frequently observed in aged GSCs, possibly because the aged cells have less severe mitochondria fragmentation compared to <italic>marf</italic> mutant GSCs. Interestingly, Drp1 expression is increased in aged GSCs, and suppressing Drp1 expression to prevent mitochondrial fission or treating the flies with rapamycin to induce autophagy reduces aging‐dependent GSC loss. Together, our results show that aging shifts mitochondrial dynamics toward fission in stem cells, and this shift impairs the maintenance of stemness factor production and cellular metabolism. Thus, mitochondrial homeostasis may be an interesting target for modulating aging‐related tissue degeneration.</p></sec><sec sec-type=\"results\" id=\"acel13191-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13191-sec-0003\"><label>2.1</label><title>Aging increases fragmented mitochondria in GSCs</title><p>To understand whether aging affects mitochondrial morphology, we first labeled germaria with antibodies for LamC, 1B1, and Vasa to delineate the various cell types. We also labeled ATP synthase 5 α subunit (ATP5ase) in the mitochondria of young (1‐week‐old) and aged ovaries (8‐week‐old) to analyze mitochondrial location and size. In young GSCs (indicated by an asterisk in Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1c</xref>), mitochondria formed a big cluster near the fusome, an observation that is in agreement with a previous report (Cox & Spradling, <xref rid=\"acel13191-bib-0013\" ref-type=\"ref\">2003</xref>); this location was not changed in aged GSCs (indicated by an asterisk in Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1d</xref>). The volume of individual mitochondria in GSCs was about 0.4 ± 0.14 μm<sup>3</sup> (<italic>n</italic> = 20 GSCs) on average, but the size ranged up to 48 μm<sup>3</sup> (data not shown). Notably, the sizes of individual mitochondria and numbers of mitochondria in a cluster could not be accurately assessed due to limitations in resolution. However, we could find that mitochondria with sizes smaller than 0.05 μm<sup>3</sup> were increased in aged GSCs compared to young GSCs (Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1e,e</xref>′, and see yellow signals in Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1c</xref>′,d′); we defined these small mitochondria as fragmented mitochondria. This difference was not correlated with the phase of the GSC cell cycle (Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1e</xref>′). In addition, total mitochondrial content (ratio of total mitochondrial volume to the GSC volume) was significantly lower in aged GSCs than in young GSCs (Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1e</xref>″). The immediate daughter cells of GSCs (CBs) also displayed a wide range of mitochondrial sizes, as well as age‐associated increases in fragmented mitochondria and decreases in mitochondrial content (Figure <xref rid=\"acel13191-sup-0001\" ref-type=\"supplementary-material\">S1</xref>A‐C), indicating that GSCs and CBs share similar responses of mitochondrial characteristics to aging.</p><p>Next, we used TEM to further examine mitochondria in GSCs of young (1‐week‐old) and aged ovaries (8‐week‐old) at high resolution (Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1</xref>f,g,f′,g′). GSCs were identified by their anterior location in the germarium and direct contact with cap cells; we measured mitochondrial areas and width to height (W/H) ratios only in GSCs where the nucleus could be clearly identified. Despite the fact that organelle orientation in the thin section will affect the size measurement, we reasoned that the stochastic nature of mitochondrial orientation should still allow us to see a difference between experimental groups if mitochondria were more fragmented in aged GSCs. Indeed, averages of mitochondrial area and length were larger in young GSCs (area: 0.11 ± 0.11 μm<sup>2</sup>; W/H ratio: 2.34 ± 1.9, 177 mitochondria from 6 GSCs), as compared to aged GSCs (area: 0.06 ± 0.04 μm<sup>2</sup>, <italic>p </italic>< 0.05; W/H ratio: 1.92 ± 1.4, <italic>p </italic>< 0.001, 129 mitochondria from 5 GSCs) (Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1h</xref>). For further analysis, mitochondria were classified into three groups according to the mean area and W/H (of mitochondria analyzed in young GSCs: mitochondria with areas bigger than 0.11 μm<sup>2</sup> and W/H ratio larger than 2.34 were considered “elongated mitochondria,” mitochondria with areas bigger than 0.11 μm<sup>2</sup> and W/H ratio smaller than 2.34 were counted as “medium mitochondria,” and mitochondria with areas smaller than 0.11 μm<sup>2</sup> were called “fragmented mitochondria” (Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1h</xref>). The respective percentages of elongated, medium, and fragmented mitochondria in young GSCs were 19.7%, 15.3%, and 65% versus 5.4%, 0.8%, and 93.8% (<italic>p </italic>< 0.001) in aged GSCs (Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1h</xref>). Thus, we found that aged GSCs exhibited high levels of fragmented mitochondria, consistent with our conclusion from fluorescence microcopy experiments. Because the mitochondria in aged GSCs were smaller than those in young cells, we suspected that mitochondria in aged cells may either undergo less fusion or more fission to yield fragmented mitochondria.</p></sec><sec id=\"acel13191-sec-0004\"><label>2.2</label><title>Aged GSCs display a preference for mitochondrial fission</title><p>To distinguish between the two possibilities described above, we first labeled mitochondria in live young (1‐week‐old) and aged ovaries (7‐week‐old) by MitoTracker, a fluorescent dye (Figure <xref rid=\"acel13191-sup-0001\" ref-type=\"supplementary-material\">S1</xref>D and E). We found that mitochondria in aged GSCs were more fragmented than those in young GSCs (insets in Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1c,d</xref>); however, we sometimes could not distinguish if analyzed mitochondria were from GSCs or somatic cells. We, therefore, expressed <italic>mito</italic>‐<italic>gfp</italic> [a marker for mitochondria (Cox & Spradling, <xref rid=\"acel13191-bib-0013\" ref-type=\"ref\">2003</xref>)] in the germline using <italic>nos</italic>‐<italic>GAL4</italic>. We made live recordings of <italic>nos</italic>><italic>mito</italic>‐<italic>gfp</italic> GSCs for 10 min (300 time points with intervals of about 2 s to generate 300 stacks, each stack containing 100 slices along the z‐axis) using lattice light‐sheet microscopy (Movie <xref rid=\"acel13191-sup-0009\" ref-type=\"supplementary-material\">S1 and</xref> Movie <xref rid=\"acel13191-sup-0010\" ref-type=\"supplementary-material\">S2</xref>), which allows us to capture images with high resolution and fast acquisition speed (Chen et al., <xref rid=\"acel13191-bib-0011\" ref-type=\"ref\">2014</xref>). Hoechst staining was used to identify cap cells according to their small oval‐shaped nuclei and anterior‐most location in the germaria; GSCs were identified by their direct contact with cap cells (left panel in Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2a</xref>). We found that over the 10‐min recording period, the mitochondria number nearly doubled (1 week‐old, 9 GSCs: 0 min, 69 ± 21 mitochondria versus 10 min, 139 ± 17 mitochondria; 8 week‐old, 5 GSCs: 0 min, 189 ± 45 mitochondria versus 10 min, 346 ± 95 mitochondria) (Figure <xref rid=\"acel13191-sup-0002\" ref-type=\"supplementary-material\">S2</xref>A,B). We suspected that this result might be due to noisy signal as a result of Mito‐GFP photobleaching, or the laser itself may induce mitochondrial fission. Given that mitochondrial number should not dramatically change under normal physiological conditions, we limited our analysis to the first 10 stacks; during this period, the mitochondrial number was steadily maintained (Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2c,d</xref>). In this experiment, we noted that aged GSCs exhibited decreased mitochondrial content and an increased proportion of fragmented mitochondria (mitochondrial size <0.05 μm<sup>3</sup>, as yellow mitochondria in the left panel of Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2a</xref>), consistent with our other results.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13191-fig-0002\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>Mitochondrial dynamics favor fission in aged GSCs. (a) Left panel: Mito‐GFP‐labeled mitochondria (blue) in 1‐week (W)‐old GSCs with Hoechst labeling (gray) were analyzed at the first 4 time points and displayed as a surface‐rendered 3D trace. The cap cells were recognized by their small ovoid nucleus shown in red and juxtaposition with GSCs. Fragmented mitochondria are shown in yellow. Right panel: tracking route of mitochondria in the GSC. Color code indicates mitochondrial route from time point 1 to 4. Scale bar, 2 μm. (b) Mitochondrial dynamics events [fission (fiss) plus fusion (fus)] of 1‐ and 8‐week‐old GSCs within the first 10 time points. (b′) Proportional difference of fission and fusion in 1‐ and 8‐week‐old GSCs. ns, no significance. (c) Representative tracks of mitochondria (shown in gray) analyzed over 4 time points are classified into three groups: mitochondrion undergoing 1 fusion and 1 fission (top panels), more fission (middle panels, only one fission track is shown as an example), and more fusion (bottom panels, only one fusion track is shown as an example). Mitochondrial routes are indicated by the color code shown in the right panel of a. White circles represent tracked mitochondrion, and blue squares indicate the analyzed time point. (d) Percentage of mitochondrial tracks of each type in 1‐ and 8‐week‐old GSCs analyzed throughout time points 1‐4, 4‐7, and 7‐10. Track numbers analyzed are shown beside each bar. ***<italic>p </italic>< 0.001, chi‐squared test. (e) Net difference (△) of fragmented mitochondrial number between two time points within the first 10 recorded from 1‐ and 8‐week GSCs. Positive and negative values indicate net preference for fission and fusion events, respectively. Dashed lines show fluctuation of fragmented mitochondrial number for each GSC; solid lines show the average fluctuation of fragmented mitochondrial number. Red solid lines shown in b′, and e are trend‐lines; slopes represent tendencies toward fusion or fission.</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13191-g002\"/></fig><p>To examine mitochondrial dynamics, we carefully tracked mitochondrial fission and fusion over three periods of four time points each, covering the first ten live images (i.e., time points 1‐4, 4‐7, and 7‐10) of young and aged GSCs (Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2a</xref>, right panel and Movie <xref rid=\"acel13191-sup-0011\" ref-type=\"supplementary-material\">S3</xref> show representative tracking routes from time points 1‐4). If a fission or fusion event occurred on a single track in each of the three time periods, the maximum total fusion‐plus‐fission events would be 3 for each track, allowing us to make a simple characterization of mitochondrial dynamics. We found that total fission and fusion events in young and aged GSCs were similar (Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2b</xref>; see examples of fission and fusion in Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2c</xref>). However, the proportional difference between fission and fusion was slightly more in aged GSCs (Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2b</xref>′), suggesting that there may be a minor excess of fission causing a very slow accumulation of fragmented mitochondria during aging. To further analyze the mitochondria tracking data, we grouped tracks into three types (Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2c</xref>): tracks with one fusion and one fission event (one fis and fus track, top panel), tracks with higher number of fission events than fusion events (fis > fus track, middle panel), and tracks with lower number of fission events than fusion events (fis < fus track, bottom panel). Our results showed that the respective proportions of one fiss and fus, fiss > fus and fiss < fus tracks were 30%, 31%, and 40% (<italic>n</italic> = 285) in young GSCs and 24%, 40%, and 36% in aged GSCs (<italic>n</italic> = 196, <italic>p </italic>< 0.001). These results showed that one‐third of mitochondrial dynamic events were balanced with equal amounts of fusion and fission in young GSCs, while these balanced tracks were decreased in aged flies (Black bar in Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2d</xref>). Moreover, mitochondria preferred to fuse with other mitochondria in young GSCs, while more mitochondria were observed undergoing fission in aged GSCs (Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2d</xref>). These results indicate that young GSCs display a slight preference for fusion, while aged GSCs show a small preference for fission.</p><p>To confirm these results, we also counted the numbers of fragmented mitochondria at each time point, as any change in their number should reflect the relative proportions of fission and fusion events. For example, if one fragmented mitochondrion is present at the first time point but two fragmented mitochondria are present at the next time point (the resulting change in number of fragmented mitochondria is +1), the cell should exhibit a net tendency toward fission. In contrast, if two fragmented mitochondria are present at the first time point but only one is present at the next time point (the resulting change in number of fragmented mitochondria is −1), the cell should exhibit a net tendency toward fusion. We averaged the changes in numbers of fragmented mitochondria between stacks for all GSCs and found that the trendline of net changes of mitochondrial dynamics in young GSCs was sloped slightly toward fusion, while in aged GSCs, the trendline sloped toward fission (Figure <xref rid=\"acel13191-fig-0002\" ref-type=\"fig\">2e</xref>). Of note, the changes in mitochondrial number varied greatly (both positive and negative) in both young and aged GSCs, implying mitochondrial fission and fusion events are highly frequent in GSCs. Taken together, our live‐imaging data show that aging of GSCs prompts a slight shift in the balance of mitochondrial dynamics toward fission.</p></sec><sec id=\"acel13191-sec-0005\"><label>2.3</label><title>Preventing mitochondrial fusion decreases GSC division and maintenance</title><p>GSC division and maintenance are reduced during aging (Kao et al., <xref rid=\"acel13191-bib-0028\" ref-type=\"ref\">2015</xref>; Pan et al., <xref rid=\"acel13191-bib-0048\" ref-type=\"ref\">2007</xref>; Zhao, Xuan, Li, & Xi, <xref rid=\"acel13191-bib-0074\" ref-type=\"ref\">2008</xref>). To understand whether mitochondrial fission is involved in this process, we introduced a mutation in the fusion regulator, <italic>marf</italic>, in GSCs by mitotic recombination (indicated by the absence of GFP) (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3a–e</xref>). As expected, <italic>marf</italic> mutant GSCs displayed fragmented mitochondria (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3c</xref>′,d″) when compared to neighboring control GSCs or the GSCs in control germaria (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3b</xref>′,b″,d′). We then assessed whether preventing fusion decreases GSC division (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3f</xref>). Because each CB or cyst is derived from one GSC division, the ratio of mutant (GFP‐negative, GFP<sup>−</sup>) and control progeny (GFP‐positive, GFP<sup>+</sup>) can reflect relative rates of division (LaFever & Drummond‐Barbosa, <xref rid=\"acel13191-bib-0032\" ref-type=\"ref\">2005</xref>). We counted the number of control and mutant cystoblasts and cysts in <italic>marf</italic> germaria containing at least one control and one mutant GSC. The relative numbers of wild‐type and mutant CBs or cysts were unaffected by early germline death (Figure <xref rid=\"acel13191-sup-0004\" ref-type=\"supplementary-material\">S4</xref>), and the numbers of progeny derived from control GSCs were approximately equal to those without GFP in mock mosaic germaria (relative division rate equal to approximately 1.0) at 1, 2, and 3 weeks after clone induction (ACI) (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3f</xref>). In contrast, division was significantly reduced in GSCs homozygous for <italic>marf<sup>E</sup></italic>, a hypomorphic allele, and for <italic>marf<sup>B</sup></italic>, a null allele (Yarosh et al., <xref rid=\"acel13191-bib-0072\" ref-type=\"ref\">2008</xref>) (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3f</xref>). We next asked whether diminished fusion reduces GSC maintenance by counting the number of germaria carrying <italic>marf</italic> mutant GSCs over time (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3g</xref> and Table <xref rid=\"acel13191-sup-0013\" ref-type=\"supplementary-material\">S1</xref>). At 3 weeks ACI, about 93 ± 4% of <italic>FRT19A</italic> control germaria (<italic>n</italic> = 336) retained at least one GFP‐negative control GSC from the first week, indicating that up to ~7% of GSCs may be naturally turned over. In 3‐week ACI mutant germaria, only 68 ± 15% (<italic>marf<sup>E</sup></italic>, <italic>n</italic> = 251) and 38 ± 9% (<italic>marf<sup>B</sup></italic>, <italic>n</italic> = 310) of mutant GSCs were maintained. Since we did not detect any apoptotic <italic>marf</italic> mutant GSCs (Figure <xref rid=\"acel13191-sup-0004\" ref-type=\"supplementary-material\">S4</xref>), we suspect that <italic>marf</italic> mutant GSCs leave the niche and undergo differentiation. These results show that mitochondrial fragmentation is detrimental to GSC division and maintenance.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13191-fig-0003\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>Disrupting mitochondrial fusion decreases GSC division and maintenance while disrupting mitochondrial fission promotes GSC–niche occupancy. (a) Mitotic recombination was used to generate GSC mutants for <italic>marf</italic> or <italic>drp1</italic>. Females carrying a wild‐type (wt, +) allele linked to a marker gene (<italic>gfp</italic>) <italic>in trans</italic> with a mutant (<italic>mut</italic>) allele were generated. FLP‐mediated recombination between FRT sites during mitotic division generates a homozygous mutant cell, identifiable by the absence of marker expression. (b‐e, h and i) <italic>FRT19A</italic> control (ctrl) (b), <italic>marf<sup>E</sup></italic> (c), <italic>marf<sup>B</sup></italic> mutant mosaic germaria (d and e), <italic>drp1<sup>2</sup></italic> (h) and <italic>drp1<sup>1</sup></italic> mutant mosaic germaria (i) with GFP (green, wt cells), 1B1 (red, fusomes), and ATP5ase (gray, mitochondria) at 1 (b‐d), 2 (e and f), and 3 weeks (w) after clone induction (ACI) (i). Wt GSCs are indicated by asterisks; GFP‐negative GSCs and their daughter cells are outlined by yellow and white dashed lines, respectively. Inserts in b and c show GFP‐negative (−) GSCs, in d and h show GFP‐positive (+) GSCs at different focal planes. b′ and b″, c′, d′, d″, h′ and h″ show higher magnifications of GFP<sup>+</sup> or GFP<sup>−</sup> GSCs with 1B1 and ATP5ase staining. In the germarium (d), GFP<sup>−</sup> GSCs only produce one GFP<sup>−</sup> GSC daughter cell, indicating a low rate of GSC division; the germarium (e) carries GFP<sup>−</sup> germ cells but not GFP<sup>−</sup> GSCs, indicating the loss of GFP<sup>−</sup> GSCs. Germaria carrying GSCs that are not all GFP‐negative are referred to as partial GSC clones, while germaria carrying GSCs that are all GFP‐negative are referred to as full GSC clones. Scale bar, 10 µm. (f and j) GSC relative division rates (ratio of GFP‐negative to GFP‐positive GSC progeny) in mosaic germaria. The number of GSCs analyzed is shown above each bar. (g) Relative percentage of GSC clones (as a proportion of total GSCs) at 1, 2, and 3 W ACI. (k) Relative percentage of germaria with partial GSC clones versus germaria with full GSC clones at 1, 2, and 3 W ACI. Numbers of germaria analyzed are shown above each bar. *<italic>p </italic>< 0.05; **<italic>p </italic>< 0.01; ***<italic>p </italic>< 0.001. Error bars, mean ± <italic>SEM</italic>.</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13191-g003\"/></fig></sec><sec id=\"acel13191-sec-0006\"><label>2.4</label><title>Forcing mitochondrial elongation promotes GSC competitiveness for niche occupancy</title><p>We also examined the impact of promoting elongated mitochondria on GSC homeostasis by generating GSCs with a mutation of mitochondrial fission regulator, <italic>drp1</italic> (indicated by the absence of GFP). GSCs homozygous for <italic>drp1<sup>1</sup></italic> or <italic>drp1<sup>2</sup></italic> null alleles (Yarosh et al., <xref rid=\"acel13191-bib-0072\" ref-type=\"ref\">2008</xref>) displayed large mitochondrial clusters that occupied a major portion of the cell (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3h</xref>,h′,h″,i,i′). However, <italic>drp1</italic> mutant GSCs exhibited comparable division and maintenance rates compared to control GSCs (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3g</xref>,j). These results indicate that preventing mitochondrial fission neither decreases GSC division nor maintenance. Interestingly, we found that the proportions of mutant germaria carrying at least one GFP‐positive GSC (partial GSC clone) decreased from 75% to 34% (<italic>drp1<sup>2</sup></italic>) and 70% to 14% (<italic>drp1<sup>1</sup></italic>), while the proportion of germaria in which all GSCs were mutant (full GSC clone) increased from 25% to 66% (<italic>drp1<sup>2</sup></italic>, 41% increase) and 30% to 86% (<italic>drp1<sup>1</sup></italic>, 56% increase) by 3‐week ACI (Figure <xref rid=\"acel13191-fig-0004\" ref-type=\"fig\">4k</xref>). In <italic>FRT40A</italic> mock mosaic germaria, only a 25% increase was observed, due to the natural loss of neighboring GFP‐positive GSCs (Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3k</xref>). These results indicate that GSCs with <italic>drp1</italic> mutation (shifting mitochondrial dynamics balance toward fusion) tend to push away neighboring control GSCs to dominate niche occupancy.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13191-fig-0004\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>Disrupting mitochondrial fusion in GSCs decreases BMP signaling, while disrupting mitochondrial fission promotes E‐cadherin expression in the GSC‐cap cell junction. (a–c) <italic>FRT40A</italic> control (ctrl) (a), <italic>marf<sup>B</sup></italic> (b) and <italic>drp1<sup>2</sup></italic> mosaic germaria (c) with GFP (green, wild‐type cells), 1B1 (gray, fusomes), LamC (gray, terminal filament [TF] and cap cell nuclear envelopes), pMad (red, BMP signaling) 1 week (W) (b and c) and 2 weeks (a) after clonal induction (ACI). (d‐f) <italic>FRT 19A</italic> control (d), <italic>marf<sup>B</sup></italic> (e) and <italic>drp1<sup>2</sup></italic> mosaic germaria (f) with GFP (green, wild‐type cells), 1B1 (blue), LamC (blue), E‐cad (gray) 1‐week ACI. (g–j) Fold changes (FCs) of pMad (g and h) and E‐cad expression (i and j) in GFP<sup>−</sup> vs GFP<sup>+</sup> GSCs in the germaria with indicated genotypes 1, 2, and 3 W ACI. Numbers of analyzed GSCs are shown above each bar. *<italic>p </italic>< 0.05; **<italic>p </italic>< 0.01; ***<italic>p </italic>< 0.001. Error bars, mean ± <italic>SEM</italic>. Scale bar, 10 μm.</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13191-g004\"/></fig></sec><sec id=\"acel13191-sec-0007\"><label>2.5</label><title>Marf promotes Dpp stemness signaling while Drp1 suppresses GSC–niche attachment</title><p>We next investigated whether regulation of GSC maintenance by mitochondrial dynamics relies on Dpp stemness signaling (Xie & Spradling, <xref rid=\"acel13191-bib-0067\" ref-type=\"ref\">1998</xref>) or E‐cadherin‐mediated GSC<bold>–</bold>niche adhesion (Song & Xie, <xref rid=\"acel13191-bib-0060\" ref-type=\"ref\">2002</xref>); both are known to be decreased during aging (Pan et al., <xref rid=\"acel13191-bib-0048\" ref-type=\"ref\">2007</xref>; Tseng et al., <xref rid=\"acel13191-bib-0063\" ref-type=\"ref\">2014</xref>). We found that in mock mosaic germaria, expression levels of phosphorylated Mad (pMad) revealed Dpp signaling (Song et al., <xref rid=\"acel13191-bib-0059\" ref-type=\"ref\">2004</xref>) was similar in GFP‐negative and GFP‐positive GSCs at 1‐, 2‐, and 3‐week ACI (Figure <xref rid=\"acel13191-fig-0004\" ref-type=\"fig\">4a</xref>,g). However, pMad expression was significantly reduced in <italic>marf<sup>E</sup></italic> or <italic>marf<sup>B</sup></italic> mutant GSCs compared to neighboring GFP‐positive control GSCs (Figure <xref rid=\"acel13191-fig-0004\" ref-type=\"fig\">4b</xref>,g). The level of pMad remained similar in <italic>drp1</italic> mutant GSCs (Figure <xref rid=\"acel13191-fig-0004\" ref-type=\"fig\">4c</xref>,h) and control GSCs over time. On the other hand, E‐cadherin expression was similar in GFP‐positive and GFP‐negative GSC niches of control and <italic>marf</italic> mutant germaria (Figure <xref rid=\"acel13191-fig-0004\" ref-type=\"fig\">4d,e,i</xref>). Meanwhile, E‐cadherin expression was significantly increased in GSC<bold>–</bold>niche junctions of <italic>drp1<sup>1</sup> or drp1<sup>2</sup></italic> mutant germaria as compared to neighboring normal GSC<bold>–</bold>niche junctions at 1‐, 2‐, and 3‐week ACI (Figure <xref rid=\"acel13191-fig-0004\" ref-type=\"fig\">4f</xref>,j). Moreover, stronger expression of E‐cadherin in <italic>drp1<sup>1</sup></italic> mutant GSC<bold>–</bold>niche junctions compared to <italic>drp1<sup>2</sup></italic> mutant GSC<bold>–</bold>niche junctions may reflect a stronger competiveness of <italic>drp1<sup>1</sup></italic> mutant GSCs for niche occupancy (see Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3k</xref>). Together, these results indicate that cells with fragmented mitochondria have decreased Dpp stemness signaling in GSCs, while those with elongated mitochondria have enhanced GSC<bold>–</bold>niche attachment.</p></sec><sec id=\"acel13191-sec-0008\"><label>2.6</label><title>Impaired mitochondrial fusion reduces egg laying</title><p>To determine whether mitochondrial dynamics affect egg production, we also knocked down <italic>marf</italic> and <italic>drp1</italic> specifically in the adult germline. To this end, we used <italic>nos</italic>‐<italic>GAL4</italic> and cultured flies at 18°C before eclosion then switched the flies to 29°C after eclosion. One‐week‐old <italic>nos</italic>><italic>marf<sup>RNAi</sup></italic> were smaller than <italic>nos</italic>><italic>drp1<sup>RNAi</sup></italic> (control) and <italic>nos</italic>><italic>drp1<sup>RNAi</sup></italic> ovaries (Figure <xref rid=\"acel13191-sup-0005\" ref-type=\"supplementary-material\">S5</xref>A–C). Compared to control GSCs, <italic>nos</italic>><italic>marf<sup>RNAi</sup></italic> GSCs had highly fragmented mitochondria, while mitochondria were elongated in <italic>nos</italic>><italic>drp1<sup>RNAi</sup></italic> GSCs (Figure <xref rid=\"acel13191-sup-0005\" ref-type=\"supplementary-material\">S5</xref>D–E), indicating the <italic>RNAi</italic> lines we used could recapitulate the mutant phenotypes. Consistently, <italic>nos</italic>><italic>marf<sup>RNAi</sup></italic> GSCs were more quickly lost from the germaria with age than the controls (Figure <xref rid=\"acel13191-sup-0005\" ref-type=\"supplementary-material\">S5</xref>G). Although our clonal analysis showed that Drp1 is not required for GSC maintenance (see Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3</xref>), 3‐week‐old <italic>nos</italic>><italic>drp1<sup>RNAi</sup></italic> GSCs were lost faster than control GSCs (Figure <xref rid=\"acel13191-fig-0005\" ref-type=\"fig\">5g</xref>), possibly because varied expression of <italic>nos</italic>‐<italic>GAL4</italic> among aged GSCs in the niche created a competitive environment (Tseng et al., <xref rid=\"acel13191-bib-0063\" ref-type=\"ref\">2014</xref>). As a consequence, <italic>nos</italic>><italic>marf<sup>RNAi</sup></italic> ovaries had a dramatic reduction of egg production compared to <italic>nos</italic>><italic>drp1<sup>RNAi</sup></italic> and control ovaries (Figure <xref rid=\"acel13191-fig-0005\" ref-type=\"fig\">5h</xref>). Thus, oogenesis appears to be disturbed when mitochondria are fragmented in the germline.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13191-fig-0005\" orientation=\"portrait\" position=\"float\"><label>FIGURE 5</label><caption><p>\n<italic>marf</italic>‐<italic>knockdown</italic> and aged GSCs display unhealthy mitochondria and reduced cellular ROS levels, while only <italic>marf</italic>‐knockdown GSCs exhibit oil accumulation. (a) Fold changes (FCs) of TMRE signals in isolated <italic>bam<sup>1</sup></italic>/<italic>bam<sup>△86</sup></italic> GSCs with indicated genotypes at 2 and 4 weeks after eclosion with or without FCCP treatment. (a′) FCs of TMRE signals in isolated <italic>bam<sup>1</sup></italic>/<italic>bam<sup>△86</sup></italic> GSCs of 1‐ and 8‐week‐old flies. (b) FCs of DHE signals in isolated <italic>bam<sup>1</sup></italic>/<italic>bam<sup>△86</sup></italic> GSCs with indicated genotypes 2 weeks after eclosion with or without paraquat treatment. (b′) FCs of DHE signals in isolated <italic>bam<sup>1</sup></italic>/<italic>bam<sup>△86</sup></italic> GSCs of 1‐ and 8‐week‐old flies. Each experiment was repeated 6 times, except TMRE analysis at 4 weeks was repeated 4 times. (c–e) Representative electron micrographs showing the anterior regions of 1‐week (w)‐old <italic>nos </italic>><italic> gfp<sup>RNAi</sup></italic> (c), <italic>nos </italic>><italic> marf<sup>RNAi</sup></italic> (d), and <italic>nos </italic>><italic> drp1<sup>RNAi</sup></italic> germaria (e). GSCs are identified as the large cells directly contacting cap cells (CpCs). N, nucleus. Asterisks indicate oil droplets. Scale bar, 1 μm. (f‐l) One‐week (w)‐old <italic>nos </italic>><italic> gfp<sup>RNAi</sup></italic> (f) <italic>nos </italic>><italic> marf<sup>RNAi</sup></italic> (g), <italic>nos </italic>><italic> drp1<sup>RNAi</sup></italic> (h), wild‐type (WT) (i), 5‐ (j) and 8‐week‐old germaria (k) stained for LamC (red, cap cell nuclei), 1B1 (red, fusomes), and BODIPY (green, natural oil). GSCs are outlined with solid lines. Scale bar, 10 μm. (l) Number (no.) of lipid droplets (LDs) in 1‐week‐old GSCs with indicated genotypes with or without L‐Carnitine treatment. (m) Average GSCs per germarium of 1‐week‐old flies with indicated genotypes with or without treated L‐Carnitine treatment. (n) Number of lipid droplets in 1‐, 5‐ and 8‐week‐old wild‐type (WT) GSCs. Numbers of analyzed GSCs are shown above each bar. *<italic>p </italic>< 0.05; **<italic>p </italic>< 0.01; ***<italic>p </italic>< 0.001. Error bars, mean ± <italic>SEM</italic>. Scale bar, 10 μm.</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13191-g005\"/></fig></sec><sec id=\"acel13191-sec-0009\"><label>2.7</label><title>Fragmented mitochondria in GSCs display low membrane potential and disrupted lipid homeostasis</title><p>The mitochondrial membrane potential results from a proton gradient across the inner membrane, which is generated by oxidative phosphorylation complexes and is thought to reflect mitochondrial functional output. To know whether altered mitochondrial dynamics affects mitochondrial membrane potential, we used the probe, TMRE (Perry, Norman, Barbieri, Brown, & Gelbard, <xref rid=\"acel13191-bib-0050\" ref-type=\"ref\">2011</xref>), in isolated GSCs and analyzed TMRE signals by flow cytometry. GSCs were isolated from the germaria with <italic>nos</italic>‐<italic>GAL4</italic> driven <italic>gfp<sup>RNAi</sup></italic>,<italic>marf<sup>RNAi</sup></italic> or <italic>drp1</italic>\n<sup>RNAi</sup> along with <italic>vasa</italic>‐<italic>gfp</italic> (for GSC isolation) and a mutation of <italic>bag of marbles</italic> (<italic>bam</italic>, encodes a master differentiation factor) to increase GSC number (Kao et al., <xref rid=\"acel13191-bib-0028\" ref-type=\"ref\">2015</xref>). Isolated 2‐week‐old <italic>gfp<sup>RNAi</sup></italic>‐knockdown (KD) GSCs were treated with FCCP, a potent mitochondrial oxidative phosphorylation uncoupler, which served as a positive control (Heytler, <xref rid=\"acel13191-bib-0023\" ref-type=\"ref\">1979</xref>). These FCCP‐treated GSCs showed dramatically reduced TMRE signals (Figure <xref rid=\"acel13191-fig-0005\" ref-type=\"fig\">5a</xref>). Compared to <italic>nos</italic>><italic>gfp<sup>RNAi</sup></italic> control GSCs, <italic>drp1</italic>‐KD GSCs exhibited similar levels of TMRE signal, while 2‐week‐old and 4‐week‐old <italic>marf</italic>‐KD GSCs displayed 19% and 71% reductions of TMRE signal, respectively (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6a</xref>). This result suggests that mitochondria with impaired fusion are less functional. Furthermore, cellular ROS levels, as detected by DHE (Benov, Sztejnberg, & Fridovich, <xref rid=\"acel13191-bib-0006\" ref-type=\"ref\">1998</xref>), in 2‐week‐old <italic>marf</italic>‐KD GSCs were reduced by 16% (Figure <xref rid=\"acel13191-fig-0005\" ref-type=\"fig\">5b</xref>), suggesting that oxidative phosphorylation activity in fusion‐defective mitochondria is attenuated. To our surprise, like <italic>gfp</italic>‐KD GSCs treated with paraquat to increase ROS (Ali, Jain, Abdulla, & Athar, <xref rid=\"acel13191-bib-0002\" ref-type=\"ref\">1996</xref>), <italic>drp1</italic>‐KD GSCs displayed slightly increased ROS levels (Figure <xref rid=\"acel13191-fig-0005\" ref-type=\"fig\">5b</xref>), indicating that oxidative phosphorylation activity is promoted in fission‐defective mitochondria. Similar reductions of membrane potential and cellular ROS were also observed in aged GSCs (8‐week‐old), as compared to young GSCs (1‐week‐old) (Figure <xref rid=\"acel13191-fig-0005\" ref-type=\"fig\">5a</xref>′,b′), in line with our observation that aged GSCs carry more fragmented mitochondria.</p><fig fig-type=\"FIGURE\" xml:lang=\"en\" id=\"acel13191-fig-0006\" orientation=\"portrait\" position=\"float\"><label>FIGURE 6</label><caption><p>Suppression of Drp1 expression or promotion of autophagy in aged GSCs delays GSC removal from the niche. (a and b) One‐ (a) and 8‐week (W)‐old germaria (b) bearing <italic>marf</italic>‐<italic>gfp</italic> with 1B1 (red, fusomes), LamC (red, cap cell nuclear envelopes), and GFP (green, Marf) labeling. (c and d) One‐ (c) and 8‐week‐old wild‐type (WT) germaria (d) with 1B1 (red), LamC (red) and GFP (green, Drp1) labeling. (e and f) Fold changes (FCs) of Marf‐GFP (e) in <italic>marf</italic>‐<italic>gfp</italic> flies and Drp1 (f) in wild‐type flies at 1 and 8 weeks old. (g) A flip‐out system was used for <italic>nos</italic>‐<italic>gal4</italic>‐driven <italic>drp1</italic> knockdown in aged GSCs; knockdowns are identified by the presence of GFP expression. In females carrying a <italic>nos</italic> promoter‐driven FRT‐flanked flip‐in GAL4/VP16 construct (<italic>nos </italic>><italic> STOP </italic>><italic> GAL4</italic>), GAL4 is not expressed, preventing expression of <italic>UAS</italic>‐<italic>gfp</italic> and <italic>UAS</italic>‐<italic>drp1<sup>RNAi</sup></italic>. GAL4 expression is turned on by removing the stop cassette through Flippase‐mediated recombination, which in turn activates expression of <italic>UAS</italic>‐<italic>gfp</italic> and <italic>UAS</italic>‐<italic>drp1<sup>RNAi</sup></italic>. (g and i) 8‐week‐old <italic>nos</italic>><italic>gfp &mCD8gfp</italic> (g) and <italic>nos</italic>><italic>gfp & drp1<sup>RNAi</sup></italic> mosaic ovaries (i) were heat‐shocked at 4 weeks old for 3 days to activate <italic>nos</italic>‐<italic>GAL4</italic>. Red arrows point to previtellogenic egg chambers. (j–l) 5‐week‐old <italic>nos</italic>><italic>gfp &mCD8gfp</italic> (i), <italic>nos</italic>><italic>gfp and drp1<sup>RNAi</sup></italic> (j) and 8‐week‐old <italic>nos</italic>><italic>gfp and drp1<sup>RNAi</sup></italic> mosaic germaria (l) heat‐shocked at 4 weeks old for 3 days with LamC (red), 1B1 (red), and GFP (green, cloned cells) labeling. (m) Percentage (%) of 5‐ or 8‐week‐old germaria (Left y‐axis) carrying partial or full GSC clones (containing 1 or ≥2 GSCs) from flies with indicated genotypes heat‐shocked at 4 weeks old. Right <italic>y</italic>‐axis shows percentage of GFP‐positive GSCs in flies with indicated genotypes. (n–q) Two‐ and 9‐week‐old germaria with or without rapamycin treatment for 1 week labeled with LamC (red) and 1B1 (red). (r) Percentage of germaria with indicated GSC number of 2‐ and 9‐week‐old flies fed with or without rapamycin, beginning at 1 and 8 weeks after eclosion, respectively. Solid lines outline GSCs and dashed lines outline GSC progeny. Numbers of analyzed GSCs are shown above each bar. *<italic>p </italic>< 0.05; **<italic>p </italic>< 0.01; ***<italic>p </italic>< 0.001. Error bars, mean ± <italic>SEM</italic>. Scale bars in a, c, j and n are 10 μm; bar in h is 0.5 mm.</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13191-g006\"/></fig><p>To further explore the dysfunction of fragmented mitochondria, we also examined lipid accumulation in <italic>marf</italic>‐KD GSCs by TEM and a lipophilic dye (BODIPY 493/503 staining), as fatty acid oxidation takes place in mitochondria (Pakhomov et al., <xref rid=\"acel13191-bib-0047\" ref-type=\"ref\">2017</xref>). We observed a clear increase in number and size of oil droplets in <italic>marf</italic>‐KD GSCs as compared to control or <italic>drp1</italic>‐KD GSCs one and 2 weeks after eclosion (Figure <xref rid=\"acel13191-fig-0005\" ref-type=\"fig\">5c</xref>–h,l, and Figure <xref rid=\"acel13191-sup-0006\" ref-type=\"supplementary-material\">S6</xref>), similar to previous observations in male GSCs (Sênos Demarco, Uyemura, D’Alterio, & Jones, <xref rid=\"acel13191-bib-0056\" ref-type=\"ref\">2019</xref>). Interestingly, promoting lipid reentry into mitochondria of 1‐week‐old <italic>marf</italic>‐KD GSCs, by feeding flies with L‐carnitine for 1 week (Longo, Frigeni, & Pasquali, <xref rid=\"acel13191-bib-0035\" ref-type=\"ref\">2016</xref>), eliminated lipid accumulation and partially rescued GSC number (Figure <xref rid=\"acel13191-fig-0005\" ref-type=\"fig\">5m</xref>, and Figure <xref rid=\"acel13191-sup-0006\" ref-type=\"supplementary-material\">S6</xref>). These observations were also in agreement with a previous report that lipid homeostasis controls <italic>Drosophila</italic> male GSC maintenance (Sênos Demarco et al., <xref rid=\"acel13191-bib-0056\" ref-type=\"ref\">2019</xref>). However, we did not observe oil accumulation in aged GSCs (Figure <xref rid=\"acel13191-fig-0005\" ref-type=\"fig\">5i</xref>–k), possibly because mitochondrial fragmentation is less severe in aged GSCs than <italic>marf</italic> mutant GSCs. Thus, the nature of mitochondrial dysfunction caused by excessive fragmentation during aging appears to be complex and multifaceted, and it is unlikely that lipid accumulation alone can explain the loss of GSCs during aging.</p></sec><sec id=\"acel13191-sec-0010\"><label>2.8</label><title>Reduced fragmented mitochondria promotes maintenance of aged GSCs</title><p>Because we saw that the balance of mitochondrial dynamics in aged GSCs shifts toward fission, we further asked whether this switch is associated with changes in Marf or Drp1 expression levels. We found that according to expression of a genomic construct, <italic>marf</italic>‐<italic>gfp</italic> (Zhang, Mishra, Hay, Chan, & Guo, <xref rid=\"acel13191-bib-0073\" ref-type=\"ref\">2017</xref>), Marf levels in young and aged GSCs were comparable (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6a,b,e</xref>). However, Drp1 expression was significantly increased in aged GSCs and their progeny, as compared to young GSCs (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6c,d,f</xref>), suggesting a role for increased Drp1 in aging‐induced mitochondrial fragmentation.</p><p>To test this idea, we used the flip‐out system, in which a transcriptional stop sequence flanked by two <italic>FRT</italic> sites was inserted between the <italic>nanos</italic> (<italic>nos</italic>) promoter and <italic>GAL4</italic> (<italic>nos</italic>><italic>STOP</italic>><italic>GAL4</italic>; Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6g</xref>) (Ma et al., <xref rid=\"acel13191-bib-0037\" ref-type=\"ref\">2014</xref>). We used heat shock to express <italic>drp1<sup>RNAi</sup></italic> or <italic>mCD8</italic>‐<italic>gfp</italic> (control) expression along with a GFP reporter specifically in germ cells of 4‐week‐old females. Strikingly, <italic>drp1</italic>‐KD mosaic ovaries of 8‐week‐old flies looked younger than <italic>mCD8</italic>‐<italic>gfp</italic>‐expressing ovaries in flies at the same age (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6h,i</xref>), as they carried many vitellogenic egg chambers (arrow heads in Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6i</xref>). In addition, consistent to our previous results (see Figure <xref rid=\"acel13191-fig-0003\" ref-type=\"fig\">3h,i</xref>), mitochondria in <italic>drp1</italic>‐<italic>KD</italic> GSCs formed a big cluster as compared to control GSCs of 8‐week‐old flies (Figure <xref rid=\"acel13191-sup-0007\" ref-type=\"supplementary-material\">S7</xref>), indicating a disruption of mitochondrial fission. Five‐ and 8‐week‐old <italic>mCD8</italic>‐<italic>gfp</italic>‐expressing mosaic germaria carried 52% and 38% GFP‐positive GSCs, respectively (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6j</xref>,k,m), indicating that 14% of GSCs were naturally lost. However, 5‐ and 8‐week‐old <italic>drp1</italic>‐KD mosaic germaria, respectively, carried 49% and 61% GFP‐positive GSCs (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6l</xref>,m), showing a net increase of <italic>drp</italic>1‐KD GSCs. In fact, 19% of 8‐week‐old <italic>drp1</italic>‐KD mosaic germaria carried at least one GFP‐positive GSC (partial GSC clone) and 81% carried only GFP‐positive GSCs (full GSC clone) (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6m</xref>). In contrast, 59% of 8‐week‐old <italic>mCD8gfp</italic>‐expressing mosaic germaria were partial clones, and 41% were full GSC clones (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6m</xref>). This result is consistent with the result of our clonal analysis showing that <italic>drp1</italic>‐deficient GSCs tend to occupy the niche. Based on these results, we conclude that decreasing <italic>drp1</italic> expression in aged GSCs prevents their loss.</p><p>Autophagy is suppressed by Target of rapamycin (TOR) (Kim, Kundu, Viollet, & Guan, <xref rid=\"acel13191-bib-0030\" ref-type=\"ref\">2011</xref>) and provides a mechanism to clear small and unhealthy mitochondria from the cell (Morita et al., <xref rid=\"acel13191-bib-0043\" ref-type=\"ref\">2015</xref>). To stimulate removal of fragmented mitochondria in aged flies, we fed aged flies for 1 week with rapamycin to suppress TOR. Two‐week‐old flies treated with or without rapamycin showed similar numbers of GSCs (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6n</xref>,o,r), while 9‐week‐old flies treated with rapamycin showed significantly higher GSC numbers, compared to flies at the same age without rapamycin feeding (Figure <xref rid=\"acel13191-fig-0006\" ref-type=\"fig\">6p</xref>,q,r). These results were reminiscent of previous studies that showed autophagy promotes stem cell fate (Boya, Codogno, & Rodriguez‐Muela, <xref rid=\"acel13191-bib-0008\" ref-type=\"ref\">2018</xref>; Zhao, Fortier, & Baehrecke, <xref rid=\"acel13191-bib-0075\" ref-type=\"ref\">2018</xref>), and aging slows autophagy activity (Cuervo, <xref rid=\"acel13191-bib-0014\" ref-type=\"ref\">2008</xref>). Together, these results suggest that preventing production of fragmented mitochondria or clearing dysfunctional mitochondria during aging may promote GSC maintenance.</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13191-sec-0011\"><label>3</label><title>DISCUSSION</title><p>Here, we have described a shift of mitochondrial dynamics toward fission that occurs in GSCs during aging. This shift occurs in parallel with changes in factors that control stem cell self‐renewal and contributes to the loss (differentiation) of GSCs. In the GSCs of young germaria, the balance of mitochondrial dynamics favors fusion, resulting in mitochondria that are relatively elongated, healthy, and functional. Under these conditions, GSCs are well maintained. Blocked mitochondrial fusion in GSCs results in highly fragmented unhealthy mitochondria with decreased membrane potential, ROS generation, fatty acid metabolism defects, and attenuated BMP stemness signaling. Such GSCs divide slowly and are quickly lost from the niche. Interestingly, promoting fatty acid reentry into mitochondria partially restores the GSC loss phenotype when <italic>marf</italic> is depleted, indicating a role for lipid metabolism in GSC maintenance. Furthermore, preventing mitochondrial fission in GSCs causes cells to accumulate elongated healthy mitochondria, which may result in slightly increased ROS generation and stronger E‐cadherin‐mediated GSC attachment to the niche. These GSCs exhibited higher competiveness for niche occupancy. In aged GSCs, increased expression of Drp1 leads to increased mitochondrial fission accompanied by low mitochondrial membrane potential, decreased ROS levels, and reduced BMP signaling. Moreover, removed fragmented mitochondria in aged GSCs enhance their maintenance, preventing their loss during aging. Our results indicate that aging alters mitochondrial dynamics, shifting the balance from fusion to fission, and contributing to stem cell loss. These results may have broad significance for stem cell aging, particularly in light of the well‐known effects of aging on tissue degeneration and the close coupling of mitochondria with cellular metabolism.</p><p>The interplay between aging and mitochondrial dynamics has been previously studied in various laboratory models. For example, in <italic>Drosophila</italic>, promoting Drp1 expression or mitophagy in midlife flies extends lifespan, with mitochondria in the muscles of those flies appearing more fragmented compared to controls (Aparicio, Rana, & Walker, <xref rid=\"acel13191-bib-0003\" ref-type=\"ref\">2019</xref>; Rana et al., <xref rid=\"acel13191-bib-0051\" ref-type=\"ref\">2017</xref>). In a <italic>C</italic>.<italic> elegans</italic> study (Morsci, Hall, Driscoll, & Sheng, <xref rid=\"acel13191-bib-0045\" ref-type=\"ref\">2016</xref>), it was revealed that mitochondria size and density in neurons are increased in midlife but decreased to early‐life levels in aged worms, while promotion of mitochondrial fusion by AMPK or dietary restriction extends lifespan (Weir et al., <xref rid=\"acel13191-bib-0066\" ref-type=\"ref\">2017</xref>). Similarly, in fungal models, reducing mitochondrial fission also extends lifespan (Scheckhuber et al., <xref rid=\"acel13191-bib-0053\" ref-type=\"ref\">2007</xref>). Thus, manipulation of mitochondrial dynamics appears to be globally related to aging in tissues across many organisms. However, it is not clear whether all tissues show similar shifts in preference of mitochondrial dynamics during aging, and it is also unknown whether age‐related phasic patterns of mitochondrial dynamics happen in all cell types and in higher organisms. In mammals, this issue appears to be somewhat complicated. In studies on skeletal muscle, both fragmented mitochondria and large, less circular mitochondria have been reported in aged tissues (Beregi, Regius, Huttl, & Gobl, <xref rid=\"acel13191-bib-0007\" ref-type=\"ref\">1988</xref>; Iqbal, Ostojic, Singh, Joseph, & Hood, <xref rid=\"acel13191-bib-0027\" ref-type=\"ref\">2013</xref>; Leduc‐Gaudet et al., <xref rid=\"acel13191-bib-0033\" ref-type=\"ref\">2015</xref>). Furthermore, reducing mitochondrial fission partially reduces neurodegeneration in mouse neuronal disease models (Manczak, Kandimalla, Fry, Sesaki, & Reddy, <xref rid=\"acel13191-bib-0038\" ref-type=\"ref\">2016</xref>), while promoting mitochondrial fission reverses certain degenerative phenotypes in <italic>Drosophila parkin</italic> and <italic>park</italic> mutants (Deng, Dodson, Huang, & Guo, <xref rid=\"acel13191-bib-0016\" ref-type=\"ref\">2008</xref>; Yang et al., <xref rid=\"acel13191-bib-0071\" ref-type=\"ref\">2008</xref>). These species and tissue‐dependent results illustrate the importance of studies that experimentally delineate the mitochondrial profile during aging in various cellular contexts, which may aid the discovery of factors that influence long‐term mitochondrial maintenance.</p><sec id=\"acel13191-sec-0012\"><label>3.1</label><title>Age‐dependent GSC loss is not due to defective fatty acid metabolism or increased ROS levels</title><p>An important function of mitochondria is to carry out long‐chain fatty acid oxidation for energy production. Forcing mitochondrial fragmentation in ovarian GSCs by disrupting <italic>marf</italic> results in reduced mitochondrial membrane potential and accumulation of oil droplets, which mostly contain stores of triglycerides composed of glycerol and three fatty acids. This droplet accumulation is not found in <italic>drp1</italic>‐depleted GSCs with elongated mitochondria. Similar results were recently reported in <italic>Drosophila</italic> male GSCs (Sênos Demarco et al., <xref rid=\"acel13191-bib-0056\" ref-type=\"ref\">2019</xref>). Interestingly, stimulating the reentry of accumulated lipids into mitochondria for oxidation by feeding <italic>nos</italic>><italic>marf<sup>RNAi</sup></italic> male and female flies with L‐carnitine decreases lipid accumulation and restores the GSC loss phenotype, indicating a contribution of fatty acid oxidation on GSC maintenance. In the study on <italic>marf</italic>‐defective male GSCs (Sênos Demarco et al., <xref rid=\"acel13191-bib-0056\" ref-type=\"ref\">2019</xref>), autophagy is found to be inhibited via TOR signaling, which is activated by excessive buildup of cytoplasmic fatty acids due to dysfunctional mitochondria. Removal of accumulated fatty acids or suppression of TOR activity promotes autophagy and prevents loss of <italic>marf</italic>‐defective male GSCs.</p><p>Although aged GSCs display more fragmented mitochondria and decreased mitochondrial membrane potential, we did not observe accumulation of oil droplets in these cells. This difference with <italic>marf</italic> mutant GSCs could be explained if only a subset of mitochondria are fragmented or if aged flies produce or consume low amounts of fatty acids. Nevertheless, feeding aged flies with rapamycin, which suppresses TOR activity to increase autophagy (Neufeld, <xref rid=\"acel13191-bib-0046\" ref-type=\"ref\">2010</xref>), significantly delayed GSC loss. This result suggests that inducing autophagy may help to clear fragmented mitochondria from aged GSCs, meaning the underlying mechanisms of GSC loss in aged <italic>marf</italic>‐defective and aged GSCs may be at least partially different.</p><p>According to the free radical theory of aging, ROS induces oxidative damage and leads to cellular dysfunction and aging (Barja, <xref rid=\"acel13191-bib-0005\" ref-type=\"ref\">2013</xref>). In support of this idea, damaged mitochondria and ROS levels are known to be increased in aged fly and mammalian brains (Chakrabarti et al., <xref rid=\"acel13191-bib-0009\" ref-type=\"ref\">2011</xref>; Scialo et al., <xref rid=\"acel13191-bib-0055\" ref-type=\"ref\">2016</xref>). Surprisingly, in aged GSCs from female <italic>Drosophila</italic>, there is an approximately 14% reduction in ROS levels compared to young GSCs. Nevertheless, constitutive overexpression of superoxide dismutase (SOD; helps remove ROS) delays GSC loss during aging (Pan et al., <xref rid=\"acel13191-bib-0048\" ref-type=\"ref\">2007</xref>). These observations might be reconciled if SOD expression keeps ROS levels low throughout the entire lifespan of the GSC. Further analysis of ROS levels in aged GSCs from male flies or GSCs in other species will help to determine whether and how the free radical theory of aging may be applicable in a cell type‐specific manner.</p></sec><sec id=\"acel13191-sec-0013\"><label>3.2</label><title>Mitochondrial dynamics control germ cell differentiation</title><p>Several lines of evidence have shown that mitochondrial dynamics control cell fate decisions (Bahat & Gross, <xref rid=\"acel13191-bib-0004\" ref-type=\"ref\">2019</xref>; Chen & Chan, <xref rid=\"acel13191-bib-0012\" ref-type=\"ref\">2017</xref>), including in stem cells. For example, Drp1‐induced mitochondrial fission is required for Notch‐mediated follicle cell differentiation during <italic>Drosophila</italic> oogenesis (Mitra, Rikhy, Lilly, & Lippincott‐Schwartz, <xref rid=\"acel13191-bib-0042\" ref-type=\"ref\">2012</xref>). Mouse embryonic neural stem cells carry elongated mitochondria and rely on glycolysis for energy production, and enhanced mitochondrial fragmentation promotes the commitment of neural stem cells to differentiation and maturation (Khacho et al., <xref rid=\"acel13191-bib-0029\" ref-type=\"ref\">2016</xref>). However, the role of mitochondrial dynamics in germ cell differentiation is less clear.</p><p>In <italic>Drosophila</italic> female GSCs, mitochondria are elongated and form clusters near the fusome (see Figure <xref rid=\"acel13191-fig-0001\" ref-type=\"fig\">1</xref>); similar mitochondrial morphology is also observed in the immediate daughter cells, CBs. Mitochondria are highly fragmented in 4‐ and 8‐cell cysts (see Figure <xref rid=\"acel13191-fig-0004\" ref-type=\"fig\">4</xref>), suggesting that fission is preferred during differentiation; meanwhile, mitochondria are elongated again in 16‐cell cysts (see Figure <xref rid=\"acel13191-fig-0004\" ref-type=\"fig\">4</xref>), which are undergoing meiosis (Lin & Spradling, <xref rid=\"acel13191-bib-0034\" ref-type=\"ref\">1993</xref>). These observations are in agreement with a previous study (Cox & Spradling, <xref rid=\"acel13191-bib-0013\" ref-type=\"ref\">2003</xref>). In this study, we show that forcing mitochondria fragmentation in a <italic>marf</italic> mutant promotes GSC differentiation, and aging‐associated mitochondrial fragmentation contributes to age‐dependent GSC loss, at least in part via the upregulation of Drp1. Although Marf is also required for <italic>Drosophila</italic> male GSC maintenance (Sênos Demarco et al., <xref rid=\"acel13191-bib-0056\" ref-type=\"ref\">2019</xref>), the balance of mitochondrial dynamics in young and aged male GSCs is not clear. Interestingly, mitochondria in <italic>drp1</italic> mutant 4‐ or 8‐cell cysts are still highly fragmented (dashed outline in Figure <xref rid=\"acel13191-sup-0008\" ref-type=\"supplementary-material\">S8</xref>A), suggesting that additional factors regulate mitochondrial fission in germ cell cysts. Furthermore, we often observed <italic>drp1</italic>‐mutant GSCs overloaded with mitochondria with proximal CBs only carrying a few mitochondria or vice versa (Figure <xref rid=\"acel13191-sup-0008\" ref-type=\"supplementary-material\">S8</xref>B,C). This observation suggests that mitochondrial fission is necessary for proper partitioning of mitochondrial mass into the two daughter cells during cell division (Mishra & Chan, <xref rid=\"acel13191-bib-0041\" ref-type=\"ref\">2014</xref>). Occasionally, we found CBs or 2‐cell cysts with very few mitochondria positioned in the posterior germarium (Figure <xref rid=\"acel13191-sup-0008\" ref-type=\"supplementary-material\">S8</xref>D), implying that these cells may be defective in differentiation; perhaps cellular metabolism cannot meet the energetic requirements for differentiation in such cells. Mitochondria elongation mediated by Mfn1 and Mfn2 (Pernas & Scorrano, <xref rid=\"acel13191-bib-0049\" ref-type=\"ref\">2016</xref>) was shown to be critical for efficient ATP production via oxidative phosphorylation during mouse spermatogonial differentiation and a metabolic shift that occurs during meiosis (Varuzhanyan et al., <xref rid=\"acel13191-bib-0064\" ref-type=\"ref\">2019</xref>). Similarly, mitochondrial maturation with maximal ATP and ROS generation also promotes GSC differentiation and oocyte formation in <italic>C</italic>.<italic> elegans</italic> (Charmpilas & Tavernarakis, <xref rid=\"acel13191-bib-0010\" ref-type=\"ref\">2020</xref>). These studies combined with our results suggest a conserved role for mitochondrial dynamics in germ cell fate decisions among species. Importantly, physiological aging drives a shift of mitochondrial dynamics toward fission in <italic>Drosophila</italic> female GSCs, an observation that might be generalizable to stem cells in different tissues.</p></sec></sec><sec sec-type=\"materials-and-methods\" id=\"acel13191-sec-0014\"><label>4</label><title>MATERIALS AND METHODS</title><sec id=\"acel13191-sec-0015\"><label>4.1</label><title>\n<italic>Drosophila</italic> strains and culture</title><p>\n<italic>Drosophila</italic> stocks were maintained at 22–25°C on standard medium, unless otherwise indicated. For aging experiments, flies were fed with normal diet plus a paste of wet yeast and food was changed daily. <italic>yw</italic> was used as a wild‐type control. The following fly strains were used in this study: Hypomorphic <italic>marf<sup>E</sup></italic> and null <italic>bam<sup>△86</sup></italic>, <italic>bam<sup>1</sup></italic>, <italic>marf<sup>B</sup></italic>, <italic>drp1<sup>1</sup></italic>, and <italic>drp1<sup>2</sup></italic> alleles have been described previously (<italic>marf</italic> and <italic>drp1</italic> mutant fly lines are kind gifts from Dr. Hugo Bellen, Department of Molecular and Human Genetics, Baylor College of Medicine) (Gonczy, Matunis, & DiNardo, <xref rid=\"acel13191-bib-0021\" ref-type=\"ref\">1997</xref>; McKearin & Spradling, <xref rid=\"acel13191-bib-0039\" ref-type=\"ref\">1990</xref>; Sandoval et al., <xref rid=\"acel13191-bib-0052\" ref-type=\"ref\">2014</xref>). <italic>UASp</italic>‐<italic>mito</italic>‐<italic>gfp</italic>, that is, human COX VIII mitochondrial targeting signal fused to the N‐terminus of EGFP, was used to monitor mitochondria (a kind gift from Dr. Allan C. Spradling, Department of Embryology, Carnegie Institution of Science) (Cox & Spradling, <xref rid=\"acel13191-bib-0013\" ref-type=\"ref\">2003</xref>). <italic>pCasper</italic>‐<italic>mfn</italic>‐<italic>egfp</italic>, a transgene construct with GFP‐tagged Mfn under the control of the endogenous Mfn promoter, was used to monitor the endogenous Mfn level (Zhang et al., <xref rid=\"acel13191-bib-0073\" ref-type=\"ref\">2017</xref>); an appropriate anti‐Marf antibody for immunostaining is lacking. <italic>UAS</italic>‐<italic>RNAi</italic> lines against <italic>marf</italic> (BL55189) and <italic>drp1</italic> (BL51483) were obtained from the Bloomington <italic>Drosophila</italic> Stock Center (BL). The efficiencies were described previously (Deng et al., <xref rid=\"acel13191-bib-0017\" ref-type=\"ref\">2016</xref>; Smith et al., <xref rid=\"acel13191-bib-0058\" ref-type=\"ref\">2019</xref>) and were tested again in this study (Figure <xref rid=\"acel13191-sup-0004\" ref-type=\"supplementary-material\">S4</xref>). The <italic>nos</italic>‐<italic>Vp16</italic>‐<italic>GAL4</italic> line was used to drive <italic>RNAi</italic> expression in the germline line (Doren, Williamson, & Lehmann, <xref rid=\"acel13191-bib-0019\" ref-type=\"ref\">1998</xref>; Tseng et al., <xref rid=\"acel13191-bib-0063\" ref-type=\"ref\">2014</xref>). Other genetic elements are described in FlyBase (<ext-link ext-link-type=\"uri\" xlink:href=\"http://flybase.bio.indiana.edu\">http://flybase.bio.indiana.edu</ext-link>). Flies expressing <italic>RNAi</italic> or <italic>mito</italic>‐<italic>gfp</italic> by <italic>nos</italic>‐<italic>GAL4</italic> were cultured at 18°C to suppress GAL4 activity during developmental stages; the flies were switched to 29°C after eclosion to activate GAL4. For the egg laying assay, five 2‐day‐old females were cultured with five <italic>w<sup>1118</sup></italic> males for 1 day at 25°C (the experiment was performed in triplicate), then transferred into plastic bottles with small holes at the bottom and capped by a molasses plate supplied with a wet yeast paste. The bottles were placed upside‐down, and the molasses plate was changed daily. Numbers of eggs on the molasses petri dish were counted.</p></sec><sec id=\"acel13191-sec-0016\"><label>4.2</label><title>Genetic mosaic analysis</title><p>Genetic mosaics were generated by FLP/FRT‐mediated mitotic recombination (Xu & Rubin, <xref rid=\"acel13191-bib-0069\" ref-type=\"ref\">1993</xref>). Three‐ to 5‐day‐old flies of the genotypes <italic>FRT19A</italic>/<italic>ubi</italic>‐<italic>gfpFLP122FRT19A</italic>, <italic>marf*FRT19A</italic>/<italic>ubi</italic>‐<italic>gfpFLP122FRT19A</italic>, <italic>hs</italic>‐<italic>flp</italic>/+;<italic> FRT40A</italic>/<italic>ubi</italic>‐<italic>gfpFRT40A</italic>, <italic>hs</italic>‐<italic>flp</italic>/+;<italic> drp1*FRT40A</italic>/<italic>ubi</italic>‐<italic>gfpFRT40A</italic> and <italic>marf*FRT19A</italic>/<italic>ubi</italic>‐<italic>gfpFLP122FRT19A</italic>; <italic>drp*FRT40A</italic>/<italic>arm</italic>‐<italic>lacZFRT40A</italic> (*represents <italic>marf</italic> or <italic>drp1</italic> mutant alleles) were generated from standard crosses and subjected to heat shock for 1 hr at 37°C, twice a day for 3 days. After heat shock, flies were cultured at 25°C and food was changed daily until dissection. Homozygous mutant cells were identified by the absence of GFP. For flip‐out clone analysis, 5 and 8‐week‐old flies of genotypes <italic>hs</italic>‐<italic>flp</italic>/+;<italic> nos </italic>≫ <italic>mcherryV40ply</italic>‐<italic>STOP </italic>> <italic>GAL4UAS</italic>‐<italic>gfp</italic>/<italic>UAS</italic>‐<italic>drp1<sup>RNAi</sup></italic>, and <italic>hs</italic>‐<italic>flp</italic>/+;<italic> nos </italic>≫ <italic>mcherryV40ply</italic>‐<italic>STOP </italic>> <italic>GAL4UAS</italic>‐<italic>GFP</italic>/+;<italic> UAS</italic>‐<italic>mcd8</italic>‐<italic>gfp</italic>/+ were generated from standard crosses and subjected to heat shock one or 2 weeks prior to dissection for 30 min at 37°C for 3 times a day for 3 days. After heat shock, flies were cultured at 25°C until dissection. Food with wet yeast paste was changed daily. RNAi‐expressing cells were recognized by the presence of GFP in flip‐out clones.</p></sec><sec id=\"acel13191-sec-0017\"><label>4.3</label><title>Immunostaining and fluorescence microscopy</title><p>Ovaries were dissected, fixed, and immunostained as described (Yang et al., <xref rid=\"acel13191-bib-0070\" ref-type=\"ref\">2013</xref>). In brief, ovaries were dissected in pre‐warmed Grace's insect medium (GIM, Lonza) and fixed with 5.3% paraformaldehyde/GIM for 13 min with gentle agitation at room temperature. Ovaries were washed in PBST (0.1% Triton X‐100 in 1X PBS) for 20 min three times and teased apart in PBST before incubating with blocking solution (5% bovine serum albumin (BSA) and 0.05% normal goat serum in PBST) for 3 hr at room temperature or 4°C overnight. Ovaries were incubated with primary antibodies (diluted in blocking solution) for 3 hr at room temperature or 4°C overnight, followed by three or four 30‐min washes with PBST. Next, ovaries were incubated with secondary antibodies (diluted in blocking solution) for 3 hr at room temperature or 4°C overnight, followed by three or four 30‐min washes with PBST. Apoptag<sup>®</sup> Fluorescein <italic>In Situ</italic> Apoptosis Detection Kit (cat#S7110, Merck) was used to detect apoptotic cells following the instruction manual with slight modifications (Tseng et al., <xref rid=\"acel13191-bib-0063\" ref-type=\"ref\">2014</xref>). In brief, ovaries were fixed, teased apart, and incubated with 300 µl of equilibration buffer for 5 min on rotator twice at room temperature. Ovaries were then incubated with a reaction mix consisting of 76 µl reaction buffer and 32 µl TdT enzyme for 1 hr at 37°C in a dark chamber. The reaction was stopped by adding 500 µl STOP/WASH solution, and samples were subsequently rinsed with PBST for 20 min before further staining. The following primary antibodies were used: mouse anti‐Hu‐li tai shao (<italic>Drosophila</italic> adducing‐related protein) (1:25; 1B1, Developmental Studies Hybridoma Bank, DSHB), mouse anti‐Lamin C (1:25; LC28.26, DSHB), rabbit anti‐GFP (1:1000; cat#TP401, Torrey Pines), rabbit anti‐Drp1 (1:100, a gift from Dr. Leo J. Pallanck, Department of Genome Sciences, University of Washington), rat anti‐DE‐cadherin (1:3, DCAD2, DSHB), rabbit anti‐Smad3 (phospho S423+S425) (1:250, ab52903, Abcam), mouse anti‐ATP5ase (1:1000, 15H4C4, Abcam), and mouse anti‐mono‐and poly‐ubiquitinylated conjugates (1:100, FK2, Enzo). Alexa Fluor 488‐ or 568‐ or 633‐conjugated goat anti‐mouse, anti‐rabbit, anti‐rat, and anti‐chicken secondary antibodies (Molecular Probes or Abcam, 1:500) were used. Samples were stained with 0.5 μg/ml DAPI (Sigma), followed by mounting in mounting solution [80% glycerol containing 20.0 µg/ml N‐propyl gallate (Sigma)]. Images of fixed ovaries were obtained using a Zeiss LSM 700 Laser Scanning confocal microscopes.</p></sec><sec id=\"acel13191-sec-0018\"><label>4.4</label><title>BODIPY 493/503 staining</title><p>Ovaries were dissected, and immunostaining was performed as described above. After immunostaining, ovaries were incubated with 50 μM BODIPI 493/503 (D3922, Thermo Fisher) in 0.1% PBST in the dark at RT for 20 min. Samples were washed 3 times with 0.1% PBST, stained with 0.5 μg/ml DAPI for 5 min, and mounted in mounting solution, as described above.</p></sec><sec id=\"acel13191-sec-0019\"><label>4.5</label><title>L‐Carnitine and rapamycin treatment</title><p>L‐carnitine (C0283‐5G, Thermo Fisher, final concentration of 25 mg/ml) or rapamycin (R0395, Sigma‐Aldrich, final concentration of 200 μM) were added to wet yeast (yeast to water ratio was 1.8 g:1 ml). One‐week‐old flies were fed wet yeast with or without L‐carnitine (or rapamycin) in vials containing molasses food for 1 week. Food was changed every day until dissection.</p></sec><sec id=\"acel13191-sec-0020\"><label>4.6</label><title>Image analysis</title><p>GSCs were identified by the position of the fusome (labeled by 1B1 staining), which is adjacent to cap cells (cap cell nuclear envelopes were labeled by LamC staining) (Hsu & Drummond‐Barbosa, <xref rid=\"acel13191-bib-0025\" ref-type=\"ref\">2011</xref>). Germaria analyzed for GSC division and expression of pMad and E‐cadherin contained at least one GFP‐negative and one GFP‐positive GSC. To measure GSC relative division rates, the number of GFP‐positive progeny (cystoblasts and cysts) was divided by the number of GFP‐positive GSCs, and this value was divided by the number of GFP‐negative progeny divided by the number of GFP‐negative GSCs in a given germarium. Each CB undergoes four more rounds of division to form 2‐, 4‐, 8‐, and 16‐cell cysts, and the cells in each cyst remain interconnected by a branched fusome. Therefore, the number of fusomes represents the number of GFP‐negative progeny derived from a GFP‐negative GSC and likewise for the fusomes carried by GFP‐positive progeny. To measure pMad expression, Image J was used to quantify the average fluorescence intensity (arbitrary units) in confocal z‐sections at the largest GSC nuclear diameter. For E‐cadherin measurement, the z‐section with strongest E‐cadherin expression in the junction between cap cells and GSC was analyzed. For oil droplet analysis, number and size of BODIPY signals in each GSC cytoplasm were analyzed by ImageJ. For mitochondria analysis, images of germaria labeled for ATP5ase were deconvoluted using MetaMorph (Molecular Devices) and analyzed by Imaris.</p></sec><sec id=\"acel13191-sec-0021\"><label>4.7</label><title>Live imaging and image processing</title><p>Ovaries of 1‐ and 8‐week‐old <italic>nos</italic>><italic>mito</italic>‐<italic>gfp</italic> flies were dissected in pre‐warmed GIM and stained with Hoechst (5 μg/ml) for 10 min at room temperature. Anterior ovarioles were dissected from Hoechst‐stained ovaries, amounted with CellTak (Corning) on 5‐mm‐diameter pre‐cleaned cover slips (Warner Instruments, 64‐0700), immersed in a PBS‐filled chamber, and imaged with lattice light‐sheet microscopy (Chen et al., <xref rid=\"acel13191-bib-0011\" ref-type=\"ref\">2014</xref>). Images were scanned with 100 z‐sections every ~2 s for 300 frames with a detection objective (Nikon, CFI Apo LWD 25XW, 1.1 NA, 2 mm WD) at a speed of 10 ms exposure per plane. Raw images taken from the light‐sheet microscope were deconvoluted by Amira (version 6.4, Thermo Fisher) with the PSF kernel acquired under the same optical condition. The deconvoluted images taken at the first 10 time points were stacked and corrected for bleaching to decrease background using ImageJ (Fiji, NIH). For each stack, the anterior of the germarium was cropped and subjected to Amira (Thermo Fisher) segmentation in order to define mitochondria by “hysteresis thresholding.” Each stack with defined mitochondria was analyzed with Imaris (Bitplane) to track mitochondrial dynamics within time points 1‐4, 4‐7, and 7‐10. Mitochondrial fusion/fission was tracked using the connected components model (Dillencourt, Samet, & Tamminen, <xref rid=\"acel13191-bib-0018\" ref-type=\"ref\">1992</xref>). By this model, spots in adjacent time points are considered connected if the spot spheres occupy some of the same space (the spheres would overlap if the two time points were merged into one image). This method essentially compares the amount of overlap between identified objects in the previous frame with those in the current frame. All objects with overlap are assigned the same track ID. For Mitotracker staining, ovaries were dissected in pre‐warmed GIM; 5 pairs of ovaries were dissected within 10 min. Ovaries were then incubated with Mitotracker Red (1:3,000, Molecular Probes) in the GIM at 25°C, gently rotating for 30 min, avoiding light. The solution was later replaced with pre‐warmed GIM to clear the background. The ovaries were mounted in pre‐warmed GIM. The anterior‐most germ cells in the germarium were considered to be GSCs in live ovarioles, as it was not feasible to label GSC fusome and niche cells. Images of live ovaries were obtained using a Zeiss LSM 700 Laser Scanning confocal microscopes.</p></sec><sec id=\"acel13191-sec-0022\"><label>4.8</label><title>Transmission electron microscopy (TEM) of adult germaria</title><p>Ovaries of were dissected in 2.5% glutaraldehyde/2% paraformaldehyde/1% tannic acid/0.1 M sodium cacodylate buffer and fixed in buffer at 4°C for overnight. Ovaries were washed for 10 min three times in 0.2 M sucrose/0.1% CaCl<sub>2</sub>/0.1 M sodium cacodylate buffer at 4°C and incubated for 2 hr in 1% OsO<sub>4</sub>, 0.1 M cacodylate buffer at room temperature. After rinsing with cold H2O three times at 4°C (Each time for 5 min), ovaries were immersed in 1% uranyl acetate for 1 hr at 4°C. Ovaries were dehydrated through a gradient of ethanol concentrations [30%, 50%, 75%, 90% for 1 time and 100% for 3 times (each time for 5 min)] at 4°C and infiltrated with Spurr's Resin (the Low Viscosity Embedding Media Spurr's Kit, Electron Microscopy Sciences) with ethanol ratios of 1:3, 1:1, and 3:1 and then with pure Spurr's Resin for times (each time for 1 hr). Finally, ovaries were polymerized at 60°C for 48 hr. Ultrathin sections were sectioned with a diamond knife (DiATOME) on a microtome and stained with 4% uranyl acetate for 20 min. After rinsing with H<sub>2</sub>O six times, ovaries were immersed in Reynolds lead citrate for 10 min. Then, slices were rinsed with H<sub>2</sub>O and mounted on copper slot grids and observed under TEM. After mounting, germarial sections were examined with a Tecnai G2 spirit TWIN transmission electron microscope (FEI Company) equipped with a Multiscan Gatan camera (Gatan) at an accelerating voltage of 120 kV. Finally, images were subjected to analysis of mitochondria using ImageJ and Amira software or movie production by Tomographic 3D Image Reconstruction.</p></sec><sec id=\"acel13191-sec-0023\"><label>4.9</label><title>Flow cytometry‐based mitochondrial membrane potential and ROS measurement</title><p>GSC dissociation for flow cytometry analysis was performed as previously described (Kao et al., <xref rid=\"acel13191-bib-0028\" ref-type=\"ref\">2015</xref>). In brief, flies with the genotypes, <italic>UAS</italic>‐<italic>marf<sup>RNAi</sup></italic>/+; <italic>vasa</italic>‐<italic>gfpbam<sup>△86</sup></italic>/<italic>nos</italic>‐<italic>GAL4bam<sup>1</sup></italic>, <italic>UAS</italic>‐<italic>drp1<sup>RNAi</sup></italic>/+;<italic> vasa</italic>‐<italic>gfpbam<sup>△86</sup></italic>/<italic>nos</italic>‐<italic>GAL4bam<sup>1</sup></italic>, <italic>vasa</italic>‐<italic>gfpbam<sup>△86</sup></italic>/<italic>nos</italic>‐<italic>GAL4bam<sup>1</sup></italic>,<italic>bam<sup>△86</sup></italic>/<italic>nos</italic>‐<italic>GAL4bam<sup>1</sup></italic> were grown at 18°C and shifted to 29°C after eclosion. Seven to twelve pairs of ovaries from each genotype were dissected in pre‐warmed GIM plus 10% FBS (GIM‐FBS) and were subsequently incubated with 0.45% Trypsin (Solution 10X, cat# 9002077, Sigma‐Aldrich) and 2.5 mg/ml collagenase (cat#17018‐029, Gibco) on a rotator at 25°C for 25 min with vigorous shaking; samples were vortexed every 5 min. Digested ovaries were filtered through a 40‐µm nylon mesh and then centrifuged at 1,000 <italic>g</italic> for 7 min to harvest the cell pellet. The pellets were resuspended in 500 µl GIM‐FBS containing 10 nM membrane potential probe TMRE (cat#T669, Thermo Fisher)/or 30 µM ROS probe DHE (cat#D11347, Invitrogen) and 0.5 µg/ml of DAPI with vigorous shaking at RT for 10 min in a dark chamber. For a positive control of membrane potential measurement, cells dissociated from <italic>bam<sup>△86</sup>vasa</italic>‐<italic>gfp</italic>/<italic>nos</italic>‐<italic>GAL4bam<sup>1</sup></italic> ovaries were co‐treated with 10 nM TMRE and 10 µM FCCP (to depolarize the mitochondrial membrane, C2920, Sigma‐Aldrich) for 10 min under the same conditions as described above. For a positive control of ROS measurement, cells dissociated from <italic>bam<sup>△86</sup>vasa</italic>‐<italic>gfp</italic>/<italic>nos</italic>‐<italic>GAL4bam<sup>1</sup></italic> ovaries were treated with 30 µM DHE and 100 µM paraquat (to induce cellular ROS, cat#3752782, Sigma‐Aldrich) for 10 min under the same conditions as described above. The stained cells were detected using an Attune NxT acoustic focusing cytometer (Thermo Fisher Scientific) at 480/530, 405/440, 480/590, and 561/585 (Excitation/Emission) to measure GSCs carrying vasa‐GFP, DAPI‐labeled dead cell, DHE, and TMRE, respectively. TMRE/or DHE intensity was measured from GFP‐positive and DAPI‐negative GSCs, and at least 10,000 GSCs’ intensity was measured and averaged for one replicate; three replicate were done for each measurement.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13191-sec-0025\"><title>CONFLICT OF INTEREST</title><p>The authors declare that there is no conflict of interest regarding the publication of this article.</p></sec><sec id=\"acel13191-sec-0026\"><title>AUTHOR CONTRIBUTIONS</title><p>O.A., C.‐H.L., A.C, and H.‐J. H. designed and interpreted the experiments and wrote the paper. C.‐H. L., S.‐H. K., and O.A. contributed to TEM image analysis; B.‐C. C., C.‐H. L., S. ‐C. H. W.‐C. T., and S.‐H. K. contributed to live image recording and analysis; C.‐K. Y. and G‐C. C. provided reagents; H‐L.C performed egg laying assay; D.L.C and Y. Y. H performed LD analysis; B.‐S. H. performed Mitotracker staining; A.C. and E. R. counted GSC number; K.‐Y. L. and Y.‐T. W. provided valuable comments and discussion. O.A. performed the remaining experiments.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0001\"><caption><p>Figure S1</p></caption><media xlink:href=\"ACEL-19-e13191-s001.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0002\"><caption><p>Figure S2</p></caption><media xlink:href=\"ACEL-19-e13191-s002.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0003\"><caption><p>Figure S3</p></caption><media xlink:href=\"ACEL-19-e13191-s003.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0004\"><caption><p>Figure S4</p></caption><media xlink:href=\"ACEL-19-e13191-s004.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0005\"><caption><p>Figure S5</p></caption><media xlink:href=\"ACEL-19-e13191-s005.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0006\"><caption><p>Figure S6</p></caption><media xlink:href=\"ACEL-19-e13191-s006.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0007\"><caption><p>Figure S7</p></caption><media xlink:href=\"ACEL-19-e13191-s007.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0008\"><caption><p>Figure S8</p></caption><media xlink:href=\"ACEL-19-e13191-s008.TIF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0009\"><caption><p>Video S1</p></caption><media xlink:href=\"ACEL-19-e13191-s009.mp4\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0010\"><caption><p>Video S2</p></caption><media xlink:href=\"ACEL-19-e13191-s010.avi\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0011\"><caption><p>Video S3</p></caption><media xlink:href=\"ACEL-19-e13191-s011.mp4\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0012\"><caption><p>Supplementary Material</p></caption><media xlink:href=\"ACEL-19-e13191-s012.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13191-sup-0013\"><caption><p>Table S1</p></caption><media xlink:href=\"ACEL-19-e13191-s013.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13191-sec-0024\"><title>ACKNOWLEDGEMENTS</title><p>We thank A. C. Spradling, H. J. Bellen, H. Imamura, H.‐C. Chen, the Bloomington Drosophila Stock Center, the VDRC Stock Center and the DSHB for <italic>Drosophila</italic> stocks and antibodies. We also thank the Taiwan fly core for ordering fly lines and reagents, core facilities in the Institute of Molecular Biology, and the Institute of Cellular and Organismic Biology, Academia Sinica for assistance with EM and image analysis, Chen‐Hui Chen and Yi‐Ching Lee for valuable comments, and Marcus Calkins for English editing. This work was supported by two thematic grants of Academia Sinica.</p></ack><sec sec-type=\"data-availability\" id=\"acel13191-sec-0028\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available upon request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13191-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13191-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13191-cit-0001\">\n<string-name>\n<surname>Ahmed</surname>, <given-names>A. S. I.</given-names>\n</string-name>, <string-name>\n<surname>Sheng</surname>, <given-names>M. H.</given-names>\n</string-name>, <string-name>\n<surname>Wasnik</surname>, <given-names>S.</given-names>\n</string-name>, <string-name>\n<surname>Baylink</surname>, <given-names>D. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32691494</article-id><article-id pub-id-type=\"pmc\">PMC7431835</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13195</article-id><article-id pub-id-type=\"publisher-id\">ACEL13195</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Paper</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Growth differentiation factor 15 protects against the aging‐mediated systemic inflammatory response in humans and mice</article-title><alt-title alt-title-type=\"left-running-head\">MOON et al.</alt-title></title-group><contrib-group><contrib id=\"acel13195-cr-0001\" contrib-type=\"author\"><name><surname>Moon</surname><given-names>Ji Sun</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"acel13195-note-0001\">\n<sup><sup>*</sup></sup>\n</xref></contrib><contrib id=\"acel13195-cr-0002\" contrib-type=\"author\"><name><surname>Goeminne</surname><given-names>Ludger J. E.</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"acel13195-note-0001\">\n<sup><sup>*</sup></sup>\n</xref></contrib><contrib id=\"acel13195-cr-0003\" contrib-type=\"author\"><name><surname>Kim</surname><given-names>Jung Tae</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13195-aff-0003\">\n<sup>3</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"acel13195-note-0001\">\n<sup><sup>*</sup></sup>\n</xref></contrib><contrib id=\"acel13195-cr-0004\" contrib-type=\"author\"><name><surname>Tian</surname><given-names>Jing Wen</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13195-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0005\" contrib-type=\"author\"><name><surname>Kim</surname><given-names>Seok‐Hwan</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0004\">\n<sup>4</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0006\" contrib-type=\"author\"><name><surname>Nga</surname><given-names>Ha Thi</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13195-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0007\" contrib-type=\"author\"><name><surname>Kang</surname><given-names>Seul Gi</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13195-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0008\" contrib-type=\"author\"><name><surname>Kang</surname><given-names>Baeki E.</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0005\">\n<sup>5</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0009\" contrib-type=\"author\"><name><surname>Byun</surname><given-names>Jin‐Seok</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0006\">\n<sup>6</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0010\" contrib-type=\"author\"><name><surname>Lee</surname><given-names>Young‐Sun</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0007\">\n<sup>7</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0011\" contrib-type=\"author\"><name><surname>Jeon</surname><given-names>Jae‐Han</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0008\">\n<sup>8</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0012\" contrib-type=\"author\"><name><surname>Shong</surname><given-names>Minho</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13195-aff-0003\">\n<sup>3</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0013\" contrib-type=\"author\"><name><surname>Auwerx</surname><given-names>Johan</given-names></name><xref ref-type=\"aff\" rid=\"acel13195-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13195-cr-0014\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Ryu</surname><given-names>Dongryeol</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-5905-6760</contrib-id><xref ref-type=\"aff\" rid=\"acel13195-aff-0005\">\n<sup>5</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13195-aff-0009\">\n<sup>9</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13195-aff-0010\">\n<sup>10</sup>\n</xref><address><email>jmpbooks@cnu.ac.kr</email><email>freefall@skku.edu</email></address></contrib><contrib id=\"acel13195-cr-0015\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Yi</surname><given-names>Hyon‐Seung</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-3767-1954</contrib-id><xref ref-type=\"aff\" rid=\"acel13195-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13195-aff-0003\">\n<sup>3</sup>\n</xref><address><email>jmpbooks@cnu.ac.kr</email><email>freefall@skku.edu</email></address></contrib></contrib-group><aff id=\"acel13195-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Research Center for Endocrine and Metabolic Diseases</named-content>\n<named-content content-type=\"organisation-division\">Chungnam National University Hospital</named-content>\n<institution>Chungnam National University School of Medicine</institution>\n<city>Daejeon</city>\n<country country=\"KR\">Republic of Korea</country>\n</aff><aff id=\"acel13195-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Laboratory of Integrative Systems Physiology</named-content>\n<institution>École Polytechnique Fédérale de Lausanne (EPFL)</institution>\n<city>Lausanne</city>\n<country country=\"CH\">Switzerland</country>\n</aff><aff id=\"acel13195-aff-0003\">\n<label><sup>3</sup></label>\n<named-content content-type=\"organisation-division\">Department of Medical Science</named-content>\n<institution>Chungnam National University School of Medicine</institution>\n<city>Daejeon</city>\n<country country=\"KR\">Republic of Korea</country>\n</aff><aff id=\"acel13195-aff-0004\">\n<label><sup>4</sup></label>\n<named-content content-type=\"organisation-division\">Department of Surgery</named-content>\n<institution>Chungnam National University School of Medicine</institution>\n<city>Daejeon</city>\n<country country=\"KR\">Republic of Korea</country>\n</aff><aff id=\"acel13195-aff-0005\">\n<label><sup>5</sup></label>\n<named-content content-type=\"organisation-division\">Department of Molecular Cell Biology</named-content>\n<institution>Sungkyunkwan University School of Medicine</institution>\n<city>Suwon</city>\n<country country=\"KR\">Republic of Korea</country>\n</aff><aff id=\"acel13195-aff-0006\">\n<label><sup>6</sup></label>\n<named-content content-type=\"organisation-division\">Department of Oral Medicine</named-content>\n<named-content content-type=\"organisation-division\">School of Dentistry</named-content>\n<institution>Kyungpook National University</institution>\n<city>Daegu</city>\n<country country=\"KR\">Republic of Korea</country>\n</aff><aff id=\"acel13195-aff-0007\">\n<label><sup>7</sup></label>\n<named-content content-type=\"organisation-division\">Department of Internal Medicine</named-content>\n<institution>Korea University College of Medicine</institution>\n<city>Seoul</city>\n<country country=\"KR\">Republic of Korea</country>\n</aff><aff id=\"acel13195-aff-0008\">\n<label><sup>8</sup></label>\n<named-content content-type=\"organisation-division\">Department of Internal Medicine</named-content>\n<named-content content-type=\"organisation-division\">School of Medicine</named-content>\n<institution>Kyungpook National University</institution>\n<city>Daegu</city>\n<country country=\"KR\">Korea</country>\n</aff><aff id=\"acel13195-aff-0009\">\n<label><sup>9</sup></label>\n<named-content content-type=\"organisation-division\">Biomedical Institute for Convergence at SKKU (BICS)</named-content>\n<institution>Sungkyunkwan University</institution>\n<city>Suwon</city>\n<country country=\"KR\">Republic of Korea</country>\n</aff><aff id=\"acel13195-aff-0010\">\n<label><sup>10</sup></label>\n<institution>Samsung Biomedical Research Institute</institution>\n<institution>Samsung Medical Center</institution>\n<city>Seoul</city>\n<country country=\"KR\">Republic of Korea</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nHyon‐Seung Yi, Research Center for Endocrine and Metabolic Diseases, Chungnam National University Hospital, Chungnam National University School of Medicine, Daejeon 35015, Republic of Korea<break/>\nEmail: <email>jmpbooks@cnu.ac.kr</email><break/>\nand<break/>\nDongryeol Ryu, Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, 16419, Republic of Korea.<break/>\nEmail: <email>freefall@skku.edu</email><break/></corresp><fn id=\"acel13195-note-0001\"><label><sup>*</sup></label><p>These authors contributed equally to this work.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>21</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13195</elocation-id><history><date date-type=\"received\"><day>18</day><month>2</month><year>2020</year></date><date date-type=\"rev-recd\"><day>19</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13195.pdf\"/><abstract id=\"acel13195-abs-0001\"><title>Abstract</title><p>Mitochondrial dysfunction is associated with aging‐mediated inflammatory responses, leading to metabolic deterioration, development of insulin resistance, and type 2 diabetes. Growth differentiation factor 15 (GDF15) is an important mitokine generated in response to mitochondrial stress and dysfunction; however, the implications of GDF15 to the aging process are poorly understood in mammals. In this study, we identified a link between mitochondrial stress‐induced GDF15 production and protection from tissue inflammation on aging in humans and mice. We observed an increase in serum levels and hepatic expression of <italic>GDF15</italic> as well as pro‐inflammatory cytokines in elderly subjects. Circulating levels of cell‐free mitochondrial DNA were significantly higher in elderly subjects with elevated serum levels of GDF15. In the BXD mouse reference population, mice with metabolic impairments and shorter survival were found to exhibit higher hepatic <italic>Gdf15</italic> expression. Mendelian randomization links reduced <italic>GDF15</italic> expression in human blood to increased body weight and inflammation. GDF15 deficiency promotes tissue inflammation by increasing the activation of resident immune cells in metabolic organs, such as in the liver and adipose tissues of 20‐month‐old mice. Aging also results in more severe liver injury and hepatic fat deposition in <italic>Gdf15</italic>‐deficient mice. Although GDF15 is not required for Th17 cell differentiation and IL‐17 production in Th17 cells, GDF15 contributes to regulatory T‐cell‐mediated suppression of conventional T‐cell activation and inflammatory cytokines. Taken together, these data reveal that GDF15 is indispensable for attenuating aging‐mediated local and systemic inflammation, thereby maintaining glucose homeostasis and insulin sensitivity in humans and mice.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13195-abs-0002\"><p>Aging‐induced GDF15 production is observed in humans and mice, which is positively correlated with systemic inflammation and mitochondrial stress. GDF15 deficiency promotes glucose intolerance as well as hepatic and adipose inflammation in old mice. GDF15 contributes to regulatory T cells‐mediated suppression of conventional T cell activation, but senescent T cells were resistant to regulatory T cells‐mediated suppression compared to conventional T cells.<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13195-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13195-g009.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13195-kwd-0001\">aging</kwd><kwd id=\"acel13195-kwd-0002\">inflammation</kwd><kwd id=\"acel13195-kwd-0003\">mitochondria</kwd><kwd id=\"acel13195-kwd-0004\">senescence</kwd><kwd id=\"acel13195-kwd-0005\">T cell</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source>Gilead Sciences Asia Ltd</funding-source></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>Chungnam National University Hospital </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100007631</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0003\"><funding-source><institution-wrap><institution>National Research Foundation of Korea </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100003725</institution-id></institution-wrap></funding-source><award-id>2017K1A1A2013124</award-id><award-id>2017R1E1A1A01075126</award-id><award-id>2018R1C1B6004439</award-id><award-id>2019M3E5D1A02068575</award-id></award-group></funding-group><counts><fig-count count=\"8\"/><table-count count=\"0\"/><page-count count=\"18\"/><word-count count=\"11737\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13181-cit-1001\">\n<string-name>\n<surname>Moon</surname>\n<given-names>JS</given-names>\n</string-name>, <string-name>\n<surname>Goeminne</surname>\n<given-names>LJE</given-names>\n</string-name>, <string-name>\n<surname>Kim</surname>\n<given-names>S.‐H.</given-names>\n</string-name>, et al. <article-title>Growth differentiation factor 15 protects against the aging‐mediated systemic inflammatory response in humans and mice</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13195</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13195</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13195-body-0001\"><sec id=\"acel13195-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Aging is a major risk factor for various chronic diseases, including type 2 diabetes, neurodegenerative diseases, and malignancies, which are closely related to systemic subclinical inflammation in the absence of overt infections in the elderly (Ortega Martinez de Victoria et al., <xref rid=\"acel13195-bib-0029\" ref-type=\"ref\">2009</xref>; Meda et al., <xref rid=\"acel13195-bib-0030\" ref-type=\"ref\">1995</xref>; Multhoff, Molls, & Radons, <xref rid=\"acel13195-bib-0034\" ref-type=\"ref\">2011</xref>; Yi et al., <xref rid=\"acel13195-bib-0054\" ref-type=\"ref\">2019</xref>). Such systemic inflammatory responses are also termed “metaflammation” or “inflammaging” in humans (Franceschi et al., <xref rid=\"acel13195-bib-0011\" ref-type=\"ref\">2007</xref>; Hotamisligil, <xref rid=\"acel13195-bib-0015\" ref-type=\"ref\">2017</xref>; Sanada et al., <xref rid=\"acel13195-bib-0041\" ref-type=\"ref\">2018</xref>). Given that mitochondrial damage contributes to various senescent processes with a distinct pro‐inflammatory secretory phenotype (Wiley et al., <xref rid=\"acel13195-bib-0048\" ref-type=\"ref\">2016</xref>), the fact that the disruption of mitochondrial function is linked to age‐related pathologies is not surprising (Lane, Hilsabeck, & Rea, <xref rid=\"acel13195-bib-0024\" ref-type=\"ref\">2015</xref>).</p><p>Progressive mitochondrial dysfunction occurs across species during the aging process (Yi, Chang, & Shong, <xref rid=\"acel13195-bib-0053\" ref-type=\"ref\">2018</xref>). Oxidative damage to cellular macromolecules, or stress arising from mitochondrial DNA (mtDNA) mutation and increased reactive oxygen species (ROS), is a key hallmark of aging physiology (Yi et al., <xref rid=\"acel13195-bib-0053\" ref-type=\"ref\">2018</xref>). Although higher levels of ROS induced by mitochondrial stress are involved in cellular damage and the inflammatory response, they also provide the first line of host defense (Pellegrino et al., <xref rid=\"acel13195-bib-0036\" ref-type=\"ref\">2014</xref>). Paradoxically, elevated ROS levels increase the lifespan of worms, flies, and mice through an adaptive response (Yi et al., <xref rid=\"acel13195-bib-0053\" ref-type=\"ref\">2018</xref>), termed mitohormesis. The secretion of mitokines during cellular stress is a critical response that may reflect disease severity acting as disease markers. They may also regulate disease progression, which makes them a potential therapeutic target for chronic diseases caused by mitochondrial dysfunction (Yi et al., <xref rid=\"acel13195-bib-0053\" ref-type=\"ref\">2018</xref>).</p><p>Growth differentiation factor 15 (GDF15) is a well‐known mitokine that is induced by defects in mitochondrial oxidative phosphorylation or by the unfolded protein response (UPR<sup>mt</sup>) pathway in mammals (Chung, Ryu, et al., <xref rid=\"acel13195-bib-0007\" ref-type=\"ref\">2017</xref>; Khan et al., <xref rid=\"acel13195-bib-0021\" ref-type=\"ref\">2017</xref>). GDF15 production is regulated by the mTORC1 kinase through an integrated mitochondrial stress response in patients with mitochondrial myopathy (Khan et al., <xref rid=\"acel13195-bib-0021\" ref-type=\"ref\">2017</xref>). Plasma GDF15 levels increase during metabolic stress‐mediated tissue inflammation, including in insulin resistance and type 2 diabetes (Kempf et al., <xref rid=\"acel13195-bib-0020\" ref-type=\"ref\">2012</xref>; Yi, <xref rid=\"acel13195-bib-0052\" ref-type=\"ref\">2019</xref>). Skeletal muscle‐specific UPR<sup>mt</sup> is also related to the promotion of lipolysis and fatty acid oxidation in adipose tissues by GDF15 production, thereby protecting the organism against high fat, diet‐induced obesity, and insulin resistance (Chung, Ryu, et al., <xref rid=\"acel13195-bib-0007\" ref-type=\"ref\">2017</xref>). Additionally, GDF15 improves alcohol‐ or chemically induced chronic liver injury by suppressing the infiltration of neutrophils, monocytes, and activated T cells in the liver (Chung, Kim, et al., <xref rid=\"acel13195-bib-0006\" ref-type=\"ref\">2017</xref>). Although the fibroblast growth factor 21 (FGF21) maintains peripheral T‐cell homeostasis by attenuating thymic immune senescence with age (Youm, Horvath, Mangelsdorf, Kliewer, & Dixit, <xref rid=\"acel13195-bib-0055\" ref-type=\"ref\">2016</xref>), the immunometabolic role of GDF15 in the aging process is incompletely understood.</p><p>In this study, we provide evidence that GDF15 exerts a protective effect on tissue inflammation in metabolic organs, such as the liver and adipose tissues, in humans and mice. Through complementary human and animal experiments supported by the reanalysis of large‐scale human transcript datasets, we demonstrate that GDF15 is required for the prevention of aging‐induced development of metabolic diseases by regulating tissue and systemic inflammation. Taken together, the immune regulatory role of GDF15 reveals the dynamic interplay between the metabolic and immune systems and contributes to delays in aging‐induced systemic inflammation.</p></sec><sec sec-type=\"results\" id=\"acel13195-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13195-sec-0003\"><label>2.1</label><title>Elderly subjects exhibit higher levels of serum GDF15 and hepatic GDF15 expression</title><p>In an experiment on 12 male C57BL/6 WT mice, we noticed that serum GDF15 levels were elevated in old (20‐month‐old) mice compared to young (8‐week‐old) mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>a). Subsequently, we recruited 70 participants of which the demographics and baseline characteristics are summarized in Table <xref rid=\"acel13195-sup-0002\" ref-type=\"supplementary-material\">S1</xref>. Again, we observed a significant positive association between the age of the study subjects and serum levels of GDF15 (Figure <xref rid=\"acel13195-fig-0001\" ref-type=\"fig\">1a</xref>). Elderly subjects (≥60 years) also exhibited significantly higher levels of serum GDF15, compared with younger subjects (≤40 years) (Figure <xref rid=\"acel13195-fig-0001\" ref-type=\"fig\">1b</xref>). We further noticed that hepatic <italic>Gdf15</italic> expression was higher in old mice (20‐month‐old) compared to young mice (8‐week‐old) (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>b). Likewise, hepatic <italic>GDF15</italic> expression was remarkably increased in elderly subjects compared with young people (Figure <xref rid=\"acel13195-fig-0001\" ref-type=\"fig\">1c</xref>). We confirmed this age‐related increase in hepatic GDF15 expression in two independent large human transcript datasets: (1) a liver microarray dataset (Innocenti et al., <xref rid=\"acel13195-bib-0017\" ref-type=\"ref\">2011</xref>) (Figure <xref rid=\"acel13195-fig-0001\" ref-type=\"fig\">1d</xref>) and (2) the RNA‐Seq data of the human Genotype‐Tissue Expression (GTEx) project (Consortium, <xref rid=\"acel13195-bib-0008\" ref-type=\"ref\">2015</xref>) (Figure <xref rid=\"acel13195-fig-0001\" ref-type=\"fig\">1e</xref>). In both datasets, GDF15 expression decreases in very young subjects (up to 30 years old), remains constant between 30 and 50 years of age, and then increases again after 50 years old. These non‐linear age effects are significant in both the microarray dataset (limma analysis, <italic>p</italic> = 4 × 10<sup>−5</sup>) and the GTEx RNA‐Seq dataset (edgeR‐zingeR analysis, <italic>p</italic> = 0.04). The former would even remain significant in a genome‐wide screen (q‐value =0.001). Average <italic>GDF15</italic> expression is 65% higher in 60‐ to 81‐year‐old subjects as compared to 20‐ to 40‐year‐old subjects (corrected for gender and ancestry, <italic>p</italic> = 0.06) (Innocenti et al., <xref rid=\"acel13195-bib-0017\" ref-type=\"ref\">2011</xref>). <italic>Gdf15</italic> is also highly expressed in murine livers compared to other tissues (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>c). If we equate 6‐months‐old mice to 30‐year‐old humans and 14‐month‐old mice to 50‐year‐old humans (Fox, <xref rid=\"acel13195-bib-0010\" ref-type=\"ref\">2007</xref>), this trend can also be observed in C57BL/6 JN mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>d) (Tabula Muris et al., <xref rid=\"acel13195-bib-0044\" ref-type=\"ref\">2018</xref>). The lower <italic>Gdf15</italic> expression in very old mice (27 months old) might be due to survival bias as only ~50% of mice reach this age.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13195-fig-0001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>GDF15 correlates positively with aging‐induced systemic inflammation in humans. (a) Correlation analysis of serum GDF15 levels in human subjects. (b) Serum levels of GDF15 in young (<bold>≤</bold>40; n = 14) and elderly (≥60; n = 24) subjects. (c) Hepatic expression of <italic>GDF15</italic> in young (<bold>≤</bold>40; n = 8) and elderly (≥60; n = 8) subjects. (d,e) The effect of age on hepatic <italic>GDF15</italic> expression in (d) a microarray dataset showing patient‐averaged hepatic log<sub>2</sub>‐transformed <italic>GDF15</italic> intensities for 202 patients (Innocenti et al., <xref rid=\"acel13195-bib-0017\" ref-type=\"ref\">2011</xref>), and (e) a GTEx RNA‐Seq dataset with log<sub>2</sub>‐transformed <italic>GDF15</italic> expression in transcripts per million (TPM) for 226 liver biopsies. Men are denoted as black circles, women as red triangles. The blue trend lines are obtained by fitting regression models with linear and quadratic age effects to the data. The transparent blue bands denote the 95% confidence intervals corresponding to these models. (f) Serum levels of TNF in young (<bold>≤</bold>40; n = 14) and elderly (≥60; n = 24) subjects. (g) Quantitation of mtDNA levels in ccf‐DNA from plasma in study participants. (h) Serum levels of GDF15 in subjects with the 20% lowest (bottom; n = 14; mean age, 46.4 years old) or 20% highest (top; n = 14; mean age, 65.5 years old) plasma levels of ccf‐mtDNA copy numbers. Data are expressed as mean ± SEM. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01 ((a): simple linear regression, (b,c), (f–h): two‐tailed t‐tests)</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13195-g001\"/></fig></sec><sec id=\"acel13195-sec-0004\"><label>2.2</label><title>GDF15 is linked to inflammation and mitochondrial stress</title><p>We also found that elderly subjects exhibited higher levels of serum TNF and increased hepatic TNF expression (Figure <xref rid=\"acel13195-fig-0001\" ref-type=\"fig\">1f</xref> and Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>e). The subjects with a higher level (top 25%) of serum GDF15 exhibited significantly elevated hepatic TNF expression compared with those with lower levels (bottom 25%) of serum GDF15 (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>f). Old subjects also showed a reduction in the frequency of naïve (CD45RA<sup>+</sup>CD45RO<sup>−</sup>) CD4<sup>+</sup> and CD8<sup>+</sup> T cells and an increase in the frequency of memory (CD45RA<sup>−</sup>CD45RO<sup>+</sup>) CD4<sup>+</sup> and CD8<sup>+</sup> T cells in the peripheral blood (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>g–i). Moreover, the absolute numbers of naïve CD4<sup>+</sup> and CD8+ T cells were reduced in older subjects, but the number of memory CD8<sup>+</sup> T cells was increased (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>j). In addition, the population of memory CD8<sup>+</sup> T cells showed a positive correlation with serum levels of GDF15 (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>k). The production of granzyme B in senescent CD4<sup>+</sup> and CD8<sup>+</sup> T cells was also higher in elderly subjects, compared with young subjects (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S1</xref>l–o).</p><p>Mitochondrial damage is closely linked with the systemic inflammatory response in human diseases, and circulating mitochondrial DNA is also associated with inflammation during aging (Picca et al., <xref rid=\"acel13195-bib-0037\" ref-type=\"ref\">2018</xref>). GDF15 is a well‐known mitokine that is secreted during mitochondrial stress and damage. Thus, we also measured mitochondria DNA (mtDNA) levels by quantitating the copy number of mitochondria DNA in circulating‐cell‐free (ccf) DNA in the plasma of young and elderly subjects. Although there is a high variability in circulating mtDNA in the elderly population which may be explained by individual differences in mitochondrial dysfunction, the mtDNA levels were significantly higher in elderly subjects compared with the younger controls (Figure <xref rid=\"acel13195-fig-0001\" ref-type=\"fig\">1g</xref>). Subjects with higher levels (top 20%) of ccf‐mtDNA copy number in plasma exhibited significantly higher levels of serum GDF15 compared with those with lower levels (bottom 20%) (Figure <xref rid=\"acel13195-fig-0001\" ref-type=\"fig\">1h</xref>). In addition, GDF15 levels were positively correlated with ccf‐mtDNA copy number in plasma (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S2</xref>a). We also found that tissue‐specific deficiency of CR6‐interacting factor‐1 (Crif1)‐induced dysfunction of mitochondrial oxidative phosphorylation exhibited an increase in serum levels of GDF15 in mice at 8 weeks of age compared with controls (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S2</xref>b) (Choi et al., <xref rid=\"acel13195-bib-0005\" ref-type=\"ref\">2020</xref>; Chung, Ryu, et al., <xref rid=\"acel13195-bib-0007\" ref-type=\"ref\">2017</xref>). Collectively, our data show that increases in both hepatic <italic>GDF15</italic> expression and serum levels of GDF15 are associated with aging‐related inflammation and mitochondrial damage.</p></sec><sec id=\"acel13195-sec-0005\"><label>2.3</label><title>Analysis of transcriptome datasets from the Genotype‐Tissue Expression (GTEx) project</title><p>To further investigate the relationship between <italic>GDF15</italic> and inflammatory response at the transcriptome level, we utilized GTEx RNA‐Seq data from the liver, adipose tissue, and skeletal muscle to observe whether <italic>GDF15</italic> expression is associated with systemic inflammation in humans. Differential expression gene analysis (DEA) was performed by dividing the data into two groups (top 25% and bottom 25% group) based on <italic>GDF15</italic> expression levels. First, DEA was performed in the liver (Figure <xref rid=\"acel13195-fig-0002\" ref-type=\"fig\">2a</xref>). The –log10(q‐value) for <italic>GDF15</italic> was equal to 191.7, confirming that each group was well‐differentiated by the expression of <italic>GDF15</italic> (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S3</xref>a). The DEA results indicated that 6,314 up‐regulated and 6307 down‐regulated genes differed between the <italic>GDF15</italic> top 25% group and the bottom 25% group (Figure <xref rid=\"acel13195-fig-0002\" ref-type=\"fig\">2b</xref>). Next, pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed to define pathways that differ between groups in terms of <italic>GDF15</italic> expression. Of the total 299 available pathways, the top 25% group with the highest <italic>GDF15</italic> levels exhibited 240 up‐regulated pathways and 4 down‐regulated pathways compared with the bottom 25% group. We found that mTOR signaling pathway and mitochondria‐related pathways were up‐regulated in the <italic>GDF15</italic> top 25% group compared to the <italic>GDF15</italic> bottom 25% group. We also observed an increase in inflammation‐related pathways, including the TNF signaling and IL‐17 signaling pathways. On the other hand, the pathways related valine, leucine and isoleucine degradation, and fatty acid degradation were significantly down‐regulated (Figure <xref rid=\"acel13195-fig-0002\" ref-type=\"fig\">2c</xref>). A correlation analysis on the GTEx liver expression data revealed that the expression levels of <italic>AP1</italic>, <italic>TNFAIP3</italic>, <italic>NOD2</italic>, and <italic>CD44</italic>, which play important roles in inflammation including TNF signaling, showed significant positive correlation with <italic>GDF15</italic> expression (Figure <xref rid=\"acel13195-fig-0002\" ref-type=\"fig\">2d</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13195-fig-0002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Analysis of related pathways regulated by <italic>GDF15</italic> expression in liver and adipose tissue in the GTEx database. (a) Distribution of 226 hepatic <italic>GDF15</italic> expression levels (log<sub>2</sub>(TPM + 0.001)) for human subjects in GTEx. The red and blue boxes represent the top 25% (n = 57) and the bottom 25% (n = 57) of the group according to <italic>GDF15</italic> expression levels, respectively. (b) Number of DEGs between the top 25% and the bottom 25% according to <italic>GDF15</italic> levels. The red, blue, and gray represent the number of up‐regulated, down‐regulated, and un‐regulated genes, respectively. (c) KEGG pathway analysis of the DEA results. The red, blue, and gray boxes indicate up‐regulated, down‐regulated, and un‐regulated pathways, respectively. Bar plots representing the up‐regulated (red) and down‐regulated (blue) pathways for significantly enriched pathways. The pathways shown in these bar plots were selected from the significant pathways (FDR <0.1) in the KEGG analysis. (d) Hepatic <italic>GDF15</italic> expression correlates positively with <italic>AP1</italic>,<italic> TNFAIP3</italic>,<italic> NOD2</italic>, and <italic>CD44</italic> gene expression. The correlation analysis was conducted by GEPIA2 in the GTEx liver dataset (R: Pearson's correlation coefficient). (e) Distribution of 633 subcutaneous adipose tissue <italic>GDF15</italic> expression levels (log<sub>2</sub>(TPM + 0.001)) for human subjects in GTEx. The red and blue boxes represent the top 25% (n = 158) and bottom 25% (n = 158) groups according to <italic>GDF15</italic> levels, respectively. (f) Number of DEGs between the top 25% and bottom 25% groups according to <italic>GDF15</italic> levels. The red, blue, and gray boxes represent the number of up‐regulated, down‐regulated, and un‐regulated genes, respectively. (g) KEGG pathway analysis of the DEA results. The red, blue, and gray boxes indicate up‐regulated, down‐regulated, and un‐regulated pathways, respectively. Bar plots represent the up‐regulated (red) and down‐regulated (blue) pathways for significantly enriched pathways. The pathways shown in these bar plots were selected from the significant pathways (FDR <0.1) in the KEGG analysis. (h) Adipose tissue <italic>GDF15</italic> expression correlates positively with <italic>AP1</italic>,<italic> TNFAIP3</italic>,<italic> NOD2</italic>,<italic> IFNG</italic>,<italic> CD44</italic>,<italic> CD11B</italic>, and <italic>CCL2</italic> gene expression. The correlation analysis was conducted by GEPIA2 in the GTEx subcutaneous adipose tissue dataset (R: Pearson's correlation coefficient)</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13195-g002\"/></fig><p>The role of GDF15 in subcutaneous adipose tissue was analyzed next. As in the liver, groups were divided into top 25% and bottom 25% groups, according to <italic>GDF15</italic> expression levels (Figure <xref rid=\"acel13195-fig-0002\" ref-type=\"fig\">2e</xref>). The q‐value for <italic>GDF15</italic> was approximately 0, indicating that each group was well‐divided in terms of <italic>GDF15</italic> expression (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S3</xref>b). For the DEA results, the top 25% group contained 9841 up‐regulated genes and 7588 down‐regulated genes, compared with the bottom 25% group (Figure <xref rid=\"acel13195-fig-0002\" ref-type=\"fig\">2f</xref>). KEGG analysis showed that inflammation‐related pathways were up‐regulated and mitochondria‐related pathways were down‐regulated, similar to what we observed in the liver (Figure <xref rid=\"acel13195-fig-0002\" ref-type=\"fig\">2g</xref>). Correlation analyses showed that, in addition to the inflammation‐related genes (<italic>AP1</italic>,<italic> TNFAIP3</italic>,<italic> NOD2</italic>, and <italic>CD44</italic>) observed in the liver, genes such as <italic>IFNG</italic>, <italic>CD11B</italic>, and <italic>CCL2</italic> also correlated positively with <italic>GDF15</italic> mRNA expression levels (Figure <xref rid=\"acel13195-fig-0002\" ref-type=\"fig\">2h</xref>). Similar data were observed in skeletal muscle (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S4</xref>a–d).</p></sec><sec id=\"acel13195-sec-0006\"><label>2.4</label><title>Role of GDF15 on longevity and metabolic phenotypes in mouse populations</title><p>To further explore the link between GDF15 and the metabolic features of aging, we analyzed whether GDF15 impacts lifespan and metabolic phenotypes in the BXD mouse genetic reference population, which is composed of ~160 genetically different mouse strains (Andreux et al., <xref rid=\"acel13195-bib-0001\" ref-type=\"ref\">2012</xref>). First, we analyzed the overall impact of hepatic <italic>Gdf15</italic> expression on murine lifespan without considering their strain (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3a, </xref>b). BXD mice with high expression (top 25%) of <italic>Gdf15</italic> transcripts in the liver lived significantly shorter than mice with low expression levels (bottom 25%) (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3a</xref>,b). Higher <italic>Gdf15</italic> expression in the liver is tightly associated with gene sets involved in mitochondrial stress and quality control (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S5</xref>a,b). To identify the <italic>Gdf15</italic>‐expression‐associated metabolic phenotypes, we also analyzed the major phenotypes of the individual BXD mice with higher (top 25%; <italic>Gdf15</italic>‐Hi) or lower expression (bottom 25%; <italic>Gdf15</italic>‐Lo) of hepatic <italic>Gdf15</italic> transcripts (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3c</xref>). The <italic>Gdf15</italic>‐Hi BXD strains placed on a normal chow diet exhibited glucose intolerance (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3d</xref>) and a lower respiratory exchange ratio (RER) compared with the <italic>Gdf15</italic>‐Lo strains (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3e</xref>,f). Although we found no differences in food intake between chow‐fed <italic>Gdf15</italic>‐Lo and <italic>Gdf15</italic>‐Hi animals, body weight (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S6</xref>a,b) and fat mass were significantly higher in the latter, whereas the lean mass was not altered (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S6</xref>c,d). The chow‐fed <italic>Gdf15</italic>‐Hi group also had heavier liver masses (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S5</xref>e) and higher serum levels of liver injury markers (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3g</xref>,h) compared to the chow‐fed <italic>Gdf15</italic>‐Lo group.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13195-fig-0003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Impact of <italic>Gdf15</italic> on metabolic phenotypes and survival in BXD mouse reference populations. (a) Violin plot visualizing the distribution of 241 BXD mice by hepatic <italic>Gdf15</italic> expression. The red and blue boxes represent the top and bottom 25% of BXD populations, respectively. (b) Kaplan–Meier plot showing the survival curves for the top (n = 37) and bottom (n = 36) 25% of mice corresponding to the red and blue squares, regardless of their BXD line (log‐rank (Mantel–Cox) test <italic>p</italic> = 0.039; Gehan–Breslow–Wilcoxon test <italic>p</italic> < 0.0001, hazard ratio = 0.5644). (c) The mean expression of hepatic <italic>Gdf15</italic> in each BXD strain at 29 weeks of age. The <italic>Gdf15</italic>‐low (blue) or <italic>Gdf15</italic>‐Hi (red) group consisted of 10 BXD lines each with, respectively, the lowest and highest hepatic <italic>Gdf15</italic> expression levels. (d–o) Metabolism and inflammation‐related phenotypes of <italic>Gdf15</italic>‐low (blue circle) and <italic>Gdf15</italic>‐Hi (red square) groups (n = 3 to 5 mice per BXD line). The area under curve for the oral glucose tolerance test (OGTT AUC) (d), the respiratory exchange ratio (RER) during day and night (e,f), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) (g, h) were obtained from the <italic>Gdf15</italic>‐low (blue) or <italic>Gdf15</italic>‐Hi (red) groups fed a normal chow diet at 29 weeks of age. The OGTT AUC (i), RER_day (j), RER_night (k), AST (l), ALT (m), plasma TNF (n), and plasma IL‐10 (o) were from the <italic>Gdf15</italic>‐low (blue) or <italic>Gdf15</italic>‐Hi (red) groups under a high‐fat diet at 29 weeks of age. Values (d–o) are mean ± SEM. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001 ((d–o): two‐tailed t tests)</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13195-g003\"/></fig><p>Then, we assessed the metabolic parameters in the same BXD strains but now fed with a high fat diet (HFD) for 21 weeks. Consistent with the data from the chow‐fed mice, the <italic>Gdf15</italic>‐Hi strain fed with a HFD developed more severe glucose intolerance (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3i</xref>) without any changes in food intake (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S6</xref>f) compared with the <italic>Gdf15</italic>‐Lo strain, but the <italic>Gdf15</italic>‐Hi group also showed a similar RER compared with the <italic>Gdf15</italic>‐Lo group (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3j</xref>,k). The <italic>Gdf15</italic>‐Hi group exhibited more severe hepatocellular injury (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3l</xref>,m), although food intake, body weight, body composition, and liver mass were not clearly altered (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S6</xref>f‐j). Moreover, the <italic>Gdf15</italic>‐Hi BXD mice were cold intolerant compared with <italic>Gdf15</italic>‐Lo mice, both when fed a chow or a HFD (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S6</xref>k,l). In the <italic>Gdf15</italic>‐Hi group, the plasma levels of TNF, interleukin 10 (IL‐10), and monocyte chemoattractant protein 1 (MCP1) were increased, but only differences in IL‐10 levels passed the significance threshold of 5% (Figure <xref rid=\"acel13195-fig-0003\" ref-type=\"fig\">3n</xref>,o and Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S6</xref>m). These data demonstrate that mice with higher hepatic <italic>Gdf15</italic> expression exhibit metabolic impairment and tissue inflammation.</p></sec><sec id=\"acel13195-sec-0007\"><label>2.5</label><title>Mendelian randomization study with human data</title><p>We then performed a Mendelian randomization through the single nucleotide polymorphism (SNP) rs7226, which is significantly associated with <italic>GDF15</italic> expression in whole blood in GTEx and has been linked to GDF15 blood concentration before (Jiang et al., <xref rid=\"acel13195-bib-0018\" ref-type=\"ref\">2018</xref>). We assessed 33 obesity‐related traits and 27 traits related to concentration of leukocytes in the blood obtained from a variety of publicly available human GWAS datasets (Table <xref rid=\"acel13195-sup-0003\" ref-type=\"supplementary-material\">S2</xref>). Increased <italic>GDF15</italic> expression was significantly associated with reduced obesity and fat mass for 24 traits at 5% false discovery rate (FDR) (Table <xref rid=\"acel13195-sup-0003\" ref-type=\"supplementary-material\">S2</xref>, top 10 shown in Figure <xref rid=\"acel13195-fig-0004\" ref-type=\"fig\">4a</xref>). This is unsurprising, as GDF15 is known to prevent obesity (Chung, Ryu, et al., <xref rid=\"acel13195-bib-0007\" ref-type=\"ref\">2017</xref>; Tsai, Lin, Brown, Salis, & Breit, <xref rid=\"acel13195-bib-0046\" ref-type=\"ref\">2016</xref>). However, increased <italic>GDF15</italic> expression through rs7226 also appears to be linked to a decreased concentration of total leukocytes, innate immune cells, and myeloid white cells (9 significant traits at 5% FDR) and an increased concentration of lymphocytes and monocytes in the blood (3 significant traits at 5% FDR) (Table <xref rid=\"acel13195-sup-0003\" ref-type=\"supplementary-material\">S2</xref>, top 10 shown in Figure <xref rid=\"acel13195-fig-0004\" ref-type=\"fig\">4b</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13195-fig-0004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Mendelian randomization of GTEx liver <italic>GDF15</italic> expression in whole blood through SNP rs7226 on 33 obesity‐related outcomes and 27 outcomes related to blood leukocyte concentrations. Wald ratio tests were used to test for statistical significance. q‐values were calculated with the Benjamini–Hochberg multiple testing FDR procedure. All estimated effect sizes <mml:math id=\"nlm-math-1\"><mml:mover accent=\"true\"><mml:mi>β</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover></mml:math> should be interpreted as average increases (for positive effect sizes) or decreases (for negative effect sizes) in the outcomes per unit increase in normalized effect size (NES) <italic>GDF15</italic> expression. Masses and weights are expressed in kg; BMI is expressed in kg/m<sup>2</sup>. Whiskers denote 95% confidence intervals. (a) Top 10 most significantly changing obesity‐related traits; (b) top 10 most significantly changing immunity‐related traits</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13195-g004\"/></fig></sec><sec id=\"acel13195-sec-0008\"><label>2.6</label><title>GDF15 depletion produces hepatic and adipose inflammatory responses during the aging process</title><p>Based on the link of GDF15 with aging, metabolism, and inflammation observed in mouse and humans and the abnormalities in metabolic tissues (liver, muscle, adipose tissue) in the BXD mouse reference population, we investigated the population and function of immune cells in the liver of 20‐month‐old WT and <italic>Gdf15</italic> knockout (KO) mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S7</xref>). We found no remarkable difference in the absolute number of liver mononuclear cells between the 20‐month‐old WT and <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S8</xref>a). In the subset analysis of liver mononuclear cells (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S7</xref>b), the population of natural killer T cells and mature CD3+ T cells did not exhibit changes in both the 20‐month‐old WT or <italic>Gdf15</italic> KO mice, but the number of natural killer cells was significantly decreased in the liver of <italic>Gdf15</italic> KO old mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S8</xref>a–c). 20‐month‐old <italic>Gdf15</italic> KO mice exhibited a higher population of CD8<sup>+</sup> T cells and a lower population of CD4<sup>+</sup> T cells in the liver compared with 20‐month‐old WT mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S8</xref>b–d). Although the frequency of naïve and memory CD4<sup>+</sup> T cells was not different between the WT and <italic>Gdf15</italic> KO old mice, the population of memory CD8<sup>+</sup> T cells and naïve CD8<sup>+</sup> T cells was, respectively, increased and decreased in the livers of the 20‐month‐old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-fig-0005\" ref-type=\"fig\">5a</xref> and Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S8</xref>e,f). However, no significant difference was found in the naïve and memory CD4<sup>+</sup> or CD8<sup>+</sup> T‐cell populations between younger WT and <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S8</xref>g–i). This means that aging can induce mitochondrial stress in cells and that aging stress is required for GDF15 deficiency‐mediated systemic inflammation in mice. Furthermore, Ly6C<sup>+</sup> inflammatory macrophages and Ly6G<sup>+</sup> neutrophils were remarkably higher in the livers of 20‐month‐old <italic>Gdf15</italic> KO mice compared with WT controls (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S8</xref>j,k).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13195-fig-0005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>GDF15‐mediated regulation of the inflammatory response in the liver and gonadal adipose tissues during aging. (a) Population size and frequency of CD44<sup>+</sup>CD62L<sup>−</sup> and CD44<sup>−</sup>CD62L<sup>−</sup> in CD4<sup>+</sup>, and CD8<sup>+</sup> T cells in liver tissues of 20‐month‐old WT (n = 6) or <italic>Gdf15</italic> KO (n = 6) mice. (b) IFN‐γ, TNF‐α, or IL‐17A producing CD4<sup>+</sup> and CD8<sup>+</sup> T cells in liver tissues of 20‐month‐old WT (n = 6) or <italic>Gdf15</italic> KO (n = 6) mice. (c,d) Representative flow cytometry plots of CD44<sup>+</sup>CD62L<sup>−</sup> and CD44<sup>−</sup>CD62L<sup>−</sup> in CD4<sup>+</sup>, and CD8<sup>+</sup> T cells in liver tissues of 20‐month‐old WT (n = 6) or <italic>Gdf15</italic> KO (n = 6) mice. (e) Percentage of CD11b and F4/80‐positive cells within the livers of 20‐month‐old WT and Gdf15 KO mice. (f) Frequencies of infiltrating macrophages, monocytes, and neutrophils in liver tissues of WT (n = 6) or <italic>Gdf15</italic> KO (n = 6) mice. (g) Transcript levels of pro‐inflammatory cytokines in liver tissues of WT (n = 6) or <italic>Gdf15</italic> KO (n = 6) mice. Data are expressed as mean ± SEM. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01 ((a,b,f,g): two‐tailed t tests)</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13195-g005\"/></fig><p>Next, we investigated the functional characteristics of hepatic CD4<sup>+</sup> and CD8<sup>+</sup> T cells from 20‐month‐old WT and <italic>Gdf15</italic> KO mice. IFN‐γ production in memory CD4<sup>+</sup> and CD8<sup>+</sup> T cells was increased in the liver of 20‐month‐old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-fig-0005\" ref-type=\"fig\">5b</xref> and Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S8</xref> l,m). In addition, the population of TNF‐α or IL‐17A‐producing memory CD4<sup>+</sup> and CD8<sup>+</sup> T cells was significantly increased in the liver of the old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-fig-0005\" ref-type=\"fig\">5b</xref> and Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S8</xref>n–p). We also compared T cells in the mesenteric lymph nodes of 20‐month‐old controls and <italic>Gdf15</italic> KO mice. The population of memory/effector CD4<sup>+</sup> and CD8<sup>+</sup> T cells was similar in the mesenteric lymph nodes from old <italic>Gdf15</italic> KO and WT mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S9</xref>a–e). Taken together, our data demonstrate that <italic>Gdf15</italic> deficiency induces a larger population of infiltrating pro‐inflammatory immune cells in the livers of older mice.</p><p>Diverse innate and adaptive immune cells reside within visceral and subcutaneous adipose tissues in mammals, where they play an essential role in the development of obesity, type 2 diabetes, and metabolic diseases (Lu, Zhao, Meng, & Zhang, <xref rid=\"acel13195-bib-0028\" ref-type=\"ref\">2019</xref>). Thus, we investigated the immune cells in the gonadal adipose tissues of 20‐month‐old WT and <italic>Gdf15</italic> KO mice. Adipose CD8<sup>+</sup> T cells were significantly increased in old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S10</xref>a,b). The subset analysis of CD4<sup>+</sup> and CD8<sup>+</sup> T cells in the gonadal adipose tissue revealed a higher population of memory T cells and a lower population of naïve T cells in old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-fig-0005\" ref-type=\"fig\">5c,d</xref> and Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S10</xref>c). Moreover, CD11b<sup>+</sup>F4/80<sup>+</sup> macrophages in gonadal fat were significantly increased in the old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S10</xref>d). Infiltrating neutrophils and monocytes were remarkably abundant in the gonadal adipose tissues of old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-fig-0005\" ref-type=\"fig\">5f</xref> and Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S10</xref>e,f). The expression of pro‐inflammatory cytokines and chemokines was also significantly up‐regulated in the gonadal adipose tissues of old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-fig-0005\" ref-type=\"fig\">5g</xref>). Collectively, <italic>Gdf15</italic> deficiency contributes to the adipose inflammatory response by increasing the population of activated T cells, inflammatory macrophages, and neutrophils in the adipose tissue of aged mice.</p></sec><sec id=\"acel13195-sec-0009\"><label>2.7</label><title>GDF15 deficiency deteriorates systemic glucose homeostasis in 20‐month‐old mice</title><p>Next, we studied the impact of <italic>Gdf15</italic> on systemic metabolic homeostasis during the aging process. We found that <italic>Gdf15</italic> depletion did not induce any significant changes in body weight in 10‐ 40‐, or 100‐week‐old mice (Figure <xref rid=\"acel13195-fig-0006\" ref-type=\"fig\">6a</xref>). Liver injury markers were increased in the serum of <italic>Gdf15</italic> KO old mice, but serum levels of triglyceride and total cholesterol were similar between WT and <italic>Gdf15</italic> KO old mice (Figure <xref rid=\"acel13195-fig-0006\" ref-type=\"fig\">6b</xref>). Serum levels of inflammatory cytokines, including TNF‐α and IL‐1β, were significantly higher in 20‐month‐old <italic>Gdf15</italic> KO mice compared with WT controls (Figure <xref rid=\"acel13195-fig-0006\" ref-type=\"fig\">6c</xref>). Moreover, hepatic steatosis (Figure <xref rid=\"acel13195-fig-0006\" ref-type=\"fig\">6d</xref>) and liver triglyceride content were elevated in 20‐month‐old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S11</xref>a). Furthermore, F4/80+ cells were increased in the livers of 20‐month‐old <italic>Gdf15</italic> KO mice (Figure <xref rid=\"acel13195-fig-0006\" ref-type=\"fig\">6e</xref> and Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S11</xref>b,c), which also showed higher hepatic expression of pro‐inflammatory cytokines and fibrotic mediators (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S11</xref>d). Old <italic>Gdf15</italic> KO mice exhibited glucose intolerance compared to age‐matched WT mice during intraperitoneal glucose tolerance tests (Figure <xref rid=\"acel13195-fig-0006\" ref-type=\"fig\">6f</xref>). <italic>Gdf15</italic> deficiency also led to significant insulin resistance in 20‐month‐old mice compared with WT mice (Figure <xref rid=\"acel13195-fig-0006\" ref-type=\"fig\">6g</xref>). These metabolic phenotypic changes may indirectly affect the increase in memory/effector T cells in the liver and adipose tissues. These data suggest that <italic>Gdf15</italic> protects the mice against aging‐induced glucose intolerance and insulin resistance, as well as tissue inflammation.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13195-fig-0006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Metabolic phenotyping of WT and <italic>Gdf15</italic> KO 20‐month‐old mice. (a) Body weight of WT and <italic>Gdf15</italic> KO mice at 10‐, 40‐, and 100‐weeks. (b) Serum levels of liver injury markers, triglyceride, and cholesterol profiles of 20‐month‐old WT (n = 6) and <italic>Gdf15</italic> KO (n = 6) mice. (c) Serum levels of pro‐inflammatory cytokines of 20‐month‐old WT (n = 6) and <italic>Gdf15</italic> KO (n = 6) mice. (d) H&E staining for liver tissues of 20‐month‐old WT (n = 6) and <italic>Gdf15</italic> KO (n = 6) mice. Scale bar, 200 μm. Arrows indicate fat accumulation. (e) Fixed adipose tissue from 20‐month‐old WT (n = 6) and <italic>Gdf15</italic> KO (n = 6) mice was stained for F4/80 antibodies. Scale bar, 200 μm. (f) Glucose tolerance tests of 20‐month‐old WT (n = 6) and <italic>Gdf15</italic> KO (n = 6) mice. (g) Blood glucose levels measured over time after intraperitoneal insulin (0.75 U/kg) injection in 20‐month‐old WT (n = 6) and <italic>Gdf15</italic> KO (n = 6) mice. Data (a–c, f, g) are expressed as mean ± SEM. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01 ((a–c, f,g): two‐tailed t tests)</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13195-g006\"/></fig></sec><sec id=\"acel13195-sec-0010\"><label>2.8</label><title>Treatment with recombinant GDF15 has no effect on T‐cell activation <italic>in vitro</italic>\n</title><p>To understand how GDF15 contributes to chronic inflammation, we investigated the effect of GDF15 addition on T‐cell activation and Th17 differentiation. Isolated naïve CD4<sup>+</sup> T cells from human subjects were stimulated with anti‐CD3 and anti‐CD28, which were concomitantly treated with or without several concentrations of recombinant GDF15. The production of IL‐2 and IFN‐γ on CD4<sup>+</sup> T‐cell activation was not significantly changed by treatment with recombinant GDF15 (Figure <xref rid=\"acel13195-fig-0007\" ref-type=\"fig\">7a</xref>,b). Moreover, co‐incubation with recombinant GDF15 did not induce any differences in the number of CD69<sup>+</sup> or CD25<sup>+</sup> T cells stimulated with anti‐CD3/CD28 (Figure <xref rid=\"acel13195-fig-0007\" ref-type=\"fig\">7c</xref>,d). Furthermore, recombinant GDF15 did not impact on IFN‐γ production upon CD8<sup>+</sup> T‐cell activation (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S12</xref>a). IL‐17‐producing effector T helper cells, known as Th17 cells, were implicated in the development of metabolic diseases as well as autoimmune disorders in the elderly (Chehimi, Vidal, & Eljaafari, <xref rid=\"acel13195-bib-0004\" ref-type=\"ref\">2017</xref>). Thus, we also assessed the function of GDF15 on Th17 cell differentiation <italic>in vitro</italic>. Th17 cells that were differentiated in the presence of recombinant GDF15 showed similar levels of IL‐17 production compared to the control cells treated with vehicle (Figure <xref rid=\"acel13195-fig-0007\" ref-type=\"fig\">7e</xref>). In addition, the expression of RORγt, the key transcriptional regulator for Th17 cell differentiation, was not different between T cells treated with or without recombinant GDF15 (Figure <xref rid=\"acel13195-fig-0007\" ref-type=\"fig\">7f</xref>). Furthermore, treatment with recombinant GDF15 did not induce a significant change in oxygen consumption rate and extracellular acidification rate (ECAR) in activated CD4<sup>+</sup> or CD8<sup>+</sup> T cells (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S12</xref>b–e). Collectively, our data show that GDF15 is not required for Th17 cell differentiation from human naïve CD4<sup>+</sup> T cells and is dispensable for IL‐17 production in Th17 cells.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13195-fig-0007\" orientation=\"portrait\" position=\"float\"><label>Figure 7</label><caption><p>The role of GDF15 in T‐cell activation and Th17 differentiation <italic>in vitro</italic>. (a) IL‐2 production from naïve CD4<sup>+</sup> T cells stimulated with anti‐CD3 (2 μg/mL)/CD28 (5 μg/mL) in the absence or presence of varying concentrations of recombinant <italic>GDF15</italic> for 72 hr. (b) IFN‐γ production from differentiated CD4<sup>+</sup> T cells under Th1 culture conditions in the presence or absence of the indicated concentrations of recombinant GDF15. (c,d) Naïve lymph node T cells were stimulated with the indicated concentrations of anti‐CD3/CD28 and several concentrations of recombinant GDF15 for 24 hr and then analyzed for CD69 and CD25 expression by FACS. (e) The population of IL‐17+ cells under Th17 differentiation‐inducing culture conditions at the indicated concentrations of recombinant <italic>GDF15</italic>. (f) The transcription of RORγt in differentiated CD4<sup>+</sup> T cells under Th17 differentiation‐inducing culture conditions at several concentrations of recombinant GDF15. Data are expressed as mean ± SEM ((a–f): one‐way ANOVA)</p></caption><graphic id=\"nlm-graphic-15\" xlink:href=\"ACEL-19-e13195-g007\"/></fig></sec><sec id=\"acel13195-sec-0011\"><label>2.9</label><title>GDF15 contributes to the regulatory T‐cell‐mediated suppression of conventional T cells</title><p>Although GDF15 is involved in aging‐induced abnormal glucose homeostasis and tissue inflammation in humans and mice, we found that GDF15 does not exhibit a direct role in T‐cell activation and Th17 differentiation. Thus, we investigated whether GDF15 is involved in regulatory T‐cell (Treg)‐mediated inhibition of the effector function of conventional T cells. Effector T cells were co‐cultured with or without Tregs at a ratio of 1:1. Tregs suppressed the proliferation and IFN‐γ production of conventional T cells stimulated with anti‐CD3/CD28 (Figure <xref rid=\"acel13195-fig-0008\" ref-type=\"fig\">8a</xref>–e). Then, we co‐cultured Tregs with conventional T cells activated by anti‐CD3/CD28 in the presence of recombinant GDF15. Intriguingly, we found that recombinant GDF15 increased Treg‐mediated suppression of the proliferation and IFN‐γ production of activated T cells <italic>in vitro</italic> (Figure <xref rid=\"acel13195-fig-0008\" ref-type=\"fig\">8a</xref>–e). Moreover, we found that recombinant GDF15 increased the expression of <italic>GDNF</italic> family receptor α‐like (GFRAL) in differentiated Tregs (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S13</xref>). These results indicate that recombinant GDF15 increases Treg‐mediated suppression of conventional T‐cell activation, and thus, likely contributes to the regulation of systemic inflammation.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13195-fig-0008\" orientation=\"portrait\" position=\"float\"><label>Figure 8</label><caption><p>GDF15 enhances Treg‐mediated suppression of T‐cell activation via IL‐10 production in Tregs. (a) Treg‐mediated suppression of the activation (% of suppression of CD25<sup>+</sup> T cells) of CD4<sup>+</sup> T cells stimulated with anti‐CD3 (2 μg/mL)/CD28 (5 μg/mL) in the absence or presence of different concentrations of recombinant <italic>GDF15</italic> for 72 hr. (b–e) IFN‐γ levels in supernatants from co‐culturing WT or IL‐10‐deficient Tregs with CD4<sup>+</sup> T cells stimulated by anti‐CD3 (2 μg/mL)/CD28 (5 μg/mL) at the indicated concentrations of recombinant GDF15 for 72 hr. (f) IL‐10 KO Treg‐mediated suppression of the activation (% of suppression of CD25<sup>+</sup> T cells) of CD4<sup>+</sup> T cells in the absence or presence of several concentrations of recombinant GDF15 for 72 hr. (g–j) IL‐10 KO Treg‐mediated suppression of IFN‐γ production of CD4<sup>+</sup> T cells in the absence or presence of several concentrations of recombinant GDF15 for 72 hr. (k) Population of senescent CD8<sup>+</sup> T cells in young (<bold>≤</bold>40) and elderly (≥60) human subjects. (l) IFN‐γ or TNF‐α production from senescent CD8<sup>+</sup> T cells in the absence or presence of several concentrations of recombinant GDF15 for 48 hr. Data are expressed as mean ± SEM. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01 ((a–h): one‐way ANOVA, (k): two‐tailed t tests, (i–l): one‐way ANOVA)</p></caption><graphic id=\"nlm-graphic-17\" xlink:href=\"ACEL-19-e13195-g008\"/></fig><p>Moreover, we showed that IL‐10‐deficient Tregs did not manage to increase the suppression of T‐cell activation and failed to reduce IFN‐γ production in activated T cells by treatment with recombinant GDF15 (Figure <xref rid=\"acel13195-fig-0008\" ref-type=\"fig\">8f</xref>–j). This suggests that IL‐10 mediates the effect of GDF15 on Treg‐mediated suppression of activated T cells. Additionally, we demonstrated that the population of senescent CD8<sup>+</sup> T cells within peripheral blood mononuclear cells (PBMCs) was significantly increased in elderly subjects compared with young subjects (Figure <xref rid=\"acel13195-fig-0008\" ref-type=\"fig\">8k</xref>). To identify the role of GDF15 on the secretion of inflammatory cytokines from senescent CD8<sup>+</sup> T cells, we co‐cultured senescent CD8<sup>+</sup> T cells with Tregs in the presence of recombinant GDF15. Intriguingly, recombinant GDF15 did not induce an increase in Treg‐mediated suppression of IFN‐γ or TNF‐α production in senescent CD8<sup>+</sup> T cells (Figure <xref rid=\"acel13195-fig-0008\" ref-type=\"fig\">8l</xref>). Collectively, these findings suggest that GDF15 increases Treg‐mediated suppression of proliferation and IFN‐γ production in conventional T cells but does not exhibit any role in the regulation of inflammatory cytokines in senescent CD8<sup>+</sup> T cells.</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13195-sec-0012\"><label>3</label><title>DISCUSSION</title><p>In this study, we focused on the role of GDF15, a mitochondrial stress‐related secretory factor, in immunometabolic homeostasis during the aging process. We discovered that GDF15‐mediated regulation of tissue‐resident immune cells in the liver and adipose tissues is a key mechanism in preventing the abrupt metabolic deterioration in aging. Thus, we suggest that GDF15 plays an essential role as a new biomarker and candidate drug target for aging and age‐related tissue inflammation.</p><p>Although the aging process is complex, mitochondrial stress and dysfunction are universal hallmarks of cellular senescence and aging. Such age‐related mitochondrial damage and dysfunction can result in the leakage of mtDNA in the circulation. Increased concentrations of plasma ccf‐mtDNA have been observed in patients with mitochondrial dysfunction‐related chronic metabolic disorders (Kintscher et al., <xref rid=\"acel13195-bib-0023\" ref-type=\"ref\">2008</xref>; Li et al., <xref rid=\"acel13195-bib-0026\" ref-type=\"ref\">2016</xref>; Zhang et al., <xref rid=\"acel13195-bib-0056\" ref-type=\"ref\">2010</xref>). Here, we found that plasma levels of ccf‐mtDNA were also significantly increased in older subjects and correlated positively with serum levels of GDF15. These findings suggest that aging is a critical factor for the elevation of plasma ccf‐mtDNA levels, which may be derived from mitochondrial stress in humans.</p><p>In this study, we found a positive correlation of GDF15 levels with aging in human subjects and in the BXD mouse genetic reference population. In apparent contrast, <italic>Gdf15</italic> KO mice showed an increase in inflammatory and organ damage markers upon aging. This is, however, consistent with a role of the GDF15 mitokine in mitohormesis. During specific periods of life, the secretion of small amounts of mitokines is adaptive as they act to correct and restore some of the underlying mitochondrial abnormalities. We hypothesize that the inherent adaptive nature of mitokine secretion can, however, become maladaptive as uncontrolled mitokine production can contribute to the aging process and disease progression (Khan et al., <xref rid=\"acel13195-bib-0021\" ref-type=\"ref\">2017</xref>). Secretion of limited “physiological” levels of mitokines, such as GDF15, may hence correct or cure, through a feedback inhibition, certain of the underpinning cellular abnormalities that lead to their secretion. Higher “supraphysiological” mitokine levels may, however, either contribute to enhance the underlying abnormalities and via a feedforward mechanism enhance disease pathogenesis or reverse the pathology, as observed in the case of FGF21 in the case of mitochondrial myopathy (Khan et al., <xref rid=\"acel13195-bib-0021\" ref-type=\"ref\">2017</xref>). In this last scenario, high mitokine levels may also serve as reliable disease biomarkers.</p><p>Although the relationship between mitochondrial stress and GDF15 induction is merely correlative and requires further confirmation in follow‐up studies, a large number of previous reports supports the role of GDF15 as a major mitokine regulating metabolic phenotype and inflammatory responses (Choi et al., <xref rid=\"acel13195-bib-0005\" ref-type=\"ref\">2020</xref>; Chung, Ryu, et al., <xref rid=\"acel13195-bib-0007\" ref-type=\"ref\">2017</xref>; Jung et al., <xref rid=\"acel13195-bib-0019\" ref-type=\"ref\">2018</xref>). We also showed previously that GDF15 deficiency exacerbated obesity and glucose intolerance, as well as alcohol‐ and carbon tetrachloride‐induced liver inflammation (Chung, Kim, et al., <xref rid=\"acel13195-bib-0006\" ref-type=\"ref\">2017</xref>; Tran, Yang, Gardner, & Xiong, <xref rid=\"acel13195-bib-0045\" ref-type=\"ref\">2018</xref>). Adeno‐associated virus expression of human GDF15 (AAV‐hGDF15) also improved body weight and metabolic profiles in diet‐induced obese mice and fat mass remained reduced in 18‐month‐old male B6D2F1 with diet‐induced obese mice, 12 months after injection with AAV‐hGDF15 (Xiong et al., <xref rid=\"acel13195-bib-0049\" ref-type=\"ref\">2017</xref>). Recombinant GDF15 furthermore improved the metabolic phenotype in <italic>ob</italic>/<italic>ob</italic> mice (Chung, Ryu, et al., <xref rid=\"acel13195-bib-0007\" ref-type=\"ref\">2017</xref>) and inhibited IL‐1β+IFN‐γ‐induced apoptosis of human islets and of a β‐cell line, leading to the prevention of diabetes in NOD mice (Nakayasu et al., <xref rid=\"acel13195-bib-0035\" ref-type=\"ref\">2020</xref>). Taken together, GDF15 is a potent hormone responsible for regulating the inflammatory and metabolic phenotypes.</p><p>An essential mechanism of immune regulation in tissue inflammation and metabolic diseases involves the action of Tregs (Ilan et al., <xref rid=\"acel13195-bib-0016\" ref-type=\"ref\">2010</xref>). In a previous report, Tregs effectively suppressed the pathological and physiological immune responses, leading to immune homeostasis (Miyara & Sakaguchi, <xref rid=\"acel13195-bib-0032\" ref-type=\"ref\">2007</xref>). In this study, we found that recombinant GDF15 increased the expression of <italic>Gfral</italic> in differentiated murine Tregs (Figure <xref rid=\"acel13195-sup-0001\" ref-type=\"supplementary-material\">S13</xref>), although further confirmation is needed to define the role of GFRAL in the differentiated Tregs. We also demonstrated that recombinant GDF15 enhances Treg‐mediated suppression of T‐cell activation by increasing IL‐10 production in Tregs. However, senescent CD8<sup>+</sup> T cells exhibited resistance to Treg‐mediated suppression of IFN‐γ or TNF‐α production in the presence of recombinant GDF15, although senescent T cells exhibited defective T‐cell receptor‐mediated proliferation capacity (Fukushima, Minato, & Hattori, <xref rid=\"acel13195-bib-0012\" ref-type=\"ref\">2018</xref>). These findings suggest that aging mediates an increase in the population of senescent T cells and may contribute to the development of systemic inflammation by avoiding Treg‐mediated suppression. Thus, even if GDF15 increases with age, it may be difficult to regulate tissue inflammation and to prevent metabolic disorders in humans and mice.</p><p>Aging is also associated with chronic inflammation in several tissues (Sanada et al., <xref rid=\"acel13195-bib-0041\" ref-type=\"ref\">2018</xref>). Lipid accumulation and infiltration of activated T cells, monocytes/macrophages, or neutrophils into metabolic organs increase tissue inflammation and dysregulation of metabolic homeostasis (Byun & Yi, <xref rid=\"acel13195-bib-0003\" ref-type=\"ref\">2017</xref>; Kintscher et al., <xref rid=\"acel13195-bib-0023\" ref-type=\"ref\">2008</xref>; Mirmiran, Bahadoran, & Azizi, <xref rid=\"acel13195-bib-0031\" ref-type=\"ref\">2014</xref>). In contrast, the depletion of these inflammatory immune cells ameliorates systemic inflammation and insulin resistance (Jung et al., <xref rid=\"acel13195-bib-0019\" ref-type=\"ref\">2018</xref>; Yang et al., <xref rid=\"acel13195-bib-0050\" ref-type=\"ref\">2010</xref>). Moreover, T‐cell aging is involved in glucose intolerance and insulin resistance in humans and mice (Yi et al., <xref rid=\"acel13195-bib-0054\" ref-type=\"ref\">2019</xref>). In this study, we demonstrated that <italic>Gdf15</italic> deficiency enhanced the infiltration of effector T cells and inflammatory macrophages into the liver and adipose tissues, accentuating liver injury, and insulin resistance in aged mice. Although we have not demonstrated an effect of recombinant GDF15 on inflammatory or metabolic phenotypes in old mice, previous studies (Choi et al., <xref rid=\"acel13195-bib-0005\" ref-type=\"ref\">2020</xref>; Chung, Kim, et al., <xref rid=\"acel13195-bib-0006\" ref-type=\"ref\">2017</xref>; Chung, Ryu, et al., <xref rid=\"acel13195-bib-0007\" ref-type=\"ref\">2017</xref>; Tran et al., <xref rid=\"acel13195-bib-0045\" ref-type=\"ref\">2018</xref>) support the idea that GDF15 can regulate metabolic homeostasis by modulating the tissue inflammatory response in various situations. However, further study is required to establish a functional role for GDF15 in aging‐mediated inflammation and metabolic diseases in humans.</p><p>Although recent studies emphasized the importance of the <italic>GDNF</italic> family receptor α‐like (GFRAL)/GDF15 signaling in hindbrain neurons (Emmerson et al., <xref rid=\"acel13195-bib-0009\" ref-type=\"ref\">2017</xref>; Mullican et al., <xref rid=\"acel13195-bib-0033\" ref-type=\"ref\">2017</xref>; Yang et al., <xref rid=\"acel13195-bib-0051\" ref-type=\"ref\">2017</xref>), previous investigations also showed peripheral effects of GDF15 (Chung, Ryu, et al., <xref rid=\"acel13195-bib-0007\" ref-type=\"ref\">2017</xref>; Kim et al., <xref rid=\"acel13195-bib-0022\" ref-type=\"ref\">2018</xref>; Lee et al., <xref rid=\"acel13195-bib-0025\" ref-type=\"ref\">2017</xref>). Moreover, treatment with recombinant GDF15 ameliorated non‐alcoholic or alcoholic fatty liver in mice, without affecting food consumption (Chung, Ryu, et al., <xref rid=\"acel13195-bib-0007\" ref-type=\"ref\">2017</xref>; Kim et al., <xref rid=\"acel13195-bib-0022\" ref-type=\"ref\">2018</xref>). In the current study, we utilized the BXD mouse reference population and GTEx human transcriptomic data to unravel a network of GDF15‐related genes and phenotypes in different human (GTEx) and mouse (BXD) tissues under basal physiological conditions. <italic>GDF15</italic> expression increased during normal aging in the mouse BXD population and in two independent human cohorts. Moreover, we demonstrated that aged mice with a higher hepatic <italic>Gdf15</italic> transcript levels had decreased health and lifespan, despite the absence of changes in food intake. Combined, these data may suggest a role of GDF15 in the regulation of survival and aging‐related metabolic diseases through a peripheral mechanism independent of GFRAL. However, further studies are needed to establish peripheral effects of GDF15‐GFRAL axis using tissue‐specific GFRAL knockout animal models.</p><p>In conclusion, GDF15 is induced by mitochondrial dysfunction and systemic inflammation in humans and mice during the aging process. Our study also reveals critical insights into the regulation of tissue inflammation by GDF15 during the aging process. Although previous studies showed that recombinant GDF15 improves the metabolic phenotype and tissue inflammation in mouse models, additional mechanistic studies will be needed to confirm GDF15 supplementation is a potential therapy against age‐related chronic diseases.</p></sec><sec id=\"acel13195-sec-0013\"><label>4</label><title>EXPERIMENTAL PROCEDURES</title><sec id=\"acel13195-sec-0014\"><label>4.1</label><title>Human subjects</title><p>Peripheral blood samples (10 ml each) were obtained from 70 study participants in the Department of Endocrinology and Metabolism outpatient clinic at Chungnam National University Hospital (CNUH). Human subjects with any of the following conditions were excluded from the study: previous coronary heart disease, uncontrolled hypertension, chronic obstructive pulmonary disease, acute or chronic kidney disease (estimated glomerular filtration rate < 30 ml/min/1.73 m<sup>2</sup>), anemia (hemoglobin < 12 g/dl), history of any malignant or inflammatory disease, current liver disease, or high plasma aspartate transaminase or alanine transaminase (>80 IU/L). Liver tissues were collected from 30 subjects who underwent lobectomy or segmentectomy at CNUH. The non‐tumor areas in the liver were isolated and used for real‐time PCR analysis. Baseline clinical characteristics of the study subjects are described in Table <xref rid=\"acel13195-sup-0002\" ref-type=\"supplementary-material\">S1</xref>. Prior to their inclusion in this study, written informed consent was obtained from all participants. This study was also approved by the Institutional Review Board of CNUH (CNUH 2015‐09‐042; CNUH 2019‐06‐063). All experiments were performed in accordance with the standards of the Declaration of Helsinki and related guidelines.</p></sec><sec id=\"acel13195-sec-0015\"><label>4.2</label><title>Mice</title><p>Male C57BL/6 WT mice were purchased from Jackson Laboratory (Bar Harbor, ME, USA), and <italic>Gdf15</italic> KO mice were provided by S. Lee, Johns Hopkins University School of Medicine (Baltimore, MD). IL‐10‐deficient mice were purchased from the Jackson Laboratory (Bar Harbor, ME) and were backcrossed with the B6 strain for more than ten generations. To generate skeletal muscle‐specific and adipocyte‐specific <italic>Crif1</italic> knockout mice, floxed Crif1 mice were crossed with <italic>Adipoq</italic>‐<italic>Cre</italic> mice (a kind gift from E. Rosen, Beth Israel Deaconess Medical Center, Boston, MA, USA) or <italic>Mlc1f</italic>‐<italic>Cre</italic> mice on a C57BL/6 background. All mice were housed in a controlled environment (12‐h light/12‐h dark cycle; humidity: 50%–60%; ambient temperature: 22 ± 2°C) and fed a normal chow diet in a specific pathogen‐free animal facility at the CNUH Preclinical Research Center. To avoid any possible effects of estrogen on GDF15 production, all <italic>in vivo</italic> experiments were conducted on male mice. WT and <italic>Gdf15</italic> KO old (20‐month‐old) mice were used to investigate the effect of GDF15 on aging‐induced systemic inflammation. All animals received humane care according to institutional guidelines, and all experiments were approved by the Institutional Review Board of CNUH. All represented BXD phenotypes in this study were reanalyzed using publicly available datasets at the GeneNetwork website (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.genenetwork.org\">http://www.genenetwork.org</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://gn2.genenetwork.org\">http://gn2.genenetwork.org</ext-link>; GeneNetwork accession numbers: GN843 and GN859) (Andreux et al., <xref rid=\"acel13195-bib-0001\" ref-type=\"ref\">2012</xref>).</p></sec><sec id=\"acel13195-sec-0016\"><label>4.3</label><title>FACS analysis</title><p>Hepatic and adipose immune cells were pre‐incubated with anti‐mouse CD16/32 Fc blocker (BD Pharmingen, USA), followed by staining with the Live/Dead marker anti‐FVD‐APC‐Cy7 (all supplied by eBioscience, San Diego, CA, USA). The fluorochrome‐conjugated antibodies used in this study were anti‐CD45, anti‐CD3, anti‐NK1.1, anti‐CD4, anti‐CD8, anti‐CD44, anti‐CD62L, anti‐CD11b, anti‐F4/80, anti‐Ly6C, anti‐Ly6C, and anti‐Siglec‐F (all supplied by eBioscience, San Diego, CA, USA). Liver mononuclear cells were stimulated with phorbol‐myristate acetate/ionomycin/brefeldin A/monensin for 5 hr <italic>in vitro</italic>. The cells were fixed and permeabilized using a Fixation/Permeabilization Buffer kit (eBioscience, San Diego, CA, USA). The permeabilized cells were washed with FACS buffer and resuspended in 1% formaldehyde and stained for intracellular cytokines with anti‐IFN‐γ‐PE‐Cy7, anti‐TNF‐α‐APC, and anti‐IL‐17A‐APC fluorochrome‐conjugated antibodies. Stained cells were analyzed using a BD LSRFortessa flow cytometer (BD Biosciences, San Jose, CA, USA), and data were analyzed using FlowJo software (FlowJo, LLC, Ashland, OR, USA).</p></sec><sec id=\"acel13195-sec-0017\"><label>4.4</label><title>Bioinformatics analysis of the Innocenti et al. dataset</title><p>We obtained the liver gene expression microarray dataset of a previous report (Innocenti et al., <xref rid=\"acel13195-bib-0017\" ref-type=\"ref\">2011</xref>) from the GEO database at <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25935\">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25935</ext-link>. The dataset contains 464 unique Agilent gene expression microarrays <italic>m</italic> corresponding to healthy human liver biopsies from 206 patients <italic>p</italic>. Available covariates include patients’ age, gender, and ancestry, defined as the first principal component in the analysis of Innocenti et al., which separated African from non‐African individuals. The 16 samples obtained from patients 325, 333, 716, and 720 were removed from the data because of inconsistencies in their age and gender annotations.</p><p>The intensities are already preprocessed: Intensities were log<sub>2</sub>‐transformed, background subtracted with the minimum method (Ritchie et al., <xref rid=\"acel13195-bib-0038\" ref-type=\"ref\">2007</xref>), and quantile normalized (Bolstad, Irizarry, Astrand, & Speed, <xref rid=\"acel13195-bib-0002\" ref-type=\"ref\">2003</xref>). For each gene <italic>i</italic>, we assumed the following linear regression model:<disp-formula id=\"acel13195-disp-0001\"><mml:math id=\"nlm-math-2\"><mml:mrow><mml:msub><mml:mi>y</mml:mi><mml:mi mathvariant=\"italic\">im</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mi>β</mml:mi><mml:mi>i</mml:mi><mml:mn>0</mml:mn></mml:msubsup><mml:mo>+</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>p</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub><mml:msubsup><mml:mi>β</mml:mi><mml:mi>i</mml:mi><mml:mtext>age</mml:mtext></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mi>x</mml:mi><mml:mrow><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>p</mml:mi><mml:mi>m</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:msubsup><mml:mi>β</mml:mi><mml:mi>i</mml:mi><mml:msup><mml:mtext>age</mml:mtext><mml:mn>2</mml:mn></mml:msup></mml:msubsup><mml:mo>+</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>b</mml:mi><mml:mo>,</mml:mo><mml:mi>p</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub><mml:msubsup><mml:mi>β</mml:mi><mml:mi>i</mml:mi><mml:mtext>ancestry</mml:mtext></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mi>β</mml:mi><mml:mi mathvariant=\"italic\">ig</mml:mi><mml:mtext>gender</mml:mtext></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mi>u</mml:mi><mml:mi mathvariant=\"italic\">ip</mml:mi><mml:mtext>patient</mml:mtext></mml:msubsup><mml:mo>+</mml:mo><mml:msub><mml:mi>ε</mml:mi><mml:mi mathvariant=\"italic\">im</mml:mi></mml:msub><mml:mo>.</mml:mo></mml:mrow></mml:math></disp-formula>where <italic>y<sub>im</sub></italic> is the preprocessed log<sub>2</sub> intensity of gene <italic>i</italic> in sample <italic>m</italic>, <mml:math id=\"nlm-math-3\"><mml:msubsup><mml:mi>β</mml:mi><mml:mi>i</mml:mi><mml:mn>0</mml:mn></mml:msubsup></mml:math> is the intercept, <mml:math id=\"nlm-math-4\"><mml:msubsup><mml:mi>β</mml:mi><mml:mi>i</mml:mi><mml:mtext>age</mml:mtext></mml:msubsup></mml:math> and <mml:math id=\"nlm-math-5\"><mml:msubsup><mml:mi>β</mml:mi><mml:mi>i</mml:mi><mml:mtext>ancestry</mml:mtext></mml:msubsup></mml:math> are the effects of age and ancestry, respectively, and <mml:math id=\"nlm-math-6\"><mml:msubsup><mml:mi>β</mml:mi><mml:mi>i</mml:mi><mml:msup><mml:mtext>age</mml:mtext><mml:mn>2</mml:mn></mml:msup></mml:msubsup></mml:math> is a quadratic effect for age. This quadratic effect allows us to capture non‐linear trends in the data. <italic>x<sub>a</sub></italic>\n<sub>,</sub>\n<italic><sub>pm</sub></italic> and <italic>x<sub>b</sub></italic>\n<sub>,</sub>\n<italic><sub>pm</sub></italic> are vectors of length 448 wherein each element corresponds to, respectively, the age and ancestry of patient <italic>p</italic> in sample <italic>m</italic>.</p><p>\n<mml:math id=\"nlm-math-7\"><mml:msubsup><mml:mi>β</mml:mi><mml:mi mathvariant=\"italic\">ig</mml:mi><mml:mtext>gender</mml:mtext></mml:msubsup></mml:math> is a dummy variable which is equal to 0 if the sample is from a male patient and equal to 1 if the sample is from a female patient. <mml:math id=\"nlm-math-8\"><mml:msubsup><mml:mi>u</mml:mi><mml:mi mathvariant=\"italic\">ip</mml:mi><mml:mtext>patient</mml:mtext></mml:msubsup></mml:math> is a random patient effect that accounts for the fact that samples originating from the same patient are correlated. <mml:math id=\"nlm-math-9\"><mml:msubsup><mml:mi>u</mml:mi><mml:mi mathvariant=\"italic\">ip</mml:mi><mml:mtext>patient</mml:mtext></mml:msubsup></mml:math> is assumed to be normally distributed (<mml:math id=\"nlm-math-10\"><mml:mrow><mml:msubsup><mml:mi>u</mml:mi><mml:mi mathvariant=\"italic\">ip</mml:mi><mml:mtext>patient</mml:mtext></mml:msubsup><mml:mo>∼</mml:mo><mml:mi>N</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mi>σ</mml:mi><mml:mrow><mml:msub><mml:mi>u</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:mfenced></mml:mrow></mml:math>). <mml:math id=\"nlm-math-11\"><mml:mrow><mml:msub><mml:mi>ε</mml:mi><mml:mi mathvariant=\"italic\">im</mml:mi></mml:msub><mml:mo>∼</mml:mo><mml:mi>N</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mi>σ</mml:mi><mml:mi>i</mml:mi><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:mfenced></mml:mrow></mml:math> is a random error term. Statistical significance was assessed with the limma R package (Smyth, <xref rid=\"acel13195-bib-0042\" ref-type=\"ref\">2004</xref>), whereby inter‐technical replicate variation was estimated by fitting gene‐wise linear mixed models with the duplicateCorrelation function, as previously described (Smyth, Michaud, & Scott, <xref rid=\"acel13195-bib-0043\" ref-type=\"ref\">2005</xref>). We used limma's empirical Bayes‐moderated F statistic to test the combined significance of the linear and quadratic age effects and corrected for multiple testing with the Benjamini–Hochberg FDR procedure.</p></sec><sec id=\"acel13195-sec-0018\"><label>4.5</label><title>Bioinformatics analysis of the Genotype‐Tissue Expression (GTEx) dataset</title><p>The Auwerx lab has access to the Genotype‐Tissue Expression (GTEx) Project version 8 through dbGaP accession number phs000424.v8.p2 (Project #10143 AgingX). To study the relation between age and <italic>GDF15</italic> expression, we assessed the raw RNA‐Seq gene count data from the livers of 226 deceased individuals. For the statistical analysis, we removed three persons for which no information on smoking and drinking status was known and one person for which no information on cocaine usage was known. Effective library sizes were calculated based on trimmed mean of M value (TMM) normalization factors (Robinson & Oshlack, <xref rid=\"acel13195-bib-0040\" ref-type=\"ref\">2010</xref>) as implemented in edgeR (Robinson, McCarthy, & Smyth, <xref rid=\"acel13195-bib-0039\" ref-type=\"ref\">2010</xref>). To account for over‐excess zero counts in low‐expressed genes, we fitted with a zero‐inflated negative binomial model as previously described (Van den Berge et al., <xref rid=\"acel13195-bib-0047\" ref-type=\"ref\">2018erge</xref> et al., <xref rid=\"acel13195-bib-0047\" ref-type=\"ref\">2018</xref>) and implemented in the zingeR R package. Sample‐level covariates included a linear and quadratic effect for age, a linear and quadratic effect for the sample's ischemic time, and dummy variables to account for the effects of gender, sample collection site, smoking status, drinking status, and cocaine usage in the past five years. The combined significance of the linear and quadratic age terms was inferred with an F test with adjusted denominator degrees of freedom. Multiple testing was controlled by DESeq2’s independent filtering procedure (Love, Huber, & Anders, <xref rid=\"acel13195-bib-0027\" ref-type=\"ref\">2014</xref>) followed by Benjamini–Hochberg FDR correction.</p><p>To further explore the role of <italic>GDF15</italic> expression in different tissues (liver, subcutaneous adipose tissue, visceral adipose tissue, and muscle), the expression profiles of GTEx version 8 were obtained from the GTEx Portal (<ext-link ext-link-type=\"uri\" xlink:href=\"https://gtexportal.org/\">https://gtexportal.org/</ext-link>). We identified differentially expressed genes by establishing two groups based on the <italic>GDF15</italic> expression level of each tissue with the R package DESeq2 (Love et al., <xref rid=\"acel13195-bib-0027\" ref-type=\"ref\">2014</xref>). The gene‐set collection of KEGG was obtained from Enrichr (<ext-link ext-link-type=\"uri\" xlink:href=\"https://amp.pharm.mssm.edu/Enrichr/\">https://amp.pharm.mssm.edu/Enrichr/</ext-link>), and the gene‐set enrichment analysis was conducted with the R package PIANO, by using the DEA results taken from DESeq2. Differentially expressed genes (DEG) with <italic>p</italic>‐value <.05 and KEGG pathways with Benjamini–Hochberg‐corrected FDR values <0.1 were considered statistically significant. The correlation analysis of <italic>GDF15</italic> and individual genes was done by Gene Expression Profiling Interactive Analysis (GEPIA; <ext-link ext-link-type=\"uri\" xlink:href=\"http://gepia2.cancer-pku.cn/\">http://gepia2.cancer‐pku.cn/</ext-link>). The full list of significantly enriched pathways in the liver, subcutaneous adipose tissue, and skeletal muscle i included in Tables <xref rid=\"acel13195-sup-0004\" ref-type=\"supplementary-material\">[Link]</xref>, <xref rid=\"acel13195-sup-0005\" ref-type=\"supplementary-material\">[Link]</xref>, <xref rid=\"acel13195-sup-0006\" ref-type=\"supplementary-material\">[Link]</xref>.</p></sec><sec id=\"acel13195-sec-0019\"><label>4.6</label><title>Mendelian randomization</title><p>We used the publicly available GTEx cis‐eQTL data in whole blood as exposure effect estimates. After clumping based on linkage disequilibrium with the ld_clump function from the ieugwasr v 0.1.4 R package (<ext-link ext-link-type=\"uri\" xlink:href=\"https://mrcieu.github.io/ieugwasr/\">https://mrcieu.github.io/ieugwasr/</ext-link>), one single nucleotide polymorphism (SNP), rs7226, was retained. rs7226 has been linked to GDF15 concentration in blood before (Jiang et al., <xref rid=\"acel13195-bib-0018\" ref-type=\"ref\">2018</xref>) and is located ~12 kb upstream of <italic>GDF15</italic> inside the fifth and last exon of <italic>PGPEP1</italic>, a gene that is known to be co‐expressed with <italic>GDF15</italic> (Ho et al., <xref rid=\"acel13195-bib-0014\" ref-type=\"ref\">2012</xref>). The estimated effects of rs7226 on 33 obesity‐related outcomes and 27 outcomes related to blood leukocyte concentrations (Table <xref rid=\"acel13195-sup-0003\" ref-type=\"supplementary-material\">S2</xref>) were obtained from the IEU GWAS database (<ext-link ext-link-type=\"uri\" xlink:href=\"https://gwas.mrcieu.ac.uk/datasets/\">https://gwas.mrcieu.ac.uk/datasets/</ext-link>) (Hemani et al., <xref rid=\"acel13195-bib-0013\" ref-type=\"ref\">2018</xref>) through the available_outcomes function from the TwoSampleMR v 0.5.3 R package (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/MRCIEU/TwoSampleMR\">https://github.com/MRCIEU/TwoSampleMR</ext-link>). We used the same package to calculate Wald ratios of the outcome effect estimates to the exposure effect estimate. The p‐values were converted to q‐values with the Benjamini–Hochberg multiple testing procedure.</p></sec><sec id=\"acel13195-sec-0020\"><label>4.7</label><title>Tregs‐mediated suppression of conventional or senescent T cells</title><p>Isolated CD4<sup>+</sup>CD25<sup>−</sup> T cells were labeled with carboxyfluorescein succinimidyl ester (CFSE) (Invitrogen, Eugene, OR, USA) just before co‐culturing with Tregs following the manufacture's guidelines. CFSE‐labeled CD4<sup>+</sup>CD25<sup>−</sup> T cells were activated with anti‐CD3 (2 μg/mL)/CD28 (5 μg/mL) in the presence of different amounts of Tregs from C57BL/6 mice. The suppressive capacity of Tregs was measured by the addition of Tregs at Treg:Teff ratios of 0.25:1, 0.5:1, and 1:1 in 96‐well round‐bottom plates in the absence or presence of the indicated concentrations of recombinant GDF15 for 72 hr. CD8<sup>+</sup>CD28<sup>−</sup> T cells were sorted by FACS Aria II (BD Bioscience, San Jose, CA, USA) using surface immunofluorescence‐conjugated antibodies and were labeled with CFSE. To assess the suppressive capacity of Tregs, the CD8<sup>+</sup>CD28<sup>−</sup> T cells were also activated with anti‐CD3 (2 μg/ml)/CD28 (5 μg/ml) in the presence of different amounts of Tregs under the indicated concentrations of recombinant GDF15. Then, IFN‐γ levels from the culture supernatants were measured, and the relative suppression of proliferation was determined by assessing the inhibition of CFSE dilution.</p></sec><sec id=\"acel13195-sec-0021\"><label>4.8</label><title>Statistical analysis</title><p>All continuous variables are reported as the mean ± SE of the mean, except if explicitly stated otherwise. Statistical analyses were performed using GraphPad PRISM software (GraphPad, San Diego, CA, USA). All data from the mouse studies were analyzed by two‐way repeated‐measures ANOVA followed by Bonferroni's multiple comparison, a one‐way ANOVA followed by Tukey's <italic>post hoc</italic> test, or a two‐tailed Student's t test. Statistical correlations were evaluated using Pearson's correlation coefficient. <italic>p</italic> values < 0.05 were considered statistically significant.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13195-sec-0023\"><title>CONFLICT OF INTEREST</title><p>No potential conflicts of interest are reported.</p></sec><sec id=\"acel13195-sec-0024\"><title>AUTHOR CONTRIBUTIONS</title><p>J.S.M, D.R., and H.Y. designed research and analyzed data. J.S.M, J.W.T., H.T.N., and S.G.K carried out all experiments. L.J.E.G., B.E.K., and J.A. helped with the omics studies. S.K., M.S., and J.A. provided critical scientific insights. J.T.K., L.J.E.G., J.A., D.R., and H.Y. wrote the manuscript. J.S.B, Y.L., and J.J. provided helpful discussion.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13195-sup-0001\"><caption><p>Figure S1‐S13</p></caption><media xlink:href=\"ACEL-19-e13195-s001.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13195-sup-0002\"><caption><p>Table S1</p></caption><media xlink:href=\"ACEL-19-e13195-s002.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13195-sup-0003\"><caption><p>Table S2</p></caption><media xlink:href=\"ACEL-19-e13195-s003.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13195-sup-0004\"><caption><p>Table S3</p></caption><media xlink:href=\"ACEL-19-e13195-s004.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13195-sup-0005\"><caption><p>Table S4</p></caption><media xlink:href=\"ACEL-19-e13195-s005.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13195-sup-0006\"><caption><p>Table S5</p></caption><media xlink:href=\"ACEL-19-e13195-s006.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13195-sup-0007\"><caption><p>Table S6</p></caption><media xlink:href=\"ACEL-19-e13195-s007.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"acel13195-sup-0008\"><caption><p>Supinfo</p></caption><media xlink:href=\"ACEL-19-e13195-s008.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13195-sec-0022\"><title>ACKNOWLEDGMENTS</title><p>This work was supported by the Basic Science Research Program, through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT, and Future Planning, Korea (NRF‐2018R1C1B6004439, NRF‐2019M3E5D1A02068575), Gilead Sciences Asia Ltd and CNUH Research Fund, 2018. M.S and S.K.K were also supported by the NRF (NRF‐2017K1A1A2013124 and NRF‐2017R1E1A1A01075126). D.R. was supported by Samsung Research Fund, Sungkyunkwan University, 2019. J.A. was supported by grants from the EPFL, the European Research Council (ERC‐AdG‐787702), the Swiss National Science Foundation (SNSF 31003A_179435), and a GRL grant of the National Research Foundation of Korea (NRF 2017K1A1A2013124). L.J.E.G. was supported by a Swiss Government Excellence Scholarship (FCS ESKAS‐Nr. 2019.0009). The Genotype‐Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health (commonfund.nih.gov/GTEx). Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI\\Leidos Biomedical Research, Inc. subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to the Broad Institute, Inc. Biorepository operations were funded through a Leidos Biomedical Research, Inc. subcontract to Van Andel Research Institute (10ST1035). Additional data repository and project management were provided by Leidos Biomedical Research, Inc. (HHSN261200800001E). The Brain Bank was supported supplements to University of Miami grant DA006227. Statistical Methods development grants were made to the University of Geneva (MH090941 & MH101814), the University of Chicago (MH090951, MH090937, MH101825, & MH101820), the University of North Carolina ‐ Chapel Hill (MH090936), North Carolina State University (MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University (MH101810), and to the University of Pennsylvania (MH101822). The GTEx datasets used for the analyses described in this manuscript were obtained from dbGaP at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/gap\">http://www.ncbi.nlm.nih.gov/gap</ext-link> through dbGaP accession number phs000424.v8.p2.</p></ack><sec sec-type=\"data-availability\" id=\"acel13195-sec-0026\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13195-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13195-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13195-cit-0001\">\n<string-name>\n<surname>Andreux</surname>, <given-names>P. A.</given-names>\n</string-name>, <string-name>\n<surname>Williams</surname>, <given-names>E. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Aging Cell</journal-id><journal-id journal-id-type=\"iso-abbrev\">Aging Cell</journal-id><journal-id journal-id-type=\"doi\">10.1111/(ISSN)1474-9726</journal-id><journal-id journal-id-type=\"publisher-id\">ACEL</journal-id><journal-title-group><journal-title>Aging Cell</journal-title></journal-title-group><issn pub-type=\"ppub\">1474-9718</issn><issn pub-type=\"epub\">1474-9726</issn><publisher><publisher-name>John Wiley and Sons Inc.</publisher-name><publisher-loc>Hoboken</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32725944</article-id><article-id pub-id-type=\"pmc\">PMC7431836</article-id><article-id pub-id-type=\"doi\">10.1111/acel.13182</article-id><article-id pub-id-type=\"publisher-id\">ACEL13182</article-id><article-categories><subj-group subj-group-type=\"overline\"><subject>Original Article</subject></subj-group><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>Aging and sex: Impact on microglia phagocytosis</article-title><alt-title alt-title-type=\"left-running-head\">YANGUAS‐CASÁS et al.</alt-title></title-group><contrib-group><contrib id=\"acel13182-cr-0001\" contrib-type=\"author\" corresp=\"yes\"><name><surname>Yanguas‐Casás</surname><given-names>Natalia</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-4368-0176</contrib-id><xref ref-type=\"aff\" rid=\"acel13182-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13182-aff-0002\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13182-curr-0001\">\n<sup>3</sup>\n</xref><address><email>nyanguas@idiphim.org</email></address></contrib><contrib id=\"acel13182-cr-0002\" contrib-type=\"author\"><name><surname>Crespo‐Castrillo</surname><given-names>Andrea</given-names></name><xref ref-type=\"aff\" rid=\"acel13182-aff-0001\">\n<sup>1</sup>\n</xref></contrib><contrib id=\"acel13182-cr-0003\" contrib-type=\"author\"><name><surname>Arevalo</surname><given-names>Maria‐Angeles</given-names></name><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-4303-9576</contrib-id><xref ref-type=\"aff\" rid=\"acel13182-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13182-aff-0002\">\n<sup>2</sup>\n</xref></contrib><contrib id=\"acel13182-cr-0004\" contrib-type=\"author\"><name><surname>Garcia‐Segura</surname><given-names>Luis Miguel</given-names></name><xref ref-type=\"aff\" rid=\"acel13182-aff-0001\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"acel13182-aff-0002\">\n<sup>2</sup>\n</xref></contrib></contrib-group><aff id=\"acel13182-aff-0001\">\n<label><sup>1</sup></label>\n<named-content content-type=\"organisation-division\">Consejo Superior de Investigaciones Científicas (CSIC)</named-content>\n<institution>Instituto Cajal</institution>\n<city>Madrid</city>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13182-aff-0002\">\n<label><sup>2</sup></label>\n<named-content content-type=\"organisation-division\">Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES)</named-content>\n<institution>Instituto de Salud Carlos III</institution>\n<city>Madrid</city>\n<country country=\"ES\">Spain</country>\n</aff><aff id=\"acel13182-curr-0001\"><label><sup>3</sup></label>Present address:\n<institution>IIS Puerta de Hierro‐Segovia de Arana (IDIPHISA)</institution>\n<city>Majadahonda</city>\n<country country=\"ES\">Spain</country>\n</aff><author-notes><corresp id=\"correspondenceTo\"><label>*</label><bold>Correspondence</bold><break/>\nNatalia Yanguas‐Casás, Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Madrid, Spain.<break/>\nEmail: <email>nyanguas@idiphim.org</email><break/></corresp></author-notes><pub-date pub-type=\"epub\"><day>29</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><volume>19</volume><issue>8</issue><issue-id pub-id-type=\"doi\">10.1111/acel.v19.8</issue-id><elocation-id>e13182</elocation-id><history><date date-type=\"received\"><day>07</day><month>4</month><year>2020</year></date><date date-type=\"rev-recd\"><day>23</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>06</day><month>6</month><year>2020</year></date></history><permissions><!--<copyright-statement content-type=\"issue-copyright\"> Copyright © 2020 The Anatomical Society and John Wiley & Sons Ltd <copyright-statement>--><copyright-statement content-type=\"article-copyright\">© 2020 The Authors. <italic>Aging Cell</italic> published by the Anatomical Society and John Wiley & Sons Ltd.</copyright-statement><license license-type=\"creativeCommonsBy\"><license-p>This is an open access article under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri content-type=\"pdf\" xlink:href=\"file:ACEL-19-e13182.pdf\"/><abstract id=\"acel13182-abs-0001\"><title>Abstract</title><p>Microglia dysfunction and activation are important hallmarks of the aging brain and are concomitant with age‐related neurodegeneration and cognitive decline. Age‐associated changes in microglia migration and phagocytic capacity result in maladaptive responses, chronic neuroinflammation, and worsened outcomes in neurodegenerative disorders. Given the sex bias in the incidence, prevalence, and therapy response of most neurological disorders, we have here examined whether the phagocytic activity of aged microglia is different in males and females. With this aim, the phagocytosis activity of male and female cells was compared in an in vitro aged microglia model and in microglia isolated from adult (5‐month‐old) or aged (18‐month‐old) mice. In both models, the phagocytosis of neural debris increased with aging in male and female cells and was higher in aged female microglia than in aged male cells. However, female aged microglia lost its ability to adapt its phagocytic activity to inflammatory conditions. These findings suggest that microglia phagocytosis of neural debris may represent a previously unexplored neuroprotective characteristic of aged microglia that may contribute to the generation of sex differences in the manifestation of neurodegenerative diseases.</p></abstract><abstract abstract-type=\"graphical\" id=\"acel13182-abs-0002\"><p>Sex differences in postnatal microglia: higher pathogen‐specific phagocytosis is detected in male cells while females show an enhanced nonspecific and neural debris phagocytosis compared to the other sex. Phagocytosis of neural debris increased with aging in male and female cells. These characteristics of the phagocytic behaviour of microglia are potential contributors to the generation of sex differences in the manifestation of neurodegenerative diseases.\n<boxed-text position=\"anchor\" content-type=\"graphic\" id=\"acel13182-blkfxd-0001\" orientation=\"portrait\"><graphic xlink:href=\"ACEL-19-e13182-g007.jpg\" position=\"anchor\" id=\"nlm-graphic-1\" orientation=\"portrait\"/></boxed-text>\n</p></abstract><kwd-group><kwd id=\"acel13182-kwd-0001\">aging</kwd><kwd id=\"acel13182-kwd-0002\">dysfunction</kwd><kwd id=\"acel13182-kwd-0003\">irresponsiveness</kwd><kwd id=\"acel13182-kwd-0004\">Microglia</kwd><kwd id=\"acel13182-kwd-0005\">neuroinflammation</kwd><kwd id=\"acel13182-kwd-0006\">phagocytosis</kwd><kwd id=\"acel13182-kwd-0007\">sex differences</kwd></kwd-group><funding-group><award-group id=\"funding-0001\"><funding-source><institution-wrap><institution>Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/100012619</institution-id></institution-wrap></funding-source></award-group><award-group id=\"funding-0002\"><funding-source><institution-wrap><institution>European Regional Development Fund </institution><institution-id institution-id-type=\"open-funder-registry\">10.13039/501100008530</institution-id></institution-wrap></funding-source><award-id>BFU2017‐82754‐R </award-id></award-group></funding-group><counts><fig-count count=\"6\"/><table-count count=\"1\"/><page-count count=\"13\"/><word-count count=\"8835\"/></counts><custom-meta-group><custom-meta><meta-name>source-schema-version-number</meta-name><meta-value>2.0</meta-value></custom-meta><custom-meta><meta-name>cover-date</meta-name><meta-value>August 2020</meta-value></custom-meta><custom-meta><meta-name>details-of-publishers-convertor</meta-name><meta-value>Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.6 mode:remove_FC converted:18.08.2020</meta-value></custom-meta></custom-meta-group></article-meta><notes><p content-type=\"self-citation\">\n<mixed-citation publication-type=\"journal\" id=\"acel13182-cit-1001\">\n<string-name>\n<surname>Yanguas‐Casás</surname>\n<given-names>N</given-names>\n</string-name>, <string-name>\n<surname>Crespo‐Castrillo</surname>\n<given-names>A</given-names>\n</string-name>, <string-name>\n<surname>Arevalo</surname>\n<given-names>M‐A</given-names>\n</string-name>, <string-name>\n<surname>Garcia‐Segura</surname>\n<given-names>LM</given-names>\n</string-name>. <article-title>Aging and sex: Impact on microglia phagocytosis</article-title>. <source xml:lang=\"en\">Aging Cell</source>. <year>2020</year>;<volume>19</volume>:<elocation-id>e13182</elocation-id>\n<pub-id pub-id-type=\"doi\">10.1111/acel.13182</pub-id>\n</mixed-citation>\n</p></notes></front><body id=\"acel13182-body-0001\"><sec id=\"acel13182-sec-0001\"><label>1</label><title>INTRODUCTION</title><p>Microglia are the primary innate immune cells of the brain and are key players in the resolution or propagation of the inflammatory process (Kettenmann, Hanisch, Noda, & Verkhratsky, <xref rid=\"acel13182-bib-0015\" ref-type=\"ref\">2011</xref>; Ransohoff & Perry, <xref rid=\"acel13182-bib-0030\" ref-type=\"ref\">2009</xref>). These glial cells perform central functions in the regulation of cell number, synaptic patterning, homeostasis maintenance, and response to pathogens by selective phagocytosis. Microglia phagocytosis is a fine‐tuned process mediated by the expression of specific receptors on the cell surface and downstream signaling pathways that, depending on their ability to recognize misfolded proteins and apoptotic cells or bind structurally conserved molecules derived from microbial pathogens (such as Toll‐like receptors; TLRs), contribute to the engulfment of cellular debris, harmful particles, or synapses (Fu, Shen, Xu, Luo, & Tang, <xref rid=\"acel13182-bib-0008\" ref-type=\"ref\">2014</xref>; Galloway, Phillips, Owen, & Moore, <xref rid=\"acel13182-bib-0009\" ref-type=\"ref\">2019</xref>).</p><p>The incidence of most neurological disorders increases with aging, and two of its common characteristics, chronic neuroinflammation and impairment of microglial responses (Bachiller et al., <xref rid=\"acel13182-bib-0002\" ref-type=\"ref\">2018</xref>), are affected by the aging process. Indeed, microglia become senescent/dystrophic and less responsive to stimulation with age, and the mechanisms underlying age‐dependent phenotypic changes vary from extrinsic environmental changes to intrinsic changes in genomic integrity (Rawji et al., <xref rid=\"acel13182-bib-0032\" ref-type=\"ref\">2016</xref>; Streit & Xue, <xref rid=\"acel13182-bib-0037\" ref-type=\"ref\">2013</xref>). The aged microglial phenotype is characterized by reduced migration and phagocytosis activity, as well as by exacerbated inflammatory responses (microglia priming) and deficits in chemotactic functions (Damani et al., <xref rid=\"acel13182-bib-0005\" ref-type=\"ref\">2011</xref>; Koellhoffer, McCullough, & Ritzel, <xref rid=\"acel13182-bib-0016\" ref-type=\"ref\">2017</xref>). Overall, age‐related alterations in the expression of receptors implicated in innate immunity and phagocytosis, and the inability to mount a normal response to injury or inflammation, limit the capacity of microglia to cope with pathogens or neurodegeneration and contribute to an increased susceptibility and neurodegeneration (Liang, Domon, Hosur, Wang, & Hajishengallis, <xref rid=\"acel13182-bib-0020\" ref-type=\"ref\">2009</xref>).</p><p>Although there are robust sex differences in the epidemiology, clinical features, and pathophysiology of many neurological disorders (The Lancet, <xref rid=\"acel13182-bib-0038\" ref-type=\"ref\">2019</xref>), little attention has been paid to the sex differences in microglia function with aging. In this study, we aimed to determine the relevance of sex in the aged phenotype of microglia, analyzing two key functional responses of these cells: migration and phagocytosis. With this aim, we studied microglia isolated from adult or aged mouse brains (from 5‐ and 18‐month‐old animals, respectively) and in an experimental aging model in vitro after isolation of microglia from neonatal mouse brains.</p></sec><sec sec-type=\"results\" id=\"acel13182-sec-0002\"><label>2</label><title>RESULTS</title><sec id=\"acel13182-sec-0003\"><label>2.1</label><title>Microglia phagocytosis is affected by aging in a sex‐specific way</title><p>As previously mentioned, microglia migration, motility (Damani et al., <xref rid=\"acel13182-bib-0005\" ref-type=\"ref\">2011</xref>; Hefendehl et al., <xref rid=\"acel13182-bib-0011\" ref-type=\"ref\">2014</xref>), and phagocytic activity are impaired with aging (Damani et al., <xref rid=\"acel13182-bib-0005\" ref-type=\"ref\">2011</xref>; Koellhoffer et al., <xref rid=\"acel13182-bib-0016\" ref-type=\"ref\">2017</xref>). Some studies had found sex differences in the phagocytic activity of microglia in early developmental stages or even in adulthood (Hanamsagar & Bilbo, <xref rid=\"acel13182-bib-0010\" ref-type=\"ref\">2016</xref>; Villa et al., <xref rid=\"acel13182-bib-0040\" ref-type=\"ref\">2018</xref>; Yanguas‐Casás et al., <xref rid=\"acel13182-bib-0042\" ref-type=\"ref\">2018</xref>); however, possible sex differences in microglia phagocytosis had not been assessed in aged brains. Therefore, we decided to focus our studies on the phagocytic responses of microglial cells directly purified from adult and aged mouse brains. For this, we performed three different engulfment assays to evaluate: (a) nonspecific phagocytosis (quantifying fluorescent bead intake); (b) pathogen‐specific phagocytosis (measuring <italic>Escherichia coli</italic> bioparticles uptake); and (c) neural debris phagocytosis (analyzing the intake of Cy<sup>TM</sup>3‐labeled neural debris) (Figure <xref rid=\"acel13182-fig-0001\" ref-type=\"fig\">1</xref>), using IFN‐γ as a pro‐phagocytic stimulus (Yanguas‐Casás et al., <xref rid=\"acel13182-bib-0042\" ref-type=\"ref\">2018</xref>). Then, we measured the amount of internalized particles in actively engulfing microglial cells in the different experimental conditions.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13182-fig-0001\" orientation=\"portrait\" position=\"float\"><label>Figure 1</label><caption><p>Phagocytosis of microglia purified from adult (5 months) or aged (18 months) mouse brain. Representative images of microglia: nonspecific bead intake (a, b), pathogen‐specific (d, e), and neural debris (g, h) phagocytosis. (c, f, i) Amount of internalized particles per cell. <sup>+++</sup>\n<italic>p</italic> < .001 effect of time measured by two‐way ANOVA followed by Bonferroni post hoc test; <sup>$$$</sup>\n<italic>p</italic> < .001 sex differences, **<italic>p</italic> < .01, ***<italic>p</italic> < .001 effect of IFN‐γ treatment measured by one‐way ANOVA followed by Tukey's post hoc test. Dots show mean ± <italic>SEM</italic>. Blue: male; Purple: female; Dark: IFN‐γ treatment</p></caption><graphic id=\"nlm-graphic-3\" xlink:href=\"ACEL-19-e13182-g001\"/></fig><p>Male and female microglia purified from adult (5 months) brains showed similar internalization of uncoated beads (nonspecific phagocytosis), <italic>E. coli</italic> bioparticles (pathogen‐specific phagocytosis), and neural debris under basal conditions (Figure <xref rid=\"acel13182-fig-0001\" ref-type=\"fig\">1</xref>). The comparison of the results obtained with microglia purified from adult and aged brains revealed that aging had significant effects on basal microglia phagocytosis of neural debris. Thus, both male and female microglia isolated from aged brain (18 months) significantly increased the internalization of neural debris compared to microglia isolated from adult brains (Figure <xref rid=\"acel13182-fig-0001\" ref-type=\"fig\">1i</xref>).</p><p>IFN‐γ treatment increased nonspecific, pathogen‐specific, and neural debris intake in both male and female microglia purified from adult brains (Figure <xref rid=\"acel13182-fig-0001\" ref-type=\"fig\">1c,f</xref>,i). However, adult female microglia showed a much higher increase in bead internalization than adult male microglia (Figure <xref rid=\"acel13182-fig-0001\" ref-type=\"fig\">1c</xref>). This sex difference was lost in microglia isolated from aged brains, in which bead phagocytosis was irresponsive to IFN‐γ stimulation in both sexes (Figure <xref rid=\"acel13182-fig-0001\" ref-type=\"fig\">1c</xref>). In addition, aged male microglia did not increase the internalization of <italic>E. coli</italic> bioparticles upon IFN‐γ stimulation (Figure <xref rid=\"acel13182-fig-0001\" ref-type=\"fig\">1f</xref>). Furthermore, IFN‐γ stimulation was ineffective to increase neural debris internalization in microglia isolated from aged female brains. Thus, aging affects microglia phagocytosis in response to an inflammatory challenge in a sex‐specific way.</p></sec><sec id=\"acel13182-sec-0004\"><label>2.2</label><title>Perinatal male and female microglia acquire a senescent‐like phenotype after 16 days in vitro</title><p>Previous studies had described an experimental model to reproduce irresponsive/senescent microglia in vitro (Caldeira et al., <xref rid=\"acel13182-bib-0004\" ref-type=\"ref\">2014</xref>); however, they did not evaluate the relevance of sex in the senescence process. For this reason, we characterized the senescent phenotype in microglia obtained separately from male and female mouse brains in this in vitro model.</p><p>We first determined β‐galactosidase activity, which is increased in the senescence phenotype, at 2 and 16 days in vitro (DIV) in male and female microglial cells. There was a time‐dependent increase in the senescent phenotype regardless of the sex (Figure <xref rid=\"acel13182-fig-0002\" ref-type=\"fig\">2a,b</xref>). We also found decreased miRNA‐124a, miRNA‐146a, and miRNA‐155 expression, a characteristic of aged microglia, at 16 DIV in both sexes (Figure <xref rid=\"acel13182-fig-0002\" ref-type=\"fig\">2c‐e</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13182-fig-0002\" orientation=\"portrait\" position=\"float\"><label>Figure 2</label><caption><p>Acquisition of senescent‐like phenotype by male and female microglia at 16 days in vitro (DIV). (a) Representative images of β‐galactosidase activity (β‐gal, senescent cells, blue) in male and female microglia at 2 and 16 DIV. Scale bar 150 µm. (b) Quantification of % of β‐gal positive cells per field. <sup>+</sup>\n<italic>p</italic> < .05 two‐way ANOVA effect of time. (c–e) miRNA expression of miRNA‐124a, miRNA‐146a, and miRNA‐155, measured as %RQ. (f–k) mRNA expression of Beclin‐1 (f), Toll‐like receptor (TLR)2 (g), TLR4 (h), interleukin (IL)‐1β (i), IL‐6 (j), and tumor necrosis factor α (TNF‐α; k), measured as %RQ. <sup>+++</sup>\n<italic>p</italic> < .001 effect of time, <sup>###</sup>\n<italic>p</italic> < .001 effect of time only in female microglia, measured by two‐way ANOVA followed by Bonferroni post hoc test. Dots show mean ± <italic>SEM</italic>. Blue: male; Purple: female</p></caption><graphic id=\"nlm-graphic-5\" xlink:href=\"ACEL-19-e13182-g002\"/></fig><p>We next evaluated the mRNA expression of other senescence markers such as Beclin‐1, which plays a central role in autophagosome formation, TLR2 and TLR4, which are associated with microglia activation, and interleukin (IL)‐1β, IL‐6, and tumor necrosis factor‐α (TNF‐α), whose expression by microglia has been shown to be altered with physiological aging and in disease (Caldeira et al., <xref rid=\"acel13182-bib-0004\" ref-type=\"ref\">2014</xref>; Koellhoffer et al., <xref rid=\"acel13182-bib-0016\" ref-type=\"ref\">2017</xref>). We found decreased Beclin‐1, TLR2, and TLR4 and increased IL‐1β mRNA expression with time, in both male and female microglia (Figure <xref rid=\"acel13182-fig-0002\" ref-type=\"fig\">2f‐i</xref>). IL‐6 mRNA expression only decreased over time on female microglia (Figure <xref rid=\"acel13182-fig-0002\" ref-type=\"fig\">2j</xref>). In contrast, TNF‐α expression remained unaffected in both sexes (Figure <xref rid=\"acel13182-fig-0002\" ref-type=\"fig\">2k</xref>).</p><p>These results show that both male and female microglia acquire a senescent phenotype when kept in culture over 16 DIV, being the effect more pronounced in male than in female cells.</p></sec><sec id=\"acel13182-sec-0005\"><label>2.3</label><title>IFN‐γ induces a sex‐specific inflammatory response in primary microglia that is altered in the in vitro aging model</title><p>We next stimulated microglial cells at 2 and 16 DIV, using IFN‐γ as a pro‐inflammatory stimulus. The levels of this cytokine are increased in the aged brain, and converging evidences point to its involvement in different mechanisms of aging (Monteiro, Roque, Marques, Correia‐Neves, & Cerqueira, <xref rid=\"acel13182-bib-0023\" ref-type=\"ref\">2017</xref>).</p><p>As observed in the previous experiment, female microglia showed increased basal levels of IL‐6 mRNA compared to male microglia at 2 DIV. However, by 16 DIV the basal mRNA levels of IL‐6 were reduced and reached male values (Figure <xref rid=\"acel13182-fig-0003\" ref-type=\"fig\">3b</xref>). In contrast, the basal levels of IL‐1β mRNA expression were increased in male microglia by 16 DIV over basal female levels (Figure <xref rid=\"acel13182-fig-0003\" ref-type=\"fig\">3a</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13182-fig-0003\" orientation=\"portrait\" position=\"float\"><label>Figure 3</label><caption><p>Evolution of the inflammatory profile of male and female microglia in an aging model in vitro. mRNA expression of interleukin (IL)‐1β (a), IL‐6 (b), and tumor necrosis factor α (TNF‐α; c) in microglial cells. <italic><sup>@@@</sup>p</italic> < .001 effect of time on IFN‐γ‐induced mRNA expression by two‐way ANOVA followed by Bonferroni post hoc test; <sup>$</sup>\n<italic>p</italic> < .05, <sup>$$</sup>\n<italic>p</italic> < .01 sex differences; *<italic>p</italic> < .05, **<italic>p</italic> < .01, ***<italic>p</italic> < .001 effect of IFN‐γ treatment measured by one‐way ANOVA followed by Tukey's post hoc test. Dots show mean ± <italic>SEM</italic>. Blue: male; Purple: female; Dark and striped: IFN‐γ treatment</p></caption><graphic id=\"nlm-graphic-7\" xlink:href=\"ACEL-19-e13182-g003\"/></fig><p>The effect of IFN‐γ was also different in male and female cells. Thus, IFN‐γ increased IL‐1β, IL‐6, and TNF‐α mRNA expression in female microglia at 2 DIV (Figure <xref rid=\"acel13182-fig-0003\" ref-type=\"fig\">3a‐c</xref>). In contrast, at 2 DIV, only the mRNA levels of IL‐1β were increased in male cells after the treatment with IFN‐γ; the mRNA levels of IL‐6 and TNF‐α remained unaffected (Figure <xref rid=\"acel13182-fig-0003\" ref-type=\"fig\">3a‐c</xref>). By 16 DIV, the effect of IFN‐γ on IL‐1β and IL‐6 mRNA levels in female microglia disappeared and the effect on TNF‐α mRNA expression was significantly reduced (Figure <xref rid=\"acel13182-fig-0003\" ref-type=\"fig\">3a‐c</xref>). In male cells at 16 DIV, IFN‐γ treatment reduced the mRNA levels of IL‐1β, in contrast to that observed in female microglia. However, IFN‐γ treatment increased the mRNA levels of TNF‐α in male cells at 16 DIV, as observed in females (Figure <xref rid=\"acel13182-fig-0003\" ref-type=\"fig\">3a‐c</xref>).</p><p>These results show that microglia derived from male or female brains show different inflammatory patterns, both under basal conditions and in response to IFN‐γ in the aging model in vitro.</p></sec><sec id=\"acel13182-sec-0006\"><label>2.4</label><title>Sex differences in microglia motility disappear after 16 DIV</title><p>In a healthy brain, microglial cells constantly monitor their immediate surroundings by extension and retraction of their motile processes, allowing homeostasis maintenance and fine‐tuning of neuronal activity. These cells also have the potential to move their soma, which allows a fast response for many pathophysiological processes (Kettenmann et al., <xref rid=\"acel13182-bib-0015\" ref-type=\"ref\">2011</xref>; Nimmerjahn, Kirchhoff, & Helmchen, <xref rid=\"acel13182-bib-0025\" ref-type=\"ref\">2005</xref>). Recent studies report that aged microglia show impaired migration and decreased motility and are unable to respond to several chemotactic stimuli in mice (Damani et al., <xref rid=\"acel13182-bib-0005\" ref-type=\"ref\">2011</xref>; Hefendehl et al., <xref rid=\"acel13182-bib-0011\" ref-type=\"ref\">2014</xref>). To test microglia motility in our aging model in vitro, we first analyzed the mRNA expression of migration‐related genes.</p><p>Results showed an effect of time and treatment in the mRNA expression of monocyte chemotactic protein 1 (MCP‐1) receptor (CCR2; Figure <xref rid=\"acel13182-fig-0004\" ref-type=\"fig\">4a</xref>), MCP‐1 (Figure <xref rid=\"acel13182-fig-0004\" ref-type=\"fig\">4b</xref>), and regulated on activation, normal T cell expressed and secreted (RANTES; Figure <xref rid=\"acel13182-fig-0004\" ref-type=\"fig\">4c</xref>). IFN‐γ treatment increased the mRNA levels of CCR2, MCP‐1, and RANTES (Figure <xref rid=\"acel13182-fig-0004\" ref-type=\"fig\">4a‐c</xref>) only in female microglia at 2 DIV and increased MCP‐1 and RANTES mRNA expression in male and female cells at 16 DIV.</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13182-fig-0004\" orientation=\"portrait\" position=\"float\"><label>Figure 4</label><caption><p>Evolution of migration capacity of microglia in an aging model in vitro. mRNA expression of monocyte chemotactic protein 1 (MCP‐1) receptor (CCR2; a), MCP‐1 (b), and regulated on activation, normal T cell expressed and secreted (RANTES; c), measured as %RQ, at 2 and 16 DIV. (d) Microglia displacement (area in µm<sup>2</sup> a single cell moves around in 1 min). (e) Area covered by microglia (total area covered by a single cell in 3 hr, expressed in µm<sup>2</sup>). <sup>%%%</sup>\n<italic>p</italic> < .001 effect of time and treatment, <sup>^^^</sup>\n<italic>p</italic> < .001 effect of time and sex, <italic><sup>@@@</sup>p</italic> < .001 effect of time on IFN‐γ‐induced mRNA expression, <sup>$$$</sup>\n<italic>p</italic> < .001 effect of sex measured by two‐way ANOVA followed by Bonferroni post hoc test; *<italic>p < .</italic>05, ***<italic>p</italic> < .001 effect of IFN‐γ treatment measured by one‐way ANOVA followed by Tukey's post hoc test. Dots show mean ± <italic>SEM</italic>. Blue: male; Purple: female; Dark and striped: IFN‐γ treatment</p></caption><graphic id=\"nlm-graphic-9\" xlink:href=\"ACEL-19-e13182-g004\"/></fig><p>Time‐lapse analysis of microglia showed that basal displacement and area covered by male microglia were higher than those of female microglia at 2 DIV (Figure <xref rid=\"acel13182-fig-0004\" ref-type=\"fig\">4d,e</xref>). IFN‐γ treatment increased motility of microglial cells regardless of the sex. Both parameters were decreased by time in male and female microglia, and the IFN‐γ‐induced increase in displacement and the area covered by the cells found at 2 DIV disappeared at 16 DIV (Figure <xref rid=\"acel13182-fig-0004\" ref-type=\"fig\">4d, e</xref>).</p><p>Therefore, microglia aged in vitro display a limited motility regardless of the sex, hence losing their physiological sex differences, and also lose the ability to respond to IFN‐γ stimulation.</p></sec><sec id=\"acel13182-sec-0007\"><label>2.5</label><title>In vitro aging alters the expression of phagocytosis receptors in microglia</title><p>As a first step to determine the effects of in vitro aging on microglia phagocytosis, we analyzed the mRNA expression of several genes implicated in this function at 2 and 16 DIV (Figure <xref rid=\"acel13182-fig-0005\" ref-type=\"fig\">5</xref>). At 2 DIV, female microglia showed higher basal mRNA levels of C‐X3‐C motif receptor 1 (CX3CR1; Figure <xref rid=\"acel13182-fig-0005\" ref-type=\"fig\">5f</xref>), mannose receptor (CD206; Figure <xref rid=\"acel13182-fig-0005\" ref-type=\"fig\">5d</xref>), macrophage scavenger receptor 1 (MSR1; Figure <xref rid=\"acel13182-fig-0005\" ref-type=\"fig\">5j</xref>), purine receptor P2Y6 (P2RY6; Figure <xref rid=\"acel13182-fig-0005\" ref-type=\"fig\">5k</xref>), and scavenger receptor class B member 1 (Scarb1; Figure <xref rid=\"acel13182-fig-0005\" ref-type=\"fig\">5l</xref>), than male microglia. IFN‐γ treatment increased the mRNA levels of CD11b, CD36, galectin‐3 (Gal3, also known as MAC‐2), CD206, MHCII, MSR1, Scarb1, TLR2, and TLR4 at 2 DIV, but only in female microglia (Figure <xref rid=\"acel13182-fig-0005\" ref-type=\"fig\">5a,c</xref>,g,i,j,l‐n).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13182-fig-0005\" orientation=\"portrait\" position=\"float\"><label>Figure 5</label><caption><p>mRNA expression of phagocytosis‐related receptors in microglia at 2 and 16 days in vitro (DIV). mRNA expression of CD11b (a), CD14 (b), CD36 (c), mannose receptor (CD206; d), CD200R1 (e), C‐X3‐C motif receptor 1 (CX3CR1; f), galectin‐3 (Gal3; g), IL‐1 receptor‐associated kinase (IRAK)‐4 (h), MHCII (i), macrophage scavenger receptor 1 (MSR1; j), purine receptor P2Y6 (P2RY6; k), scavenger receptor class B member 1 (Scarb1; l), Toll‐like receptor (TLR)2 (m), TLR4 (n), and triggering receptor expressed on myeloid cells 2 (TREM2; o). <sup>+</sup>\n<italic>p</italic> < .05, <sup>+++</sup>\n<italic>p</italic> < .001 effect of time measured by two‐way ANOVA followed by Bonferroni post hoc test. *<italic>p</italic> < .05, **<italic>p</italic> < .01, ***<italic>p</italic> < .001 effect of IFN‐γ treatment, <sup>$$</sup>\n<italic>p</italic> < .01, <sup>$$$</sup>\n<italic>p</italic> < .001 sex differences measured by one‐way ANOVA followed by Tukey's post hoc test. Dots show mean ± <italic>SEM</italic>. Blue: male; Purple: female; Dark and striped: IFN‐γ treatment</p></caption><graphic id=\"nlm-graphic-11\" xlink:href=\"ACEL-19-e13182-g005\"/></fig><p>Compared to 2 DIV, the mRNA expression levels of all genes tested decayed in microglia from both sexes at 16 DIV, except for CD36, which only decayed in female microglia, and IRAK4, which was downregulated in male microglia only. The effect of IFN‐γ treatment disappeared at 16 DIV in all cases except for MHCII (Figure <xref rid=\"acel13182-fig-0005\" ref-type=\"fig\">5</xref>). These results show that: (a) Female microglia at 2 DIV are more sensitive than male microglia to IFN‐γ treatment, regarding its effect on the mRNA expression of phagocytosis‐related genes, and (b) the aging process profoundly alters the expression of genes related to phagocytosis in microglia under basal conditions and after stimulation with IFN‐γ.</p></sec><sec id=\"acel13182-sec-0008\"><label>2.6</label><title>Microglia aged in vitro show similar alterations in phagocytosis activity as microglia purified from aged brains</title><p>To investigate whether microglia phagocytosis was affected by sex in our aging model in vitro, we performed the same engulfment assays we had previously tested in microglia isolated from adult and aged male and female brains, using IFN‐γ as a pro‐phagocytic stimulus (Yanguas‐Casás et al., <xref rid=\"acel13182-bib-0042\" ref-type=\"ref\">2018</xref>).</p><p>Male and female cells isolated from newborn brains showed different internalization patterns under basal conditions. Female microglia showed a higher basal internalization of fluorescent beads (Figure <xref rid=\"acel13182-fig-0006\" ref-type=\"fig\">6c</xref>) and neural debris (Figure <xref rid=\"acel13182-fig-0006\" ref-type=\"fig\">6i</xref>), while male microglia showed enhanced internalization of <italic>E. coli</italic> bioparticles (Figure <xref rid=\"acel13182-fig-0006\" ref-type=\"fig\">6f</xref>). The aging process in vitro imitated the effect of natural in vivo aging on microglia phagocytosis. Thus, both male and female microglia increased the internalization of neural debris with in vitro aging (Figure <xref rid=\"acel13182-fig-0006\" ref-type=\"fig\">6i</xref>), as it was observed in the comparison between microglia isolated from adult and aged brains. However, cells obtained from newborns showed an increased phagocytic activity in comparison with those isolated from adult and aged animals (Figure <xref rid=\"acel13182-fig-0001\" ref-type=\"fig\">1</xref>), in agreement with the fact that perinatal microglia present enhanced basal phagocytosis compared to adult microglia (Galloway et al., <xref rid=\"acel13182-bib-0009\" ref-type=\"ref\">2019</xref>; Lenz & Nelson, <xref rid=\"acel13182-bib-0019\" ref-type=\"ref\">2018</xref>).</p><fig fig-type=\"Figure\" xml:lang=\"en\" id=\"acel13182-fig-0006\" orientation=\"portrait\" position=\"float\"><label>Figure 6</label><caption><p>Microglia phagocytic capacity in an aging model in vitro. Representative images of microglia: nonspecific bead intake (a, b), pathogen‐specific (d, e), and neural debris (g, h) phagocytosis. (c, f, i) Amount of internalized particles per cell. <sup>++</sup>\n<italic>p</italic> < .01 effect of time in male and female microglia measured by two‐way ANOVA followed by Bonferroni post hoc test; <sup>^^</sup>\n<italic>p < </italic>.<italic>01</italic> effect of time in male microglia measured by two‐way ANOVA followed by Bonferroni post hoc test <sup>$$$</sup>\n<italic>p</italic> < .001 sex differences, *<italic>p</italic> < .05, ***<italic>p</italic> < .001 effect of IFN‐γ treatment measured by one‐way ANOVA followed by Tukey's post hoc test. Dots show mean ± <italic>SEM</italic>. Blue: male; Purple: female; Dark: IFN‐γ treatment</p></caption><graphic id=\"nlm-graphic-13\" xlink:href=\"ACEL-19-e13182-g006\"/></fig><p>IFN‐γ treatment increased nonspecific, pathogen‐specific, and neural debris intake in both male and female microglia at 2 DIV (Figure <xref rid=\"acel13182-fig-0006\" ref-type=\"fig\">6c,f</xref>,i). However, after IFN‐γ stimulation female microglia showed a much higher increase in the internalization of fluorescent beads and neural debris than male microglia at 2 DIV (Figure <xref rid=\"acel13182-fig-0006\" ref-type=\"fig\">6c</xref>). This sex difference was lost in aged (16 DIV) microglia, in which the increase in bead and neural debris phagocytosis upon IFN‐γ stimulation was not observed, neither in males nor in females (Figure <xref rid=\"acel13182-fig-0006\" ref-type=\"fig\">6c,i</xref>). Indeed, neural debris phagocytosis was even decreased upon IFN‐γ stimulation in female microglia at 16 DIV (Figure <xref rid=\"acel13182-fig-0006\" ref-type=\"fig\">6i</xref>). Thus, as it was observed in the comparison of microglia isolated from adult and aged brains, in vitro aging also affects microglia phagocytosis in response to an inflammatory challenge in a sex‐dependent fashion.</p></sec></sec><sec sec-type=\"discussion\" id=\"acel13182-sec-0009\"><label>3</label><title>DISCUSSION</title><p>Our present findings, showing sex differences in the basal phagocytic activity of aging microglia and in their response to inflammatory stimuli, extend the results of previous studies that have characterized physiological and pathological sex differences in microglia during development and in adult life (Bordeleau, Carrier, Luheshi, & Tremblay, <xref rid=\"acel13182-bib-0003\" ref-type=\"ref\">2019</xref>; Rahimian, Cordeau, & Kriz, <xref rid=\"acel13182-bib-0029\" ref-type=\"ref\">2019</xref>). While those studies suggest that microglia are involved in the generation of sex differences in neurodevelopmental and psychiatric disorders and in the neurodegenerative response after acute traumatic brain injury or stroke in young animals, our findings have implications for the possible role of microglia in the generation of sex differences in the response of the aging brain to neurodegenerative conditions.</p><p>Reproducing microglia aging in vitro using long‐term murine cultures provides a useful tool to study senescent microglia, given the limitations of isolating degenerating microglia from the aged brains for experimentation, as only the more resistant microglia will survive the isolation procedure, while susceptible microglia are lost in this process (Njie et al., <xref rid=\"acel13182-bib-0026\" ref-type=\"ref\">2012</xref>). Microglia isolated from newborn and adult brains maintain sex‐specific features when in culture, such as postnatal sex differences in the phagocytic and migratory activity (Villa et al., <xref rid=\"acel13182-bib-0040\" ref-type=\"ref\">2018</xref>; Yanguas‐Casás et al., <xref rid=\"acel13182-bib-0042\" ref-type=\"ref\">2018</xref>). Our findings also indicate that microglia derived from newborn male or female mice brains acquire a similar phenotype when cultured during 16 DIV. This phenotype is characterized by increased β‐galactosidase activity, decreased expression of specific miRNAs and mRNA levels of Beclin‐1, TLR2, and TLR4, and reduced motility and mRNA expression of motility‐related genes (Caldeira et al., <xref rid=\"acel13182-bib-0004\" ref-type=\"ref\">2014</xref>; Sieber, Claus, Witte, & Frahm, <xref rid=\"acel13182-bib-0035\" ref-type=\"ref\">2011</xref>). Furthermore, microglia maintained for 16 DIV show an altered inflammatory response when exposed to IFN‐γ stimulation.</p><p>Although all these modifications are compatible with the phenotype of senescent microglia (Caldeira et al., <xref rid=\"acel13182-bib-0004\" ref-type=\"ref\">2014</xref>; Scheiblich, Trombly, Ramirez, & Heneka, <xref rid=\"acel13182-bib-0033\" ref-type=\"ref\">2020</xref>; Sieber et al., <xref rid=\"acel13182-bib-0035\" ref-type=\"ref\">2011</xref>), additional methods and molecular markers would be necessary to confirm cellular senescence in 16 DIV microglia cultures. Moreover, it is important to note that the molecular mechanisms that determine aging process of microglia in vitro may differ from those that cause microglia dysfunction in the aged brain (Stojiljkovic et al., <xref rid=\"acel13182-bib-0036\" ref-type=\"ref\">2019</xref>). For instance, the expression of Gal3 and MHCII in phagocytic cells has been shown to increase with age (Shobin et al., <xref rid=\"acel13182-bib-0034\" ref-type=\"ref\">2017</xref>). Here, we find that the mRNA expression of Gal3 decays in the aged phenotype of microglia, while MHCII mRNA expression levels remained unaffected. The expression of these markers is also linked to microglia priming (Holtman et al., <xref rid=\"acel13182-bib-0013\" ref-type=\"ref\">2015</xref>); therefore, these differences may be due to a different priming state of microglial cells aged in vitro or to the infiltration of macrophages and the expression of these markers by other cell types besides microglia in the aged brain. However, with independence of the differences in the triggering mechanisms, the observed functional characteristics of microglia aged in vitro are reminiscent of the functional changes that occur in microglia in vivo with the aging process, such as impaired inflammatory response (Scheiblich et al., <xref rid=\"acel13182-bib-0033\" ref-type=\"ref\">2020</xref>), decreased process motility, soma movement, and cellular migration and recruitment in the injured tissue compared to young microglia (Damani et al., <xref rid=\"acel13182-bib-0005\" ref-type=\"ref\">2011</xref>; Hefendehl et al., <xref rid=\"acel13182-bib-0011\" ref-type=\"ref\">2014</xref>).</p><p>In this study, our principal aim was to analyze the influence of aging and sex on microglia phagocytosis, a functional response of these cells that are involved in the regulation of brain tissue homeostasis under physiological and pathological conditions. Our findings indicate that donor's age had significant effects on basal sex differences in microglia phagocytosis in vitro. Thus, microglia isolated from newborn female brains showed a higher basal internalization of fluorescent beads and neural debris than male microglia. In contrast, male microglia from newborn animals showed higher internalization of <italic>E. coli</italic> bioparticles than female microglia. These sex differences disappeared in microglia isolated from adult (5 months) brains, which showed a similar basal internalization of fluorescent beads, <italic>E. coli</italic> bioparticles, and neural debris in both sexes.</p><p>Our results are in agreement with previous findings of sex differences in the activity of phagocytosis of developing microglia in vitro and in vivo (Weinhard et al., <xref rid=\"acel13182-bib-0041\" ref-type=\"ref\">2018</xref>; Yanguas‐Casás et al., <xref rid=\"acel13182-bib-0042\" ref-type=\"ref\">2018</xref>) and suggest that there are transient functional sex differences in basal microglia phagocytosis during postnatal development. During this period, male microglia showed preferentially pathogen‐specific phagocytosis, at least of gram‐negative bacteria substrate, while female microglia had enhanced nonspecific and neural debris phagocytosis. These differences, which may represent a sex‐dependent priority to trigger microbial pattern recognition‐mediated phagocytosis in developing male microglia or cellular debris clearance, induced phagocytosis in developing female microglia.</p><p>The results of the quantification of mRNA phagocytosis‐related genes are compatible with the functional sex differences detected in the phagocytosis assays. Thus, higher IRAK4 mRNA levels in microglia isolated from newborn male animals compared to female microglia are consistent with a higher capacity of male cells to internalize <italic>E. coli</italic>‐coated bioparticles, since IRAK4 mediates upregulation of scavenger receptors (Doyle et al., <xref rid=\"acel13182-bib-0006\" ref-type=\"ref\">2004</xref>) by TLR2 and TLR4. These receptors can recognize both microbial patterns and danger‐associated molecular patterns and mediate brain injury‐induced inflammation and microglia phagocytosis under pathological conditions (Fiebich, Batista, Saliba, Yousif, & de Oliveira, <xref rid=\"acel13182-bib-0007\" ref-type=\"ref\">2018</xref>). In addition, higher mRNA levels of CD206, MSR1, and Scarb1 in microglia isolated from newborn female brains are compatible with the increased basal nonspecific and neural debris phagocytosis of these cells, given that these genes encode for scavenger receptors that are important in the innate host response to bacterial and fungal pathogens (Husemann, Loike, Anankov, Febbraio, & Silverstein, <xref rid=\"acel13182-bib-0014\" ref-type=\"ref\">2002</xref>). However, in the absence of protein data these molecular interpretations remain speculative. Nevertheless, with independence of the possible molecular mechanisms, the observed functional differences in phagocytosis may have important consequences for the generation of sex differences in brain structure and adult brain function (Bordeleau et al., <xref rid=\"acel13182-bib-0003\" ref-type=\"ref\">2019</xref>; VanRyzin, Pickett, & McCarthy, <xref rid=\"acel13182-bib-0039\" ref-type=\"ref\">2018</xref>) and in the long‐term psychiatric consequences of perinatal infections (Ardalan et al., <xref rid=\"acel13182-bib-0001\" ref-type=\"ref\">2019</xref>). Further studies are still necessary to determine the mechanisms that generate the observed sex differences in the activity of phagocytosis of microglia, which may involve the perinatal sex hormone environment together with the cellular actions of sex chromosome genes (Loke, Harley, & Lee, <xref rid=\"acel13182-bib-0021\" ref-type=\"ref\">2015</xref>; VanRyzin et al., <xref rid=\"acel13182-bib-0039\" ref-type=\"ref\">2018</xref>).</p><p>In spite of the fact that basal sex differences in newborn microglia phagocytosis disappeared in adult microglia, cells from both ages responded to IFN‐γ stimulation by increasing nonspecific and pathogen‐specific phagocytic activity. However, an important finding in our study is that aging impaired the increase in nonspecific phagocytosis and reduced the increase in pathogen‐specific phagocytosis upon IFN‐γ stimulation in microglia of both sexes. The impaired effect of IFN‐γ on the stimulation of nonspecific and pathogen‐specific phagocytosis in aged microglia is consistent with the observed alterations in the mRNA expression of different molecules with in vitro aging. These include CD11b, CD14, CD36, CD206, CD200R1, CX3CR1, Gal3, MSR1, P2RY6, Scarb1, TLR2, TLR4, and TREM2, which are involved in different steps of microglia activation and the regulation of phagocytosis (Husemann et al., <xref rid=\"acel13182-bib-0014\" ref-type=\"ref\">2002</xref>; Neumann, Kotter, & Franklin, <xref rid=\"acel13182-bib-0024\" ref-type=\"ref\">2009</xref>).</p><p>One of the most relevant results of this study is that, while nonspecific and pathogen‐specific phagocytosis decay during the acquisition of the senescent phenotype, in agreement with the reported decrease in phagocytosis activity of aged microglia (Damani et al., <xref rid=\"acel13182-bib-0005\" ref-type=\"ref\">2011</xref>; Koellhoffer et al., <xref rid=\"acel13182-bib-0016\" ref-type=\"ref\">2017</xref>), neural debris phagocytosis increases with aging in male and female microglia. This effect was detected both in microglia isolated from aged brains and in microglia aged in vitro. We may speculate that the increase in neural debris uptake in aged microglia is more likely mediated through the deregulation of CX3CL1‐CX3CR1 and CD200‐CD200R1 axes, which have been correlated with microglia activation and exacerbated phagocytic responses (Oria et al., <xref rid=\"acel13182-bib-0028\" ref-type=\"ref\">2018</xref>; Raoul et al., <xref rid=\"acel13182-bib-0031\" ref-type=\"ref\">2010</xref>), rather than by a selective surface expression of debris clearance receptors in microglial cells, as mRNA expression of these receptors decays with time. However, further studies are necessary to unveil the precise mechanism and determine the functional consequences of this increase in phagocytosis activity by aging microglia. Nevertheless, as neuronal vulnerability increases with age, increased phagocytosis of neural debris might contribute to maintain homeostasis in the aged brain. Indeed, clearance of cellular debris from the parenchyma is essential to avoid further degeneration and exacerbated inflammatory responses in the brain under pathological conditions (Neumann et al., <xref rid=\"acel13182-bib-0024\" ref-type=\"ref\">2009</xref>). Alternatively, it may be hypothesized that the increased phagocytosis activity of neural debris by aged microglia could be part of the cell priming process associated with microglia senescence and may, therefore, contribute to brain deterioration with aging.</p><p>Another important observation in our study is that the phagocytosis of neural debris was impaired only in aged female microglia under inflammatory conditions elicited by IFN‐γ stimulation. This effect of aging may be associated with the marked decrease in P2RY6 mRNA expression detected in aged female microglia, since P2RY6 mediates UDP‐evoked phagocytosis of debris from damaged neurons (Koizumi et al., <xref rid=\"acel13182-bib-0017\" ref-type=\"ref\">2007</xref>). We can only speculate on the possible consequences of the impaired phagocytosis of neural debris by aging female microglia under inflammatory conditions. However, considering that brain aging is associated with increased neuroinflammation, our finding may implicate a decreased efficiency of microglia to maintain homeostasis in the aged female brain compared to the aged male brain or, on the contrary, a better control of the inflammatory stimulation of microglia phagocytosis by female cells to avoid further damage. In either case, the different phagocytic response of male and female microglia to inflammation may contribute to the generation of the well‐characterized sex differences in the incidence of neurodegenerative diseases with aging (Loke et al., <xref rid=\"acel13182-bib-0021\" ref-type=\"ref\">2015</xref>; The Lancet, <xref rid=\"acel13182-bib-0038\" ref-type=\"ref\">2019</xref>). Even though IFN‐γ is a factor aged microglia are physiologically exposed to (Monteiro et al., <xref rid=\"acel13182-bib-0023\" ref-type=\"ref\">2017</xref>), we cannot rule out that other inflammatory stimuli in vivo may elicit a different response or affect the one we are describing.</p><p>Finally, it should be emphasized that the sex‐specific alterations in the phagocytosis activity of aged microglia are associated with modifications in the inflammatory response that are also different between male and female cells and occur in parallel with impaired cell motility, a characteristic of microglia in several neurological diseases (O'Connor, Borsig, & Heikenwalder, <xref rid=\"acel13182-bib-0027\" ref-type=\"ref\">2015</xref>; Raoul et al., <xref rid=\"acel13182-bib-0031\" ref-type=\"ref\">2010</xref>). The combination of all these circumstances most probably will magnify the consequences of sex dimorphic microglia changes in the aged brain.</p></sec><sec id=\"acel13182-sec-0010\"><label>4</label><title>EXPERIMENTAL PROCEDURES</title><sec id=\"acel13182-sec-0011\"><label>4.1</label><title>Animals</title><p>Postnatal P0‐P2 CD1 male and female mouse pups and C57BL/6 male mice were raised in our in‐house colony (Instituto Cajal, CSIC), and 8‐week‐old C57BL/6 female mice were obtained from Envigo. In all cases, mice were maintained under standard conditions (22°C with 50% ± 10% relative humidity and 12‐hr light/dark cycle) until the experimental procedures. Animal handling and care were performed in compliance with European Union guidelines 2010/63/EU and Spanish regulations (R.D. 53/2013) regarding the use and care of laboratory animals, and all protocols were approved by our local Animal Care and Ethics Committee (Comité de Ética de Experimentación Animal del Instituto Cajal) and by the Madrid regional government (Consejería del Medio Ambiente y Territorio, Comunidad de Madrid. Ref. PROEX 134/17).</p></sec><sec id=\"acel13182-sec-0012\"><label>4.2</label><title>Reagents</title><p>DNase I, papain, and dispase II were purchased from Sigma‐Aldrich. IFN‐γ was purchased from PeproTech EC, Ltd. Roswell Park Memorial Institute medium 1640 (RPMI), Dulbecco's modified Eagle's medium (DMEM), heat‐inactivated fetal bovine serum (FBS), and heat‐inactivated horse serum (HS) were purchased from Gibco BRL. Antibiotic–antimitotic, GlutaMAX<sup>TM</sup> was purchased from Thermo Fisher Scientific. Percoll was purchased from GE Healthcare.</p></sec><sec id=\"acel13182-sec-0013\"><label>4.3</label><title>Microglia purification from adult mouse brain</title><p>5‐ and 18‐month‐old C57BL/6 male and female mice (4–5 animals per experimental group) were anesthetized with pentobarbital (Dolethal, 50 mg/kg body weight, intraperitoneal) and perfused transcardially with 0.9% saline. Adult or aged microglia were isolated as previously described (Lee & Tansey, <xref rid=\"acel13182-bib-0018\" ref-type=\"ref\">2013</xref>). Brain tissue was minced and digested at 37°C for 30 min with gentle shaking in a buffer containing papain, dispase II, and DNase I followed by mechanical dissociation. After neutralization of the reaction, cells were centrifuged and filtered by a 40‐µm mesh. Microglia fraction was obtained in a 30%–70% SIP Percoll gradient by centrifugation of the cells at 500g and 18°C for 30 min, with no brake. After centrifugation, myelin was discarded and 6ml of the interphase was collected and mixed with 40 ml RPMI. The cells were centrifuged at 500g and 18°C for 7 min with regular brake, and the pellet was resuspended on 2ml RPMI. After counting the cells, they were centrifuged at 168g for 10 min and resuspended in 180μL MACS buffer. Afterward, 10 μl/10<sup>7</sup> cells of CD11b microbeads were added to the mix. After washing, magnetically labeled cells were collected using MACS column system (Miltenyi Biotec).</p></sec><sec id=\"acel13182-sec-0014\"><label>4.4</label><title>Microglia cultures from newborn mouse brain</title><p>Primary cultures of microglial cells were obtained from newborn (P0) to 2‐day‐old (P2) CD1 mouse forebrains. Pups were sexed via measurement of anogenital distance, and a separate cohort of animals was used for each experiment. Homogenized forebrains from male or female pups were grown separately in DMEM supplemented with 10% FBS, 10% HS, and P/S (DMEM 10:10:1) in 75‐cm<sup>2</sup> flasks, coated with poly‐<sc>l</sc>‐lysine (10 μg/ml) as described previously (Mecha et al., <xref rid=\"acel13182-bib-0022\" ref-type=\"ref\">2011</xref>). Briefly, after reaching confluence, cells were shaken at 230 rpm for 3 hr at 37°C. Detached cells were centrifuged at 168g for 10 min. To avoid the estrogenic effects of phenol red, purified microglia were plated in warm antibiotic‐ and phenol red‐free RPMI 1640 supplemented with 0.1% FBS. All the subsequent procedures were carried out using this medium.</p></sec><sec id=\"acel13182-sec-0015\"><label>4.5</label><title>In vitro aging model</title><p>An age‐like phenotype was induced in microglia cultures as previously described (Caldeira et al., <xref rid=\"acel13182-bib-0004\" ref-type=\"ref\">2014</xref>), with minor modifications. After microglia purification from newborn brains, the cells were seeded on 6‐well plates coated with poly‐<sc>l</sc>‐lysine (10 μg/ml) at a density of 100,000 cells/cm<sup>2</sup> for PCR analysis, 25,000 cells/cm<sup>2</sup> for senescence assays, or 50,000 cells/cm<sup>2</sup> for phagocytosis assays. Cells were maintained for 2, 10, or 16 days at 37º C and 5% CO<sub>2</sub> in RPMI medium containing 0.5% FBS. The cells were incubated for at least 12 hr in antibiotic‐free serum‐free RPMI prior to IFN‐γ (20 ng/ml) treatment. Senescence, gene expression, phagocytic capacity, and motility of the cells were evaluated at the three time points.</p></sec><sec id=\"acel13182-sec-0016\"><label>4.6</label><title>Cell senescence</title><p>Microglia senescence was evaluated using the Senescence Cells Histochemical Staining Kit (Sigma) according to the manufacturer's protocol. Microglia were seeded at a density of 21,000 cells/cm<sup>2</sup> and kept at 37°C in fresh RPMI with 0.5% FBS and P/S for 2 or 16 days. At the selected times, cells were washed with PBS, fixed for 7 min at room temperature, and stained for 2 hr at 37°C. Images for quantification of β‐galactosidase (senescent)‐positive cells were acquired using a 10× lens by phase contrast in a Leica DMI6000 microscope.</p></sec><sec id=\"acel13182-sec-0017\"><label>4.7</label><title>RNA purification of microglial cells and qPCR</title><p>To study microglial mRNA expression by quantitative PCR, microglial cells were seeded at a density of 100,000 cells/cm<sup>2</sup> and lysed 24h after treatment with IFN‐γ at each time point (2 or 16 DIV). Total RNA was extracted using an Illustra RNAspin Mini RNA Isolation Kit (GE Healthcare) to assess the mRNA expression levels of the set of genes listed in Table <xref rid=\"acel13182-tbl-0001\" ref-type=\"table\">1</xref>. First‐strand cDNA was synthesized from 0.75 µg RNA using M‐MLV reverse transcriptase (Promega) according to the manufacturer's protocol.</p><table-wrap id=\"acel13182-tbl-0001\" xml:lang=\"en\" content-type=\"Table\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Mouse primers for quantitative PCR</p></caption><table frame=\"hsides\" rules=\"groups\"><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><col style=\"border-right:solid 1px #000000\" span=\"1\"/><thead valign=\"top\"><tr style=\"border-bottom:solid 1px #000000\"><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Gene</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Accession #</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Forward primer 5′‐3′</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Reverse primer 5′‐3′</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Amplicon size (bp)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Beclin‐1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CTGGACAAGCTCAAGAAAACCAA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GCAAGCGACCCAGTCTGAAA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">100</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CCR2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CTCTGCAAACAGTGCCCAGTT</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">AACCGAGACCTCTTGCTCCCC</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">93</td></tr><tr><td align=\"left\" rowspan=\"2\" colspan=\"1\">CD11b (Itgam)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"2\" colspan=\"1\">ATGGACGCTGATGGCAATACC</td><td align=\"left\" rowspan=\"2\" colspan=\"1\">TCCCCATTCACGTCTCCCA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">203</td></tr><tr><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">206</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CD14</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CGTTGACGAGGACCCTCAGA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GCAGTGGCCTTGTCAGGAA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">100</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CD36</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">AGGTGATGGGTCTTCACCAG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">ATTTGTGGTTGGTTGCCAAGG</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">110</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CD200R1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CATAGGATGCATTTGTCTTTTGAAA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GCTGCATTTCATCCTCCTCAATA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">98</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CD206 (Mannose Receptor)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGTTGGATTGAGGCCTGAAA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">AACGTCCCTTTGTTTTGAACATC</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">66</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">CX3CR1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TGTCCTTTCTCTTTGTGAACATGA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGCGGCGGCCATCTT</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">56</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">Gal3 (MAC2)</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CTAATCAGGTGAGCGGCACAG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TCCTTGAGGGTTTGGGTTTCC</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">102</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IL‐1b</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGTGTGTGACGTTCCCATTA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CCGACAGCACGAGGCTTT</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">74</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IL‐6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GAAACCGCTATGAAGTTCCTCTCTG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TGTTGGGAGTGGTATCCTCTGTGA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">136</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">IRAK4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGCAATCTGAAGTCCCCTCGT</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TCTGACGTTCCTCGCTTCCT</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">104</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">MCP‐1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TGTTGGCTCAGCCAGATGCAGTTA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TACAGCTTCTTTGGGACACCTGCT</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">131</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">MHCII</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TTCCAGCCCCCATGTCAG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">ACAACCCCAGGGCACAGA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">54</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">MSR1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GAGTGTAGGCGGATCAACCC</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CACTGGCCTTGGTGGAAGAT</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">96</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">P2RY6</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CCAGTGCCAGGTTCAGGGTGTA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GCGTCTACCGTGAGGATTTCA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">159</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">RANTES</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">ATATGGCTCGGACACCACTC</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CGAGTGACAAAGACGACTGC</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">126</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">SCARB1</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GATGTCACACCTGTCCGCA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CCTGGCTCACAGGCCATTTA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">138</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TLR2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TGTCCGCAATCATAGTTTCTGATG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">AGCAGAGAAGTGAAGCCCCT</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">145</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TLR4</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGCTCCTGGCTAGGACTCTGA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TCTGATCCATGCATTGGTAGGT</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">114</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TNF‐α</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GAAAAGCAAGCAGCCAACCA</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">CGGATCATGCTTTCTGTGCTC</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">106</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">TREM2</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GCACCTCCAGGAATCAAGAG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GGGTCCAGTGAGGATCTGAA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">200</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">RPL13A</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TACCAGAAAGTTTGCTTACCTGGG</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">TGCCTGTTTCCGTAACCTCAAG</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">151</td></tr><tr><td align=\"left\" rowspan=\"1\" colspan=\"1\">RPS29</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">\n</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">ACATGTTCAGCCCGTATTTGC</td><td align=\"left\" rowspan=\"1\" colspan=\"1\">GCCGCGTCTGCTCCAA</td><td align=\"char\" char=\".\" rowspan=\"1\" colspan=\"1\">56</td></tr></tbody></table><permissions><copyright-holder>John Wiley & Sons, Ltd</copyright-holder></permissions></table-wrap><p>Diluted cDNA was amplified by real‐time PCR in a 15 µl volume reaction in a 7500 Real‐Time PCR System (Applied Biosystems) with Power SYBR<sup>®</sup> Green reagent (Applied Biosystems). Gene expression was determined with 7500 Software v2.0.4 using ROX as passive reference dye. A standard curve with varying dilutions of each sample mix was performed for each set of primers to ensure the presence of unique amplification products. cDNA amplification was done in conventional Applied Biosystems cycling parameters (40 cycles of changing temperatures, first at 95°C for 15 s and then 60°C for 1 min).</p><p>Cycle threshold (<italic>C</italic>\n<sub>t</sub>) values for all analyzed genes ranged between 23 and 31. Data were represented using the comparative <italic>C</italic>\n<sub>t</sub> method, and for a valid ΔΔ<italic>C</italic>\n<sub>t</sub> value, we verified that the efficiency of amplification of the target and the reference gene was approximately equal (the absolute value of the slope of Δ<italic>C</italic>\n<sub>t</sub> versus log relative concentration should be between −0.1 and 0.1). The <italic>C</italic>\n<sub>t</sub> was determined for each target gene in duplicate. Δ<italic>C</italic>\n<sub>t</sub> was calculated by the difference between the <italic>C</italic>\n<sub>t</sub> of each target gene and the <italic>C</italic>\n<sub>t</sub> of an artificial BestKeeper reference gene based on the <italic>C</italic>\n<sub>t</sub> values of two independent reference genes: RPL13A and RPS29 calculated using the BestKeeper<sup>©</sup> software (<ext-link ext-link-type=\"uri\" xlink:href=\"http://gene-quantification.com/bestkeeper.html\">http://gene‐quantification.com/bestkeeper.html</ext-link>), which helps determine stable housekeeping <italic>genes</italic>, differentially regulated target <italic>genes,</italic> and sample integrity.</p></sec><sec id=\"acel13182-sec-0018\"><label>4.8</label><title>MicroRNA purification</title><p>MicroRNAs were purified following manufacturer's protocol of Isolation Kit <italic>mir</italic>Vana<sup>™</sup>. After acid phenol:chloroform extraction, miRNAs were isolated after washing with different ethanol concentrations, and collected with RNase‐free water. Less than 5 ng/µl miRNA were used in the retrotranscription/amplification phase. Following TaqMan<sup>®</sup> Advanced miRNA Assays manufacturer's protocol, Poly(A) tailing reaction, adaptor ligation, and reverse transcription (RT) were made prior to miR amplification. After this, we proceeded with polymerase chain reaction (PCR), using different TaqMan Advanced miRNA Assay (20X): 480902_mir (miRNA‐124a), 481546_mir (miRNA‐146a), and 481328_mir (miRNA‐155). We used 18S (Mm03928990_g1, Thermo Fisher) as a housekeeping gene. cDNA amplification by real‐time quantitative PCR was done in 20 µl volume reaction in a 7500 Real‐Time PCR System (Applied Biosystems) with TaqMan Fast Advanced Master Mix (Thermo Fisher). Gene expression was determined with 7500 Software v2.0.4 and represented using the comparative <italic>C</italic>\n<sub>t</sub> (cycle threshold) method.</p></sec><sec id=\"acel13182-sec-0019\"><label>4.9</label><title>Neuron debris production and labeling</title><p>Neurons were obtained from the brains of mouse embryos as previously described (Hilgenberg & Smith, <xref rid=\"acel13182-bib-0012\" ref-type=\"ref\">2007</xref>). Mouse embryos were sexed prior to brain extraction, and brains were minced and digested with a trypsin solution at 37°C for 15 min, and after mechanical disaggregation, the cells were resuspended in neurobasal medium containing B27, ampicillin, and GlutaMAX<sup>TM</sup>. After pelleting the cells by centrifugation at 168g for 5 min, neurons were resuspended in sodium carbonate buffer (0.1 M NaHCO<sub>3</sub>‐Na<sub>2</sub>CO<sub>3</sub>, pH 9.3) for optimal labeling and sonicated in a bar sonicator. Neuron debris was labeled using Cy<sup>TM</sup>3 Mono‐Reactive Dye Pack (Amersham Biosciences) according to the manufacturer's specifications and kept stored at 4°C until use.</p></sec><sec id=\"acel13182-sec-0020\"><label>4.10</label><title>Phagocytosis assays</title><p>To determine the microglia phagocytosis, cells were seeded on 10‐mm‐diameter glass coverslips coated with poly‐<sc>l</sc>‐lysine 10 μg/ml for in vitro microglia cultures or 50 μg/ml for adult mouse brain‐derived microglia at a density of 50,000 or 25,000 cells/cm<sup>2</sup>, respectively. After 24‐hr incubation in serum‐free RPMI or IFN‐γ (20 ng/ml) treatment, the cells were washed twice with warm RPMI medium and the phagocytosis reagents were added for 1 hr. Fluorescent beads (0.5 µl/well; Fluoresbrite<sup>®</sup> YG Carboxylate Microspheres 1.00 µm, Polysciences, Inc.), Cy<sup>TM</sup>3‐labeled neural debris (5:1 microglia:neuron debris ratio), or <italic>E. coli</italic> bioparticles (1 µl/well; pHrodo<sup>TM</sup> Green <italic>E. coli</italic> BioParticles<sup>TM</sup> Conjugate for Phagocytosis, Invitrogen<sup>TM</sup>, Thermo Fisher Scientific) were added in warm RPMI at the selected wells. Afterward, cells were washed twice with warm PBS and fixed with 4% paraformaldehyde. Microglial cells were stained with rabbit anti‐Iba1 antibody (Wako Pure Chemical Industries; 1:500 dilution) followed by incubation with a goat anti‐rabbit Alexa 594‐conjugated secondary antibody (1:1,000) or guinea pig anti‐Iba1 antibody (Synaptic Systems) followed by incubation with a goat anti‐guinea pig Alexa 488‐conjugated secondary antibody (1:1,000). After washing with PBS, glass coverslips were mounted on slides with VECTASHIELD Antifade Mounting Medium with DAPI (Vector Laboratories). The Z‐stack images were visualized on a Leica TCS‐SP5 confocal system. Bead intake was quantified as beads inside the cell, and neural debris and <italic>E. coli</italic> bioparticle phagocytosis were quantified by measuring the raw intensity density using the Fiji software and maintaining the same threshold restrictions for all the experimental conditions. We characterized microglia phagocytosis as the amount of internalized bioparticles per cell in actively engulfing cells. Percentages of phagocytic microglia in each condition can be found on Table <xref rid=\"acel13182-sup-0001\" ref-type=\"supplementary-material\">S1</xref>. In all cases, quantifications were performed in 50 cells from 5 fields per experimental condition per experiment, with five independent experiments being performed.</p></sec><sec id=\"acel13182-sec-0021\"><label>4.11</label><title>Time‐lapse acquisition of microglia motility</title><p>To study microglial motility, male and female microglial cells were seeded at a density of 25,000 cells/cm<sup>2</sup> in Multiwell 6‐well plastic plates (FALCON, Corning Incorporated—Life Sciences) coated with poly‐<sc>l</sc>‐lysine (10 µg/ml).</p><p>Microglia motility was analyzed 2 or 16 days after microglia seeding, in cells obtained from the same cell culture. Phase‐contrast images of three fields per well were acquired every 2 min for 3 hr with a 203 0.70 DRY Leica DMI6000B lens in a time‐lapse Leica AF 6500–7000 microscope and analyzed using Fiji software. Microglia motility was evaluated using two parameters: total area covered by the cells during the acquisition (expressed as the area in mm<sup>2</sup> covered by a single cell during a 3‐hr period) and the wandering of the cells (displacement, measured as the area in µm<sup>2</sup> a single cell moves around in 1 min). Changes in cell shape and brightness did not allow a reliable automatic tracking, and therefore, this possibility was discarded. To determine the total area covered by each microglial cell, acquired images were stabilized and cropped, and the series of images was stacked with the StackReg plug‐in. The area covered by each cell was measured in the projection in µm<sup>2</sup>. Total path distance wandered by each cell was determined by tracking the trajectory of each cell body during the acquisition period with the Manual Tracking plug‐in.</p></sec><sec id=\"acel13182-sec-0022\"><label>4.12</label><title>Statistical analysis</title><p>The data in bar graphs are expressed as the mean ± <italic>SEM</italic>, and the data in scattered plots are expressed as the median ± range of a representative replicate, being the cells from the same replicate at the three time points analyzed when in vitro. GraphPad Prism software version 5.0 for Windows and SPSS 22 software (IBM Corporation) was used for the statistical analysis. Normality of the data was assessed with the Kolmogorov–Smirnov test, to satisfy the assumption of normality for the analysis of variance (ANOVA). Whenever normality was not achieved, statistical significance was determined with nonparametric tests (Kruskal–Wallis and post hoc pairwise comparisons with Mann–Whitney <italic>U</italic> test). One‐way ANOVA was used for comparison of multiple samples, followed by a Tukey's post hoc test to determine the statistical significance. Interactions between sex and treatment, sex and time, and time and treatment were determined using the two‐way ANOVA interaction model, with post hoc Bonferroni's comparisons. Statistical significance was set at <italic>p</italic> < .05 in all cases.</p></sec></sec><sec sec-type=\"COI-statement\" id=\"acel13182-sec-0024\"><title>CONFLICT OF INTEREST</title><p>The authors of the manuscript declare no conflict of interest. They certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent‐licensing arrangements), or nonfinancial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.</p></sec><sec id=\"acel13182-sec-0025\"><title>AUTHOR CONTRIBUTIONS</title><p>NYC conceived and designed the study, and analyzed and interpreted the data. ACC and NYC conducted the experimental procedures. LMGS and MAA advised on the experimental design and contributed materials and animals. All authors contributed to manuscript writing and revision and approved the final version.</p></sec><sec sec-type=\"supplementary-material\"><title>Supporting information</title><supplementary-material content-type=\"local-data\" id=\"acel13182-sup-0001\"><caption><p>Table S1</p></caption><media xlink:href=\"ACEL-19-e13182-s001.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec></body><back><ack id=\"acel13182-sec-0023\"><title>ACKNOWLEDGMENTS</title><p>We thank Ms. Elisa Baides Rosell for excellent technical assistance. The study was supported by a grant from Agencia Estatal de Investigación (AEI), co‐funded by Fondo Europeo de Desarrollo Regional (FEDER): BFU2017‐82754‐R and by CIBERFES.</p></ack><sec sec-type=\"data-availability\" id=\"acel13182-sec-0027\"><title>DATA AVAILABILITY STATEMENT</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></sec><ref-list content-type=\"cited-references\" id=\"acel13182-bibl-0001\"><title>REFERENCES</title><ref id=\"acel13182-bib-0001\"><mixed-citation publication-type=\"journal\" id=\"acel13182-cit-0001\">\n<string-name>\n<surname>Ardalan</surname>, <given-names>M.</given-names>\n</string-name>, <string-name>\n<surname>Chumak</surname>, <given-names>T.</given-names>\n</string-name>, <string-name>\n<surname>Vexler</surname>, <given-names>Z.</given-names>\n</string-name>, & <string-name>\n<surname>Mallard</surname>, <given-names>C.</given-names>\n</string-name> (<year>2019</year>). <article-title>Sex‐dependent effects of perinatal inflammation on the brain: Implication for neuro‐psychiatric disorders</article-title>. <source xml:lang=\"en\">International Journal of Molecular Sciences</source>, <volume>20</volume>(<issue>9</issue>), <fpage>2270</fpage>\n<pub-id pub-id-type=\"doi\">10.3390/ijms20092270</pub-id>\n</mixed-citation></ref><ref id=\"acel13182-bib-0002\"><mixed-citation publication-type=\"journal\" id=\"acel13182-cit-0002\">\n<string-name>\n<surname>Bachiller</surname>, <given-names>S.</given-names>\n</string-name>, <string-name>\n<surname>Jimenez‐Ferrer</surname>, <given-names>I.</given-names>\n</string-name>, <string-name>\n<surname>Paulus</surname>, <given-names>A.</given-names>\n</string-name>, <string-name>\n<surname>Yang</surname>, <given-names>Y.</given-names>\n</string-name>, <string-name>\n<surname>Swanberg</surname>, <given-names>M.</given-names>\n</string-name>, <string-name>\n<surname>Deierborg</surname>, <given-names>T.</given-names>\n</string-name>, & <string-name>\n<surname>Boza‐Serrano</surname>, <given-names>A.</given-names>\n</string-name> (<year>2018</year>). <article-title>Microglia in neurological diseases: A road map to brain‐disease dependent‐inflammatory response</article-title>. <source xml:lang=\"en\">Frontiers in Cellular Neuroscience</source>, <volume>12</volume>, <fpage>488</fpage>\n<pub-id pub-id-type=\"doi\">10.3389/fncel.2018.00488</pub-id>\n<pub-id pub-id-type=\"pmid\">30618635</pub-id></mixed-citation></ref><ref id=\"acel13182-bib-0003\"><mixed-citation publication-type=\"journal\" id=\"acel13182-cit-0003\">\n<string-name>\n<surname>Bordeleau</surname>, <given-names>M.</given-names>\n</string-name>, <string-name>\n<surname>Carrier</surname>, <given-names>M.</given-names>\n</string-name>, <string-name>\n<surname>Luheshi</surname>, <given-names>G. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Nat Commun</journal-id><journal-id journal-id-type=\"iso-abbrev\">Nat Commun</journal-id><journal-title-group><journal-title>Nature Communications</journal-title></journal-title-group><issn pub-type=\"epub\">2041-1723</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807791</article-id><article-id pub-id-type=\"pmc\">PMC7431837</article-id><article-id pub-id-type=\"publisher-id\">17756</article-id><article-id pub-id-type=\"doi\">10.1038/s41467-020-17756-7</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>T-cells produce acidic niches in lymph nodes to suppress their own effector functions</article-title></title-group><contrib-group><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Wu</surname><given-names>Hao</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Estrella</surname><given-names>Veronica</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Beatty</surname><given-names>Matthew</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Abrahams</surname><given-names>Dominique</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0003-2039-2825</contrib-id><name><surname>El-Kenawi</surname><given-names>Asmaa</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0003-1066-6132</contrib-id><name><surname>Russell</surname><given-names>Shonagh</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ibrahim-Hashim</surname><given-names>Arig</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-6906-9925</contrib-id><name><surname>Longo</surname><given-names>Dario Livio</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Reshetnyak</surname><given-names>Yana K.</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Moshnikova</surname><given-names>Anna</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Andreev</surname><given-names>Oleg A.</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Luddy</surname><given-names>Kimberly</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Damaghi</surname><given-names>Mehdi</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kodumudi</surname><given-names>Krithika</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0001-9718-5010</contrib-id><name><surname>Pillai</surname><given-names>Smitha R.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Enriquez-Navas</surname><given-names>Pedro</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Pilon-Thomas</surname><given-names>Shari</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-9945-9473</contrib-id><name><surname>Swietach</surname><given-names>Pawel</given-names></name><address><email>pawel.swietach@dpag.ox.ac.uk</email></address><xref ref-type=\"aff\" rid=\"Aff6\">6</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-8888-7747</contrib-id><name><surname>Gillies</surname><given-names>Robert J.</given-names></name><address><email>robert.gillies@moffitt.org</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.468198.a</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9891 5233</institution-id><institution>Department of Cancer Physiology, </institution><institution>H. Lee Moffitt Cancer Center and Research Institute, </institution></institution-wrap>Tampa, FL 33612 USA </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.13402.34</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1759 700X</institution-id><institution>Cancer Institute, Second Affiliated Hospital, </institution><institution>Zhejiang University School of Medicine, </institution></institution-wrap>310058 Hangzhou, P.R. China </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.468198.a</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9891 5233</institution-id><institution>Department of Immunology, </institution><institution>H. Lee Moffitt Cancer Center and Research Institute, </institution></institution-wrap>Tampa, FL 33612 USA </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.5326.2</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 1940 4177</institution-id><institution>Institute of Biostructures and Bioimaging (IBB), </institution><institution>National Research Council of Italy (CNR), </institution></institution-wrap>Turin, Italy </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.20431.34</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0416 2242</institution-id><institution>Department of Physics, </institution><institution>University of Rhode Island, </institution></institution-wrap>Kingston, RI 02881 USA </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.4991.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 8948</institution-id><institution>Department of Physiology, Anatomy and Genetics, </institution><institution>University of Oxford, </institution></institution-wrap>Parks Road, Oxford, OX1 3PT England UK </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>4113</elocation-id><history><date date-type=\"received\"><day>21</day><month>11</month><year>2019</year></date><date date-type=\"accepted\"><day>13</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">The acidic pH of tumors profoundly inhibits effector functions of activated CD8 + T-cells. We hypothesize that this is a physiological process in immune regulation, and that it occurs within lymph nodes (LNs), which are likely acidic because of low convective flow and high glucose metabolism. Here we show by in vivo fluorescence and MR imaging, that LN paracortical zones are profoundly acidic. These acidic niches are absent in athymic Nu/Nu and lymphodepleted mice, implicating T-cells in the acidifying process. T-cell glycolysis is inhibited at the low pH observed in LNs. We show that this is due to acid inhibition of monocarboxylate transporters (MCTs), resulting in a negative feedback on glycolytic rate. Importantly, we demonstrate that this acid pH does not hinder initial activation of naïve T-cells by dendritic cells. Thus, we describe an acidic niche within the immune system, and demonstrate its physiological role in regulating T-cell activation.</p></abstract><abstract id=\"Abs2\" abstract-type=\"web-summary\"><p id=\"Par2\">T-cell activation primarily occurs in the lymph nodes, highly organized and specialized secondary lymphoid organs. Here the authors show that the acidic extracellular pH in lymph node paracortical zones limits cytokine production by effector T-cells, but does not alter their activation by antigen-presenting cells.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Immunology</kwd><kwd>Physiology</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/100000054</institution-id><institution>U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)</institution></institution-wrap></funding-source><award-id>R01 CA077575</award-id><award-id>U54 CA193489</award-id><award-id>P30 CA076292</award-id><principal-award-recipient><name><surname>Gillies</surname><given-names>Robert J.</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution>U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)</institution></funding-source></award-group></funding-group><funding-group><award-group><funding-source><institution>U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)</institution></funding-source></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/100010629</institution-id><institution>Fulbright Association</institution></institution-wrap></funding-source><award-id>0001</award-id><principal-award-recipient><name><surname>Gillies</surname><given-names>Robert J.</given-names></name></principal-award-recipient></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par3\">Lymph nodes (LNs) are anatomically and physiologically complex organs that receive inputs from both lymphatic and blood vasculatures, and consist of discrete zones for processing and activating T and B cells (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>). Despite their well-characterised histology and recent insights into the functional interplay between various resident cell-types and epithelial structures<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>, relatively little is known of the physiological microenvironment of LNs in situ, and how it may influence immune cell functions. Notably, acidosis is known to inhibit effector T-cell functions under cell culture conditions and in solid tumours in vivo<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>, but the relevance of this observation in the context of LN physiology has not been determined. Oxygen tension in LNs has been reported to be low<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>, and since hypoxic tissues are generally acidic via increased glucose fermentation, we hypothesized that LNs are also acidic.<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Extracellular spaces of lymph node paracortical zones are acidic.</title><p><bold>a</bold> Cartoon of lymph node (LN) showing zones occupied by T and B cells, blood vessels (B.V.) and the medulla (Me). Histological section of inguinal LN showing T-cell marker CD3 in paracortical zone (<italic>N</italic> = 3). <bold>b</bold> B6 mouse injected with pHLIP (40 µM in 60 µl) into footpad, followed by intravital imaging of inguinal LN 24 h later in window chamber. Left: composite image collected with ×1.6 objective for pHLIP-Cy5.5 (red; excited at 633 nm), autofluorescence (green; excited at 514 nm) and vasculature (blue; determined from transmission images). Right: montage of pHLIP fluorescence collected in overlapping fields of view with ×10 objective, summed across the depth of the LN (<italic>n</italic> = 10 mice). Experiment repeated on B6 mouse injected via the intraperitoneal cavity with <bold>c</bold> 200 µl of 12.5 mg/kg omeprazole (OME) and 1 mg/kg of bafilomycin (BAF) (<italic>n</italic> = 4). or <bold>d</bold> 200 µl of 5.2 mg/kg (5-N,N-dimethyl)amiloride (DMA) and 3.9 mg/kg acetazolamide (ATZ) 24 h prior to imaging (<italic>n</italic> = 3). <bold>e</bold> Experiment performed on athymic nude mouse, with same imaging settings, showing absence of pHLIP signal (<italic>n</italic> = 8). <bold>f</bold> Summary data for mean pHLIP fluorescence within LN boundary. Significance tested by one-way ANOVA with multiple comparisons (<italic>N</italic> = 5, 10, 4, 4, 8, 11); two sided at 5% significance. <italic>p</italic>-values compared to control: Depleted: <italic>P</italic> = 0.0237, Nude: <italic>P</italic> = 0.0004. <bold>g</bold> Intravital imaging of pH-sensitive cSNARF1 fluorescence in inguinal LN. Mice were injected with 70 kDa dextran-conjugated cSNARF1 into the tail-vein (20 mg/ml in 100 µl). Measurements on control mice, or mice treated with LPS (<italic>n</italic> = 4). <bold>h</bold> Statistical distribution of pHe data analyzed by Gaussian mixed models to separate pixels into clusters, representing compartments. Plots shows the pH-distribution in each of the LN compartments, averaged for all LNs. Note that compared to footpad injections, tail-vein injections detect an additional compartment corresponding to blood vessels. <bold>i</bold> Summary data for each LN compartment from 4, 3, 4, 3 LNs, respectively. <bold>j</bold> MRI-CEST pH imaging of control (B6) mice injected via i.v. with a 300 µl bolus of Isovue 370. pHe maps in inguinal LN region-of-interest are overlaid on anatomical T<sub>2</sub>-weighted images. Mean ± SEM pHe measured in B6 (<italic>n</italic> = 6) and BALB/c (<italic>n</italic> = 5) mice. <bold>k</bold> Intravital imaging for hypoxic regions using 12.5 nmoles of ImageIT-Green hypoxic probe injected into B6 mice via the footpad in a 50 µl volume. As a positive control, LNs were made anoxic by bubbling PBS with N<sub>2</sub> and including the O<sub>2</sub>-scavenger dithionite (1 mM), followed by cessation of circulation by cervical dislocation. Upper panels: composite image collected with ×1.6 objective for ImageIT-Green (green; excited at 514 nm) and vasculature (blue; determined from transmission images). Bottom panels: montage of ImageIT-Green fluorescence collected in overlapping fields of view with ×10 objective, summed across the depth of the LN (<italic>N</italic> = 10 control and 10 anoxia). (Scale bars = 1.0 mm for <bold>b</bold>–<bold>e</bold>, <bold>k</bold>; 0.5 mm for <bold>g</bold>).</p></caption><graphic xlink:href=\"41467_2020_17756_Fig1_HTML\" id=\"d30e580\"/></fig></p><p id=\"Par4\">Given the exquisite pH-sensitivity of cytokine release<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR4\">4</xref>,<xref ref-type=\"bibr\" rid=\"CR6\">6</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>, LN acidity may have physiological consequences. For example, it may be advantageous to refrain from secreting inflammatory cytokines into a confined space of the LN. Aberrant activation of densely packed T-cells can, for example, induce immunopathological responses in both lymphoid and nonlymphoid tissues and, for that reason, many checkpoints are in place to prevent overactive lymphocytes in these organs<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref>–<xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. While these checkpoints are active under physiological conditions, a pathologically overactive immune response can negatively impact lymph node structure and function. Persistent immune activation within lymphoid tissue, as seen with human immunodeficiency virus (HIV), results in lymph node fibrosis, often restricted to the T-cell zone, leading to diminished lymph node function and reduced peripheral T-cell numbers<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>–<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. Additionally, high levels of cytokines accumulating within the T-cell zone would have detrimental effects on the acquisition of adaptive immunity. For example, IFNγ, whose expression is potently inhibited at low pH, alters T-cell polarization and homeostasis, can induce apoptosis, and inhibit lymphangiogenesis<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref>–<xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>. However, without direct measurements of pH in intact LNs, the physiological significance of this postulated regulatory influence is untested.</p><p id=\"Par5\">Here, we use in vivo fluorescence and magnetic resonance imaging to identify acidic niches in LNs. We further show that the source of this acidity is the T-cells themselves, based on measurements of lactic acid release and intracellular pH (pHi) in vitro, and the lack of acidity in LNs from athymic nude or lymphodepleted mice. We interpret the mechanism of LN acidity in terms of a steady-state between activated acid production and inhibitory feedback on glycolysis. We further show that the low extracellular pH (pHe) of LNs does not impair the ability of T-cells to become activated by antigen-presenting cells (APCs), whereas it does suppress elaboration of cytokine production. Our findings identify localized acidosis as a critical component of the adaptive immune response.</p></sec><sec id=\"Sec2\" sec-type=\"results\"><title>Results</title><sec id=\"Sec3\"><title>Paracortical zones are acidic niches inside the lymph node</title><p id=\"Par6\">The pHe of inguinal LNs in C57BL/6 (B6) mice was probed using pH-Low Insertion Peptide (pHLIP) a short peptide that undergoes a conformational change at low pH to make it membrane penetrant, where it can be persistent<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>–<xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>. To visualize areas of pHLIP insertion<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>, the peptide was conjugated to the fluorophore Cy5.5, which emits in the far-red range for optimal signal-to-background ratio. To deliver the construct to the LN, injections (50 µl of 40 µM solution in PBS) were made into the right footpad (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S1</xref>). After either 4 h or 24 h, the mouse was put under anaesthesia and its right inguinal LN was surgically exposed for intravital imaging in a window chamber, which was then mounted on an inverted laser-scanning confocal microscope<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. Fluorescence and transmission images were taken with excitation lasers alternating between 633 and 514 nm, using either a low (×1.6) or higher power (×10) objective. Fluorescence (650–700 nm) excited at 633 nm revealed the distribution of pHLIP, which accumulates in acidic niches. High-power excitation at 514 nm evoked autofluorescence (550–650 nm), which was used to delineate the LN outline (hence size). Once optimized, the same imaging settings were applied consistently in all experiments using pHLIP. The ratio of transmission images acquired alternately at the two excitation wavelengths generated a ratiometric map that identified the vasculature on the basis of haemoglobin absorbance properties (greater absorbance at 514 nm, compared to 633 nm). Images obtained at various depths through an open pinhole and the 10x objective were summed to generate a projection of total fluorescence across the z-axis of the LN (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S2a–c</xref>). To cover the entire area of the LN, high-resolution imaging was performed in overlapping fields of view, and the montage was assembled offline (MATLAB Control Point Selection tool). Analysis of pHLIP fluorescence indicated acidity in T-cell rich (CD3+) paracortical zones, with the notable absence of signal in B-zones in outer regions of the cortex (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>, Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S2a</xref>). These data were further quantified in terms of the frequency-distribution of fluorescence intensity in the LN (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S2d</xref>). This analysis indicated no difference between animals injected with pHLIP at 4 h or 24 h prior to imaging.</p><p id=\"Par7\">It is plausible that the source of acidity is an H<sup>+</sup>-ion transport process of LN tubular structures, such as high endothelial venules<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref>,<xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. Transporters underpinning such fluxes include omeprazole (OME)-sensitive P-type H<sup>+</sup>/K<sup>+</sup>-ATPases, bafilomycin (BAF)-sensitive V-type H<sup>+</sup>-ATPases or 5-(N,N-dimethyl)amiloride (DMA)-sensitive Na<sup>+</sup>/H<sup>+</sup> exchangers. Alternatively, acidity may be attributable to the catalytic activity of membrane-tethered, acetazolamide (ATZ)-sensitive carbonic anhydrases. To test for the involvement of these acid-handling proteins, mice received intraperitoneal injections of pairs of inhibitors (OME/BAF or DMA/ATZ) and a footpad injection of pHLIP, 24 h prior to imaging. These pharmacological interventions did not, however, abolish acidic niches in LNs arguing against the involvement of their target-proteins in acidifying the microenvironment (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c/d</xref>, Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S2b</xref>). An alternative source of acidity may relate to the metabolic activity of T-cells residing in paracortical zones. Indeed, activated T-cells are known to have a substantial capacity to acidify media in vitro because of their high glycolytic fluxes<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR4\">4</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>. To test this mechanism, pHLIP was injected into athymic nude mice that lack T-cells. In these animals, pHLIP no longer accumulated in paracortical zones (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e</xref>, Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S2c</xref>), and its mean fluorescence decreased by ~80% (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f</xref>). Because the LNs of nude mice may become altered by long-term effects of T-cell deficiency, we also tested whether acute lymphodepletion would result in decreased LN acidity. Figure <xref rid=\"MOESM2\" ref-type=\"media\">S3</xref> shows that injection of anti-CD4 and anti-CD8 antibodies successfully depleted 80% of CD3+ cells in the spleen and inguinal LN (Table <xref rid=\"MOESM2\" ref-type=\"media\">S1</xref>). As shown in Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S2d</xref> and analysed in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f</xref>, the LNs of acutely lymphodepleted mice had a significant, >50% decrease in pHLIP labelling. A statistical analysis of LN-averaged pHLIP fluorescence shows that LN pH is related to the number of T-cells residing in the LN (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f</xref>).</p><p id=\"Par8\">While the spatial resolution of pHLIP fluorescence is excellent, acquired images lack the quantitative power to define the level of pHe in the LN. To determine this, B6 mice were injected with the membrane-impermeable 70kDa-dextran derivative of cSNARF1 (cSNARF1-Dex), a ratiometric dye that provides a calibratable readout of pH<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>. The settings used for imaging (×10 objective, excitation 514 nm, emission measured simultaneously at 580 ± 20 nm and 640 ± 20 nm) were optimised to minimise artefacts due to autofluorescence (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S4a</xref>). A solution of cSNARF1-Dex dye in PBS (100 µL of 20 mg/mL) was injected into either the tail-vein or in the footpad, and fluorescence was subsequently measured by intravital imaging in anesthetized mice using a window chamber similar to that used for pHLIP imaging (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S4b, c</xref>). These cSNARF1-Dex images were ratioed offline and converted to pHe maps using a calibration curve determined in vitro in buffered saline (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S4d</xref>). High-quality images could be acquired 1 h after a tail-vein injection or 3 h after footpad injection. Specifically, tail-vein delivery of cSNARF1-Dex allowed for concurrent measurements of the pH inside blood vessels, which conveniently served as a reference for alkaline pHe. Analysis of pHe maps showed distinct areas of profound acidity in paracortical areas of the LN (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g</xref>). The intensity histograms of pHe within the LN boundary were analysed by mixed Gaussian modelling to determine the number of compartments that best described the observed distribution (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1h</xref>). Notably, footpad injections produced a two-compartment distribution, whereas in tail-vein injections an additional alkaline compartment was observed, which was attributable to blood vessels. Irrespective of injection protocol, the most acidic LN compartments had a mean pHe of ~6.3, and were surrounded by regions of mean pHe ~6.7 (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1i</xref>). The pH inside blood vessels was ~7.1, as expected from venous blood draining from an acidic organ. To test if these acidic niches could become ‘diluted’ in an enlarged LN, or chemically neutralized with buffers, experiments were performed on mice injected with lipopolysaccharide (LPS; 100 ng/kg i.p. 48 h prior to imaging) to induce inflammation; or receiving oral bicarbonate (200 mmol/L of NaHCO<sub>3</sub> ad libitum 10 days before imaging), which has been shown to neutralize the acidic pH of tumors<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. LPS treatment resulted in a significant (50%) increase in LN volume (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S5</xref>), but no effect on pHe (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1i</xref>). There was also was no effect of NaHCO<sub>3</sub> on pHe, arguing that the acidic niches in LNs are robustly regulated to a specific acidic level by a means of a feedback (“pH-stat”) mechanism, which could not be disrupted by organ enlargement or systemic base-loading. Consistent with this hypothesis, the LNs of NaHCO<sub>3</sub>-treated mice contained ~50% higher concentrations of lactate (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S6a, b</xref>), despite no effect on pHe (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1i</xref>). In this scenario, the raised buffering power allows a greater cumulative glycolytic flux, reported in terms of lactate build-up because the magnitude of the negative feedback via pHe is lessened (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S6a, b</xref>). To confirm that T-cells are the source of lactate, experiments on nude mice LNs showed signficantly lower [lactate] compared to untreated control mice (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S6c</xref>).</p><p id=\"Par9\">Since intravital imaging may potentially introduce artefacts from the necessary surgery, confirmation of low pHe in the LN was sought using a noninvasive method based on chemical exchange saturation transfer (CEST) magnetic resonance imaging imaging<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref>,<xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. With this technique, images (albeit with inferior resolution to fluorescence microscopy) are collected from different saturation frequencies in mice injected with the CT contrast agent iopamidol (Isovue; Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S7a</xref>). This compound has ionizable secondary amides that resonate at different frequencies (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S7b</xref>), which can be interrogated using frequency-specific excitations. Since these resonances have distinct pH profiles (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S7c</xref>), the ratios of saturation occurring at the two frequencies report pHe (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S7d</xref>). The inguinal LNs of B6 and BALB/c mice were determined to have a mean pH of ~6.4 (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1j</xref>), which is consistent with measurements obtained by cSNARF1 fluorescence microscopy.</p><p id=\"Par10\">Previous reports have suggested the existence of hypoxic regions within the LN using flow cytometry of LN derived cells exposed to the hypoxia adduct, pimonidazole<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Depletion of oxygen could influence T-cell functions, either by acting alongside the influence of low pH, or as the dominant modulator, with low pHe merely being a collateral epiphenomenon<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. To seek evidence for hypoxic niches and their relationship with pHe, B6 mice were injected intraperitoneally with the hypoxic probe pimonidazole; and 1 h later, inguinal LNs were excised and fixed for immunohistochemistry. The pattern of pimonidazole staining was sparse and weak, which argued against substantial hypoxia (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S8a</xref>). As a further test, the fluorescent hypoxia probe ImageIT-Green was injected into the footpad of B6 mice and, after 4 h, the inguinal LN was surgically exposed for intravital imaging (excitation at 488 nm, emission 540 ± 20 nm) in a window chamber. ImageIT-Green fluorescence is irreversibly increased in regions with O<sub>2</sub> tension lower than 5%, and thus provides an independent assessment of hypoxic niches, even when the LN is surgically exposed to the atmosphere. LNs emitted only low levels of autofluorescence above background (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S8b</xref>), and similarly low levels of fluorescence following injection of ImageIT, indicating that oxygen tension in the LN is normally greater than 5% (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1k</xref>). As a positive control, LN anoxia was produced by bathing the organ in deoxygenated (N<sub>2</sub>-bubbled) PBS that contained the oxygen-scavenger, dithionite (1 mM), followed by cessation of circulation by means of cervical dislocation. Under these conditions, ImageIT fluorescence (and hence hypoxia) was abundant (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1k</xref>). Thus, LN paracortical zones are profoundly acidic, but not substantially hypoxic.</p></sec><sec id=\"Sec4\"><title>Acidic niches result from activated T-cells lactic acid</title><p id=\"Par11\">When activated in vitro, T-cells undergo a dramatic increase in aerobic glycolysis, considered necessary for engaging effector T-cell functions<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR4\">4</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>. Activation of T-cells with plate-bounded anti-CD3ε antibody and soluble anti-CD28 antibody had a rapid and robust effect on the extracellular acidification rate (ECAR), measured by a Seahorse extracellular flux (XF) analyser (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>). Note that although ECAR was lower in T-cells incubated at a lower pH, addition of CD28 Ab increased the glycolytic ECAR at both low and high pH. ECAR can be converted, using data for buffering capacity, to a quantitative H<sup>+</sup> production rate (PPR) generated by T-cells, 48 h after their activation (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). At pHe 7.4, the PPR of activated T-cells, isolated from B6 mice, was equivalent to ~10 mmoles/min/(L of intracellular volume), after accounting for buffering capacity (β = 3.78 mM/pH) and intra/extracellular volume-ratios (chamber volume of 2.28 µL, cell radius cell radius = 5.14 µm, SD = 0.68 µm, number of cells 100,000). This glycolytic rate is high; comparable to the most metabolically-active cancer cells<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref>–<xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>, and may underpin the low pHe observed in vivo in paracortical zones (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a–j</xref>), provided that the flux is sufficiently large in relation to fluid clearance by perfusion of the LN. Steady-state lactate levels in inguinal LNs of B6 mice were 9.4 ± 3.5 mM (<italic>N</italic> = 8), which is higher than levels in LNs of nude mice (2.1 ± 0.9 mM; <italic>N</italic> = 8; Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S6c</xref>). Assuming that the fluid clearance is comparable, these data indicate that the products of T-cell glycolysis accumulate in LNs and are not rapidly washed away with perfusion.<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Feedback regulation of T-cell glycolysis by pH establishes an acidic extracellular milieu at the steady-state.</title><p><bold>a</bold> Time course of extracellular acidification rate (ECAR) was measured by Seahorse in B6 T-cells (Mean ± SD, <italic>n</italic> = 7 biological samples.). Injection of activating antibody (or vehicle for control) at 20 min evoked an increase in ECAR, due to the activation of T-cell glycolysis. <bold>b</bold> Proton production rate (PPR) measured by Seahorse in B6 or OT-II T-cells is reduced under acidic conditions. In paired experiments on B6 or OT-II T-cells, oxygen consumption rate (OCR), measured by Seahorse, is increased under acidic conditions (two-tailed, unpaired <italic>t</italic>-test, mean ± SD, <italic>n</italic> = 8 biological samples. PPR (B6, <italic>p</italic> = 1.35E-11; OT-II, <italic>p</italic> = 1.62E-11), OCR (B6, <italic>p</italic> = 2.22E-5; OT-II, <italic>p</italic> = 1.14E-5). Asterisks (***) represent <italic>p</italic> < 0.0001). <bold>c</bold> Glucose consumption and lactate production as a function of pHe in OT-I T-cells, expressed as mean ± SD; <italic>n</italic> = 3 biological samples. Significance tested by one-way ANOVA with multiple comparisons <italic>p</italic> < 0.001. <bold>d</bold> Schematic diagram of feedback loop between lactic acid production by glycolysis, and its inhibitory feedback by extracellular pH. <bold>e</bold> Time course of pHe measured in 60 µl volumes of 5 mM HEPES-buffered media containing no cells, nonactivated T-cells or activated (CD3-coated plates, then incubated in media containing 2 µg/ml CD28) T-cells at the densities indicated. <italic>n</italic> = 3 biological replicates. Data shown as mean ± S.E.M. Activated T-cells acidify the restricted extracellular volume towards pH 6.3 within several hours. <bold>f</bold> Schematic representation of mathematical model used to simulate the relationship between extracellular pH and lactate for a system featuring glycolytic lactic acid production and feedback inhibition by extracellular pH, as determined from panel <bold>e</bold> (i.e. linear inhibition towards zero production at pH 6.3), for a LN paracortex of intracellular volume fraction v<sub>i</sub>, and fluid turnover (perfusion) of τ. (<bold>g</bold>) Results of simulation for extracellular pH (upper panel) and lactate (lower panel). Black line shows the combination of v<sub>i</sub> and τ that simulates experimentally observed data for pHe (6.3; Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>) and lactate (9.4 mM; Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S5</xref>). <bold>h</bold> Replotting of the best-fit curves from panel <bold>g</bold>. Red dashed line shows solution of this mathematical problem using the literature value for τ of 20 min. This indicates that ~70% of the paracortical zone is occupied by T-cells, engaged in lactic acid production, the source of low pHe measured in LNs.</p></caption><graphic xlink:href=\"41467_2020_17756_Fig2_HTML\" id=\"d30e983\"/></fig></p><p id=\"Par12\">When metabolic flux measurements were repeated in media at a reduced pHe, glycolytic flux decreased profoundly, along with a concomitant increase in the O<sub>2</sub> consumption rate, OCR (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). Although the increase in OCR (and hence oxidative ATP production) was modest, it is sufficient to compensate for a loss of glycolytic ATP production, assuming a ratio of ca. 18:1 (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). These results were also observed with OT-II (CD4+) T-cells stimulated with OVA<sub>323–339</sub> (ISQAVHAAHAEINEAGR) peptide (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). These observations would argue that once pHe had attained a low level, further acidification would be curtailed, leading to steady-state. Since Seahorse measurements of rate are performed over short periods of time, they cannot determine if the high glycolytic rate of activated T-cells could be sustained long enough to produce a meaningful accumulation of lactic acid in vivo. To test this, OT-I (CD8+) T-cells, stimulated with OVA<sub>257–264</sub> (SIINAFEKL) peptide, were plated at low density (500,000 cells/ml) at pH = 7.4. After 24 h, the media had accumulated ~10 mM lactate (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c</xref>), confirming the cells’ capacity to sustain a high glycolytic flux. Both lactate production and glucose consumption decreased with decreasing pHe (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c</xref>), consistent with ambient acidity feeding back negatively on glycolysis.</p><p id=\"Par13\">The scheme shown in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2d</xref> illustrates the proposed relationship between extracellular acidity and T-cell glycolysis. Here, pHe is predicted to stabilize at an acidic level once the inhibitory feedback reaches a level that fully blocks any further acid production by glycolysis. To determine if steady-state pHe could be attained within a reasonable time-frame, T-cells from B6 mice were resuspended in lightly buffered media (2 mM Hepes + 2 mM Mes) and plated at either 7.5 or 15 million/mL in small (60 µL) inspection chambers. Media of low buffering capacity (~3.78 mM/pH over the range 6.0–7.5) were chosen for this experiment to allow metabolism from a relatively low density of cells to measurably affect pH. Dextran-conjugated cSNARF1 (0.25 µg/µl) was added to media to report extracellular acidification in real-time (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2e</xref>). In this in vitro system, nonactivated (naïve) cells represented LN-resident T-cells, and in separate experiments, T-cells were activated to enhance metabolic rate further. Both activated and naive T-cells caused a progressive decrease in pHe, until this reached an acidic steady-state of pHe = 6.3 within 2–10 h for activated and nonactivated T-cells, respectively. Notably, in vivo pH measurements using dextran-cSNARF-1 (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g, h</xref>) also showed a pHe of ~6.3. The average density of T-cells in LNs of B6 mice is 800 million/mL (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S9</xref>). At these densities, activated and naïve T -cells will, respectively, produce ~14 and 1.5 mM H<sup>+</sup> per hour while in the inguinal LN, and even at the lower rate, a steady-state pHe of 6.3 is attainable within 16 h, assuming restricted capillary perfusion. Activation of T-cells thus serves to hasten the rate at which pHe stabilizes at its pH-stat. In summary, our in vitro findings and in vivo correlates indicate that activated T-cells can profoundly and rapidly acidify their milieu, and maintain it at a reduced pHe.</p><p id=\"Par14\">To relate the in vitro findings to the conditions that prevail inside LNs, a mathematical model was used to simulate steady-state pHe and lactate levels. The volume of a mouse inguinal LN is typically 2.5 µL<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>, half of which is occupied by the paracortex<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>, and contains 1–4 million T-cells<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. Only <10% of flow from the afferent vessel supplying LNs perfuses the central regions comprising the cortex and medulla, with the remaining flow takes a peripheral route<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. Central flow is equivalent to 5% of LN volume per minute<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>, suggesting that the turnover of fluid in the central region occurs in ~20 min, i.e. a relatively slow wash-out which would favour metabolite build-up. The mathematical model, presented schematically in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2f</xref>, includes variables describing glycolytic rate, its pHe-dependence, fractional volume of the intracellular space (v<sub>i</sub>), and turnover of extracellular fluid (τ). Briefly, the intracellular compartment releases lactic acid into a poorly-perfused extracellular space buffered with CO<sub>2</sub>/HCO<sub>3</sub><sup>−</sup>, where lactate and H<sup>+</sup> can accumulate and inhibit, via pHe, the glycolytic rate. Results of simulations for various combinations of v<sub>i</sub> and τ are shown in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2g</xref>; the thick line shows those combination that predict experimentally measured values for pHe (6.3; Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g</xref>) and lactate (9.4 mM; Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S6c</xref>). Replotting these curves in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1h</xref> shows the range of τ and v<sub>i</sub> compatible with experimental data. Since τ is estimated<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup> to be ~20 min, the best-fitting v<sub>i</sub> is predicted to be ~0.7, i.e. a combined T-cell volume of ~0.9 µL (70% of half the LN paracortex volume, 1.25 µL) that equates to 1.5 million T-cells. These values are well within the range of measurements in LNs, arguing that the degree of acidity measured in LNs can be adequately described by T-cell metabolism, as shown in the model Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2d</xref>.</p></sec><sec id=\"Sec5\"><title>Acid inhibits T-cell lactic acid efflux and glycolysis</title><p id=\"Par15\">Glycolytic inhibition at low pHe was studied further in terms of its dynamics using the Seahorse analyser. Injection of a volume of HCl acid, determined a priori to reduce the pH of lightly (2 mM) HEPES/MES-buffered medium from 7.4 to 6.6, triggered a rapid fall in extracellular acidification rate (ECAR) in activated T-cells from B6 mice, which reversed with an injection of NaOH that restored pHe back to 7.4 (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a</xref>). Thus, the effect of acidosis on glycolytic rate is acute and reversible, and its mechanism may involve a dynamic resetting of pHi, which was tested in T-cells loaded with cSNARF1, calibrated with nigericin (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S10a</xref>) and imaged confocally. Changes in pHe, produced by switching between superfusates titrated to pH 7.4 or pH 6.6, evoked dynamic changes in pHi in the same direction but with a short delay and of reduced amplitude (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>). To determine if these pHi shifts were stable over a longer period of time, T-cells were equilibrated in media over a range of pHe, and their pHi was measured once it reached steady-state. Upon decreasing pHe from 7.4 to 6.6, pHi stably decreased by ~0.2 pH units (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3c</xref>). Critically, this transduction of a pHe change into a sustained pHi signal allows access to a myriad of protonatable targets in the cytoplasm<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>, including enzymes in the glycolytic pathway<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>, such as highly pH-sensitive phosphofructokinase-1 (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S10b</xref>).<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Mechanism of T-cell glycolysis inhibition by low pH.</title><p><bold>a</bold> An injection of HCl abruptly reduces extracellular acidification rate (ECAR) in OT1 and B6 T-cells; this reverses upon an injection of NaOH. NaCl injections performed as sham controls. Solutions were lightly buffered with 2 mM HEPES/MES mixture and titrated to desired pH. Mean ± SEM (<italic>n</italic> = 4 biological replicates). <bold>b</bold> A reduction in extracellular pH (pHe) evokes a delayed fall in intracellular pH (pHi), as measured from cSNARF1 fluorescence (2 mM HEPES/MES mixture). Mean of 10 time course recordings; error bars not shown for clarity. <bold>c</bold> Fluorescence imaging of cells under superfusion with CO<sub>2</sub>/HCO<sub>3</sub><sup>−</sup> buffer. Cells co-loaded with cSNARF1 (red) to report pH and Hoechst-33342 (blue) to exclude nuclear areas from the analysis. Plot shows relationship between pHe and pHi at the steady-state in OT1 and B6 cells. Note the transmembrane [H<sup>+</sup>] gradient, shown in inset, inverts near resting pHi. Mean ± SEM of 5 recordings of fields of view containing 40–60 cells. <bold>d</bold> Western blot for MCT1 (48 kDa) and MCT4 (43 kDa) relative to actin (42 kDa) on lysates collected from B6 T-cells that had been incubated at pHe 7.4 or 6.6 (N = 3). (See Supplementary Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S17</xref> for full blot). <bold>e</bold> Measuring total MCT activity from the rate of pHi change driven by transmembrane lactate efflux. T-cells under superfusion were equilibrated with one of the three conditions, 30 mM lactate at pHe 7.4, 15 mM lactate at pHe 6.9 or 7.5 mM lactate at pHe 6.6. Note that, for lower pHe, the lactate concentration was reduced to ensure that comparable levels of lactic acid are present at equilibrium. Rapid switching to lactate-free solution at the same pHe evoked net lactate efflux. Apparent permeability to lactic acid can be calculated from the rate of pHi change, buffering capacity and transmembrane gradient. To confirm that the ensuing pHi response was not rate-limited by the speed of solution exchange, one solution was labelled with fluorescein sulphonic acid (FS) and the rate of fluorescence-change indicated an exchange time constant of 2.6 s. Mean ± SEM of 10 cells per condition. <bold>f</bold> Apparent membrane permeability for NH<sub>3</sub> (added as 15 mM NH<sub>4</sub>Cl; <italic>n</italic> = 21) acetic acid (Ac; 30 mM NaAcetate; <italic>n</italic> = 12) and lactic acid (7.5–30 mM Na Lactate) at high (<italic>n</italic> = 12), intermediate (<italic>n</italic> = 6), and low (<italic>n</italic> = 8) pHe. Indicated experiments performed in the presence of MCT inhibitors AR-C (AR-C155858; 10 µM; <italic>n</italic> = 7) and SR (SR13800; 10 µM;; <italic>n</italic> = 7). Mean ± S.E.M. of 7–15 cells per condition. Box shows median and 25–75% percentiles and whiskers show 10–90% percentile. <bold>g</bold> Steady-state relationship between pHe and pHi mapped for 2 mM HEPES/MES solution containing either normal (140 mM) or reduced [Cl] (7 mM), iso-osmotically substituted with gluconate to offset pHi at constant pHe. Mean±SEM of 6 recordings with 40–60 cells each. <bold>h</bold> EACR, calibrated to units of lactic acid-production rate (mM/min), is shown not to be a unique function of pHe; Data shown are Mean ± S.D., <italic>n</italic> = 14 wells over two independently seeded plates. <bold>i</bold> Data from <bold>g</bold> and <bold>h</bold> analyzed to generate a relationship between metabolic rate, extrapolated to lactate-free conditions (see Eq. (<xref rid=\"Equ1\" ref-type=\"\">1</xref>)). Best-fit is a simple function of pHi, described by a Hill curve.</p></caption><graphic xlink:href=\"41467_2020_17756_Fig3_HTML\" id=\"d30e1223\"/></fig></p><p id=\"Par16\">The coupling between pHe and pHi arises from changes in transmembrane acid-base traffic, including that carried by H<sup>+</sup>-monocarboxylate co-transporters (MCTs). Low pHe thermodynamically hinders H<sup>+</sup>-lactate export<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>, leading to an intracellular retention of H<sup>+</sup> and lactate ions. Both MCT1 and MCT4 are present in T-cells, and their expression remains stable even at low pHe (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d</xref>; S<xref rid=\"MOESM2\" ref-type=\"media\">17</xref>). Flux carried aboard MCTs was quantified from the rate of pHi change evoked by the withdrawal of extracellular lactate, which was performed using a rapid perfusate switching system<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup> (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3e</xref>). MCT transport capacity, quantified in terms of an apparent permeability to lactic acid, was 180 µm/s in T-cells from B6 mice (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3f</xref>). The measured permeability to acetic acid (a smaller organic acid) and NH<sub>3</sub> (a gas) were lower (~110 and ~30 µm/s, respectively), indicating that lactate is transported by means of a protein-facilitated process. This transporter is likely a MCT, as the inhibitors AR-C155858 and SR13800 (10 µM) reduced lactic acid permeability to 20 µm/s, i.e. to the level of protein-unassisted permeability across the lipid matrix (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S10c</xref>, Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3f</xref>). When measurements were repeated at lower pHe (6.6 and 6.9), lactic acid permeability was reduced substantially, consistent with a thermodynamic inhibition of MCT1. Reduced MCT transport capacity ultimately leads to an intracellular acidification and lactate retention, both of which feedback negatively on glycolysis. Given that glycolytic flux is ultimately limited by the rate of end-product removal, this action of pHe can explain the glycolytic suppression attained at low pHe, even in activated T-cells with high glycolytic capacity<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>,<xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>.</p><p id=\"Par17\">To confirm that changes in pHi mediate the pHe-glycolysis relationship, ECAR measurements were performed under conditions that selectively manipulated pHi at constant pHe. To raise pHi in acidic media (a ‘rescue’), NaCl in the perfusate was reduced by iso-osmotic replacement with Na-gluconate (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3g</xref>). This ionic substitution alters the transmembrane Cl<sup>−</sup>-driving force for acid-loaders (including Cl<sup>−</sup>/HCO<sub>3</sub><sup>−</sup> exchangers), which leads to import of HCO<sub>3</sub><sup>−</sup> into cells. Strikingly, the glycolytic rate (J<sub>glyco</sub>), reported as ECAR, was not a unique function of pHe (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3h</xref>); instead, it could be described by a simple mathematical function of pHi and intracellular [lactate]:<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{J}}_{{\\mathrm{glyco}}} = {\\mathrm{J}}_{{\\mathrm{glyco}}}^{{\\mathrm{MAX}}} \\times \\frac{{{\\mathrm{K}}^{\\mathrm{n}}}}{{\\left[ {\\mathrm{H}} \\right]{\\mathrm{i}}^{\\mathrm{n}} + {\\mathrm{K}}^{\\mathrm{n}}}} \\times \\frac{{\\mathrm{Q}}}{{\\left[ {{\\mathrm{lactate}}} \\right]{\\mathrm{i}} + {\\mathrm{Q}}}}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">J</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">glyco</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mi mathvariant=\"normal\">J</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">glyco</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">MAX</mml:mi></mml:mrow></mml:msubsup><mml:mo>×</mml:mo><mml:mfrac><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">K</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">n</mml:mi></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mi mathvariant=\"normal\">H</mml:mi></mml:mrow></mml:mfenced><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">i</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">n</mml:mi></mml:mrow></mml:msup><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">K</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mfrac><mml:mrow><mml:mi mathvariant=\"normal\">Q</mml:mi></mml:mrow><mml:mrow><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mi mathvariant=\"normal\">lactate</mml:mi></mml:mrow></mml:mfenced><mml:mi mathvariant=\"normal\">i</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">Q</mml:mi></mml:mrow></mml:mfrac></mml:math><graphic xlink:href=\"41467_2020_17756_Article_Equ1.gif\" position=\"anchor\"/></alternatives></disp-formula>Where K and Q are the apparent binding constants for H<sup>+</sup> and lactate ions, respectively, and n is the Hill coefficient for the binding of H<sup>+</sup> ions. Knowing the transmembrane pH gradient (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3c</xref>), metabolic rate (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>), and membrane permeability to lactic acid (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3f</xref>), it is possible to predict cytoplasmic [lactate] at steady-state (Table <xref rid=\"MOESM2\" ref-type=\"media\">S2</xref>) and, by best-fitting these data to Eq. (<xref rid=\"Equ1\" ref-type=\"\">1</xref>), describe the relationship between pHi and glycolytic flux (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3i</xref>). This relationship, determined for both B6 and OT1 T-cells, was highly cooperative (<italic>n</italic> = 4.39) and with half-maximal activation near resting pHi (−log(K) = 7.095), i.e. consistent with a highly pH-sensitive response. The effect of end-product inhibition by lactate ions was best described by Q of 2.1 mM. To test this model, some predictions of Eq. (<xref rid=\"Equ1\" ref-type=\"\">1</xref>) were confirmed experimentally. At constant pHe and pHi, the addition of lactate is expected to reduce glycolysis by end-product inhibition. Since the L- and D- isoforms are transport substrates for MCT, both will similarly influence transmembrane traffic, but only the L-isomer will produce end-product inhibition via stereo-specific lactate dehydrogenase (LDH). Indeed, the L-isoform produced a stronger inhibition of ECAR (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S10d</xref>; to 21% of control vs. 58% of control with D-lactate). Consistent with this, Eq. (<xref rid=\"Equ1\" ref-type=\"\">1</xref>) predicts that, at constant pHi, L- and D-lactate would respectively reduce J<sub>glyco</sub> to 24% and 44% of control.</p></sec><sec id=\"Sec6\"><title>Acid suppresses cytokine release but not T-cell activation</title><p id=\"Par18\">The powerful inhibition of glycolysis at low pHe is expected to suppress effector T-cell functions<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>. In keeping with previous findings<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>, CD3/CD28-activated T-cells isolated from B6 mice had dramatically reduced interferon-gamma (IFNγ) secretion when incubated for 24 h at pHe 6.6, compared to time-matched controls incubated at pHe 7.4 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref>). The reduction in measured IFNγ at low pH was not an artefact of a conformational disruption to the epitope detected by the ELISA method because immunoreactivity for known quantities of synthetic IFNγ remained stable over a wide range of pHe (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S11a</xref>). Acid inhibition of IFNγ elaboration was titratable in four different strains of T-cells: (i) B6 T-cells stimulated with anti-CD3/anti-CD28, (ii) OT-I (CD8+) T-cells stimulated with OVA<sub>257–264</sub> peptide, (iii) Pmel (CD8+) T-cells stimulated with gp-100 peptide, and (iv) OT-II (CD4+) T-cells stimulated with OVA<sub>323–339</sub> peptide (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4b</xref>). The inhibitory effect of acid was reversible: T-cells stimulated at pHe 7.4 had reduced IFNγ secretion when restimulated at pH 6.6, but could readily resume IFNγ production when transferred back to an alkaline environment (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c</xref>). The actions of acidity were not limited to IFNγ, as IL-2 release was also inhibited at low pHe (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4d</xref>). Acid inhibition of cytokine release was not attributable merely to a generalized failure of exocytosis, as measurements on a large panel of cytokines indicated that, whilst most had reduced secretion at low pHe, some cytokines (MDC, MIG, and IP-10) showed greater secretion in acidic conditions (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4e</xref>; Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S11b, c</xref>). Although there was an inhibitory effect on cytokine secretion, acidosis did not consistently inhibit the proliferation rate of B6, Pmel, OT-I, or OT-II T-cells stimulated with anti-CD3 antibodies (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4f</xref>) or specific antigen (data provided with review).<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>T-cell effector functions are inhibited at acidic pH.</title><p><bold>a</bold> Interferon γ (IFNγ) production from C57BL/6 (B6) T-cells is reduced at low pHe, as determined by ELISA; <italic>n</italic> = 3, <italic>p</italic> = 0.00013. <bold>b</bold> INFγ production, measured over a range of pHe in T-cells from B6 mice as well as three antigen-specific strains. <italic>n</italic> = 3. IFNγ levels were compared with those at pHe 7.4 within each strain. B6 (pHe 6.8, <italic>p</italic> = 0.0034; pHe 6.6, <italic>p</italic> = 0.00013), OT-I (pHe 6.6, <italic>p</italic> = 0.0013), Pmel-1 (pHe 6.8, <italic>p</italic> = 0.017; pHe 6.6, <italic>p</italic> = 6.25E-6), OT-II (pHe 6.6, <italic>p</italic> = 0.046). <bold>c</bold> Time course of IFNγ levels in media following pH-manoeuvres that demonstrate the reversal of acid inhibition upon subsequent exposure to alkaline pH (rescue experiment); <italic>n</italic> = 3. <bold>d</bold> Interleukin-2 (IL-2) release, measured by ELISA in Pmel-1 and OT-II T-cells and a Jurkat leukaemia cell line, is reduced at low pHe; <italic>n</italic> = 3. Pmel-1, p = 8.77E-6; OT-II, <italic>p</italic> = 6.85E-9; Jurkat, <italic>p</italic> = 5.64E-8. <bold>e</bold> Relationship between cytokine levels at low and high pH, determined in paired experiments by the Cytokine Beads Array (CBA) assay. For most cytokines, with the exception of those highlighted in red (IP-10, MIG, MDC), acidic conditions evoked a reduction in release. <bold>f</bold> Rate of B6 cell proliferation measured by CellTrace Violet assay. <bold>g</bold> IFNγ production was measured, by ELISA, at the end of a 24 h preconditioning period (no OVA added) at either pHe 6.6 or 7.4, and then at the end of a consecutive 24 h period in the presence of antigen (OVA) at pHe 7.4. IFNγ production can be activated irrespective of whether cells had been preconditioned at pHe 6.6 or 7.4; <italic>n</italic> = 3. pHe 6.6 precondition, <italic>p</italic> = 2.82E-5; pHe 7.4 precondition, <italic>p</italic> = 4.25E-7. Asterisks (****) represent <italic>p</italic> < 0.0001. <bold>h</bold> IFNγ production by T-cells activated with dendritic cells (DC) and antigen (OVA) measured after 24 h at pHe 6.6 or 7.4, followed by measurements at the end of a subsequent 24 h period without stimulation at pHe 7.4 (rest). T-cells can become activated by DC/OVA at acidic or alkaline pHe, and fully retain the capacity to produce cytokines when transferred to alkaline media; <italic>n</italic> = 3. pHe 6.6 activation, <italic>p</italic> = 3.22E-7; pHe 7.4 activation, <italic>p</italic> = 0.29. Asterisks (****) represent <italic>p</italic> < 0.0001. <bold>i</bold> Flow cytometry. Intracellular IFNγ staining of T-cells activated with DC and antigen (OVA) measured after 24 h of treatment in either pHe 6.6 or 7.4 (top panels). Cells were then transferred to pHe 7.4 to rest in the absence of DC and OVA, and measurements were performed after 3 h of resting. All the experiments were repeated at least twice and expressed as mean ± SD and analyzed by two-tailed, unpaired <italic>t</italic>-test unless indicated otherwise. Significance level: *<italic>p</italic> < 0.05; **<italic>p</italic> < 0.01; ***<italic>p</italic> < 0.001; ****<italic>p</italic> < 0.0001.</p></caption><graphic xlink:href=\"41467_2020_17756_Fig4_HTML\" id=\"d30e1593\"/></fig></p><p id=\"Par19\">While we have shown that a major effect of pHe on T-cell activation is mediated via inhibitions of glycolysis, it does not rule out specific acid-mediated ligand interactions; such as VISTA binding to co-inhibitor PSGL-1<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup>. Hence, it was important to investigate other candidates for parallel mechanisms linking acidosis with reduced cytokine elaboration. Other potential H<sup>+</sup>-sensing mechanisms include, <italic>inter alia</italic>, activation of acid-sensing receptors or channels<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup> or the modulation of Ca<sup>2+</sup> signalling<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. Two acid-sensing G-protein coupled receptors, GPR65 (TDAG8) and GPR68 (OGR1), are expressed in T-cells<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>, but activated T-cells obtained from <italic>Gpr65</italic>- or <italic>Gpr68-</italic>knockout mice remained inhibited under acidic conditions (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S12a</xref>). Furthermore, small-molecule inhibitors of OGR1 and GPR4 (BA-39-PQ30-1, NE-52-QQ57-1, gifts from Novartis) failed to rescue cytokine release under acidic conditions (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S12b, c</xref>). Inhibition of TRPV1, an acid-sensing ion channel, did not rescue IFNγ production either (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S12d</xref>). Acid-sensing ion channel (ASIC)<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref>,<xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup> isoforms 1 and 3 are also expressed in T-cells<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>, but the potent and specific inhibitors, A-317567, APETx2, and psalmotoxin<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref>,<xref ref-type=\"bibr\" rid=\"CR52\">52</xref></sup>, were unable to restore T-cell function at low pHe (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S12e–g</xref>). Similarly, amiloride and cariporide showed no ‘rescue’ effect on IFNγ (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S12h, i</xref>). The only treatments found that at least partially raised IFNγ production at acidic pH were phorbol esters and histone deacetylase inhibitors (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S12j/k</xref>), but these effects were only modest. T-cell Ca<sup>2+</sup> signalling is thought to respond to low pHe through the pH-sensitivity of Ora1 Ca<sup>2+</sup> channels<sup><xref ref-type=\"bibr\" rid=\"CR53\">53</xref>,<xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>. However, low pHe did not meaningfully change store-operated Ca<sup>2+</sup> entry interrogated by a standard protocol (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S13</xref>). Thus, ruling out the contribution of these other mechanisms leads us to conclude that inhibition of glycolysis via the intracellular build-up of lactate and H<sup>+</sup> ions are responsible for T-cell inactivation by low pH.</p><p id=\"Par20\">Results thus far indicate that T-cells residing in restricted niches of paracortical zones will produce an acidic steady-state pHe as their lactic acid output comes into balance with feedback inhibition through reduced MCT activity and glycolytic flux (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3e</xref>). The attained steady-state pHe is sufficient to suppress cytokine secretion (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S12a–k</xref>), and while this would protect the LN from damage caused by cytokines, a comparable inhibitory effect on the ability for T-cells to undergo activation would be deleterious to the acquisition of adaptive immunity. To test if exposure to acidity also affects antigen activation, naïve T-cells isolated from OT-I mice were incubated, without stimulation, at either pHe 6.6 or 7.4 for 24 h. Cells were subsequently activated with OVA<sub>257–264</sub> peptide at pHe 7.4. Measurements of IFNγ secretion performed 24 h later showed significant IFNγ secretion (measured by ELISA) in both groups, indicating that preconditioning at low pHe did not impair the ability of T-cells to be activated (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4g</xref>). The extent of T-cell activation was also assayed in terms of the proportion of IFNγ positive (IFNγ<sup>+</sup>) cells, determined by flow cytometry at 3 h and 24 h after activation with peptide. A significant increase in the percentage of IFNγ<sup>+</sup> cells was observed after 24 h in both the control and acid preconditioned groups (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S14</xref>; S<xref rid=\"MOESM2\" ref-type=\"media\">18</xref>).</p><p id=\"Par21\">To evaluate whether low pHe can affect T-cell activation by APCs, the process was modelled in vitro by co-culturing T-cells with monocyte-derived dendritic cells (DCs) and antigen (OVA<sub>257–264</sub>) at either pHe 7.4 or 6.6. No differences in the expression of the DC marker CD40 were observed at either low or control pHe, thus any potential actions of acid cannot be argued in terms of insufficient stimulation by DCs (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S15a</xref>). DCs efficiently took-up FITC-tagged OVA protein (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S15b</xref>) and presented OVA<sub>257–264</sub> peptide equally avidly at pHe 6.6 and 7.4, indicating that acidosis did not impair the ability of DCs to process and present antigen to T-cells (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S15c</xref>). These findings are in broad agreement with prior reports demonstrating that low pHe does not attenuate DC antigen-presenting activity<sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref>,<xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>. T-cells activated by DCs at the reduced pHe of 6.6 produced less IFNγ at 24 h after primary activation, but a further 24-h period in alkaline conditions without the continued presence of DCs fully restored IFNγ production (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4h</xref>). Intriguingly, only a 3-h period of rest at pHe 7.4 was sufficient to increase the number of IFNγ-positive cells measured by flow cytometry (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4i</xref>; S<xref rid=\"MOESM2\" ref-type=\"media\">18</xref>), indicating that the inhibitory actions exerted on T-cells by acidosis in the LN can be reversed once the T-cells re-enter the circulation. The restoration of effector functions, assayed in terms of IFNγ-positive cells, was also observed when T-cells were activated with OVA<sub>257–264</sub> peptide, irrespective of the presence of DCs (Fig. <xref rid=\"MOESM2\" ref-type=\"media\">S16</xref>). In summary, T-cell effector functions could be restored promptly upon exposure to alkaline conditions, even if the activation by DCs had occurred at acidic pHe.</p></sec></sec><sec id=\"Sec7\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par22\">We describe a naturally occurring acidic niche in the body, one of a few sites wherein low pH may play an integral role in normal physiological function. Specifically, our results demonstrate a potential role for the LN microenvironment in shaping T-cell biology. Within the structurally-restricted extracellular spaces of paracortical zones, T-cells activated by antigen-presenting cells (e.g. DCs) produce an acidic environment, set by the balance between the enhanced capacity to generate lactic acid glycolytically and the ensuing negative feedback exercised by acid inhibition of MCT and glycolytic enzymes. Whilst this low pHe does not block the process of activation by antigen, it will suppress the production and release of many (but not all) cytokines, thereby possibly protecting the LN from premature and unwarranted release of inflammatory and anti-inflammatory cytokines. The complexity of these cytokines’ interactions within a LN are poorly understood and perhaps one function of this acid-induced inhibition of T-cells is just to simplify this milieu within the confined space of a LN. Once outside the acidic LN, effector functions of egressing T-cell become rapidly uninhibited. This effect of pH on T-cells is consistent with the emerging notion that “the role of extracellular acidosis is not clearly immunosuppressive, but can have both promoting and suppressive effects on different classes of immune cells”<sup><xref ref-type=\"bibr\" rid=\"CR57\">57</xref></sup>. Our mechanism explains the apparent paradox of how the LN is able to host processes that underpin T-cells activation, while at the same time suppressing T-cells from invoking their effector functions while in residence. This physiological mechanism may, however, be exploited by tissues seeking to evade immune surveillance, such as solid tumours. In the case of tumours, however, acidity can be manipulated, as demonstrated by the efficacy of systemic buffers on improving T-cell checkpoint blockade therapy<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>. Tertiary lymphoid structures (TLS) are ectopic lymphoid-like organs found in nonlymphoid tissues, which develop under conditions of persistent chronic inflammation, such as in tumours, in autoimmune syndromes, and inflammatory disorders<sup><xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>. Some TLSs exist as sophisticated, segregated structures that bear resemblance to LNs<sup><xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>. It is plausible that these TLSs, sharing structural similarities with LNs, would also manifest an acidic pH, therefore locally inhibit T-cell-dependent immune functions. Accumulating evidence supports that TLSs are important in antitumoural immunity<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>, therefore increasing T-cell function by selectively manipulating the pH of tumour-associated TLSs, may benefit immunotherapy.</p></sec><sec id=\"Sec8\"><title>Methods</title><sec id=\"Sec9\"><title>Isolation and activation of T-cells</title><p id=\"Par23\">Female B6 (C57BL/6), Pmel, OT-I, OT-II and TDAG8-knockout (TDAG8 KO) mice on the C57BL/6 background were bred and housed at the Animal Research Facility of the H. Lee Moffitt Cancer Center and Research Institute (Tampa, FL). Naïve T -cells were isolate from mice spleen using T-cell column (R&D system). T-cells from B6 mice and TDAG8-knockout mice were cultured in complete medium with 5 µg/mL plate-bounded anti-CD3 antibody and 2 µg/mL soluble anti-CD28 antibody for 48 h. Isolated Pmel, OT-I and OT-II T- cells are cultured in complete medium with 5 µg/mL gp-100 <sub>25–33</sub> peptide, 10 µg/mL OVA <sub>SIINFEKL</sub> peptide and 10 µg/mL OVA <sub>323–339</sub> peptide, respectively. IFN-gamma was measured by ELISA (BD Biosciences). All animal experiments were approved by the Institutional Animal Care and Use Committee and performed in accordance with the U.S. Public Health Service Policy and National Research Council Guidelines. Jurkat cells were maintained in RPMI-1640 medium with 5% FBS. Jurkat cells were stimulated with phorbol 12-myristate 13-acetate (PMA, Cat#8139, Sigma–Aldrich) and phytohemagglutinin, M form (PHA-M, Cat# 10576015, Gibco) for 24 h.</p></sec><sec id=\"Sec10\"><title>Animals</title><p id=\"Par24\">All animals were maintained under Institutional Animal Care and Use Committee (IACUC) at H. Lee Moffitt Cancer Center. Eight-to ten-week old Balb/c, C57BL/6, and <italic>nu/nu</italic> mice (male, 22–25 g) were purchased from The Jackson Laboratory and housed in ventilated isolette cages at ambient temperature and humidity with 12 h light dark cycles.</p></sec><sec id=\"Sec11\"><title>LN lactate measurement</title><p id=\"Par25\">Inguinal lymph nodes (LNs) excised from a consistent anatomical location were surgically remove from immunocompetent C57BL/6 (B6) or nude mice, weighted and flash frozen in liquid nitrogen immediately. Tissue was homogenized in 0.2 mL 80% methanol and the supernatants obtained after 10 min of centrifugation at 15,000 × <italic>g</italic> were collected for biochemical analysis. Lactate concentration was measured by a fluorometric method using Lactate Assay Kit (BioVision, inc. Cat#K607).</p></sec><sec id=\"Sec12\"><title>Seahorse measurements of metabolism</title><p id=\"Par26\">Extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) were measured by Seahorse XF96 Analyzer (Agilent). Cells were cultured with bicarbonate-free RPMI-1640 medium with 2 mM HEPES and 2 mM MES. The buffering capacity was determined to calculate the proton production rate (PPR).</p></sec><sec id=\"Sec13\"><title>Flow cytometry</title><p id=\"Par27\">Fresh isolated T -cells were activated at pHe 7.4 or pHe 6.6 for 72 h. Cells were collected and wash by PBS twice, then stained in FACS buffer with the following antibodies for flow cytometric analysis: CD3, CD4, CD8, CD44, and CD62L (see Supplementary Table <xref rid=\"MOESM2\" ref-type=\"media\">S3</xref> for antibody information). Live/Dead fixable near-IR (Invitrogen) was used to exclude dead cells before analysis. To analyze intracellular marker IFNγ, cells were incubated with 1 µL/mL GoldgiPlug (BD Bioscience) for 3 h, stained with surface marker and Live/Dead dye, fixed and permeabilized by Fixation/Permeabilization Solution Kit (BD Biosciences), and then stained with anti-IFNγ antibody. Samples are analyzed by LSR II Flow Cytometer (BD Biosciences). Multiple antibody lot numbers were used and each was validated by the flow cytometry core facility according to the manufacturer prior to used and titered for appropriate staining by us. In general, antibodies were used at a dilution of 1 ul per 100 ul staining buffer per 10<sup>6</sup> cells.</p></sec><sec id=\"Sec14\"><title>Antibodies</title><p id=\"Par28\">Anti-pimonidazole antibody (#PAb2627, a rabbit polyclonal antibody) was purchased from Hyproxyprobe, Inc (Burlington, MA) and used at a 1:100 dilution; anti-CD3 antibody (#M3072, a rabbit monoclonal antibody) was purchased from Spring Bioscience Corp. (Pleasanton, CA) and used at a 1:100 dilution; anti-CD28 antibody (37.51, 16-0281-82) was purchased from Thermofisher (Waltham, MA) and used at a concentration of 1 ug/mL; anti-CD4 antibody (GK1.5, BE0003-1) was purchased from Bioexcell (Lebanon, NH) and used at a concentration of 3 ug/ul; anti-CD8 antibody (2.43, BE0061) was purchased from Bioexcell (Lebanon, NH) and used at a concentration of 3ug/ul.</p></sec><sec id=\"Sec15\"><title>In vivo depletion of CD4 and CD8 T-cells</title><p id=\"Par29\">C57B6 mice were injected IP with CD4 (GK1.5) and CD8 (2.43) depleting antibodies at a dosage of 300 ug/mouse for three consecutive days to initiate depletion. Depleted state was then maintained by additional dosing every 3 days until initiation of imaging studies. Depletion status was verified by flow cytometry on isolated lymph nodes and spleen of depleted and nondepleted mice.</p></sec><sec id=\"Sec16\"><title>Cytokine beads array assay</title><p id=\"Par30\">T-cells were activated for 48 h and restimulated at pHe 7.4 or pHe 6.6 for 24 h. Culture medium was collected for cytokine beads array analysis according to the manufacturer’s manual (BioLegend). Briefly, 25 µL culture medium was sequentially mixed with antibody-conjugated beads, detection antibody and SA-PE. Washed samples were analyzed by flow cytometer.</p></sec><sec id=\"Sec17\"><title>Cell proliferation assay</title><p id=\"Par31\">Fresh prepared T-cells were washed by PBS twice and stained with 2 uM CellTrace Voilet (Invitrogen) in PBS for 10 min, and incubated in complete medium for another 20 min to quench residual dye. After two wash with complete medium, cells were activated at pHe 7.4 or pHe 6.6 for 72 h. After activation, cells were collected and stained with surface marker and live/dead dye for before analysis.</p></sec><sec id=\"Sec18\"><title>Cytotoxicity assay</title><p id=\"Par32\">For the cell lysis assay, the Xcelligence system (Roche Diagnostics) was used to monitor cellular events without incorporation of radioactive labels. Fifty microliter of complete media (CM) was added to 96XE-plates. Twenty thousand target cells (B16 or B16 pulsed with OVA peptide) were seeded into the wells of 96XE-Plates in 50 μL of CM and incubated on the Real Time Cell Analyzer overnight in a CO2 incubator to monitor cell adhesion and growth. Effector cells (OT-I T-cells) activated for 24 h with OVA peptide in media at pH 6.6 or pH 7.4 were added to plate at 25:1 ratio in a volume of 100 μL/well. Co-cultures were assessed by the system for 20 h. Results are expressed as percent lysis determined from Cellular Index (CI) normalized as (nCI): % of lysis = [nCI (no effector) − nCI (effector)]/nCi (no effector) × 100.</p></sec><sec id=\"Sec19\"><title>Phosphofructokinase-1 (PFK-1) activity assay</title><p id=\"Par33\">T-cells from spleens of B6 mice were activated for 48 h and washed by PBS twice, and homogenized in M-PER Mammalian Protein Extraction Reagent (cat#78501, ThermoFisher) with Halt<sup>TM</sup> Protease Inhibitor Cocktail (1:100, cat#87786, ThermoFisher). The supernatants, after 10 min of centrifugation at 15,000 × <italic>g</italic>, were collected for enzymatic analysis. Ten microlotre of supernatants were added to 2 mL of reaction buffer (50 mM HEPES, 1 mM ATP, 1 mM fructose-6-phosohate, 2 mM MgCl<sub>2</sub>, 0.2 mM NADH, 1 U/mL aldolase, 5 U/mL triosephosphate isomerase, 1 U/mL α-glycerophosphate dehydrogenase, pH varied between 6.6 and 7.4) to initiate the reaction and the change in absorbance at 340 nm were measured spectrophotometrically every 10 s to calculated the enzyme activity.</p></sec><sec id=\"Sec20\"><title>Immunochemistry</title><p id=\"Par34\">Murine inguinal lymph nodes were surgically removed from C57BL/6 mice, fixed in formalin and paraffin embedded. Slides were prepared with 4-µm thick tissue slices and stained using a Ventana Discovery XT automated system (Ventana Medical Systems) as per manufacturer’s protocol with proprietary reagents. The primary antibodies were used to detect pimonidazole (1:100, Hyproxyprobe #PAb2627) and CD3 (1:100, Spring Bioscience #M3072) expression. Slides were incubated with Ventana OmniMap Secondary Antibody followed by Ventana ChromoMap kit to detect the proteins staining and then slides were counterstained with Hematoxylin.</p></sec><sec id=\"Sec21\"><title>Superfusion</title><p id=\"Par35\">Superfusion experiments were performed in a plastic chamber (Pecon, TempController 2000-2) supplied by a solution line with a switcher that changed between one of two lines (the other being diverted to the waste bottle). The plastic chamber was mounted on a confocal microscope and heated to 37 °C by small scale temperature incubator (The Cube Life Imaging Services). Solution exchange was attained with a time constant of 2.6 s. Solution flows were 2–4 ml/min.</p></sec><sec id=\"Sec22\"><title>Solutions and media</title><p id=\"Par36\">(i) Solutions for seahorse experiments: 2 mM HEPES, 2 mM MES, 5.3 mM KCl, 5.6 mM Na-Phosphate, 11 mM glucose, 133 mM NaCl, 0.4 mM MgCl<sub>2</sub>, 0.42 mM CaCl<sub>2</sub>, titrated to given pH with NaOH. For reduced Cl<sup>−</sup> experiments, 133 mM NaCl was replaced with 133 Na-Gluconate and MgCl<sub>2</sub> and CaCl<sub>2</sub> were raised to 0.74 and 1.46 mM, respectively, to account for gluconate-divalent binding. Amount of dilute HCl or NaOH added to medium to reduce pH to target level was determined empirically. Solutions for pH measurements under superfusion: For pH 7.4, 133 mM NaCl, 5.3 mM KCl, 10 mM Glucose, 1 mM CaCl<sub>2</sub>, 1 mM MgCl<sub>2</sub>, 22 mM NaHCO<sub>3</sub>. For lower pH, NaHCO<sub>3</sub> was reduced (compensated by NaCl) to attain a target pH, according to the Henderson Hasselbalch equation (pH = 6.15 + log([HCO<sub>3</sub><sup>−</sup>]/[CO<sub>2</sub>]), where [CO<sub>2</sub>] is 1.2 mM for 5%. All solutions were bubbled in 5% CO<sub>2</sub>. (iii) Solutions for Ca<sup>2+</sup> imaging: For pH 7.4, 133 mM NaCl, 5.3 mM KCl, 0.8 mM MgCl<sub>2</sub>, 0.9 mM Na-Phosphate, 22 mM NaHCO<sub>3</sub> and either 1.8 mM CaCl<sub>2</sub>, 0.5 mM CaCl<sub>2</sub> or 0.5 mM EGTA. For pH 6.6, NaHCO<sub>3</sub> was reduced to 2.75 mM and NaCl raised accordingly. All solutions were bubbled with 5% CO<sub>2</sub>/balanced air. (iv) Calibration solutions for nigericin: 145 mM KCl, 1 mM MgCl<sub>2</sub>, 0.5 mM EGTA, 10 mM HEPES, 10 mM MES and pH adjusted with NaOH to required level.</p></sec><sec id=\"Sec23\"><title>Confocal imaging</title><p id=\"Par37\">Imaging was performed on an SP5 system (Leica Microsystems). Cellular measurements were performed with an oil-immersion ×63 objective and intravital microscopy was performed with a dry ×1.6 or ×10 objective. The following excitation (ex) and emission (em) wavelengths were used: dextran-conjugated cSNARF1 (cat#D3304, Invitrogen): 514 nm ex, 580/640 nm em (during lymph node imaging); Hoechst 34580 (cat#H21486, ThermoFisher): 405 nm ex, 420 nm; FuraRed: 488 nm ex, 585/685 nm; pHLIP-Cy5.5: 633 nm ex, >700 nm. Settings were optimized to obtain maximal quality under the constraints of temporal resolution.</p></sec><sec id=\"Sec24\"><title>Lymph node imaging</title><p id=\"Par38\">The inguinal lymph node is exposed for imaging on the confocal microscope by performing a midline incision to separate the skin from the peritoneum. The skin is pinned down and the excess fat around the inguinal lymph node is carefully removed with sterile Dumont #5 forceps. Once cleared and exposed, a 3D printed window chamber, with a 12 mm in diameter window, is placed over the lymph node area. The chamber is secured using a tissue adhesive (3 M Vetbond #1469SB) and 12 mm Micro coverslip (cat# 72226-01 Electron microscopy sciences). We ensure that the lymph node region underneath the coverslip does not dryout by injecting 200 µl of 1× PBS. Mice were kept under anesthesia (1.5% isofluorane) throughout the surgical procedure and in a 37 °C warming chamber during imaging. To measure pH in the inguinal lymph node, mice were injected with dextran-conjugated cSNARF1 (cat# D3304, Invitrogen) at a concentration of 20 mg/ml in 100 µl via their tail-vein or footpad. To determine whether inflammation or buffering would change the pH of the lymph node microenvironment, mice were treated with lipopolysaccharide (LPS, cat# L3012, Sigma–Aldrich) at a concentration of 1000 ng/kg (i.p. injection) for 48 h or provided mice with 400 mM NaHCO<sub>3</sub> ad libitum for 9–10 days, respectively, before imaging the mice with dextran-conjugated cSNARF1.</p></sec><sec id=\"Sec25\"><title>Image analysis</title><p id=\"Par39\">Cytoplasmic pH was measured by gating pixels according to a threshold level of Hoechst signal within cSNARF1-positive pixels. Fluorescence at 580 and 640 nm was averaged, background offset and ratioed for each particle representing a cell. For time course experiments, pHi was probed in the entire cell to allow for faster acquisition rates. Buffering capacity was measured from the change in weak acid/base concentration, assuming passive equilibration, and the pH change. Transmembrane acid-base fluxes were therefore calculated as the product of pH change and buffering capacity. Cell surface area/volume ratio assumed spherical symmetry i.e. 3/radius. For intravital microscopy, image montages were constructed with in-house software that aligned fluorescence or anatomical landmarks.</p></sec><sec id=\"Sec26\"><title>CEST imaging</title><p id=\"Par40\">Magnetic resonance data were acquired with a 7T horizontal Bruker scanner equipped with nested 205/120/HDS gradient insert and a bore size of 310 mm. A 35 mm Litzcage coil (Doty Scientific) was used to carry out all experiments. Before imaging, animals were placed in an induction chamber and anesthetized with 3% isoflurane delivered in 1.5 litre/min oxygen ventilation. After complete induction, animals were restrained in a custom-designed holder and inserted into the magnet while constantly receiving isoflurane (1–3%) within the 0.6 litre/min oxygen ventilation. Body temperature (37° ± 1 °C) and respiratory functions were monitored continuously (SAII 177 System) during the experimental time. Coronal T<sub>2</sub>-weighted fast spin-echo multislice images were acquired with TE/TR [echo time/repetition time] = 31 ms/2271 ms, field of view (FOV) = 80 × 30 mm<sup>2</sup>, matrix = 256 × 96, yielding a spatial in-plane resolution of 312 μm and with slice thickness of 1.5 mm. These images were used as anatomical reference for the pH map. MRI-CEST pH images were acquired by adapting a previously described protocol<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>; TE/TR = 10 ms/10 s, the saturation used was 3 µT during 5 s, same FOV than T2w images but matrix = 171 × 64. Only one slide was imaged containing the inguinal lymph node. Animals were injected with ISOVUE370 (Bracco Imaging, Milano, Italy) at 300 ul iv bolus injection; followed by an i.v. infusion of 300 µl/h. To create the CEST maps, an in-lab designed Matlab code was used. The pH values were calculated after a calibration curve done in the same system with 20 mM ISOVUE370 phantoms titrated at several pH values in the range 5.5–8.</p></sec><sec id=\"Sec27\"><title>Statistics</title><p id=\"Par41\">All statistical tests had a significance level of 5% in a two-tailed test. For comparisons between two samples, a <italic>t</italic>-test was used. For more than two samples, a one-way ANOVA with multiple comparisons was used.</p></sec><sec id=\"Sec28\"><title>Reporting summary</title><p id=\"Par42\">Further information on research design is available in the <xref rid=\"MOESM3\" ref-type=\"media\">Nature Research Reporting Summary</xref> linked to this article.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec29\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41467_2020_17756_MOESM1_ESM.pdf\"><caption><p>Peer Review File</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41467_2020_17756_MOESM2_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"41467_2020_17756_MOESM3_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media></supplementary-material></sec></sec></body><back><app-group><app id=\"App1\"><sec id=\"Sec30\"><title>Source data</title><p id=\"Par45\"><media position=\"anchor\" xlink:href=\"41467_2020_17756_MOESM4_ESM.xlsx\" id=\"MOESM4\"><caption><p>Source Data</p></caption></media></p></sec></app></app-group><fn-group><fn><p><bold>Peer review information</bold>\n<italic>Nature Communications</italic> thanks Kevin Brindle and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Hao Wu, Veronica Estrella.</p></fn><fn><p>These authors jointly supervised this work: Pawel Swietach, Robert J. Gillies.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> is available for this paper at 10.1038/s41467-020-17756-7.</p></sec><ack><title>Acknowledgements</title><p>This work was supported by U.S. Department of Health & Human Services | NIH| National Cancer Institute (NCI) - R01 CA077575 [Gillies]. U.S. Department of Health & Human Services | NIH| National Cancer Institute (NCI) - U54 CA193489 [Gillies]. U.S. Department of Health & Human Services | NIH| National Cancer Institute (NCI) - P30 CA076292 [Gillies]. Fulbright Association - 0001 [Swietach]. European Research Council “SURVIVE” #723997 [Swietach]. U.S. Department of Health & Human Services |NIH| National Cancer Institute (NCI) - F99 CA234942 [Russell]. U.S. Department of Health & Human Services | NIH| National Cancer Institute (NCI) - R01 GM073857 [Reshtenyak/Andreev]. Associazione Italiana Ricerca Cancro |AIRC| MFAG 2017 - ID. 20153 [Longo]. China Scholarship Council 201706325051 [Wu]. Natural Science Foundation of Zhejiang Province, China LY17H160036 [Wu]. U.S. Department of Health & Human Services |NIH| National Cancer Institute (NCI) - R01 CA239219 [Gillies/Pilon Thomas].</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>Conception or design of the work: H.W., V.E., D.A., A.I-H., K.L., S.P-T., P.S., R.J.G. Data collection: H.W., V.E., M.B., A. E-K., S.R., D.A., A.I-H., T.N., A.M., O.A., K.L., M.D., K.K., S.R.P., P.E-N., P.S. Data analysis and interpretation: H.W., V.E., M.B., D.L.L., Y.R., O.A., P.E-N, S.P-T., P.S., R.J.G. Drafting the article: S.P-T., P.S., R.J.G. Critical revision of the article. H.W., V.E., M.B., D.A., A.I-H., Y.R., O.A., K.L., S.P-T., P.S., R.J.G. Final approval of the version to be published. H.W., V.E., M.B., Y.R., S.P-T., P.S., R.J.G.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>High-resolution image data are available upon request from either of the corresponding authors (PS or RJG). All other relevant data are available in the article, supplementary information, or from the corresponding authors (PS or RJG) upon reasonable request. Source data are provided with this paper.</p></notes><notes notes-type=\"data-availability\"><title>Code availability</title><p>Custom code was developed for image processing of data in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g–i</xref>, and for steady-state modeling of Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2g, h</xref>. It allowed ad hoc image stitching, curve fitting, etc. It is available upon request to corresponding author PS.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par43\">R.J.G. has research support form Helix Biopharma, who makes acid pH targeting agents. These agents were not used in the current work. O.A.A. and Y.K.R. are founders of pHLIP, Inc. They have shares in the company, but the company did not fund any part of the work reported in this paper, which was carried out in their academic laboratories. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807848</article-id><article-id pub-id-type=\"pmc\">PMC7431838</article-id><article-id pub-id-type=\"publisher-id\">70919</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70919-w</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Progressive patterns of neurological disability in multiple sclerosis and neuromyelitis optica spectrum disorders</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Akaishi</surname><given-names>Tetsuya</given-names></name><address><email>t-akaishi@med.tohoku.ac.jp</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Takahashi</surname><given-names>Toshiyuki</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Misu</surname><given-names>Tatsuro</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Abe</surname><given-names>Michiaki</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ishii</surname><given-names>Tadashi</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fujimori</surname><given-names>Juichi</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Aoki</surname><given-names>Masashi</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fujihara</surname><given-names>Kazuo</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Nakashima</surname><given-names>Ichiro</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.69566.3a</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2248 6943</institution-id><institution>Department of Neurology, </institution><institution>Tohoku University Graduate School of Medicine, </institution></institution-wrap>Seiryo-machi 1-1, Aoba-ku, Sendai, Miyagi 980-8574 Japan </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412757.2</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0641 778X</institution-id><institution>Department of Education and Support for Regional Medicine, </institution><institution>Tohoku University Hospital, </institution></institution-wrap>Sendai, Japan </aff><aff id=\"Aff3\"><label>3</label>Department of Neurology, National Hospital Organization Yonezawa National Hospital, Yonezawa, Japan </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412755.0</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2166 7427</institution-id><institution>Department of Neurology, </institution><institution>Tohoku Medical and Pharmaceutical University, </institution></institution-wrap>Sendai, Japan </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411582.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 1017 9540</institution-id><institution>Department of Multiple Sclerosis Therapeutics, </institution><institution>Fukushima Medical University, </institution></institution-wrap>Fukushima, Japan </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13890</elocation-id><history><date date-type=\"received\"><day>18</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">The progressive patterns of neurological disability in multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) and the significance of clinical relapses to the progressions of neurological disability in these diseases have not been fully elucidated. In this study, to elucidate the impact of relapses to the progression of accumulated neurological disability and to identify the factors to affect the progression of neurological disability in MS and NMOSD, we followed 62 consecutive MS patients and 33 consecutive NMOSD patients for more than 5 years with the clinical symptoms, relapse occurrence, and Expanded Disability Status Scale (EDSS) in the chronic phase. All enrolled MS patients were confirmed to be negative for serum anti-myelin oligodendrocyte glycoprotein antibody. As a result, patients with NMOSD showed significantly severer neurological disability at 5 years from onset than MS patients. Progression in EDSS score was almost exclusively seen after clinical attacks in NMOSD, whereas progression could be observed apart from relapses in MS. Neurological disability did not change without attacks in NMOSD, whereas it sometimes spontaneously improved or deteriorated apart from relapses in MS (p < 0.001). In patients with MS, those with responsible lesions primarily in spinal cord were more likely to show such spontaneous improvement. In conclusion, clinical deterioration in NMOSD patients is irreversible and almost exclusively takes place at the timing of clinical attacks with stepwise accumulation of neurological disability. Meanwhile, changes in EDSS score can be seen apart from relapses in MS patients. Neurological disability in MS patients is partly reversible, and the patients with disease modifying drugs sometimes present spontaneous improvement of the neurological disability.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Neurology</kwd><kwd>Multiple sclerosis</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) are major autoimmune-related neurological diseases that predominantly impairs the central nervous system (CNS) but have distinct pathophysiological mechanisms<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. Both diseases typically present recurrent clinical attacks with lesions in cerebrum, optic nerves, brainstem, and spinal cord<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. At present, MS is diagnosed based on the dissemination of lesions in time and space<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>–<xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>, whereas NMOSD is diagnosed both by the clinical history and the presence of serum anti-aquaporin-4 antibody (AQP4-IgG)<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>.</p><p id=\"Par3\">The accumulation of neurological impairment in MS is thought to be mainly comprised of the following two components: subclinical progressive brain atrophy and recurrent clinical relapses during which the responsible lesions are contrast-enhanced in MRI<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Based on this concept, clinical course of MS is generally categorized into the following three subtypes: primary progressive MS (PPMS), relapsing–remitting MS (RRMS), and secondary progressive MS (SPMS)<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref>,<xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. Meanwhile, progressive pattern of neurological impairment in NMOSD has not been so much studied until now, although the subsequent neurological disability is generally severer in NMOSD than in MS<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>–<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. From before, the progression of neurological disability in NMOSD has been empirically thought to occur mainly at the timing of clinical attacks<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>, but whether it occurs even during the intermittent period between attacks or not has not been proved yet. Because the expected clinical course will surely affect the achieved outcomes in clinical studies enrolling the patients with these diseases, elucidating the characteristic clinical course and factors that affect the progression of neurological disability in these diseases has many clinical significances.</p><p id=\"Par4\">In this study, we enrolled an enough number of MS and NMOSD patients who have their clinical onset between 2000 and 2015, and followed their neurological disability every year from the first visit to our hospital. The progressive pattern of their neurological disability was evaluated with other information such as patient background and relapses to identify the clinical factors that mainly regulate the progression of neurological disability in each of MS and NMOSD.</p></sec><sec id=\"Sec2\"><title>Methods</title><sec id=\"Sec3\"><title>Patients</title><p id=\"Par5\">For this study, a total of 62 consecutive MS patients and 33 consecutive AQP4-IgG-positive NMOSD patients, who had their clinical onset between 2000 and 2015 and treated in our university hospital were initially collected. These initial cohorts were prospectively followed up, among which 57 MS patients (91.9%) and 31 NMOSD patients (93.9%) were followed and treated in our university hospital for more than 5 years from their onset. All enrolled MS patients were confirmed to be negative for the presence of serum anti-myelin oligodendrocyte glycoprotein (MOG) antibody by utilizing the cell-based assay method<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. The presence of serum AQP4-IgG in the enrolled NMOSD patients was also confirmed based on the cell-based assay method<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>.</p><p id=\"Par6\">All of the 5 MS patients who was not followed for more than 5 years dropped out because of moving. One of the 2 NMOSD patients who was not followed for more than 5 years died because of a malignant tumor 2 years after the onset of NMOSD, but another patient dropped out because of moving.</p></sec><sec id=\"Sec4\"><title>Collected data</title><p id=\"Par7\">In these patients, comprehensive clinical information was collected, such as onset age, sex, follow up period as of 2019, types of relapse preventive therapies, type and timing of clinical attacks, and the titer of serum anti-AQP4 antibody. The irreversible neurological impairment was evaluated with the Expanded Disability Status Scale (EDSS) in every 1 or 2 years in these patients<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. Because neurological disability can drastically exacerbate and recover in the acute and subacute phases of clinical relapses with proper treatments with immune-suppressants, EDSS scores within 3 months from the last relapse were not used in this study. Relapses in MS and NMOSD were defined by the presence of clinically evidenced gadolinium-enhanced T1-weighted lesions with the neurological symptoms sustained for more than 24 h in the absence of fever or infection<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>.</p><p id=\"Par8\">As for other clinical information, data about the number of cerebral lesions at 5 years from the onset and the site of lesion responsible for the neurological disability (i.e. cerebral, optic nerves, brainstem, spinal cord) were also collected in MS patients. To identify the factors that affect the neurological disability in MS, we also evaluated the gray matter volume and white matter lesion volume in some of them with a volumetric method as previously reported<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>.</p></sec><sec id=\"Sec5\"><title>Statistical analysis</title><p id=\"Par9\">Comparisons of two distributions were performed with either of the Student’s t-test or Mann–Whitney U test, based on the normality of distributions. Comparisons of frequencies were performed with either of the chi-squared test or Fisher’s exact test, based on the size in each cell. A value of p < 0.05 was regarded to be statistically significant. The analyses were conducted using either SPSS Statistics Base 22 software (IBM, Armonk, NY, USA) or MATLAB R2015a (MathWorks, Natick, MA, USA).</p></sec><sec id=\"Sec6\"><title>Ethical approval</title><p id=\"Par10\">This study was approved by the institutional review board of the Tohoku University Graduate School of Medicine (approval number: 2010589), and was carried out in accordance with the standards stated in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.</p></sec><sec id=\"Sec7\"><title>Informed consent</title><p id=\"Par11\">Informed consent was obtained from all individual participants included in the study.</p></sec></sec><sec id=\"Sec8\"><title>Results</title><sec id=\"Sec9\"><title>Cohort demographics</title><p id=\"Par12\">The total follow-up period with EDSS evaluation in the initially collected MS patients with onset between 2000 and 2015 was 337 person-year (62 patients) as of Oct 2019; that in the initially collected NMOSD patients with onset between 2000 and 2015 was 229 person-year (33 patients). Patients’ background in both groups are listed in Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref> and compared between the groups. As for therapeutic interventions, in MS group, 58 of the 62 patients have been treated with disease modifying drugs (DMDs), such as interferon (IFN) beta, glatiramer acetate, fingolimod, or natalizumab. In the 58 patients with DMDs, 26 were initially treated with IFN beta, but later changed DMD to fingolimod. Other 2 patients have been treated with oral prednisolone (PSL) and the remaining 2 patients are untreated. In NMOSD group, 32 of the 33 patients were treated with low-dose oral PSL (< 20 mg/day) with or without other immune-suppressants (i.e. azathioprine, cyclosporine, tacrolimus). The remaining 1 NMOSD patient has not been treated with any kind of relapse preventive therapies.<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Patient background and clinical course in MS and NMOSD.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">MS (n = 62)</th><th align=\"left\">NMOSD (n = 33)</th><th align=\"left\">p-value</th></tr></thead><tbody><tr><td align=\"left\">Male:Female</td><td align=\"left\">12:50</td><td align=\"left\">3:30</td><td align=\"left\">0.25</td></tr><tr><td align=\"left\">Onset age (mean ± SD)</td><td align=\"left\">29.9 ± 8.6</td><td align=\"left\">48.5 ± 13.8</td><td align=\"left\">< 0.0001</td></tr><tr><td align=\"left\">Total EDSS follow-up period (person-year)</td><td align=\"left\">337</td><td align=\"left\">229</td><td align=\"left\">–</td></tr><tr><td align=\"left\">EDSS follow-up years per capita (median, IQR)</td><td align=\"left\">6 (3–7)</td><td align=\"left\">7 (5–9)</td><td align=\"left\">0.0460</td></tr><tr><td align=\"left\" colspan=\"4\"><bold>Cross-sectional EDSS</bold></td></tr><tr><td align=\"left\">EDSS at 5 years from onset (median, IQR)</td><td align=\"left\">1.5 (1.0–2.0; n = 53)</td><td align=\"left\">3.0 (2.0–5.0; n = 31)</td><td align=\"left\">< 0.0001</td></tr><tr><td align=\"left\">EDSS at 10 years from onset (median, IQR)</td><td align=\"left\">1.5 (1.0–2.0; n = 20)</td><td align=\"left\">4.5 (3.0–6.5; n = 18)</td><td align=\"left\">0.0002</td></tr><tr><td align=\"left\" colspan=\"4\"><bold>EDSS annual deterioration (person-year)</bold></td></tr><tr><td align=\"left\">Total</td><td align=\"left\">28/337 (8.3%)</td><td align=\"left\">20/229 (8.7%)</td><td align=\"left\">0.86</td></tr><tr><td align=\"left\">(With relapse)</td><td align=\"left\">11 (3.3%)</td><td align=\"left\">13 (5.7%)</td><td align=\"left\">0.16</td></tr><tr><td align=\"left\">(Without relapse)</td><td align=\"left\">17 (5.0%)</td><td align=\"left\">7 (3.1%)</td><td align=\"left\">0.29</td></tr><tr><td align=\"left\" colspan=\"4\"><bold>EDSS annual improvement (person-year)</bold></td></tr><tr><td align=\"left\">Total</td><td align=\"left\">32/337 (9.5%)</td><td align=\"left\">4/229 (1.7%)</td><td align=\"left\">0.0001</td></tr><tr><td align=\"left\">(With relapses)</td><td align=\"left\">5 (1.5%)</td><td align=\"left\">2 (0.9%)</td><td align=\"left\">0.71</td></tr><tr><td align=\"left\">(Without relapses)</td><td align=\"left\">27 (8.0%)</td><td align=\"left\">2 (0.9%)</td><td align=\"left\">< 0.0001</td></tr><tr><td align=\"left\" colspan=\"4\"><bold>EDSS annually unchanged (person-year)</bold></td></tr><tr><td align=\"left\">Total</td><td align=\"left\">277/337 (82.2%)</td><td align=\"left\">205/229 (89.5%)</td><td align=\"left\">0.0161</td></tr><tr><td align=\"left\">(With relapses)</td><td align=\"left\">22 (6.5%)</td><td align=\"left\">8 (3.5%)</td><td align=\"left\">0.13</td></tr><tr><td align=\"left\">(Without relapses)</td><td align=\"left\">255 (75.7%)</td><td align=\"left\">197 (86.0%)</td><td align=\"left\">0.0026</td></tr></tbody></table><table-wrap-foot><p>All EDSS scores were evaluated in the chronic phase more than 3 months after the last clinical episodes; the scores within 3 months from the last episodes were not used in this study.</p><p><italic>EDSS</italic> Expanded Disability Status Scale, <italic>IQR</italic> interquartile range (25–75 percentile), <italic>MS</italic> multiple sclerosis, <italic>NMOSD</italic> neuromyelitis optica spectrum disorders, <italic>SD</italic> standard deviation.</p></table-wrap-foot></table-wrap></p></sec><sec id=\"Sec10\"><title>Progression of neurological disability in total</title><p id=\"Par13\">The progressions of EDSS in both disease groups by years from the onset, irrespective of the length of follow-up period or the occurrence of relapses, are shown in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>. In NMOSD group, most of the changes in EDSS took place as deterioration in neurological disability; in MS group, deterioration and improvement in EDSS score were similarly observed. As a consequence, during the whole follow-up period, the cross-sectional distribution of EDSS score was worse in NMOSD group than in MS group.<fig id=\"Fig1\"><label>Figure 1</label><caption><p>EDSS progression in each patient with MS or NMOSD. The cross-mark shows that the patient passed away because of malignancy. Patients with NMOSD are likely to show a stepwise progression of neurological impairment at each occasion of clinical attack, whereas patients with MS show gradual improvement or deterioration of neurological impairment irrespective of the relapse occurrence. <italic>AQP4-IgG</italic> anti-aquaporin-4 autoantibodies, <italic>EDSS</italic> expanded disability status scale, <italic>MS</italic> multiple sclerosis, <italic>NMOSD</italic> neuromyelitis optica spectrum disorders.</p></caption><graphic xlink:href=\"41598_2020_70919_Fig1_HTML\" id=\"MO1\"/></fig></p><p id=\"Par14\">Within the initially enrolled patients, 53 MS patients were evaluated with EDSS at 5 years from the onset and 20 MS patients were evaluated at 10 years from the onset. In NMOSD group, all 31 patients were evaluated with EDSS at 5 years from the onset and 18 patients were evaluated at 10 years from the onset. The distributions of EDSS in MS and NMOSD groups at 5 and 10 years from the onset are listed in the middle of Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>. The score of EDSS was much worse in NMOSD group than in MS group both at 5 years and 10 years from the onset.</p></sec><sec id=\"Sec11\"><title>Impact of relapses to the progression of neurological disability</title><p id=\"Par15\">Data regarding to the relationship between attacks and the progression of irreversible neurological disability, irrespective of relapses, are summarized in the lower half of Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>. Within the 20 occasions of EDSS annual deterioration in NMOSD patients, 13 occasions (65.0%) took place at the timing of relapses. Meanwhile, within the 28 occasions of EDSS deterioration in MS patients, 11 occasions (39.3%) took place at the timing of relapses.</p><p id=\"Par16\">When focusing on the period without clinical relapses, 255 (85.3%) of the 299 person-years of follow-up in MS group showed unchanged EDSS score, whereas 197 (95.6%) of the 206 follow-up years in NMOSD group showed unchanged EDSS score (p = 0.0002, Fisher’s exact test). In other words, neurological disability in NMOSD hardly changes without clinical attacks, whereas that in MS is more likely to change without relapses.</p><p id=\"Par17\">To visually confirm the difference in the impact of relapse to neurological disability between MS and NMOSD, we depicted line graphs of chronological change in EDSS for each patient by the relapse occurrence as shown in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>. As described above, relapses were more likely to be accompanied by EDSS deterioration in NMOSD than in MS (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>A). During the period without relapses, EDSS did not change at all in almost all NMOSD patients, whereas EDSS was more likely to change without relapses in MS patients (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>B).<fig id=\"Fig2\"><label>Figure 2</label><caption><p>EDSS progression by relapse occurrence in MS and NMOSD. Neurological disability did not change without attacks in NMOSD, whereas it often spontaneously improved or deteriorated irrespective of the relapses in MS. Consequently, suppressing attack occurrence is the most important objective in NMOSD, whereas facilitating spontaneous improvement could be one of the possible therapeutic strategies in MS. <italic>AQP4-IgG</italic> anti-aquaporin-4 autoantibodies, <italic>EDSS</italic> expanded disability status scale, <italic>MS</italic> multiple sclerosis, <italic>NMOSD</italic> neuromyelitis optica spectrum disorders.</p></caption><graphic xlink:href=\"41598_2020_70919_Fig2_HTML\" id=\"MO2\"/></fig></p></sec><sec id=\"Sec12\"><title>Spontaneous improvement of neurological disability in MS</title><p id=\"Par18\">As can be seen in the figures, not a few MS patients with proper treatments with DMDs showed a spontaneous EDSS improvement during their clinical course irrespective of relapse occurrence. The frequency of EDSS annual improvement in MS and NMOSD patients is shown in the bottom of Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>. Improvement in EDSS was observed in only a few occasions in NMOSD patients. Meanwhile, EDSS annual improvement was observed in 32 occasions of the followed 337 person-years, which was much higher than that in NMOSD patients (p = 0.0001, Fisher’s exact test).</p><p id=\"Par19\">In MS group, the prevalence of minimally clinically important difference in EDSS score in the first 5 years was evaluated. Based on a previous literature, minimal clinically important difference was defined as 1.0 point change when the EDSS score was 0–5.0, and as 0.5 point change when the EDSS score was 5.5–8.5<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>. As a result, there were 12 patients whose EDSS improved with clinically important difference in the first 5 years from the onset, and 7 patient whose EDSS deteriorated with clinically important difference in the first 5 years from the onset. The characteristics of these two groups are summarized in Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>. The baseline level of EDSS score was not significantly different between the improved group and the deteriorated group, but the number of cerebral lesions at 5 years from the onset (e.g. periventricular, subcortical) and the lesion site that is responsible for the neurological disability were significantly different between the groups. The number of cerebral lesions was significantly higher in the deteriorated group than in the improved group. The responsible lesion site was mainly located in the spinal cord in the improved group, whereas that was mainly located in the cerebrum (p = 0.0063) or brainstem (p = 0.0090) in the deteriorated group. The type of DMDs was not different between the two groups. For reference, the sites of myelitis that was responsible for the neurological disability in the improved group were cervical in 5 patients and thoracic in the other 5 patients.<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Patients with MS who showed clinically important difference in EDSS score in the first 5 years.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Improved group</th><th align=\"left\">Deteriorated group</th><th align=\"left\">p</th></tr></thead><tbody><tr><td align=\"left\">n (male:female)</td><td align=\"left\">12 (4:8)</td><td align=\"left\">7 (2:5)</td><td align=\"left\">–</td></tr><tr><td align=\"left\">Follow-up year</td><td align=\"left\">5.6 ± 2.1</td><td align=\"left\">5.9 ± 1.6</td><td align=\"left\">0.77</td></tr><tr><td align=\"left\">Onset age</td><td align=\"left\">28.8 ± 7.8</td><td align=\"left\">22.7 ± 6.0</td><td align=\"left\">0.0911</td></tr><tr><td align=\"left\">OB positivity</td><td align=\"left\">6/8 (75%)</td><td align=\"left\">6/6 (100%)</td><td align=\"left\">0.47</td></tr><tr><td align=\"left\">IgG-index</td><td align=\"left\">0.93 ± 0.30</td><td align=\"left\">0.80 ± 0.17</td><td align=\"left\">0.34</td></tr><tr><td align=\"left\" colspan=\"4\"><bold>Findings at 5 years from the onset (*median, IQR)</bold></td></tr><tr><td align=\"left\">Relapses in the first 5 years*</td><td align=\"left\">0 (0–1)</td><td align=\"left\">1 (0–3)</td><td align=\"left\">0.26</td></tr><tr><td align=\"left\">Number of cerebral lesions*</td><td align=\"left\">2 (1–7)</td><td align=\"left\">14 (13–18)</td><td align=\"left\">0.0003</td></tr><tr><td align=\"left\">WML volume (cc)</td><td align=\"left\">3.8 ± 3.1</td><td align=\"left\">27.4 ± 8.7</td><td align=\"left\">0.0004</td></tr><tr><td align=\"left\">Grey matter volume (cc)</td><td align=\"left\">892 ± 62</td><td align=\"left\">899 ± 29</td><td align=\"left\">0.84</td></tr><tr><td align=\"left\">EDSS*</td><td align=\"left\">1.0 (1.0–2.0)</td><td align=\"left\">1.0 (1.0–4.0)</td><td align=\"left\">0.70</td></tr><tr><td align=\"left\" colspan=\"4\"><bold>Primarily responsible site of lesion for neurological disability (n)</bold></td></tr><tr><td align=\"left\">Cerebral</td><td align=\"left\">2/12 (16.7%)</td><td align=\"left\">6/7 (85.7%)</td><td align=\"left\">0.0063</td></tr><tr><td align=\"left\">Optic nerves</td><td align=\"left\">0/12 (0.0%)</td><td align=\"left\">1/7 (14.3%)</td><td align=\"left\">0.37</td></tr><tr><td align=\"left\">Brainstem</td><td align=\"left\">0/12 (0.0%)</td><td align=\"left\">4/7 (57.1%)</td><td align=\"left\">0.0090</td></tr><tr><td align=\"left\">Myelitis</td><td align=\"left\">10/12 (83.3%)</td><td align=\"left\">2/7 (28.6%)</td><td align=\"left\">0.0449</td></tr><tr><td align=\"left\" colspan=\"4\"><bold>Relapse preventive therapies (n; allowing duplication)</bold></td></tr><tr><td align=\"left\">IFN-beta</td><td align=\"left\">10/12 (83.3%)</td><td align=\"left\">7/7 (100.0%)</td><td align=\"left\">0.51</td></tr><tr><td align=\"left\">Fingolimod</td><td align=\"left\">8/12 (66.7%)</td><td align=\"left\">4/7 (57.1%)</td><td align=\"left\">1.00</td></tr></tbody></table><table-wrap-foot><p>Clinical data between the MS patients who showed clinically important improvement in EDSS over 5 years follow-up (i.e. improved group) and those who showed clinically important deterioration in EDSS over 5 years follow-up (i.e. deteriorated group) was compared. Minimal clinically important difference in EDSS score was defined as 1.0 point change when the EDSS score was < 5.5, and as 0.5 point change when it was 5.5–8.5. Brain volumetry was performed using a volumetric program offered by Icometrix (Leuven, Belgium).</p><p><italic>EDSS</italic> expanded disability status scale, <italic>IFN</italic> interferon, <italic>IgG</italic> immunoglobulin-G, <italic>IQR</italic> interquartile range (25–75 percentile), <italic>OB</italic> oligoclonal band, <italic>WML</italic> white matter lesion.</p><p>*median (interquartile range [IQR]).</p></table-wrap-foot></table-wrap></p></sec></sec><sec id=\"Sec13\"><title>Discussion</title><p id=\"Par20\">Based on the results of this study, progressive pattern of neurological disability in MS and NMOSD was suggested to be largely different. Progression of EDSS in NMOSD mainly took place at the timing of each attack occurrence, and it did not deteriorate for long time if there is no attack occurrence. Meanwhile, progression of EDSS in MS mainly took place irrespective of the relapse occurrence as previously known<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref>,<xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. More importantly, EDSS in MS patients with DMDs often showed spontaneous sustainable improvement, which was hardly observed in the NMOSD patients. Such spontaneous EDSS improvement was likely to be seen in MS patients with neurological impairment based on myelitis and with less cerebral lesions.</p><p id=\"Par21\">One notable finding of this study was that neurological impairment in MS could be spontaneously improved, suggesting that damages in oligodendrocytes could be partially reversible or replaceable. Such spontaneous improvement was mainly seen in MS patients with responsible lesions in the spinal cord, whereas the MS patients with responsible lesions in the cerebrum or brainstem did not show such spontaneous remission. It is widely known that MS patients with more cerebral lesions and larger cerebral lesions are likely to show accelerated neurological disability and faster rate of global brain atrophy<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref>–<xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. It may be better in upcoming clinical trials to distinguish the MS patients with only a few cerebral lesions, who are expected not to show EDSS deterioration in the following several years, and those with much more cerebral lesions, who are likely to show EDSS deterioration in the following several years.</p><p id=\"Par22\">The absence of spontaneous change in EDSS score independent from attacks in NMOSD patients supports the importance of taking the preceding relapse frequency in the last several years into consideration at the enrollment of clinical trials with NMOSD patients<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>. Also, this study indicated that one of the most appropriate primary outcomes to be evaluated in trials with NMOSD patients is the subsequent attack frequency or the severity of neurological damage in each attack, as neurological disability in NMOSD shows stepwise accumulation as a result of attacks<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>. Meanwhile, in clinical trials in MS, relapse frequency would not be among the most important enrollment criteria or outcomes to be evaluated, because EDSS improvement and deterioration often take place irrespective of the relapse occurrence. Suppression of neurological disability progression irrespective of relapses or facilitation of spontaneous EDSS improvement would be more desirable therapeutic outcome in MS patients. Since not a few MS patients showed spontaneous improvement in neurological disability, the achievement rate of EDSS recovery may be another possible outcome to be evaluated in the future clinical trials in MS.</p><p id=\"Par23\">There are several limitations in this study. First, almost all of the enrolled patients were Asian, except for one Caucasian female MS patient. Thus, whether the suggested spontaneous EDSS improvement in MS patients with DMDs can be also observed in MS patients of Caucasians and African-Americans is to be elucidated in the future clinical researches. Second, almost all of the enrolled patients were treated with standard relapse preventive therapies. Thus, the natural progressive patterns of neurological disability without any treatments are not known. Moreover, most of the studied MS patients in this study changed DMDs from IFN-β to fingolimod during the follow-up period. Thus, whether different types of DMDs may produce different rates of spontaneous remission in MS patients or not is unconcluded. However, such possibility seems to be unlikely between IFN-β and fingolimod, as there was no significant difference in the progression of neurological disability between these two drugs in a previous randomized double-blind study<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. Lastly, we did not study EDSS progression in patients with serum anti-MOG antibody, which is expected to be elucidated in the future researches.</p></sec><sec id=\"Sec14\"><title>Conclusions</title><p id=\"Par24\">NMOSD patients with relapse-preventive therapies show EDSS progression exclusively at the timing of each clinical attack, and EDSS does not deteriorate without attacks, suggesting that preventing clinical attacks is the most important therapeutic target in NMOSD. Meanwhile, MS patients treated with DMDs often show spontaneous improvement or deterioration in EDSS independent from relapses. Because the neurological disability in MS is reversible to some extent, therapeutic strategies to facilitate such spontaneous improvement in MS would be a promising therapeutic strategy in the future.</p></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><ack><title>Acknowledgements</title><p>This study was not funded.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>T.A., T.T. and I.N. wrote the main manuscript text. T.A. prepared figures and tables. T.T., T.M., K.F. and I.N. collected the data. T.I., M. Aoki, K.F. and I.N. supervised the study process. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"letter\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Infect Control Hosp Epidemiol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Infect Control Hosp Epidemiol</journal-id><journal-id journal-id-type=\"publisher-id\">ICE</journal-id><journal-title-group><journal-title>Infection Control and Hospital Epidemiology</journal-title></journal-title-group><issn pub-type=\"ppub\">0899-823X</issn><issn pub-type=\"epub\">1559-6834</issn><publisher><publisher-name>Cambridge University Press</publisher-name><publisher-loc>New York, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32734852</article-id><article-id pub-id-type=\"pmc\">PMC7431840</article-id><article-id pub-id-type=\"publisher-id\">S0899823X20003803</article-id><article-id pub-id-type=\"doi\">10.1017/ice.2020.380</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Letter to the Editor</subject></subj-group></article-categories><title-group><article-title>Validation of a small-size pooling approach targeting hospital surveillance of SARS-CoV-2 infection</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-4029-6865</contrib-id><name><surname>Petrucca</surname><given-names>Andrea</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"a1\">\n<sup>1</sup>\n</xref><xref ref-type=\"corresp\" rid=\"cor1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Borro</surname><given-names>Marina</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"a2\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"a3\">\n<sup>3</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lionetto</surname><given-names>Luana</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"a2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"a3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Gentile</surname><given-names>Giovanna</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"a2\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"a3\">\n<sup>3</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Alari</surname><given-names>Antonella</given-names></name><degrees>Biol</degrees><xref ref-type=\"aff\" rid=\"a1\">\n<sup>1</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Simmaco</surname><given-names>Maurizio</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"a2\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"a3\">\n<sup>3</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Santino</surname><given-names>Iolanda</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"a1\">\n<sup>1</sup>\n</xref><xref ref-type=\"aff\" rid=\"a2\">\n<sup>2</sup>\n</xref></contrib></contrib-group><aff id=\"a1\"><label>1</label>Microbiology Unit, Sant’Andrea Hospital, <city>Rome</city>, <country>Italy</country></aff><aff id=\"a2\"><label>2</label>Department of Neurosciences, Mental Health and Sensory Organs, <institution>Sapienza University of Rome</institution>, <city>Rome</city>, <country>Italy</country></aff><aff id=\"a3\"><label>3</label>Sant’Andrea Hospital, <city>Rome</city>, <country>Italy</country></aff><author-notes><corresp id=\"cor1\"><bold>Author for correspondence:</bold> Andrea Petrucca, E-mail: <email>apetrucca@ospedalesantandrea.it</email></corresp></author-notes><pub-date publication-format=\"electronic\" date-type=\"pub\"><day>30</day><month>7</month><year>2020</year></pub-date><fpage>1</fpage><lpage>2</lpage><history><date date-type=\"received\"><day>24</day><month>6</month><year>2020</year></date><date date-type=\"rev-recd\"><day>22</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Society for Healthcare Epidemiology of America 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>The Society for Healthcare Epidemiology of America</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (<uri xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"S0899823X20003803a.pdf\"/><counts><fig-count count=\"1\"/><ref-count count=\"9\"/><page-count count=\"2\"/></counts></article-meta></front><body><p>\n<italic>To the Editor</italic>—The ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), started in December 2019 as a large viral pneumonia outbreak in the city of Wuhan, China.<sup><xref rid=\"r1\" ref-type=\"bibr\">1</xref></sup> The disease spread from Wuhan to other countries, and the World Health Organization declared it a pandemic by March 11, 2020 (<uri xlink:href=\"https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen\">https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen</uri>). With vaccine development currently underway, the rapid identification of disease carriers and their close contacts represents the only effective measure to limit SARS-CoV-2 spreading.<sup><xref rid=\"r2\" ref-type=\"bibr\">2</xref></sup>\n</p><p>Hospitals are hotbeds for SARS-CoV-2 transmission; healthcare workers (HCWs) are at high risk of being infected and of further transmitting the virus to vulnerable patients.<sup><xref rid=\"r3\" ref-type=\"bibr\">3</xref></sup> Thus, infection control strategies based on SARS-CoV-2 testing in HCWs and patients are necessary.<sup><xref rid=\"r4\" ref-type=\"bibr\">4</xref></sup> Unfortunately, this type of disease surveillance is limited by the overwhelming demand for SARS-CoV-2 molecular diagnostic analyses.<sup><xref rid=\"r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"r5\" ref-type=\"bibr\">5</xref>,<xref rid=\"r6\" ref-type=\"bibr\">6</xref></sup>\n</p><p>To increase COVID-19 testing capacity, procedures based on pooling of naso-oral pharyngeal (NOP) swab specimens have been recently proposed.<sup><xref rid=\"r7\" ref-type=\"bibr\">7</xref>–<xref rid=\"r9\" ref-type=\"bibr\">9</xref></sup> However, the validation of the sample pooling approach is crucial to assess its diagnostic accuracy and to avoid false-negative results. Recent studies describing the detection of SARS-CoV-2 RNA in pools of 5 to 32 samples reported false-negative rates up to 10% for large groups, suggesting that smaller sample pools are a good compromise to increase sample processing capacity while maintaining test reliability.<sup><xref rid=\"r6\" ref-type=\"bibr\">6</xref>–<xref rid=\"r9\" ref-type=\"bibr\">9</xref></sup> Since 5-sample pools were shown to efficiently detect SARS-CoV-2 RNA in RT-PCR assays,<sup><xref rid=\"r7\" ref-type=\"bibr\">7</xref></sup> we chose to test and validate this approach using a high-throughput RNA extraction and amplification platform. The Sant’ Andrea Hospital of Rome (Italy) has put in place a SARS-CoV-2 surveillance program focused on the periodic screening of HCWs and preventive screening of patients (before hospitalization). In total, 2,035 people from the surveillance program (1,437 HCWs and 598 patients) were enrolled in this study. The molecular diagnostic workflow we used for SARS-CoV-2 detection included the following elements: (1) NOP swab sampling using the COPAN UTM-RM virus transport medium (Copan Diagnostics, Murrieta, CA); (2) automated specimen RNA extraction and amplification with the Versant kPCR molecular system (Siemens Healthineers AG, Erlangen, Germany). Viral nucleic acid detection was carried out using the detection kit for 2019 novel coronavirus (2019-nCoV) RNA (PCR-fluorescence probing; Daan Gene, Sun Yat University, Guangzhou, Guandong, China), an RT-PCR assay which simultaneously detects the viral nucleocapsid (N) and Orf1ab genes.</p><p>We first tested a small set of NOP swab pools to assess the lower detection limit of the method, then we validated the method on a larger set of sample pools from Sant’ Andrea Hospital HCWs and patients. Each sample was analyzed both individually and as a part of a pool of 5 specimens (200 μL each). The small set consisted of 10 pools, each including 1 SARS-CoV-2–positive sample and 4 negative NOP samples. For each pool, 2 technical replicates were prepared and analyzed. The PCR cycle threshold (Ct) values of individually tested positive samples ranged from 33.3 to 38.1 for the N gene and from 34.1 to 38.7 for the Orf1ab gene, whereas Ct values obtained from their corresponding pools were between 34.3 and 38.9 for the N gene and between 35 and 40 for the Orf1ab gene (Fig. <xref ref-type=\"fig\" rid=\"f1\">1</xref>). The Ct value differences (ΔCt) between individual and pooled positive samples ranged from 0.3 to 2.3 for N and from 0.4 to 1.8 for Orf1ab (Fig. <xref ref-type=\"fig\" rid=\"f1\">1</xref>). No false-positive amplification signals were obtained using an analogous set of sample pools consisting of only SARS-CoV-2–negative NOP specimens.</p><p>\n<fig id=\"f1\" orientation=\"portrait\" position=\"float\"><label>Fig. 1.</label><caption><p>Influence of pooling samples strategy on the sensitivity of RT-PCR. Cycle threshold (Ct) values obtained from individual positive naso-oral pharyngeal swabs (NOP) samples (P1–P10; black symbols) and from their corresponding pooled samples, run in duplicate (open and grey symbols). Circles and squares indicate Ct values of the N and Orf1ab genes, respectively. Connecting brackets indicate the change in Ct (ΔCt) between individual NOP positive samples and their corresponding pools. The horizontal dotted line represents the Ct limit of our RT-PCR assay to assign a positive detection of SARS-Cov-2 RNA in NOP specimens.</p></caption><graphic xlink:href=\"S0899823X20003803_fig1\"/></fig>\n</p><p>We next performed a validation of the pooling strategy to assess the diagnostic performance and benefits of this approach. Daily during the first 3 weeks of April 2020, we analyzed an average of 96.9 individual NOP samples and their corresponding 19.38 pools collected from Sant’ Andrea Hospital HCWs and patients (2,035 individual samples and 407 pools). In total, 36 patients (1.7 %) were identified as SARS-CoV-2–positive through the analysis of individual samples as well as of their corresponding pools. Interestingly, all SARS-CoV-2–positive study participants belonged to the HCW group. In individually tested positive NOP specimens, the average Ct value for the N gene was 29.6 (±4.7) and the average Ct value for the Orf1ab gene was 31.1 (± 5.6). In pooled samples, the average Ct value for the N gene was 31.7 (±5.9) and the average Ct value for the Orf1ab gene was 33.8 (±6.1).</p><p>The diagnostic accuracy of the 5-sample pooling strategy was excellent, showing sensitivity, specificity, and positive and negative predictive values of 100%. The tests required to complete individual NOP sample and pool analysis were, respectively, 2,035 and 587 (407 pools plus 36×5 = 180 tests to confirm single samples included in positive pools). Summarizing, the small-pooling approach saved 1,448 tests, corresponding to 71.1% of the total cost of laboratory reagents required for individual sample analysis (ie, 15 RNA extraction and RT-PCR amplification kits). In our hands, it was possible to run at least 2 consecutive analytical sessions per day, allowing the reanalysis of individual samples from positive pools within 24 hours, which is a standard laboratory turnaround time for SARS-CoV-2 diagnostics in Italy.</p><p>When COVID-19 incidence is low, as in our study (below 2%), the small-pooling approach significantly reduces the use of laboratory resources and simultaneously increases the number of screened people. The number of positive pools to be reanalyzed increases in relation to SARS-CoV-2 incidence, consequently worsening TAT and cost–benefit ratio. In conclusion, the described approach represents an optimal strategy for surveillance programs in late pandemic phases when screening of a large population is needed.</p></body><back><ack><title>Acknowledgments</title><p>None.</p></ack><sec id=\"s1\" sec-type=\"other\"><title>Financial support</title><p>No financial support was provided relevant to this article.</p></sec><sec id=\"s2\" sec-type=\"other\"><title>Conflicts of interest</title><p>All authors report no conflicts of interest relevant to this article.</p></sec><ref-list id=\"reflist1\"><title>References</title><ref id=\"ref1\"><label>1.</label><mixed-citation publication-type=\"journal\" id=\"r1\">\n<string-name>\n<surname>Zhu</surname>\n<given-names>N</given-names>\n</string-name>, <string-name>\n<surname>Zhang</surname>\n<given-names>D</given-names>\n</string-name>, <string-name>\n<surname>Wang</surname>\n<given-names>W</given-names>\n</string-name>, <etal>et al.</etal>\n<article-title>Novel coronavirus from patients with 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807836</article-id><article-id pub-id-type=\"pmc\">PMC7431841</article-id><article-id pub-id-type=\"publisher-id\">70634</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70634-6</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Comparative analysis, distribution, and characterization of microsatellites in Orf virus genome</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Sahu</surname><given-names>Basanta Pravas</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0001-6807-010X</contrib-id><name><surname>Majee</surname><given-names>Prativa</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Singh</surname><given-names>Ravi Raj</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sahoo</surname><given-names>Anjan</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-4762-4553</contrib-id><name><surname>Nayak</surname><given-names>Debasis</given-names></name><address><email>nayakdn@iiti.ac.in</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.450280.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1769 7721</institution-id><institution>Discipline of Biosciences and Biomedical Engineering, </institution><institution>Indian Institute of Technology Indore, </institution></institution-wrap>Indore, MP 453 552 India </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412372.1</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2292 0631</institution-id><institution>College of Veterinary Science and Animal Husbandry, </institution></institution-wrap>Bhubaneswar, 751003 India </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13852</elocation-id><history><date date-type=\"received\"><day>1</day><month>3</month><year>2019</year></date><date date-type=\"accepted\"><day>1</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Genome-wide in-silico identification of microsatellites or simple sequence repeats (SSRs) in the Orf virus (ORFV), the causative agent of contagious ecthyma has been carried out to investigate the type, distribution and its potential role in the genome evolution. We have investigated eleven ORFV strains, which resulted in the presence of 1,036–1,181 microsatellites per strain. The further screening revealed the presence of 83–107 compound SSRs (cSSRs) per genome. Our analysis indicates the dinucleotide (76.9%) repeats to be the most abundant, followed by trinucleotide (17.7%), mononucleotide (4.9%), tetranucleotide (0.4%) and hexanucleotide (0.2%) repeats. The Relative Abundance (RA) and Relative Density (RD) of these SSRs varied between 7.6–8.4 and 53.0–59.5 bp/kb, respectively. While in the case of cSSRs, the RA and RD ranged from 0.6–0.8 and 12.1–17.0 bp/kb, respectively. Regression analysis of all parameters like the incident of SSRs, RA, and RD significantly correlated with the GC content. But in a case of genome size, except incident SSRs, all other parameters were non-significantly correlated. Nearly all cSSRs were composed of two microsatellites, which showed no biasedness to a particular motif. Motif duplication pattern, such as, (C)-x-(C), (TG)-x-(TG), (AT)-x-(AT), (TC)- x-(TC) and self-complementary motifs, such as (GC)-x-(CG), (TC)-x-(AG), (GT)-x-(CA) and (TC)-x-(AG) were observed in the cSSRs. Finally, in-silico polymorphism was assessed, followed by in-vitro validation using PCR analysis and sequencing. The thirteen polymorphic SSR markers developed in this study were further characterized by mapping with the sequence present in the database. The results of the present study indicate that these SSRs could be a useful tool for identification, analysis of genetic diversity, and understanding the evolutionary status of the virus.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Data mining</kwd><kwd>Pox virus</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Contagious ecthyma or Orf is a zoonotic viral disease of sheep, goats, and other small ruminants characterized by proliferative skin lesions in and around the oral cavity in the form of erythematous macule, papule, vesicle, pustule, and scabs. The causative agent is the Orf virus (ORFV), a member of the genus Parapoxvirus of the Poxviridae family. The virus is highly contagious, quite stable in the environment, and remains in the infectious form in wools or animal excreta for months to years<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. The disease is manifested by proliferative lesions on the mouth and muzzle that usually get resolved in 1–2 months<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. These facial and oral lesions in lambs may interfere with suckling, while lesions on the udder may interfere in feeding neonates. Similarly, foot lesions often cause transient lameness in infected animals, and together all these results in poor health and loss of body weight. Lesions progress through all clinical stages but are generally non-proliferative and usually resolve within 2–3 weeks. ORFV specific antibodies do not seem to confer protective immunity, although the IgG2 isotype is believed to provide some defense against ORFV infection<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. As IgG2 is not secreted in the colostrum of ruminants, lamb and kids don’t get required protection<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>. Although Orf is normally non-fatal in adults, often comes with high morbidity (up to 100%). While in neonates, Orf can be life-threating as it interferes with suckling of milk from the infected udder or predisposing the animals to the secondary bacterial or fungal infections<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. For these reasons, the mortality rate may reach up to 15%<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. There is increasing evidence of ORFV to cross‐infect other species of animals such as camels, gazelles, reindeers, musk ox, and Japanese serows<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>.</p><p id=\"Par3\">The virus can infect humans, particularly those who are closely associated with animal handling. Zoonosis occurs most frequently during lambing, shearing, docking, drenching, or slaughtering of affected animals<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. Orf infections in humans appear in hand<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup> but occasionally seen in the face<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>, nose<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>, axilla<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>, scalp<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>, genitals<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>, urethral<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>, and pericanthal eyelid skin and the wound heals spontaneously. However, in immunosuppressive individuals, large-sized poorly healing lesions could remain for an extended period up to a couple of months<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>. This possesses a significant health risk to animal-handlers and veterinarians who often get infected by direct contact and develop painful pustular lesions in the skins. Complications of Orf with secondary bacterial infections are potentially life-threatening and need urgent medical attention.</p><p id=\"Par4\">The ORFV is a classic epitheliotropic virus, having a double-stranded DNA genome with a higher (64%) GC content<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. The genome consists of central conserved and terminal variable domains with size varying from 134 to 139 kbp having ~ 130 putative genes, 88 of which are conserved to Chordopoxviruses<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. Having such a devastating character, this virus has got less attention in terms of genomic information, which is evident from the availability of only eleven complete genome sequences worldwide. Several conserved genomic regions such as envelope protein B2L (ORFV011), F1L (ORFV059), and A32L (ORFV108) were used for ORFV identification and phylogenetic tree construction<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. Still, there is a lack of clarity regarding the real diversity of ORFV due to the absence of a reliable system for virus identification, which consists of hypermutable regions such as microsatellites rather than conventional conserved genes.</p><p id=\"Par5\">Simple sequence repeats (SSRs), also known as microsatellites, refer to mono-, di-, tri-, tetra-, penta- and hexanucleotide sequence units that are repeated in tandem in a genome<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. Those short motifs of DNA are distributed ubiquitously in the genome of eukaryotes<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>, and prokaryotes<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>, and is regarded as the most variable type of DNA sequence within the viral genome<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref>,<xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>. The microsatellites may be classified as either simple or compound, depending on the constituent of nucleotide sequences. The interruptions present in the microsatellite will give rise to interrupted pure, compound, interrupted compound, complex and interrupted complex types. Two or more microsatellites resides directly adjacent to each other to form compound microsatellites by interruption of repeats<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. Due to its unique characteristics, these SSRs play a major role in meiotic recombination<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref>–<xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>, the evolution of species<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>, genome mapping<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>, differentiation of viral strains<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>, studying population genetics<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>, and secondary structure formation<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Many studies have highlighted the presence of microsatellite repeats in viruses, such as menovirus<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>, vesicular stomatitis virus<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>, hepatitis C virus<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>, and human respiratory syncytial virus (RSV)<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. Here, we report for the first time a comparative analysis of microsatellites with respect to the abundance, distribution, composition, and polymorphism of SSRs within ORFV through in-silico approach, followed by the development and characterization of thirteen microsatellites markers. Using these tools, we further tested its usefulness by screening the viral genome from an ORFV outbreak and constructing a concatenated phylogenetic tree, which elucidated that the investigated virus closely related to the Chinese isolate. These markers could be used as a tool for making multiplex PCR assays for virus identification, strain demarcation, and evolutionary analysis.</p></sec><sec id=\"Sec2\"><title>Materials and methods</title><sec id=\"Sec3\"><title>Genome sequences</title><p id=\"Par6\">The publicly available eleven complete genome sequences of ORFV isolates obtained from the NCBI database (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov\">www.ncbi.nlm.nih.gov</ext-link>) were used for genome-wide in-silico microsatellites analysis. To compare genomic sequences of different lengths, we calculated the Relative Density (RD) and Relative Abundance (RA) values. RD is defined as the total length (bp) contributed by each microsatellite per kilobase (kb) of sequence analyzed whereas; RA is the number of microsatellites present per kb of the genome (kb). Among all the strains, we have chosen OV-SA00 (Acc. number: AY386264) as the reference to evaluate the polymorphism of microsatellites through in-silico approach as well as the development of SSRs for Indian origin ORFV (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>).<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Overview of microsatellites in ORFV complete genome sequences.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Sr. no.</th><th align=\"left\">Acc. no.</th><th align=\"left\">Names of the strains</th><th align=\"left\">Year of strain isolation</th><th align=\"left\">Size (bp)</th><th align=\"left\">Country</th><th align=\"left\">Host</th><th align=\"left\">GC content (%)</th><th align=\"left\">Total no of SSRs</th><th align=\"left\">RA</th><th align=\"left\">RD</th><th align=\"left\">Total no of cSSRs</th><th align=\"left\">cRA</th><th align=\"left\">cRD</th><th align=\"left\">% of cSSR</th></tr></thead><tbody><tr><td align=\"left\">S1</td><td align=\"left\">AY386264</td><td align=\"left\">OV-SA00</td><td align=\"left\">2004</td><td align=\"left\">139,962</td><td align=\"left\">USA</td><td align=\"left\">Goat</td><td char=\".\" align=\"char\">63.44</td><td align=\"left\">1,181</td><td char=\".\" align=\"char\">8.43</td><td char=\".\" align=\"char\">59.5</td><td char=\".\" align=\"char\">107</td><td char=\".\" align=\"char\">0.76</td><td char=\".\" align=\"char\">16.98</td><td char=\".\" align=\"char\">9.06</td></tr><tr><td align=\"left\">S2</td><td align=\"left\">AY386263</td><td align=\"left\">OV-IA82</td><td align=\"left\">2004</td><td align=\"left\">137,241</td><td align=\"left\">USA</td><td align=\"left\">Lamb</td><td char=\".\" align=\"char\">64.33</td><td align=\"left\">1,089</td><td char=\".\" align=\"char\">7.93</td><td char=\".\" align=\"char\">55.66</td><td char=\".\" align=\"char\">98</td><td char=\".\" align=\"char\">0.67</td><td char=\".\" align=\"char\">14.51</td><td char=\".\" align=\"char\">8.99</td></tr><tr><td align=\"left\">S3</td><td align=\"left\">DQ184476</td><td align=\"left\">NZ2</td><td align=\"left\">2006</td><td align=\"left\">137,820</td><td align=\"left\">New Zealand</td><td align=\"left\">Sheep</td><td char=\".\" align=\"char\">64.34</td><td align=\"left\">1,082</td><td char=\".\" align=\"char\">7.85</td><td char=\".\" align=\"char\">55.42</td><td char=\".\" align=\"char\">95</td><td char=\".\" align=\"char\">0.68</td><td char=\".\" align=\"char\">14.11</td><td char=\".\" align=\"char\">8.78</td></tr><tr><td align=\"left\">S4</td><td align=\"left\">HM133903</td><td align=\"left\">D1701</td><td align=\"left\">2011</td><td align=\"left\">134,038</td><td align=\"left\">Germany</td><td align=\"left\">Sheep</td><td char=\".\" align=\"char\">63.69</td><td align=\"left\">1,038</td><td char=\".\" align=\"char\">7.74</td><td char=\".\" align=\"char\">54.34</td><td char=\".\" align=\"char\">83</td><td char=\".\" align=\"char\">0.61</td><td char=\".\" align=\"char\">12.13</td><td char=\".\" align=\"char\">7.99</td></tr><tr><td align=\"left\">S5</td><td align=\"left\">KF234407</td><td align=\"left\">NA11</td><td align=\"left\">2015</td><td align=\"left\">137,080</td><td align=\"left\">China</td><td align=\"left\">Sheep</td><td char=\".\" align=\"char\">63.63</td><td align=\"left\">1,049</td><td char=\".\" align=\"char\">7.65</td><td char=\".\" align=\"char\">53.54</td><td char=\".\" align=\"char\">87</td><td char=\".\" align=\"char\">0.63</td><td char=\".\" align=\"char\">12.78</td><td char=\".\" align=\"char\">8.29</td></tr><tr><td align=\"left\">S6</td><td align=\"left\">KP010353</td><td align=\"left\">YX</td><td align=\"left\">2015</td><td align=\"left\">138,231</td><td align=\"left\">China</td><td align=\"left\">Goat</td><td char=\".\" align=\"char\">63.75</td><td align=\"left\">1,099</td><td char=\".\" align=\"char\">7.95</td><td char=\".\" align=\"char\">55.4</td><td char=\".\" align=\"char\">90</td><td char=\".\" align=\"char\">0.65</td><td char=\".\" align=\"char\">12.89</td><td char=\".\" align=\"char\">8.18</td></tr><tr><td align=\"left\">S7</td><td align=\"left\">KP010354</td><td align=\"left\">GO</td><td align=\"left\">2018</td><td align=\"left\">139,866</td><td align=\"left\">China</td><td align=\"left\">Goat</td><td char=\".\" align=\"char\">63.6</td><td align=\"left\">1,114</td><td char=\".\" align=\"char\">7.96</td><td char=\".\" align=\"char\">55.61</td><td char=\".\" align=\"char\">97</td><td char=\".\" align=\"char\">0.69</td><td char=\".\" align=\"char\">13.81</td><td char=\".\" align=\"char\">8.7</td></tr><tr><td align=\"left\">S8</td><td align=\"left\">KP010355</td><td align=\"left\">NP</td><td align=\"left\">2015</td><td align=\"left\">132,111</td><td align=\"left\">China</td><td align=\"left\">Goat</td><td char=\".\" align=\"char\">63.76</td><td align=\"left\">1,054</td><td char=\".\" align=\"char\">7.97</td><td char=\".\" align=\"char\">56.02</td><td char=\".\" align=\"char\">86</td><td char=\".\" align=\"char\">0.65</td><td char=\".\" align=\"char\">12.8</td><td char=\".\" align=\"char\">8.15</td></tr><tr><td align=\"left\">S9</td><td align=\"left\">KP010356</td><td align=\"left\">SJ1</td><td align=\"left\">2015</td><td align=\"left\">139,112</td><td align=\"left\">China</td><td align=\"left\">Goat</td><td char=\".\" align=\"char\">63.63</td><td align=\"left\">1,126</td><td char=\".\" align=\"char\">8.09</td><td char=\".\" align=\"char\">57.01</td><td char=\".\" align=\"char\">99</td><td char=\".\" align=\"char\">0.71</td><td char=\".\" align=\"char\">13.74</td><td char=\".\" align=\"char\">8.79</td></tr><tr><td align=\"left\">S10</td><td align=\"left\">KY053526</td><td align=\"left\">OV-HN3/12</td><td align=\"left\">2012</td><td align=\"left\">136,643</td><td align=\"left\">China</td><td align=\"left\">Sheep</td><td char=\".\" align=\"char\">63.67</td><td align=\"left\">1,036</td><td char=\".\" align=\"char\">7.58</td><td char=\".\" align=\"char\">53.04</td><td char=\".\" align=\"char\">84</td><td char=\".\" align=\"char\">0.61</td><td char=\".\" align=\"char\">12.31</td><td char=\".\" align=\"char\">8.18</td></tr><tr><td align=\"left\">S11</td><td align=\"left\">MG712417</td><td align=\"left\">SY17</td><td align=\"left\">2016</td><td align=\"left\">140,413</td><td align=\"left\">China</td><td align=\"left\">Sheep</td><td char=\".\" align=\"char\">63.81</td><td align=\"left\">1,087</td><td char=\".\" align=\"char\">7.74</td><td char=\".\" align=\"char\">54.28</td><td char=\".\" align=\"char\">92</td><td char=\".\" align=\"char\">0.65</td><td char=\".\" align=\"char\">12.97</td><td char=\".\" align=\"char\">8.46</td></tr></tbody></table></table-wrap></p></sec><sec id=\"Sec4\"><title>Microsatellites identification, investigation, and statistical analysis</title><p id=\"Par7\">For identification of perfect mono, di, tri, tetra, penta, hexa as well as compound microsatellites, IMEx software<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup> was utilized. Microsatellites from genomes were extracted using the ‘Advance-Mode’ of IMEx using the parameters previously used for RNA viruses<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup> and DNA viruses<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. The parameters used were as follows: type of repeat: perfect; repeat size: all; minimum repeat number: 6, 3, 3, 3, 3, 3 for mono, di, tri, tetra, penta and hexanucleotide repeats, respectively. The maximum distance allowed between any two SSRs (dMAX) is 10 nucleotides. Other parameters were used as default. Compound microsatellites (cSSRs) were not standardized in order to determine real composition.</p></sec><sec id=\"Sec5\"><title>Multiple sequence alignment and identification of polymorphic SSRs</title><p id=\"Par8\">The microsatellites of OV-SA00 were considered for the identification of polymorphic microsatellites as well as consensus motifs. Sequences were first transferred to BioEdit version 7.2.5 software<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup> and aligned by CLUSTAL W<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup> module and checked manually for the presence of polymorphism. The Circos plot was generated using the Circos software to map the genome size, CDS, SSRs distribution, cSSRs distribution, and GC content in ORFV (OV-SA00) genome.</p></sec><sec id=\"Sec6\"><title>Disease outbreak and sample data collection</title><p id=\"Par9\">The study did not involve experiments on live vertebrates. Rather, samples were collected from the diseased goats (showing the symptoms of Orf) those reported for veterinary care where scab samples were collected by veterinary professionals as a routine practice. In October and November 2017, an outbreak of ORFV was noticed in Black Bengal goats in the Eastern-Indian state of Odisha with the geographical location (20.4625° N, 85.8830° E). Tissue samples in the form of scabs from four suspected goats were collected at both infective and recovery/convalescent phase and simultaneously treated for wounds with 2% boro glycerine and parenteral application of Enrofloxacin @ 5 mg/kg IM (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>).<fig id=\"Fig1\"><label>Figure 1</label><caption><p>ORFV infection in goat. Representative figure depicting clinical cases of ORFV infection in Black Bengal goat having proliferative lesions around the lip recorded in the study area.</p></caption><graphic xlink:href=\"41598_2020_70634_Fig1_HTML\" id=\"MO1\"/></fig></p><p id=\"Par10\">About 5 g of tissue samples were collected from each animal and subsequently dissolved in phosphate-buffered saline (PBS, pH 7.2) added with antibiotics and antifungal supplements in a labeled sterile tube. The homogenized samples were then treated with tissue lysis buffer containing proteinase K, and the mixture was incubated at 56 °C overnight. Finally, the mixture was passed through a column, and DNA was purified from the column by using the standard phenol–chloroform method as described by Sambrook et al.<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup> and stored at − 20 °C until further use. The suspected samples collected during this outbreak produced the expected PCR-amplified fragment size of 140 bp using ORFV specific primers orf1 and orf2<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup> having nucleotide sequences Orf1: 5′-CGCAGACGTGGCTGAGTACGT-3′ and Orf2: 5′-TGAGCTGGTTGGCGCTGTCCT-3′, which confirmed the presence of the virus.</p></sec><sec id=\"Sec7\"><title>Development of polymorphic SSRs</title><p id=\"Par11\">The polymorphic microsatellites identified through in-silico approach were further validated through in-vitro approach using ORFV positive clinical sample. Motifs located within defined flanking regions were PCR amplified using specially designed SSR-PCR primer pairs by Primer3Plus web tool (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi/\">https://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi/</ext-link>). The primer length was kept between 18 and 22 bp with product size in the range of 130–200 bp. For proper annealing to the template DNA, the annealing temperature was adjusted between 54 and 61 °C. The thermal cycling conditions for all genes were as follows: initial denaturation step at 95 °C for 5 min, with 35 cycles of denaturation at 95 °C for 50 s, with varying annealing temperature for each set of primers (55–61 °C) and extension step at 72 °C for 90 s with a final extension at 72 °C for 7 min. PCR amplification was performed in a Thermal Cycler system 2,720 (Applied Biosystems, USA) (Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>).<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Characteristics of the 13 microsatellite markers developed for the ORFV.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Primer name</th><th align=\"left\">Sequence</th><th align=\"left\">Expected size (bp)</th><th align=\"left\">Target repeat</th><th align=\"left\">Functional region of the genome</th><th align=\"left\">ORF</th><th align=\"left\">Position in genome</th><th align=\"left\">Temp (°C)</th><th align=\"left\">No. of variants</th></tr></thead><tbody><tr><td align=\"left\">ORFV-SSR-1</td><td align=\"left\"><p>F-CACCACCATTAACACCACCA</p><p>R-AAAGGGTTCGCAAGTACACC</p></td><td align=\"left\">166</td><td align=\"left\">(CA)<sub>3</sub></td><td align=\"left\">Hypothetical protein</td><td align=\"left\">ORF005</td><td char=\"–\" align=\"char\">4,974–4,979</td><td align=\"left\">55</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-2</td><td align=\"left\"><p>F-GACCGTGGCGAGATCCAC</p><p>R-CACCCTTATTGCCATTCAGC</p></td><td align=\"left\">159</td><td align=\"left\">(GGC)<sub>3</sub></td><td align=\"left\">Ankyrin repeat protein</td><td align=\"left\">ORF008</td><td char=\"–\" align=\"char\">7,290–7,298</td><td align=\"left\">55</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-3</td><td align=\"left\"><p>F-ATCTTTATGGGCGCTGAATG</p><p>R-CCCAGTGTAGAGGCCAATTC</p></td><td align=\"left\">151</td><td align=\"left\">(A)<sub>7</sub></td><td align=\"left\">Intergenic region</td><td align=\"left\"/><td char=\"–\" align=\"char\">7,406–7,412</td><td align=\"left\">56</td><td align=\"left\">3</td></tr><tr><td align=\"left\">ORFV-SSR-4</td><td align=\"left\"><p>F-ATGAGCACAATGCAGACCAG</p><p>R-GAGCAGACACTGCCTACGAC</p></td><td align=\"left\">130</td><td align=\"left\">(CG)<sub>3</sub></td><td align=\"left\">Hypothetical protein</td><td align=\"left\">ORF015</td><td char=\"–\" align=\"char\">13,445–13,450</td><td align=\"left\">58</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-5</td><td align=\"left\"><p>F-TCAAAGTCCTCGTCCGAGTT</p><p>R-CACATTCACCGAGGAGCAG</p></td><td align=\"left\">168</td><td align=\"left\">(TAC)<sub>3</sub></td><td align=\"left\">DNA-binding phosphoprotein</td><td align=\"left\">ORF032</td><td char=\"–\" align=\"char\">34,352–34,360</td><td align=\"left\">56</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-6</td><td align=\"left\"><p>F-ATGACCTAGAGCCCGTGGAC</p><p>R-GAGCAGGTCATTCGTGGAG</p></td><td align=\"left\">172</td><td align=\"left\">(GAG)<sub>3</sub></td><td align=\"left\">Virion core protein</td><td align=\"left\">ORF088</td><td char=\"–\" align=\"char\">93,996–94,004</td><td align=\"left\">55</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-7</td><td align=\"left\"><p>F-GCCGCCACTACTTCAGAAAC</p><p>R-CTAGAGCCAGCGCAGGTACA</p></td><td align=\"left\">200</td><td align=\"left\">(T)<sub>6</sub></td><td align=\"left\">Intergenic region</td><td align=\"left\"/><td char=\"–\" align=\"char\">117,434–117,439</td><td align=\"left\">60</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-8</td><td align=\"left\"><p>F-TTTACGTGAAGGCGTTCCT</p><p>R-TGAGGCACTTCCTGGACATC</p></td><td align=\"left\">159</td><td align=\"left\">(A)<sub>6</sub></td><td align=\"left\">GM-CSF/IL-2 inhibition factor-like protein</td><td align=\"left\">ORF117</td><td char=\"–\" align=\"char\">118,261–118,266</td><td align=\"left\">58</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-9</td><td align=\"left\"><p>F-TTCCTAGGTGCGTTCAGAGG</p><p>R-GAGCTGTCGGGGATCTCG</p></td><td align=\"left\">155</td><td align=\"left\">(CAC)<sub>3</sub></td><td align=\"left\">Ankyrin repeat protein</td><td align=\"left\">ORF121</td><td char=\"–\" align=\"char\">121,158–121,166</td><td align=\"left\">54</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-10</td><td align=\"left\"><p>F-TCACTACGAGACCCCTGACC</p><p>R-AGTGCTTCATTGGGAAGTCG</p></td><td align=\"left\">164</td><td align=\"left\">(C)<sub>6</sub></td><td align=\"left\">Ankyrin repeat protein</td><td align=\"left\">ORF121</td><td char=\"–\" align=\"char\">121,625–121,630</td><td align=\"left\">61</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-11</td><td align=\"left\"><p>F-CACAGATGCGTATTGTGTTGAG</p><p>R-TTCAGTTGGTCTTTCATCTGGA</p></td><td align=\"left\">156</td><td align=\"left\">(AGT)<sub>3</sub></td><td align=\"left\">IL-10-like protein</td><td align=\"left\">ORF127</td><td char=\"–\" align=\"char\">128,736–128,744</td><td align=\"left\">57</td><td align=\"left\">2</td></tr><tr><td align=\"left\">ORFV-SSR-12</td><td align=\"left\"><p>F-AGTTATCGGTCGGATTCTCG</p><p>R-GCGCAATACGAGAGTGAACA</p></td><td align=\"left\">150</td><td align=\"left\">(AGTTAC)<sub>3</sub></td><td align=\"left\">Intergenic region</td><td align=\"left\">–</td><td char=\"–\" align=\"char\">129,259–129,276</td><td align=\"left\">55</td><td align=\"left\">3</td></tr><tr><td align=\"left\">ORFV-SSR-13</td><td align=\"left\"><p>F-GTTCTCCCGCTGGATAAATG</p><p>R-CGAGGAAGACGTCGTACAGC</p></td><td align=\"left\">160</td><td align=\"left\">(CGC)<sub>3</sub></td><td align=\"left\">Putative serine/threonine protein kinase</td><td align=\"left\">ORF130</td><td char=\"–\" align=\"char\">134,033–134,041</td><td align=\"left\">55</td><td align=\"left\">2</td></tr></tbody></table></table-wrap></p><p id=\"Par12\">The amplified products were resolved by electrophoresis in a 3% agarose gel. The PCR amplified products, stained with ethidium bromide, were visualized and photographed using a Gel Doc™ XR + System with Image Lab™ Software (Bio-Rad®). Subsequently, the amplified products were purified using QIAquick® purification kit (QIAGEN, USA) and the purified fragments were sent for sequencing using 3100 ABI sequencer (Applied Biosystems, USA) as described by Sanger et al.<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>. All sequences obtained were analyzed and verified twice in each direction.</p></sec><sec id=\"Sec8\"><title>Sequencing data analysis and phylogenetic tree construction</title><p id=\"Par13\">The sequencing results of the developed SSR markers were aligned by using discontiguous-MegaBLAST to identify specific regions among the reads (microsatellites) within the ORFV genome<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup>. Next, the sequencing results were subjected to the BLASTx analysis, which compares translational products of the nucleotide query sequence to protein databases (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi\">https://www.ncbi</ext-link>. nlm.nih.gov). A concatenated phylogenetic tree was constructed using the bootstrap consensus tree building method of neighbor-joining with bootstrap value 500 through MEGA 5 to elucidate the genetic relationship of the outbreak sample with the global strains of ORFV.</p></sec></sec><sec id=\"Sec9\"><title>Results</title><sec id=\"Sec10\"><title>Distribution of SSRs and cSSRs in ORFV genome</title><p id=\"Par14\">Our study revealed a large number of SSRs scattered throughout the ORFV genomes varying from 1,036 to 1,181 in number with an average of 1,092 per genome. The RA and RD ranged from 7.6–8.4 and 53.0–59.5, respectively, in the analyzed ORFV genomes. However, previous reports in other DNA viruses such as human papillomaviruses (HPVs), the RA and RD ranged from 3.6–8.3 and 23.9–59.1<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>. In the case of Herpesviruses, RA and RD occurred to be 4.1–13.3 and 26.9–102.9<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. On examining the SSR unit size classes, dinucleotide repeats were found to be most abundant (76.9%), followed by trinucleotide (17.7%) and mononucleotide repeats (4.9%) in all the genomes. Tetranucleotide and hexanucleotide repeats were least in number and represented 0.4% and 0.2% within the ORFV genome, respectively. There were no SSRs with pentanucleotide repeats observed in the ORFV genome. Approximately 90% and 10% of microsatellite motifs were distributed within coding and noncoding regions. Among the non-coding region, 4.8% are present in the UTR, while 5.4% in the intergenic regions, where functional protein and hypothetical protein occupied 68.8% and 21%, respectively. The genome-wide scan revealed the presence of 83–107 cSSRs, with an average of 93 occurrences per genome. In the case of compound microsatellites, the calculated RA and RD ranged from 0.6–0.8 and 12.1–17.0. However, in other DNA viruses such as HPVs, RA, and RD exhibited 0–1.2 and 0–27.3, whereas, in Herpesviruses, the RA and RD occurred 0.1–1.8 and 2.2–35.1<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref>,<xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. Approximately 89.5% and 10.5% of microsatellite motifs were distributed within coding or non-coding regions, respectively. Among the non-coding region, 5.0% were represented in the UTR while 5.5% in the intergenic region, where functional protein and hypothetical protein occupied 60.7% and 28.8%, respectively (Figure S2).</p><p id=\"Par15\">The percentage of individual microsatellites being part of compound microsatellite (cSSR%) ranged from 7.9 to 9.0 (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>). Based on dMAX value, the maximum distance between any two adjacent microsatellites and if the distance separating two microsatellites is less than or equivalent to dMAX, than microsatellites are classified as cSSRs<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>. To determine the impact of dMAX, all the studied genome sequences were chosen to determine the variability of cSSRs with increasing dMAX. The value of dMAX was set between 10 and 100 by Microsatellite Identification Search Analysis (MISA)<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>. Our analysis revealed an overall increase in the number of cSSRs with higher dMAX value and attained a plateau (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>).<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Frequency of cSSRs in relation to varying dMAX (10–100) across eleven ORFV complete genomes represented on the right side of the graph. A higher cSSR incidence was observed with increasing dMAX in the genomes.</p></caption><graphic xlink:href=\"41598_2020_70634_Fig2_HTML\" id=\"MO2\"/></fig></p></sec><sec id=\"Sec11\"><title>Genomic parameters influencing SSR and cSSR distribution</title><p id=\"Par16\">We tested for the correlation between genome size and GC content with the incidence, RA, RD of SSRs and cSSRs. Except incidence (R<sup>2</sup> = 0.6162, <italic>p</italic> > 0.05), all other parameters such as RA and RD of SSRs had no correlation (R<sup>2</sup> = 0.002374, <italic>p</italic> > 0.05; R<sup>2</sup> = 0.18, <italic>p</italic> < 0.05) with the genome size and GC content (R<sup>2</sup> = 0.09377, <italic>p</italic> < 0.05, R<sup>2</sup> = 0.00126, <italic>p</italic> > 0.05; R<sup>2</sup> = 0.08129, <italic>p</italic> < 0.05). The regression analysis of cSSRs showed significant correlation with the incidence (R<sup>2</sup> = 0.6483, <italic>p</italic> > 0.05) and RA (R<sup>2</sup> = 0.4823, <italic>p</italic> > 0.05) while displayed non-significant correlation with RD (R<sup>2</sup> = 0.3759, <italic>p</italic> < 0.05). On the contrary, the GC content was weakly correlated with the number (R<sup>2</sup> = 0.02903, <italic>p</italic> > 0.05), RD (R<sup>2</sup> = 0.004839, <italic>p</italic> < 0.05) and RA (R<sup>2</sup> = 0.03917, <italic>p</italic> < 0.05) of cSSRs.</p></sec><sec id=\"Sec12\"><title>The frequency of classified repeat types</title><p id=\"Par17\">The overall frequency of mononucleotide repeats A/T (64.1%), dinucleotide repeat motif CG/GC (81.6%) were the most prevalent than poly G/C (35.9%), GA/TC (5.0%), AC/GT (4.5%), AG/CT (3.9%), CA/TG (3.6%) and AT/TA (1.4%), respectively. Analysis of the classified tri-repeat types revealed that the ORFV genome had 30 types of trinucleotide from which CGC/GCG, GCC/GGC, CAG/CTG, AGC/GCT, CCG/CGG were abundantly present exhibiting 18.2%, 14.5%, 6.3%, 6.2%, and 6.3%, respectively. The most common tetra and hexanucleotide repeats were CGAG/CTCG (34.9%), ACTC/GAGT (18.6%), GTGA/TCAC (9.3%) and AGTTAC/GTAACT (15.0%), ACACTC/GAGTGT (15.0%), respectively. However, the accession specific analysis illustrated that the frequency of mono, di, tri repeats varied from each other (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>a–c).<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Types of SSRs distribution. (<bold>A</bold>) Distribution of different motifs of mononucleotide SSRs within ORFV genomes, (<bold>B</bold>) distribution of different motifs of dinucleotide SSRs within ORFV genomes, and (<bold>C</bold>) distribution of different motifs of trinucleotide SSRs within ORFV genomes.</p></caption><graphic xlink:href=\"41598_2020_70634_Fig3_HTML\" id=\"MO3\"/></fig></p></sec><sec id=\"Sec13\"><title>Motif complexity of compound microsatellites</title><p id=\"Par18\">Compound microsatellites (cSSRs) are composed of two or more adjacent individual microsatellites. Generally, cSSR having the pattern like, m1-xn-m2, m1-xn-m2-xn-m3 are considered as ‘2-microsatellite’ and ‘3-microsatellite’, respectively<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>. Majority of cSSRs were composed of two motifs, followed by tri, tetra, and penta-motifs (Supplementary file <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). Interestingly, two long stretches of cSSR were composed of identical motifs repeated 12 times, which was exclusively found in the genome of AY386264. The CTG–CAG compound microsatellite composed of self-complementary motifs has been proposed to be created by recombination<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup>. However, our study showed no such compound microsatellites which contained self-complementary motifs, suggesting that these compound microsatellites were not likely to be derived from recombination. Motifs exhibiting the form [m1]n-xn-[m2]n can be termed as SSR-couples and are represented the maximum time in the genome. In this study, SSR couples, such as (CG)-x-(GC), (GC)-x-(GC), (GC)-x-(CGC), (GT)-x-(GC), (GC)-x-(CG), (CT)-x-(C) were presented in all analyzed genome. A number of self-complementary motifs such as (CG)3-x1-(GC)3, (CG)4-x1-(GC)3, (CG)3-x7-(GC)3, (CG)3-x0-(GC)3, (GC)3-x8-(CG)3, (CG)3-x7-(CG)3, (GC)3-x0-(CG)3, (CG)3-x4-(GC)3 have been observed in ORFV, which played a pivotal role in secondary structure formation. Motif duplication is one of the phenomena in which a similar motif is located on both ends of the spacer sequence, for example (CA)n-(X)y-(CA)z. About 22.1% of the total cSSR were made up of duplicated sequences having the motif pattern (GC)-x-(GC), (CG)-x-(CG), (GA)-x-(GA), (CA)-x-(CA), (CT)-x-(CT), (TC)-x-(TC), (CA)-x-(CA)-x-(CA), (A)-x-(A), (AG)-x-(AG)-x-(AG)-x-(AG)-x-(AG)-x-(AG)3-x-(AG)-x1-(AG)-x-(AG)-x-(AG)-x-(AG)-x-(AG)-x-(AG), (AG)-x-(AG), (C)-x-(C), (CA)-x-(CA), and (CT)-x-(CT)-x-(CT)-x-(CT)-x-(CT)-x-(CT)-x-(CT)-x-(CT)-x-(CT)-x-(CT)-x-(CT)-x-(CT)-x-(CT) (Supplementary file <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>).</p></sec><sec id=\"Sec14\"><title>Identification of polymorphic microsatellites through in silico approach</title><p id=\"Par19\">For a polymorphic microsatellite, the length of the repeat block should be non-identical with that of the other sequences in the database, and this length difference must be a multiple of the repeat unit<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR30\">30</xref>,<xref ref-type=\"bibr\" rid=\"CR52\">52</xref></sup>. For the identification of a polymorphic microsatellite, eleven strains of ORFV were used, where (AY386264) acted as the reference. A total thirteen number of polymorphic microsatellites were observed; among these, two were observed within the hypothetical protein, three in the intergenic regions, and rest eight in the protein-coding/genic regions. The polymorphic genic region containing the microsatellites encodes several important proteins such as Ankyrin repeat protein (ANK protein), DNA-binding phosphoprotein, virion core protein, Granulocyte–macrophage colony-stimulating factor (GM-CSF), Interleukin 10 protein (IL-10), Putative serine/threonine-protein kinase protein (Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>, Figure S3). The Circos map provides a clear vision regarding the SSRs and cSSRs distribution and other related details in ORFV (OV-SA00) genome (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>).<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Circos plot showing the Genome size, CDS, Distribution of SSRs, selected SSR markers, cSSRs and GC content in ORFV (OV-SA00) genome. From outer track to inner track: Genome size, CDS, SSRs, selected SSR markers (Black lines within the SSR), cSSRs and GC content.</p></caption><graphic xlink:href=\"41598_2020_70634_Fig4_HTML\" id=\"MO4\"/></fig></p></sec><sec id=\"Sec15\"><title>Development and characterization of SSR markers</title><p id=\"Par20\">All clinical samples collected during the outbreak were found to be positive for ORFV tested by producing the desired PCR amplicon size of 140 bp (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>).<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Clinical samples evaluation by universal OFRV primers. Electrophoresis gel showing the PCR amplicon of four suspected ORFV clinical samples collected from Black Bengal goats. M: 100 bp DNA ladder; -C: Negative control (PCR using nuclease-free water as DNA template); 1–4: Clinical samples.</p></caption><graphic xlink:href=\"41598_2020_70634_Fig5_HTML\" id=\"MO5\"/></fig></p><p id=\"Par21\">We chose all thirteen polymorphic markers to validate in-vitro. Hence, PCR was set with each primer sets to amplify the DNA isolated from a positive clinical sample. The SSR name, primer sequences, expected size, targeted motif, functional region, protein motif position, gene, ORF number, and annealing temperature, were summarized in Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>. All the SSR markers produced reliable and reproducible PCR products with the expected molecular size (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6</xref>).<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Clinical sample validation using SSR markers. Electrophoresis gel showing the PCR amplicon of the developed SSR markers in ORFV. SSR markers from SSR1 to SSR13; M:50 bp DNA ladder; -C: Negative control (PCR using nuclease-free water as DNA template).</p></caption><graphic xlink:href=\"41598_2020_70634_Fig6_HTML\" id=\"MO6\"/></fig></p><p id=\"Par22\">The amplified SSRs were further characterized by sequencing, mapping with the GenBank database through BLASTn and BLASTx. The results of BLASTn alignment revealed a 100% of query coverage and a high identity percentage (91–100%) between the respective sequencing product and their equivalent genes from the published OV-SA00 isolate genome sequence. The results of BLASTx alignment revealed various degrees of query coverage (38–96%) and a high identity percentage (91–100%) with their equivalent amino acid sequences (Table <xref rid=\"Tab3\" ref-type=\"table\">3</xref>).<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Alignment of the 13 sequenced microsatellite markers (partial) against the complete genome present in the NCBI database.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">SSR</th><th align=\"left\" colspan=\"3\">BLASTn</th><th align=\"left\" colspan=\"3\">BLASTx</th></tr><tr><th align=\"left\">Query cover (%)</th><th align=\"left\">E value</th><th align=\"left\">Identity (%)</th><th align=\"left\">Query cover</th><th align=\"left\">E value</th><th align=\"left\">Identity</th></tr></thead><tbody><tr><td align=\"left\">ORFV-SSR-1</td><td align=\"left\">100</td><td align=\"left\">6.00E−81</td><td char=\".\" align=\"char\">96</td><td align=\"left\">52%</td><td align=\"left\">0.41</td><td align=\"left\">91%</td></tr><tr><td align=\"left\">ORFV-SSR-2</td><td align=\"left\">100</td><td align=\"left\">5.00E−76</td><td char=\".\" align=\"char\">100</td><td align=\"left\">81%</td><td align=\"left\">2.00E−19</td><td align=\"left\">100%</td></tr><tr><td align=\"left\">ORFV-SSR-3</td><td align=\"left\">100</td><td align=\"left\">2.00E−50</td><td char=\".\" align=\"char\">91</td><td align=\"left\">Intergenic</td><td align=\"left\">Intergenic</td><td align=\"left\">Intergenic</td></tr><tr><td align=\"left\">ORFV-SSR-4</td><td align=\"left\">100</td><td align=\"left\">5.00E−60</td><td char=\".\" align=\"char\">100</td><td align=\"left\">96%</td><td align=\"left\">1.00E−18</td><td align=\"left\">100%</td></tr><tr><td align=\"left\">ORFV-SSR-5</td><td align=\"left\">100</td><td align=\"left\">6.00E−80</td><td char=\".\" align=\"char\">99</td><td align=\"left\">55%</td><td align=\"left\">3.00E−12</td><td align=\"left\">100%</td></tr><tr><td align=\"left\">ORFV-SSR-6</td><td align=\"left\">100</td><td align=\"left\">1.00E−67</td><td char=\".\" align=\"char\">95</td><td align=\"left\">41%</td><td align=\"left\">6.00E−05</td><td align=\"left\">100%</td></tr><tr><td align=\"left\">ORFV-SSR-7</td><td align=\"left\">100</td><td align=\"left\">2.00E−85</td><td char=\".\" align=\"char\">97</td><td align=\"left\">Intergenic</td><td align=\"left\">Intergenic</td><td align=\"left\">Intergenic</td></tr><tr><td align=\"left\">ORFV-SSR-8</td><td align=\"left\">100</td><td align=\"left\">5.00E−65</td><td char=\".\" align=\"char\">92</td><td align=\"left\">65%</td><td align=\"left\">2.00E−18</td><td align=\"left\">100%</td></tr><tr><td align=\"left\">ORFV-SSR-9</td><td align=\"left\">100</td><td align=\"left\">4.00E−67</td><td char=\".\" align=\"char\">97</td><td align=\"left\">67%</td><td align=\"left\">5.00E−17</td><td align=\"left\">100%</td></tr><tr><td align=\"left\">ORFV-SSR-10</td><td align=\"left\">100</td><td align=\"left\">6.00E−75</td><td char=\".\" align=\"char\">99</td><td align=\"left\">71%</td><td align=\"left\">2.00E−07</td><td align=\"left\">100%</td></tr><tr><td align=\"left\">ORFV-SSR-11</td><td align=\"left\">100</td><td align=\"left\">2.00E−74</td><td char=\".\" align=\"char\">99</td><td align=\"left\">98%</td><td align=\"left\">9.00E−30</td><td align=\"left\">100%</td></tr><tr><td align=\"left\">ORFV-SSR-12</td><td align=\"left\">100</td><td align=\"left\">2.00E−78</td><td char=\".\" align=\"char\">92</td><td align=\"left\">Intergenic</td><td align=\"left\">Intergenic</td><td align=\"left\">Intergenic</td></tr><tr><td align=\"left\">ORFV-SSR-13</td><td align=\"left\">100</td><td align=\"left\">3.00E−23</td><td char=\".\" align=\"char\">100</td><td align=\"left\">38%</td><td align=\"left\">1.00E−15</td><td align=\"left\">100%</td></tr></tbody></table></table-wrap></p><p id=\"Par23\">The concatenated phylogenetic tree showed the ORFV of our study closely related to Chinese isolate (MG712417) (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>). We observed the presence of 2–3 alleles within ORFV genomes.<fig id=\"Fig7\"><label>Figure 7</label><caption><p>The concatenated phylogenetic tree was constructed using the bootstrap consensus tree building method of neighbor-joining with bootstrap value 500 using MEGA 5. Black triangle represents the ORFV isolates of present investigation showing its relationship with eleven global strains.</p></caption><graphic xlink:href=\"41598_2020_70634_Fig7_HTML\" id=\"MO7\"/></fig></p></sec></sec><sec id=\"Sec16\"><title>Discussion</title><p id=\"Par24\">Microsatellites, otherwise known as short tandem repeats (STRs), or a variable number of tandem repeats (VNTRs) are being used to discriminate various viruses, such as human cytomegalovirus (hCMV)<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref>,<xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>, white spot syndrome virus (WSSV)<sup><xref ref-type=\"bibr\" rid=\"CR53\">53</xref>–<xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>, Herpes Simplex virus type 1<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref>,<xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>, Herpes Simplex virus type 2<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>, Herpesvirus 3<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup>, Herpesvirus 6<sup><xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>, Adenovirus<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>, Ostreid herpesvirus 1<sup><xref ref-type=\"bibr\" rid=\"CR64\">64</xref>,<xref ref-type=\"bibr\" rid=\"CR65\">65</xref></sup>, Marek’s disease virus 1<sup><xref ref-type=\"bibr\" rid=\"CR66\">66</xref></sup>, and Spodoptera littoralis multiple nucleopolyhedrovirus (SpliMNPV)<sup><xref ref-type=\"bibr\" rid=\"CR67\">67</xref></sup> due to its polymorphic in nature. To get the insight into the microsatellite in ORFV, we have employed a comparative genomics approach for development and characterization through in-silico and in-vitro analysis and validated our findings using samples collected from the recent Orf outbreak for the first time.</p><p id=\"Par25\">The specific parameters, such as its incidence, RA and RD of SSR and cSSR in ORFV genomes, show abundance variation as compared to their genome size and GC content due to the heterogeneity of ORFVs. Until now, limited full-length ORF genomes exist in the database. Based on our analysis, we observed little variation in RA and RD in ORFV. However, in other viruses such as HPVs<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup> and Herpesviruses<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>, higher variation in RA and RD were reported. The large variation with the parameters was not observed in ORFV, probably due to the lack of enough size difference in the genome. However, a limited number of complete genome sequences are available for this virus, in comparison to HPV and herpesviruses, which act as a constraint to get the optimal range. Correlation analysis confirmed that incidence of both SSRs and cSSRs, RA of cSSRs were dependent on genome size, but independent of GC content, which was similar to that of HPV<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>, but opposite to HIV<sup><xref ref-type=\"bibr\" rid=\"CR68\">68</xref></sup>, potexvirus, carlavirus, and tobamovirus<sup><xref ref-type=\"bibr\" rid=\"CR69\">69</xref>–<xref ref-type=\"bibr\" rid=\"CR71\">71</xref></sup>. The distribution of microsatellite in the viral genome is pathogen-specific rather than host-specific. The increase of cSSR is predominant when dMAX approaches 10–90 bp and further decreases with the increase of dmax (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>). This may be due to the occurrence of SSR in the overlapping regions of increasing dMAX. The ORFV genomes have more SSR within coding regions than non-coding regions in comparison with other DNA virus, such as herpes simplex virus. This might be due to higher relaxed selection pressure on coding regions in comparison to the non-coding region in the respective virus.</p><p id=\"Par26\">The cSSRs percentages of ORFV ranges from 7.9 to 9.0%, which is lower in comparison to HIV-1, 0–24.2%<sup><xref ref-type=\"bibr\" rid=\"CR68\">68</xref></sup>, Geminivirus, 0–27.2%<sup><xref ref-type=\"bibr\" rid=\"CR72\">72</xref></sup>, Herpesvirus, 8.1–33.3%<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. Generally, the number of compound microsatellites decreases with an increase in complexity<sup><xref ref-type=\"bibr\" rid=\"CR73\">73</xref></sup>. Moreover, the lack of sufficient genomic resources from diverse geographical locations may contribute to a stagnant range of cSSRs%. In ORFV, 22.1% of cSSRs were composed of similar motifs, probably contributed by genome duplication. Some study suggests that genome duplication may be helpful for the repeat tendency mechanism<sup><xref ref-type=\"bibr\" rid=\"CR74\">74</xref></sup>, which promotes the expansion of genome size such as yeast<sup><xref ref-type=\"bibr\" rid=\"CR75\">75</xref>,<xref ref-type=\"bibr\" rid=\"CR76\">76</xref></sup>.</p><p id=\"Par27\">In ORFV genomes, the poly A/T repeats were significantly more prevalent than poly G/C repeats, similar to eukaryotic and prokaryotic genomes<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. The presence of mononucleotide repeats in Mengovirus and Encephalomyocarditis virus affect virus growth in murine cell culture<sup><xref ref-type=\"bibr\" rid=\"CR77\">77</xref></sup>. In the case of ORFV, its significance needs further validation. In this study, we also observed the microsatellites having polymorphism in poly A/T (ORF117), poly C/G (ORF121), within the important immune-regulatory genes, such as in GM-CSF and ANK protein, respectively (Supplementary file <xref rid=\"MOESM2\" ref-type=\"media\">2</xref>). GM-CSF secreted by a variety of cell types triggers neutrophil, monocyte, and eosinophil myelopoiesis and stimulate early events in immune responses, controlling the differentiation and function of antigen-presenting dendritic cells. IL-2 is a T-cell-derived lymphokine that stimulates T-cell and NK cell activation and proliferation and activated-B-cell proliferation<sup><xref ref-type=\"bibr\" rid=\"CR78\">78</xref>,<xref ref-type=\"bibr\" rid=\"CR79\">79</xref></sup>. ANK protein leads to the down-regulation of hypoxia-induced factor (HIF) activity and regulates energy metabolism, angiogenesis, the apoptotic cascade, the <italic>NF-kB</italic> signaling pathway, and cell cycle regulation<sup><xref ref-type=\"bibr\" rid=\"CR80\">80</xref></sup>. The functional effects of this polymorphism in these regions require further investigations.</p><p id=\"Par28\">Dinucleotide CG/GC is more prevalent in most of the ORFV genomes, similar to that of DNA viruses such as HPVs<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>, Caulimoviruses, Geminiviruses<sup><xref ref-type=\"bibr\" rid=\"CR52\">52</xref>,<xref ref-type=\"bibr\" rid=\"CR81\">81</xref></sup>. CG/GC repeat could form Z‐conformation or other alternative secondary DNA to facilitate the recombination activity<sup><xref ref-type=\"bibr\" rid=\"CR82\">82</xref></sup>. In our study, the polymorphism within dinucleotide (AC/CA)3 and (CG/CG)3 observed within the hypothetical protein. Dinucleotide repeats have the highest slippage rate as compared to any other type of repeats<sup><xref ref-type=\"bibr\" rid=\"CR81\">81</xref></sup>. Among 257 viral genomes examined in a published study, the highest number of dinucleotide SSRs were found when compared to the other types<sup><xref ref-type=\"bibr\" rid=\"CR83\">83</xref></sup>. Dinucleotide repeats are also speculated to be recombination hot spots<sup><xref ref-type=\"bibr\" rid=\"CR84\">84</xref>,<xref ref-type=\"bibr\" rid=\"CR85\">85</xref></sup>. In this study, the presence of higher di-nucleotide repeats over tri-nucleotide repeats suggests a possible role of hosts in the evolution of di-nucleotide repeats within poxvirus genomes. Inconsistency frequency of SSRs in different accession of the same virus may be attributed to instability because of a higher slippage rate<sup><xref ref-type=\"bibr\" rid=\"CR86\">86</xref></sup>.</p><p id=\"Par29\">Trinucleotide motif ATA/TAA/AAT or ATT/TTA/TAT were most prevalent in most genomes of poxvirus whereas in other DNA virus GAG/AGA was most prevalent in HPVs and AAG/GAA in caulimoviruses. The higher density of trinucleotide repeats was observed compared to any other repeat type within coding regions of eukaryotic and prokaryotic genomes<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Interestingly, dynamic mutations within trinucleotide repeats responsible for the development of some diseases in humans<sup><xref ref-type=\"bibr\" rid=\"CR87\">87</xref></sup>, as well as viral enzymes that interfere pathogenicity of Influenza virus<sup><xref ref-type=\"bibr\" rid=\"CR88\">88</xref></sup>. Our study revealed the presence of trinucleotide CGC/GCG and GCC/GGC repeats to be most prevalent than others. The trinucleotide polymorphism was observed in some immunoregulatory genes such as ANK protein (GGC/GCC)<sub>3</sub> (ORF008), IL-10 protein (AGT/ACT)<sub>3</sub> (ORF127) and structural genes virion core protein (GAG/CTC)<sub>3</sub>, Putative serine/threonine-protein kinase (CGC/GCG)<sub>3</sub>, which needs further functional evaluation.</p><p id=\"Par30\">Three polymorphic SSRs such as (A/T)<sub>7</sub>, (T/A)<sub>6</sub>, (AGTTAC/ GTAACT)<sub>3</sub> were observed within non-coding regions. The microsatellite present within the non-coding reasons was evolutionarily neutral and can be utilized as an excellent molecular marker<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. Finally, we have characterized, those polymorphic markers present at non-genic as well as coding (genic) regions. These genic microsatellites, however, may provide adaptive variation important to viral evolution and genetic variability, perhaps similar to the functionally important mononucleotide runs found in VSV<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup> and RSV<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup> and virulence of avian influenza virus encephalo-myocarditis virus<sup><xref ref-type=\"bibr\" rid=\"CR89\">89</xref>,<xref ref-type=\"bibr\" rid=\"CR90\">90</xref></sup>. It is noteworthy to mention that, recently, the microsatellite present in HSV-1 glycoprotein coding region US4 was useful for strain differentiation<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. The concatenated tree, which was constructed utilizing sequence information of characterized markers, confirmed that the ORFV of the present study closely related to Chinese isolate (MG712417). Our previous report, as well as several other studies, observed a similar pattern of relationship<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref>,<xref ref-type=\"bibr\" rid=\"CR91\">91</xref></sup>. We speculate that trans-boundary and cross-species transfer of ORFV isolates could have resulted in this, as India is geographically adjacent to China. It is interesting to observe the presence of a number of the alleles (2–3) within ORFV genomes indicates the existence of polymorphism within microsatellites, which could act as a useful tool to estimate the diversity<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup>. Using a single repeated mononucleotide was able to follow the dynamics of transmission of a human adenovirus during an epidemic<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. Therefore, microsatellites constitute a potentially powerful tool for epidemiological studies of the transmission routes and evolution of ORFV and other related poxviruses. This study provides an important new type of molecular markers useful to investigate questions not only related to epidemiology but also for deciphering the diversity of the virus. However, the characterized microsatellites of the present study are not biased to the particular strain, which indicates the presence of recombinant strains circulating within the Indian subcontinent. This information is not concrete, which requires validation by several whole-genome sequence analysis of ORFV isolates from Indian origin. So far, our understanding of the functional and evolutionary role of microsatellites in ORFV biology is limited, which needs further in-depth evaluation and possible implementation.</p><p id=\"Par31\">In conclusion, the study of microsatellites in ORFV genome is a key step towards better understanding the nature, function, and evolutionary biology of the species. Our preliminary results can be considered as a useful tool for ORFV strain demarcation, diversity estimation, and evolutionary analysis. Our next plan is to characterize several ORFV strain complete genome from Indian origin through next-generation sequencing to get a better insight into genome organization, development of a suitable multiplex panel, which can be utilized as an effective tool for virus identification, genotyping and evolutionary analysis of the respective virus.</p></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec17\"><p>\n<supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41598_2020_70634_MOESM1_ESM.docx\"><caption><p>Supplementary Information 1.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41598_2020_70634_MOESM2_ESM.xlsx\"><caption><p>Supplementary Information 2.</p></caption></media></supplementary-material></p></sec></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p>is available for this paper at 10.1038/s41598-020-70634-6.</p></sec><ack><p>BPS is thankful to the University Grant Commission (UGC, Govt India) for providing a Ph.D. fellowship as financial support.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>B.P.S. and A.S. have collected the samples. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"in-brief\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Ir J Psychol Med</journal-id><journal-id journal-id-type=\"iso-abbrev\">Ir J Psychol Med</journal-id><journal-id journal-id-type=\"publisher-id\">IPM</journal-id><journal-title-group><journal-title>Irish Journal of Psychological Medicine</journal-title></journal-title-group><issn pub-type=\"ppub\">0790-9667</issn><issn pub-type=\"epub\">2051-6967</issn><publisher><publisher-name>Cambridge University Press</publisher-name><publisher-loc>Cambridge, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32408926</article-id><article-id pub-id-type=\"pmc\">PMC7431842</article-id><article-id pub-id-type=\"pii\">S0790966720000403</article-id><article-id pub-id-type=\"doi\">10.1017/ipm.2020.40</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Perspective Piece</subject></subj-group></article-categories><title-group><article-title>Fallout from the COVID-19 pandemic – should we prepare for a tsunami of post viral depression?</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Lyons</surname><given-names>D.</given-names></name><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"cor1\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Frampton</surname><given-names>M.</given-names></name><xref ref-type=\"aff\" rid=\"a2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Naqvi</surname><given-names>S.</given-names></name><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Donohoe</surname><given-names>D.</given-names></name><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Adams</surname><given-names>G.</given-names></name><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Glynn</surname><given-names>K.</given-names></name><xref ref-type=\"aff\" rid=\"a3\"><sup>3</sup></xref></contrib></contrib-group><aff id=\"a1\"><label>1</label>Department of Psychiatry of Later Life, <institution>St. Patrick’s Mental Health Services James’s St</institution>, <city>Dublin 8</city>, <country>Ireland</country></aff><aff id=\"a2\"><label>2</label>Department of Old Age Psychiatry, Carew House, <institution>St. Vincent’s University Hospital</institution>, Elm Park, <city>Dublin 4</city>, <country>Ireland</country></aff><aff id=\"a3\"><label>3</label>Department of Psychiatry, <institution>University Hospital Limerick</institution>, <city>Limerick</city>, <country>Ireland</country></aff><author-notes><corresp id=\"cor1\"><label>*</label>Address for correspondence: Dr D. Lyons, St. Patrick’s Mental Health Services James’s St, Dublin 8, Ireland. (Email: <email>dlyons@stpatsmail.com</email>)</corresp></author-notes><pub-date publication-format=\"electronic\" date-type=\"pub\"><day>15</day><month>5</month><year>2020</year></pub-date><fpage>1</fpage><lpage>6</lpage><history><date date-type=\"received\"><day>20</day><month>4</month><year>2020</year></date><date date-type=\"rev-recd\"><day>10</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>11</day><month>5</month><year>2020</year></date></history><permissions><copyright-statement>© College of Psychiatrists of Ireland 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>College of Psychiatrists of Ireland</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (<uri xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"S0790966720000403a.pdf\"/><abstract abstract-type=\"normal\"><p>The current COVID-19 pandemic is not just a medical and social tragedy, but within the threat of the outbreak looms the potential for a significant and persistent negative mental health impact, based on previous experience with other pandemics such as Severe Acute Respiratory Syndrome (SARS) in 2003 and the earlier H1N1 outbreak of 1918. This piece will highlight the links between depression and viral illnesses and explore important overlaps with myalgic encephalomyelitis/chronic fatigue syndrome, potentially implicating inflammatory mechanisms in those exposed to a range of viral agents. While containment of psychological distress currently focuses on social anxiety and quarantine measures, a second wave of psychological morbidity due to viral illness may be imminent.</p></abstract><kwd-group><title>Key words:</title><kwd>Coronavirus</kwd><kwd>COVID-19</kwd><kwd>major depressive disorder</kwd><kwd>myalgic encephalomyelitis</kwd></kwd-group><counts><ref-count count=\"37\"/><page-count count=\"6\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>For most of human, history infectious diseases were responsible for the greatest burden of premature death and morbidity, and global pandemics over the centuries have threatened the survival of entire populations. Notable outbreaks that were seared into collective memory owing to their associated mass casualties included diseases such as smallpox, cholera and influenza. Widespread immunization through safe and effective vaccine usage and increased deployment of antibiotics considerably reduced the toll of infectious diseases, at least in developed countries, by the middle of the 20th century. Emerging pandemic viral infections remain a constant threat to human health, however, many entering the human population (as is allegedly the case with COVID-19) from contact with animals (Holmes <italic>et al</italic>. <xref rid=\"r18\" ref-type=\"bibr\">2017</xref>).</p><p>Compared with antibiotics to treat bacterial infection, relatively few antiviral drugs have been developed to treat emerging viral infections and their complications; therefore, breaking the chain of transmission is a crucial intervention in containing any outbreak of novel viruses. The unprecedented public health measures undertaken across the world, since China first reported cases of the novel Coronavirus in December 2019, have necessarily entailed significant social disruption and jeopardized the economic prospects of entire communities. While the negative psychological effects of prolonged quarantine measures may also seem obvious, does the recent outbreak of COVID-19 sweeping around the world potentially carry a second layer of psychological morbidity in the form of depression and mood disorder in its’ wake? This piece will consider how post viral psychogenic sequelae are conceptualized and highlight certain factors for clinical contemplation, once the acute infective phase of coronavirus has passed.</p></sec><sec sec-type=\"other\" id=\"s2\"><title>The role of inflammation</title><p>Clinical or major depressive disorder interacts with disability and medical illness in a variety of ways that are complex and often with a bidirectional relationship, especially in respect of cardiovascular illness (Blazer & Hybels, <xref rid=\"r3\" ref-type=\"bibr\">2005</xref>). The development of mood disorder has also been linked to inflammation (Howren <italic>et al</italic>. <xref rid=\"r19\" ref-type=\"bibr\">2009</xref>), and experimental activation of inflammatory reactions has been demonstrated to induce symptoms of mood disorders in both human and animal studies (Eisenberger <italic>et al</italic>. <xref rid=\"r12\" ref-type=\"bibr\">2010</xref>). In particular, decreased cellular immunity results in the formation of neuromodulators and cytokine peptides or interleukins, which are hypothesized to penetrate the brain when the blood– central nervous system (CNS) barrier is compromised during time of stress, infection and inflammation (Irani & Lang, <xref rid=\"r20\" ref-type=\"bibr\">2008</xref>). Immune components such as proinflammatory cytokines and brain-reactive antibodies are theorized to induce changes in neurotransmitter and neuroendocrine function, such as hypercortisolism, and it has long been appreciated that cortisol hypersecretion is potentially related to a range of psychiatric disorders (Pivonello <italic>et al</italic>. <xref rid=\"r29\" ref-type=\"bibr\">2015</xref>). Although the mechanisms for interaction between mental health difficulties and communicable diseases, namely infections, may still be the subject of speculation, in relation to specific triggers for psychiatric episodes, it seems not unreasonable to assume that they are far from being solely psychosocial in origin.</p></sec><sec sec-type=\"other\" id=\"s3\"><title>Remembering ME</title><p>Myalgic encephalomyelitis (abbreviated to ME), but also known as chronic fatigue syndrome (CFS) is a complex, disabling chronic illness characterized by extreme fatigue that is not explained by any underlying medical condition, which is said to affect 0.76–3.28% of the world-wide population (Johnston <italic>et al</italic>. <xref rid=\"r21\" ref-type=\"bibr\">2013</xref>). Symptoms constellations associated with ME include musculoskeletal pain, headaches, sore throat, tender lymph nodes, concentration and memory difficulties, unrefreshing sleep and exacerbation of these symptoms with what is felt to be the cardinal feature of the condition, namely post exertional malaise in response to minimal physical or cognitive exertion (Fukuda <italic>et al</italic>. <xref rid=\"r13\" ref-type=\"bibr\">1994</xref>). The term ‘benign myalgic encephalomyelitis’ was first deployed in relation to what appeared to be an infective but low-mortality outbreak (in sporadic and epidemic fashion) at the Royal Free Hospital in London in the 1950s (Wojcik <italic>et al</italic>. <xref rid=\"r36\" ref-type=\"bibr\">2011</xref>). By the 1980s, following a further outbreak of an illness resembling infectious mononucleosis in the United States, an initial link to the Epstein Barr virus was suggested and working groups established to reach consensus about diagnostic criteria, with Fukuda et al. finally publishing diagnostic criteria in 1994. The illness has remained somewhat controversial over the intervening years, with patient groups feeling the condition has been somewhat trivialized by medics, who failed to agree on the etiology, seriousness or prevalence of ME/CFS and patients themselves being frustrated by persistent professional reference to psychological components of CFS, which they rejected as offensive (Dumit, <xref rid=\"r11\" ref-type=\"bibr\">2006</xref>). This was not helped by the popular media which initially, despite being supportive of efforts to raise awareness of ME/CFS and to highlight an organic attribution, subsequently nicknamed the condition the ‘yuppie flu’ in the 1990s.</p></sec><sec sec-type=\"other\" id=\"s4\"><title>At risk of ME/chronic fatigue?</title><p>A recent review of peri-onset events reported by subjects meeting ME/CFS criteria identified the most common peri-onset events as being infection-related episodes (64%) as opposed to stressful incidents (34%) or exposure to environmental toxins (20%) (Chu <italic>et al</italic>. <xref rid=\"r7\" ref-type=\"bibr\">2019</xref>). In their prospective, population-based cohort study in Denmark, Benros <italic>et al</italic>. (<xref rid=\"r2\" ref-type=\"bibr\">2013</xref>), using 78 million person years of follow-up drawn from Danish longitudinal registers, found that any history of hospitalization for infection increased the risk of mood disorders by 62% with many displaying the symptom of prominent fatigue as a hallmark. With 32% of their study participants who had mood disorder having had a previous hospital contact for an infection, they speculated that these associations seemed compatible with an immunologic hypothesis for the development of depression and mood disorder in subgroups of patients. The Danish group pointed to the symptom overlap emanating from systemic infection and depression and the symptoms common to both which they termed ‘sickness behavior’ including fatigue, apathy, reduced social interaction, impaired concentration and sleep disturbance, which they felt could become rather chronic and progress to major depression in some cases. Benros also noted that the number of infections and autoimmune disorders increased the risk of mood disorders in a dose–response relationship. ME/CFS is consistently more prevalent in females (who also have higher rates of autoimmune disorders) than in males, as 60–85% of all cases in the United States were women, most commonly aged between 40 and 60 years (Dinos <italic>et al</italic>. <xref rid=\"r10\" ref-type=\"bibr\">2009</xref>). Psychological factors such as pre-existing depressive and anxiety disorders, perfectionistic personality type and a childhood trauma history were predisposing factors identified in a review by Lievesley <italic>et al</italic>. (<xref rid=\"r22\" ref-type=\"bibr\">2014</xref>).</p></sec><sec sec-type=\"other\" id=\"s5\"><title>Links with which viruses?</title><p>Because ME/CFS may begin as a flu-like illness with a sudden onset, various infectious causes have been proposed right from the outset of clinical observation of this condition, but it should be emphasized that the exact pathogenic mechanism is unclear. A 2016 report by the Institute of Medicine (which is a US-based NGO) concludes that ME/CFS is a biologically based illness but that markers and abnormalities are not yet sensitive enough to be useful as a diagnosis (Unger <italic>et al</italic>. <xref rid=\"r32\" ref-type=\"bibr\">2016</xref>). While in the majority of cases, there appears to be no conclusive evidence for chronic viral infection, it has been plausibly proposed that viruses could act via ‘a hit and run’ mechanism: this theory proposes that viruses trigger the disease, cause immune abnormalities and leave in their wake a dysfunctional immune system and/or autoimmunity (Rasa <italic>et al</italic>. <xref rid=\"r30\" ref-type=\"bibr\">2018</xref>). Although various viral, and even microbial infections, are considered to be possible triggers for a subsequent diagnosis of ME/CFS, studies have been conducted on the association of ME/CFS with Epstein–Barr virus (EBV), cytomegalovirus (CMV), human herpesviruses type 6 and 7, human parvovirus and enteroviruses (Strauss <italic>et al</italic>. <xref rid=\"r31\" ref-type=\"bibr\">1985</xref>; Holmes <italic>et al</italic>. <xref rid=\"r17\" ref-type=\"bibr\">1987</xref>; Martin, <xref rid=\"r25\" ref-type=\"bibr\">1997</xref>).</p><p>The Toronto-based psychiatrist and sleep specialist Henry Moldofsky studied the long-term adverse effects of the Severe Acute Respiratory Syndrome (SARS) on a subgroup of patients, the majority of whom were healthcare workers and who remained unable to return to their former occupation (Moldofsky & Patcai, <xref rid=\"r27\" ref-type=\"bibr\">2011</xref>). SARS is a viral respiratory disease that surfaced in Asia in the early 2000s caused by the first identified strain of the SARS coronavirus species. Although the majority (93.5%) of the sickest patients admitted to hospitals in Toronto survived (Booth <italic>et al</italic>. <xref rid=\"r4\" ref-type=\"bibr\">2003</xref>), longer term outcomes surveillance by Moldofsky’s group found a profile of symptoms such as daytime fatigue, myalgia, weakness and depression very reminiscent of ME/CFS in the cases that remained occupationally and functionally impaired. Of note, Moldofsky’s small sample of 22 cases (which represented only 8% of those who recovered from SARS) had not been exposed to lengthy periods of quarantine and had similar outcomes to a more widely selected, ostensibly recovered, population of Toronto SARS patients who were subsequently surveyed. In a larger study of 107 such patients, similar problems with pain, reduced vitality and impaired physical, mental and social functioning were revealed in up to 82% of patients, who had returned to unmodified work, 1-year post initial infection (Herridge <italic>et al</italic>. <xref rid=\"r16\" ref-type=\"bibr\">2003</xref>).</p><p>The relevant literature (Wang <italic>et al</italic>. <xref rid=\"r35\" ref-type=\"bibr\">2015</xref>) also purports to conclude that other viral agents are linked to depression and anxiety in developed countries, not only EBV, Borna virus disease and Varicella-Zoster virus but also human immunodeficiency virus (Van den Heuvel, <xref rid=\"r2\" ref-type=\"bibr\">2013</xref>), influenza A (H1N1) (Manjunatha <italic>et al</italic>. <xref rid=\"r24\" ref-type=\"bibr\">2011</xref>) and other influenza viruses. Coughlin (<xref rid=\"r8\" ref-type=\"bibr\">2012</xref>) in his review however acknowledges that frameworks for understanding linkages between mood disorder and anxiety are not yet sufficiently robust, but their further exploration offers potential for prevention of psychological distress through vaccination and via improved treatment of the viral illness directly, as is the case with Hepatitis C and HIV/AIDS. Gale et al. found the strongest associations between virus exposure and depression in a sample of US adults existing for subjects who were seropositive for Herpes Simplex Virus type-2, but to a lesser extent for CMV (Gale, <xref rid=\"r30\" ref-type=\"bibr\">2018</xref>). They found no association with depression and Hepatitis A and B or herpes simplex type 1 infection.</p></sec><sec sec-type=\"other\" id=\"s6\"><title>Coronavirus and depression and/or sickness behavior – a classic false dichotomy?</title><p>At the level of the pathogen–immune system interface, it is important to appreciate that there may be differences as well as similarities between sickness behavior and clinical depression (Dantzer, <xref rid=\"r9\" ref-type=\"bibr\">2001</xref>). Symptoms such as fatigue, sleep and appetite disturbance, decreased social interaction and loss of interest in usual activities are seen in both clinical depression and sickness behavior related to viral infections (Vollmer-Conna, <xref rid=\"r34\" ref-type=\"bibr\">2001</xref>). Clinically, however, the core psychological symptoms of depression (hopelessness, worthlessness, pessimism and guilt) would be more typical of depression than sickness alone (Gelder <italic>et al</italic>. <xref rid=\"r15\" ref-type=\"bibr\">2001</xref>). Okusaga et al., while speculating on an infection to mood rather than mood to infection causality direction, highlighted an association between seropositivity for influenza and coronaviruses and a subsequent history of mood disorders. In addition, seropositivity for influenza B was concerningly associated with suicidal behavior and a lifetime history of psychotic symptoms in patients with mood disorders (Okusaga <italic>et al</italic>. <xref rid=\"r28\" ref-type=\"bibr\">2011</xref>). It remains unclear whether the viruses themselves or the immune response to them are the main culprit in leading to mood disorder, but it is worth noting that both influenza and coronaviruses are potentially neurotropic and have been isolated from the CNS (Xu <italic>et al</italic>. <xref rid=\"r37\" ref-type=\"bibr\">2005</xref>). Cytokines involved in the immune response against influenza infection enhance activation of the HPA axis as well as reportedly causing a depletion of tryptophan in the brain. In people who developed mood disorder post infection, Okusaga et al. failed to note neurological complications of viral illness or evidence of encephalitis due to direct effects of viral infection, reinforcing the view of an immune basis as being the main culprit leading to mood disorder in their sample.</p><p>When one considers the entire symptomatic spectrum associated with mood disorder – both emotional/cognitive and the full range of physical symptoms (which encompass the so-called sickness behavior which we more readily associate perhaps with infection), it becomes possible to re-conceptualize the diversity of mood disorder in terms of etiology and perhaps ultimately remediation. A Western conceptualization puts affective symptoms front and center, whereas non-Western patients who meet Diagnostic and Statistical Manual criteria for major depression report primarily somatic symptoms, reflecting in part cultural differences in the stigmatization of mental illness (Canli, <xref rid=\"r6\" ref-type=\"bibr\">2014</xref>).</p></sec><sec sec-type=\"other\" id=\"s7\"><title>Can 1918 teach us anything?</title><p>Comparisons are being currently made between the present COVID-19 pandemic and the so-called Spanish Flu pandemic of 1918–1919, as we try to acclimatize ourselves to the rapidly changing social circumstances of 2020. While little formal research was conducted on the long-term impact of the Spanish flu on mental health, Sven-Erik Mamelund (<xref rid=\"r23\" ref-type=\"bibr\">2010</xref>) studied asylum hospitalization during the period in question and found that the number of first-time hospitalizations due to influenza related mental disorders increased by an average annual factor of 7.2 in the 6 years after the pandemic. Spanish flu survivors reported sleep disturbances, depression, dizziness and difficulty coping at work, and increased death rates due to suicide were noted in the United States, according to Mamelund. Psychiatrists and neurologists first reported encountering encephalitis lethargica symptoms in 1916 in Austria and France but by 1919, it had become common throughout much of the World. Although many clinicians (at the time and subsequently) surmised an association between encephalitis lethargica and the Spanish flu, no conclusive evidence of causality exists (Beiner, <xref rid=\"r1\" ref-type=\"bibr\">2006</xref>). Although the psychological reaction to the Spanish flu may have been either squeezed in terms of perceived importance by the conflict of World War I and its conclusion, if history teaches us anything it is to expect a swathe of mental health challenges following in tow of the present pandemic.</p></sec><sec sec-type=\"other\" id=\"s8\"><title>Preparing for the worst – hoping for the best</title><p>As we have seen, the documented connection between viral pandemics and psychological stress is not new. It was the American psychiatrist Karl A. Menninger who urged colleagues to awaken from complacency in relation to the emerging connection between the 1918 pandemic and psychiatric complications, by realizing that although the influenza virus now as then most commonly affects the respiratory system, the burden on neuropsychiatric disease was under-recognized (Menninger, <xref rid=\"r26\" ref-type=\"bibr\">1919</xref>). Yet the citizenry, of the developed and developing world alike, navigating the present pandemic are experiencing unique and profound economic shocks after a relative period of global prosperity, stability and peace. Developed economies are promising fiscal safety nets and stimulus to counter economic aftershocks, but few are reassured that they will be either sustainable or adequate in the medium to long term. For struggling health systems (and that appears to be the majority), it is immediate counter-measures to flatten the infective curve that take priority, and few if any, are giving consideration to or anticipating neuropsychiatric manifestations, that may take months or years to appear. Part of those counter-measures include mass quarantine to safeguard more vulnerable members of society and the negative psychological effects of self-isolation have received publicity and continue to attract clinical concern from public health officials who must balance the distress associated with restriction of liberty with acceleration of the spread of COVID-19. In a useful review by Brooks <italic>et al</italic>. (<xref rid=\"r5\" ref-type=\"bibr\">2020</xref>) with reference to the present pandemic, she and her colleagues argue for an appeal to altruism in respect of public compliance with restrictive measures, but that in turn, authorities should provide adequate information about the rationale for the lockdown and should only extend quarantine measures if absolutely necessary.</p></sec><sec sec-type=\"other\" id=\"s9\"><title>What can psychiatrists do?</title><p>In the current phase of the pandemic, psychiatry could add a collective voice to quell the calls for premature easing of social distance and other mitigation measures, by reminding sceptics that not only may a reduction in mortality be achieved but also potentially many cases of neuropsychiatric illness, associated with significant long-term economic burden and individual distress. We can listen for and be attuned to the symptoms of post viral depression, when cases begin to appear and educate our colleagues about these conditions as genuine entities deserving of care, support and rehabilitation. In this way, perhaps many of the previous mistakes around interacting with individuals with ME/CFS can be avoided. In future, perhaps we will attempt to prevent neurotropic respiratory viral infections more aggressively by reducing risk factors such as smoking, obesity and the metabolic syndrome associated with many of our treatments. Those recovering from viral infections may need closer monitoring in terms of suicide risk and we may consider even prophylactic use of antidepressants for a brief time in treating patients at risk.</p><p>In the meantime, we must never cease to counsel those who have had to come to terms with socially unsupported grief or to reassure those who once regarded a nursing home as a safe sanctuary and who now experience a level of personal as well as collective insecurity, in fearing an undignified, lonely demise. We will do well to help people attempt to reframe the cocooning experience as a period of nurturing self-sufficiency and self-awareness, to talk up the benefits of routine and exercise, getting the correct balance of rest and activity for those recovering from viral infection to avoid exacerbating their fatigue. We should speak out on behalf of those with mental illness for access to basic subvention and supplies including medication. We will not only observe the present unplanned and unwelcome social experiences and share in many of them because of COVID-19 but also be mindful of factors such as mutual solidarity and resilience, adaptability, flexibility to work and interact differently through technology use – all of which will almost certainly lessen the impact of the current pandemic.</p></sec><sec sec-type=\"other\" id=\"s12\"><title>Financial Support</title><p>This article received no specific grant from any funding agency, commercial or not-for-profit sectors.</p></sec><sec sec-type=\"other\" id=\"s10\"><title>Conflict of interest</title><p>The authors have no conflict of interest to disclose</p></sec><sec sec-type=\"other\" id=\"s11\"><title>Ethical Standards</title><p>The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committee on human experimentation with the Helsinki Declaration of 1975, as revised in 2008. 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ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.492573.e</institution-id><institution>Lunenfeld Tanenbaum Research Institute, </institution><institution>Sinai Health System, </institution></institution-wrap>Toronto, ON M5G 1X5 Canada </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.17063.33</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2157 2938</institution-id><institution>Department of Surgical Oncology, </institution><institution>University of Toronto, </institution></institution-wrap>Toronto, ON M5G 2M9 Canada </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.424444.6</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 1103 8547</institution-id><institution>Laboratory of Physiopathology, Alimentation and Biomolecules LR17ES03, </institution><institution>Higher Institute of Biotechnology, Sidi Thabet, University of Manouba, </institution></institution-wrap>Ariana, 2020 Tunisia </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.17063.33</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2157 2938</institution-id><institution>Department of Radiation Oncology, </institution><institution>University of Toronto, </institution></institution-wrap>Toronto, ON M5T 1P5 Canada </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.17063.33</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2157 2938</institution-id><institution>Department of Laboratory Medicine and Pathology, </institution><institution>University of Toronto, </institution></institution-wrap>Toronto, ON M5S 1A8 Canada </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>3</volume><elocation-id>448</elocation-id><history><date date-type=\"received\"><day>5</day><month>6</month><year>2019</year></date><date date-type=\"accepted\"><day>20</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Polo like kinase 4 (Plk4) is a tightly regulated serine threonine kinase that governs centriole duplication. Increased Plk4 expression, which is a feature of many common human cancers, causes centriole overduplication, mitotic irregularities, and chromosomal instability. Plk4 can also promote cancer invasion and metastasis through regulation of the actin cytoskeleton. Herein we demonstrate physical interaction of Plk4 with FAM46C/TENT5C, a conserved protein of unknown function until recently. FAM46C localizes to centrioles, inhibits Plk4 kinase activity, and suppresses Plk4-induced centriole duplication. Interference with Plk4 function by FAM46C was independent of the latter’s nucleotidyl transferase activity. In addition, FAM46C restrained cancer cell invasion and suppressed MDA MB-435 cancer growth in a xenograft model, opposing the effect of Plk4. We demonstrate loss of FAM46C in patient-derived colorectal cancer tumor tissue that becomes more profound with advanced clinical stage. These results implicate FAM46C as a tumor suppressor that acts by inhibiting Plk4 activity.</p></abstract><abstract id=\"Abs2\" abstract-type=\"web-summary\"><p id=\"Par2\">Kazazian and colleagues identify FAM46C/TENT5C as an interactor and negative regulator of PLK4 activity in centriole duplication. The authors also find that FAM46C expression strongly reduces cancer cell invasion, uncovering a role for FAM46C as tumor suppressor.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Cancer models</kwd><kwd>Gastrointestinal cancer</kwd><kwd>Metastasis</kwd><kwd>Tumour-suppressor proteins</kwd><kwd>Cancer genetics</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par3\">Polo-like kinase 4 (Plk4) is a serine threonine kinase found most abundantly at the centriole<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>, but also localized to the MTOCs of acentriolar cells<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup> and to the lamellipodia of migrating cells<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Plk4 is required for centriole duplication, and its activity is closely tied to cell cycle progression, increasing during S phase and peaking at G2/M<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR6\">6</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>. Increased Plk4 expression is found in a spectrum of common human malignancies<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref>–<xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>, and results in centriole overduplication and aneuploidy<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>–<xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>. While germline Plk4 haploidy is insufficient to suppress tumor development in adult life<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>, inhibition of Plk4 kinase activity reduces cancer cell proliferation and migration<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR9\">9</xref>,<xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>, indicating a potential for oncogenic function. In keeping with the latter, Plk4 depletion from malignant cells inhibits tumor progression in xenograft models<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>, while upregulation of Plk4 can promote tumorigenesis in vivo<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>. With this preclinical evidence as well as its association with aggressive tumor behavior and chemo-resistance in breast cancer patients, Plk4 is currently under investigation as a therapeutic target in Phase I/II clinical trials<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>. Identification of an expanded Plk4 bio-interactome has sparked further interest in the therapeutic potential of its members<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>.</p><p id=\"Par4\">Plk4 is a pivotal actor in the precise and highly regulated process of centriolar duplication, a process that is evolutionarily conserved in eukaryotes. The effectors that act downstream of Plk4 to realize spatially correct recruitment of microtubules to the nascent daughter centriole include STIL, SAS-6, and CEP135/CPAP<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR22\">22</xref>–<xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. Upstream regulation of Plk4 itself remains incompletely understood, but appears to involve multiple sophisticated systems to maintain tight temporal and spatial control of both protein level and kinase activity. Plk4 is localized to the centriole through interactions with scaffolding proteins<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref>–<xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>, and its kinase activity is subject to regulation by autophosphorylation<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR32\">32</xref>–<xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>, acetylation<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup> and dephosphorylation<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref>,<xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. Plk4 substrate STIL, together with its interactor CEP85, in turn regulate Plk4 kinase activity and localization, critically contributing to the control of centriolar duplication<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR23\">23</xref>,<xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref>,<xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. Plk4 protein is rapidly degraded through βTRCP-mediated ubiquitination, which is triggered by Plk4 trans-autophosphorylation within a phosphodegron in the linker region positioned between the kinase domain and Polo Box domains<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref>,<xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. Intramolecular interaction between the third polo box motif (PB3) and the linker serves to suppress kinase activity, and disruption of this interaction, for instance through binding to STIL<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>, relieves the inhibition, culminating in phosphorylation of Plk4 substrates<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. Tight regulation of Plk4 kinase activity is essential to maintain precise coordination of the centrosome and cell cycles, ensuring diploidy and avoiding other adverse consequences of centriolar overduplication<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR13\">13</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>.</p><p id=\"Par5\">In a search for additional functional Plk4 interactors, we identified FAM46C/TENT5C, a ≈ 47 kDa protein recently confirmed to function as a non-canonical poly(A) RNA polymerase in multiple myeloma cell lines<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. Here we show that FAM46C localizes to centrioles throughout the cell cycle, physically interacts with Plk4 kinase/PB-1/PB-2 domains, and impairs Plk4 kinase activity, restraining centriole duplication. In a spheroid model, FAM46C depletion promoted invasion of HeLa cancer cells into the surrounding matrix. When Plk4 was inactivated by centrinone B, however, FAM46C depletion had no effect, indicating that the suppressive effect of FAM46C on cancer cell invasion is mediated through its inhibition of Plk4 activity. Furthermore, in a mouse xenograft model of human cancer using MDA MB-435 cells, FAM46C inhibited tumor progression in opposition to Plk4. In the cancer cell lines studied here, the inhibitory effect of FAM46C on Plk4 activity was not dependent on its poly(A) RNA polymerase function. We also show for the first time that unlike other centriolar Plk4 interactors, FAM46C is depleted in human colorectal cancer tumor tissue, compared with paired normal mucosa samples taken from the same patient. Furthermore, the FAM46C/Plk4 ratio declined markedly with advancing clinical cancer stage. Taken together, these results reveal a tumor suppressor function of FAM46C/TENT5C through inhibition of Plk4 kinase activity.</p></sec><sec id=\"Sec2\" sec-type=\"results\"><title>Results</title><sec id=\"Sec3\"><title>FAM46C interacts with Plk4 and localizes to the centriole</title><p id=\"Par6\">Of the sixty-five and seventeen potential Plk4 interactors identified by yeast 2– hybrid (Y2H) screens of the <italic>Drosophila</italic> and human ORFeomes, respectively (DroID:, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.droidb.org\">http://www.droidb.org</ext-link> and HuRI: <ext-link ext-link-type=\"uri\" xlink:href=\"http://interactome.baderlab.org\">http://interactome.baderlab.org</ext-link>), only four were shared between species (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>): Plk4 itself, an interaction that is expected given its known functional homodimerization<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref>,<xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>; βTRCP, also a well-known physical and functional interactor across species<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref>,<xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR41\">41</xref>,<xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>; FAM46C; and FAM46B. The FAM46/TENT5 proteins are conserved across eukaryotes (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1a, b</xref>)<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup> but were until recently of unknown function. Human FAM46C and FAM46B are two of four differentially expressed family members (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1a, c</xref>), that based on sequence were predicted to function as non-canonical poly(A) RNA polymerases<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>, with recent confirmation of selective mRNA stabilization by FAM46C in multiple myeloma cells<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. While FAM46A and FAM46D interacted with several other proteins in Y2H screens (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1d</xref>;<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref>,<xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>), they did not interact with Plk4. Of the four HsFAM46/TENT5 family members, only hFAM46C interacted with hPlk4 in reciprocal co-immunoprecipitation experiments (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>).<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>FAM46C is a conserved protein that interacts with Plk4.</title><p><bold>a</bold> Diagram summarizing results of yeast two-hybrid screens for Plk4/SAK interactors from the HuRI (the human reference protein interactome mapping project, <ext-link ext-link-type=\"uri\" xlink:href=\"http://interactome.baderlab.org\">http://interactome.baderlab.org</ext-link>) and DroID (Drosophila interactions database, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.droidb.org\">http://www.droidb.org</ext-link>) databases, showing overlap of four interactors, including <italic>H. Sapiens</italic> FAM46C/<italic>D. Melanogaster</italic> CG30497. <italic>Hs</italic>.FAM46C is a protein of 391 amino acids (aa) that contains a nucleotidyltransferase (NTase) domain<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>, that has been recently renamed TENT5C. <bold>b</bold> Immunoblots of Flag-Plk4 and four individual <italic>Hs</italic>.RFP-FAM46 proteins after coexpression in HEK293T cells, showing reciprocal co-immunoprecipitation of Plk4 only with FAM46C. Uncropped blots for this and subsequent figures can be found in Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">10</xref>. <bold>c</bold> Localization of FAM46C to centrioles, identified by staining with centriole markers including centrin, CEP120 (daughter centriole marker) and ODF2 (mother centriole marker), and overlap with Plk4. Representative immunofluorescence images of U2OS cells labeled with antibodies to: top panel, centrin (green) and FAM46C (red); second panel, Plk4 (green), FAM46C (red), centrin (blue); third panel, CEP120 (green, daughter), Plk4 (red), FAM46C (blue), and with Hoechst (blue). The right panels/inserts show magnified centrosomes (boxed in white). Bottom panels show magnified centrosomes labeled with antibodies to CEP120 (green, preferential labeling of daughter), ODF2 (red, top row, preferential labeling of mother), centrin (red, bottom row) and blue (FAM46C). FAM46C localizes predominantly to the mother centriole in unsynchronized cells (see Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4b</xref> for localization in synchronized cells). Cartoon summarizes putative FAM46C localization in relation to centriolar markers probed in this study, showing localization at the proximal mother centriole (M mother, D daughter; Bar 1 µm). <bold>d</bold> Left panel, representative immunoblot of Luciferase or FAM46C siRNA transfected U2OS cell extracts probed using anti-FAM46C antibody, with β-tubulin as loading control, demonstrating 87% depletion of FAM46C. Right panels, representative immunofluorescence images of Luciferase or FAM46C siRNA-transfected U2OS cell centrosomes labeled with antibodies to centrin (red) and FAM46C (green). FAM46C depletion was confirmed and resulted in a rosette-like centriolar phenotype. Bar: 1 µm. <bold>e</bold> Left panel, immunoblot showing expression of RFP and RFP-FAM46C in U2OS cells transfected with RFP or RFP-FAM46C, respectively, using anti-FAM46C and anti-RFP antibodies with β-tubulin as a loading control, representative of <italic>n</italic> = 3 independent experiments. Right panel, localization of exogenous FAM46C to centrioles shown in representative immunofluorescence images of U2OS cells transfected with RFP-FAM46C (red) for 42 h and labeled with antibodies to Plk4 (green) and centrin (blue, bottom panels/inserts) and with Hoechst (blue, top panel). The bottom panels/inserts show a magnified centrosome (boxed in white). Bars: 10 µm, inserts 1 µm.</p></caption><graphic xlink:href=\"42003_2020_1161_Fig1_HTML\" id=\"d30e704\"/></fig></p><p id=\"Par7\">An antibody raised to full-length hFAM46C stained centrioles, confirmed by centrin positivity (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c, d</xref>). At high power, the centriolar localization of endogenous FAM46C partially overlapped with that of endogenous Plk4 (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c</xref>). Upon siFAM46C-mediated depletion, endogenous FAM46C was no longer visualized at centrioles (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>). Interestingly, a rosette-type centriolar over-duplication phenotype was frequently observed in FAM46C-depleted cells. RFP-tagged hFAM46C also localized specifically to centrioles, partially overlapping with endogenous Plk4 (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e</xref>), and with the other centriolar proteins CPAP, CP110, and CEP135 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2a</xref>). Of note, centriolar staining for FAM46C, whether endogenous or exogenous, was consistently observed at centrin-positive structures in all cells, and FAM46C appeared to be a ubiquitous component of centrioles. In reciprocal co-immunoprecipitation experiments, FAM46C did not interact with CEP135, nor with centriolar scaffolding proteins CEP152 or CEP192 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2b–d</xref>). RFP-FAM46C was found in the same centriolar fraction as FLAG-Plk4 in extracts from doubly transfected cells that were separated by sucrose gradient (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3a</xref>). Taken together, these data suggested the possibility of a specific functional interaction between FAM46C and Plk4.</p></sec><sec id=\"Sec4\"><title>FAM46C regulates centriole duplication in a Plk4-dependent manner</title><p id=\"Par8\">To investigate the function of FAM46C, knockdown was achieved by shFAM46C (four individual constructs), confirmed by RT-PCR and immunoblot (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>). FAM46C depletion led to an increase in centriolar number in U2OS cells, with an increased proportion of cells that had >4 centrioles, and a corresponding decrease in cells with 2–4 centrioles (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>), again frequently accompanied by development of a centriolar phenotype that was reminiscent of the well-described Plk4 overexpression rosette (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref><sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>). The supernumerary centrioles appeared structurally normal by electron microscopy, which also revealed evidence of multiple daughter centrioles arising from the same mother, as typically seen with Plk4 overexpression (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3b</xref>). The supernumerary centrioles generated in response to FAM46C depletion also each stained positive for SAS-6, in keeping with their generation via the Plk4 pathway (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c</xref>;<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>). Furthermore, these SAS-6-positive and centrin-positive foci were arranged in a typical Plk4 overexpression rosette.<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>FAM46C regulates centriole duplication.</title><p><bold>a</bold> Depletion of FAM46C in U2OS cells using four individual FAM46C shRNAs, confirmed by reduction of FAM46C mRNA levels (left panel) and reduction of FAM46C protein levels (right panel) relative to RFP shRNA cell lines, <italic>n</italic> = 2 independent experiments, *<italic>p</italic> < 0.001 vs. RFP shRNA. <bold>b</bold> Centriolar overduplication phenotype in U2OS cells depleted of FAM46C. Left panels show representative immunofluorescence images of U2OS cells labeled with antibodies to centrin (red) and pericentrin (green), and with Hoechst (blue). The bottom panels/inserts show magnified centrosomes (boxed in white) for each condition. Right panel, bar graph showing proportion of cells with indicated number of centrioles per cell, quantified by scoring centrin-positive foci. <italic>n</italic> = 3 independent experiments with >50 cells measured in each, *<italic>p</italic> < 0.001 vs. RFP shRNA. <bold>c</bold> Downstream marker of Plk-4 driven centriolar duplication SAS-6 is amplified by FAM46C depletion, as shown in representative immunofluorescence images (left panels) of Luciferase- or FAM46C- siRNA transfected U2OS cells labeled with antibodies to SAS-6 (red), centrin (green), FAM46C (blue, bottom panels/inserts) and with Hoechst (blue, top panels). Right panel: Bar graph showing proportion of cells with more than four SAS-6 positive centrioles, <italic>n</italic> = 3. Bars: 10 µm; insert 1 µm. <bold>d</bold> Depletion of FAM46C in MDA MB-435 cells using two individual FAM46C shRNAs. Left panel, confirmation of reduction of FAM46C mRNA levels relative to Luciferase shRNA cell lines, <italic>n</italic> = 2 independent experiments, *<italic>p</italic> = 0.016 vs. Luciferase shRNA. Right panels, viability and proliferation of MDA MB-435 cells, treated as indicated. Cells were labeled with Hoechst and Propidium Iodide, then imaged using the Celigo Cell Imaging Cytometer at the indicated times. Dead cells were distinguished from the live cells based on the mean intensity of the Propidium Iodide signal. Number of independent experiments was 3 for Viability, 3 for Proliferation. p = NS vs. control, using ANOVA with Bonferroni correction. Bottom panel, bar graph showing proportion of cells with indicated number of centrioles per cell, quantified by scoring centrin-positive foci. <italic>n</italic> = 4 independent experiments with >60 cells measured in each, *<italic>p</italic> < 0.015 vs. Luciferase shRNA. <bold>e</bold> Reduction in centriole number in response to RFP-FAM46C expression for 42 h in U2OS cells. Bar graph shows proportion of cells with indicated number of centrioles per cell, quantified by scoring centrin-positive foci. <italic>n</italic> = 2 independent experiments with >50 cells measured in each, *<italic>p</italic> = 0.0098 vs. RFP. Bars: 10 µm, inset 1 µm. Data are means ± SEM.</p></caption><graphic xlink:href=\"42003_2020_1161_Fig2_HTML\" id=\"d30e825\"/></fig></p><p id=\"Par9\">Of note, the centriolar over-duplication phenotype observed with FAM46C depletion was lost after ≈3 weeks’ culture of shFAM46C cells, in association with recovery of FAM46C expression, which could potentially reflect a proliferative disadvantage<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR14\">14</xref>,<xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. Given that in myeloma cell lines, FAM46C’s RNA polymerase activity regulates transcription of a suite of genes that control proliferation and arrest<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>, we investigated the acute effect of FAM46C depletion on viability and growth in culture. At 48 h in culture, MDA MB-435 cells depleted of FAM46C showed no significant difference in viability or proliferative rate vs. controls, while simultaneously displaying a higher number of centrioles/cell (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2d</xref>). Thus, it appeared unlikely that cell cycle arrest or delay accounted for the increase in centrioles/cell. It is noteworthy that the centriole overduplication phenotype induced by FAM46C depletion was observed not only in U2OS cells, which typically tolerate and display a high level of centriole amplification, but also in the melanoma line MDA MB-435.</p><p id=\"Par10\">In HeLa cells synchronized by Aphidicolin block and release, FAM46C staining was observed at centrioles throughout the ensuing phases of the cell cycle (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4a, b</xref>). Co-staining with antibody to ODF2, which localizes preferentially to the mother centriole, or with antibody to CEP120, which localizes preferentially to the daughter centriole, showed that FAM46C is localized predominantly to the mother centriole (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c</xref>, bottom panels; Supplementary Figs. <xref rid=\"MOESM1\" ref-type=\"media\">4b, c</xref>; <xref rid=\"MOESM1\" ref-type=\"media\">5a</xref>). Super resolution fluorescence imaging of U2OS cells confirmed FAM46C localization to centrioles, and revealed a ring-like distribution on the mother centriole, adjacent to and partially overlapping with Plk4 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4d</xref>). In T-REx YFP-Plk4 U2OS cells induced to display a Plk4-driven centriole overduplication phenotype by treatment with tetracycline, FAM46C appeared to be concentrated at the central mother centriole, surrounded by CEP120- and Plk4- positive nascent daughters (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4c, d</xref>).</p><p id=\"Par11\">Depletion or inhibition of Plk4 results in gradual loss of centrioles over subsequent cell cycles; in a variety of human cell lines, treatment with siPlk4 for 48–72 h results in a significant increase in the proportion of cells with only 1 centriole<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref>,<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. In U2OS cells depleted of Plk4, some FAM46C staining was still observed on the single residual centriole (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5a</xref>). Increased FAM46C expression was also associated with an altered centriolar profile: a higher proportion of RFP-FAM46C cells had only 1 centriole, while fewer possessed >4 centrioles, as compared with RFP control (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2e</xref>, Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5b</xref>). The shFAM46C centriole phenotype was partially rescued by RFP-FAM46C (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5b</xref>), arguing against an off-target effect and providing further evidence that FAM46C was indeed regulating centriole number.</p><p id=\"Par12\">The localization of FAM46C and Plk4 to centrioles throughout the cell cycle (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4b</xref>;<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>), in addition to the physical interaction of FAM46C with Plk4 observed in co-immunoprecipitation experiments, taken together with the rosette-like centriolar configuration conferred by depletion of FAM46C, suggested that FAM46C might normally function to limit Plk4 activity and/or abundance. In addition, expression of FAM46C was cell cycle-dependent and peaked in mitosis (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">6</xref>), similar to that of Plk4<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>. Indeed, Plk4-induced centriole overduplication was abrogated by RFP-FAM46C, manifest as both reduction in centriole number and suppression of rosette formation (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a</xref>). Furthermore, the centriolar overduplication phenotype induced by depletion of FAM46C was not observed when Plk4 was itself depleted (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>). While it is conceivable that epistasis could account for the inability of FAM46C depletion to overcome the centriolar phenotype induced by Plk4 depletion, a more likely explanation is that the centriolar overduplication observed upon FAM46C depletion is dependent on Plk4 expression.<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>FAM46C interacts functionally with Plk4 to regulate centriole duplication.</title><p><bold>a</bold> Suppression of Plk4 overduplication phenotype by FAM46C shown in representative immunofluorescence images of U2OS T-REx YFP-Plk4 (green) cells with Plk4 expression induced by tetracycline (Tet+) and transfected with RFP or RFP-FAM46C X40h (top panels), with quantification of centriole numbers (bottom panel). Cells were labeled with antibodies to FAM46C (red, stains endogenous FAM46C in left panel, and both endogenous and transfected FAM46C in right panel), and centrin (blue, inserts), and with Hoechst (blue, top panels). The inserts show magnified centrosomes (boxed in white) for each condition. Bar graph shows proportion of cells with indicated number of centrioles per cell, quantified by scoring centrin-positive foci. <italic>n</italic> = 3 independent experiments with 80 cells measured in each, *<italic>p</italic> < 0.034 vs. RFP + Tet. <bold>b</bold> The FAM46C shRNA centriolar overduplication phenotype is dependent on Plk4 expression, as illustrated by representative immunofluorescence images of U2OS RFP shRNA or FAM46C shRNA cells transfected with Luciferase siRNA or Plk4 siRNA-A X48h (top panels), and labeled with antibodies to centrin (red) and pericentrin (green), and with Hoechst (blue). Inserts to the right show magnified centrosomes (boxed in white) for each condition. Bottom panel: Bar graph showing proportion of cells with indicated number of centrioles per cell, quantified by scoring centrin-positive foci. <italic>n</italic> = 2 independent experiments with >50 cells measured in each. Data are means ± SEM.</p></caption><graphic xlink:href=\"42003_2020_1161_Fig3_HTML\" id=\"d30e940\"/></fig></p><p id=\"Par13\">In cells treated with hydroxyurea to effect an S phase arrest, the centriole cycle becomes uncoupled from the cell cycle, and centriole duplication continues, with a resultant increase in centriole number; this phenomenon is augmented by tetracycline-induced Plk4 expression (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5c</xref>). In cells under hydroxyurea arrest, which would minimize any potential differences in cell cycle progression, FAM46C overexpression nevertheless prevented the increase in centriole count per cell otherwise seen with Plk4 upregulation (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5d</xref>), showing that this effect was intimately linked to the centriole cycle, and supporting the hypothesis that FAM46C acted in opposition to Plk4.</p></sec><sec id=\"Sec5\"><title>FAM46C inhibits Plk4 autophosphorylation and interacts with the Plk4 kinase and PB-1/PB-2 domains</title><p id=\"Par14\">We surmised that FAM46C inhibited Plk4 function, and examined its effect on Plk4 kinase activity. In an in vitro kinase assay, the autophosphorylation of full-length wild-type FLAG-Plk4 was markedly reduced in the presence of FAM46C, with a clear dose response (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref>). By contrast, comparable levels of FAM46A had no discernible effect on Plk4 autophosphorylation (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7a</xref>). While the association between Plk4 and FAM46C demonstrated in Y2H and reciprocal co-immunoprecipitation experiments (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>) suggested a direct interaction, it was possible that FAM46C instead interfered with activation of Plk4 by upstream regulators, in particular STIL and/or CEP85<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>. To explore this, we performed an in vitro kinase assay using His-tagged Plk4 kinase domain+linker (AA1–390) purified from bacterial extract on a nickel column. By this method, we were able to generate and purify large amounts of Plk4(1–390) and GST-FAM46C and test their interaction independent of other cellular proteins. Autophosphorylation of the Plk4 fragment was inhibited by purified GST-FAM46C, with a dose-response effect (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4b</xref>), indicating that the inhibitory effect of FAM46C on Plk4 kinase activity was mediated through a direct physical interaction between the two proteins. In transient co-expression experiments, increasing levels of FAM46C appeared to stabilize wild-type Plk4 protein (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c</xref>). This would be consistent with reduced Plk4 autophosphorylation, required for its ubiquitination and degradation, providing further evidence for inhibition of Plk4 kinase activity by FAM46C. Centriolar loading of SAS-6 is another read-out of Plk4 activity, since it is dependent on phosphorylation of its interacting partner STIL by Plk4<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>. As noted, upon FAM46C depletion, SAS-6 was observed at each of the nascent centrioles in the resulting centriolar rosette (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c</xref>), confirming the downstream effect of upregulated Plk4 activity, of which this centriolar configuration is a hallmark.<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>FAM46C regulates Plk4 kinase activity.</title><p><bold>a</bold> In vitro kinase assay showing dose-dependent reduction in wild-type (wt) Plk4 autophosphorylation by FAM46C. Increasing amounts of FAM46C are indicated by wedge, specified for each lane under the colloidal blue-stained gel of input proteins. Autoradiographs show incorporation of γ-<sup>33</sup>P during incubation, reflecting active phosphorylation. The kinase-dead construct FLAG-Plk4 K41M lacks autophosphorylation. <bold>b</bold> In vitro kinase assay using bacterially expressed purified proteins, showing dose-dependent reduction in Plk4 kinase domain (1–390) autophosphorylation by GST-FAM46C. Increasing amounts of GST-FAM46C are indicated by wedge. <bold>c</bold> Increase in Plk4 protein level in response to forced expression of FAM46C, shown in representative immunoblot of HEK293T cells transfected with FLAG-Plk4 wt or FLAG-Plk4 K41M and increasing amounts of RFP-FAM46C, as indicated, using anti-FLAG and anti-RFP antibodies, with γ-tubulin as a loading control. The effect on kinase-dead Plk4 protein level was much less pronounced. <bold>d</bold> Immunoblots of RFP-FAM46C and the indicated wild-type Plk4 fragment, after coexpression in HEK293T cells in a reciprocal coimmunoprecipitation assay. Right panel, summary of domain-dependent interactions between Plk4 fragments coexpressed with RFP-FAM46C as in left panel, showing interaction of RFP-FAM46C with the Plk4 kinase domain and PB1-2.</p></caption><graphic xlink:href=\"42003_2020_1161_Fig4_HTML\" id=\"d30e1014\"/></fig></p><p id=\"Par15\">FAM46C/TENT5C was recently confirmed as an active non-canonical poly(A) polymerase that enhances mRNA stability and thereby alters gene expression<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. FAM46C’s nucleotidyl transferase activity is dependent on residues D90 and D92, and mutation of these residues to alanine results in abrogation of catalytic activity. Importantly, catalytically inactive mutant FAM46C D90/92A retained the ability to suppress Plk4 activity, similar to wild-type FAM46C (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7b</xref>). Furthermore, the ability of FAM46C to suppress Plk4-induced centriole overduplication did not require nucleotidyl transferase activity (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7c</xref>). These observations support the hypothesis that FAM46C physically interacts with Plk4 to inhibit its kinase activity, rather than regulating Plk4 and/or centriole duplication through transcriptional regulation.</p><p id=\"Par16\">While the FAM46C sequence contains two consensus Plk4 phosphorylation sites (S74, T354)<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>, it did not appear that FAM46C was a substrate for full-length Plk4 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref>). However, co-immunoprecipitation experiments using deletion constructs showed that FAM46C interacted with the isolated Plk4 kinase domain, and to a lesser extent with polobox domains PB-1/PB-2, and not with PB3 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4d</xref>). Of note, FAM46C co-immunoprecipitated with the kinase-dead Plk4 K41M (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8a</xref>), implying that the physical interaction was not dependent on Plk4 kinase activity. As expected from its inability to autophosphorylate and induce its own degradation, Plk4 K41M was more abundant intracellularly than wild-type Plk4 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c</xref>). However, there was a limited stabilizing influence of FAM46C on Plk4 K41M (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c</xref>), again implying that the functional effect of FAM46C depended on Plk4 kinase activity. Trans-autophosphorylation of exogenous kinase-dead Plk4 by endogenous wild-type Plk4 with which it is dimerized has been described<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>, and the minor stabilization of Plk4 K41M observed in the presence of RFP-FAM46C may have reflected such homodimerization with endogenous Plk4, the latter being susceptible to regulation by FAM46C. The significant stabilizing effect of FAM46C on wild-type Plk4 protein level could also have potentially been due to an increase in Plk4 mRNA level mediated through FAM46C’s poly(A) RNA polymerase activity, but if this were the dominant mechanism, one might expect a corresponding increase in K41M protein stability, which was not observed. The phosphodegron that mediates Plk4 autophosphorylation-dependent degradation was not required for interaction with FAM46C (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4d</xref>), and FAM46C interacted with non-degradable Ser293A/Thr297A mutant Plk4 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8b</xref>). These results argue against the possibility that FAM46C occluded the phosphodegron and suggest instead that direct interaction with the Plk4 kinase domain/ PB-1/PB-2 may be of greatest relevance to the inhibition of Plk4 kinase activity by FAM46C. N terminal/ DUF domain fragments of FAM46C interacted with Plk4, whereas a C terminal fragment did not, and while full-length FAM46C suppressed Plk4 autophophorylation, the non-interactive C terminal fragment had no apparent effect (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8c, d</xref>). Interestingly, comparing hFAM46C to paralogs FAM46A, B and D, there is greater divergence in the N terminal versus the C terminal sequence, an observation that, taken together with the failure of the C terminal fragment to bind to or inhibit the kinase activity of Plk4, could underlie the specific interaction of FAM46C with Plk4 (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>; Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7a</xref>).</p></sec><sec id=\"Sec6\"><title>FAM46C suppresses the growth of MDA MB-435 cancer xenografts</title><p id=\"Par17\">Deletion and/or point mutation of FAM46C, found in 25–30% of patients with multiple myeloma, portends an adverse prognosis<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref>–<xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup>. Forced FAM46C expression suppresses myeloma cell growth, with altered expression of genes involved in survival signaling pathways<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref>,<xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>, and faster proliferation of B-lymphocytes harvested from FAM46C knock-out mice. To test the effect of FAM46C on tumor growth in vivo, we employed an aggressive human cancer cell line xenograft model in nude mice. Using two different shFAM46C constructs in MDA MB-435 cells, we confirmed that the resultant tumors retained reduced FAM46C expression in vivo up to 3 weeks after implantation (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>). Tumors formed by shFAM46C cells progressed rapidly compared to shLuciferase controls (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5b–d</xref>). Tumors formed by Plk4-depleted MDA MB-435 cells (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>) displayed modestly reduced growth (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5b–d</xref>), similar to previous findings in an MDA MB-231 model<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. The rapidity of tumor growth in the MDA MB-435 model necessitated early sacrifice, precluding assessment of tissue invasion and distant metastases. Compound mutant cells with knockdown of both Plk4 and FAM46C (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5e</xref>) showed an intermediate phenotype, consistent with a functional interaction in vivo whereby FAM46C restrained the oncogenic effect of Plk4 (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5f</xref>). Of note, there was a trend to reduced Plk4 mRNA in cells depleted of FAM46C, which could potentially reflect poly(A) RNA polymerase activity of the latter. Despite this, depletion of FAM46C enhanced tumor progression (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5f</xref>), suggesting that the dominant effect was to liberate Plk4 kinase activity. Depletion of Plk4 did not appear to affect FAM46C expression, at least in MDA MB-435 derived xenografts.<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>FAM46C suppresses MDA MB-435 xenograft tumor growth.</title><p><bold>a</bold>–<bold>d</bold> Tumors were generated by injecting MDA MB-435 cells transduced with shRNAs as indicated, and suspended in Matrigel, subcutaneously into the right flank of nude mice, and followed over the ensuing 3 weeks. <bold>a</bold> Quantification of FAM46C and Plk4 mRNA levels in tumors at 3 weeks, showing persistence of FAM46C or Plk4 depletion, as appropriate, relative to control, *<italic>p</italic> < 0.001 vs. Luciferase (Luc) shRNA. Quantification is relative to the housekeeping gene RPII. <bold>b</bold> Representative images of flank tumors in nude mice at the indicated times after injection. <bold>c</bold> Tumor volume of xenografts at indicated times after injection, showing larger tumor size of FAM46C shRNA compared to Luciferase shRNA xenografts at 2.5 and 3 weeks, **<italic>p</italic> < 0.012 vs. Luc shRNA, and showing smaller size of Plk4 shRNA compared to Luciferase shRNA xenografts at 3 weeks. <bold>d</bold> Corresponding tumor weights at time of sacrifice, which was 3 weeks after injection, showing greater weight of FAM46C shRNA xenografts, *<italic>p</italic> < 0.008 vs. Luc shRNA. <bold>e</bold>, <bold>f</bold> Functional interaction between Plk4 and FAM46C was examined using compound mutant MDA MB-435 cells in a nude mouse xenograft model. <bold>e</bold> Quantification of FAM46C and Plk4 mRNA levels in tumors at 3 weeks after injection, showing successful co-depletion. Dashed line indicates shLuc control, to which other conditions are normalized. Top panel, *<italic>p</italic> < 0.019 vs. Luciferase+Plk4 shRNA. Bottom panel, *<italic>p</italic> < 0.001 vs. Luciferase+FAM46C shRNA. <bold>f</bold> Tumor volume of xenografts at indicated times, showing a partial rescue of the smaller tumor size that occurs with Plk4 depletion alone in Plk4 + FAM46C shRNA xenografts, *<italic>p</italic> < 0.033 vs. Plk4 + FAM46C shRNA. Right panel, corresponding tumor weights showing an intermediate tumor weight in the Plk4 + FAM46C shRNA xenografts vs. Luciferase+Plk4 shRNA and Luciferase+FAM46C shRNA tumors, <italic>n</italic> = 10 mice per condition. Data are means ± SEM.</p></caption><graphic xlink:href=\"42003_2020_1161_Fig5_HTML\" id=\"d30e1176\"/></fig></p></sec><sec id=\"Sec7\"><title>FAM46C is depleted in human colorectal cancer</title><p id=\"Par18\">Observational studies of clinical cancers and xenografts indicate that high Plk4 expression is associated with aggressive behavior and treatment resistance<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref>,<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>,<xref ref-type=\"bibr\" rid=\"CR55\">55</xref>–<xref ref-type=\"bibr\" rid=\"CR57\">57</xref></sup>. For instance, recurrence-free survival was significantly poorer in Tamoxifen-treated breast cancer patients with high vs. low Plk4 expression (42% vs. 66%, GSE6532)<sup><xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>. In a cohort of patients with synchronous or metachronous liver metastases from colorectal adenocarcinoma (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>), we examined the expression of 48 genes in the hPlk4 interactome as defined by mass spectrometry<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup> plus 10 additional related genes including FAM46C (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>), in banked paired samples of primary tumor (T) and adjacent normal mucosa (NM), microdissected to isolate cancer cells and intestinal mucosal cells, respectively. As noted in previous colorectal cancer patient cohorts<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref>,<xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>, Plk4 was increased in T vs. matched NM (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6a, b</xref>). By contrast, FAM46C expression was consistently reduced in the tumor samples (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6a, b</xref>). This depletion in tumor tissue was not observed for the other centriolar proteins that interact with Plk4 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">9</xref>). The ratio of FAM46C to Plk4 expression was markedly reduced in T vs. NM in this patient cohort (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6c</xref>). Analysis of expression array data generated by Kim et al. in an independent cohort of colorectal cancer patients with liver metastases<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup> similarly reveals depletion of FAM46C levels relative to Plk4 in colorectal cancer specimens (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6d</xref>). Data from another independent cohort of colorectal cancer patients of all TNM stages<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup> show a significant reduction in FAM46C/Plk4 ratio in primary tumor (T) vs. NM (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6e</xref>), and further depletion of FAM46C with clinical stage progression, as apparent in our own initial patient cohort (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6f</xref>). While Plk4 is best recognized for its role in centriole duplication and accurate chromosomal segregation<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref>,<xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>, we and others have demonstrated its promotion of EMT and cell invasion<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. Here we show that FAM46C suppresses cancer cell invasion in a spheroid model, while FAM46C depletion promotes invasion (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6g, h</xref>). Importantly, when spheroids were treated with the highly selective Plk4 kinase inhibitor centrinone B, the stimulating effect of FAM46C depletion on invasion was lost (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6h</xref>), demonstrating the functional dependence of the latter on Plk4 activity. The causal link between FAM46C and Plk4 demonstrated in these invasion experiments implies that FAM46C can additionally restrain non-centriolar oncogenic functions of Plk4.<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>FAM46C is depleted in human colorectal cancer.</title><p><bold>a</bold> Distribution of Plk4 (top panel) and FAM46C (bottom panel) expression levels in colorectal primary tumor (T) compared with paired normal mucosa (NM) in microdissected specimens from 13 cases of advanced colorectal cancer. T/NM ratio is displayed on a log scale. Plk4 and FAM46C expression were measured relative to the control GAPDH. Each patient case is represented by a single vertical unit. All patients had synchronous or metachronous liver metastases; this cohort is described in further detail in Table <xref rid=\"MOESM1\" ref-type=\"media\">S1</xref>. <bold>b</bold> Summary of expression levels of Plk4 (top) and FAM46C (bottom) in 13 cases of colorectal cancer, as in <bold>a</bold>, showing increased Plk4 and decreased FAM46C in primary tumor (T) vs. paired normal colonic mucosa (NM). Data are log-transformed means ± SEM. <bold>c</bold>–<bold>e</bold> Ratio of FAM46C/Plk4 expression in colorectal primary tumor (T) vs. paired normal mucosa (NM), assayed using qPCR (<bold>c</bold> *<italic>p</italic> < 0.003, vs. NM, <italic>n</italic> = 13 patient cohort as in <bold>a</bold> above), or derived from RNA-seq data of Kim et al., 2014<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup> (<bold>d</bold> **<italic>p</italic> < 0.0001, vs. NM, <italic>n</italic> = 18), or The Cancer Genome Atlas (TCGA-COAD), 2012<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup> (<bold>e</bold> **<italic>p</italic> < 0.0001, vs. NM, <italic>n</italic> = 39). <bold>f</bold> Top panel, FAM46C expression in primary tumor (T)/paired normal mucosa (NM) in 13 patients with colorectal cancer, as in <bold>a</bold> above, sorted by stage at time of primary presentation: Stage I, <italic>n</italic> = 1; Stage II, <italic>n</italic> = 2; Stage III, <italic>n</italic> = 2; Stage IV, <italic>n</italic> = 8. Bottom panel, FAM46C expression in colorectal primary tumor determined by RNA-seq, in a cohort of 439 patients, from TCGA-COAD, 2012<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup>, sorted by stage: Stage I, <italic>n</italic> = 74; Stage II, <italic>n</italic> = 175; Stage III, <italic>n</italic> = 128; Stage IV, <italic>n</italic> = 62. *<italic>p</italic> < 0.005 vs. Stage I. <bold>g</bold>, <bold>h</bold> Representative brightfield images of HeLa cells plated on ultra-low attachment plates, which form spheres over 4 days in culture after addition of Matrigel. <bold>g</bold> Transfection with RFP-FAM46C suppressed the 3D invasion of cells into surrounding matrix, at the indicated times. Quantification of invasion is shown in right panel, <italic>n</italic> = 8 independent experiments, *<italic>p</italic> < 0.01 vs. RFP alone. <bold>h</bold> FAM46C knockdown using shRNA enhanced the invasion of spheroids into surrounding matrix. Synchronous treatment with the selective Plk4 inhibitor centrinone B prevented the enhanced invasion seen with FAM46C knockdown, indicating that the enhancement is dependent on Plk4 activity. Quantification of invasion is shown in right panels, <italic>n</italic> = 7 independent experiments for each panel, *<italic>p</italic> < 0.001 vs. GFP shRNA without centrinone B (top panel) and p = NS with centrinone B (bottom panel). Data are means ± SEM. Bars: 300 µm.</p></caption><graphic xlink:href=\"42003_2020_1161_Fig6_HTML\" id=\"d30e1409\"/></fig></p></sec></sec><sec id=\"Sec8\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par19\">The best described and understood role of Plk4 is as a driver of centriole duplication, but other distinct functions have recently been uncovered, including regulation of the actin cytoskeleton with promotion of cell motility<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR64\">64</xref></sup>. Mounting evidence has implicated Plk4 in cancer progression, which can be mediated through Plk4-induced centriolar amplification, amongst other possible mechanisms<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR9\">9</xref>,<xref ref-type=\"bibr\" rid=\"CR13\">13</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. In this study, we show that FAM46C/TENT5C interacts physically with Plk4 in co-immunoprecipitation experiments, and suppresses Plk4 activity. FAM46C restrains centriole duplication and cancer cell invasion in opposition to Plk4, and acts as a tumor suppressor in a human cancer xenograft model. Furthermore, we show that FAM46C is significantly depleted in human colorectal cancer, which correlates with cancer progression. These results reveal FAM46C as a modulator of centriolar duplication via its inhibition of Plk4, and implicate it as a tumor suppressor in common human epithelial malignancies.</p><p id=\"Par20\">Our interest in FAM46C as a potential Plk4 interactor was prompted by the results of yeast 2 hybrid screens, and the specific localization of FAM46C to centrioles we observed here was not anticipated. Taken together with the evidence of physical interaction in vitro, and functional interaction in regulation of centriole duplication and of xenograft tumor growth, the similarity in centriolar localization of FAM46C and Plk4 suggests the possibility of a functional interaction at the centriole; however, further study using super-resolution microscopy will be necessary to elucidate the details of this relationship. The requirements for centriolar localization of FAM46C remain to be determined, but seem unlikely to involve the scaffolding proteins CEP192 or CEP152.</p><p id=\"Par21\">The evidence we have uncovered in the present experiments suggests that binding of FAM46C to Plk4 may occur predominantly within the kinase domain of the latter, but is not dependent on kinase activity, and does not result in phosphorylation of FAM46C. Further investigation of the mechanism of inhibition of Plk4 kinase activity will determine its dependence on direct physical interaction. Analysis of FAM46C/TENT5C sequence and domain structure predicted its function as a non-canonical poly(A) RNA polymerase, and mutation in the polymerase domain renders it catalytically inactive<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>. We found that mutant FAM46C that was catalytically inactive for nucleotidyl transferase activity retained the ability to suppress Plk4 activity, as measured in both kinase and centriole duplication assays. Thus it appears unlikely that the effect of FAM46C on Plk4-dependent centriole duplication is related to its RNA polymerase function, at least in the cancer cell types we have studied here, which include osteosarcoma, melanoma and colorectal adenocarcinoma. In multiple myeloma cell lines, depletion of FAM46C increases proliferation while restoration of functional wild-type FAM46C causes cell death, consistent with the transcriptional regulation of cell survival and death genes observed in those cell lines<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>. In the solid tumor cells studied here, viability was not affected, and alterations in Plk4-dependent centriole duplication antedated changes in proliferation. Moreover, Plk4 mRNA levels were reduced by FAM46C depletion in vivo, the latter nevertheless resulting in enhanced xenograft growth. Taken together, these data are more consistent with unrestrained Plk4 kinase activity as the principal mechanism of tumor promotion, rather than regulation of RNA stability. Inhibition of Plk4 kinase activity and restraint of Plk4-induced centriole overduplication by mutant FAM46C that has no nucleotidyl transferase activity further support the conclusion that the restraining effect on centriole duplication is independent of FAM46C’s RNA polymerase function. Stabilization of Plk4 protein levels was dependent on reduced Plk4 kinase activity, rather than increased Plk4 mRNA expression, further implicating suppression of Plk4 kinase activity through direct interaction with the kinase and/or PB-1/PB-2 domains.</p><p id=\"Par22\">Regulation of Plk4 function occurs at both the protein and kinase activity levels, with a complex interplay between the two inherent in the enzyme’s autophosphorylation-dependent degradation, and the requirement to achieve a threshold level of Plk4 kinase activity to trigger its self-destruction<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Stabilization of wild-type Plk4 protein levels by FAM46C is consistent with its function as an endogenous inhibitor of Plk4 kinase activity. The synthetic Plk4 inhibitor centrinone/centrinone B causes loading of centrioles with inactive wild-type Plk4; withdrawal of centrinone then triggers multiple rounds of unfettered centriole duplication<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. We found that knockdown of endogenous FAM46C increases centriole duplication in a Plk4-dependent manner, creating centriolar rosettes characteristic of forced Plk4 expression or activation. There is no apparent structural similarity between FAM46C/TENT5C and any of the pharmacologic Plk4 inhibitors previously described, which in addition to centrinone/centrinone B also include CFI-400945<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref>,<xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup> and YLT-11<sup><xref ref-type=\"bibr\" rid=\"CR65\">65</xref></sup>; each of these agents was designed to block the ATP-binding pocket of Plk4.</p><p id=\"Par23\">Regulation of Plk4 function can also be mediated through its most intimate functional interactors, such as STIL. Binding of CDK1-CyclinB to STIL during mitosis prevents its binding to Plk4, delaying phosphorylation of STIL by Plk4, and the resulting recruitment of SAS-6 and triggering of centriolar biogenesis, until G1<sup><xref ref-type=\"bibr\" rid=\"CR66\">66</xref></sup>. Of note, phosphorylated STIL enhances the centriolar anchoring of Plk4; the normal restriction of procentriole formation to the single spot of Plk4 concentration on the mother centriole is disrupted by overexpression of either STIL or Plk4. While we have shown here that FAM46C physically interacts with Plk4 within its kinase/ PB-1/PB-2 domains, suppressing its autophosphorylation, the impact on Plk4-STIL interaction remains to be determined. However, depletion of endogenous FAM46C does culminate in enhanced recruitment of endogenous SAS-6 to procentrioles, implying that activity of the Plk4-STIL axis is indeed upregulated.</p><p id=\"Par24\">Loss or mutation of FAM46C is a common secondary genetic event in multiple myeloma, and predicts inferior survival<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref>–<xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>. The reason(s) for this association remained obscure while the function of FAM46C was unknown. Members of the FAM46 family were long speculated to function as RNA polymerases, but definitive evidence of nucleotide transferase activity was uncovered only recently<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>, in recognition of which the FAM46C gene was renamed terminal nucleotidyl transferase 5C (TENT5C). It regulates gene expression through polyadenylation-induced enhancement of mRNA stability. As for the specific mRNAs targeted by FAM46C-induced polyadenylation, in multiple myeloma lines these were shown to be highly enriched for proteins that are targeted to the ER<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. Mroczek et al. propose that loss of FAM46C is associated with ER stress in multiple myeloma, making this tumor type particularly vulnerable to proteasome inhibition. The role of FAM46C/TENT5C poly(A) polymerase activity in other types of cancer requires further study. Previous reports of FAM46C depletion in hepatocellular cancer have suggested an association with tumor progression and adverse patient prognosis<sup><xref ref-type=\"bibr\" rid=\"CR67\">67</xref></sup>, foreshadowing our present analysis of FAM46C in colorectal cancer. These prognostic correlations could be related to altered expression of multiple genes involved in cell survival/death, or, as we suggest here, to unopposed oncogenic kinase activity of Plk4. It should be acknowledged that these are not mutually exclusive mechanisms. Intriguingly, the anti-proliferative and anti-invasive effects of norcantharidin on hepatoma cells depended on its specific upregulation of FAM46C expression<sup><xref ref-type=\"bibr\" rid=\"CR67\">67</xref>,<xref ref-type=\"bibr\" rid=\"CR68\">68</xref></sup>. Lack of FAM46C/TENT5C function, now revealed as a frequent and prognostic feature in human colorectal cancer as well as in multiple myeloma and hepatocellular carcinoma, can promote tumor progression by allowing unrestrained Plk4 activity. Restoration of FAM46C function warrants investigation as a therapeutic strategy in treatment-resistant cancers driven by Plk4.</p></sec><sec id=\"Sec9\"><title>Methods</title><sec id=\"Sec10\"><title>Phylogenetic tree</title><p id=\"Par25\">Based on multiple sequence alignments generated by Clustal Omega (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ebi.ac.uk/Tools/msa/clustalo/\">https://www.ebi.ac.uk/Tools/msa/clustalo/</ext-link>) as in Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>, an unrooted phylogenetic tree was generated for select FAM46C orthologs and the human FAM46C paralogs, FAM46 A, B and D.</p></sec><sec id=\"Sec11\"><title>Cell culture, transfection</title><p id=\"Par26\">Cells were grown at 37 °C in Dulbecco’s modified Eagle Medium (DMEM: HEK293T), Roswell Park Memorial Institute medium-1640 (RPMI: MDA-MB-435), DMEM-F12 (RPE-1) or McCoy’s 5A medium (U2OS) supplemented with 10% fetal bovine serum (FBS). Transient transfection was performed using PEI transfection reagent (Sigma), Lipofectamine 2000 or Lipofectamine RNAiMAX (Invitrogen) according to manufacturer’s instructions and as described<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. A pool of four FAM46C siRNAs was utilized (M-020700 SMARTpool siGENOME siRNA, Dharmacon). U2OS cells were incubated with 8 mM hydroxyurea for 16 h to effect cell cycle arrest. U2OS Flp-In T-Rex YFP-Plk4 cells were treated with Tetracycline at a concentration of 1 µg/mL X16-24h to stimulate YFP-Plk4 expression. Following 24 h of tetracycline induction, cells were transfected with RFP, RFP-FAM46C, RFP-FAM46C D90/D92A x42h and fixed and stained with centriole markers for immunofluorescence.</p></sec><sec id=\"Sec12\"><title>Cell line derivation</title><p id=\"Par27\">HEK293T, RPE-1 and MDA MB-435 cell lines were a kind gift from the Tony Pawson laboratory (Lunenfeld Tanenbaum Research Institute, Toronto), and U2OS cells were a kind gift from the Laurence Pelletier laboratory (Lunenfeld Tanenbaum Research Institute).</p></sec><sec id=\"Sec13\"><title>Stable cell lines</title><p id=\"Par28\">Stable cell lines were generated as Flp-In U2OS T-REx cells, or U2OS and MDA MB-435 cells expressing Plk4 (SHCLNG-NM_014264, Sigma), Luciferase (SHC007, Sigma), RFP (Tony Pawson laboratory, Lunenfeld Tanenbaum Research Institute) or FAM46C (SHCLND-NG_017709, Sigma) short hairpin RNAs (shRNAs) through lentiviral infection as described<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. Stable cells transduced with shRNAs were studied within 3 weeks of infection. For transfection in the rescue protocol, 100,000 U2OS FAM46C shRNA (two shRNA constructs utilized) or RFP shRNA cells were seeded onto 6-well plates and transfected after 24 h with 1 μg of RFP-FAM46C or RFP X42h. RFP-FAM46C and RFP expression were confirmed with immunofluorescence. Cells were tested for mycoplasma contamination.</p></sec><sec id=\"Sec14\"><title>Plasmid constructs</title><p id=\"Par29\">Vectors were constructed using Invitrogen’s Gateway system as described<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. The Flag-CEP192 (1–2436), Flag-CEP135 and BirA*-Flag-CEP152 vectors were kindly provided by Laurence Pelletier’s laboratory (Lunenfeld Tanenbaum Research Institute, Toronto, Canada). The Plk4 Non-Degradable (ND) mutant, with two mutations within the <italic>DSGIIT</italic> degron (Ser293A and Thr297A), and FAM46C catalytically inactive mutant (D90A and D92A), were synthesized from the PLK4 and FAM46C entry vectors (Gateway) using PCR-based site-directed mutagenesis (QuikChange II Site-Directed Mutagenesis Kit, Agilent Technologies). The Plk4 ND and FAM46C D90/92A mutants were cloned into the pDEST N-terminal 3xFlag vector. Plk4 deletion mutants were cloned as described<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. FAM46C deletion mutants (N-term 1–199, C-term 193–391 and DUF 17–336) were generated by PCR using Phusion High Fidelity DNA Polymerase (M0530, New England Biolabs) according to manufacturer’s instructions.</p><p id=\"Par30\">FAM46A, B, C and D entry vectors (Gateway) were obtained from the Lunenfeld Tanenbaum Research Institute OpenFreezer reagent repository and cloned into the pDEST mCherry destination vector (Gateway). All constructs were validated by sequencing.</p></sec><sec id=\"Sec15\"><title>Co-immunoprecipitation, immunoblotting</title><p id=\"Par31\">These were performed as described<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. In brief, HEK293T cells transfected using PEI transfection reagent (Sigma) X24h were lysed using TNTE lysis buffer (2 mM Tris-HCl,pH7.5, 120 mM NaCl, 1%TritonX-100, 1 mM EDTA), with protease inhibitor cocktail, 5mMNaF and 2mMNaOva. Beads were pre-washed and blocked with 5% BSA. Extracts were centrifuged (14,000 rpm) X10min, and supernatants immunoprecipitated with anti-FLAG M2 affinity gel (Sigma; A2220) X1.5 h, or incubated with rabbit polyclonal mCherry, RFP or GFP antibodies (Abcam) X1h followed by immunoprecipitation with ProteinG Sepharose beads (GE Healthcare) X30min. Beads were washed 6x with lysis buffer supplemented with 500 mM NaCl, boiled in Laemmli sample buffer, and analyzed using SDS polyacrylamide gel electrophoresis (SDS-PAGE) followed by immunoblotting.</p></sec><sec id=\"Sec16\"><title>Anti-FAM46C antibody production</title><p id=\"Par32\">Full length FAM46C was cloned using the pET system into a bacterial expression vector containing a C-term 6×His tag. Recombinant protein was purified from <italic>Escherichia coli</italic> using Ni-NTA beads (Novagen) and used for immunization; rabbit immune sera were affinity-purified using standard procedures (Pacific Immunology Corp.). Prior incubation of anti-FAM46C with antigen eliminated centriolar staining, and staining was markedly reduced by depletion of FAM46C using siRNA (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>).</p></sec><sec id=\"Sec17\"><title>Immunofluorescence</title><p id=\"Par33\">Cells were fixed, permeabilized and blocked using Methanol (−20 °C, 10 min), 0.5% Nonidet-P40 (Bioshop, 20 min), and 0.2% Fish Gelatin/PBS X1h. Antibody incubations were in the blocking solution overnight (4 °C), and slides were mounted in Immuno-mount medium (Thermo). Immunofluorescence images were collected using the Olympia Deconvolution fluorescence microscope or Deltavision Elite DV imaging system equipped with a sCMOS 2048×2048 pixels2 camera (GE Healthcare). Z stacks (0.2 μm apart) were used, and images were deconvolved and maximum intensity projected using softWoRx software (Applied Precision). Images were collected using 60X and 100×1.4 NA oil objectives (Olympus). High- and super-resolution imaging were performed following standard procedures<sup><xref ref-type=\"bibr\" rid=\"CR69\">69</xref></sup> at the Network Biology Collaborative Centre (NBCC), a facility supported by Canada Foundation for Innovation, the Ontarian Government, and Genome Canada and Ontario Genomics Institute (OGI-139).</p></sec><sec id=\"Sec18\"><title>Antibodies</title><p id=\"Par34\">Antibodies used for immunofluorescence in this study were FAM46C (generated for this study, as above, 1:200), centrin (clone 20H5, Millipore, 1:1000), pericentrin (Sigma, 1:1000), Plk4 (NB100–894, Novus Biologicals Canada and MABC544, Millipore, 1:300), SAS-6 (sc-82360, Santa Cruz, 1:400), CPAP<sup><xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup>, CP110 (A301-343A-1, Bethyl Laboratories, 1:1000), Cep135<sup><xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup>, CEP120<sup><xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup> (kind gift of Dr. M. Mahjoub, Washington University, St. Louis, 1:3000), ODF2 (H00004957-M01, Cedarlane/Abnova, 1:100). Secondary antibodies were conjugated to Alexa Fluor 488, 546, 594, 633, or 647 (Life Technologies, 1:800). DNA was detected using Hoechst. YFP and mCherry/RFP were visualized directly. The antibodies used for immunoblotting were: anti-β-tubulin (Sigma-Aldrich, 1:1000), anti-γ-tubulin (Sigma-Aldrich, 1:1000), anti-FLAG M2 (F1804, Sigma-Aldrich, anti-RFP (Abcam, 1:1000), anti-mCherry (Abcam, 1:500), anti-FAM46C (generated for this study, as above, 1:1000), anti-GFP (ab290, Abcam, 1:1000), anti-Cyclin B1 (4135, Cell Signaling, 1:1000), anti-Cyclin D1 (2926, Cell Signaling, 1:1000), anti-Phosphohistone H3 (9701, Cell Signaling, 1:1000).</p></sec><sec id=\"Sec19\"><title>RNA extraction, real-time RT-PCR</title><p id=\"Par35\">These were performed as described<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. In brief, RNA was isolated using the RNeasy mini kit (QIAGEN), treated with RNase-free DNase (Invitrogen) and reverse transcribed with SuperScriptII reverse transcriptase (18064-014, Invitrogen) using Random Primers (48190-011, Invitrogen). Real-time RT-PCR was performed using SYBR Green PCR Master Mix (Applied Biosystems) on an ABI 7900HT apparatus. Quantifications were normalized to control endogenous GAPDH, with data generated by PCR software (SDS2.2.2, Applied Biosystems) analyzed using the 2<sup>−∆∆Ct</sup> method. Primers for FAM46C were as follows, F: ggccacgttttggtcaaagac and R: gggaacacagaaccacatctc.</p></sec><sec id=\"Sec20\"><title>Cell cycle analyses</title><p id=\"Par36\">HeLa cells were grown to 70% confluence and treated with 2 µg/mL aphidicolin x24h, washed then treated again with 2 µg/mL aphidicolin for another 24 h. Cells were then washed with PBS, and DMEM + FCS added for release. At the indicated times after release cells were lysed and collected for Immunoblotting or cells grown on slides were fixed and stained with centriole markers for Immunofluorescence. In other cell cycle analyses, RPE-1 cells were grown to 70% confluence and treated with 2 mM thymidine (Sigma) X20h, washed with PBS and DMEM-F12 + FCS added for release. After 8 h cells were again treated with 2 mM thymidine X15h and again released. At the indicated times after release cell pellets were collected, fixed in cold 70% ethanol, washed with PBS and resuspended in PBS supplemented with 0.5 mg/ml RNase A (30 min, 37 °C). Propidium iodide (Invitrogen) was added to a final concentration of 10 µg/mL (15 min, room temperature) followed by analysis with a flow cytometer (BD Cytoff). Data were processed using Flow Jo software. For cell cycle immunoblot analysis, cells were harvested with Laemmli buffer and boiled (5 min, 100 °C). Lysates were run on 12% acrylamide SDS gels and transferred onto nitrocellulose (PALL Life Sciences BioTrace) using a BD Transfer Blot SD. Blots were blocked in Licor Blocking Buffer. Primary antibodies in 1:1 Licor Blocking buffer: PBS with 0.1% Tween20 (PBS-T) were incubated overnight at 4 °C. Blots were washed in PBS-T. Blots were then incubated in LICOR secondary fluorescent antibodies (1:30,000) in 1:1 Licor Blocking buffer: PBS-T X1h. Blots were washed in PBS-T and visualized using the LICOR scanner.</p></sec><sec id=\"Sec21\"><title>Electron microscopy</title><p id=\"Par37\">For electron microscopy, HEK293T samples were fixed in 2.5% glutaraldehyde solution buffered with PBS for 2 h. After they were washed three times in phosphate buffer, cells were postfixed with 1% glutaraldehyde for 1 h, then washed in buffer and distilled water. Cells were then dehydrated in a graded series of ethanols and flat-embedded in a mixture of Epon and Araldite. After polymerization for 48 h at 60 °C, coverslips were immersed in liquid nitrogen and sections were obtained with an ultramicrotome. Sections were stained with uranyl acetate and Reynold’s lead citrate. Images were obtained with a Philips CM10 electron microscope.</p></sec><sec id=\"Sec22\"><title>Xenograft studies in mice</title><p id=\"Par38\">All protocols were approved by the Toronto Centre for Phenogenomics (TCP) Animal Care Committee. 0.5 × 10<sup>6</sup> MDA MB-435 Luciferase, Plk4 or FAM46C shRNA cells were injected subcutaneously in the right flank of female 5wk-old NCr Nude mice (Taconic Biosciences). Tumor growth was monitored by palpation, size was measured with calipers, and volume calculated assuming an ellipsoid shape. Mice were sacrificed at 3wk post-injection. Tumors were harvested and frozen in liquid nitrogen for RNA extraction.</p></sec><sec id=\"Sec23\"><title>Real time RT-PCR of xenograft lysates</title><p id=\"Par39\">Tissue was placed in Ambion RNAlater-ICE solution overnight at –20 °C, then disrupted and homogenized in RLT buffer (RNeasy mini kit Qiagen) supplemented with β-mercaptoethanol using a rotor-stator homogenizer. Lysates were centrifuged X10min (4 °C; 14,000 rpm) and the supernatant used for RNA purification using the RNeasy mini kit following manufacturer’s protocol. Real time RT-PCR was performed using SYBR Green PCR Master Mix (Applied Biosystems) on an ABI 7900HT apparatus, and normalized to RPII.</p></sec><sec id=\"Sec24\"><title>qPCR</title><p id=\"Par40\">qPCR reactions were prepared with TaqMan Universal PCR Master Mix no AmpErase UNG (4324018, Applied Biosystems) and TaqMan Gene Expression Assays 20X Hs00214530_m1 (FAM46C) and Hs00179514_m1 (PLK4), and run on the Applied Biosystems 7900HT instrument. Conditions for the reactions were according to TaqMan protocol. GAPDH was used as a housekeeping gene. For each gene, quantities were extrapolated from standard curves made with control cDNA prepared from a pool of cells. Expression for each gene was calculated as a ratio relative to GAPDH, and primary expression was compared to normal for each individual patient.</p></sec><sec id=\"Sec25\"><title>In vitro kinase assay</title><p id=\"Par41\">HEK293T cells were transfected with FLAG-Plk4 WT, kinase-dead FLAG-Plk4 K41M, FLAG-FAM46A, FLAG-FAM46C WT, FLAG-FAM46C D90/92 A or FLAG-FAM46C 193–391 using PEI transfection reagent (Sigma). After 24 h, cells were lysed using TNTE lysis buffer (20 mM Tris-HCl, pH 7.5, 120 mM NaCl, 1% Triton X-100, 1 mM EDTA), with protease inhibitor cocktail, 5 mM NaF and 2 mM NaOva phosphatase inhibitors. Extracts were centrifuged at 14,000 rpm x 10 min (4 °C), and the supernatants were immunoprecipitated with anti-Flag M2 affinity beads (Sigma; A2220) for 1 h (4 °C). The beads were washed three times with lysis buffer supplemented with an additional 500 mM NaCl and twice with kinase buffer (25 mM Tris HCl, pH 7.5, 25 mM MgCl2, 15 mM sodium glycerolphosphate, 0.5 mM NaOva, 2 mM EDTA, 25 mM NaF, 1 mM DTT and 1.25 μg BSA). The beads were then incubated with 0.25–2.25 μg of FAM46C recombinant protein (Origene), or with eluted protein FAM46A, FAM46C WT, FAM46C 193–391 or FAM46C D90/92A, in 30 μl kinase buffer containing 100 μM ATP with 10 μCi [γ-<sup>33</sup>P or γ-<sup>32</sup>P] ATP. For elution of FLAG-FAM46A, FLAG-FAM46C WT, FLAG-FAM46C 193–391 or FLAG-FAM46C D90/92A, the beads were washed three times with lysis buffer, and protein eluted with 15 µg 3XFLAG-Peptide (APExBIO,A6001) with gentle mixing X30 min. Kinase reactions were performed at 30 °C for 30 min and terminated by adding Laemmli sample buffer. Proteins were separated by SDS–PAGE gel electrophoresis, stained with Colloidal Blue (Invitrogen), and dried using a Bio-Rad gel dryer. Phosphorylation was visualized by autoradiography (Typhoon FLA 9500, GE Healthcare).</p><p id=\"Par42\">For experiments using bacterially expressed and purified recombinant human GST-FAM46C and His-Plk4 (1–390), constructs were expressed in BL21 <italic>E. coli</italic> and purified using glutathione resin (NEB) or HisPur Ni-NTA resin (Thermo Fisher Scientific), according to manufacturers’ instructions. In vitro kinase reactions were performed using kinase buffer containing 100 μM ATP with 10 μCi [γ-<sup>33</sup>P] ATP as above, with protein levels determined by Coomassie blue staining.</p></sec><sec id=\"Sec26\"><title>3D spheroid invasion assay</title><p id=\"Par43\">HeLa cell spheroids, untreated or treated as indicated, were generated by seeding 2500 or 2800 cells into Corning Costar Ultra-Low Attachment 96-well plates (7007, Corning) in DMEM + 10%FCS and incubating at 37 °C. Transient transfection with RFP or RFP-FAM46C was performed for 6 h on day 3 using Lipofectamine 2000 (Invitrogen). Matrigel matrix (354234, Corning) was dispensed into wells on day 4 to initiate invasion (<italic>t</italic> = 0 h). Centrinone B (5690, TOCRIS Bioscience), dissolved in DMSO and used at a final concentration of 500 nM, was dispensed into selected wells after solidification of Matrigel. Spheroids were imaged at 24-hour intervals using the In Cell Analyzer 6000 (GE Healthcare Life Sciences), equipped with a Nikon 4X/0.20 NA, Plan Apo objective and 2048×2048 sCMOS camera. Quantification was performed by outlining invasion on the ImageJ software, then calculating average distance of invasion from the center of the spheroid using the following equation:<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{Average}}\\,{\\mathrm{invasion}}\\,{\\mathrm{(}} \\upmu \\mathrm{m}{\\mathrm{)}} = ({\\mathrm{x}}\\,{\\mathrm{axis}}\\,{\\mathrm{length}}/2 + {\\mathrm{y}}\\,{\\mathrm{axis}}\\,{\\mathrm{length}}/2)/2$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mi mathvariant=\"normal\">Average</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">invasion</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">(</mml:mi><mml:mi mathvariant=\"normal\">μ</mml:mi><mml:mi mathvariant=\"normal\">m</mml:mi><mml:mi mathvariant=\"normal\">)</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">x</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">axis</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">length</mml:mi><mml:mo>/</mml:mo><mml:mn>2</mml:mn><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">y</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">axis</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">length</mml:mi><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:math><graphic xlink:href=\"42003_2020_1161_Article_Equa.gif\" position=\"anchor\"/></alternatives></disp-formula></p><p id=\"Par44\">The outer perimeter of the invadopods extending into the Matrigel matrix was outlined and two measures each of the distance through the center of the spheroid in the x and y axes were taken and averaged to determine degree of invasion.</p></sec><sec id=\"Sec27\"><title>Colorectal cancer specimen tissue processing, microdissection, RNA isolation, and pre-amplification</title><p id=\"Par45\">All protocols were approved by the Sinai Health Systems and University Health Network Research Ethics Boards. Fresh frozen matched pairs of primary colorectal cancer tumors and corresponding liver metastases, along with normal colonic tissue, were obtained from the University Health Network and Lunenfeld Tanenbaum Research Institute BioBanks. Sections on RNAse free PET membrane slides (11505190, Leica Microsystems) were stained with hematoxylin and eosin according to standard protocols, washed with RNAse free water then laser capture microdissected using the Zeiss PALM MicroBeam (ZEISS) to isolate strictly tumor tissues from each frozen section. RNA from microdissected tissue was isolated using the PicoPure RNA isolation kit (KIT0204, Arcturus), including DNAse treatment (79254, RNase-Free DNase Set, Qiagen). Normal colonic mucosal tissue was placed in Ambion RNAlater-ICE solution overnight at –20 °C, then disrupted and homogenized by rotor/stator in buffer XB with β-ME added (5 µl β-ME /500 μl buffer). RNA was isolated using PicoPure RNA isolation kit, including DNase treatment. RNA was quantified using NanoDrop. 15 ng RNA for each sample was reverse-transcribed using SuperScript IV First Strand Synthesis System (18091050, Invitrogen) and random hexamers. cDNA was preamplified with TaqMan PreAmp Master Mix (4391128, Applied Biosystems) and the pooled assay mix (0.2X each assay), and then the preamplified cDNA was diluted as recommended.</p></sec><sec id=\"Sec28\"><title>TCGA-COAD</title><p id=\"Par46\">Transcriptome and clinical datasets from TCGA-COAD<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup> and Kim et al.<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup> were downloaded using the NIH National Cancer Institute GDC Data Portal Data release 8.0 <ext-link ext-link-type=\"uri\" xlink:href=\"https://portal.gdc.cancer.gov/\">https://portal.gdc.cancer.gov/</ext-link>, cBioPortal for Cancer Genomics v1.8.3 <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.cbioportal.org/\">http://www.cbioportal.org/</ext-link><sup><xref ref-type=\"bibr\" rid=\"CR71\">71</xref>,<xref ref-type=\"bibr\" rid=\"CR72\">72</xref></sup> and CRN Nexus <ext-link ext-link-type=\"uri\" xlink:href=\"http://syslab4.nchu.edu.tw/\">http://syslab4.nchu.edu.tw/</ext-link><sup><xref ref-type=\"bibr\" rid=\"CR73\">73</xref></sup>.</p></sec><sec id=\"Sec29\"><title>Sucrose gradient</title><p id=\"Par47\">Isolation of centrosomes was performed by sucrose gradient density centrifugation<sup><xref ref-type=\"bibr\" rid=\"CR74\">74</xref></sup>. 70% confluent U2OS cells were co-transfected with FLAG-Plk4 and RFP-FAM46C X24h. Cells were lysed and protein extracts separated through a sucrose gradient by ultracentrifugation at 10,400 G for 30 min to sediment the proteins on a sucrose cushion and at 11,2700 G X1h for final centrosomal purification. The gradient was fractionated from the bottom, yielding fourteen sucrose fractions that were precipitated with ethanol and chloroform, then subjected to SDS/PAGE followed by immunoblotting.</p></sec><sec id=\"Sec30\"><title>Statistics and reproducibility</title><p id=\"Par48\">Statistical significance and <italic>p</italic> values were assessed by analysis of variance with Bonferroni correction and Student’s <italic>t</italic> tests using Prism software (GraphPad Software, La Jolla, CA). Error bars reflect SEM. Number of replicates and samples sizes are stated in the respective Figure Legend for each figure, with n value corresponding to independent experiments.</p></sec><sec id=\"Sec31\"><title>Reporting summary</title><p id=\"Par49\">Further information on research design is available in the <xref rid=\"MOESM4\" ref-type=\"media\">Nature Research Reporting Summary</xref> linked to this article.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec32\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"42003_2020_1161_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"42003_2020_1161_MOESM2_ESM.pdf\"><caption><p>Description of Additional Supplementary Files</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"42003_2020_1161_MOESM3_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM4\"><media xlink:href=\"42003_2020_1161_MOESM4_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM5\"><media xlink:href=\"42003_2020_1161_MOESM5_ESM.pdf\"><caption><p>Peer Review File</p></caption></media></supplementary-material></sec></sec></body><back><fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> is available for this paper at 10.1038/s42003-020-01161-3.</p></sec><ack><title>Acknowledgements</title><p>The authors acknowledge Laurence Pelletier, Jim Dennis, Anne-Claude Gingras, Julie Brill, Gabrielle Boul, and Johnny Tkach for helpful discussions, and Mona Reid and Johnny Tkach for technical assistance. This work was supported by a grant from The Cancer Research Society (CJS) and by the Sydney Cooper Program for the Prevention of Cancer Progression, Mount Sinai Hospital, Toronto, Canada.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>K.K., Y.H., and C.J.S. conceived of the study and K.K. and Y.H. conducted the majority of the experiments. K.K. and C.J.S. wrote the manuscript, to which W.J. contributed. C.M.M.L. and D.N. conducted the spheroid assays, to which R.X. contributed, and D.N. performed the sucrose gradient centrifugation. M.K. performed laser capture microdissection, enabled by F.S.W.Z., and A.P. identified tumor tissue and normal mucosa in colorectal cancer specimens. J.T. and K.P. contributed to the mouse xenograft experiments. A.S. performed the cell cycle analysis in RPE-1 cells. H.W. conducted histologic examination of murine xenograft tumors.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>The datasets generated and/or analyzed as part of the current study are available from the corresponding author upon reasonable request. Source data are made available as “Supplementary Data <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>”.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par50\">The authors declare no competing interests.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Sillibourne</surname><given-names>JE</given-names></name><etal/></person-group><article-title>Autophosphorylation of polo-like kinase 4 and its role in centriole duplication</article-title><source>Mol. Biol. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"brief-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">BJPsych Open</journal-id><journal-id journal-id-type=\"iso-abbrev\">BJPsych Open</journal-id><journal-id journal-id-type=\"publisher-id\">BJO</journal-id><journal-title-group><journal-title>BJPsych Open</journal-title></journal-title-group><issn pub-type=\"epub\">2056-4724</issn><publisher><publisher-name>Cambridge University Press</publisher-name><publisher-loc>Cambridge, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32799958</article-id><article-id pub-id-type=\"pmc\">PMC7431844</article-id><article-id pub-id-type=\"doi\">10.1192/bjo.2020.79</article-id><article-id pub-id-type=\"pii\">S2056472420000794</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Short Report</subject></subj-group></article-categories><title-group><article-title>Impact of the COVID-19 pandemic and initial period of lockdown on the mental health and well-being of adults in the UK</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"true\">https://orcid.org/0000-0003-4026-6439</contrib-id><name><surname>White</surname><given-names>Ross G.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"/><xref ref-type=\"corresp\" rid=\"cor1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Van Der Boor</surname><given-names>Catharina</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"/></contrib></contrib-group><aff id=\"aff1\">Institute of Population Health, <institution>University of Liverpool</institution>, <country>UK</country></aff><aff id=\"aff2\">Institute of Population Health, <institution>University of Liverpool</institution>, <country>UK</country></aff><author-notes><corresp id=\"cor1\"><bold>Correspondence:</bold> Ross G. White. Email: <email>ross.white@liverpool.ac.uk</email></corresp></author-notes><pub-date publication-format=\"electronic\" date-type=\"collection\" iso-8601-date=\"2020-09\"><month>9</month><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><volume>6</volume><issue>5</issue><elocation-id>e90</elocation-id><history><date date-type=\"received\"><day>24</day><month>4</month><year>2020</year></date><date date-type=\"rev-recd\"><day>08</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>The Author(s)</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">http://creativecommons.org/licenses/by/4.0/</ali:license_ref><license-p>This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (<uri xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"S2056472420000794a.pdf\"/><abstract abstract-type=\"normal\"><title>Summary</title><p>The impact of the COVID-19 pandemic on mental health and well-being were assessed in a convenience sample of 600 UK adults, using a cross-sectional design. Recruited over 2 weeks during the initial phase of lockdown, participants completed an online survey that included COVID-19-related questions, the Hospital Anxiety and Depression Scale, the World Health Organization (Five) Well-Being Index and the Oxford Capabilities Questionnaire for Mental Health. Self-isolating before lockdown, increased feelings of isolation since lockdown and having COVID-19-related livelihood concerns were associated with poorer mental health, well-being and quality of life. Perceiving increased kindness, community connectedness and being an essential worker were associated with better mental health and well-being outcomes.</p></abstract><kwd-group><title>Keywords</title><kwd>Anxiety disorders</kwd><kwd>depressive disorders</kwd><kwd>coronavirus</kwd><kwd>COVID-19</kwd><kwd>pandemics</kwd></kwd-group><counts><table-count count=\"1\"/><ref-count count=\"11\"/><page-count count=\"4\"/></counts></article-meta></front><body><p>On 24 March 2020, the UK introduced a range of ‘lockdown’ restrictions intended to slow the progression of the COVID-19 outbreak. Emerging evidence indicates that the COVID-19 pandemic is associated with adverse mental health outcomes for healthcare workers in China.<sup><xref rid=\"ref1\" ref-type=\"bibr\">1</xref></sup> Specifically, being female and having an intermediate level of seniority were associated with experiencing severe depression, anxiety and distress. A study conducted with the general population in Italy indicated that COVID-19-related stressful life events were associated with increased odds of post-traumatic stress, depression, anxiety, insomnia, perceived stress and adjustment disorder symptoms.<sup><xref rid=\"ref2\" ref-type=\"bibr\">2</xref></sup> There is, however, limited data relating to the potential impact of the COVID-19 pandemic on levels of well-being and quality of life (QoL). The capability approach,<sup><xref rid=\"ref3\" ref-type=\"bibr\">3</xref></sup> which focuses specifically on the extent to which people have the freedom to engage in valued forms of being and doing, provides a potentially important framework for understanding the impact of the COVID-19 pandemic and the associated lockdown. The lockdown restrictions associated with the pandemic have posed an inherent risk of isolation and a reduction in social connectedness. Research has highlighted that social connections can have positive effects on health and well-being.<sup><xref rid=\"ref4\" ref-type=\"bibr\">4</xref></sup> The current study, which is part of a programme of research aimed at tracking the impact of the COVID-19 outbreak, investigated whether mental health, well-being and QoL outcomes in UK adults are associated with experiencing symptoms of COVID-19, being in a group vulnerable to COVID-19 (the question read ‘I am classified as being in a vulnerable group in terms of COVID-19 (aged 70 or above, heart disease, lung disease, pregnant, etc)’), being categorised as an ‘essential worker’, experiencing COVID-19-related isolation and local community interactions. Further, the study explored if participants' level of social support was associated with mental health and well-being outcomes.</p><sec sec-type=\"methods\" id=\"sec1\"><title>Method</title><p>The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human participants were approved by the Central University Research Ethics Committees, University of Liverpool (reference 7633). Written informed consent was obtained from all participants.</p><p>A cross-sectional design was used. A convenience sample recruited via social media forums (Twitter, Facebook, Reddit) completed an online survey. Data was collected over 2 weeks in the initial lockdown period (31 March to 13 April 2020). To be eligible, people had to be adults (≥18 years), speak English and be living in the UK at the time of the COVID-19 outbreak.</p><p>The survey included demographic questions; COVID-19-related questions; the Hospital Anxiety and Depression Scale<sup><xref rid=\"ref5\" ref-type=\"bibr\">5</xref></sup> (HADS; higher scores on the subscales indicate higher levels of depression and anxiety symptoms); the World Health Organization (Five) Well-Being Index (WHO-5),<sup><xref rid=\"ref6\" ref-type=\"bibr\">6</xref></sup> a measure of well-being (higher scores indicate higher levels of well-being); the Oxford Capabilities Questionnaire for Mental Health (OXCAP-MH),<sup><xref rid=\"ref7\" ref-type=\"bibr\">7</xref></sup> a measure of QoL (higher scores indicate higher levels of QoL) and the Multidimensional Scale of Perceived Social Support<sup><xref rid=\"ref8\" ref-type=\"bibr\">8</xref></sup> (higher scores indicate higher levels of perceived social support).</p><p>A total of 600 participants (74% female, mean age 36.75 years, s.d. 13.44, range 18–76 years) completed at a minimum the demographic and COVID-19-related questions. Participants were mainly White (93.6%) and employed (65%). Around a quarter of participants (26.3%) self-reported currently receiving treatment for mental disorders, including mood disorders (18%) and neurotic, stress-related and somatoform disorders (14.3%). No participants had been diagnosed with COVID-19.</p></sec><sec sec-type=\"results\" id=\"sec2\"><title>Results</title><p>The mean scores on the HADS Anxiety subscale (mean 10.23, s.d. 4.98) and HADS Depression subscale (mean 7.57, s.d. 4.39) exceeded the normal range (i.e. scores of 0–7). The mean scores on the WHO-5 and OXCAP-MH were 10.43 (s.d. 5.40) and 69.45 (s.d. 11.91), respectively. Female participants reported significantly higher levels of anxiety symptoms (<italic>t</italic>(195.73) = −2.21, <italic>P</italic> = 0.028) than males (female mean 10.51, s.d. 4.85; male mean 9.33, s.d. 5.29). There were no significant differences in the level of depression symptoms, well-being and QoL between males and females.</p><p>Being in a vulnerable group (12.5%) or experiencing symptoms of COVID-19 (11.7%) were not associated with significant differences in mental health and well-being outcomes (see <xref rid=\"tab01\" ref-type=\"table\">Table 1</xref>). Participants who self-isolated before lockdown owing to symptoms of COVID-19 (11.8%) had higher levels of anxiety (<italic>t</italic>(584) = 2.77, <italic>P</italic> = 0.006) and depression (<italic>t</italic>(550) = 2.83, <italic>P</italic> = 0.005) symptoms, and lower levels of well-being (<italic>t</italic>(534) = −2.29, <italic>P</italic> = 0.022) and QoL (<italic>t</italic>(466)= −3.56, <italic>P</italic> < 0.001), relative to those who did not. Participants who felt more isolated than usual during lockdown (69%) had higher levels of anxiety (<italic>t</italic>(513) = −5.95, <italic>P</italic> < 0.001) and depression (<italic>t</italic>(250.86)= −7.77, <italic>P</italic> < 0.001) symptoms, and lower levels of wellbeing (<italic>t</italic>(191.84) = 6.18, <italic>P</italic> < 0.001) and QoL (<italic>t</italic>(441) = 4.16, <italic>P</italic> < 0.001).\n<table-wrap id=\"tab01\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Between-group analyses for HADS, OXCAP-MH and WHO-5</p></caption><alternatives><graphic xlink:href=\"S2056472420000794_tab1\"/><table frame=\"hsides\" rules=\"groups\"><col align=\"left\" width=\"1*\" span=\"1\"/><col align=\"left\" width=\"1*\" span=\"1\"/><col align=\"char\" char=\".\" width=\"1*\" span=\"1\"/><col align=\"char\" char=\".\" width=\"1*\" span=\"1\"/><col align=\"char\" char=\".\" width=\"1*\" span=\"1\"/><col align=\"char\" char=\".\" width=\"1*\" span=\"1\"/><col align=\"char\" char=\".\" width=\"1*\" span=\"1\"/><thead><tr><th align=\"left\" colspan=\"1\" rowspan=\"1\">Question</th><th align=\"center\" colspan=\"1\" rowspan=\"1\">Response</th><th align=\"center\" colspan=\"1\" rowspan=\"1\">Mean</th><th align=\"center\" colspan=\"1\" rowspan=\"1\">s.d.</th><th align=\"center\" colspan=\"1\" rowspan=\"1\"><italic>t</italic>-value</th><th align=\"center\" colspan=\"1\" rowspan=\"1\">d.f.</th><th align=\"center\" colspan=\"1\" rowspan=\"1\"><italic>P-</italic>value</th></tr></thead><tbody><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Being in a ‘vulnerable group’</td></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Depression</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">7.80</td><td colspan=\"1\" rowspan=\"1\">4.65</td><td colspan=\"1\" rowspan=\"1\">0.48</td><td colspan=\"1\" rowspan=\"1\">549</td><td colspan=\"1\" rowspan=\"1\">0.629</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">7.53</td><td colspan=\"1\" rowspan=\"1\">4.36</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Anxiety</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">9.81</td><td colspan=\"1\" rowspan=\"1\">5.15</td><td colspan=\"1\" rowspan=\"1\">−0.75</td><td colspan=\"1\" rowspan=\"1\">546</td><td colspan=\"1\" rowspan=\"1\">0.454</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.29</td><td colspan=\"1\" rowspan=\"1\">4.97</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">OXCAP-MH</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">68.73</td><td colspan=\"1\" rowspan=\"1\">13.20</td><td colspan=\"1\" rowspan=\"1\">−0.63</td><td colspan=\"1\" rowspan=\"1\">465</td><td colspan=\"1\" rowspan=\"1\">0.527</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">69.71</td><td colspan=\"1\" rowspan=\"1\">11.59</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">WHO-5</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">10.44</td><td colspan=\"1\" rowspan=\"1\">5.83</td><td colspan=\"1\" rowspan=\"1\">0.04</td><td colspan=\"1\" rowspan=\"1\">532</td><td colspan=\"1\" rowspan=\"1\">0.970</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.41</td><td colspan=\"1\" rowspan=\"1\">5.34</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Experienced symptoms of COVID-19</td></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Depression</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">7.68</td><td colspan=\"1\" rowspan=\"1\">3.40</td><td colspan=\"1\" rowspan=\"1\">0.22</td><td colspan=\"1\" rowspan=\"1\">546</td><td colspan=\"1\" rowspan=\"1\">0.825</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">7.55</td><td colspan=\"1\" rowspan=\"1\">4.46</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Anxiety</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">10.56</td><td colspan=\"1\" rowspan=\"1\">4.74</td><td colspan=\"1\" rowspan=\"1\">0.56</td><td colspan=\"1\" rowspan=\"1\">544</td><td colspan=\"1\" rowspan=\"1\">0.573</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.19</td><td colspan=\"1\" rowspan=\"1\">5.01</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">OXCAP-MH</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">66.78</td><td colspan=\"1\" rowspan=\"1\">14.64</td><td colspan=\"1\" rowspan=\"1\">−1.60</td><td colspan=\"1\" rowspan=\"1\">65.72</td><td colspan=\"1\" rowspan=\"1\">0.113</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">70.02</td><td colspan=\"1\" rowspan=\"1\">11.32</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">WHO-5</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">9.82</td><td colspan=\"1\" rowspan=\"1\">5.46</td><td colspan=\"1\" rowspan=\"1\">−0.99</td><td colspan=\"1\" rowspan=\"1\">530</td><td colspan=\"1\" rowspan=\"1\">0.322</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.52</td><td colspan=\"1\" rowspan=\"1\">5.40</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Self-isolated before lockdown owing to symptoms of COVID-19</td></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Depression</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">9.00</td><td colspan=\"1\" rowspan=\"1\">4.36</td><td colspan=\"1\" rowspan=\"1\">2.83</td><td colspan=\"1\" rowspan=\"1\">550</td><td colspan=\"1\" rowspan=\"1\">0.005**</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">7.37</td><td colspan=\"1\" rowspan=\"1\">4.36</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Anxiety</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">11.83</td><td colspan=\"1\" rowspan=\"1\">4.74</td><td colspan=\"1\" rowspan=\"1\">2.77</td><td colspan=\"1\" rowspan=\"1\">548</td><td colspan=\"1\" rowspan=\"1\">0.006**</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.02</td><td colspan=\"1\" rowspan=\"1\">4.98</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">OXCAP-MH</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">64.42</td><td colspan=\"1\" rowspan=\"1\">13.25</td><td colspan=\"1\" rowspan=\"1\">−3.56</td><td colspan=\"1\" rowspan=\"1\">466</td><td colspan=\"1\" rowspan=\"1\"><0.001***</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">70.28</td><td colspan=\"1\" rowspan=\"1\">11.43</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">WHO-5</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">8.98</td><td colspan=\"1\" rowspan=\"1\">5.29</td><td colspan=\"1\" rowspan=\"1\">−2.29</td><td colspan=\"1\" rowspan=\"1\">534</td><td colspan=\"1\" rowspan=\"1\">0.022*</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.63</td><td colspan=\"1\" rowspan=\"1\">5.39</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Agree that they felt more isolated than usual during lockdown</td></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Depression</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">8.20</td><td colspan=\"1\" rowspan=\"1\">4.31</td><td colspan=\"1\" rowspan=\"1\">−7.77</td><td colspan=\"1\" rowspan=\"1\">250.86</td><td colspan=\"1\" rowspan=\"1\"><0.001***</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">5.16</td><td colspan=\"1\" rowspan=\"1\">3.68</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Anxiety</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">10.91</td><td colspan=\"1\" rowspan=\"1\">4.69</td><td colspan=\"1\" rowspan=\"1\">−5.95</td><td colspan=\"1\" rowspan=\"1\">513</td><td colspan=\"1\" rowspan=\"1\"><0.001***</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">7.99</td><td colspan=\"1\" rowspan=\"1\">5.14</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">OXCAP-MH</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">68.53</td><td colspan=\"1\" rowspan=\"1\">11.46</td><td colspan=\"1\" rowspan=\"1\">4.16</td><td colspan=\"1\" rowspan=\"1\">441</td><td colspan=\"1\" rowspan=\"1\"><0.001***</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">73.67</td><td colspan=\"1\" rowspan=\"1\">11.00</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">WHO-5</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">9.64</td><td colspan=\"1\" rowspan=\"1\">5.07</td><td colspan=\"1\" rowspan=\"1\">6.18</td><td colspan=\"1\" rowspan=\"1\">191.84</td><td colspan=\"1\" rowspan=\"1\"><0.001***</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">13.17</td><td colspan=\"1\" rowspan=\"1\">5.67</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Identified as an essential worker</td></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Depression</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">7.01</td><td colspan=\"1\" rowspan=\"1\">4.04</td><td colspan=\"1\" rowspan=\"1\">−2.18</td><td colspan=\"1\" rowspan=\"1\">400.76</td><td colspan=\"1\" rowspan=\"1\">0.030*</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">7.84</td><td colspan=\"1\" rowspan=\"1\">4.54</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Anxiety</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">9.83</td><td colspan=\"1\" rowspan=\"1\">4.79</td><td colspan=\"1\" rowspan=\"1\">−1.30</td><td colspan=\"1\" rowspan=\"1\">546</td><td colspan=\"1\" rowspan=\"1\">0.194</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.42</td><td colspan=\"1\" rowspan=\"1\">5.08</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">OXCAP-MH</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">70.30</td><td colspan=\"1\" rowspan=\"1\">11.40</td><td colspan=\"1\" rowspan=\"1\">0.97</td><td colspan=\"1\" rowspan=\"1\">465</td><td colspan=\"1\" rowspan=\"1\">0.332</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">69.18</td><td colspan=\"1\" rowspan=\"1\">12.02</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">WHO-5</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">10.82</td><td colspan=\"1\" rowspan=\"1\">5.12</td><td colspan=\"1\" rowspan=\"1\">1.18</td><td colspan=\"1\" rowspan=\"1\">532</td><td colspan=\"1\" rowspan=\"1\">0.238</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.24</td><td colspan=\"1\" rowspan=\"1\">5.54</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Agree that the COVID-19 outbreak was threatening their livelihood</td></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Depression</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">8.08</td><td colspan=\"1\" rowspan=\"1\">4.49</td><td colspan=\"1\" rowspan=\"1\">−2.55</td><td colspan=\"1\" rowspan=\"1\">544</td><td colspan=\"1\" rowspan=\"1\">0.011*</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">7.13</td><td colspan=\"1\" rowspan=\"1\">4.23</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Anxiety</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">10.67</td><td colspan=\"1\" rowspan=\"1\">4.93</td><td colspan=\"1\" rowspan=\"1\">−1.90</td><td colspan=\"1\" rowspan=\"1\">542</td><td colspan=\"1\" rowspan=\"1\">0.058</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">9.86</td><td colspan=\"1\" rowspan=\"1\">4.96</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">OXCAP-MH</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">68.05</td><td colspan=\"1\" rowspan=\"1\">11.92</td><td colspan=\"1\" rowspan=\"1\">2.73</td><td colspan=\"1\" rowspan=\"1\">461</td><td colspan=\"1\" rowspan=\"1\">0.007**</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">71.02</td><td colspan=\"1\" rowspan=\"1\">11.47</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">WHO-5</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">10.20</td><td colspan=\"1\" rowspan=\"1\">5.39</td><td colspan=\"1\" rowspan=\"1\">0.86</td><td colspan=\"1\" rowspan=\"1\">528</td><td colspan=\"1\" rowspan=\"1\">0.391</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.61</td><td colspan=\"1\" rowspan=\"1\">5.39</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Agree that people's kindness toward others in their local area had increased</td></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Depression</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">7.29</td><td colspan=\"1\" rowspan=\"1\">4.22</td><td colspan=\"1\" rowspan=\"1\">2.25</td><td colspan=\"1\" rowspan=\"1\">551</td><td colspan=\"1\" rowspan=\"1\">0.025*</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">8.20</td><td colspan=\"1\" rowspan=\"1\">4.70</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Anxiety</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">10.13</td><td colspan=\"1\" rowspan=\"1\">4.84</td><td colspan=\"1\" rowspan=\"1\">0.75</td><td colspan=\"1\" rowspan=\"1\">548</td><td colspan=\"1\" rowspan=\"1\">0.455</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.47</td><td colspan=\"1\" rowspan=\"1\">5.29</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">OXCAP-MH</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">71.09</td><td colspan=\"1\" rowspan=\"1\">11.12</td><td colspan=\"1\" rowspan=\"1\">−4.56</td><td colspan=\"1\" rowspan=\"1\">467</td><td colspan=\"1\" rowspan=\"1\"><0.001***</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">65.74</td><td colspan=\"1\" rowspan=\"1\">12.75</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">WHO-5</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">10.85</td><td colspan=\"1\" rowspan=\"1\">5.24</td><td colspan=\"1\" rowspan=\"1\">−2.85</td><td colspan=\"1\" rowspan=\"1\">535</td><td colspan=\"1\" rowspan=\"1\">0.005**</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">9.42</td><td colspan=\"1\" rowspan=\"1\">5.62</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td colspan=\"7\" align=\"left\" rowspan=\"1\">Agree that since the COVID-19 outbreak commenced they felt more connected to the members of their local community</td></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Depression</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">7.07</td><td colspan=\"1\" rowspan=\"1\">4.08</td><td colspan=\"1\" rowspan=\"1\">2.11</td><td colspan=\"1\" rowspan=\"1\">552</td><td colspan=\"1\" rowspan=\"1\">0.035*</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">7.87</td><td colspan=\"1\" rowspan=\"1\">4.56</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">HADS Anxiety</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">10.00</td><td colspan=\"1\" rowspan=\"1\">4.87</td><td colspan=\"1\" rowspan=\"1\">0.85</td><td colspan=\"1\" rowspan=\"1\">549</td><td colspan=\"1\" rowspan=\"1\">0.395</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">10.37</td><td colspan=\"1\" rowspan=\"1\">5.05</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">OXCAP-MH</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">72.00</td><td colspan=\"1\" rowspan=\"1\">10.02</td><td colspan=\"1\" rowspan=\"1\">−3.87</td><td colspan=\"1\" rowspan=\"1\">467</td><td colspan=\"1\" rowspan=\"1\"><0.001***</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">67.76</td><td colspan=\"1\" rowspan=\"1\">12.74</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"2\" valign=\"top\" colspan=\"1\">WHO-5</td><td colspan=\"1\" rowspan=\"1\">Yes</td><td colspan=\"1\" rowspan=\"1\">11.24</td><td colspan=\"1\" rowspan=\"1\">5.06</td><td colspan=\"1\" rowspan=\"1\">−2.83</td><td colspan=\"1\" rowspan=\"1\">536</td><td colspan=\"1\" rowspan=\"1\">0.005**</td></tr><tr><td colspan=\"1\" rowspan=\"1\">No</td><td colspan=\"1\" rowspan=\"1\">9.90</td><td colspan=\"1\" rowspan=\"1\">5.55</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr></tbody></table></alternatives><table-wrap-foot><fn id=\"tfn1_1\" fn-type=\"other\"><p>HADS, Hospital Anxiety and Depression Scale; OXCAP-MH, Oxford Capabilities Questionnaire for Mental Health; WHO-5, World Health Organization (Five) Well-Being Index.</p></fn><fn id=\"tfn1_2\" fn-type=\"other\"><p>*<italic>P</italic> < 0.05; **<italic>P</italic> < 0.01; ***<italic>P</italic> < 0.001.</p></fn></table-wrap-foot></table-wrap></p><p>Participants who were essential workers (32%) had significantly lower levels of depression symptoms (<italic>t</italic>(400.76) = −2.18, <italic>P</italic> = 0.030). Participants who agreed that the COVID-19 outbreak was threatening their livelihood (46.0%) had higher levels of depression symptoms (<italic>t</italic>(544) = −2.55, <italic>P</italic> = 0.011) and lower QoL (<italic>t</italic>(461) = 2.73, <italic>P</italic> = 0.007).</p><p>Participants who agreed that people's kindness toward others in their local area had increased since the COVID-19 outbreak (68.8%) had lower levels of depression symptoms (<italic>t</italic>(551) = 2.25, <italic>P</italic> = 0.025), and higher QoL (<italic>t</italic>(467) = −4.56, <italic>P</italic> < 0.001) and well-being (<italic>t</italic>(535) = −2.85, <italic>P</italic> = 0.005). Similarly, participants who agreed that they had felt more connected to the members of their local community since the COVID-19 outbreak (40.0%) had lower levels of depression symptoms (<italic>t</italic>(552) = 2.11, <italic>P</italic> = 0.035), and higher QoL (<italic>t</italic>(467) = −3.87, <italic>P</italic> < 0.001) and well-being (<italic>t</italic>(536) = −2.83, <italic>P</italic> = 0.005).</p><p>The level of perceived social support had significant negative correlations with levels of depression (<italic>r</italic> = −0.33, <italic>P</italic> < 0.001) and anxiety (<italic>r</italic> = −0.17, <italic>P</italic> < 0.001) symptoms, and significant positive correlations with QoL (<italic>r</italic> = 0.52, <italic>P</italic> < 0.001) and well-being (<italic>r</italic> = 0.29, <italic>P</italic> < 0.001).</p></sec><sec sec-type=\"discussion\" id=\"sec3\"><title>Discussion</title><p>This study sought to investigate the impact of the COVID-19 outbreak on the mental health and well-being of a convenience sample of UK adults. The levels of anxiety and depression symptoms for the sample were markedly higher than normative data derived for the UK adult population's levels of anxiety (females 6.78, s.d. 4.23; males 5.51, s.d. 4.04) and depression (females 4.12, s.d. 3.78; males 3.83, s.d. 3.74)<sup><xref rid=\"ref9\" ref-type=\"bibr\">9</xref></sup> symptoms.</p><p>Higher levels of depression symptoms were associated with participants having to self-isolate before lockdown owing to symptoms of COVID-19, feeling more isolated than usual during lockdown or agreeing that the COVID-19 pandemic was threatening their livelihood. On the other hand, agreeing that people's kindness toward others had increased, agreeing that they felt more connected to people in the local community, and working in an essential job were associated with significantly lower levels of depression symptoms. Notably, the mean depression score for the essential workers (mean 7.01, s.d. 4.04) remained at the upper limit of the normal range. These findings are open to interpretation, but it may be that the importance of their work and/or public acknowledgment of their efforts buffered against higher levels of depression symptoms.</p><p>The significant findings relating to isolation (self-isolating before lockdown or feeling more isolated during lockdown) and levels of perceived social support highlight the importance of exploring innovative ways to maintain connection and social support during periods of lockdown and beyond. These findings, although correlational in nature, are consistent with the thesis that psychological resources associated with social connectedness can serve as a ‘social cure’ for mental health difficulties.<sup><xref rid=\"ref10\" ref-type=\"bibr\">10</xref></sup></p><p>Comparatively high levels of both well-being and QoL were associated with participants agreeing that levels of kindness in the local area had increased, and that they felt more connected to others in the local community during the COVID-19 pandemic. QoL, but not well-being, was comparatively lower in participants who indicated that their livelihood was threatened by the COVID-19 pandemic. We propose that the OXCAP-MH, as a multidimensional measure of QoL that incorporates a focus on a range of factors including non-health issues and welfare inequalities, is a valuable measure for assessing how COVID-19 and related restrictions are potentially affecting people.</p><p>There were a number of important limitations associated with the current study. The convenience sample relied on people who had access to online social media forums. Consistent with other studies that have used social media for recruitment,<sup><xref rid=\"ref11\" ref-type=\"bibr\">11</xref></sup> males and Black, Asian and minority ethnic community members were comparatively under-represented in the sample. The cross-sectional nature of the analyses limits the conclusions that can be drawn. However, forthcoming academic papers from the authors will track the impact of the COVID-19 pandemic and lockdown restrictions on mental health and well-being over time.</p><p>The study highlights that although there was no association between personal experience of COVID-19 symptoms and being part of a group vulnerable to the effects of COVID-19, and mental health and well-being, factors related to isolation and COVID-19-related livelihood concerns were in fact associated with poorer mental health and well-being. On the other hand, perceiving increased kindness and connectedness in local areas were associated with better mental health and well-being outcomes. Further research aimed at mitigating the mental health and well-being effects of public health emergencies is required.</p></sec></body><back><notes id=\"nts1\" notes-type=\"other\"><title>Data availability</title><p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p></notes><notes id=\"nts2\" notes-type=\"other\"><title>Author contributions</title><p>R.G.W. formulated the research questions, designed the study, conducted the study, analysed the data and wrote the article. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Disaster Med Public Health Prep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Disaster Med Public Health Prep</journal-id><journal-id journal-id-type=\"publisher-id\">DMP</journal-id><journal-title-group><journal-title>Disaster Medicine and Public Health Preparedness</journal-title></journal-title-group><issn pub-type=\"ppub\">1935-7893</issn><issn pub-type=\"epub\">1938-744X</issn><publisher><publisher-name>Cambridge University Press</publisher-name><publisher-loc>New York, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32660663</article-id><article-id pub-id-type=\"pmc\">PMC7431845</article-id><article-id pub-id-type=\"pii\">S1935789320002529</article-id><article-id pub-id-type=\"doi\">10.1017/dmp.2020.252</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Concepts in Disaster Medicine</subject></subj-group></article-categories><title-group><article-title>Biosafety for Dental Patients During Dentistry Care After COVID-19: A Review of the Literature</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Siles-Garcia</surname><given-names>Adriana Abigail</given-names></name><degrees>DDS student</degrees><xref ref-type=\"aff\" rid=\"a1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Alzamora-Cepeda</surname><given-names>Anais Gabriela</given-names></name><degrees>DDS student</degrees><xref ref-type=\"aff\" rid=\"a1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Atoche-Socola</surname><given-names>Katherine Joselyn</given-names></name><degrees>MSc</degrees><xref ref-type=\"aff\" rid=\"a2\"/></contrib><contrib contrib-type=\"author\"><name><surname>Peña-Soto</surname><given-names>Claudio</given-names></name><degrees>MSc</degrees><xref ref-type=\"aff\" rid=\"a3\"/></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-0010-5948</contrib-id><name><surname>Arriola-Guillén</surname><given-names>Luis Ernesto</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"a4\"/><xref ref-type=\"corresp\" rid=\"cor1\"/></contrib></contrib-group><aff id=\"a1\">School of Dentistry, <institution>Universidad Científica del Sur</institution>, <city>Lima</city>, <country>Peru</country></aff><aff id=\"a2\">Division of Oral Rehabilitation, School of Dentistry, <institution>Universidad Científica del Sur</institution>, <city>Lima</city>, <country>Peru</country></aff><aff id=\"a3\">Division of Periodontology, School of Dentistry, <institution>Universidad Científica del Sur</institution>, <city>Lima</city>, <country>Peru</country></aff><aff id=\"a4\">Division of Orthodontics, School of Dentistry, <institution>Universidad Científica del Sur</institution>, <city>Lima</city>, <country>Peru</country></aff><author-notes><corresp id=\"cor1\">Correspondence and reprint requests to Luis Ernesto Arriola-Guillén, PhD, Av. Arequipa 4861, Miraflores, <city>Lima</city>, <country>Peru</country> (e-mail: <email>luchoarriola@gmail.com</email>).</corresp></author-notes><pub-date publication-format=\"electronic\" date-type=\"pub\"><day>14</day><month>7</month><year>2020</year></pub-date><fpage>1</fpage><lpage>6</lpage><history><date date-type=\"received\"><day>04</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>04</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© Society for Disaster Medicine and Public Health, Inc. 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Society for Disaster Medicine and Public Health, Inc.</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (<uri xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"S1935789320002529a.pdf\"/><abstract abstract-type=\"normal\"><p>The world is currently changing due to coronavirus disease 2019 (COVID-19), and the field of dentistry is no stranger to this. The care of patients in the dental office involves very strict biosafety protocols, and patients must be aware of the protection barriers implemented to allow satisfactory, safe dental care. The purpose of this study was to synthesize and analyze the management of the current biosafety standards for dental patients since the arrival of the COVID-19 pandemic. A bibliographic search of the main sources of information including MEDLINE (by means of PubMed), Scopus, Science Direct, SCIELO, and Google Scholar was carried out. Articles published without language restriction, systematic reviews, literature reviews, and observational studies were included. We identified the biosafety measures that must be taken before, during, and after dental practice following the arrival of COVID-19. The main measures include telephone triage, temperature taking on arrival at the office, the organization of the waiting room, washing hands before entering the office, knowing the auxiliary radiographic exams of choice and what type of treatment can be performed, albeit with restrictions. In conclusion, dental patients must comply with all the biosafety measures established by international protection standards and implemented by dentists before, during, and after dental practice to reduce the possibility of COVID-19 infection.</p></abstract><kwd-group><title>Key Words:</title><kwd>Biosafety</kwd><kwd>patients</kwd><kwd>dentistry</kwd><kwd>COVID-19</kwd><kwd>SARS-CoV-2</kwd></kwd-group><counts><table-count count=\"1\"/><ref-count count=\"43\"/><page-count count=\"6\"/></counts></article-meta></front><body><p>During the first 6 mo of 2020, the world has been exposed to a fearsome virus that has spread rapidly. This recent viral disease was named coronavirus disease 2019 (COVID-19) and is caused by a beta-coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).<sup><xref rid=\"r1\" ref-type=\"bibr\">1</xref></sup> On December 31, the Wuhan Municipal Health Department (Hubei Province, China) notified the World Health Organization (WHO) of several cases of pneumonia of unknown origin, and due to the aggressive spread of the virus, a global pandemic was declared on March 11, 2020.<sup><xref rid=\"r2\" ref-type=\"bibr\">2</xref></sup> The main routes of transmission of this virus are by close contact and by contact through droplets of the respiratory tract when an infected individual coughs, sneezes, or speaks.<sup><xref rid=\"r3\" ref-type=\"bibr\">3</xref>,<xref rid=\"r4\" ref-type=\"bibr\">4</xref></sup> Various organizations at the international level have implemented a series of measures to stop the advance of COVID-19.<sup><xref rid=\"r5\" ref-type=\"bibr\">5</xref>-<xref rid=\"r7\" ref-type=\"bibr\">7</xref></sup>\n</p><p>Dentistry is one of the health professions most affected by this virus due to direct contact with the oral cavity of patients, because the dissemination of small droplets during sneezing or talking is the main route of transmission. Therefore, biosafety measures must be efficient to avoid possible cross-infection. The use of a mask is a very important protection barrier, especially the FFP2 masks with valves or the N95, with which 95% of aerial particles are filtered, being very helpful in an environment with high production of spray or splashes contaminated with saliva or blood.<sup><xref rid=\"r8\" ref-type=\"bibr\">8</xref></sup> Likewise, recent research has demonstrated the efficacy of the use of mouthwashes before dental care, which could help decrease the amount of bacterial load by 68.4%.<sup><xref rid=\"r9\" ref-type=\"bibr\">9</xref></sup>\n</p><p>Patients in dental services are exposed to COVID-19 infection if dental professionals do not comply with the biosafety protection measures implemented by the COVID-19 regulations, which include the number and type of patients attended, facial barriers, body protection, disinfection of environments, and social distancing.<sup><xref rid=\"r9\" ref-type=\"bibr\">9</xref></sup> It is important to point out that protection protocol measures should not only involve the personnel who provide dental care, but also the patients to reduce cross-contagion.<sup><xref rid=\"r10\" ref-type=\"bibr\">10</xref>,<xref rid=\"r11\" ref-type=\"bibr\">11</xref></sup> Faulty control of patient protection can lead to contamination of the office environment, the personnel and even the patients themselves, further increasing contagion.<sup><xref rid=\"r11\" ref-type=\"bibr\">11</xref></sup>\n</p><p>In the event of any dental emergency, it is essential to acquire information through the medical history in which the patient must report the presence of any symptoms, such as respiratory distress, dry cough, fever, or odynophagia. In case of suspicion, the patient should be referred to hospital emergency services for testing, subsequent confirmation, and treatment if necessary.<sup><xref rid=\"r12\" ref-type=\"bibr\">12</xref></sup> However, if the presence of the disease is ruled out, dental consultation can be carried out following the prevailing biosafety parameters. It is recommended to focus only on emergencies, acute pain, trauma, and infections of dental origin. Therefore, during dental care, patients require personal protective equipment (disposable shoe covers, cap, etc.), and protective equipment during clinical procedures should include the use of the rubber dam and high-power saliva suctioning as far as possible.<sup><xref rid=\"r13\" ref-type=\"bibr\">13</xref></sup>\n</p><p>With the declaration of lockdown, many dental treatments have been postponed, and professionals are only allowed to attend dental emergencies. However, after the lifting of the lockdown and care is restored, the “new normality” will require spacing of patient visits to meet biosafety measures, with protection barriers for the patient and disinfection of the dental environment after each procedure. Therefore, the purpose of this article was to describe the management of the latest biosafety standards for dental patients since the arrival of COVID-19, seeking to improve biosafety protocols and general protection during dental care.</p><sec sec-type=\"methods\" id=\"s1\"><title>METHODS</title><p>The bibliographic search was performed using the main data sources from the international health science literature (MEDLINE) by means of PubMed, Scopus, ScienceDirect, SCIELO, and Google Scholar. The search was performed without language restriction from the source of the information until May 28, 2020. Systematic reviews and literature reviews were included in this research. Letters to the Editor, individual opinions, and books were excluded (<xref rid=\"tbl1\" ref-type=\"table\">Table 1</xref>).</p><p>\n<table-wrap id=\"tbl1\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p>Questionnaire for Patients Before Dental Care</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/></colgroup><thead><tr><th colspan=\"1\" rowspan=\"1\">Questions</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">1) Do you have a fever or have you had a fever in the last 14 days?</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">2) Have you experienced a recent onset of respiratory problems, such as coughing or respiratory distress, in the last 14 days?</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">3) In the last 14 days, have you traveled to an already documented area of COVID-19?</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">4) Have you come into contact with a patient with confirmed COVID-19 infection in the last 14 days?</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">5) Have you come into contact with people who have had fever or documented respiratory problems in the last 14 days?</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">6) Have you had contact with many unknown people or have you recently participated in an event or meeting?</td></tr></tbody></table><graphic xlink:href=\"S1935789320002529_tab1\"/></alternatives></table-wrap>\n</p><sec id=\"s1-1\" sec-type=\"other\"><title>Considerations to Be Taken Before Dental Care</title><p>On any suspicion of viral infection, dental appointments must be canceled and the patient should be advised to immediately go to the hospital. On the other hand, in the absence of symptoms and with the need for a dental appointment, a questionnaire will give by phone to rule out a possible infectious process, and if the patient is considered virus-free, an appointment will be scheduled (<xref rid=\"tbl1\" ref-type=\"table\">Table 1</xref>). Once in the office, the temperature of the patient is measured with a digital thermometer on the forehead to identify possible fever. The thermometer must be disinfected with 70% ethyl alcohol after each use as recommended by the WHO.<sup><xref rid=\"r14\" ref-type=\"bibr\">14</xref></sup> If the patient is in an acute febrile state, dental care will be stopped, the appointment rescheduled, and the patient will be advised to go to the doctor.<sup><xref rid=\"r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"r15\" ref-type=\"bibr\">15</xref></sup>\n</p><p>In the case of patients with a temperature below to 98.6°F, or 37.3°C, but with an affirmative answer on the questionnaire (<xref rid=\"tbl1\" ref-type=\"table\">Table 1</xref>), the treatment will be postponed to 14 days after the exposure event. The patient is instructed to initiate quarantine and report any fever or flu-like symptoms to the local health department.<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref></sup> On the other hand, patients with a temperature above of 98.6°F, or 37.3°C, and an affirmative answer to the questionnaire will be considered as suspicious or at risk for COVID-19, and the appointment will be postponed, and the patient referred to emergency medical services for diagnosis.<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref></sup>\n</p></sec><sec id=\"s1-2\" sec-type=\"other\"><title>Considerations for Patients to Take During the Dental Consultation</title><sec id=\"s1-2-1\" sec-type=\"other\"><title>Use of Rinses</title><p>Many authors recommend the use of mouthwashes administered before dental care to help decrease the number of bacteria and/or viruses. However, some research has shown that the use of chlorhexidine is not effective for eliminating COVID-19. Nonetheless, there are 2 antiseptic options with oxidative content that favorably decreases the salivary load of the virus without causing damage to the oral mucosa, and these are: hydrogen peroxide diluted at 1%, povidone 0.2% or cetylpyridinium chloride (CPC) 0.05-0.1%.<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref>-<xref rid=\"r17\" ref-type=\"bibr\">17</xref></sup> According to the studies available, the oral mouthwash of choice is hydrogen peroxide because COVID-19 is vulnerable to oxidation.<sup><xref rid=\"r18\" ref-type=\"bibr\">18</xref></sup> To obtain 15 mL of rinse at a concentration of 1%, 5 mL of 10 volume hydrogen peroxide increasing to 10 mL of distilled water can be used.<sup><xref rid=\"r17\" ref-type=\"bibr\">17</xref></sup>\n</p></sec><sec id=\"s1-2-2\" sec-type=\"other\"><title>Aerosol Control</title><p>A recent study showed there are around 38 types of microorganisms in the air in a dental office, including <italic>Legionella pneumophila</italic>, the causative agent of severe pneumonia.<sup><xref rid=\"r19\" ref-type=\"bibr\">19</xref>-<xref rid=\"r21\" ref-type=\"bibr\">21</xref></sup> On the other hand, several studies have shown that many dental procedures produce contaminated aerosols, and droplets remain in the environment for a considerable length of time.<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref>,<xref rid=\"r22\" ref-type=\"bibr\">22</xref></sup> As described in several studies, COVID-19 is transmitted through the air when an infected individual coughs, laughs, sneezes, and speaks to a susceptible individual in close physical proximity. Therefore, disease propagation is the most important concern in clinics and hospitals, because it is difficult to avoid the generation of large amounts of droplets and aerosols mixed with the patient’s saliva and even blood during dental practice. Dental instruments, such as high-speed handpieces use aerosol oil to make the turbine rotate at high speed and run under running water.<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref>,<xref rid=\"r23\" ref-type=\"bibr\">23</xref></sup> The particles generated are so small that they are able to remain in the air for a long period of time before depositing on environmental surfaces or in the respiratory tract. Therefore, COVID-19 has the potential to spread through aerosols from infected people.<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref></sup>\n</p><p>Several health institutions have recommended that rotating instruments be used as little as possible. In specific cases, chemical or mechanical techniques for caries removal can be considered. If the use of rotating instrumentation is really necessary, absolute isolation is recommended. Furthermore, ultrasonic instrumentation is effective in removing plaque and calculus deposits, and manual scaling and root planning is recommended, as both techniques achieve the same result.<sup><xref rid=\"r21\" ref-type=\"bibr\">21</xref>,<xref rid=\"r24\" ref-type=\"bibr\">24</xref></sup> When lockdown has been lifted, the use of rotating instruments will be reincorporated, maintaining rigorous compliance with the established biosafety protocols.</p></sec></sec><sec id=\"s1-5\" sec-type=\"other\"><title>Hand Washing Before and After Dental Care</title><p>Hand washing with plenty of soap and water is an extremely important element for infection control for both patients and professionals alike.<sup><xref rid=\"r25\" ref-type=\"bibr\">25</xref></sup> Several studies have confirmed that adequate hand washing can break the transmission cycle of respiratory diseases and reduce the risk of transmission by 6 to 14%.<sup><xref rid=\"r24\" ref-type=\"bibr\">24</xref></sup> There have also been reports of fecal-oral transmission of COVID-19, further emphasizing the need for handwashing in dental practice. Although hand washing is a general requirement, compliance is relatively low, making infection control a great challenge during care.<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref></sup>\n</p></sec><sec id=\"s1-6\" sec-type=\"other\"><title>Precautions to Consider During Dental Care</title><p>Currently, some researchers have considered pediatric populations as one of the most vulnerable to possible contagion by COVID-19. In addition to the transmission routes presented by this virus (inhalation of droplets, sneezing, direct contact, and saliva), additional risks in the care of pediatric patients involve the use of removable orthodontic devices and auxiliary elements. These devices induce a higher risk of contamination if biosafety protocols are not optimally followed. On the other hand, pediatric patients should always be accompanied by someone who will also be in close contact with the operator, increasing the risk of cross-contamination.</p><p>Due to the long incubation period of approximately 2 to 14 days, the signs and symptoms of infected pediatric patients may go unnoticed as the symptomatology may be mild.<sup><xref rid=\"r26\" ref-type=\"bibr\">26</xref></sup> For this reason, both parents and infants should be considered potential carriers of COVID-19.</p><p>Upon arrival at the dental office, the child and the accompanying person should be asked to wash their hands and face, using alcohol gel for the hands. Preferably, disposable surgical footwear covers will be placed over the shoes. Before proceeding with the treatment, the previously mentioned mouthwash of choice will be used by cleaning with a gauze impregnated with the rinse to reduce the risk of ingestion.<sup><xref rid=\"r27\" ref-type=\"bibr\">27</xref>,<xref rid=\"r28\" ref-type=\"bibr\">28</xref></sup>\n</p><p>During the treatment, only the patient, operator, and assistant should be present, although if necessary, a maximum of 1 accompanying person will be allowed. The patient must be able to collaborate with the treatment being carried. Children unable to collaborate with the treatment should be referred for care under sedation or general anesthesia.<sup><xref rid=\"r28\" ref-type=\"bibr\">28</xref></sup>\n</p><p>At the end of the treatment, the air space will be cleaned with a disinfectant spray, waiting 2 min before opening the door. The patient and accompanying person will leave the unit and will be instructed to wash their hands and face before leaving the dental office.</p><p>On the other hand, the generation of greater quantity of fluids and droplets during oral surgery procedure leaves dentists exposed to possible contagions of diseases that could be transmitted to patients if adequate protection barriers are not implemented.<sup><xref rid=\"r23\" ref-type=\"bibr\">23</xref>,<xref rid=\"r29\" ref-type=\"bibr\">29</xref>-<xref rid=\"r31\" ref-type=\"bibr\">31</xref></sup> It is important to emphasize that, in the absence of adequate safety elements, the health of both the patients and the work team cannot be guaranteed; therefore, dental procedures cannot be undertaken.<sup><xref rid=\"r30\" ref-type=\"bibr\">30</xref></sup>\n</p><p>Several studies have suggested that a minimum distance of 1-2 m must be maintained, with no bodily contact, such as kissing or hugging. It has also been suggested to have a container at the entrance of the office to disinfect footwear using a bleach solution of 1/5 of sodium hypochlorite in 4/5 of water, 800 mL of water + 200 mL of sodium hypochlorite remaining at 10,000 ppm, which should be changed every 4 h.<sup><xref rid=\"r17\" ref-type=\"bibr\">17</xref>,<xref rid=\"r30\" ref-type=\"bibr\">30</xref></sup> All these considerations should be taken into account when preparing for a consultation to rule out any type of infection and decide whether to continue or cancel an appointment.</p><p>The recommendations state that patients should wash their hands with soap and water or antibacterial gel for a minimum time of 20 s. After that, documents requiring a signature, such as informed consent, medical records, and forms required by the insurers, can be handled.<sup><xref rid=\"r21\" ref-type=\"bibr\">21</xref></sup> Electronic banking services should be used for payment because receiving bills or coins requires their placement in plastic bags and being sprayed with disinfectant. In addition, pens have also been described as possible vehicles of the spread of the virus<sup><xref rid=\"r31\" ref-type=\"bibr\">31</xref></sup>; therefore, each worker and patient should have their own pen for individual use.</p><p>Subsequently, before entering the consultation, the patient should perform a new hand wash and thereafter remain with their hands on their chest and not touch anything.<sup><xref rid=\"r29\" ref-type=\"bibr\">29</xref></sup> The literature recommends patients washing their hands twice before entering the procedure and three times thereafter. The patient is instructed where to stay, and if the office must be left, the entire protocol should be repeated upon reentry if the use of washrooms should be avoided, and if necessary, the area should immediately be disinfected.<sup><xref rid=\"r23\" ref-type=\"bibr\">23</xref>,<xref rid=\"r30\" ref-type=\"bibr\">30</xref></sup>\n</p><p>On the other hand, extraoral radiographic examinations such as panoramic radiography or cone beam computed tomography (CT) are considered good alternatives to reduce contact with the patient saliva. Nevertheless, the cost of these auxiliary examinations for the patient and their requirement by the clinician must be considered.<sup><xref rid=\"r17\" ref-type=\"bibr\">17</xref></sup>\n</p><p>Before starting the procedure, the dentist should ensure that an anesthetic boost is not necessary. It is ideal to consider the use of truncal anesthesia, plus an infiltrative supplement. According to the surgical technique to be used in the area to be treated, the use of resorbable sutures is recommended to decrease session time. The area is irrigated with needles, and aspirated to reduce the amount of spray. Patients with trauma or maxillofacial infections generally present an elevated temperature. However, according to the epidemiological history, etiology, clinical examination, blood test, and chest CT scan, this differs from that of COVID-19.<sup><xref rid=\"r16\" ref-type=\"bibr\">16</xref>,<xref rid=\"r29\" ref-type=\"bibr\">29</xref></sup>\n</p><p>After the procedure, all disposable materials are removed from the patient and hand washing is requested. Recommendations for postoperative care and the prescription of medication are given to the patient,<sup><xref rid=\"r21\" ref-type=\"bibr\">21</xref></sup> followed by another hand washing and exit from the dental office. When exiting, the patient should avoid touching office surfaces, and finally, hands should be washed with antibacterial gel.<sup><xref rid=\"r31\" ref-type=\"bibr\">31</xref></sup>\n</p><p>Ideally, care in these cases should be carried out in rooms with negative pressure, such as wards, reducing the number of staff present.<sup><xref rid=\"r16\" ref-type=\"bibr\">16</xref>,<xref rid=\"r31\" ref-type=\"bibr\">31</xref></sup> Alternatively, patients should receive care in an isolated room with good ventilation. In life-threatening cases, the patient should be immediately admitted to a hospital and chest CT performed, if available, because reverse transcription polymerase chain reaction testing takes a long time.<sup><xref rid=\"r32\" ref-type=\"bibr\">32</xref></sup>\n</p><p>Dental emergencies are closely related to pain management, and endodontists are closely involved with the evaluation and treatment of odontogenic pain and inflammation. It is highly likely that dental practices can treat some patients with asymptomatic COVID-19 infections. A recent article described a set of recommendations for the management of dental emergencies, which concluded that the use of ibuprofen 600 mg with paracetamol 500 mg would be effective for symptomatic irreversible pulpitis, symptomatic apical periodontitis, acute apical abscess.<sup><xref rid=\"r33\" ref-type=\"bibr\">33</xref>-<xref rid=\"r35\" ref-type=\"bibr\">35</xref></sup>\n</p></sec></sec><sec sec-type=\"discussion\" id=\"s2\"><title>DISCUSSION</title><p>The aim of this research was to present the biosafety rules and protocols that patients should take into account if they require dental care after the appearance of COVID-19. With the active spread of this virus, patients should be treated according to rigorous biosafety parameters to avoid cross-contamination before and during dental care especially, since the full practice of dentistry has been reopened in different cities and because the dental urgencies and emergencies cannot be postponed in most of the cases.</p><p>Before consultation, all patients, symptomatic or otherwise, should answer a questionnaire, followed by temperature measurement to rule out any infectious process. This care protocol must be maintained because all patients are considered as possible carriers. If a patient presents fever, the appointment will be cancelled and postponed, and the patient will be referred to emergency medical services to definitive rule out possible infection. Patients should await care within a safe environment, strictly following biosafety protocols. In the waiting room, it is recommended to remove magazines, brochures, or any other means or surface through which the virus can be transmitted among the patients present in that space. Constant disinfection of the environment should be maintained, and patients should be seated at least 1 m away from each other in a ventilated environment. Patients should not be accompanied, but in the case of accompanying persons, they will remain outside until the end of the dental care.<sup><xref rid=\"r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"r15\" ref-type=\"bibr\">15</xref>,<xref rid=\"r35\" ref-type=\"bibr\">35</xref>-<xref rid=\"r37\" ref-type=\"bibr\">37</xref></sup>\n</p><p>Studies have shown that the use of precare oral rinses decreases the bacterial and/or viral load. Although COVID-19 still remains with the application of mouthwashes, it has been found to be susceptible to oxidation. Therefore, based on the results of many studies, the mouthwash of choice is hydrogen peroxide, which has oxidative capacity while not causing damage to the oral mucosa.<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref>-<xref rid=\"r18\" ref-type=\"bibr\">18</xref></sup> One aspect of controversy is the cytotoxicity that the mentioned rinse can present. However, a study comparing three types of mouthwashes indicated that diluted rinses, such as in this case, would not have any additional adverse effects even if used as for prophylactic purposes.<sup><xref rid=\"r39\" ref-type=\"bibr\">39</xref></sup> In addition, to prevent the spread of dental aerosols (released particles less than 50 microns in diameter) that are produced from dental instruments, such as like ultrasonic scalers, air-water syringes, dental handpieces when using rotating systems, which are a source of emission of microorganisms and even with droplets that can be produced and could generate a risk of contagion during dental care, many authors suggest the use of chemical or mechanical techniques for the elimination of caries. If rotary instrumentation must be used, absolute isolation is indicated, and if scaling and root planning are necessary, it is recommended to use a manual technique.</p><p>All specialties follow their own procedures in dental care. Each patient must strictly comply with the biosafety regulations. This review suggests a protocol to follow from admission to the dental office, the procedure, and the end of treatment. In the area of pediatrics, the application of a mouthwash should be modified to avoid toxicity due to ingestion, and an accompanying person may be allowed provided this is really necessary. On the other hand, in oral surgery, great importance is given to hand washing; it is suggested to perform hand washing twice before and three times after the procedure. In addition, once admitted to the consultation, the hands of the patient must remain on their chest and not touch any object, because in the event of violating this rule or leaving the office, the entire protocol must be repeated upon re-entry. Dental emergencies are closely related to the area of endodontics and maxillofacial surgery, the treatments of which are all associated with pain management. For this, according to many studies, medication for primary management is essential, among which the combination of ibuprofen 600 mg + paracetamol 500 mg is of note.<sup><xref rid=\"r26\" ref-type=\"bibr\">26</xref>-<xref rid=\"r31\" ref-type=\"bibr\">31</xref>,<xref rid=\"r33\" ref-type=\"bibr\">33</xref>-<xref rid=\"r35\" ref-type=\"bibr\">35</xref>,<xref rid=\"r39\" ref-type=\"bibr\">39</xref>-<xref rid=\"r41\" ref-type=\"bibr\">41</xref></sup>\n</p><p>Although several studies have described the current parameters of care to be implemented after the appearance of COVID-19, established and organized protocols for dental consultation remain to be developed.<sup><xref rid=\"r42\" ref-type=\"bibr\">42</xref>,<xref rid=\"r43\" ref-type=\"bibr\">43</xref></sup> It is recommended to standardize the biosafety standards in an orderly and sequential way to guide patients and provide knowledge regarding care in the dental office, with possible variations, depending on the characteristics of the patient and the treatment required. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"other\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Nat Commun</journal-id><journal-id journal-id-type=\"iso-abbrev\">Nat Commun</journal-id><journal-title-group><journal-title>Nature Communications</journal-title></journal-title-group><issn pub-type=\"epub\">2041-1723</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807769</article-id><article-id pub-id-type=\"pmc\">PMC7431846</article-id><article-id pub-id-type=\"publisher-id\">17723</article-id><article-id pub-id-type=\"doi\">10.1038/s41467-020-17723-2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Q&A</subject></subj-group></article-categories><title-group><article-title>Julia Greer answers questions about additive manufacturing</article-title></title-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>3993</elocation-id><permissions><copyright-statement>© Springer Nature Limited 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\" abstract-type=\"Standfirst\"><p id=\"Par1\">Professor Julia R Greer is a materials scientist at the <ext-link ext-link-type=\"uri\" xlink:href=\"https://jrgreer.caltech.edu/people/\">California Institute of Technology</ext-link>. Her group focuses on designing, fabricating and characterising micro- and nano-architected materials using 3D lithography, nanofabrication, and additive manufacturing (AM) techniques for a multitude of applications ranging from biological devices to damage-tolerant fabrics.</p></abstract><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><p id=\"Par2\">Tell us a little bit about you and what sparked your interest in additive manufacturing.</p><p id=\"Par3\">When I was a graduate student, my PhD research was about the mechanical properties of nanomaterials. It was a very fundamental study where I made very small nanostructures, deformed them, and measured their strength. But in the back of my mind, I was always wondering: these structures are a little bit like Lego, so can we do something useful with them, maybe give them fun or interesting shapes? It’s the same concept as the trusses making up Eiffel tower, or using tiny building blocks to build a crane.</p><p id=\"Par4\">Before tenure, you are not really encouraged to think outside the box. So it is really only after I got tenure that I felt like I was in a position to pursue this idea. It was a leap of faith, which coincided with the development of a technique called two-photon lithography (TPL). I went to Germany to visit a company called <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.nanoscribe.com/en/\">Nanoscribe</ext-link>, which spun out of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.kit.edu/english/\">Karlsruhe Institute of Technology</ext-link>. They demonstrated the capabilities of the technique and I was sold: they even printed a little Statue of Liberty and a “Welcome Julia” sign for my visit! I realised that this was the instrument that was going to enable me to achieve the scientific dream that I had, which was to use these nanomaterials to make something larger out of these small, tiny things, and together with a colleague, we bought the countryʼs (USA) second Nanoscribe instrument.</p><p id=\"Par5\">One of the ways in which I am really fortunate is that Caltech students are brilliant, they educate and inspire me: one of the great things that came out of getting acquainted with the Nanoscribe together is all the things that are not spelled out in the manual. We explored what happens when you coat the structure you print with another material, or when you modify the resin, and when you have a cascade of chemical reactions… . This is what catalysed my transition to additive manufacturing. Additive manufacturing was a springboard for us to explore what else could be done with these materials. There is a lot of potential out there! We were pioneers in terms of 3D architected nanomaterials and in the development of different synthesis techniques (for example to make smart textiles, or materials that are simultaneously ultralightweight and ultrastiff). Today, my group’s niche is in the discovery and demonstration of these unexpected phenomena in 3D nano-architectures made of a broad range of materials.</p><p id=\"Par6\">How has additive manufacturing changed your field? Is there anything specific that it has enabled or made possible? Tell us how it impacted your research.</p><p id=\"Par7\">Additive manufacturing has hugely impacted my field. It is exploding right now, and is a topic of conversation and discussion at national agencies and the National Academies: for example,  additive and digital manufacturing have been highlighted by the US Department of Energy and the National Materials and Manufacturing Board. There is no conference these days without an additive manufacturing symposium! It is enabling many research groups and allowing for explorations that wouldn’t be possible without it: some examples are multi-functional, reconfigurable smart materials and devices, new device physics, materials research….</p><p id=\"Par81\">There has always been a duality between material and structure, and additive manufacturing enables us to couple these properties. We can envision a day where we have a backpack with supplies and we can use natural resources and the natural habitat to set up a little 3D-printer and create power and energy sources and whatever else we may need: solid state batteries, fuel cells… Additive manufacturing allows us to dream big, which is what I like doing. While I’m not sure of the timeframe, it really paves the way for a future world where everything is going to be done by design according to utility.</p><p id=\"Par9\">What does additive manufacturing have to do to be more widely adopted? Are there any specific hurdles it has to overcome?</p><p id=\"Par10\">There are currently two big gaps: one is a lack of computational capabilities that are able to accurately describe and predict properties, and another is scalability.</p><p id=\"Par11\">We currently have computational models that are high fidelity but are expensive in terms of time and not particularly generalisable or ones that are less computationally expensive but canʼt capture the physics of the additive manufacturing process. Nearly all models are idealised and lack the ability to account for defects or to accurately predict or describe properties of materials that contain some defects. What we need is for models not only to capture the processing parameters and properties that emerge during the additive manufacturing process, but also to be able to make in-process decisions to adjust the final product. We are effectively looking for in situ diagnostic capabilities as well as the ability to process the acquired data in real time. Today, we just don’t have reliable models, and the bottlenecks are computational bandwidth and our predictive capabilities. Using machine learning techniques and genetic algorithms can be one way to accommodate defect detection and accommodate large amounts of data, which will then give rise to the ability to predict defects in a reliable way. Navigating this infinite parameter space is the real challenge: Once we can do that and successfully achieve in-process decision making capabilities, developing and refining the manufacturing techniques will be straightforward.</p><p id=\"Par12\">The other issue is scalability: how can production be stepped up while retaining the desired properties for these materials to be inserted in useful applications? To this end, I’ve co-founded a company specifically focused on scalability that makes smart textiles: from very porous filters to ultralightweight mechanically resilient sheets. We had to deviate from two-photon lithography as it is not amenable to mass production, and develop a different method for patterning large areas.</p><p id=\"Par13\">Looking forward: where do you see additive manufacturing going next?</p><p id=\"Par14\">Looking at the next 5 years, more and more real companies are looking into 3D printing, which is one aspect of additive manufacturing. 3D-printing is becoming mainstream technology for component development where cost isn’t a factor: for example, in biomedical, and space and airborne applications. I believe these niche applications where either the current cost of manufacturing is high or the economic aspects of the produced part are less important will be the first ones to truly adopt additive manufacturing and make it proliferate. You could argue that today we are able to print fully degradable plastic bags using polylactic acid, for example, but the reality is that it is impossible to compete with the economics of current high-density polyethylene (HDPE)/low-density polyethylene (LDPE) plastic bag production.</p><p id=\"Par15\">The manufacturing community is currently focussing on scalability and the development of computational techniques, both being hurdles I talked about above. Current buzzing topics to apply to additive manufacturing are machine learning techniques and genetic algorithms, which have previously been developed in other contexts.</p><p id=\"Par16\">For you, is there a difference between additive manufacturing and 3D printing? If they are not equivalent, how do they differ?</p><p id=\"Par17\">They are different! 3D-printing is layer-by-layer shaping of a three-dimensional structure. Additive manufacturing is more complex than that, involving chemical synthesis and clever algorithms, which ulitmately leverage on material and structural effects. The materials that can be produced using additive manufacturing are far from equilibrium, and it wouldn’t be possible to create and shape these configurations without additive manufacturing. Understanding how a material behaves at each relevant  lengthscale in a structure where everything is essentially chemically derived ins a challenge! Some of these may be detrimental, and others become useful features. There is so much more to learn!</p></body></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807894</article-id><article-id pub-id-type=\"pmc\">PMC7431847</article-id><article-id pub-id-type=\"publisher-id\">70964</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70964-5</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Application of unmanned aerial vehicle (UAV) thermal infrared remote sensing to identify coal fires in the Huojitu coal mine in Shenmu city, China</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>He</surname><given-names>Xiaoyuan</given-names></name><address><email>81556924@qq.com</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Xingke</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Luo</surname><given-names>Zheng</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Guan</surname><given-names>Tao</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.440661.1</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9225 5078</institution-id><institution>School of Earth Science and Resources, </institution><institution>Chang’an University, </institution></institution-wrap>Xi’an, 710054 China </aff><aff id=\"Aff2\"><label>2</label>Aerial Photogrammetry and Remote Sensing Bureau of China National Administration of Coal Geology, Xi’an, 710199 China </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13895</elocation-id><history><date date-type=\"received\"><day>6</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>6</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">China is a major coal-producing country that consumes large amounts of coal every year. Due to the existence of many small coal kilns using backward mining methods, numerous worked-out areas have been formed. The coal mines were abandoned with no mitigation, so air penetrates into the roadways and contacts the coal seams; as a result, the residual coal seams spontaneously ignite to form coal fires. These coal fires have burned millions of tons of valuable coal resources and caused serious environmental problems. To implement fire suppression more effectively, coal fire detection is a key technology. In this paper, thermal infrared remote sensing from unmanned aerial vehicle combined with a surface survey is used to identify the range of coal fires in the Huojitu coal mine in Shenmu city. The scopes and locations of the fire zones are preliminarily delineated, which provides an accurate basis for the development of fire suppression projects.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Environmental sciences</kwd><kwd>Fossil fuels</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><p id=\"Par2\">A coal fire is a subsurface phenomenon that causes not only losses of valuable natural resources but also environmental problems, such as surface cracks, subsidence and collapse, and atmospheric pollution, eventually endangering human security<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. The term coal fire refers to a spontaneous combustion phenomenon in which a coal body is in contact with air and oxidizes to burn under natural conditions that occur in exposed coal seams or underground, so as in coal waste and storage piles<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>. Coal fires occur everywhere such as the United States, South Africa, India, Australia, Indonesia, China and Canada<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>,<xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>, which are major coal-producing countries. With plenty of thick and shallow coal beds, underground coal fires are burning in Ningxia, Xinjiang, Inner Mongolia and Shaanxi provinces in Northern China<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. The fires in China are usually triggered by human interference<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup> because human alteration of the natural environment of coal seams makes them easier to oxidize and spontaneously combust. The Jurassic coalfield is located in the six counties, namely, Dingbian, Fugu, Hengshan, Yuyang, Jingbian and Shenmu in Yulin city in northern Shaanxi; it covers 27,000 km<sup>2</sup> in area and contains 138.8 billion tons of proven reserves of coal resources<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Jurassic coalfields in northern Shaanxi still have a large amount of coal resources, and burnt rocks(clinkers) are widely distributed during the geo-historical period<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>; these rocks have been baked or melted by the burning of underlying coal beds, providing evidence of past coal bed fires<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup> and showing that conditions are suitable for spontaneous combustion in this area. In recent years, with a large number of developed coal resources, human activities change the environment and thus improving the chance of spontaneous coal combustion, the Jurassic coal fields in northern Shaanxi have caused many surface and underground coal fires, mainly distributed in Shenmu, such as in the Longyan, Tanyaoqu, and Huojitu coal mines (HCM) (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>b).\n<fig id=\"Fig1\"><label>Figure 1</label><caption><p>The location of the HCM: (<bold>a</bold>) shows the location of Yulin city in China. (<bold>b</bold>) show the HCM located in the Shenmu city; (<bold>c</bold>) is a three-dimensional based on digital orthophoto map (DOM) and digital surface model (DSM) that are acquired on 16 February 2019, red and magenta bands indicate 1<sup>−2</sup> and 2<sup>–2</sup> coal fire respectively. (<bold>d</bold>)–(<bold>h</bold>) ground survey, (<bold>d</bold>) and (<bold>e</bold>) are gas and smoke are emitted along the surface fissures.(<bold>f</bold>) is 1<sup>−2</sup> and 2<sup>–2</sup> coal seams in outcrop, 1<sup>–2</sup> coal seam covered by loess for extinguishing fire. (<bold>g</bold>) is an open fire in mine-out area. (<bold>h</bold>) is coal tar from vent.</p></caption><graphic xlink:href=\"41598_2020_70964_Fig1_HTML\" id=\"MO1\"/></fig></p><p id=\"Par3\">Accurate detection of the coal fire combustion centre, range and depth is a major problem for coal fire exploration technology, and it is also the basis of coal fire suppression projects. It is difficult to determine the extent of underground coal fires because of a few surface factors, including vegetation, rock and soil cover over the coal bed<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. At present, principal detection methods include remote sensing, borehole temperature measurements, and geochemical and geophysical measurements<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. Since the early 1960s, remote sensing has become a convenient and useful tool for the monitoring and detection of coal fires based on surface temperature anomalies<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR13\">13</xref>–<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup><sup>.</sup> Interferometric synthetic aperture radar (ISAR) can detect subtle surface deformation but the subsidence may not always equal to an active fire region<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. Several researchers have used magnetic methods to characterize underground coal fires<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref>–<xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>. Magnetic surveys provide high-resolution coal fire mapping and delineate previously burned, active coal fire and non-coal fire regions, and the magnetic properties of materials change with temperature<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>. To date, only a few geophysical or remote sensing methods have been applied for coal fire detecting in Shaanxi Province, but there are some problems. For example, due to the destruction of the original terrain, a magnetic exploration line cannot be measured through an active coal fire or artificial cliff, or the scattered distribution of highly magnetic burnt rock from other places on the surface affects the results obtained by the magnetic method, so the magnetic method cannot effectively delineate the coal fire boundary. Radon measurements are not possible or are not accurate due to surface fissures and detached loess. In the past, airborne thermal infrared (TIR) remote sensing had been suitable for the identification of large fires, but small-scale mines are not easy to identify, or the data are not precise enough. Although providing high temporal and spatial resolution, imaging systems mounted on manned airborne platforms are limited by high operational complexity and costs<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. The development of unmanned aerial vehicle (UAV) remote sensing with low-altitude level has the characteristics of high spatial resolution, frequency and cost performance, and can complement satellite remote sensing capabilities, alleviating the contradiction between high spatial resolution and temporal resolution<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref>,<xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. This method provides the safe and rapid probe of thermal areas, often occur in dangerous or inaccessible terrain<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref>–<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. In this paper, due to the destruction of the original landforms and interference factors, geophysical surveys are not available in fire zones, so low-altitude UAV-based TIR remote sensing, ground surveys, and boreholes are used to comprehensively detect and delineate coal fire areas in the Huojitu coal mine (HCM) in Shenmu city, northern Shaanxi.</p><p id=\"Par4\">The HCM is located approximately 4.5 km west of Daliuta town, Shenmu city, Shaanxi Province (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a), between N 39° 15′ 16.75″–N 39° 15′ 49.53″ and E 110° 9′ 48.50″–E 110° 10′ 1.94″; it has a typical arid continental monsoon climate covering an area of 2.71 km<sup>2</sup>. From east to west, the length of the mining area is approximately 600–2,500 m, and north to south, the width is approximately 300–1,500 m, with most of the original landforms altered. The area is located on the northern Shaanxi slope of the Ordos Basin, a monoclinic structure inclined to the northwest. The strata are gentle and nearly horizontal at 1°–3°, and there is no fault or magmatic activity.</p><p id=\"Par5\">The coal seams in HCM are mined in the Middle-Lower Jurassic Yan'an Formation (J<sub>1–2</sub><italic>y</italic>), the only exposed unit in the study area. In the regional area, the Yan'an Formation (J<sub>2</sub><italic>y</italic>) is separated by disconformities from the underlying Upper Triassic Yongping Formation (T<sub>3</sub><italic>y</italic>) and the overlying Middle Jurassic Zhiluo Formation (J<sub>2</sub><italic>z</italic>). The thickness of the Yan'an Formation is generally between 260 and 316 m and is divided into five sections, each of which contains a coal group, and the coal groups are numbered 1–5 in order from bottom to top (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>). The study area is covered mainly by sandstones, siltstones, shales and coals. The major mining in the HCM occurred in the 1<sup>–2</sup> and 2<sup>–2</sup> coal seams; 1<sup>–2</sup> has a thickness from 2.80 to 11.38 m with an average of 10.20 m, and the average elevation of the 1<sup>–2</sup> coal floor is 1,073 m above sea level; 2<sup>–2</sup> has a thickness from 4.48 to 5.25 m with an average of 4.75 m, and the floor elevation 1,037 m above sea level (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>f), the pillar and room method was adopted between 1985 and 2001. Opencast mining activity (2002–2011) often leads to bulk volume of mining wastes and large fresh rock surfaces<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. From 1985 to 2011, due to years of random mining and excavation of small coal kilns and the typical dry natural climatic conditions, under which coal seams are prone to spontaneous combustion, the residual coal seams are oxidized and can be naturally ignited. Mining of the 1<sup>–2</sup> coal on the east and west sides was completed, and a pit was formed with the lowest elevation at 1,040 m and residual 2<sup>–2</sup> coal pillars in the middle stripper platform to be mined with the highest elevation at 1,156 m and residual 1<sup>–2</sup> coal pillars; the mining formed steep terrain (an escarpment) with relief of approximately 116 m (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>c). According to a ground survey, the coal fire is mainly located on the escarpment wall where the coal seam was deeply excavated with a large-scale worked-out area, and copious gas and smoke are emitted along the surface fissures. The hot fumes reach temperatures as high as 340.0 °C, locally separating out coal tar. Sulfur and mirabilite are also found around some gas vents (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>d, e, h). This process occurs because the pit accelerates the flow of oxygen penetrating the combustion zone, thereby exacerbating the fire's spread to the west and east of the pit. The lower layer of coal belongs to the China Shenhua Group. With the low-rank coal beds rich in volatile matter and dry climate, as the rooms between pillars collapse and allow air to enter from the surface<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>, the coal in the HCM is prone to spontaneous combustion. The coal beds in the HCM are nearly horizontal, and when a fire starts, it first spreads along the outcrop. The Yulin Bureau of Natural Resources and Planning has carried out some fire-fighting measures in the HCM, such as loess cover and water injection, but the fires are still active, indicating that these measures have not been wholly successful.<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Schematic diagram of the numbering of coal seams in the Yan'an Formation.</p></caption><graphic xlink:href=\"41598_2020_70964_Fig2_HTML\" id=\"MO2\"/></fig></p><sec id=\"Sec2\"><title>Methodology</title><p id=\"Par6\">TIR is a remote sensing method that detects variations in heat on Earth’s surface<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. The use of airborne TIR for mapping and studying coal fires has greater resolution and availability than satellite TIR<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup> imagery. Recent advances in UAVs equipped with global positioning systems (GPSs) and digital cameras are reducing the cost of collecting imagery<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. High-resolution thermal cameras have been successfully mounted on aircraft platforms and on UAVs, increasingly using high-performance sensors with smaller size and weight and greater spectral and spatial resolutions. The thermal cameras can reach centimetre-scale ground resolution and provide sufficient accuracy.</p><p id=\"Par7\">In our research, TIR Zenmuse XT2 cameras mounted onto a UAV DJI M210 were used to acquire data. The Zenmuse XT2 gimbal and cameras, which included a forward looking infrared detector and a visual camera, provided both infrared and visual images simultaneously. The forward looking infrared camera performed high-sensitivity infrared scanning at 640/30 fps and was equipped with an uncooled vanadium oxide (VOx) microbolometer to measure longwave radiation in the spectral range 7.5 ~ 13.5 μm and a temperature range of -20 to 135 °C (high gain); it had a 25 mm lens and acquired image frames of 640 × 512 pixels as raw 8-bit digital numbers (DNs) at the rate of less than 9 Hz. The visual camera captured 4 K videos and 12 megapixel photos (<ext-link ext-link-type=\"uri\" xlink:href=\"https://support.pix4d.com/hc/en-us\">https://support.pix4d.com/hc/en-us</ext-link>). Several studies showed that TIR surveys conducted during the fall or predawn were best for detecting coal fires<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref>–<xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>, but that RGB orthophotos were best obtained during the day. To acquire both types of data simultaneously, the flight was carried out from 7 a.m. to 10 a.m. on 22 October 2019, which was a cloudy day; the heating caused by sunlight was very small, and solar radiation was negligible. As described previously literature, an appropriate flight plan was determined using the DJI Ground Station software<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. The flight plan was then uploaded to the quadcopter’s flight controller using the DJI Vision App<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. Accordingly, both in-flight navigation and image capture were autonomous<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. The internal time of the camera was set to the GPS time prior to the flight to ensure that the images could be easily synchronized with the position data in the UAV GPS log file<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. Ground control points (GCPs) measured with differential RTK GPS were established before the flight so that the resulting orthophoto imagery and digital elevation models (DEMs) could be accurately georeferenced and tested<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref>,<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. On a clear day with good visibility, the flight was conducted at a relatively low angle with respect to the horizon. Three flights were conducted with a flight altitude of 300 m, the frontal overlap rate was 75%, and the side overlap was 65% to obtain high-accuracy results. When there is high overlap between 2 images, the common area captured is larger, and the key points can be matched. Therefore, the main rule is to maintain high overlap between the images. The route length was approximately 21.6 km, and 431 overlapping images were processed using the Pix4D software (<ext-link ext-link-type=\"uri\" xlink:href=\"https://support.pix4d.com/hc/en-us\">https://support.pix4d.com/hc/en-us</ext-link>). A digital orthophoto image and a digital surface model (DSM) of the mining area were generated, with a ground resolution of 3.8 cm.</p><p id=\"Par8\">A program was written in Python code to calculate the maximum and minimum grey values of the TIR image, which were used to invert the surface temperature, and the thermal anomaly distribution area caused by coal fires and the locations of fire area were accurately determined (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>), with a ground resolution of 40 cm (pixel size). During the flight, the ground temperature was measured simultaneously to obtain the key parameters for temperature calibration and inversion of the TIR image; the measurement used a TIR thermometer Fluke 62 Mini, with a temperature measurement range of -30 ~ 500 °C, and a temperature error of ± 2 °C (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.fluke.com\">https://www.fluke.com</ext-link>). The original 8-bit raw DNs were collected by the TIR sensor, which in the thermal imagery represented at-sensor radiance. Matching pixels were extracted from the thermal imagery, and based on the matching reference temperatures, linear regression was calculated<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. After mosaicking and co-registration, the DN values were converted to absolute temperature in °C based on an empirical line correction<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>, according to the radiation conduction equation and the Plank function. With this empirical relationship, we converted the whole thermal mosaic into absolute temperature, assuming a constant emissivity of 0.95<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. A similar method can be found in the literature <sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref>,<xref ref-type=\"bibr\" rid=\"CR30\">30</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>.<fig id=\"Fig3\"><label>Figure 3</label><caption><p>TIR image of HCM (Bright colors represent high temperature abnormalities).</p></caption><graphic xlink:href=\"41598_2020_70964_Fig3_HTML\" id=\"MO3\"/></fig></p></sec><sec id=\"Sec3\"><title>Results</title><sec id=\"Sec4\"><title>UAV thermal infrared</title><p id=\"Par9\">Airborne TIR technology has a wide detection range and high image resolution, which provides great spatial detail for mapping coal fires<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref>–<xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>, especially based on UAVs. Thermal anomalies induced by underground coal fires can be extracted from TIR data using an exclusion method<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>, together with field temperature measurements. TIR data are widely used to delineate subtle surface thermal anomalies associated with underground coal fires<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>.</p><p id=\"Par10\">In our research, the digital orthophoto map (DOM) and the TIR image (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>) obtained after processing the raw data collected by the drone. The image was clear, and the colours were bright. The TIR data and the RGB orthophoto relied upon ground control points (GCPs) or orientation measurements from inertial measurement units (IMUs) to enable accurate georeferencing of the imagery. Then the thermal infrared DOM was registered with the RGB DOM, and the registration error was 0.4 pixels. From the DOM, the coal seams, fissures, burnt rocks, gangue, pool, backfill area, coal washing plant, initial landforms, residential areas, etc., could be clearly interpreted based on multi-scale segmentation and were verified with surface surveys (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). Thermal anomalies in the fire zone are very obvious and appears as high-brightness spots or bands on the TIR remote sensing image. The difference in brightness reflects the temperature difference of the fire zone. The thermal anomaly packet is extracted and superimposed on the DOM to interpret four banded fire zones and seven small temperature anomalies (Tables <xref rid=\"Tab1\" ref-type=\"table\">1</xref>, <xref rid=\"Tab2\" ref-type=\"table\">2</xref>).The development of vents, cracks, subsidence on the surface result from underground coal fires. Such features sporadically extent in spatial and vary in dimension from a few to tens of metres<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>. Combining the investigation of surface fissures and cracks with or without smoke, and temperature measurements, the fire transition zone is delineated. Other areas are normal areas, which are non-fire zones. From east to west, the study area is divided into four coal fire zones I, II, III and IV; other fires are small and sparse, mostly burning gangue extending 30–100 m. The fire zone has the characteristics of high temperature, heat waves, flames, and new burnt rocks. It is the centre of the coal fire zone with many open fires according to the field survey (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). Slumping land surface, large and wide fissures are evidently visible along the edges of the fire. Combustion leads to subsidence and many cracks (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>), which are very dangerous and basically make access difficult. In the fire transition zone, chimneys form in the fire zone, flames are basically not seen, fissures and cracks develop, and smoke is emitted from the cracks with high-temperature gas. This transition zone is the margin of the coal fire zone. The coal fire area refers to the cumulative area of fire zones and fire transition zones.<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Digital orthophoto map (DOM) of the HCM and superimposed thermal anomaly. Divided into I, II, III, and IV four coal fire zones by thermal anomalies and surface survey. Green box show the location of Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>.</p></caption><graphic xlink:href=\"41598_2020_70964_Fig4_HTML\" id=\"MO4\"/></fig><table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Coal fires statistics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Fire Number</th><th align=\"left\">Ignite coal seam</th><th align=\"left\">Thickness of coal(m)</th><th align=\"left\">Coal fire area (m<sup>2</sup>)</th><th align=\"left\">Fire zone area (m<sup>2</sup>)</th><th align=\"left\">Fire transition zone area (m<sup>2</sup>)</th></tr></thead><tbody><tr><td align=\"left\">I</td><td align=\"left\">1<sup>–2</sup></td><td char=\".\" align=\"char\">10.20</td><td char=\".\" align=\"char\">75,634.96</td><td char=\".\" align=\"char\">33,521.64</td><td char=\".\" align=\"char\">42,113.32</td></tr><tr><td align=\"left\">II</td><td align=\"left\">1<sup>–2</sup></td><td char=\".\" align=\"char\">10.20</td><td char=\".\" align=\"char\">143,848.09</td><td char=\".\" align=\"char\">49,760.82</td><td char=\".\" align=\"char\">94,087.27</td></tr><tr><td align=\"left\">III-E</td><td align=\"left\">2<sup>–2</sup></td><td char=\".\" align=\"char\">4.75</td><td char=\".\" align=\"char\">43,458.82</td><td char=\".\" align=\"char\">31,132.91</td><td char=\".\" align=\"char\">12,325.91</td></tr><tr><td align=\"left\">III-W</td><td align=\"left\">2<sup>–2</sup></td><td char=\".\" align=\"char\">4.75</td><td char=\".\" align=\"char\">24,480.31</td><td char=\".\" align=\"char\">14,143.17</td><td char=\".\" align=\"char\">10,337.14</td></tr><tr><td align=\"left\">IV</td><td align=\"left\">1<sup>–2</sup></td><td char=\".\" align=\"char\">2.80</td><td char=\".\" align=\"char\">46,037.48</td><td char=\".\" align=\"char\">11,573.49</td><td char=\".\" align=\"char\">34,463.99</td></tr><tr><td align=\"left\" colspan=\"3\">Sum</td><td char=\".\" align=\"char\">333,459.66</td><td char=\".\" align=\"char\">140,132.03</td><td char=\".\" align=\"char\">193,327.63</td></tr></tbody></table></table-wrap><table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Small high temperature anomalies statistics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Number</th><th align=\"left\" colspan=\"2\">Coordinate</th><th align=\"left\" rowspan=\"2\">Area (m<sup>2</sup>)</th><th align=\"left\" rowspan=\"2\">Property</th><th align=\"left\" rowspan=\"2\">Surface temperature range(°C)</th></tr><tr><th align=\"left\">Longitude(E)</th><th align=\"left\">Latitude(N)</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">110° 10′ 37.52″</td><td align=\"left\">39° 16′ 2.51″</td><td char=\".\" align=\"char\">1,366.90</td><td align=\"left\">1<sup>–2</sup> coal pile, open fire</td><td char=\"–\" align=\"char\">103.0–225.0</td></tr><tr><td align=\"left\">2</td><td align=\"left\">110° 10′ 44.66″</td><td align=\"left\">39° 15′ 59.46″</td><td char=\".\" align=\"char\">331.65</td><td align=\"left\">1<sup>–2</sup> coal pile, open fire</td><td char=\"–\" align=\"char\">50.2–101.6</td></tr><tr><td align=\"left\">3</td><td align=\"left\">110° 11′ 1.62″</td><td align=\"left\">39° 15′ 45.76″</td><td char=\".\" align=\"char\">4,968.13</td><td align=\"left\">Gangue</td><td char=\"–\" align=\"char\">36.6–89.6</td></tr><tr><td align=\"left\">4</td><td align=\"left\">110° 11′ 0.88″</td><td align=\"left\">39° 15′ 36.76″</td><td char=\".\" align=\"char\">3,092.70</td><td align=\"left\">Gangue</td><td char=\"–\" align=\"char\">46.0–102.5</td></tr><tr><td align=\"left\">5</td><td align=\"left\">110° 10′ 28.67″</td><td align=\"left\">39° 15′ 24.91″</td><td char=\".\" align=\"char\">1,346.92</td><td align=\"left\">2<sup>−2</sup> coal seam, open fire</td><td char=\"–\" align=\"char\">30.2–253.6</td></tr><tr><td align=\"left\">6</td><td align=\"left\">110°10′17.08″</td><td align=\"left\">39° 15′ 17.99″</td><td char=\".\" align=\"char\">10,480.00</td><td align=\"left\">Gangue</td><td char=\"–\" align=\"char\">36.4–103.0</td></tr><tr><td align=\"left\">7</td><td align=\"left\">110° 10′ 10.65″</td><td align=\"left\">39° 15′ 16.26″</td><td char=\".\" align=\"char\">3,401.00</td><td align=\"left\">Gangue</td><td char=\"–\" align=\"char\">45.7–153.8</td></tr></tbody></table></table-wrap><fig id=\"Fig5\"><label>Figure 5</label><caption><p>Fissures and subsidence in the fire zone (the location show in Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). <bold>a</bold>. DOM; <bold>b</bold>. Interpret fissures and subsidence on DOM; <bold>c</bold> and <bold>d</bold> field photos (Camera direction 158° and 95° respectively).</p></caption><graphic xlink:href=\"41598_2020_70964_Fig5_HTML\" id=\"MO5\"/></fig></p><p id=\"Par11\">Coal fire zone I is a long northwest–southeast strip along the overhanging wall of the pit, ranging from 45 to 225 m wide and approximately 816 m long. There are four roadway openings, six open fires and four collapses with many fissures and cracks, which emit smoke and gas. The area of the fire zone is 33,521.64 m<sup>2</sup>, the area of the fire transition zone is approximately 42,113.32 m<sup>2</sup>, and the total area of this coal fire is approximately 75,634.96 m<sup>2</sup>.</p><p id=\"Par12\">Fire zone II corresponds to fire zone I and is is also a long northwest-southeast strip along the overhanging wall of the pit, 30–253 m in width, 1,225 m in length; there are eight open flames and four subsidence events with smoke emissions. The area of the fire zone is 49,760.82 m<sup>2</sup>, the fire transition zone is 94,087.27 m<sup>2</sup>, and the total area of the coal fire is 143,848.09 m<sup>2</sup>.</p><p id=\"Par13\">Fire zone III is composed of two bay-shaped areas III-E and III-W, separated by 80 m sidewalks piled with gangue. The roadways at the bottom of the two areas are connected, and the roof of the coal seam is burnt rock. The fire zone width is 45–135 m, and its length is 260–340 m, with two laneway entrances; there are ten open flames, and three collapses with smoke emerging from the cracks around the areas of subsidence. The area of the fire zone is 45,276.08 m<sup>2</sup>, the fire transition zone is approximately 22,663.05 m<sup>2</sup>, and the total area of this coal fire is 67,939.13 m<sup>2</sup>.</p><p id=\"Par14\">Fire zone IV is a long northwest-southeast strip along the overhanging wall of the pit, ranging from 40–155 m in width and 545 m in length, with three laneway entrances, one collapsed spot and six open flames. Many fissures and cracks emit smoke and gas. The area of the fire zone is 11,573.49 m<sup>2</sup>, the fire transition zone is approximately 34,463.99 m<sup>2</sup>, and the total area of the coal fire is 46,037.48 m<sup>2</sup>.</p><p id=\"Par15\">In pace with eliminating the underlying coal, the overburden subsides and the air conducts into the burning area and hot gas escape from there though the tension cracks, which promotes combustion. As time going, coal burns deeper into the mountain slope, leading to the overlying rocks to gradually subside into the burned-out void<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. Therefore, the direction of fire advance is from the fire zone to the fire transition zone.</p><p id=\"Par16\">TIR can detect the location of coal fire based on surface signatures<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref>–<xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup> but cannot be seen into the subsurface, so the true range of the subsurface burning region cannot be delineated merely from this technique. It is successful to identify and delineated the surface fires with depths less than 10 m, but hard to identify fires deeper than 30 m, because it is need a long time (approximately a decade) to conduct the heat to the surface<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>. Therefore, remote sensing is predominant in revealing near-surface fires but has difficulties identifying fires at greater depths<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref>,<xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>.</p><p id=\"Par17\">Delineation of the subsurface fire is essential to extinguish fire project, including surface subsidence and temperatures, cracks and fissures. Investigation of these variables can approximately identify the areal extent of the fire<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. Field geological work includes the investigation of fire areas, crack, smoke, laneways and old kiln wellheads, and the measurement of surface temperatures. Ground real information about coal fires in the HCM has been acquired from using portable thermometers. A field survey was conducted during the month of November 2019 to obtain results for validation. Temperature were measured at different heights in the opencast mine to comprehend the connection between the thermal anomalies due to subsurface coal fires and background temperatures (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6</xref>). The high-temperature points and cracks are basically located in the coal fire zone and effectively determine the coal fire range.<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Temperature measurement and drilling location (Black dots represent temperature measurement location with °C; Red solid circles represent holes drilled into burnt rock, red open circles represent the holes drilled into worked-out areas or fissures, red circles with black dots represent the holes drilled into non-fire areas).</p></caption><graphic xlink:href=\"41598_2020_70964_Fig6_HTML\" id=\"MO6\"/></fig></p></sec><sec id=\"Sec5\"><title>Drilling</title><p id=\"Par18\">Based on the preliminary delineation of the coal fire zone, the centre of the fire zone is determined by deploying drill holes. A corresponding bore is placed on each side of the fire transition zone and the non-fire zone. One borehole is drilled inside first; if it is a high-temperature hole, drilling continues outside; otherwise, drilling is stopped. Using a drill, the lithologies are determined by the characteristics of fragments carried by the wind pressure, such as sandstone, burnt rocks and coal (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>a, b, c). Temperature is measured from top to bottom every 5 m through the borehole (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>d), which can approximately verify and modify the boundaries of coal fires. Figure <xref rid=\"Fig6\" ref-type=\"fig\">6</xref> shows the drilling locations, and Table <xref rid=\"Tab3\" ref-type=\"table\">3</xref> shows the drilling characteristics. Red solid circles represent holes drilled into burnt rock with high temperature, red open circles represent holes drilled into worked-out areas or fissures and red circles with black dots represent the holes drilled into non-fire areas with low temperature.<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Drilling and temperature measurement in the hole.</p></caption><graphic xlink:href=\"41598_2020_70964_Fig7_HTML\" id=\"MO7\"/></fig><table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Drilling characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Bore no</th><th align=\"left\">Latitude</th><th align=\"left\">Longitude</th><th align=\"left\">Altitude (m)</th><th align=\"left\">Depth (m)</th><th align=\"left\">Temperature (°C)<break/>Top–bottom (5 m interval)</th><th align=\"left\">Feature</th></tr></thead><tbody><tr><td align=\"left\">ZK01</td><td align=\"left\">39° 15′ 49.47″</td><td align=\"left\">110° 10′ 3.69″</td><td char=\".\" align=\"char\">1,108.00</td><td char=\".\" align=\"char\">37.90</td><td align=\"left\">13.8–14.2–14.1–14.6–14.8–15.2–16.5–17.7–18 (37.50 m)</td><td align=\"left\">28.60–37.90 m is 1<sup>–2</sup> coal seams, and the opening is 20 m from the surface fissure of the fire zone, which release smoke and hot gases(53.0 °C)</td></tr><tr><td align=\"left\">ZK03</td><td align=\"left\">39° 15′ 45.05″</td><td align=\"left\">110° 10′ 8.12″</td><td char=\".\" align=\"char\">1,109.00</td><td char=\".\" align=\"char\">32.50</td><td align=\"left\">42.0–63.0–84.0–123.0–175.0–196.0–232.0–240.0 (32.50 m)</td><td align=\"left\">29.00 m is 1<sup>–2</sup> coal roofs and reach worked out area; the hole is 6 m from the surface fissure of the fire zone, which release hot gases(68.0 °C)</td></tr><tr><td align=\"left\">ZK04</td><td align=\"left\">39° 15′ 45.36″</td><td align=\"left\">110° 10′ 9.09″</td><td char=\".\" align=\"char\">1,111.00</td><td char=\".\" align=\"char\">37.20</td><td align=\"left\">20.0–19.8–17.2–18.3–19.2–18.6–18.5–17.5–17.6 (37.2 m)</td><td align=\"left\">30.70 m is 1<sup>–2</sup> coal roofs, ZK03 and ZK04 are 25.65 m apart</td></tr><tr><td align=\"left\">ZK05</td><td align=\"left\">39° 15′ 40.08″</td><td align=\"left\">110° 10′ 13.36″</td><td char=\".\" align=\"char\">1,122.00</td><td char=\".\" align=\"char\">51.60</td><td align=\"left\">35.1–39.3–42.1–46.3–51.8–58.2–71.8–82.6–79.4–55.1–37.6–39.1 (51.60 m)</td><td align=\"left\">38.50–49.10 m is 1<sup>–2</sup> coal, the middle (35.00–40.00 m) of the temperature is high</td></tr><tr><td align=\"left\">ZK06</td><td align=\"left\">39° 15′ 40.67″</td><td align=\"left\">110° 10′ 14.19″</td><td char=\".\" align=\"char\">1,125.00</td><td char=\".\" align=\"char\">46.90</td><td align=\"left\">18.1–18.1–18.1–18.2–18.1–18.1–18.3–17.5–16.6–18.2–88.5 (46.90 m)</td><td align=\"left\">43.20 m is 1<sup>–2</sup> coal roofs, and 45.90 m is worked out area; There are gases at the orifice</td></tr><tr><td align=\"left\">ZK07</td><td align=\"left\">39° 15′ 36.09″</td><td align=\"left\">110° 10′ 21.92″</td><td char=\".\" align=\"char\">1,111.00</td><td char=\".\" align=\"char\">32.80</td><td align=\"left\">16.6–16.8–16.7–16.8–16.9–17.4–40.4–53.8 (32.80 m)</td><td align=\"left\">27.40 m is 1<sup>–2</sup> coal roofs, and 30.80 m is worked out area</td></tr><tr><td align=\"left\">ZK08</td><td align=\"left\">39° 15′ 31.97″</td><td align=\"left\">110° 10′ 28.02″</td><td char=\".\" align=\"char\">1,129.00</td><td char=\".\" align=\"char\">51.20</td><td align=\"left\">29.8–30.0–30.7–31.3–31.4–31.7–32.3–32.2–32–31.8–31.3–29.7 (51.20 m)</td><td align=\"left\">45.20 m is 1<sup>–2</sup> coal roofs, and 49.00 m is worked out area</td></tr><tr><td align=\"left\">ZK09</td><td align=\"left\">39° 15′ 26.78″</td><td align=\"left\">110° 10′ 33.07″</td><td char=\".\" align=\"char\">1,147.00</td><td char=\".\" align=\"char\">47.00</td><td align=\"left\">17.5–16.2–15.7–16.6–19.8–20.5–19.8–20.6–21.3–20.6–25.8 (47.0 m)</td><td align=\"left\">42.30 m is fissure (or worked out area)</td></tr><tr><td align=\"left\">ZK10</td><td align=\"left\">39° 15′ 27.39″</td><td align=\"left\">110° 10′ 33.80″</td><td char=\".\" align=\"char\">1,147.50</td><td char=\".\" align=\"char\">79.90</td><td align=\"left\">26.0–26.0–26.0–25.7–26.0–25.9–25.7–25–24.7–24.8–24.3–25.4–26.3–25.8–25.9–22.9–17.4 (79.90 m)</td><td align=\"left\">67.12–78.5 m is 1<sup>–2</sup> coal seams</td></tr><tr><td align=\"left\">ZK02</td><td align=\"left\">39° 15′ 35.10″</td><td align=\"left\">110° 10′ 17.04″</td><td char=\".\" align=\"char\">1,104.00</td><td char=\".\" align=\"char\">32.90</td><td align=\"left\">28.8–32.9–35.3–38.9–40.7–44.5–46.5–50.2 (32.90 m)</td><td align=\"left\">24.50 m is 1<sup>–2</sup> coal roofs, 29.30 m is worked out area</td></tr><tr><td align=\"left\">ZK11</td><td align=\"left\">39° 15′ 34.21″</td><td align=\"left\">110° 10′ 47.22″</td><td char=\".\" align=\"char\">1,128.00</td><td char=\".\" align=\"char\">51.70</td><td align=\"left\">12.7–12.5–12.6–12.2–12.2–12.4–12.7–12.9–14.3–16.6–17.8–17.8 (51.70 m)</td><td align=\"left\">38.90–49.40 m is 1<sup>–2</sup> coal seams</td></tr><tr><td align=\"left\">ZK12</td><td align=\"left\">39° 15′ 39.38″</td><td align=\"left\">110° 10′ 42.67″</td><td char=\".\" align=\"char\">1,126.00</td><td char=\".\" align=\"char\">51.70</td><td align=\"left\">13.1–12.9–12.7–12.6–13.5–13.6–14.2–15.3–18.5–20.1–22.2–22.8 (51.70 m)</td><td align=\"left\">40.80–50.80 m is 1<sup>–2</sup> coal seams</td></tr><tr><td align=\"left\">ZK13</td><td align=\"left\">39° 15′ 46.87″</td><td align=\"left\">110° 10′ 31.44″</td><td char=\".\" align=\"char\">1,135.00</td><td char=\".\" align=\"char\">18.80</td><td align=\"left\">192.0–210.0–212.0–214.0–213.8 (18.80 m)</td><td align=\"left\">Borehole collapse and Burnt Rock</td></tr><tr><td align=\"left\">ZK14</td><td align=\"left\">39° 15′ 48.81″</td><td align=\"left\">110° 10′ 30.29″</td><td char=\".\" align=\"char\">1,134.50</td><td char=\".\" align=\"char\">23.50</td><td align=\"left\">44.0–49.0–58.0–64.2–68.8–73.2 (23.50 m)</td><td align=\"left\">Backfill area with a large amount of loose crushed stones and gangue, and the color of the stone changes a lot</td></tr><tr><td align=\"left\">ZK15</td><td align=\"left\">39° 15′ 47.18″</td><td align=\"left\">110° 10′ 25.40″</td><td char=\".\" align=\"char\">1,143.00</td><td char=\".\" align=\"char\">75.20</td><td align=\"left\">18.4–18.5–18.5–18.6–18.9–18.1–18–17.7–17.6–17.5–17.4–17.1–16.7–16.4–15.9–20(75.00 m)</td><td align=\"left\">64.90–75.20 m is 1<sup>–2</sup> coal seams, rich in water</td></tr><tr><td align=\"left\">ZK16</td><td align=\"left\">39° 15′ 51.09″</td><td align=\"left\">110° 10′ 31.29″</td><td char=\".\" align=\"char\">1,134.66</td><td char=\".\" align=\"char\">18.80</td><td align=\"left\">34.5–37.2–41.2–40.8–41.0 (18.80 m)</td><td align=\"left\">Loose dump, steam at the orifice, cracks below 16.80 m</td></tr><tr><td align=\"left\">ZK17</td><td align=\"left\">39° 15′ 43.15″</td><td align=\"left\">110° 10′ 35.32″</td><td char=\".\" align=\"char\">1,123.00</td><td char=\".\" align=\"char\">9.40</td><td align=\"left\">78.0–160.0–210.0 (9.40 m)</td><td align=\"left\">6.20 m see red and hard burnt rocks with rock fragment temperature 135.0 °C</td></tr><tr><td align=\"left\">ZK18</td><td align=\"left\">39° 15′ 43.21″</td><td align=\"left\">110° 10′ 31.40″</td><td char=\".\" align=\"char\">1,154.00</td><td char=\".\" align=\"char\">79.90</td><td align=\"left\">22.5–22.6–22.5–22.7–22.6–22.9–23.4–23.5–23.7–24.5–24.7–25.6–26.6–27.9–28.4–32.5–34.0 (79.90 m)</td><td align=\"left\">73.40 m is 1<sup>–2</sup> coal roofs, 75.20 m is worked out area</td></tr><tr><td align=\"left\">ZK19</td><td align=\"left\">39° 15′ 27.37″</td><td align=\"left\">110° 9′ 38.41″</td><td char=\".\" align=\"char\">1,151.00</td><td char=\".\" align=\"char\">79.90</td><td align=\"left\">14.3–14.6–15.1–15.2–15.9–16.9–18.2–19.7–20.1–21.6–23.3–25.6–28.6–32.5–40.6–48.6–50.5 (79.90 m)</td><td align=\"left\">74.40–77.20 m is 1<sup>–2</sup> coal seams</td></tr><tr><td align=\"left\">ZK20</td><td align=\"left\">39° 15′ 18.95″</td><td align=\"left\">110° 9′ 37.62″</td><td char=\".\" align=\"char\">1,154.00</td><td char=\".\" align=\"char\">75.20</td><td align=\"left\">12.5–13.2–13.1–13.3–13.5–13.4–13.7–14.9–13.4–13.7–14.9–14.3–14.7–15–15.6–15.7(75.00 m)</td><td align=\"left\">72.60 m is fissure (or worked out area), 74.20 m is 1<sup>–2</sup> coal roofs</td></tr><tr><td align=\"left\">ZK21</td><td align=\"left\">39° 15′ 28.54″</td><td align=\"left\">110° 10′ 1.98″</td><td char=\".\" align=\"char\">1,069.60</td><td char=\".\" align=\"char\">37.60</td><td align=\"left\">35.2–39.4–42.2–49.7–62.5–82.9–81.9–48.8–36.6 (37.60 m)</td><td align=\"left\">28.20–32.90 m is 2<sup>–2</sup> coal seam, 4.70 m in thickness</td></tr><tr><td align=\"left\">ZK22</td><td align=\"left\">39° 15′ 27.24″</td><td align=\"left\">110° 9′ 53.96″</td><td char=\".\" align=\"char\">1,069.00</td><td char=\".\" align=\"char\">28.20</td><td align=\"left\">47.4–67.5–70–75.4–125.3–149.6–155.7 (28.20 m)</td><td align=\"left\">26.80 m is worked out area</td></tr><tr><td align=\"left\">ZK23</td><td align=\"left\">39° 15′ 28.83″</td><td align=\"left\">110° 9′ 54.42″</td><td char=\".\" align=\"char\">1,071.00</td><td char=\".\" align=\"char\">32.90</td><td align=\"left\">23.5–26.3–31.6–41.6–52.1–52.9–55.6–57.1 (32.90 m)</td><td align=\"left\">28.20–30.20 m is 2<sup>–2</sup> coal seam, 30.20 m is worked out area with water</td></tr></tbody></table></table-wrap></p><sec id=\"Sec6\"><title>Coal fire zone I</title><p id=\"Par19\">The main coal seam of spontaneous combustion is the 1<sup>–2</sup> coal. The field investigation finds that the four laneways in the fire zone I have high temperatures of 168.0–432.0 °C and a distance of approximately 50.00 m. Borehole ZK11 is approximately 30.00 m west of fire zone I and has not been drilled into the roadway, although it is above the laneway. It is a low-temperature hole (17.8 °C) and covers approximately 39.00 m of rocks in the 1<sup>–2</sup> coal roof. ZK12 is approximately 20.00 m from the edge of the high-temperature (225.0 °C) of fire zone I, and it is a low-temperature hole (22.8 °C); the cap rock of the 1<sup>–2</sup> coal is approximately 40.80 m. With thick overburdens, although the ZK11 and ZK12 holes are relatively close to the fire zone, due to the integrity of the strata and insufficient oxygen supply, the fire does not spread far and may only burn near the laneways. The roadways function as chimneys and only emit a certain amount of smoke. ZK13 is approximately 5 m from the smoke point at the boundary of the fire zone; it is located in the back-filled area and drilled to the red burnt rock and is a high-temperature hole (213.0 °C). Then, proceeding 65.00 m outward, and ZK14 is implemented in the middle of the two bifurcated fire zones; the temperature at the bottom of the hole is 73.2 °C. ZK15 with low temperature controls the boundary of the western bifurcation fire zone, which is approximately 25 m from the surface smoke point (46.0 °C). ZK16 is located in the back-fill area, approximately 10 m from the smoke point (37.0 °C); the maximum temperature in the hole is 41.2 °C, and the eastern boundary of the fire zone can be determined. According to the above information, the TIR anomaly bifurcates on the surface to the east and west and may indicate a single fire under-ground. The ZK17 hole close to the fire zone is only 9.40 m deep and difficult to drill deeper due to the high temperature; at 6.20 m, high-temperature hard burnt rocks appear (210.0 °C). ZK18 is approximately 13 m from a crack without smoke emission and approximately 28.00 m from the crack with smoke (24.4 °C), and the bottom of the hole is 34.0 °C.</p></sec><sec id=\"Sec7\"><title>Coal fire zone II</title><p id=\"Par20\">The main coal seam with spontaneous combustion in the fire zone is the 1<sup>–2</sup> coal. ZK01 is approximately 14 m from the a surface crack with smoke (53.0 °C), and approximately 31 m from a surface high-temperature point (120.0 °C); the bottom temperature is 18.0 °C, and the borehole is considered a non-fire zone; thus, the range between ZK01 and near-surface cracks can roughly delineate the fire zone boundary, which is consistent with the initial fire zone boundary, so no further holes are implemented. Similarly, ZK03 is approximately 6 m from the surface crack with smoke (68.0 °C) and is a high-temperature hole (240 °C), which is determined to be a fire zone. The corresponding hole ZK04 approximately 25 m from ZK03 is a low-temperature hole (17.6 °C), so ZK03 and ZK04 are judged to mark the borders of the fire zone. ZK05 is approximately 22 m from the overhanging fire zone and approximately 24 m from the surface crack with smoke (79.0 °C), and the temperatures in the hole from bottom to top at 5 m intervals are 39.1, 37.6, 55.1, 79.4, 82.6, 71.8, 58.2, 51.8, 46.3, 42.1, 39.3 and 35.1 (°C); these numbers indicate that the temperatures at the bottom and top of the hole are low and those in the middle are high. The top of the 1<sup>–2</sup> coal is 1,083.5 m in elevation, and the abnormal temperature (79.4 °C) starts at 1,080.4 m, which is 3 m below the coal roof. The temperature anomaly zone is consistent with the burning depth of the 1<sup>–2</sup> coal and is related to the baking in the overhanging fire zone. ZK06 with vapour and toxic gas at the orifice is approximately 27 m northeast of ZK05, and the temperature at the bottom of the hole is 88.5 °C. Therefore, ZK05 and ZK06 determine the transition zone, and the true boundary of the coal fire area can be extrapolated a few metres north of ZK06. Due to the topography, ZK07 fails to reach the platform in the fire zone and is drilled into the worked-out area, and the temperature at the bottom of the hole is 53.8 °C, which marks the transition fire zone. ZK02 is approximately 32 m east of the crack at the edge of the cantilevered fire zone. It is drilled to the goaf, and the bottom temperature of the hole is 50.2 °C, which indicates the transition fire zone. ZK08 is approximately 50 m from the fire zone below the overhanging wall and is drilled into the worked-out area. Referring to the adjacent ZK10, the goaf with a temperature of 29.7 °C should be at the top of the 1<sup>–2</sup> coal. Due to the cap with a thickness of approximately 45 m, the oxygen supply is insufficient, and no combustion has occurred at the bottom. ZK09 is approximately 30 m from the active fire, and owing to the existence of cracks, the target layer 1<sup>–2</sup> coal is not drilled, and the temperature at the bottom of the hole is 25.8 °C. Therefore, ZK10 is drilled 25 m towards the back side, and the thickness of the 1<sup>–2</sup> coal is approximately 11.38 m; this is a normal low-temperature hole.</p><p id=\"Par21\">In summary, the fire zone of the coal seam slowly burns into the mountains along the steep walls, and the coal seam that is completely covered does not burn underground due to insufficient oxygen supply. The burning rate of coal seams is mainly related to the thickness of the overlying strata and the development of fractures. The fire is extinguished naturally where the overburden layer is so enough intact and thick that fractures fail to reach the surface to supply more air<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>.</p></sec><sec id=\"Sec8\"><title>Coal fire zone III</title><p id=\"Par22\">The main coal seam with spontaneous combustion in the fire zone is the 2<sup>–2</sup> coal, which is located in the lowest part of the mining area where the wall overhangs pit. In the eastern fire zone III-E, the overhanging wall shows the 1<sup>–2</sup> coal with a thickness 1–2 m and ongoing combustion, and the top covers burnt rocks.</p><p id=\"Par23\">ZK21 is approximately 50 m from a crack with hot gas emission, the highest temperature in the hole is 82.9 °C, and the temperature is abnormally high at 1,042–1,052 m on the roof of the coal seam. According to the field survey, the 2<sup>–2</sup> coal roof is exposed at the bottom of the pit with a few open fires, and it can be inferred that the abnormally high temperature in ZK21 is caused by baking, which results in smouldering. ZK22 is approximately 35 m from the crack with hot gas and smoke in the III-W fire zone and is drilled into worked-out area. Because ZK22 is a high-temperature hole (155.7 °C), ZK23 is implemented at a greater interval. The hole is approximately 50 m from ZK22 and 84 m from a crack at the edge of the fire zone, and it is still drilled to a worked-out area with a temperature of 57.1 °C. ZK22 and ZK33 may be connected via roadways with a cap thickness of approximately 28 m. It can be inferred from the exposed laneway with open fire to ZK23 that the fire extends approximately 100 m along the roadway and that the temperature decreases from 407 °C to approximately 60 °C.</p></sec><sec id=\"Sec9\"><title>Coal fire zone IV</title><p id=\"Par24\">The main seam with spontaneous combustion in the fire zone is the 1<sup>–2</sup> coal. ZK19 is approximately 35 m from a crack, and the temperature at the bottom of the hole is 50.5 °C. Because the terrain is steep and difficult to reach, ZK20 is approximately 90 m away from the cracks, and the temperature at the bottom of the hole is 15.7 °C, which is a non-fire zone. From the comparison of ZK19 and ZK20, it can be seen that the cantilevered fire zone has a baking heating effect on the coal seams smouldering at a short distance, and the temperatures of the strata far from the overhanging walls tend to be normal.</p><p id=\"Par25\">The above drilling data show that the preliminarily delineated fire area is basically accurate, and only some parts need to be modified.</p></sec></sec></sec><sec id=\"Sec10\"><title>Discussion and conclusion</title><p id=\"Par26\">The largest coal consumer, China experiences the most coal fires in the world. Therefore, it is important for China to monitor and execute coal fire evaluation, and suitable suppression work<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. The government of China has got a clear understanding of this hazard and its impacts on the economy and health, with initiatives for fighting coal fires since 1988. Supposing know the depth of the coal fires, coal fire-fighting teams could fight the fires more successfully and efficiently<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>. To this end, systematic quantification and investigation of actual scenarios of coal seams are always critical issues for the coal fire research community<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>.</p><p id=\"Par27\">Many surface and underground coal fires in northern Shaanxi, such as those in the Longyan, Tanyaoqu, and Huojitu coal mines, are generally less than 10 km<sup>2</sup>, most of them are 2–3 km<sup>2</sup>, and the coal fire distribution is even smaller. In these coal mines, satellite imagery (> 0.5 m) often provides inadequate detail about fissures and coal fire information, and imaging carried out by traditional airborne platforms (< 0.1 m) can provide high temporal and spatial resolutions but with high costs<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref>,<xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. Satellite and conventional platforms are limited in weather, the availability of aircraft, and satellite orbits<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Magnetic surveys offer a method for the detection, delineation and monitoring of coal fires<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>, but in the HCM, ground disturbances and destruction block access to coal fires, so the magnetic method cannot effectively delineate the coal fires. Contrast to traditional airborne remote sensing, UAV remote sensing provides fine spatial and higher temporal resolution and low-price to satisfy the critical requirements of spectral, spatial, and temporal resolutions<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>. This technique commits to offer the swift and safe survey of thermal areas, often current in dangerous and inaccessible terrain<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>.</p><p id=\"Par28\">As Greene (1969) described previously, it is easy to detect fires less than 10 m in depth on TIR; fires between 10 and 30 m are detected only when the heat is transported to the surface by cracks or is conducted to the surface for several years or more; greater than 30 m in depth, detecting fires at the surface require a decade, or more<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>. In our research, TIR remote sensing technology is very effective in monitoring the high-temperature and thermally anomalous regions formed by surface and near-surface(< 10 m) coal fires, especially in open flame areas. However, it is very troublesome to identify coal fires more than 10 m deep with intact cover; for example, at the positions of ZK5, ZK6, ZK22, and ZK23, with no thermal abnormalities on the TIR image, the borehole temperatures are abnormally high. Therefore, remote sensing interpretation of RGB orthophoto images, ground investigations and drilling are needed to compensate for the shortcomings of TIR images. Surface subsidence, cracks, fissures, hot gas and smoke are all manifestations of the development of coal fires. They are thermally anomalous areas that expand outward from the open flame area and may mark the locations of the next open flame areas.</p><p id=\"Par29\">Our study demonstrates a low cost and effective technique to detect the main coal fires in northern Shaanxi based on UAV remote sensing and provides an accurate basis for fire suppression projects.</p></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><ack><title>Acknowledgements</title><p>The authors thank Yulin Bureau of Natural Resources and National Natural Science Foundation of China (Grant No. 41702144) for providing funds to investigate coalfires in the Huojitu coal mine.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>X.H. and X.Y. contributed to the main manuscript text. Z.L. and T.G. prepared the statistic, figures and tables. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807855</article-id><article-id pub-id-type=\"pmc\">PMC7431848</article-id><article-id pub-id-type=\"publisher-id\">70363</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70363-w</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Photon-counting spectral basis component material decomposition for musculoskeletal radiographs</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\" equal-contrib=\"yes\"><name><surname>Beck</surname><given-names>Stefanie</given-names></name><address><email>stefanie.beck@tum.de</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Sellerer</surname><given-names>Thorsten</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mechlem</surname><given-names>Korbinian</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bodden</surname><given-names>Jannis</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Meurer</surname><given-names>Felix</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sauter</surname><given-names>Andreas</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Herzen</surname><given-names>Julia</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Pfeiffer</surname><given-names>Franz</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Pfeiffer</surname><given-names>Daniela</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.6936.a</institution-id><institution-id institution-id-type=\"ISNI\">0000000123222966</institution-id><institution>Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, </institution><institution>Technical University of Munich, </institution></institution-wrap>81675 Munich, Germany </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.6936.a</institution-id><institution-id institution-id-type=\"ISNI\">0000000123222966</institution-id><institution>Chair of Biomedical Physics, Department of Physics and Munich School of Bioengineering, </institution><institution>Technical University of Munich, </institution></institution-wrap>85748 Garching, Germany </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13889</elocation-id><history><date date-type=\"received\"><day>10</day><month>12</month><year>2019</year></date><date date-type=\"accepted\"><day>8</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">As a very fast and non-invasive examination, conventional X-ray radiography is well established as the first line diagnostic imaging method of the human bone system. While major bone injuries such as fractures and dislocations are usually easily detectable on conventional X-ray images, more subtle injuries such as microfractures are often missed, leading to mistreatment and potential long-term consequences. The technology of Photon-Counting Dual-Energy Radiography (PCDER) yields the possibility to decompose conventional X-ray images into basis material images such as bone- and soft-tissue-equivalence images. The obtained basis material images offer significant advantages in terms of image contrast and image details over the raw attenuation image which shows an overlap of bone and soft tissue. Whereas the advantages of bone- and soft-tissue-equivalence images have been broadly discussed referring to bone subtraction images in the detection of pulmonary diseases, this method has not been considered for the analysis of musculoskeletal images until present. In this study we show that basis component equivalence images have high potential to improve the diagnostic accuracy of the detection of minor bone lesions during clinical trauma imaging. A reader study performed by three experienced radiologists compares the image quality of basis material images to a standard radiograph image of a non-fractured cadaveric hand.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Bone</kwd><kwd>Trauma</kwd><kwd>Bone imaging</kwd><kwd>Radiography</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/501100001659</institution-id><institution>Deutsche Forschungsgemeinschaft</institution></institution-wrap></funding-source><award-id>GRK2274</award-id></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Conventional X-ray radiography is the standard procedure for first line diagnosis of skeletal disorders. Although many injuries of the human bone system are missed during the initial radiological examination. The overall percentage of missed fractures in the extremities is estimated to be about 3.7%<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. This problem particularly affects fractures of the hand and the wrist due to the high absolute numbers of hand and wrist fractures combined to the particularly high percentage of misdiagnosed fractures of wrist (4.1%) and hand (5.4%)<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. The misdiagnosis often results in a delay of adequate therapy and thus the healing process and may cause further complications and long-term consequences such as non-union, wrist arthritis, avascular necrosis and disability<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>. It is also known that about 70% of the initially missed fractures are diagnosed in a second review<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>.</p><p id=\"Par3\">The reason for the failing diagnosis in the first examination underlies the subtlety of the injuries but also the limited spatial resolution and contrast properties of the available X-ray images. Conventional X-ray radiography shows an overall projection of the entire scanned area, with superposition of bone and tissue components yielding a limited image contrast and quality of the bone system. The superposition of bone and soft tissue in the conventional images is one of the reasons why many subtle bone injuries are not visualized properly on conventional X-ray images, while CT or MR reduce this superposition showing considerably better sensitivities for fracture detection<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>–<xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>.</p><p id=\"Par4\">Spectral imaging offers a potential solution to this problem. Dual-energy radiography exploits the energy dependency of the attenuation caused by an object. Hence different materials can be discriminated by analysing the difference of the attenuation signals detected with different X-ray spectra<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>–<xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. This provides new prospects in medical imaging, since it allows material separation and provides quantitative information concerning the material composition.</p><p id=\"Par5\">Several clinical studies revealed a diagnostic benefit of material decomposition images in the field of dual-energy subtraction chest radiography, where bone- and tissue-selective images have resulted in an improved detection ratio of pulmonary nodules and masses<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>–<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>.</p><p id=\"Par6\">In this survey, we present a proof-of-principle dual-energy phantom radiography experiment, evaluating the diagnostic value of material decomposition images in musculoskeletal diagnosis, which has been enabled by using a new class of hybrid-pixel photon-counting imaging detectors.</p><p id=\"Par7\">Photon-counting detectors (PCDs) were originally developed at CERN for the application as particle tracking detectors in high-energy physics<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. Since the concept provides several advantages over the clinically commonly used energy-integrating flat-panel detector technology, great efforts have been made during the last years to make the technology suitable for use in state-of-the-art imaging applications<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref>,<xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>. While flat-panel detectors<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref>,<xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup> utilize a scintillator crystal to convert X-rays into visible light which is eventually detected by an array of photodiodes, in PCDs the incident radiation is directly converted into an electrical signal using a semiconductor sensor layer. Thereby, this absence of a separate layer to convert X-rays into light generally results in a higher spatial resolution as the image is less blurred during the signal formation process<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. Apart from the signal formation process, both detector types strongly differ regarding the processing of the acquired electrical signal. In flat panel based detector systems the electrical charge generated in the photodiodes within one readout cycle is integrated resulting in a loss of spectral information<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. In contrast, PCDs have pixel-wise readout electronics which allow to process the signal generated by each photon individually. This is realized by comparing the electrical pulse generated by a photon to a threshold and only incrementing the attached counting logic if the registered pulse exceeds the set threshold level<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>. The described implementation provides two advantages. Firstly, setting the threshold higher than the electronic noise level results in a complete elimination of readout noise. Secondly, as the height of the electrical pulse is proportional to the energy deposited by a photon, the implementation of several thresholds allows to sort the incoming photons into distinct energy bins, which provides partly energetically resolved measurements<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>. A comprehensive review on the technical principles and clinical applications of photon counting detectors is given in Refs.<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref>,<xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>.</p><p id=\"Par8\">As a sample, we used an ex-vivo human hand. For imaging, a basis material decomposition algorithm<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup> was applied to obtain a bone- and a soft-tissue-equivalence image and a bone-tissue-overlay image.</p><p id=\"Par9\">We performed a reader study in which three experienced radiologists evaluated the diagnostic value and image quality of the two material images (bone and tissue) and of the bone-tissue-overlay image and compared them to the conventional X-ray image of the same sample.</p></sec><sec id=\"Sec2\"><title>Results</title><p id=\"Par10\">Figure <xref rid=\"Fig1\" ref-type=\"fig\">1</xref> shows the conventional radiograph, the basis material images and the bone-tissue-overlay image of an ex vivo human hand and the distal parts of radius and ulna. The proximal, intermediate and distal phalanx of the index finger of the hand was ablated post-mortem. The uncommon posture of the hand was caused by the rigor mortis and results in a stacked position of the thumb and the metacarpal bone of the index finger. In addition, the intermediate and distal phalanges of the fingers two to five are flexed and superposed in the projection, resulting in a diminished accuracy and diagnostic value in these areas. The images show long bones (radius, ulna and metacarpals) as well as short bones (all carpal bones) allowing the observers to consider different bone types in their evaluation.<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Multiple-contrast X-ray radiographs of an ex-vivo human hand, generated by a spectral photon-counting detector. (<bold>a</bold>) Conventional radiograph image of an ex-vivo human hand. (<bold>b</bold>) Bone- and (<bold>c</bold>) soft-tissue-equivalence image obtained from material decomposition. (<bold>d</bold>) Bone-tissue-overlay image generated by superposing the two basis component images. In the overlay image, the bone-components are coloured in blue.</p></caption><graphic xlink:href=\"41598_2020_70363_Fig1_HTML\" id=\"MO1\"/></fig></p><p id=\"Par11\">The conventional radiograph is shown in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a. Figure <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>b,c show the bone and tissue equivalence image, respectively. Figure <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>d shows the bone-tissue-overlay image generated by superposing the two basis material images. In the overlay image, the bone-components are coloured in blue.</p><p id=\"Par12\">The three observers evaluated the image quality and diagnostic potential of each image individually as well as the image quality and diagnostic potential of the combination of both basis material images (bone and tissue) and of the combination of all three reconstructed images (basis component images and bone-tissue-overlay image). All three observers also evaluated the image quality and diagnostic potential of the corresponding conventional X-ray image (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>).<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Quality criteria and descriptive rating scales used for the reader study.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\" colspan=\"5\"><bold>Image quality for evaluation of bone structures</bold></td></tr><tr><td align=\"left\">5 = excellent</td><td align=\"left\">4 = good</td><td align=\"left\">3 = moderate</td><td align=\"left\">2 = bad</td><td align=\"left\">1 = not appropriate/applicable</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Visualization of diagnostic details</bold></td></tr><tr><td align=\"left\">5 = excellent</td><td align=\"left\">4 = good</td><td align=\"left\">3 = moderate</td><td align=\"left\">2 = bad</td><td align=\"left\">1 = not appropriate/applicable</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Artefacts</bold></td></tr><tr><td align=\"left\">5 = no</td><td align=\"left\">4 = minor</td><td align=\"left\">3 = major</td><td align=\"left\">2 = bad</td><td align=\"left\">1 = unacceptable</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Overall image quality</bold></td></tr><tr><td align=\"left\">5 = excellent</td><td align=\"left\">4 = good</td><td align=\"left\">3 = moderate</td><td align=\"left\">2 = bad</td><td align=\"left\">1 = unacceptable</td></tr><tr><td align=\"left\" colspan=\"5\"><bold>Diagnostic acceptability</bold></td></tr><tr><td align=\"left\">5 = fully acceptable</td><td align=\"left\">4 = probably acceptable</td><td align=\"left\">3 = acceptable only under limited conditions</td><td align=\"left\">2 = bad</td><td align=\"left\">1 = unacceptable</td></tr></tbody></table></table-wrap></p><p id=\"Par13\">An overview of the complete results of the reader study is shown in Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>. All three observers evaluated the bone-equivalence image to have a higher image quality than the conventional radiograph for assessing bony structures and a better visualization of diagnostic details, such as trabecular structure or compact bone limits, corresponding to a detail level commonly used in clinical practice at which the differentiability of trabecular and compact bone structure is an important requirement as to the quality of conventional X-ray images in clinical routine<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. In addition, one of the readers considered the overall image quality of the bone-equivalence image to be better than of the traditional X-ray image while the other two readers did not find an appreciable difference between the two images as to this criterion. Also, all three observers considered the diagnostic acceptability of the bone-equivalence image to be higher than the one of the conventional X-ray image referring to the evaluation of the bony structure.<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Detailed results of the reader study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Reader 1</th><th align=\"left\">Reader 2</th><th align=\"left\">Reader 3</th><th align=\"left\">Average score</th><th align=\"left\">Standard deviation</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"6\"><bold>Conventional X-ray (63 points)</bold></td></tr><tr><td align=\"left\">Image quality for evaluation of bony structure</td><td align=\"left\">4</td><td align=\"left\">4</td><td align=\"left\">4</td><td char=\".\" align=\"char\">4.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Visualization of diagnostic detail</td><td align=\"left\">4</td><td align=\"left\">4</td><td align=\"left\">4</td><td char=\".\" align=\"char\">4.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Artefacts</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">4</td><td char=\".\" align=\"char\">4.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Overall image quality</td><td align=\"left\">5</td><td align=\"left\">4</td><td align=\"left\">4</td><td char=\".\" align=\"char\">4.33</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Diagnostic acceptability</td><td align=\"left\">4</td><td align=\"left\">4</td><td align=\"left\">4</td><td char=\".\" align=\"char\">4.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\" colspan=\"6\"><bold>Bone-equivalence image (73 points)</bold></td></tr><tr><td align=\"left\">Image quality for evaluation of bony structure</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Visualization of diagnostic detail</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Artefacts</td><td align=\"left\">5</td><td align=\"left\">4</td><td align=\"left\">5</td><td char=\".\" align=\"char\">4.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Overall image quality</td><td align=\"left\">5</td><td align=\"left\">4</td><td align=\"left\">5</td><td char=\".\" align=\"char\">4.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Diagnostic acceptability</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\" colspan=\"6\"><bold>Tissue-equivalence image (30 points)</bold></td></tr><tr><td align=\"left\">Image quality for evaluation of bony structure</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Visualization of diagnostic detail</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Artefacts</td><td align=\"left\">4</td><td align=\"left\">4</td><td align=\"left\">3</td><td char=\".\" align=\"char\">3.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Overall image quality</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">2</td><td char=\".\" align=\"char\">2.33</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Diagnostic acceptability</td><td align=\"left\">3</td><td align=\"left\">2</td><td align=\"left\">1</td><td char=\".\" align=\"char\">2.00</td><td char=\".\" align=\"char\">0.82</td></tr><tr><td align=\"left\" colspan=\"6\"><bold>Bone-tissue-overlay image (47 points)</bold></td></tr><tr><td align=\"left\">Image quality for evaluation of bony structure</td><td align=\"left\">3</td><td align=\"left\">2</td><td align=\"left\">3</td><td char=\".\" align=\"char\">2.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Visualization of diagnostic detail</td><td align=\"left\">3</td><td align=\"left\">3</td><td align=\"left\">3</td><td char=\".\" align=\"char\">3.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Artefacts</td><td align=\"left\">4</td><td align=\"left\">4</td><td align=\"left\">3</td><td char=\".\" align=\"char\">3.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Overall image quality</td><td align=\"left\">3</td><td align=\"left\">3</td><td align=\"left\">2</td><td char=\".\" align=\"char\">2.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Diagnostic acceptability</td><td align=\"left\">4</td><td align=\"left\">4</td><td align=\"left\">3</td><td char=\".\" align=\"char\">3.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\" colspan=\"6\"><bold>Bone-tissue-image (74 points)</bold></td></tr><tr><td align=\"left\">Image quality for evaluation of bony structure</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Visualization of diagnostic detail</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Artefacts</td><td align=\"left\">5</td><td align=\"left\">4</td><td align=\"left\">5</td><td char=\".\" align=\"char\">4.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Overall image quality</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Diagnostic acceptability</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\" colspan=\"6\"><bold>Bone-tissue-overlay-image (74 points)</bold></td></tr><tr><td align=\"left\">Image quality for evaluation of bony structure</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Visualization of diagnostic detail</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Artefacts</td><td align=\"left\">5</td><td align=\"left\">4</td><td align=\"left\">5</td><td char=\".\" align=\"char\">4.67</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">Overall image quality</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr><tr><td align=\"left\">Diagnostic acceptability</td><td align=\"left\">5</td><td align=\"left\">5</td><td align=\"left\">5</td><td char=\".\" align=\"char\">5.00</td><td char=\".\" align=\"char\">0.00</td></tr></tbody></table><table-wrap-foot><p>For each image combination subject to evaluation, the table shows the rating of each criteria and each reader, the overall score obtained by summing up all points obtained, and the average score with corresponding standard deviation obtained for each quality criteria subject to analysis.</p></table-wrap-foot></table-wrap></p><p id=\"Par14\">The tissue equivalence image as a stand-alone image was not considered to be of major diagnostic value. Besides the fact that this image is not appropriate for the evaluation of bony structure nor for the visualization of diagnostic details of the bone, two of the three readers considered the overall image quality to be bad, while one considered it to be moderate. Furthermore, one of the readers considered the diagnostic acceptability to be valid only under limited conditions while the other two readers rated it to be bad or even unacceptable.</p><p id=\"Par15\">The overlay image was also not considered to yield any advantages regarding the diagnostic value and image quality as a stand-alone image. The readers rated the overlay image with a lower score than the bone-equivalence image and even lower than the conventional radiograph image in all five categories.</p><p id=\"Par16\">The additional information provided by the simultaneous viewing of the soft-tissue- and the bone-equivalent image did not relevantly improve the observers' assessment compared to the visualization of the bone-equivalent image alone. Only one of the three observers considered the overall image quality to be better if visualizing both images simultaneously. All other criteria were evaluated equally when only observing the bone-equivalence image alone than when observing both basis material images.</p><p id=\"Par17\">The additional information of the overlay image, when observed simultaneously with the two basis component images did not provide any additional diagnostic value or image quality improvement. All three observers granted the combination of bone image, soft tissue image and overlay image the exact same ratings than the sole combination of the bone and the soft tissue image.</p><p id=\"Par18\">Figure <xref rid=\"Fig2\" ref-type=\"fig\">2</xref> shows a scatterplot indicating the individual ratings assigned by each of the 3 readers to each image or image combination and for each quality criteria subject to this study. The figure illustrates the distribution of the scores assigned by the different readers to each image and quality criteria and it shows that the 2 image combinations (i.e. bone- and tissue-image and bone- and tissue- and overlay-image) obtained the best overall scores, followed by the bone-equivalence image stand-alone, that obtained equal results except for the overall image quality, where its rating is slightly lower.<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Individual scores reader study. Scatter plot showing the ratings given by each of the 3 readers to each image or image combination for each of the quality criteria subject to evaluation, i.e. image quality for evaluation of bony structure (blue), visualization of diagnostic detail (red), artefacts (grey), overall image quality (yellow) and diagnostic acceptability (green). Best results were obtained by the combination of bone- and tissue-image and by the combination of bone- and tissue- and overlay-image (green), closely followed by the bone-equivalence image alone.</p></caption><graphic xlink:href=\"41598_2020_70363_Fig2_HTML\" id=\"MO2\"/></fig></p><p id=\"Par19\">Figure <xref rid=\"Fig3\" ref-type=\"fig\">3</xref> shows the overall scores obtained by the different images or image combinations. The conventional radiograph image obtained a total score of 63 points out of the maximum of 75 possible points in the sum of all quality criteria. The bone-equivalence image obtained 73 of a maximum of 75 possible points in the sum of all quality criteria. Since one of the readers detected a slight improvement in the overall image quality, the combination of bone-equivalence image and tissue equivalence image obtained 74 of a maximum of 75 possible quality criteria points. The addition of the bone-tissue-overlay image did not provide any additional value.<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Overall scores reader study. Overall scores obtained by the different images and image combinations subject to evaluation. The bone equivalence image as well as the image combinations show best results yielding an improvement when compared to the conventional X-ray image.</p></caption><graphic xlink:href=\"41598_2020_70363_Fig3_HTML\" id=\"MO3\"/></fig></p></sec><sec id=\"Sec3\"><title>Discussion</title><p id=\"Par20\">Conventional X-ray images are the state-of-the-art tool used for the diagnosis of fractures and other musculoskeletal lesions in clinical routine. Though small lesions such as microfractures or minor non-displaced fractures are not always easy to be identified in conventional X-ray images, particularly at the diagnosis of hand and wrist fractures, e.g. the scaphoid, which may lead to insufficient treatment and rehabilitation<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>.</p><p id=\"Par21\">Material decomposition methods yield the possibility to filter the conventional X-ray images in order to obtain a bone-equivalence image showing only the attenuation caused by bony-material and excluding the effects caused by soft tissue, which are overlaying in conventional radiography<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>, promising a potentially higher image quality and an increased level of diagnostic confidence and hence a prospectively improved approach to the limitations faced in the musculoskeletal diagnostics with conventional radiographies.</p><p id=\"Par22\">Dual-energy radiography has been analysed regarding the advantages of tissue-equivalence images in subtraction chest radiography<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>–<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup> and R. Panta et al. have presented first studies concerning the diagnostic quality of spectral CT Images of musculoskeletal body parts<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. In our study, we present first results of bone equivalence radiographs offering improved image quality for the diagnostic evaluation of bony structures.</p><p id=\"Par23\">Our study shows that basis material decomposition and particularly bone-equivalence images have a high potential of improving the diagnostic outcome of radiograph examinations of musculoskeletal disorders, suggesting considerably better image quality and diagnostic detail of bony structures and a higher overall image quality and diagnostic acceptability than conventional radiography.</p><p id=\"Par24\">While these results are very promising, the limitations and drawbacks of the study must be considered.</p><p id=\"Par25\">Firstly, our study was based on only one sample of an ex-vivo human hand. Further samples of the same and other bony structures of the human body should be examined for revalidation of the obtained results and for the evaluation of the transferability of these results to other anatomical structures such as a leg or an arm, where the amount of soft tissue is considerably higher compared to the hand.</p><p id=\"Par26\">Additionally, the used ex-vivo human hand did not contain any fractures, microdamage or tumours, as to the diagnostic findings of a laboratory CT-scan of the hand by two experienced radiologists. Although the image quality and diagnostic detail of the bony structure has been assessed to be better in bone-equivalence images than in conventional radiography, future studies will be necessary to evaluate the clinical potential of bone-equivalence radiographs for the detection of osseous pathologies such as e.g. fractures, tumours and infectious diseases.</p><p id=\"Par27\">Furthermore, the unnatural position of the hand caused by rigor-mortis resulting in a superposition of the projections of some bony structures, as well as the ablation of the index finger is unfavourable to the image analysis. Yet the image includes enough long and short bones visible without restrictions in order to evaluate the image quality in an adequate manner. Regardless, an evaluation of a human hand sample in an adequate position should be conducted in order to extend the evaluation of the bony structures and include the small long bones of the distal phalanxes of the fingers.</p><p id=\"Par28\">Finally, it has to be mentioned, that the reader study was conducted by a small number of observers. Though the ratings of the three readers show a clearly equal tendency and an obvious result, the significance of the study ought to be enhanced by a larger pool of observers in further surveys.</p><p id=\"Par29\">In conclusion, our results show that spectral photon-counting material decomposition and the resulting bone- and tissue-equivalence images provide promising improvements in the image quality and diagnostic value of musculoskeletal radiograph images.</p></sec><sec id=\"Sec4\"><title>Methods</title><p id=\"Par30\">The ex-vivo human hand was retrieved from a human body donor who had given written informed consent to donate his body after deceasing for medical research and medical education after his death in accordance to German law and international ethical guidelines. All experimental protocols were approved by the ethics committee at Klinikum rechts der Isar (application number 455/18 S). The sample was preserved in formalin.</p><p id=\"Par31\">The experimental measurements were performed at a stationary, lab-bench CT setup, shown in Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>. The X-ray source (XWT-160 SE, X-RAY WorX, Garbsen, Germany) has a tungsten target and a 2 mm thick beryllium window. The image data was acquired with a commercially available photon-counting detector (Flite X1, Direct Conversion, Danderyd, Sweden)<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>, with two thresholds per pixel and an integrated charge-sharing correction, which allows for dual-energy acquisition in a single shot. The detector has a cadmium telluride (CdTe) sensor with a thickness of 750 µm providing a high quantum efficiency in the relevant energy range. Due to the integrated charge-sharing correction<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup> the detector has a box-like point-spread-function<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup> resulting in an effective resolution of 100 µm and a field-of-view of 15.3 × 1.3 cm<sup>2</sup>. Based on the limited field-of-view of the detector the sample was scanned in vertical direction. The acquisition parameters are listed in Table <xref rid=\"Tab3\" ref-type=\"table\">3</xref>. The position of the energy thresholds was selected with the objective of obtaining minimum noise in the decomposed basis-material images. Thereby, the optimal settings depend on the source spectrum and the composition and size of the measured sample. The estimation of the optimal threshold setting was done based on an approach described in Ref.<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>.<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Experimental set-up. (<bold>a</bold>) X-ray source. (<bold>b</bold>) Linear-stages with calibration phantoms. (<bold>c</bold>) Sample stage. (<bold>d</bold>) Photon-counting detector.</p></caption><graphic xlink:href=\"41598_2020_70363_Fig4_HTML\" id=\"MO4\"/></fig><table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Acquisition parameters of the photon counting detector used for the experimental measurements of this study.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\">Tube voltage</td><td align=\"left\">110 kVp</td></tr><tr><td align=\"left\">Tube loading</td><td align=\"left\">3.5 mAs</td></tr><tr><td align=\"left\">Beam filtration</td><td align=\"left\">0.1 mm Cu</td></tr><tr><td align=\"left\">Threshold position</td><td align=\"left\">23, 55 keV</td></tr><tr><td align=\"left\">Source to isocentre</td><td align=\"left\">134 cm</td></tr><tr><td align=\"left\">Isocentre to detector</td><td align=\"left\">16 cm</td></tr><tr><td align=\"left\">Physical pixel size</td><td align=\"left\">100 µm</td></tr></tbody></table></table-wrap></p><p id=\"Par32\">The acquired dual-energy data was decomposed into photoelectric absorption and Compton scatter equivalent line-integrals using a maximum-likelihood method<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. Afterwards, those line-integrals were transformed to bone and soft-tissue equivalent line-integrals by a change of the corresponding vector basis<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. The maximum-likelihood estimation of the basis-material line-integrals was done with an empirical forward-model<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. Prior to the acquisition of the sample the free parameters of the utilized forward-model were tuned by calibration measurements as described in reference<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. Thereby, step wedges made of polyvinylchloride (PVC) and polyoxymethylene (PMMA) were used to create a set of 64 calibration points. The step wedges were mounted to movable linear stages to acquire dual-energy data of the 64 thickness combinations of 0–32 mm PVC and 0–64 mm PMMA. The materials PVC and PMMA were chosen for calibration since their energy-dependent attenuation properties resemble those of bone and soft-tissue accurately. The quantitative accuracy of the decomposed line-integral values was evaluated in prior studies in numerical simulations<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup> as well as experimental measurements<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>.</p><p id=\"Par33\">Three experienced radiologists with 4, 5 and 6 years of experience in musculoskeletal diagnosis, respectively, assessed the image quality and diagnostic value of the conventional X-ray image, of the two material images (bone and tissue) and of the bone-tissue-overlay image. Furthermore, the observers also evaluated the diagnostic value of the combination of both material images as well as of the combination of all three images (i.e. bone, soft tissue and bone-tissue-overlay).</p><p id=\"Par34\">The readers assessed the images by rating the following quality criteria:<list list-type=\"bullet\"><list-item><p id=\"Par35\">Image quality for evaluation of bony structures (IQBS)</p></list-item><list-item><p id=\"Par36\">Visualization of diagnostic details (VDD) such as trabecular structure and compact bone limits</p></list-item><list-item><p id=\"Par37\">Artefacts (A)</p></list-item><list-item><p id=\"Par38\">Overall image quality (OIQ)</p></list-item><list-item><p id=\"Par39\">Overall diagnostic acceptability (DA)</p></list-item></list></p><p id=\"Par40\">The trabecular structure and compact bone limits were analysed according to the detail level corresponding to the one commonly used in clinical practice for the diagnosis of conventional radiography images and at the standard at which the differentiability of trabecular and compact bone structure is an important requirement to the quality of conventional X-ray images in clinical routine<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. It does not pretend to meet the resolution and detail level of existing technologies for the detailed evaluation of trabecular structures for the evaluation of e.g. osteoporosis by techniques like fractal analysis<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>.</p><p id=\"Par41\">The evaluation was conducted for each image separately with a delay time of 2 weeks and in randomized order to minimize recall bias. In the first 4 sequences only one of the images (conventional radiograph, bone-equivalence image, tissue-equivalence image and bone-tissue-overlay image, respectively) was evaluated. In the fifth sequence bone- and tissue-equivalence image were available to the readers and evaluated conjointly and in the last sequence bone-, tissue- and overlay-image were available to the readers and evaluated conjointly. The evaluation was done by using a 5-point descriptive rating scale for each quality criteria, ranging from 1 point (poor quality) to 5 points (excellent quality). The descriptive ratings for the five above named evaluation criteria are listed in detail in Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>.</p><p id=\"Par42\">The results of the reader study were interpreted by considering the results for each quality criteria separately (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>) as well as by observing the obtained overall scores of each image or image combination (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>). The scatterplot in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref> shows all individual scores assigned to the images and quality criteria by the readers. In order to distinguish between the ratings given by the different readers to each image or image combination, each reader has been assigned a different shape indicator in the scatter plot (star for reader 1, triangle for reader 2 and circle for reader 3). The colours of the shapes of the scatter plot indicate the quality criteria that was rated. The overall scores shown in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref> were obtained by summing up the total number of points received by each image or image combination subject to evaluation. For each image or image combination all points obtained by all readers and for any of the quality criteria were added to obtain the overall score attributed to the image or image combination.</p></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Stefanie Beck and Thorsten Sellerer.</p></fn></fn-group><ack><title>Acknowledgements</title><p>This study has received funding by the Deutsche Forschungsgemeinschaft (DFG) under Grant No. GRK2274. We kindly thank our colleagues from the Department for Diagnostic and Interventional Radiology of the Klinikum rechts der Isar, Technische Universität München, Munich, Germany for participating in our reader study as observers. Open access funding provided by Projekt DEAL.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>T.S., K.M., J.H and F.P. prepared the sample, performed the measurements and generated the material decomposition images. The reader study was conceived and conducted by S.B., J.B, F.M. A.S. and D.P. The data analysis was done by S.B. and D.P. 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L.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Phan</surname><given-names>J.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Woodrow</surname><given-names>K. A.</given-names></name><address><email>woodrow@uw.edu</email></address><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><aff id=\"Aff1\"><institution-wrap><institution-id institution-id-type=\"GRID\">grid.34477.33</institution-id><institution-id institution-id-type=\"ISNI\">0000000122986657</institution-id><institution>Department of Bioengineering, </institution><institution>University of Washington, </institution></institution-wrap>Seattle, WA 98195 USA </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>14032</elocation-id><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><related-article related-article-type=\"corrected-article\" ext-link-type=\"doi\" xlink:href=\"10.1038/s41598-020-64846-z\" id=\"d30e71\"/><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><p id=\"Par1\">Correction to: <italic>Scientific Reports</italic>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.1038/s41598-020-64846-z\">https://doi.org/10.1038/s41598-020-64846-z</ext-link>, published online 14 May 2020</p><p id=\"Par200\">The original Article contained errors which have now been corrected.</p><p id=\"Par2\">The authors identified an error which was found in the central composite design of experiments. The potential measurement and resulting ∆E measurement for a run testing one of the boundary conditions for feature height (height = 0.01 mm) was incorrect. Fixing this error did not significantly change the mean ∆E value for all of the runs (p-value = 0.8080). When a new model was fit to the corrected data, changes were observed in the calculated intercept and several of the equation coefficients. The new model resulted in a reduction in the predicted ∆E for feature heights less than 3 mm (average difference in ∆E =  − 722 V/m) and feature heights greater than 8 mm (average difference in ∆E =  − 243 V/m), and an increase in ∆E for feature heights between 4 mm and 8 mm (average difference in ∆E = 130 V/m) (Figure <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>). The change in predicted ∆E was only statistically significant (p-value < 0.05) for feature heights less than 3 mm.<fig id=\"Fig1\"><label>Figure 1</label><caption><p>.</p></caption><graphic xlink:href=\"41598_2020_70777_Fig1_HTML\" id=\"MO10\"/></fig></p><p id=\"Par3\">Correction of this error affects Table 1 and Figure 2, the correct versions of which now appear in the PDF and HTML versions of the Article. The original, incorrect version of Figure 2 appears below as Figure <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>. The original, incorrect version of Table 1 appears below as Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>.<fig id=\"Fig2\"><label>Figure 2</label><caption><p>.</p></caption><graphic xlink:href=\"41598_2020_70777_Fig2_HTML\" id=\"MO1\"/></fig><table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Statistical analysis of finite element method simulations.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Sum of squares</th><th align=\"left\">df</th><th align=\"left\">Mean square</th><th align=\"left\">F value</th><th align=\"left\">p-value, Prob > F</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"6\"><bold>Full factorial design</bold></td></tr><tr><td align=\"left\">Model</td><td align=\"left\">6.05E7</td><td align=\"left\">6</td><td align=\"left\">1.00E7</td><td align=\"left\">228</td><td align=\"left\">1.37E-20</td></tr><tr><td align=\"left\">A-PDMS thickness</td><td align=\"left\">2.08E7</td><td align=\"left\">1</td><td align=\"left\">2.08E7</td><td align=\"left\">472</td><td align=\"left\">9.34E-18</td></tr><tr><td align=\"left\">B-Spacing</td><td align=\"left\">6.18E5</td><td align=\"left\">1</td><td align=\"left\">6.18E5</td><td align=\"left\">14.0</td><td align=\"left\">9.56E-04</td></tr><tr><td align=\"left\">C-Width</td><td align=\"left\">6.39E5</td><td align=\"left\">1</td><td align=\"left\">6.39E5</td><td align=\"left\">14.4</td><td align=\"left\">8.15E-04</td></tr><tr><td align=\"left\">D-Height</td><td align=\"left\">2.13E7</td><td align=\"left\">1</td><td align=\"left\">2.13E7</td><td align=\"left\">482</td><td align=\"left\">7.26E-18</td></tr><tr><td align=\"left\">E-Shape</td><td align=\"left\">8.59E5</td><td align=\"left\">1</td><td align=\"left\">8.59E5</td><td align=\"left\">19.4</td><td align=\"left\">1.70E-04</td></tr><tr><td align=\"left\">AD</td><td align=\"left\">1.62E7</td><td align=\"left\">1</td><td align=\"left\">1.62E7</td><td align=\"left\">367</td><td align=\"left\">1.81E-16</td></tr><tr><td align=\"left\" colspan=\"6\"><bold>Central composite design</bold></td></tr><tr><td align=\"left\">Model</td><td align=\"left\">5.09E7</td><td align=\"left\">7</td><td align=\"left\">7.28E6</td><td align=\"left\">58.2</td><td align=\"left\">9.78E-13</td></tr><tr><td align=\"left\">A-PDMS thickness</td><td align=\"left\">1.32E7</td><td align=\"left\">1</td><td align=\"left\">1.32E7</td><td align=\"left\">105</td><td align=\"left\">7.18E-10</td></tr><tr><td align=\"left\">B-Spacing</td><td align=\"left\">1.91E5</td><td align=\"left\">1</td><td align=\"left\">1.91E5</td><td align=\"left\">1.53</td><td align=\"left\">0.229</td></tr><tr><td align=\"left\">C-Width</td><td align=\"left\">1.52E5</td><td align=\"left\">1</td><td align=\"left\">1.52E5</td><td align=\"left\">1.21</td><td align=\"left\">0.281</td></tr><tr><td align=\"left\">D-Height</td><td align=\"left\">2.70E7</td><td align=\"left\">1</td><td align=\"left\">2.70E7</td><td align=\"left\">216</td><td align=\"left\">7.16E-13</td></tr><tr><td align=\"left\">AD</td><td align=\"left\">7.13E6</td><td align=\"left\">1</td><td align=\"left\">7.13E6</td><td align=\"left\">57.0</td><td align=\"left\">1.51E-07</td></tr><tr><td align=\"left\">A<sup>2</sup></td><td align=\"left\">3.12E6</td><td align=\"left\">1</td><td align=\"left\">3.12E6</td><td align=\"left\">24.9</td><td align=\"left\">5.29E-05</td></tr><tr><td align=\"left\">D<sup>2</sup></td><td align=\"left\">162</td><td align=\"left\">1</td><td align=\"left\">162</td><td align=\"left\">0.00129</td><td align=\"left\">0.972</td></tr></tbody></table></table-wrap></p><p id=\"Par4\">The correction of the error also necessitates textual changes, as below.</p><p id=\"Par5\">There was an error in the ‘Results and Discussion’ section, subsection ‘Electrospun fiber conformation to 3D collector pattern depends on insulative layer thickness and feature height’, where:</p><p id=\"Par6\">“PVA fibers deposited only on the surface of the fully conductive collector without the insulative layer, but fibers deposited densely in the patterns on the insulated two-layer collector (Fig. 4a,b). These results are in agreement with the simulations, which calculated a 365 V/m higher ΔE for the collector with the insulative PDMS layer.”</p><p id=\"Par7\">now reads:</p><p id=\"Par8\">“PVA fibers deposited only on the surface of the fully conductive collector without the insulative layer, but fibers deposited densely in the patterns on the insulated two-layer collector (Fig. 4a,b). These results are in agreement with the simulations, which calculated a 330 V/m higher ΔE for the collector with the insulative PDMS layer.”</p><p id=\"Par9\">There was another error in the same section and subsection where:</p><p id=\"Par10\">“The PDMS-based collectors were then scaled down by approximately 10-fold to verify that the patterning effect was valid at smaller length scales (conical pattern: 269 ± 5 μm diameter, 522 ± 6 μm height, 1400 μm spacing, 400 μm insulative PDMS layer thickness). Based on the simulations (ΔE = 882 V/m), we expected to observe fiber deposition in the patterns for this collector. We observed a similar patterning effect for this microscale collector, with fibers deposited densely within the patterns in 1–2 minutes and little to no deposition on the collector surface (Fig. 4c).”</p><p id=\"Par11\">now reads:</p><p id=\"Par12\">“The PDMS-based collectors were then scaled down by approximately 10-fold to verify that the patterning effect was valid at smaller length scales (conical pattern: 269 ± 5 μm diameter, 522 ± 6 μm height, 1400 μm spacing, 400 μm insulative PDMS layer thickness). Although the simulations calculated a ΔE value less than 0 V/m (− 119 V/m), we observed a similar patterning effect for this microscale collector, with fibers deposited densely within the patterns in 1–2 minutes and little to no deposition on the collector surface (Fig. 4c). These results suggest that experimental fiber patterning is possible at a wider range of ΔE values than those predicted by the simulations”.</p><p id=\"Par13\">Another error was in that subsection where:</p><p id=\"Par14\">“This collector contained conical patterns (364 ± 16 μm diameter, 777 ± 20 μm height, 1600 μm spacing), and an insulative PDMS layer that ranged from 400 to 580 μm. Our simulations calculated that the ΔE ranged from 647 V/m for the 400 μm PDMS thickness to 705 V/m for the 580 μm PDMS thickness.”</p><p id=\"Par15\">now reads:</p><p id=\"Par16\">“This collector contained conical patterns (364 ± 16 μm diameter, 777 ± 20 μm height, 1600 μm spacing), and an insulative PDMS layer that ranged from 400 to 580 μm. Our simulations calculated that the ΔE was 48 V/m higher for the 580 μm PDMS thickness compared to the 400 μm PDMS thickness.”</p><p id=\"Par17\">There were errors in the ‘Materials and Methods’ where:</p><p id=\"Par18\">“The results of the central composite design were used to construct a quadratic model (R<sup>2</sup> = 0.9488) that can be used to predict ΔE for any given set of control factor inputs (Eq. <xref rid=\"Equa\" ref-type=\"\">1</xref>).<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$ \\Delta E = 1286 + \\left( {293*thickness} \\right) - \\left( {35*spacing} \\right) + \\left( {31*width} \\right) - \\left( {963*height} \\right) + \\left( {106*thickness*height} \\right) - \\left( {53*thickness^{2} } \\right) + \\left( {0.382*height^{2} } \\right)\"$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:mn>1286</mml:mn><mml:mo>+</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>293</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:mfenced><mml:mo>-</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>35</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>s</mml:mi><mml:mi>p</mml:mi><mml:mi>a</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>31</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>d</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:mfenced><mml:mo>-</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>963</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>106</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfenced><mml:mo>-</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>53</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>0.382</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:msup><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfenced><mml:mo>\"</mml:mo></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70777_Article_Equa.gif\" position=\"anchor\"/></alternatives></disp-formula></p><p id=\"Par19\">now read:</p><p id=\"Par20\">“The results of the central composite design were used to construct a quadratic model (R<sup>2</sup> = 0.9958) that can be used to predict ΔE for any given set of control factor inputs (Eq. <xref rid=\"Equb\" ref-type=\"\">1</xref>)”.<disp-formula id=\"Equb\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$ \\Delta E = 63 + \\left( {231*thickness} \\right) - \\left( {35*spacing} \\right) + \\left( {31*width} \\right) - \\left( {455*height} \\right) + \\left( {106*thickness*height} \\right) - \\left( {46*thickness^{2} } \\right) - \\left( {42*height^{2} } \\right)\"$$\\end{document}</tex-math><mml:math id=\"M4\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:mn>63</mml:mn><mml:mo>+</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>231</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:mfenced><mml:mo>-</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>35</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>s</mml:mi><mml:mi>p</mml:mi><mml:mi>a</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>31</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>w</mml:mi><mml:mi>i</mml:mi><mml:mi>d</mml:mi><mml:mi>t</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:mfenced><mml:mo>-</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>455</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>106</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfenced><mml:mo>-</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>46</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>t</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:msup><mml:mi>s</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfenced><mml:mo>-</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>42</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:msup><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mfenced><mml:mo>\"</mml:mo></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70777_Article_Equb.gif\" position=\"anchor\"/></alternatives></disp-formula></p><p id=\"Par21\">These errors have now been corrected in the PDF and HTML versions of the Article.</p><p id=\"Par22\">The overall conclusions of the Article are unaffected by these corrections.</p></body></article>\n"
] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Nat Commun</journal-id><journal-id journal-id-type=\"iso-abbrev\">Nat Commun</journal-id><journal-title-group><journal-title>Nature Communications</journal-title></journal-title-group><issn pub-type=\"epub\">2041-1723</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807784</article-id><article-id pub-id-type=\"pmc\">PMC7431850</article-id><article-id pub-id-type=\"publisher-id\">17651</article-id><article-id pub-id-type=\"doi\">10.1038/s41467-020-17651-1</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>SLIT2/ROBO1-signaling inhibits macropinocytosis by opposing cortical cytoskeletal remodeling</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-2103-6454</contrib-id><name><surname>Bhosle</surname><given-names>Vikrant K.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-9433-3716</contrib-id><name><surname>Mukherjee</surname><given-names>Tapas</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0001-7294-878X</contrib-id><name><surname>Huang</surname><given-names>Yi-Wei</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0003-3798-3306</contrib-id><name><surname>Patel</surname><given-names>Sajedabanu</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Pang</surname><given-names>Bo Wen (Frank)</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff3\">3</xref><xref ref-type=\"aff\" rid=\"Aff14\">14</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Guang-Ying</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Glogauer</surname><given-names>Michael</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref><xref ref-type=\"aff\" rid=\"Aff5\">5</xref><xref ref-type=\"aff\" rid=\"Aff6\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wu</surname><given-names>Jane Y.</given-names></name><xref ref-type=\"aff\" rid=\"Aff7\">7</xref><xref ref-type=\"aff\" rid=\"Aff8\">8</xref><xref ref-type=\"aff\" rid=\"Aff9\">9</xref></contrib><contrib contrib-type=\"author\"><name><surname>Philpott</surname><given-names>Dana J.</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-0795-4160</contrib-id><name><surname>Grinstein</surname><given-names>Sergio</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff10\">10</xref><xref ref-type=\"aff\" rid=\"Aff11\">11</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0003-1714-1929</contrib-id><name><surname>Robinson</surname><given-names>Lisa A.</given-names></name><address><email>lisa.robinson@sickkids.ca</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff3\">3</xref><xref ref-type=\"aff\" rid=\"Aff12\">12</xref><xref ref-type=\"aff\" rid=\"Aff13\">13</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.42327.30</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0473 9646</institution-id><institution>Program in Cell Biology, </institution><institution>The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, </institution></institution-wrap>686 Bay Street, Toronto, ON M5G 0A4 Canada </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.17063.33</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2157 2938</institution-id><institution>Department of Immunology, </institution><institution>University of Toronto, </institution></institution-wrap>Medical Sciences Building, 1 King’s College Circle, Toronto, ON M5S 1A8 Canada </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.17063.33</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2157 2938</institution-id><institution>Institute of Medical Science, </institution><institution>University of Toronto, </institution></institution-wrap>Medical Sciences Building, 1 King’s College Circle, Toronto, ON M5S 1A8 Canada </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.17063.33</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2157 2938</institution-id><institution>Faculty of Dentistry, </institution><institution>University of Toronto, </institution></institution-wrap>101 Elm Street, Toronto, ON M5G 2L3 Canada </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.231844.8</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0474 0428</institution-id><institution>Department of Dental Oncology and Maxillofacial Prosthetics, </institution><institution>University Health Network, </institution></institution-wrap>Princess Margaret Cancer Centre, 610 University Avenue, Toronto, ON M5G 2C1 Canada </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.416166.2</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0473 9881</institution-id><institution>Centre for Advanced Dental Research and Care, </institution><institution>Mount Sinai Hospital, </institution></institution-wrap>600 University Avenue, Toronto, ON M5G 1X5 Canada </aff><aff id=\"Aff7\"><label>7</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.16753.36</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2299 3507</institution-id><institution>Department of Neurology, </institution><institution>Northwestern University Feinberg School of Medicine, </institution></institution-wrap>Chicago, IL 60611 USA </aff><aff id=\"Aff8\"><label>8</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.16753.36</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2299 3507</institution-id><institution>Center for Genetic Medicine, </institution><institution>Northwestern University Feinberg School of Medicine, </institution></institution-wrap>Chicago, IL 60611 USA </aff><aff id=\"Aff9\"><label>9</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.16753.36</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2299 3507</institution-id><institution>Lurie Cancer Center, </institution><institution>Northwestern University Feinberg School of Medicine, </institution></institution-wrap>Chicago, IL 60611 USA </aff><aff id=\"Aff10\"><label>10</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.17063.33</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2157 2938</institution-id><institution>Department of Biochemistry, </institution><institution>University of Toronto, </institution></institution-wrap>Medical Sciences Building, 1 King’s College Circle, Toronto, ON M5S 1A8 Canada </aff><aff id=\"Aff11\"><label>11</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.415502.7</institution-id><institution>Keenan Research Centre of the Li Ka Shing Knowledge Institute, </institution><institution>St. Michael’s Hospital, </institution></institution-wrap>290 Victoria Street, Toronto, ON M5C 1N8 Canada </aff><aff id=\"Aff12\"><label>12</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.42327.30</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0473 9646</institution-id><institution>Division of Nephrology, </institution><institution>The Hospital for Sick Children, </institution></institution-wrap>555 University Avenue, Toronto, ON M5G 1X8 Canada </aff><aff id=\"Aff13\"><label>13</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.17063.33</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2157 2938</institution-id><institution>Department of Paediatrics, Faculty of Medicine, </institution><institution>University of Toronto, </institution></institution-wrap>555 University Avenue, Toronto, ON M5G 1X8 Canada </aff><aff id=\"Aff14\"><label>14</label>Present Address: BenchSci, Suite 201, 559 College Street, Toronto, ON M6G 1A9 Canada </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>4112</elocation-id><history><date date-type=\"received\"><day>26</day><month>2</month><year>2019</year></date><date date-type=\"accepted\"><day>8</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Macropinocytosis is essential for myeloid cells to survey their environment and for growth of RAS-transformed cancer cells. Several growth factors and inflammatory stimuli are known to induce macropinocytosis, but its endogenous inhibitors have remained elusive. Stimulation of Roundabout receptors by Slit ligands inhibits directional migration of many cell types, including immune cells and cancer cells. We report that SLIT2 inhibits macropinocytosis in vitro and in vivo by inducing cytoskeletal changes in macrophages. In mice, SLIT2 attenuates the uptake of muramyl dipeptide, thereby preventing NOD2-dependent activation of NF-κB and consequent secretion of pro-inflammatory chemokine, CXCL1. Conversely, blocking the action of endogenous SLIT2 enhances CXCL1 secretion. SLIT2 also inhibits macropinocytosis in RAS-transformed cancer cells, thereby decreasing their survival in nutrient-deficient conditions which resemble tumor microenvironment. Our results identify SLIT2 as a physiological inhibitor of macropinocytosis and challenge the conventional notion that signals that enhance macropinocytosis negatively regulate cell migration, and vice versa.</p></abstract><abstract id=\"Abs2\" abstract-type=\"web-summary\"><p id=\"Par2\">Macrophages survey their surroundings using macropinocytosis, but its regulation is unclear. Here, the authors report that SLIT2, a known inhibitor of Rac GTPases, is an endogenous inhibitor of macropinocytosis, and that SLIT2 limits the uptake of NOD2 ligands into immune cells and subsequent release of the inflammatory chemokine, CXCL1, in vivo.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Chemokines</kwd><kwd>Cell biology</kwd><kwd>Cytoskeleton</kwd><kwd>Phagocytes</kwd><kwd>NOD-like receptors</kwd></kwd-group><funding-group><award-group><funding-source><institution>Queen Elizabeth II/Heart and Stroke Foundation of Ontario (HSFO) Graduate Scholarship in Science and Technology</institution></funding-source></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/100000002</institution-id><institution>U.S. Department of Health & Human Services | National Institutes of Health (NIH)</institution></institution-wrap></funding-source><award-id>RO1CA175360</award-id><award-id>RO1NS107396</award-id><principal-award-recipient><name><surname>Wu</surname><given-names>Jane Y.</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution>U.S. Department of Health & Human Services | National Institutes of Health (NIH)</institution></funding-source></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/501100000024</institution-id><institution>Gouvernement du Canada | Canadian Institutes of Health Research (Instituts de Recherche en Santé du Canada)</institution></institution-wrap></funding-source><award-id>FDN-14333</award-id><principal-award-recipient><name><surname>Philpott</surname><given-names>Dana J.</given-names></name></principal-award-recipient></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par3\">Macrophages, which are specialized cells of the innate immune system, perform diverse functions, including host defense against pathogens<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. Pro-inflammatory macrophages use various tools to combat invading microorganisms, including secretion of inflammatory mediators, phagocytosis of the pathogen, and production of reactive oxygen species. These cellular processes are carefully regulated by dynamic changes in the macrophage cytoskeleton<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. During immune surveillance, macrophages, and immature dendritic cells (iDCs) constitutively sample their extracellular surroundings via macropinocytosis, a phenomenon brought about by active plasma membrane ruffling induced by remodeling of the cortical cytoskeleton<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>. These cells also internalize bacterial pathogen-associated molecular patterns (PAMPs), such as muramyl dipeptide (MDP), via macropinocytosis, thereby initiating an immune response<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Intracellular pathogens, including some viruses, exploit macropinocytosis to facilitate their entry into the host cells<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>. Several growth factors, including colony stimulating factor 1 (CSF1), secreted by the host, as well as some pathogen-derived proteins such as IpaC from <italic>Shigella</italic> are known to induce macropinocytosis<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref>,<xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. It is estimated that macrophages can consume liquid equal to their cell volume every hour<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. This would leave them particularly vulnerable to the entry of soluble PAMPs and viruses, via macropinocytosis, under specific conditions.</p><p id=\"Par4\">Macropinocytosis is a near-universal feature of cancers driven by mutations in the RAS family of oncogenes<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>–<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. RAS-transformed tumors account for approximately one third of all malignancies in humans<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>. Among the RAS family members, KRAS mutations are the most common in cancer, especially in adenocarcinomas such as pancreatic adenocarcinoma (PDAC)<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>, and they are associated with poorer response to the conventional therapies and worse prognosis<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>. KRAS-transformed cancer cells use macropinocytosis to internalize large proteins, such as albumin, from their extracellular environment, to subsequently break them down into amino acids, which enter the cell metabolism to promote cancer cell survival<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. This might be particularly relevant in the tumor core, which contains low concentrations of amino acids, including glutamine (Glut)<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. Inhibition of macropinocytosis could be of therapeutic benefit in this context<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. However, no physiological factor that can inhibit macropinocytosis has been identified.</p><p id=\"Par5\">Spatiotemporal regulation of activity of Rho-family of small GTPases is essential to bring about localized and reversible changes in the cellular actin cytoskeleton<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. Accordingly, multiple steps during macropinocytosis are regulated by small Rho GTPases, Rac1 and Cdc42<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. The conserved Slit family of neuronal guidance cues, together with their transmembrane Roundabout (Robo) receptors, act as repellents during development of the central nervous system by regulating actin cytoskeletal rearrangements in migrating neurons and projecting axons<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>. Slit proteins (SLIT1-3) undergo proteolytic cleavage in vivo, with N-terminal fragments binding to Robo receptors to induce signaling<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>. A growing number of recent studies suggest that Slit-Robo signaling also has potent, localized, tissue-specific effects outside the nervous system<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref>–<xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. Of the four mammalian Robo receptors (ROBO1-4), ROBO3 does not bind to Slit proteins<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref>,<xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>, and ROBO4 is exclusively expressed in endothelial cells<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. Among Slit proteins, SLIT1 is predominantly expressed in the nervous system, while SLIT2 and to a lesser extent SLIT3 are also found in peripheral tissues<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. We and others have shown that SLIT2, together with ROBO1, inactivates Rac1/2 to regulate actin networks in immune cells, and as a result, inhibits the directed migration of neutrophils<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref>–<xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>, monocytes<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>, dendritic cells<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>, and T lymphocytes<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup> toward chemotactic stimuli in vitro and in vivo<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref>–<xref ref-type=\"bibr\" rid=\"CR33\">33</xref>,<xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>. Although primary murine macrophages were recently shown to express ROBO1 and ROBO3<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>, the actions of SLIT2 on macrophage functions have been largely unexplored.</p><p id=\"Par6\">The role played by Slit-Robo signaling in tumor progression vs. suppression also remains a topic of active investigation and appears to be context dependent<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref>,<xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>. Nonetheless, <italic>SLIT2</italic> has been shown to be silenced in several invasive tumors and in cancer cell lines, including PDAC cells, and conversely, high levels of <italic>SLIT2</italic> mRNA are associated with suppressed tumor growth in vivo<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>. Although SLIT2-induced tumor suppression has been presumed to be due inhibition of cancer cell migration, its effects on cancer cell growth have not been carefully elucidated.</p><p id=\"Par7\">We report here that SLIT2 prevents macrophage spreading not by inhibiting Rac activation, but rather, by activating RhoA. We further demonstrate that SLIT2 can inhibit macropinocytic uptake of bacterial PAMPs and subsequent upregulation of inflammatory chemokines in vivo, thereby modulating macrophage immune responses. We show that SLIT2, in a ROBO1-dependent manner, negatively impacts the survival of KRAS-transformed cancer cells by inhibiting macropinocytosis, thus limiting protein uptake in a Glut-deprived state similar to the tumor microenvironment found in vivo. Our work identifies an endogenous inhibitor of macropinocytosis with potent effects both in vitro and in vivo.</p></sec><sec id=\"Sec2\" sec-type=\"results\"><title>Results</title><sec id=\"Sec3\"><title>NSlit2 induces actin remodeling in macrophages in a ROBO1-dependent manner</title><p id=\"Par8\">We first investigated which Slit-binding Robo receptors are expressed in macrophages and found that <italic>Robo1</italic>, but not <italic>Robo2</italic>, messenger RNA (mRNA) is expressed in the RAW264.7 macrophage cell line and in primary murine bone marrow-derived macrophages (BMDM) (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>). Using immunoblotting, ROBO1 protein was detected in RAW264.7 cells as well as in primary macrophages derived from human peripheral blood mononuclear (MDM) cells (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1a</xref>). To investigate the effects of SLIT2 on macrophage cytoskeleton, we incubated cells with control vehicle, or with recombinant bioactive NSlit2 (the amino-terminal fragment of SLIT2), or with other recombinant SLIT2 proteins, CSlit2 and Slit2ΔD2, which do not bind to the Robo receptors (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>)<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref>,<xref ref-type=\"bibr\" rid=\"CR30\">30</xref>,<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. The endotoxin levels in all recombinant Slit protein preparations were below 0.05 EU/ml (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1b</xref>; Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). Treatment with NSlit2 significantly reduced macrophage spreading, as seen by reduction in their total three-dimensional surface area (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c, d</xref> and Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1c</xref>). To quantify the circularity of cells, we calculated the ‘shape factor’ using Volocity 6.3 software<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. Shape factor values range from 0 to 1, the latter defining a perfectly circular shape (see “Methods” for mathematical calculation). Only NSlit2 treatment significantly increased rounding in human and murine macrophages (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c, e</xref>; Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1d</xref>). To determine whether these effects of NSlit2 were dependent on ROBO1, we used a specific siRNA to knockdown its expression in RAW264.7 cells (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f</xref>; Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1e</xref>). NSlit2-induced inhibition of cell spreading was abolished by ROBO1 knockdown (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g</xref>). Furthermore, in ROBO1-deficient macrophages, NSlit2 failed to induce the rounding (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1h</xref>). To validate this finding using an independent experimental approach, we pre-incubated NSlit2 with the soluble N-terminal fragment of the human ROBO1 receptor (Robo1N) containing the NSlit2-binding Ig1 domain<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. Robo1N has previously been shown to act as an NSlit2 antagonist<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref>,<xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. Treatment with Robo1N alone did not have any effect on cell spreading or rounding (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1f, g</xref>). However, Robo1N-treated NSlit2 failed to reduce cell spreading (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1f</xref>) as well as to increase cell rounding (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1g</xref>). These findings suggest that actions of NSlit2 on the macrophage cytoskeleton are mediated by its binding to the ROBO1 receptor.<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>NSlit2 induces actin remodeling in macrophages in a ROBO1-dependent manner.</title><p><bold>a</bold> Total RNA was isolated from murine brain and kidney, as well as from RAW264.7 cells and primary murine bone marrow-derived macrophages (BMDM). <italic>Robo1</italic> and <italic>Robo2</italic> mRNA expression were investigated by RT-PCR. <italic>Gapdh</italic> was used as a loading control. <bold>b</bold> A schematic representation of full-length human SLIT2 isoform 1 (accession number- NP_004778.1) protein, and recombinant NSlit2, CSlit2, and Slit2ΔD2 proteins used in this study. <bold>c</bold> RAW264.7 cells were incubated with vehicle (phosphate-buffered saline), NSlit2, CSlit2, or Slit2ΔD2 for 15 min at 37 °C and allowed to spread on poly-<sc>d</sc>-lysine-coated coverslips for 1 h. Cells were fixed, permeabilized, and incubated with AF-488-conjugated phalloidin (green) to label polymerized actin. Scale bar, 25 μm. <bold>d</bold>, <bold>e</bold>, <bold>g</bold>, <bold>h</bold> All data are presented as mean ± standard error of mean (SEM). Comparisons between the groups were made by one-way analysis of variance (ANOVA), followed by post hoc Tukey’s multiple comparison test. <italic>n</italic> = 50 cells per treatment group per experiment over three independent experiments. <bold>d</bold> Experiments were performed as in (<bold>c</bold>) and cell surface area was measured using Volocity 6.3 software. ****<italic>p</italic> < 0.0001, NSlit2 vs vehicle, CSlit2, or Slit2ΔD2. <bold>e</bold> Shape factor for cells in (<bold>c</bold>) using Volocity 6.3 software. ****<italic>p</italic> < 0.0001, NSlit2 vs vehicle, CSlit2, or Slit2ΔD2. <bold>f</bold> ROBO1 levels were knocked down in RAW264.7 cells using specific siRNA. Protein levels (band intensity) were quantified using ImageJ software, version 1.51v and normalized to corresponding β-actin levels. <italic>n</italic> = 3 independent experiments (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1e</xref>). Comparison between the two groups was made by unpaired, two-tailed t-test. ***<italic>p</italic> = 0.0005, scrambled vs Robo1 siRNA. <bold>g</bold> ROBO1 protein levels were knocked down in RAW264.7 cells. After 72 h, experiments were performed as in (<bold>c</bold>). Cell surface area was measured as in (<bold>d</bold>). ****<italic>p</italic> < 0.0001 for the indicated comparisons and <italic>p</italic> = 0.9887, NSlit2 vs Slit2ΔD2 in ROBO1 knockdown conditions. (<bold>h</bold>) Shape factor for cells in (<bold>g</bold>) was determined as in (<bold>e</bold>). ****<italic>p</italic> < 0.0001 for the indicated comparisons and <italic>p</italic> = 0.4268, NSlit2 vs Slit2ΔD2 in ROBO1 knockdown conditions. Source data for (<bold>a</bold>, <bold>d</bold>, <bold>e</bold>, <bold>f–h</bold>) are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17651_Fig1_HTML\" id=\"d30e981\"/></fig></p><p id=\"Par9\">SLIT2 has previously been shown to inhibit human and murine monocyte chemotaxis by inactivating Rac1<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. In addition, it has been reported that NSlit2 can inactivate Rac1 and Cdc42 GTPases in RAW264.7 cells<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref>,<xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>. To determine whether inactivation of Rac1 is responsible for NSlit2-induced inhibition of macrophage spreading, we transiently expressed a constitutively active form of RAC1(Q61L)<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup> in RAW264.7 cells. In line with previous reports<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref>,<xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>, the Q61L mutant increased macrophage spreading (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2a</xref>). The Q61L mutant, however, failed to prevent the decrease in cell spreading following exposure to NSlit2 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2a</xref>). To further validate this finding, we studied the effects of NSlit2 on primary BMDM from Rac1/2 double knockout (Rac1/2 DKO) mice. Rac1/2 DKO macrophages were smaller than the wild-type (WT) counterparts (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>). However, exposure of Rac1/2 DKO macrophages to NSlit2 further reduced their spreading (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>). The Arp2/3 protein complex is an important downstream effector of both Rac1 and Cdc42 GTPases mediating actin polymerization<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. Exposure to CK-666, a potent Arp2/3 inhibitor<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>, could not reverse the NSlit2-induced cell spreading phenotype (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2b</xref>). The results indicate that NSlit2-induced inhibition of macrophage spreading is independent of its actions on Rac1/2 and Cdc42 activities.<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>NSlit2 activates RhoA and results in cytoskeletal rearrangement in macrophages.</title><p>(<bold>a</bold>, <bold>d</bold>, <bold>e</bold>, <bold>f</bold>) Data are presented as boxplots where middle line is the median; lower and upper hinges correspond to the first and third quartiles; whiskers represent minimum and maximum values. <italic>n</italic> = 50 cells per treatment group per experiment over three independent experiments. Comparisons between the groups were made by one-way ANOVA in (<bold>b</bold>, <bold>e</bold>, <bold>f</bold>) and two-way ANOVA in (<bold>a</bold>, <bold>d</bold>), followed by post hoc Tukey’s multiple comparison test. <bold>a</bold> Primary BMDM from wild-type (WT) or Rac1/2 double knockout (DKO) mice were cultured for 10 days, and cell spreading assays were performed as described in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c</xref>. Cell surface area was measured as in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>. ****<italic>p</italic> < 0.0001, WT vs DKO BMDM and <italic>p</italic> < 0.0001, NSlit2 vs vehicle, CSlit2, or Slit2ΔD2. <bold>b</bold> RAW264.7 macrophages were incubated with vehicle, NSlit2, CSlit2 or Slit2ΔD2 for 15 min at 37 °C and active and total RhoA levels were measured using a G-LISA assay. **<italic>p</italic> = 0.0063, 0.0093, and 0.0053 for NSlit2 vs vehicle, CSlit2, Slit2ΔD2, respectively. <bold>c</bold> RAW264.7 cells were incubated with the RhoA/B/C inhibitor, TAT-C3, for 4 h followed by vehicle, NSlit2, CSlit2, or Slit2ΔD2 treatment for 15 min and cell spreading was performed as described in Fig.<xref rid=\"Fig1\" ref-type=\"fig\">1c</xref>. Scale bar, 20 μm. <bold>d</bold> Surface area for cells in (<bold>c</bold>) was measured as in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>. ****<italic>p</italic> < 0.0001, vehicle vs TAT-C3 and <italic>p</italic> = 0.8877, 0.9470, and 0.9952 for NSlit2 vs vehicle, CSlit2, Slit2ΔD2 in TAT-C3-treated conditions, respectively. <bold>e</bold>, <bold>f</bold> Experiments were performed as in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c</xref> but cells were first incubated with a formin inhibitor, SMIFH2, for 30 min. <bold>e</bold> Cell surface area was measured as in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>. <italic>p</italic> = 0.9220, 0.8356, and 0.7215 for NSlit2 vs vehicle, CSlit2, Slit2ΔD2, respectively. <bold>f</bold> Shape factor was measured as in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e</xref>. <italic>p</italic> = 0.7557, 0.6594, and 0.9950 for NSlit2 vs vehicle, CSlit2, Slit2ΔD2 respectively. Source data for (<bold>a</bold>, <bold>b</bold>, <bold>d</bold>, <bold>e</bold>, <bold>f</bold>) are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17651_Fig2_HTML\" id=\"d30e1161\"/></fig></p></sec><sec id=\"Sec4\"><title>NSlit2 activates RhoA and results in cytoskeletal rearrangement in macrophages</title><p id=\"Par10\">We asked whether NSlit2 inhibited cell spreading through its effects on RhoA, which is the most abundantly expressed Rho-family GTPase member in monocytes/macrophages<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>. We used absorbance-based G-LISA assays to measure total and active RhoA levels in RAW264.7 cells. NSlit2 treatment significantly increased the active RhoA levels (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). We next tested the effects of TAT-C3 exoenzyme, a cell-permeable inhibitor of RhoA, RhoB, and RhoC, but not Rac1 nor Cdc42<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup>, on macrophage spreading. Cells incubated with TAT-C3 were significantly larger than the control counterparts (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c, d</xref>). In macrophages treated with TAT-C3, NSlit2 no longer inhibited cell spreading (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c, d</xref>). To verify this, we reduced the expression of RhoA using a siRNA (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2c</xref>). Exposure to NSlit2 failed to attenuate spreading in RhoA-deficient cells (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2d</xref>). Overall, these data indicate that NSlit2 inhibits the spreading of macrophages primarily through the activation of RhoA.</p><p id=\"Par11\">Several downstream effectors of RhoA, such as Rho-associated coiled-coil containing protein kinases (ROCK1/2) and diaphanous-related formins, are known to regulate actin polymerization via distinct mechanisms<sup><xref ref-type=\"bibr\" rid=\"CR52\">52</xref></sup>. To determine which of these two pathways mediates NSlit2-induced cytoskeletal changes, we selectively blocked their actions. In the presence of Y-27632, a ROCK1/2 inhibitor<sup><xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup>, NSlit2 still inhibited macrophage spreading (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2e</xref>). In contrast, SMIFH2, an inhibitor of formin homology 2 domains<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>, completely abrogated the NSlit2-induced reduction in cell surface area (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2e</xref>; Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2f</xref>) and increase in the cell rounding (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2f</xref>; Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2f</xref>). These findings suggest that NSlit2 inhibits macrophage spreading primarily by augmenting RhoA-mediated formin activation.</p></sec><sec id=\"Sec5\"><title>NSlit2-induced RhoA activation in macrophages is mediated by inactivation of MYO9B</title><p id=\"Par12\">Backer S. et al. recently reported that NSlit2 activates TRIO, a dual Rho/Rac guanine nucleotide exchange factor (GEF), in murine embryonic fibroblasts and neuronal cells<sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref></sup>. We found that <italic>Trio</italic> mRNA is expressed at low levels in RAW264.7 cells but not in primary murine BMDM (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3a</xref>). Liu et al. have shown that NSlit2 mediates RhoA activation, via inactivation of the Fyn kinase, a member of the Src-family tyrosine kinases, in rat oligodendrocyte precursor cells, but not in mature oligodendrocytes<sup><xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>. SLIT2-ROBO1-signaling has also been shown to mediate pan-Src activation in cancer cells<sup><xref ref-type=\"bibr\" rid=\"CR57\">57</xref>,<xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>. However, NSlit2 failed to induce a significant inactivation of Fyn (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3b</xref>) or activation of pan-Src kinases (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3c</xref>) in RAW264.7 macrophages.</p><p id=\"Par13\">We next investigated the effects of NSlit2 on myosin IXb (MYO9B), an atypical motor protein, with multiple F-actin binding sites within its head (N-terminal) region and a RhoGAP domain in its tail (C-terminal) region<sup><xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>. Using immunoblotting, we confirmed higher endogenous MYO9B levels in RAW264.7 macrophages as compared to human embryonic kidney (HEK293T) cells (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3d</xref>). The highly conserved RhoGAP domain of MYO9B alone can inactivate RhoA in cells<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>, and we, therefore, transiently expressed the human MYO9B RhoGAP domain (MYO9B-GAP)-containing plasmid in RAW264.7 and HEK293T cells (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3e</xref>). In the presence of bio-inactive Slit2ΔD2, the expression of MYO9B-GAP induced excessive spreading of macrophages (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a, b</xref>), which was not reversed by the NSlit2 treatment (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a, b</xref>). NSlit2-induced cell rounding was also prevented by MYO9B-GAP expression (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a, c</xref>). In HEK293T cells stably overexpressing the ROBO1 receptor (HEK293T-ROBO1) but low endogenous MYO9B, NSlit2 did not affect cell spreading (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3f</xref>) or rounding (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3g</xref>) significantly. Upon exogenous expression of MYO9B-GAP in these cells, exposure to NSlit2 resulted in reduced spreading (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3f</xref>) and increased rounding (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3g</xref>). To verify our findings, we incubated cells with blebbistatin, an inhibitor of the myosin II family of proteins, which reverses the MYO9B dominant negative phenotype<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>. Blebbistatin treatment did not completely reverse NSlit2-induced inhibition of cell spreading (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d, e</xref>) but prevented NSlit2-induced cell rounding (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d, f</xref>). Together, these findings suggest that the SLIT2-ROBO1-MYO9B-RhoA pathway results in decreased cell spreading and increased cell rounding in macrophages.<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>NSlit2-induced RhoA activation in macrophages is mediated by inactivation of MYO9B.</title><p><bold>a</bold> A cDNA plasmid encoding c-myc-tagged MYO9B-GAP (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3e</xref>) was expressed in RAW264.7 cells. After 48 h, cells were incubated with NSlit2 or Slit2ΔD2 for 15 min at 37 °C and allowed to spread on poly-<sc>d</sc>-lysine-coated coverslips for 1 h. Cells were fixed, permeabilized, and incubated with AF-488-conjugated phalloidin (green) and an antibody directed to c-Myc (red). Scale bar, 25 μm. (<bold>b</bold>, <bold>c</bold>, <bold>e</bold>, <bold>f</bold>) Data are presented as mean ± SEM. Comparisons between the groups were made by ANOVA, followed by post hoc Tukey’s multiple comparison test. <italic>n</italic> = 50 cells per treatment group per experiment over three independent experiments. <bold>b</bold> Experiments were performed as in (<bold>a</bold>). Cell surface area was measured as in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>. ****<italic>p</italic> < 0.0001, for the indicated comparisons and <italic>p</italic> = 0.3388, MYO9B-GAP NSlit2 vs MYO9B-GAP Slit2ΔD2. <bold>c</bold> Shape factor for cells in (<bold>a</bold>) was measured as in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e</xref>. ****<italic>p</italic> < 0.0001, for the indicated comparisons and <italic>p</italic> = 0.6416, MYO9B-GAP NSlit2 vs MYO9B-GAP Slit2ΔD2. <bold>d</bold> Experiments were performed as Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c</xref> but cells were first incubated with the myosin II inhibitor, blebbistatin, for 30 min. Scale bar, 25 μm. <bold>e</bold> Cell surface area was measured for (<bold>d</bold>) as in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>. **<italic>p</italic> = 0.0032, 0.0043, and 0.0022 for NSlit2 vs vehicle, CSlit2, and Slit2ΔD2, respectively. <bold>f</bold> Shape factor for cells in (<bold>d</bold>) was measured as in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e</xref>. <italic>p</italic> = 0.7848, 0.5049, and 0.6879 for NSlit2 vs vehicle, CSlit2, and Slit2ΔD2, respectively. Source data for (<bold>b</bold>, <bold>c</bold>, <bold>e</bold>, <bold>f</bold>) are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17651_Fig3_HTML\" id=\"d30e1413\"/></fig></p></sec><sec id=\"Sec6\"><title>NSlit2 inhibits constitutive and induced macropinocytosis in vitro and in vivo</title><p id=\"Par14\">We next investigated the effects of NSlit2 on macrophage macropinocytosis, a process dependent on the actin cytoskeleton<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup>. Macropinosomes are defined as endocytic vacuoles larger than 0.2 µm in diameter<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. As a first step, we verified the effects of modulating RhoA signaling on macropinocytosis. Inhibition of RhoA/B/C, using TAT-C3, induced a twofold increase in constitutive macropinocytosis by RAW264.7 macrophages (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a, b</xref>). Conversely, activation of RhoA/B/C using Rho Activator II significantly decreased average number of macropinosomes per cell (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a, b</xref>). There were also changes in the average macropinosome size with TAT-C3 and Rho Activator II treatments (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c</xref>). Next, we examined the effect of NSlit2 on constitutive macropinocytosis. Exposure to NSlit2 significantly reduced the average number of macropinosomes per cell (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4d, e</xref>) but did not produce a statistically significant change in the macropinosome size (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4f</xref>). In RhoA-deficient macrophages, constitutive macropinocytosis was significantly enhanced (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4a, b</xref>). RhoA knockdown partially, but significantly, restored macropinocytosis after exposure to NSlit2 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4a, b</xref>). To explain this result, we investigated the specific effect of NSlit2-induced Rho activation on its inhibition of Rac activity<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. As expected, pharmacological inhibition of Rho activity with TAT-C3 resulted in an increase in total active Rac levels in macrophages, as measured by Rac1/2/3 G-LISA (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4c</xref>). NSlit2 treatment, following TAT-C3, further reduced the Rac activity (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4c</xref>). These findings suggest that NSlit2 inhibits macropinocytosis in vitro by activation of RhoA, as well as through inactivation of Rac1, possibly via SRGAP2, a Rac1-specific slit-robo GTPase activating protein (srGAP)<sup><xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>, expressed in macrophages (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4d</xref>).<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>NSlit2 inhibits constitutive and induced macropinocytosis in vitro and in vivo.</title><p><bold>a</bold> RAW264.7 cells were treated with RhoA/B/C inhibitor, TAT-C3, or Rho Activator II for 4 h, then incubated with Tetramethylrhodamine (TMR)-labeled 70 kDa dextran (red) for 15 min at 37 °C. Cells were washed and fixed. Bright-field images were captured and cell boundaries are demarcated with dashed lines. Scale bar, 20 μm. (<bold>b</bold>, <bold>c</bold>, <bold>e</bold>, <bold>f</bold>) macropinosomes (number and size) were measured using ImageJ software, version 1.51v. All data are presented as mean ± SEM. Comparisons between groups were made by Kruskal–Wallis ANOVA, followed by Dunn’s multiple comparisons test. <italic>n</italic> = 50 cells per treatment group per experiment over three independent experiments. <bold>b</bold> Macropinosomes were counted for cells in (<bold>a</bold>). ****<italic>p</italic> < 0.0001, vehicle vs TAT-C3 and Rho Activator II vs vehicle or TAT-C3. <bold>c</bold> Macropinosome diameters were measured for cells in (<bold>a</bold>). ***<italic>p</italic> = 0.0002, TAT-C3 vs Rho Activator II. <bold>d</bold> RAW264.7 cells were incubated with vehicle, NSlit2, CSlit2, or Slit2ΔD2 for 15 min at 37 °C and macropinocytosis assays performed as described in (<bold>a</bold>). Scale bar, 10 μm. <bold>e</bold> Macropinosomes were counted for cells in (<bold>d</bold>). ****<italic>p</italic> < 0.0001, NSlit2 vs vehicle, CSlit2, or Slit2ΔD2. <bold>f</bold> Macropinosome diameters were measured for cells in (<bold>d</bold>). <italic>p</italic> = 0.1913, 0.2509, and 0.4802 for NSlit2 vs vehicle, CSlit2, Slit2ΔD2, respectively. <bold>g</bold> Schematic of in vivo treatments to investigate macropinocytosis. Mice were injected intraperitoneally (i.p.) with sodium periodate to induce peritonitis. After 48 h, saline vehicle, NSlit2, CSlit2, or Slit2ΔD2 was administered intravenously. After 18 h, TMR-labeled 70 kDa dextran (red) was administered i.p. After 30 min, mice were euthanized, and peritoneal macrophages were isolated, attached to poly-<sc>d</sc>-lysine-coated coverslips, fixed and incubated with FITC-conjugated anti-CD68 Ab. <bold>h</bold> In vivo macropinocytosis was performed as described in (<bold>g</bold>). <italic>n</italic> = 6 mice per treatment group. Scale bar, 10 μm. <bold>i</bold> Macropinosomes were counted for cells in (<bold>h</bold>). Thirty cells per animal per treatment group were analyzed. ***<italic>p</italic> = 0.0006, 0.0003, 0.0008 for NSlit2 vs vehicle, CSlit2, Slit2ΔD2 respectively. Source data for (<bold>b</bold>, <bold>c</bold>, <bold>e</bold>, <bold>f</bold>, <bold>i</bold>) are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17651_Fig4_HTML\" id=\"d30e1580\"/></fig></p><p id=\"Par15\">We asked whether NSlit2 could inhibit inducible macropinocytosis in primary BMDM incubated with CSF1<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Exposure to NSlit2 also blocked CSF1-induced macropinocytosis in BMDM (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4e-f</xref>). We next examined whether NSlit2 could prevent macrophage macropinocytosis in vivo. We previously reported that administration of recombinant NSlit2 inhibits monocyte/macrophage recruitment to the peritoneal cavity in a murine model of sterile peritonitis<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. To test whether NSlit2 could suppress macropinocytosis in this model, 48 h after induction of peritonitis to allow for the initial monocyte/macrophage migration<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>, we administered NSlit2 followed by intraperitoneal injection of fluorescently labeled dextran (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4g</xref>). Macrophages were identified by immunofluorescent labeling of CD68 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4g</xref>). Mice treated with NSlit2 displayed significantly less macropinocytosis by the peritoneal macrophages compared to mice treated with vehicle, CSlit2, or Slit2ΔD2 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4h, i</xref>). Together, these findings indicate that NSlit2 inhibits macropinocytosis by macrophages in vitro and in vivo.</p></sec><sec id=\"Sec7\"><title>NSlit2 inhibits MDP uptake and NOD2-mediated NF-κB activation in macrophages</title><p id=\"Par16\">Constitutive macropinocytosis in macrophages and iDCs has been implicated in the uptake of soluble antigens from the extracellular fluid<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. MDP, a peptidoglycan motif found in bacteria, is taken up by primary human macrophages via macropinocytosis<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>, and binds to the intracellular Nucleotide-binding oligomerization domain containing 2 (NOD2) receptor. MDP binding to NOD2 induces NF-κB-mediated secretion of pro-inflammatory cytokines, including the chemokine, C-X-C motif ligand 1 (CXCL1)<sup><xref ref-type=\"bibr\" rid=\"CR64\">64</xref></sup>. We found that upon exposure to NSlit2, intake of FITC-conjugated MDP (MDP-FITC) by primary BMDM was significantly reduced (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a, b</xref>). We next assessed the effects of NSlit2 on MDP-induced NF-κB activation. In the absence of MDP, NSlit2 did not affect NF-κB activity, as assessed by measuring the activation of the p65 (phospho-Ser536) subunit of NF-κB (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5c, d</xref>). However, NSlit2, but not Slit2ΔD2, blocked MDP-induced NF-κB activation (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5c, d</xref>). These findings indicate that NSlit2 inhibits macropinocytosis, thereby attentuating NOD2-mediated NF-κB activation in macrophages by preventing the cellular entry of NOD2 ligands. We next examined the effects of NSlit2 on NOD2-induced secretion of CXCL1 in a mouse model of sterile peritonitis<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref>,<xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. After the exposure of peritoneal macrophages to MDP, CXCL1 can be detected in serum as early as 2 h<sup><xref ref-type=\"bibr\" rid=\"CR64\">64</xref></sup>. We found that treatment with either NSlit2 or the pharmacological macropinocytosis inhibitor, 5-(N-Ethyl-N-isopropyl)amiloride (EIPA)<sup><xref ref-type=\"bibr\" rid=\"CR65\">65</xref></sup>, reduced the MDP-induced rise in serum CXCL1 levels (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5e</xref>). In line with our previously published results<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>, NSlit2 also diminished the MDP-induced influx of immune cells into the peritoneal cavity (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5a</xref>). Remarkably, even after normalizing for the number of cells in the peritoneal cavity, NSlit2 attentuated the levels of CXCL1 detected in the serum (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5f</xref>), suggesting that this effect of NSlit2 is independent of its action on cell migration. Both NSlit2 and EIPA also reduced the levels of CXCL1 detected locally in the peritoneal exudate (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5b</xref>). These results demonstrate that exogenously administered NSlit2 potently inhibits MDP signaling in vivo by inhibiting macropinocytosis.<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>NSlit2 inhibits MDP uptake and NOD2-mediated NF-κB activation in macrophages.</title><p>(<bold>a</bold>, <bold>b</bold>, <bold>d</bold>–<bold>h</bold>) All data are presented as mean ± SEM. Comparisons between groups were made by Kruskal–Wallis ANOVA, followed by Dunn’s multiple comparisons test. <bold>a</bold>, <bold>b</bold> Macropinosome analysis was performed using ImageJ software, version 1.51v. <italic>n</italic> = 50 cells per treatment group per experiment over three independent experiments. <bold>a</bold> BMDM were incubated with vehicle, NSlit2, CSlit2, or Slit2ΔD2 for 15 min, then with FITC-conjugated muramyl dipeptide (MDP) for 30 min. ****<italic>p</italic> < 0.0001, NSlit2 vs vehicle, CSlit2, or Slit2ΔD2. <bold>b</bold> Experiments were performed as in (<bold>a</bold>). MFI of the FITC channel was measured. ****<italic>p</italic> < 0.0001, NSlit2 vs vehicle, CSlit2, or Slit2ΔD2. <bold>c</bold> Experiments were performed as described in (<bold>a</bold>). Total protein lysates were collected 5 min after MDP treatment. <bold>d</bold> Quantification of the phospho-p65/total-p65 ratio from 4 independent experiments as described in (<bold>c</bold>) using ImageJ software, version 1.51v. *<italic>p</italic> = 0.0278, MDP vs vehicle; *<italic>p</italic> = 0.0192, NSlit2 + MDP vs MDP alone; *<italic>p</italic> = 0.0272 NSlit2 + MDP vs Slit2ΔD2 + MDP; and <italic>p</italic> > 0.9999, Slit2ΔD2 + MDP vs MDP alone. <bold>e</bold> Mice were injected i.p. with sodium periodate to induce sterile peritonitis. After 72 h, saline vehicle, NSlit2, or EIPA was administered i.p. One hour later, MDP was administered i.p. After 2 h, blood and peritoneal exudates were collected. Serum CXCL1 levels were determined by ELISA. <italic>n</italic> = 4 animals per treatment group. **<italic>p</italic> = 0.0048, MDP vs vehicle; *<italic>p</italic> = 0.0219, NSlit2 + MDP vs MDP alone; and *<italic>p</italic> = 0.0122, EIPA + MDP vs MDP alone. <bold>f</bold> Experiments were performed as described in (<bold>e</bold>). Fold changes in serum CXCL1 were normalized for total number of cells in the peritoneal exudates. **<italic>p</italic> = 0.0019, MDP vs vehicle; *<italic>p</italic> = 0.0455, NSlit2 + MDP vs MDP alone; *<italic>p</italic> = 0.0388, EIPA + MDP vs MDP alone. <bold>g</bold> Serum samples were collected from mice as described in (<bold>e</bold>). Peritoneal membranes were collected in cold PBS. SLIT2 protein levels were measured using ELISA. <italic>n</italic> = 10 animals per group. <bold>h</bold> Sterile peritonitis was induced in mice as in (<bold>e</bold>). After 72 h, vehicle, Robo1N, or EIPA and Robo1N together was administered i.p. One hour later, MDP was administered i.p. After 2 h, blood and peritoneal exudates were collected. Fold changes in serum CXCL1 were normalized as described in (<bold>f</bold>). <italic>n</italic> = 4 animals per treatment group. ***<italic>p</italic> = 0.0003, Robo1N + MDP vs vehicle, **<italic>p</italic> = 0.0086, MDP vs vehicle; *<italic>p</italic> = 0.0486, Robo1N + MDP vs MDP alone; and *<italic>p</italic> = 0.0365, Robo1N + MDP vs. Robo1N + MDP + EIPA. Source data for (<bold>a–h</bold>) are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17651_Fig5_HTML\" id=\"d30e1816\"/></fig></p><p id=\"Par17\">We next sought to understand how endogenous SLIT proteins regulate macropinocytosis. As a first step, we used a recently validated ELISA kit<sup><xref ref-type=\"bibr\" rid=\"CR66\">66</xref>,<xref ref-type=\"bibr\" rid=\"CR67\">67</xref></sup> to measure the SLIT2 protein levels in the serum and peritoneal membrane samples of adult C57BL6/J mice. The levels of endogenous SLIT2 in the peritoneum were ~10 times higher than those detected in serum (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5g</xref>). Because two recent studies have reported that SLIT3 protein is enriched in some extra-neuronal peripheral tissues such as bone marrow, we used a validated ELISA kit<sup><xref ref-type=\"bibr\" rid=\"CR68\">68</xref></sup> to measure SLIT3, and found that SLIT3 levels in murine serum and peritoneal membrane samples are similar (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5c</xref>)<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR68\">68</xref></sup>. We found that SLIT2 levels in peritoneal membrane samples are significantly higher than those of SLIT3 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5d</xref>).</p><p id=\"Par18\">We next tested the effect of blocking endogenous SLIT2 on MDP signaling by administering a single intraperitoneal dose of soluble Robo1N. Robo1N itself did not upregulate CXCL1 (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5h</xref>) but significantly augmented MDP-induced CXCL1 in serum (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5h</xref>) as well as in the peritoneal fluid (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5e</xref>). The effects of Robo1N and MDP were reversed by the macropinocytosis inhibitor, EIPA (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5h</xref> and Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5e</xref>). The results demonstrate that endogenous SLIT2 is a physiological inhibitor of macropinocytosis.</p></sec><sec id=\"Sec8\"><title>NSlit2 attenuates protein uptake by macropinocytosis in cancer cells and restricts their growth in a ROBO1-dependent manner</title><p id=\"Par19\">Macropinocytosis is an important nutritional adaptation to maintain the supply of amino acids, especially Glut, in RAS-transformed tumor cells in vitro and in vivo<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. In PDAC, low levels of <italic>SLIT2</italic> mRNA are associated with worse prognosis in patients<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. We, therefore, asked if SLIT2 could inhibit macropinocytosis, and therefore the growth of RAS-transformed cancer cells. It has previously been reported that PDAC-dervied PANC-1 cells express the ROBO1 receptor<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup> but intestinal adenocarcinoma-derived DLD-1 cells do not;<sup><xref ref-type=\"bibr\" rid=\"CR69\">69</xref>,<xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup> even though both cell lines have oncogenic KRAS mutations and perform robust macropinocytosis<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. We first tested whether these cell lines depend on macropinocytosis for protein intake and for their survival, when Glut availibility in the extracellular media is restricted to levels typically found in the tumor microenvironment<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. Cell viability was reduced by more than fourfold when grown in Glut-deprived culture medium (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6a</xref>). This effect could be reversed by addition of protein in the form of 2% albumin to the medium (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6a</xref>), but was blocked by treatment with the macroinocytosis inhibitor, EIPA (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6a</xref>). NSlit2 treatment significantly reduced albumin uptake in ROBO1-expressing PANC-1 cells (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6b</xref>) but not in ROBO1-deficient DLD-1 cells (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6c</xref>). Finally, the addition of NSlit2 to the medium significantly decreased the viability of PANC-1 cells (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6d</xref>) but no such effect was observed for DLD-1 cells (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6e</xref>). These findings suggest that NSlit2 is a potent inhbitor of macropinocytosis in KRAS-transformed tumor cells, in a ROBO1-dependent manner.<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>NSlit2 attenuates protein uptake by macropinocytosis in cancer cells and restricts their growth in a ROBO1-dependent manner.</title><p><bold>a–d</bold> All data are presented as mean ± SEM. Comparisons between the groups were made by Kruskal–Wallis ANOVA, followed by Dunn’s multiple comparisons test. PANC-1 and DLD-1 treatments are represented by black and gray symbols, respectively. <bold>a</bold>, <bold>d</bold>\n<italic>n</italic> = 8 independent replicates per treatment group per experiment over three independent experiments. <bold>a</bold> Oncogenic KRAS-expressing PANC-1 and DLD-1 cells were grown for 6 days in glutamine-free medium supplemented with high glutamine (Glut) (0.5 mM), low Glut (0.2 mM), 2% albumin. The macropinocytosis inhibitor, EIPA, was added, where indicated, for the last 2 days. Cell viability was measured on Day-6 using a MTT assay kit. Data are represented relative to the values obtained for 0.5 mM Glut supplementation. **<italic>p</italic> = 0.0015 and 0.0012, 0.5 mM vs 0.2 mM Glut for PANC-1 and DLD-1 cells, respectively; **<italic>p</italic> = 0.0013 and 0.0036, 0.2 mM Glut vs 0.2 mM Glut + 2% Alb for PANC-1 and DLD-1 cells, respectively; and *<italic>p</italic> = 0.0276 and 0.0360, 0.2 mM Glut + 2% Albumin vs 0.2 mM Glut + 2% Albumin + 25 µM EIPA for PANC-1 and DLD-1 cells, respectively. <bold>b</bold>, <bold>c</bold>\n<italic>n</italic> = 50 cells per treatment group per experiment over three independent experiments. <bold>b</bold> PANC-1 cells were incubated with vehicle, NSlit2, or Slit2ΔD2 for 15 min at 37 °C and macropinocytosis assays performed as described in Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref> using Albumin-FITC in place of the TMR-labeled dextran. Mean fluorescence intensity (MFI) of the FITC channel per cell was measured using ImageJ software, version 1.51 v. ****<italic>p</italic> < 0.0001, NSlit2 vs vehicle, or Slit2ΔD2. <bold>c</bold> Experiments were performed as in (<bold>b</bold>) using DLD-1 cells instead of PANC-1 cells. <italic>p</italic> = 0.4469 and 0.2049, NSlit2 vs vehicle or Slit2ΔD2, respectively. (<bold>d</bold>) Cell viability assays were performed as described in (<bold>a</bold>) with addition of 0.2 mM glutamine, 2% albumin, NSlit2 and Slit2ΔD2 to the medium for 6 days, as indicated. **<italic>p</italic> = 0.0061 and >0.9999, 0.2 mM Glut + 2% Albumin vs 0.2 mM Glut + 2% albumin + NSlit2 for PANC-1 and DLD-1 cells, respectively. **<italic>p</italic> = 0.0048 and >0.9999, 0.2 mM Glut + 2% Albumin + NSlit2 vs 0.2 mM Glut + 2% Albumin + Slit2ΔD2 for PANC-1 and DLD-1 cells, respectively. Source data for (<bold>a–d</bold>) are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17651_Fig6_HTML\" id=\"d30e2006\"/></fig></p></sec></sec><sec id=\"Sec9\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par20\">SLIT2 protein is widely expressed in adult, non-neuronal tissues<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>, but its actions on tissue-resident immune cells, such as macrophages, remain largely unexplored. We report here that primary murine and human macrophages express a single NSlit2 receptor, ROBO1. These results are in keeping with recent work showing that murine BMDM express ROBO1<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. We found that exposure to bioactive NSlit2, but not to the bio-inactive Slit2ΔD2 peptide, inhibited macrophage spreading and induced cell rounding, in a ROBO1-dependent manner. ROBO1 and SLIT2 global knockout mice fail to survive beyond 3 weeks<sup><xref ref-type=\"bibr\" rid=\"CR71\">71</xref>,<xref ref-type=\"bibr\" rid=\"CR72\">72</xref></sup>, and hypomorphic deletion of ROBO1 in mice results in osteopenia<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. To inhibit SLIT2/ROBO1 signaling, we, therefore, used soluble Robo1N, which contains the Slit-binding Ig1 domain of the human ROBO1 receptor, and which has been previously shown to act as an NSlit2 antagonist<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. Our findings add to a body of work demonstrating that SLIT2/ROBO1 signaling significantly impacts the rearrangement of the actin cytoskeleton in immune cells<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref>,<xref ref-type=\"bibr\" rid=\"CR73\">73</xref></sup>.</p><p id=\"Par21\">We observed that the effects of NSlit2 to inhibit macrophage spreading and to induce cell rounding did not occur through inactivation of Rac1/2, but instead, through activation of RhoA. These results are in contrast to the previous studies demonstrating that NSlit2 binding to ROBO1 influences actin dynamics by recruiting and activating GAPs, including srGAPs, that in turn inhibit activation of Rac1/2 and Cdc42<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref>,<xref ref-type=\"bibr\" rid=\"CR73\">73</xref>–<xref ref-type=\"bibr\" rid=\"CR75\">75</xref></sup>. Our group previously reported that SLIT2, acting via ROBO1, inhibits monocyte chemotaxis by preventing chemokine-induced activation of Rac1 and Cdc42 GTPases. In line with those results, in the present study, we found that NSlit2 treatment attenuated MDP-induced immune cell migration into the peritoneal cavity (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5a</xref>). We previously demonstrated that SLIT2 inhibits post-adhesion stabilization of monocytes tethered to the activated endothelial cells<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Our group has also reported that NSlit2 impairs neither phagocytosis nor reactive oxygen species production in neutrophils-cellular processes dependent on activation of Rac and Cdc42 and importantly, that NSlit2-treated mice clear bacteria as effectively as their vehicle-treated counterparts<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. NSlit2 does not affect Rac1 and Cdc42 activities in human and murine platelets<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. Le et al. recently reported that NSlit2 interacts with ROBO1 to enhance contraction of mammary myoepithelial cells and their consequent pulling on the extracellular matrix by activating Rac1<sup><xref ref-type=\"bibr\" rid=\"CR76\">76</xref></sup>. Similarly, in retinal vascular endothelial cells, SLIT2 signaling through ROBO1 and ROBO2 also activates Rac1 and promotes lamellipodial formation<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>. Overall, accumulating evidence suggests that differences in phenotypic effects and the signaling pathways transduced after exposure to NSlit2 are dependent on the cell type, the tissue milieu, and the biologic context.</p><p id=\"Par22\">SLIT2/ROBO1 signaling has been reported to activate RhoA in other cell types by either inactivation of Fyn kinase<sup><xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>, or activation of TRIO<sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref></sup>. SLIT2 can mediate RhoA inhibition via calcium signaling in neurons<sup><xref ref-type=\"bibr\" rid=\"CR77\">77</xref></sup>. Following a brief exposure to NSlit2, we observed a significant increase in RhoA activity (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>), and yet the same treatment failed to inactivate Fyn (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3b</xref>). Our results are in keeping with the previous study which observed Fyn inactivation only after prolonged exposure to SLIT2<sup><xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>. In line with another study<sup><xref ref-type=\"bibr\" rid=\"CR78\">78</xref></sup>, we detected <italic>Trio</italic> mRNA in transformed RAW264.7 cells but not in primary BMDM (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3a</xref>). This suggests that SLIT2-induced stimulation of TRIO might contribute to RhoA activation in RAW264.7 cells but cannot explain the phenotype we observed in primary macrophages.</p><p id=\"Par23\">We report here that in macrophages, NSlit2 binding to ROBO1 leads to cytoskeletal rearrangement by inactivating MYO9B, thereby promoting activation of RhoA, the most abundantly expressed Rho-family member in monocytes and macrophages<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>. Our results are in line with a study by Geutskens et al. reporting that SLIT3 activates RhoA in monocytes<sup><xref ref-type=\"bibr\" rid=\"CR79\">79</xref></sup>. However, the mechanism of SLIT3-induced RhoA activation remains to be elucidated. Our findings are consistent with recent work demonstrating that exposure to NSlit2 causes inactivation of MYO9B in human lung carcinoma cells<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>, and inactivation of the MYO9B ortholog in <italic>Caenorhabditis elegans</italic><sup><xref ref-type=\"bibr\" rid=\"CR80\">80</xref></sup>. The present study’s findings are in contrast to our previous observation that, in rat fibroblasts, NSlit2 inhibited transforming growth factor-β-induced actin stress fiber formation, possibly by inhibiting RhoA<sup><xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup>. In Schwann cells, SLIT2-induced RhoA (and ROCK) activity is needed for reversal of soma translocation but not for the collapse of the leading front<sup><xref ref-type=\"bibr\" rid=\"CR81\">81</xref></sup>. Differential effects of NSlit2 to activate or inactivate RhoA/Rac1/Cdc42 in different cell types may depend on the relative expression of the Rho-family GTPases as well as the Slit/Robo effector proteins, such as srGAPs and MYO9B. Notably, MYO9B is ubiquitously expressed in immune cells, with particularly high expression in monocytes and macrophages<sup><xref ref-type=\"bibr\" rid=\"CR82\">82</xref></sup>. Accordingly, we found that HEK293T cells, which express low levels of endogenous MYO9B (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3d</xref>), only become rounded in response to NSlit2 after transient expression of MYO9B RhoGAP domain (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3g</xref>), which inactivates RhoA in transfected cells<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>. A recent study implicates MYO9B in the regulation of constitutive activity of RhoA to generate self-limiting cellular contraction patterns in the human osteosarcoma cell line, U2OS<sup><xref ref-type=\"bibr\" rid=\"CR83\">83</xref></sup>. If this function of MYO9B is conserved in macrophages remains to be elucidated. To determine whether NSlit2-induced activation of RhoA results in downstream activation of Rho kinases, formins and/or myosin II, we used selective inhibitors of each of these pathways. Rock1/2 inhibition had no demonstrable effects, whereas formin inhibition reversed NSlit2′s effects on both macrophage spreading and rounding. These data are in keeping with recent reports that formins can initiate actomyosin contraction<sup><xref ref-type=\"bibr\" rid=\"CR84\">84</xref>,<xref ref-type=\"bibr\" rid=\"CR85\">85</xref></sup>. Furthermore, MYO9B-deficient murine macrophages are also round in morphology and spread less after treatment with Rho kinase inhibitor, suggesting a role of formins in the regulation of macrophage spreading<sup><xref ref-type=\"bibr\" rid=\"CR82\">82</xref></sup>. Finally, inhibition of myosin II activity in the presence of NSlit2 led to attenuation of macrophage spreading, but did not induce cell rounding. The exact role of myosin II in cell spreading is not clear. In murine fibroblasts, nonmuscle myosin IIa inhibits spreading by increasing retrograde F-actin flow<sup><xref ref-type=\"bibr\" rid=\"CR86\">86</xref></sup>. However, recent work by Oakes et al. suggests that cell spreading is regulated by forces not generated by myosin motors<sup><xref ref-type=\"bibr\" rid=\"CR87\">87</xref></sup>. Our results suggest that RhoA-mediated formin activation is necessary for NSlit2-induced actomyosin changes and that NSlit2-induced myosin II activation is needed for cell rounding but not for spreading.</p><p id=\"Par24\">Macrophages and iDCs sample their surroundings via macropinocytosis, which can be either constitutive or induced by growth factors. Both types require acute changes in the cortical cytoskeleton which are brought about by interactions between membrane phosphoinositides and active forms of small Rho-family GTPases, such as Rac1 and Cdc42<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. We demonstrate here that activation of RhoA signaling suppresses constitutive macropinocytosis of macrophages. In line with this observation, exposure to NSlit2 potently inhibited macropinocytosis. We observed that RhoA-deficient macrophages exhibit limited inhibition of macropinocytosis in the presence of NSlit2 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4b</xref>), which may reflect partial, rather than complete knockdown of RhoA. Another possible explanation is that in RhoA-deficient macrophages, NSlit2 could inhibit macropinocytosis by preventing activation of Rac1 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4c</xref>). Accordingly, we found SRGAP2, a Rac1-specific GAP that acts as an effector of Slit-Robo signaling<sup><xref ref-type=\"bibr\" rid=\"CR88\">88</xref>,<xref ref-type=\"bibr\" rid=\"CR89\">89</xref></sup>, is highly expressed in murine macrophages. Chabaud et al. recently proposed that constitutive macropinocytosis and tissue infiltration by iDCs are mutually exclusive, and that both processes are regulated by cellular localization of nonmuscle myosin IIa<sup><xref ref-type=\"bibr\" rid=\"CR90\">90</xref></sup>. In contrast, we found that SLIT2 negatively regulates migration<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup> as well as macropinocytosis in macrophages. Another neuronal repellent cue, Semaphorin 3a, induces macropinocytosis in neurons<sup><xref ref-type=\"bibr\" rid=\"CR91\">91</xref></sup>, which might be due to the activation of Rac1<sup><xref ref-type=\"bibr\" rid=\"CR92\">92</xref>,<xref ref-type=\"bibr\" rid=\"CR93\">93</xref></sup>. Additional studies are needed to completely elucidate the molecular machinery by which NSlit2 suppresses macropinocytosis and cell migration, including its actions on Rho-family small GTPases other than RhoA/Rac1/Cdc42, which are also expressed in macrophages<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>.</p><p id=\"Par25\">Little is currently known about the physiological relevance of macropinocytosis by immune cells in an in vivo setting<sup><xref ref-type=\"bibr\" rid=\"CR94\">94</xref>,<xref ref-type=\"bibr\" rid=\"CR95\">95</xref></sup>. We demonstrate here that not only in vitro, but also in vivo NSlit2 significantly attenuates macropinocytosis. The functional implications may be quite important given the key role of macropinocytosis in immune surveillance by macrophages and iDCs<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Some viruses, such as Ebolavirus<sup><xref ref-type=\"bibr\" rid=\"CR96\">96</xref></sup>, use the macropinocytic route to enter host cells<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. In addition, constitutive macropinocytosis, a unique function of macrophages and iDCs, enables them to engulf soluble bacterial PAMPs, such as MDP, that bind to intracellular pathogen recognition receptors (PRRs), such as NOD2. In the present study, we found that NSlit2 attenuated the ability of a bacterial peptidoglycan, MDP, to be taken up by macrophages and to subsequently induce the activation of NF-κB via NOD2<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR97\">97</xref></sup>. Additional work is needed to investigate whether NSlit2 has a therapeutic potential in inflammatory conditions associated with hyper-activation of NOD2 signaling<sup><xref ref-type=\"bibr\" rid=\"CR98\">98</xref></sup>. It will be intriguing to see if and how SLIT2 affects the entry of ligands for other cytosolic PRRs, such as NOD1, in future studies.</p><p id=\"Par26\">Pathological stimuli such as LPS, which are associated with initial transient augmentation followed by long-term suppression of macropinocytosis in iDCs<sup><xref ref-type=\"bibr\" rid=\"CR99\">99</xref>,<xref ref-type=\"bibr\" rid=\"CR100\">100</xref></sup>, have also been reported to reduce <italic>Slit2</italic> mRNA levels in tissues in vivo<sup><xref ref-type=\"bibr\" rid=\"CR101\">101</xref></sup>. Guan et al. reported that SLIT2 inhibits directed migration of dendritic cells in vitro and in vivo<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>. SLIT2-ROBO1 signaling also inhibits podosome formation in iDCs<sup><xref ref-type=\"bibr\" rid=\"CR102\">102</xref></sup>. In addition to macrophages, iDCs also exhibit robust constitutive macropinocytosis, and this is their predominant means of antigen uptake<sup><xref ref-type=\"bibr\" rid=\"CR103\">103</xref></sup>. Interestingly, endogenous levels of <italic>Slit2</italic> mRNA are increased following antigen sensitization in vivo<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref>,<xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>, but the physiological significance of this phenomenon remains to be elucidated. Future work will investigate whether SLIT2 causes long-term suppression of macropinocytosis in iDCs.</p><p id=\"Par27\">MDP signaling results in the upregulation of circulating levels of pro-inflammatory cytokines, including CXCL1, in mice<sup><xref ref-type=\"bibr\" rid=\"CR64\">64</xref></sup>. Interestingly, we found that in a murine model of sterile peritonitis, both NSlit2 and a pharmacologic inhibitor of macropinocytosis, EIPA, attenuated the MDP-induced CXCL1 secretion, locally in the peritoneal exudate (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5b</xref>), and systemically in serum (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5e, f</xref>). The effects of NSlit2 on MDP-NOD2 signaling are summarized in Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5f</xref>. We found that endogenous SLIT2 protein is concentrated in the peritoneal membrane, but SLIT3 is not (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5g</xref>, Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5c, d</xref>). Currently, no commercially available, validated ELISA kit is available to measure SLIT1 protein in murine tissues. However, Wu et al. previously reported that <italic>Slit1</italic> mRNA is not detected in adult rodent non-neuronal tissues<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. In line with those findings, Burgstaller et al. recently used mass spectrometry to demonstrate the presence of SLIT2 and SLIT3 proteins in the lung, but they did not detect any SLIT1<sup><xref ref-type=\"bibr\" rid=\"CR104\">104</xref></sup>. We, therefore, infer that SLIT2 is the major endogenous Slit protein present locally in the peritoneal membrane.</p><p id=\"Par28\">Because SLIT2 or ROBO1 knockout mice do not survive postnatally beyond 3 weeks<sup><xref ref-type=\"bibr\" rid=\"CR71\">71</xref>,<xref ref-type=\"bibr\" rid=\"CR72\">72</xref></sup>, we used soluble Robo1N fragment to block the effect of endogenous SLIT2 in vivo. MDP-induced secretion of CXCL1 was significantly enhanced in Robo1N-treated mice, and this effect was blocked by EIPA (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5h</xref>). Together, these data suggest that endogenous SLIT2 negatively regulates MDP signaling and the consequent secretion of inflammatory chemokines, at least in part, by inhibiting macropinocytosis. While the present study focused on acute MDP-NOD2 signaling, chronic NOD2 activation has been implicated in the development of tolerance to bacterial PAMPs in intestinal macrophages<sup><xref ref-type=\"bibr\" rid=\"CR105\">105</xref></sup>. Recent studies also highlight the physiological role of NOD2 signaling in non-immune cells<sup><xref ref-type=\"bibr\" rid=\"CR106\">106</xref>,<xref ref-type=\"bibr\" rid=\"CR107\">107</xref></sup>. Future studies will investigate how SLIT2 affects these aspects of NOD2 signaling.</p><p id=\"Par29\">During systemic infection producing sepsis, vascular leak may occur, and SLIT2 may influence these changes in the vascular integrity. Indeed, several studies have highlighted the vaso-protective role of SLIT2, which prevents increased vascular permeability induced by pro-inflmmatory stimuli in vivo<sup><xref ref-type=\"bibr\" rid=\"CR108\">108</xref>–<xref ref-type=\"bibr\" rid=\"CR110\">110</xref></sup>. These actions of SLIT2 were proposed to occur through the endothelial cell-specific Roundabout receptor, ROBO4<sup><xref ref-type=\"bibr\" rid=\"CR111\">111</xref></sup>, but a more recent study challenges this idea<sup><xref ref-type=\"bibr\" rid=\"CR112\">112</xref></sup>. In addition, the role of SLIT2 in maintaining vascular integrity under basal conditions is not clear. Furthermore, the differential contributions of endothelial ROBO1 and ROBO2 receptors in mediating the actions of SLIT2 on vascular integrity and permeability remain to be elucidated<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>. A better understanding of how Slit and Robo proteins coordinate the functions of immune cells and endothelial cells is needed to reveal the interplay of these pathways in shaping immune responses associated with infection and inflammation.</p><p id=\"Par30\">Recent studies from independent groups have shed light on complex signaling pathways involved in the metabolic reprogramming in KRAS-transformed cells, which use macropinocytosis as the primary route for the uptake of large proteins, such as albumin, in a nutrient-deficient tumor microenvironment<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. In another recent study, Göhrig et al. reported that SLIT2 expression is absent in the KRAS-transformed PDAC cell lines, MiaPaCa-1 and PANC-1. In addition, ectopic expression of SLIT2 resulted in reduced primary tumor size and secondary neural invasion in vivo<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. To test the hypothesis that SLIT2 could inhibit macropinocytosis in cancer cells, we chose two KRAS-transformed adenocarcinoma cell lines, PANC-1 and DLD-1. Both cell lines were able to take up albumin, via macropinocytosis, when grown in Glut-deprived culture medium (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6a</xref>). This uptake was blocked by NSlit2 only in ROBO1-expressing PANC-1 cells<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>, but not in DLD-1 cells, which lack ROBO1 (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6d</xref>)<sup><xref ref-type=\"bibr\" rid=\"CR69\">69</xref>,<xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup>. This could, in part, explain the reduced primary tumor size observed in SLIT2-expressing PDACs<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. Based on current knowledge, ROBO1 plays either a tumor suppressor or a promoter role, depending on the tissue involved and type of cancer<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. Further elucidation of mechanisms by which SLIT2/ROBO1 signaling simultaneously inhibits cancer cell migration and macropinocytosis will be important for potential development of new oncologic therapies.</p><p id=\"Par31\">We here provide evidence for an endogenous inhibitor of macropinocytosis. In addition, we provide insight into the physiological relevance of SLIT2 signaling in cells performing macropinocytosis to limit the entry of immunogenic microbial products, which activate cytosolic PRRs. In summary, NSlit2, acting together with ROBO1, has pleiotropic effects on recruitment, adhesion, and immune activation of monocytes and macrophages, not only though inhibition of Rac and Cdc42, but also via inactivation of MYO9B and consequent activation of RhoA. Our study challenges the notion that signals that inhibit cell migration enhance macropinocytosis, and vice versa. We also provide evidence that SLIT2 can attenuate the growth of KRAS-transformed cancer cells by inhibiting macropinocytosis. Our results suggest that NSlit2, in addition to inhibiting cancer cell migration, may also directly inhibit the macropinocytic uptake of nutrients by cancer cells, thereof inhibiting tumor growth. Further studies will explore the precise mechanisms by which SLIT2-induced signaling is spatiotemporally regulated in different cell types and diverse tissue microenvironments.</p></sec><sec id=\"Sec10\"><title>Methods</title><sec id=\"Sec11\"><title>Reagents and antibodies</title><p id=\"Par32\">Phalloidin dyes labeled with AF-488 or AF-594, DAPI (4′,6-diamidino-2-phenylindole dihydrochloride), Hoechst 33342 dye, and rabbit polyclonal anti-ROBO1 antibody were purchased from Thermo Fisher Scientific (Rockford, IL, USA). Total and active RhoA, and active Rac1/2/3 G-LISA kits, Rho inhibitor I (TAT-C3), Rho activator II (CN03), and mouse monoclonal anti-RhoA antibody were bought from Cytoskeleton Inc. (Denver, CO, USA). Rabbit monoclonal antibodies against total NF-κB p65 and phospho-NF-κB p65 (Ser536) were from Cell Signaling Technology (Danvers, MA, USA). Anti-CD68 and Anti-c-Myc antibodies were bought from BioLegend (San Diego, CA, USA) and Abcam (Cambridge, MA, USA), respectively. Tetramethylrhodamine (TMR)-labeled, 70,000 MW, lysine fixable dextran was from Life Technologies (Carlsbad, CA, USA). Lympholyte-H® was bought from Cedarlane (Burlington, ON, Canada). The recombinant murine and human CSF1 were purchased from PeproTech (Rocky Hill, NJ, USA). Paraformaldehyde (PFA; 16% wt/vol) was bought from Electron Microscopy Sciences (Hatfield, PA, USA). Dako mounting medium was from Agilent technologies (Santa Clara, CA, USA). CK-666 and SMIFH2 were bought from Sigma-Aldrich Canada (Oakville, ON, Canada). Blebbistatin was from Abcam (Cambridge, MA, USA). SuperblockTM blocking buffer in TBS was from Thermo Fisher Scientific (Rockford, IL, USA). Unconjugated MDP and MDP conjugated with FITC (MDP-FITC) were purchased from InvivoGen (San Diego, CA, USA). All cell culture media, and buffer solutions, unless mentioned otherwise, were purchased from Wisent (St-Bruno, QC, Canada). ELISA kits for measuring murine CXCL1, SLIT2, and SLIT3 proteins were purchased from R&D Systems (Minneapolis, MN, USA), CUSABIO (Wuhan, China), and LSBio (Seattle, WA, USA), respectively. The DC<sup>TM</sup> protein assay kit was from Bio-Rad Laboratories (Mississauga, ON, Canada). RNeasy® Plus Mini Kit and Superscript VILO III Mastermix were purchased from Qiagen Canada (Toronto, ON, Canada) and Thermo Fisher Scientific (Rockford, IL, USA), respectively. JumpStart™ REDTaq® ReadyMix™ Reaction Mix was bought from Sigma-Aldrich Canada (Oakville, ON, Canada). Recombinant Robo1N protein was purchased from R&D Systems (Minneapolis, MN, USA).</p></sec><sec id=\"Sec12\"><title>Plasmids</title><p id=\"Par33\">The pEGFP-C3-<italic>RAC1</italic>-Q61L plasmid was from S.G.’s laboratory<sup><xref ref-type=\"bibr\" rid=\"CR113\">113</xref></sup>. The pEGFP-C3 and pEYFP-N1 plasmids were purchased from Takara Bio USA, Inc. (formerly Clontech, Mountain View, CA, USA). The pcDNA<sup>TM</sup>3.1<sup>(+)</sup>-<italic>ROBO1</italic> plasmid was a kind gift from Dr. Tony Pawson’s group (University of Toronto, Toronto, ON, Canada). The pEYFP-N1-<italic>ROBO1</italic> plasmid was made by digesting pcDNA<sup>TM</sup>3.1(+)-<italic>ROBO1</italic> and pEYFP-N1 plasmids with NheI and HindIII restriction enzymes and ligating <italic>ROBO1</italic>-coding sequence (CDS) into the pEYFP-N1 backbone. A plasmid with partial <italic>MYO9B</italic> CDS in pCMV-SPORT6 vector was purchased from PlasmID Repository of Dana-Farber/Harvard Cancer Center DNA Resource Core. The partial CDS encoded for C-terminal amino acid residues 1600–1780, which includes the RhoGAP domain (amino acid residues 1703–1888), of human MYO9B protein isoform 1 (accession—NP_004136.2). The CDS was put into pcDNA<sup>TM</sup>3.1<sup>(+)</sup> vector (Thermo Fisher Scientific, Rockford, IL, USA) with six myc tags between EcoRI and EcoRV restriction sites. The silencing RNAs (siRNA) against mouse RhoA (sense 5′–3′ sequence- AGCCCUGAUAGUUUAGAAAtt) and mouse Robo1 (sense 5′–3′ sequence- GGGAAGAACUGUGACGUUU) were bought from Ambion® (Life Technologies, Carlsbad, CA, USA) and DharmaconTM (GE Healthcare, Chicago, IL, USA), respectively.</p></sec><sec id=\"Sec13\"><title>Recombinant Slit proteins purification and endotoxin testing</title><p id=\"Par34\">The full-length SLIT2 protein (human SLIT2 isoform 1 accession number: NP_004778.1) has 4 leucine-rich repeats (LRR; D1-D4), 9 EGF-like domains, a Laminin G-like (LamG) domain, and a C-terminal cysteine knot-like (CK) domain (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>). Recombinant NSlit2 included AA 26-1118 of SLIT2. In Slit2ΔD2, the ROBO1/2-binding D2 LRR (AA 235–444) was removed from NSlit2 and replaced with a short linker<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. CSlit2 comprised AA 1268–1525 of full-length SLIT2 protein<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Human <italic>SLIT2</italic> cDNA (encoding AA 26–1529 of the protein, NP_004778.1) was amplified using forward (5′-CTATCTAGACCTCAGGCGTGCCCGGCGCAGTGC-3′) and reverse (5′-CTAGGATCCGGACACACACCTCGTACAGC-3′) primers and cloned into the pTT28 vector (a kind gift from Dr. Yves Durocher, Montreal, QC, Canada) which contains a C-terminal (His)8G tag<sup><xref ref-type=\"bibr\" rid=\"CR114\">114</xref></sup> between the NheI and BamHI restriction sites. Large-scale preparation of recombinant proteins was performed by transient transfection of HEK293-EBNA1 cells as described previously<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. Briefly, HEK293-EBNA1 cells were transfected with 1 μg/ml cDNA<sup><xref ref-type=\"bibr\" rid=\"CR115\">115</xref></sup> and culture medium was collected 5 days after the transfection. The recombinant proteins were isolated using Fractogel-cobalt column purification of the conditioned medium<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. NSlit2, CSlit2, and Slit2ΔD2 preparations were tested for endotoxin levels using a ToxinsensorTM Chromogenic LAL Endotoxin Assay Kit (GenScript, Piscataway, NJ, USA). Endotoxin levels in all preparations were less than 0.05 EU/ml and are presented in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>.</p></sec><sec id=\"Sec14\"><title>Primary monocyte/macrophage isolation and cell culture</title><p id=\"Par35\">For BMDM isolation, 8–12-week-old mice were euthanized using the CO<sub>2</sub> inhalation method and femurs and tibias were cleaned and excised. The bone marrow cells were pelleted and resuspended in 1 ml of phosphate-buffered saline (PBS) and filtered through 100 µm nylon mesh. Filtered cells were centrifuged at 5500 g for 5 min and cultured in RPMI 1640 medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 25 ng/ml murine CSF1 and 1% penicillin/streptomycin/amphotericin B for 7–10 days.</p><p id=\"Par36\">The protocol for human participation and blood donation (#1000060065) was reviewed and approved by The Hospital for Sick Children Research Ethics Board, Toronto, ON, Canada. Written, informed consent was obtained from all participants before the blood donation. Peripheral blood mononuclear cells from 60 ml of healthy donor blood were separated using Lympholyte-H. The cells were plated and grown in two 10-cm dishes in RPMI 1640 medium supplemented with 10% heat-inactivated FBS and 25 ng/ml of human CSF1 and 1% penicillin/streptomycin/amphotericin B for 7–10 days.</p><p id=\"Par37\">RAW264.7, HEK293T, DLD-1, and PANC-1 cell lines were purchased from the American Type Culture Collection (ATCC, Manassas, Virginia, USA) and cultured in Dulbecco’s Modified Eagle Medium (DMEM) medium supplemented with 10% heat-inactivated FBS and 1% penicillin/streptomycin/amphotericin B (complete DMEM). HEK293T-ROBO1 (HEK293T cells stably expressing human ROBO1) cells were grown in complete DMEM supplemented with 1000 µg/ml of G418. HEK293-EBNA1-6E cells were cultured in FreestyleTM F17 culture medium from Thermo Fisher Scientific (Rockford, IL, USA).</p></sec><sec id=\"Sec15\"><title>Cell transfections</title><p id=\"Par38\">RAW264.7 cells were transiently transfected with either pEGFP-C3-RAC1-Q61L or the control pEGFP-C3 plasmid (Takara Bio USA Inc., CA, USA) using the AmaxaTM Nucleofactor II system (Lonza, Basel, Switzerland). Transfections were done using the manufacturer’s recommended protocol (D32 setting) and the experiments were performed 48 h (h) after transfection. The MYO9B-GAP-Flag or Flag plasmids were transfected in RAW264.7 cells using Viromer® Red transfection reagent (Lipocalyx GmbH, Halle, Germany), per the manufacturer’s instructions. Cells were used for spreading assays 48 h after transfection. The siRNA transfections were done using HiPerFect® transfection reagent (Qiagen, Hilden, Germany) in six-well plates as per the manufacturer’s instructions (final siRNA concentration- 50 nM) for 72 h.</p></sec><sec id=\"Sec16\"><title>Immunoblotting</title><p id=\"Par39\">Cells were washed with ice-cold PBS thrice and lysed using 1× RIPA (Abcam) buffer (500 µl per well for a 6-well plate) supplemented with protease inhibitor cocktail (Sigma-Aldrich Canada, Oakville, ON). In the case of the phospho-protein blotting, 1× phosphatase inhibitor (Sigma-Aldrich Canada, Oakville, ON) was added to the lysis buffer. Protein concentration was determined using Bio-Rad DC protein assay (Bio-Rad laboratories, Mississauga, ON, Canada). Fifty micrograms of protein per sample was loaded and proteins were separated by SDS-PAGE. The proteins were transferred to a PVDF membrane and blocked in 5% fat-free milk in Tris-buffered saline (TBS) containing 0.05% Tween-20 for 1 h. For phospho-proteins, SuperBlockTM in TBS was used instead of milk. All primary antibodies were incubated as indicated in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>. The membrane was washed three times with TBST for 10 min each time and HRP-conjugated secondary antibodies (1:5000) were added for 1 h at room temperature. The membrane was washed with TBST, treated with SupersignalTM Chemiluminescent substrate (Thermo Fisher Scientific, Rockford, IL, USA) and visualized on a ChemiDoc MP imaging system (Bio-Rad laboratories, Mississauga, ON, Canada). Band intensity was quantified using ImageJ software, version 1.51v. Following phospho-protein blotting, the same membrane was stripped using RestoreTM western blot stripping buffer (Thermo Fisher Scientific, Rockford, IL, USA) and probed for total protein levels. Detailed information for all primary antibodies used in this study is provided in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>.</p></sec><sec id=\"Sec17\"><title>RNA isolation, reverse transcription (RT) and polymerase chain reaction (PCR)</title><p id=\"Par40\">Total RNA was isolated from cells and tissues using RNeasy® Plus Mini Kit from Qiagen and was stored at −80 °C until use. RT was performed using Superscript VILO Mastermix with following conditions: 25 °C 10 min, 42 °C 1 h, 85 °C 5 min and products were stored at −20 °C until PCR. The PCR was performed with JumpStart REDTaq reaction mix using the following cycling conditions: initial activation at 94 °C 2 min, followed by 35 cycles of 94 °C (30 s), 57 °C (30 s), 72 °C (2 min) and final extension at 72 °C for 5 min. PCR products were run on 2% agarose gel.</p><p id=\"Par41\">All primer sequences for PCR are provided in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">3</xref>.</p></sec><sec id=\"Sec18\"><title>In vitro recombinant Slit2 and other pharmacological inhibitor treatments</title><p id=\"Par42\">All treatments were performed in independent triplicates. Cells were serum-starved for 1 h before all treatments. For Slit treatments, cells were suspended using Accutase® (Stemcell technologies, San Diego, CA) and incubated with 30 nM of purified human NSlit2, CSlit2, and Slit2ΔD2, respectively, in serum- and antibiotic-free RPMI 1640 for 15 min at 37 °C in the suspended state. Cells (5 × 10<sup>4</sup> cells per well) were plated on 12-well coverslips pre-coated with poly-<sc>d</sc>-lysine, incubated for 1 h at 37 °C and then fixed with 4% PFA and permeabilized with Triton X-100 (0.1%) for 10 min each. Cells were incubated with phalloidin labeled with AF-488 (or AF-594 when used for cells expressing GFP or YFP-tagged plasmids) and DAPI and coverslips were mounted on 1-mm-thick glass slides. For Robo1N in vitro experiments, 30 nM NSlit2 was pre-incubated with 90 nM Robo1N (molar ratio- 1:3) for 1 h before adding to the cells. For Rho Activator II and TAT-C3 treatments, cells were incubated with the reagents (2 µg/ml) for 4 h before Slit treatments. For all other pharmacologic inhibitors, cells were pre-treated before adding recombinant Slit as follows: Blebbistatin (50 µM, 30 min), SMIFH2 (10 µM, 30 min), CK-666 (100 µM, 60 min), Y-27632 (10 µM, 30 min).</p></sec><sec id=\"Sec19\"><title>Confocal microscopy</title><p id=\"Par43\">All confocal images were acquired using a spinning disk confocal microscope (Leica DMi8) equipped with a Hamamatsu C9100-13 EM-CCD camera and 63× (NA- 1.4) or 40× (NA- 1.3) oil immersion objectives. Cell surface area and cell rounding (shape factor) were measured using Volocity 6.3 software (PerkinElmer, Waltham, MA, USA).</p><p id=\"Par44\">Shape factor is a measure of how similar a three-dimensional shape is to a perfect sphere. It is defined as the ratio of the surface area of a sphere with the same volume as the given object (in this case, a cell) to the surface area of the object. The shape factor is 1 for a perfect sphere; becoming smaller for more irregular shapes.</p></sec><sec id=\"Sec20\"><title>RhoA activation, total RhoA, and Rac1/2/3 activation G-LISA assays</title><p id=\"Par45\">Raw 264.7 cells were serum starved for 4 h prior to the assay. Cells were then treated with 30 nM of purified NSlit2, CSlit2, or Slit2ΔD2 in serum-free RPMI 1640 medium for 15 min at 37 °C. Cells were lysed with protein lysis buffer containing the protease inhibitor mixture provided in the kits. The cleared protein lysate supernatants were normalized to the same amount of protein input using the protein detection reagent included in the kit. Activated RhoA, activated Rac1/2/3, and total RhoA assays were performed according to the manufacturer’s instruction (Cytoskeleton, Denver, CO). The results were read at 490 nm by a Molecular Devices Visible Light Plate Reader (Molecular Devices VersaMax 190).</p></sec><sec id=\"Sec21\"><title>In vitro macropinocytosis</title><p id=\"Par46\">Cells were gently lifted using accutase and plated on 18 mm coverslips in a 12-well plate 24 h prior to the assay. All solutions were prepared in serum-free, phenol red-free RPMI medium containing calcium. Following Slit treatments (30 nM) for 15 min at 37 °C, TMR-conjugated 70 kDa dextran (100 µg/ml) or albumin-FITC (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6b, c</xref>, 500 µg/ml) was added to the media and incubated for 15 min at 37 °C. Cells were washed with PBS (5 min), trypsin (20 s) and PBS (5 min) to remove all unbound TMR-dextran and fixed with 4% PFA and stained for nuclei with Hoechst dye. The micrographs were acquired using a spinning disk confocal microscope (Leica DMi8) equipped with Hamamatsu C9100-13 EM-CCD camera and 63× (NA- 1.4) objective. Macropinosomes were quantified using the measurement tool in ImageJ software, version 1.51v from areas of two-dimensional particles from projections of three-dimensional Z-stacks<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR116\">116</xref></sup>. Macropinosomes were defined as TMR-dextran-positive intracellular vesicles larger than 200 nm in diameter<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>.</p></sec><sec id=\"Sec22\"><title>Cancer cell Glut deprivation viability assay</title><p id=\"Par47\">DLD-1 and PANC-1 cells were seeded (10<sup>4</sup> cells per well) in a 96-well plate and grown in DMEM without <sc>l</sc>-Glut supplemented with 0.5 mM Glut (physiological levels), 0.2 mM Glut (low Glut condition), and 2% albumin, when indicated, for 6 days. The macropinocytosis inhibitor, EIPA (25 µM), was added to the medium for the last 2 days only, where indicated. In some experiments, 30 nM NSlit2 or Slit2Δ2 was added and the medium was changed every 24 h, for 6 days. At the end of all treatments, cell viability was assessed using a MTT assay kit (Roche Diagnostics purchased from Sigma-Aldrich, Oakville, ON, Canada).</p></sec><sec id=\"Sec23\"><title>In vitro MDP-FITC treatment</title><p id=\"Par48\">Primary murine BMDM were suspended using accutase and attached to uncoated 18 mm coverslips 24 h prior to the treatment. BMDM were serum-starved for 1 h and treated with either serum-free medium (control) or NSlit2, CSlit2, or Slit2ΔD2 (30 nM each) at 37 °C for 15 min. MDP-FITC (1 µg/ml) was added to the medium and cells were incubated at 37 °C for an additional 30 min. The cells were fixed using 4% PFA and prepared for confocal imaging as described earlier in the ‘In vitro macropinocytosis’ section. For immunoblotting experiments, BMDM were grown in six-well plates. Following the Slit and MDP treatments as described above, cells were lysed using RIPA buffer 5 min after the MDP treatment.</p></sec><sec id=\"Sec24\"><title>Animal work</title><p id=\"Par49\">In vivo macropinocytosis and MDP treatment protocols (Animal User Protocol—44731) were reviewed and approved by the Animal Care Committee of The Hospital for Sick Children (Toronto, ON, Canada) in accordance with guidelines established by the Canadian Council on Animal Care (CCAC). The generation of Rac1/2 double KO (DKO) mice has been described previously<sup><xref ref-type=\"bibr\" rid=\"CR117\">117</xref>,<xref ref-type=\"bibr\" rid=\"CR118\">118</xref></sup> and was carried out by M.G.’s lab in accordance with the Guide for the Humane Use and Care of Laboratory Animals and approved by the University of Toronto Animal Care Committee. Eight-to-twelve-week-old mice were used for all in vivo experiments.</p><p id=\"Par50\">Sample sizes (<italic>n</italic>) for all in vivo experiments were calculated using G*Power software version 3.1.9.2 (Universität Düsseldorf, Germany)<sup><xref ref-type=\"bibr\" rid=\"CR119\">119</xref></sup>.</p></sec><sec id=\"Sec25\"><title>In vivo macropinocytosis</title><p id=\"Par51\">The murine model of sterile peritonitis has been described previously<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. Six animals were used for each of the four treatment groups. On day-0, C57BL/6J mice were treated with sodium periodate (200 µg in 200 µl) by a single intraperitoneal (i.p.) injection to induce sterile peritonitis. Forty-eight hours after the periodate injection, animals were injected with either saline, or equivalent molar amounts of recombinant Slit2 proteins, namely, NSlit2 (1 µg), CSlit2 (0.2 µg), or Slit2ΔD2 (1 µg) by an intravenous (i.v.) route. On day 3, animals were injected i.p. with TMR-labeled 70 kDa dextran (200 µg) and euthanized after 30 min (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4g</xref>). The peritoneal macrophages were isolated as described by Ray et al.<sup><xref ref-type=\"bibr\" rid=\"CR120\">120</xref></sup> and centrifuged at 300 × <italic>g</italic> for 10 min. The cell pellet was resuspended in serum-free RPMI 1640 and allowed to attach to poly-<sc>d</sc>-lysine-coated coverslips for 1 h. The coverslips were washed with PBS twice and cells were fixed with 4% PFA and blocked using 5% goat serum. The coverslips were incubated with anti-CD68 antibody, a pan monocyte/macrophage marker, conjugated with AF-488 (BioLegend, San Diego, CA, USA) in 5% goat serum overnight at 4 °C. The coverslips were washed with PBS three times and incubated with Hoechst dye in the blocking solution for 1 h at 4 °C. The coverslips were washed with PBS three times and mounted using Dako mounting medium.</p></sec><sec id=\"Sec26\"><title>In vivo MDP treatment</title><p id=\"Par52\">Four animals were used for each treatment group. On day-0, C57BL/6J were treated with sodium periodate (200 µg in 200 µl) by a single i.p. injection to induce sterile peritonitis. Seventy-two hours after the periodate injection, saline vehicle, or NSlit2 (1 µg), or EIPA (25 mg/kg body weight in 200 µl) was administered i.p. After 1 h, MDP (10 ng) was administered i.p. After 2 h, blood was collected by intracardiac puncture under isoflurane anesthesia. Mice were euthanized immediately following the blood collection, and peritoneal exudates were collected by lavaging the peritoneal cavity with 5 ml cold PBS. Cells in the peritoneal exudate were counted using a hemocytometer. For serum collection, blood was allowed to clot for 2 h at room temperature and centrifuged at 1000 × <italic>g</italic> for 15 min. Serum aliquots were stored at −20 °C until the assay. Serum CXCL1 levels were determined by ELISA. For experiments involving in vivo Robo1N treatment, 72 h after sodium periodate treatment, saline vehicle, Robo1N (7 µg) or EIPA (25 mg/kg body weight), and Robo1N together were administered i.p. One hour later, MDP (10 ng) was administered i.p. After 2 h, blood was collected by intracardiac puncture. Mice were euthanized immediately, and the peritoneal exudates were collected. Murine CXCL1 ELISA was performed using a microplate reader as per the manufacturer’s recommendations (R&D Systems, DY453).</p></sec><sec id=\"Sec27\"><title>Peritoneal membrane isolation</title><p id=\"Par53\">Serum samples were collected from C57BL/6J mice (ten males and ten females) as described before and animals were euthanized immediately after the blood collection. Under aseptic conditions, the abdominal cavity was opened by removing the skin and underlying muscle but keeping the peritoneal membrane intact. The peritoneal membrane was separated from underlying adipose tissue and organs and collected in cold PBS. The membrane was rinsed with PBS once and resuspended in RIPA buffer. Samples were homogenized with a tissue homogenizer and stored at −20 °C overnight. Two freeze-thaw cycles were performed to break cell membranes and homogenates were centrifuged at 5000 × <italic>g</italic> for 5 min at 4 °C. Supernatants were stored at −80 °C.</p></sec><sec id=\"Sec28\"><title>Murine SLIT2 and SLIT3 ELISA</title><p id=\"Par54\">SLIT2 and SLIT3 ELISA were performed using a microplate reader as per the manufacturer’s recommendations (CUSABIO CSB-E11039m and LSBio LS-F-7173, respectively). Briefly, 100 µl samples and standards were incubated in pre-coated assay wells at 37 °C for 2 h. Samples were removed and wells were then incubated with biotin antibody at 37 °C for 1 h, with avidin antibody for 1 h, and TMB substrate for 30 min. Wells were thoroughly washed three times between incubation steps. After incubation with the TMB substrate, 50 µl stop solution was added to each well and the optical density was read immediately. Wavelength correction was applied by subtracting readings at 540 nm from those at 450 nm.</p></sec><sec id=\"Sec29\"><title>Statistical analyses</title><p id=\"Par55\">All data are presented as mean ± standard error of mean (SEM). Statistical tests were performed using GraphPad Prism 7 software (San Diego, CA, USA) and are described in the corresponding figure legends. <italic>p</italic> < 0.05 was considered statistically significant. All <italic>p</italic> values are reported up to four decimal places.</p></sec><sec id=\"Sec30\"><title>Biological materials</title><p id=\"Par56\">Unique biological materials used in this study will be made available upon a reasonable request from corresponding author (L.A.R.).</p></sec><sec id=\"Sec31\"><title>Reporting summary</title><p id=\"Par57\">Further information on research design is available in the <xref rid=\"MOESM3\" ref-type=\"media\">Nature Research Reporting Summary</xref> linked to this article.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec32\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41467_2020_17651_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41467_2020_17651_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"41467_2020_17651_MOESM3_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media></supplementary-material></sec></sec></body><back><app-group><app id=\"App1\"><sec id=\"Sec33\"><title>Source data</title><p id=\"Par60\"><media position=\"anchor\" xlink:href=\"41467_2020_17651_MOESM4_ESM.zip\" id=\"MOESM4\"><caption><p>Source Data</p></caption></media></p></sec></app></app-group><fn-group><fn><p><bold>Peer review information</bold>\n<italic>Nature Communications</italic> thanks Colin Watts and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> is available for this paper at 10.1038/s41467-020-17651-1.</p></sec><ack><title>Acknowledgements</title><p>We thank Paul Paroutis and Kimberly Lau for their assistance in imaging analysis. We thank Dr. Spencer Freeman for scientific advice and discussion. We thank Dr. Marvin Estrada for his technical assistance for in vivo work. This work was supported by grants from Canadian Institutes of Health Research (CIHR) to L.A.R. (MOP111083 and MOP136896). B.W.P. was supported by Queen Elizabeth II/Heart and Stroke Foundation of Ontario (HSFO) Graduate Scholarship in Science and Technology. J.Y.W. is supported by National Institutes of Health grants (RO1CA175360 and RO1NS107396). D.J.P. is supported by CIHR grant, FDN-14333.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>Conceptualization; V.K.B., S.G., L.A.R.; Methodology; V.K.B., T.M., Y.W.H.; Investigation; V.K.B., T.M., Y.W.H., S.P., B.W.P., G.Y.L.; Writing—Original Draft: V.K.B.; Writing—Review & Editing: V.K.B., L.A.R., T.M., D.J.P., S.G.; Resources: L.A.R., D.J.P., S.G., M.G., J.Y.W.; Funding acquisition: L.A.R.; Supervision: L.A.R. All authors reviewed and approved the final submission.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>All data supporting the findings of this study are available within the paper, its <xref rid=\"MOESM1\" ref-type=\"media\">supplementary information files</xref>, and source data are provided as a Source Data file linked to this article. Source data underlying Figs. <xref rid=\"Fig1\" ref-type=\"fig\">1(a, d–h)</xref>, <xref rid=\"Fig2\" ref-type=\"fig\">2(a, b, d–f)</xref>, <xref rid=\"Fig3\" ref-type=\"fig\">3(b, c, e, f)</xref>, <xref rid=\"Fig4\" ref-type=\"fig\">4(b, c, e, f, i)</xref>, <xref rid=\"Fig5\" ref-type=\"fig\">5(a–h)</xref>, <xref rid=\"Fig6\" ref-type=\"fig\">6(a–d)</xref>, and Supplementary Figs. <xref rid=\"MOESM1\" ref-type=\"media\">1(a, c–e)</xref>, <xref rid=\"MOESM1\" ref-type=\"media\">2(a–e)</xref>, <xref rid=\"MOESM1\" ref-type=\"media\">3(a–d, f, g)</xref>, <xref rid=\"MOESM1\" ref-type=\"media\">4(b–d, f)</xref>, <xref rid=\"MOESM1\" ref-type=\"media\">5(a–c, e)</xref> are provided as a Source Data file linked to this article. Source data are provided with this paper.</p></notes><notes notes-type=\"data-availability\"><title>Code availability</title><p>This study did not generate any new code. Source data are provided with this paper.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par58\">The authors declare no competing interests.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Wynn</surname><given-names>TA</given-names></name><etal/></person-group><article-title>Macrophage biology in development, homeostasis and disease</article-title><source>Nature</source><year>2013</year><volume>496</volume><fpage>445</fpage><lpage>455</lpage><pub-id pub-id-type=\"pmid\">23619691</pub-id></element-citation></ref><ref id=\"CR2\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Mostowy</surname><given-names>S</given-names></name><name><surname>Shenoy</surname><given-names>AR</given-names></name></person-group><article-title>The cytoskeleton in cell-autonomous immunity: structural determinants of host defence</article-title><source>Nat. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"other\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">BJPsych Int</journal-id><journal-id journal-id-type=\"iso-abbrev\">BJPsych Int</journal-id><journal-id journal-id-type=\"publisher-id\">BJI</journal-id><journal-title-group><journal-title>BJPsych International</journal-title></journal-title-group><issn pub-type=\"ppub\">2056-4740</issn><issn pub-type=\"epub\">2058-6264</issn><publisher><publisher-name>Cambridge University Press</publisher-name><publisher-loc>Cambridge, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmc\">PMC7431851</article-id><article-id pub-id-type=\"doi\">10.1192/bji.2020.42</article-id><article-id pub-id-type=\"pii\">S2056474020000422</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Special Paper</subject></subj-group></article-categories><title-group><article-title>Memory First Aid: remote memory service and webinar-based dementia training for non-medical graduates in Nepal, India, Pakistan and Sri Lanka</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-4849-7963</contrib-id><name><surname>Jha</surname><given-names>Arun</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Williams</surname><given-names>Shehan</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Singh</surname><given-names>Bhaweshwar</given-names></name><xref ref-type=\"aff\" rid=\"aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Pradhan</surname><given-names>Prabhat</given-names></name><xref ref-type=\"aff\" rid=\"aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bhatt</surname><given-names>Khem Raj</given-names></name><xref ref-type=\"aff\" rid=\"aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Afridi</surname><given-names>Muhammad Iqbal</given-names></name><xref ref-type=\"aff\" rid=\"aff6\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tomar</surname><given-names>Rahul</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mukhopadhaya</surname><given-names>Kaushik</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">1</xref></contrib></contrib-group><aff id=\"aff1\"><label>1</label>Consultant Old Age Psychiatrist, <institution>Hertfordshire Partnership University NHS Foundation Trust</institution>, <city>St Albans</city>, <country>UK</country>. Email: <email>arunjhauk@gmail.com</email></aff><aff id=\"aff2\"><label>2</label>Professor in Psychiatry, Faculty of Medicine, <institution>University of Kelaniya</institution>, <country>Sri Lanka</country></aff><aff id=\"aff3\"><label>3</label>Professor of Zoology, Institute of Gerontology and Geriatrics, <institution>LN Mithila University</institution>, <city>Darbhanga</city>, <country>India</country></aff><aff id=\"aff4\"><label>4</label>Executive Member, <institution>Alzheimer's and Related Dementias Society</institution>, <city>Kathmandu</city>, <country>Nepal</country></aff><aff id=\"aff5\"><label>5</label>Assistant Professor, Central Department of Psychology, <institution>Tribhuvan University</institution>, <city>Kathmandu</city>, <country>Nepal</country></aff><aff id=\"aff6\"><label>6</label>Professor and Head, Department of Psychiatry and Behavioural Sciences, <institution>Jinnah Postgraduate Medical Centre</institution>, <city>Karachi</city>, <country>Pakistan</country></aff><pub-date publication-format=\"electronic\" date-type=\"pub\"><day>30</day><month>7</month><year>2020</year></pub-date><fpage>1</fpage><lpage>3</lpage><history><date date-type=\"received\"><day>23</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Authors 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>The Authors</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><ali:license_ref xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\">http://creativecommons.org/licenses/by/4.0/</ali:license_ref><license-p>This is an Open Access article, distributed under the terms of Creative Commons Attribution licence (<uri xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"S2056474020000422a.pdf\"/><abstract abstract-type=\"normal\"><p>The prevalence of dementia is rising in low-resource countries, where specialist memory services are almost non-existent. The COVID-19 pandemic has created opportunities for innovative remote healthcare. Research shows a lack of dementia literacy and help-seeking behaviour for memory-related problems among older adults in South Asian countries. This paper proposes a remote memory service model and virtual dementia training in South Asian countries, called Memory First Aid (MFA). MFA offers help to a person experiencing memory difficulties until appropriate professional help is received. The MFA course is a 12-h webinar-based package consisting of four weekly modules. It covers dementia awareness and clinical features. The aim is to develop a non-medical workforce able to screen and assess older people with suspected dementia.</p></abstract><kwd-group><title>Keywords</title><kwd>Dementia</kwd><kwd>first aid</kwd><kwd>remote</kwd><kwd>South Asia</kwd><kwd>Nepal</kwd></kwd-group><counts><fig-count count=\"2\"/><table-count count=\"2\"/><ref-count count=\"11\"/><page-count count=\"3\"/></counts></article-meta></front><body><p>Dementia is a rapidly growing public health problem affecting around 50 million people worldwide, with approximately 60% living in low- and middle-income countries (LMICs). This figure is set to triple by 2050. International reports indicate a growing number of people with dementia in South Asian countries<sup><xref rid=\"ref1\" ref-type=\"bibr\">1</xref>,<xref rid=\"ref2\" ref-type=\"bibr\">2</xref></sup> (<xref rid=\"tab01\" ref-type=\"table\">Table 1</xref>).\n<table-wrap id=\"tab01\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Projected population and number of people with dementia in South Asia in 2015–2050</p></caption><alternatives><graphic xlink:href=\"S2056474020000422_tab1\"/><table frame=\"hsides\" rules=\"groups\"><col align=\"left\" width=\"1*\" span=\"1\"/><col align=\"char\" char=\".\" width=\"1*\" span=\"1\"/><col align=\"char\" char=\".\" width=\"1*\" span=\"1\"/><col align=\"char\" char=\".\" width=\"1*\" span=\"1\"/><col align=\"char\" char=\".\" width=\"1*\" span=\"1\"/><thead><tr><th align=\"left\" rowspan=\"2\" colspan=\"1\"/><th align=\"left\" rowspan=\"2\" colspan=\"1\">Population (thousands), 2015</th><th align=\"center\" colspan=\"3\" rowspan=\"1\">Estimated number of people with dementia (thousands)</th></tr><tr><th align=\"center\" colspan=\"1\" rowspan=\"1\">    2015</th><th align=\"center\" colspan=\"1\" rowspan=\"1\">  2030</th><th align=\"left\" colspan=\"1\" rowspan=\"1\">2050</th></tr></thead><tbody><tr><td colspan=\"1\" rowspan=\"1\">India</td><td colspan=\"1\" rowspan=\"1\">1 282 390</td><td colspan=\"1\" rowspan=\"1\">4031</td><td colspan=\"1\" rowspan=\"1\">6743</td><td colspan=\"1\" rowspan=\"1\">12 542</td></tr><tr><td colspan=\"1\" rowspan=\"1\">Nepal</td><td colspan=\"1\" rowspan=\"1\">28 441</td><td colspan=\"1\" rowspan=\"1\">78</td><td colspan=\"1\" rowspan=\"1\">134</td><td colspan=\"1\" rowspan=\"1\">285</td></tr><tr><td colspan=\"1\" rowspan=\"1\">Sri Lanka</td><td colspan=\"1\" rowspan=\"1\">21 612</td><td colspan=\"1\" rowspan=\"1\">147</td><td colspan=\"1\" rowspan=\"1\">262</td><td colspan=\"1\" rowspan=\"1\">463</td></tr><tr><td colspan=\"1\" rowspan=\"1\">Pakistan</td><td colspan=\"1\" rowspan=\"1\">188 144</td><td colspan=\"1\" rowspan=\"1\">450</td><td colspan=\"1\" rowspan=\"1\">712</td><td colspan=\"1\" rowspan=\"1\">1422</td></tr><tr><td colspan=\"1\" rowspan=\"1\">Bangladesh</td><td colspan=\"1\" rowspan=\"1\">160 411</td><td colspan=\"1\" rowspan=\"1\">460</td><td colspan=\"1\" rowspan=\"1\">834</td><td colspan=\"1\" rowspan=\"1\">2193</td></tr><tr><td colspan=\"1\" rowspan=\"1\">Total Asia Pacific</td><td colspan=\"1\" rowspan=\"1\">3 991 793</td><td colspan=\"1\" rowspan=\"1\">   23 279</td><td colspan=\"1\" rowspan=\"1\">  39 409</td><td colspan=\"1\" rowspan=\"1\">70 981</td></tr></tbody></table></alternatives><table-wrap-foot><fn id=\"tfn1_1\" fn-type=\"other\"><p>Source: Alzheimer's Disease International & Alzheimer's Australia.<sup><xref rid=\"ref1\" ref-type=\"bibr\">1</xref></sup></p></fn></table-wrap-foot></table-wrap></p><p>Many South Asians view memory loss as a normal part of ageing or understand symptoms of dementia through religious belief.<sup><xref rid=\"ref3\" ref-type=\"bibr\">3</xref></sup> Providing affordable and sustainable dementia care services in these countries poses numerous challenges. COVID-19 has forced rapid changes in global healthcare, with a significant increase in remote consultations to enable people to access healthcare during physical distancing. Remote healthcare poses specific challenges for memory services owing to patients’ cognitive impairment and the reliance of the clinician on relatives. In May 2020, NHS England's London Clinical Network distributed its <italic>Guidance on Remote Working for Memory Services during COVID-19</italic> to staff (this is not on the LCN website but copies may be found online). At the same time, we planned an innovative webinar-based dementia course called Memory First Aid (MFA). MFA aspires to train a pool of non-medical graduates in South Asian countries to offer dementia screening and brief assessment. The course is adapted from mental health first aid courses run in Australia,<sup><xref rid=\"ref4\" ref-type=\"bibr\">4</xref></sup> Nepal<sup><xref rid=\"ref5\" ref-type=\"bibr\">5</xref></sup> and elsewhere. We have also planned a post-COVID-19 remote memory service based on the Rural and Remote Memory Clinic (RRMC) project, which reported high patient and caregiver satisfaction with telehealth video conferencing.</p><sec sec-type=\"other\" id=\"sec1\"><title>The Memory First Aid pathway and action plan</title><p><xref ref-type=\"fig\" rid=\"fig01\">Figure 1</xref> depicts the MFA pathway, which consists of screening, assessment and post-diagnostic support. On this pathway, the local branch of Memory First Aid International will organise awareness-raising events along with the nearest Alzheimer's Society and/or similar organisations. The local MFA centre will have a helpline to offer a free memory screening service. All individuals who screen positive will be offered a brief initial assessment using the Rowland Universal Dementia Assessment Scale (RUDAS) cognitive test.\n<fig id=\"fig01\" fig-type=\"fig\" orientation=\"portrait\" position=\"float\"><label>Fig. 1</label><caption><p>Components of the Memory First Aid pathway.</p></caption><graphic xlink:href=\"S2056474020000422_fig1\"/></fig></p><p>RUDAS is a copyright-free instrument particularly useful for people in low- and middle-income countries where literacy or education is low.<sup><xref rid=\"ref6\" ref-type=\"bibr\">6</xref></sup> RUDAS is a short interview-based questionnaire that assesses multiple cognitive domains, including memory recall, visuospatial orientation, praxis, visuoconstructional drawing, judgement and language. It has been validated in Nepal (Nepali-RUDAS)<sup><xref rid=\"ref7\" ref-type=\"bibr\">7</xref></sup> and is relevant to all South Asian countries.</p><p>In any first aid course, participants learn an action plan for the best way to help someone who is injured or ill. For example, in the UK, when ambulance paramedics are trained to recognise the symptoms of stroke, they are taught to remember the mnemonic FAST, which stands for: Face (can the person smile?), Arms (can the person raise both arms?), Speech problems (can the person speak clearly and understand what you say?) and Time (If you see any of these three signs, it's time to call 999). The MFA course provides an action plan on how to help a person experiencing memory difficulties. Its mnemonic is SSAD: Suspect dementia, Screen for Alzheimer's disease, Assess cognition and organise Diagnosis (<xref ref-type=\"fig\" rid=\"fig02\">Fig. 2</xref>).\n<fig id=\"fig02\" fig-type=\"fig\" orientation=\"portrait\" position=\"float\"><label>Fig. 2</label><caption><p>The Memory First Aid action plan. AD, Alzheimer's disease.</p></caption><graphic xlink:href=\"S2056474020000422_fig2\"/></fig></p></sec><sec sec-type=\"other\" id=\"sec2\"><title>Course content</title><p>The MFA course teaches volunteers how to recognise the symptoms and signs of Alzheimer's dementia, how to screen older people with memory problems for dementia, how to offer basic cognitive assessment, and how to organise diagnostic assessment for people with suspected dementia.</p><p>MFA is a 12-hour webinar-based course consisting of four modules (3 h each) delivered over 4–6 weeks. The course is based on tier 1 and tier 2 of the Dementia Training Standards Framework developed by NHS Health Education England in 2018.<sup><xref rid=\"ref8\" ref-type=\"bibr\">8</xref></sup> Tier 1 is related to dementia awareness raising, in terms of knowledge, skills and attitudes for all those working in health and care settings. Tier 2 is about knowledge, skills and attitudes for roles that have regular contact with people living with dementia. <xref rid=\"tab02\" ref-type=\"table\">Table 2</xref> lists the key subject areas and learning outcomes for the four MFA modules.\n<table-wrap id=\"tab02\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Key subject areas and learning outcomes for the modules of the Memory First Aid course</p></caption><alternatives><graphic xlink:href=\"S2056474020000422_tab2\"/><table frame=\"hsides\" rules=\"groups\"><col align=\"left\" width=\"1*\" span=\"1\"/><col align=\"left\" width=\"1*\" span=\"1\"/><col align=\"left\" width=\"1*\" span=\"1\"/><thead><tr><th align=\"left\" colspan=\"1\" rowspan=\"1\">Module</th><th align=\"left\" colspan=\"1\" rowspan=\"1\">Subject area</th><th align=\"left\" colspan=\"1\" rowspan=\"1\">Key learning outcomes</th></tr></thead><tbody><tr><td colspan=\"1\" rowspan=\"1\">1</td><td colspan=\"1\" rowspan=\"1\">Dementia awareness</td><td colspan=\"1\" rowspan=\"1\">The learner will:\n<list list-type=\"bullet\"><list-item><p>know the meaning of the term dementia and the importance of family caregivers</p></list-item><list-item><p>be aware of the prevalence of dementia in South Asia</p></list-item><list-item><p>be able to recognise signs of various types of dementia, particularly vascular dementia and Alzheimer's disease</p></list-item><list-item><p>know what actions individuals can take to reduce the risk of dementia or to delay onset</p></list-item><list-item><p>know why early diagnosis of dementia is important</p></list-item><list-item><p>be aware of the impact of dementia on individuals, families and society</p></list-item><list-item><p>be able to communicate effectively and compassionately with individuals who have dementia</p></list-item><list-item><p>understand reasons why a person with dementia may exhibit signs of distress and how behaviours seen in people with dementia may be a way of communicating unmet needs</p></list-item></list></td></tr><tr><td colspan=\"1\" rowspan=\"1\">2</td><td colspan=\"1\" rowspan=\"1\">Dementia identification, assessment and diagnosis</td><td colspan=\"1\" rowspan=\"1\">The learner will:\n<list list-type=\"bullet\"><list-item><p>be sensitive to people's vision, hearing, language and literacy problems and to sociocultural norms</p></list-item><list-item><p>know the most common types of dementia and their causes</p></list-item><list-item><p>know why early diagnosis of dementia is important and the likely outcomes if assessment and treatment are delayed</p></list-item><list-item><p>know the progressive nature of dementia and some of the major impairments and difficulties people may face as dementia progresses</p></list-item><list-item><p>be able to appropriately refer patients to access nearest specialist services and support networks</p></list-item></list></td></tr><tr><td colspan=\"1\" rowspan=\"1\">3</td><td colspan=\"1\" rowspan=\"1\">Pharmacological interventions in dementia care</td><td colspan=\"1\" rowspan=\"1\">The learner will:\n<list list-type=\"bullet\"><list-item><p>know the most common medications used to treat the symptoms of dementia</p></list-item><list-item><p>know the main risks and benefits of using antipsychotics, antidepressants, anxiolytics, anticonvulsants and cognitive enhancers and be aware of the impact drugs may have on daily living, including common side-effects such as taste disturbances and a dry mouth</p></list-item></list></td></tr><tr><td colspan=\"1\" rowspan=\"1\">4</td><td colspan=\"1\" rowspan=\"1\">Health and well-being in dementia care</td><td colspan=\"1\" rowspan=\"1\">The learner will:\n<list list-type=\"bullet\"><list-item><p>be able to communicate the importance of exercise for the patient and caregiver as well as to prevent dementia</p></list-item><list-item><p>understand the importance for individuals with dementia to maintain good physical, mental and oral health through food, drink, exercise and a healthy lifestyle that includes social engagement</p></list-item><list-item><p>understand triggers and responses to stressed and distressed behaviours</p></list-item><list-item><p>understand the role of family and carers in supporting the health and well-being of people with dementia</p></list-item><list-item><p>be aware of the benefits and limitations of medication to manage behavioural and psychological problems, including associated risks</p></list-item></list></td></tr></tbody></table></alternatives></table-wrap></p></sec><sec sec-type=\"other\" id=\"sec3\"><title>Pilot testing</title><p>Evaluation of the course at three pilot sites – Kathmandu in Nepal, Darbhanga in India and Colombo in Sri Lanka – will commence in September 2020. Its effectiveness will be measured using the pre- and post-test Alzheimer's Disease Knowledge Scale (ADKS).<sup><xref rid=\"ref9\" ref-type=\"bibr\">9</xref></sup> This method has been previously used by authors in the UK for a similar course, Dementia First Aid, for family caregivers of people with early dementia.<sup><xref rid=\"ref10\" ref-type=\"bibr\">10</xref></sup> The success of the programme will be measured by the number of people with suspected dementia being screened and diagnosed. Patient and caregiver satisfaction with telescreening will be evaluated using the Telehealth Satisfaction Scale (TeSS).<sup><xref rid=\"ref11\" ref-type=\"bibr\">11</xref></sup> If successful, the MFA course and tele-memory service will be rolled out to other South Asian countries.</p></sec></body><back><notes id=\"nts1\" notes-type=\"other\"><title>Author contributions</title><p>A.J. conceived the idea and all authors were involved in writing the article.</p></notes><sec id=\"nts2\" sec-type=\"COI-statement\"><title>Declaration of interest</title><p>None.</p><sec sec-type=\"supplementary-material\" id=\"sec4\"><title>Supplementary material</title><supplementary-material content-type=\"local-data\" id=\"S2056474020000422sup001\"><p>For supplementary material accompanying this paper visit http://dx.doi.org/10.1192/bji.2020.42.</p><media xlink:href=\"S2056474020000422sup001.zip\" mimetype=\"application\" mime-subtype=\"zip\" orientation=\"portrait\" id=\"d38e565\" position=\"anchor\"><caption><p>click here to view supplementary material</p></caption></media></supplementary-material></sec></sec><ref-list id=\"reflist1\"><title>References</title><ref id=\"ref1\"><label>1</label><mixed-citation publication-type=\"other\" id=\"cite1\"><collab>Alzheimer's Disease International, Alzheimer's Australia</collab>. <source>Dementia in the Asia Pacific Region</source>. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Infect Control Hosp Epidemiol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Infect Control Hosp Epidemiol</journal-id><journal-id journal-id-type=\"publisher-id\">ICE</journal-id><journal-title-group><journal-title>Infection Control and Hospital Epidemiology</journal-title></journal-title-group><issn pub-type=\"ppub\">0899-823X</issn><issn pub-type=\"epub\">1559-6834</issn><publisher><publisher-name>Cambridge University Press</publisher-name><publisher-loc>New York, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32729441</article-id><article-id pub-id-type=\"pmc\">PMC7431852</article-id><article-id pub-id-type=\"publisher-id\">S0899823X20003761</article-id><article-id pub-id-type=\"doi\">10.1017/ice.2020.376</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Original Article</subject></subj-group></article-categories><title-group><article-title>How to keep patients and staff safe from accidental SARS-CoV-2 exposure in the emergency room: Lessons from South Korea’s explosive COVID-19 outbreak</article-title></title-group><contrib-group><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Kim</surname><given-names>Yun Jeong</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"afn1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Choe</surname><given-names>Jae Young</given-names></name><degrees>MD, MS</degrees><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"afn1\"><sup>a</sup></xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-4666-0672</contrib-id><name><surname>Kwon</surname><given-names>Ki Tae</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"a2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"a3\"><sup>3</sup></xref><xref ref-type=\"corresp\" rid=\"cor1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Hwang</surname><given-names>Soyoon</given-names></name><degrees>MD, MS</degrees><xref ref-type=\"aff\" rid=\"a2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"a3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Choi</surname><given-names>Gyu-Seog</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"a4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Sohn</surname><given-names>Jin Ho</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"a5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kim</surname><given-names>Jong Kun</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Yeo</surname><given-names>In Hwan</given-names></name><degrees>MD, MS</degrees><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Cho</surname><given-names>Yeon Joo</given-names></name><degrees>MD, MS</degrees><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Ham</surname><given-names>Ji Yeon</given-names></name><degrees>MD, MS</degrees><xref ref-type=\"aff\" rid=\"a6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Song</surname><given-names>Kyung Eun</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"a6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Lee</surname><given-names>Nan Young</given-names></name><degrees>MD, PhD</degrees><xref ref-type=\"aff\" rid=\"a6\"><sup>6</sup></xref></contrib></contrib-group><aff id=\"a1\"><label>1</label>Department of Emergency Medicine, School of Medicine, <institution>Kyungpook National University</institution>, <city>Daegu</city>, <country>Korea</country></aff><aff id=\"a2\"><label>2</label>Division of Infectious Diseases, Department of Internal Medicine, School of Medicine, <institution>Kyungpook National University</institution>, <city>Daegu</city>, <country>Korea</country></aff><aff id=\"a3\"><label>3</label>Department of Infection Control, <institution>Kyungpook National University Chilgok Hospital</institution>, <city>Daegu</city>, <country>Korea</country></aff><aff id=\"a4\"><label>4</label>Colorectal Cancer Center, Kyungpook National University Chilgok Hospital, School of Medicine, <institution>Kyungpook National University</institution>, <city>Daegu</city>, <country>Korea</country></aff><aff id=\"a5\"><label>5</label>Department of Otolaryngology-Head and Neck Surgery, School of Medicine, <institution>Kyungpook National University</institution>, <city>Daegu</city>, <country>Korea</country></aff><aff id=\"a6\"><label>6</label>Department of Clinical Pathology, School of Medicine, <institution>Kyungpook National University</institution>, <city>Daegu</city>, <country>Korea</country></aff><author-notes><corresp id=\"cor1\"><bold>Author for correspondence:</bold> Ki Tae Kwon, E-mail: <email>ktkwon@knu.ac.kr</email></corresp><fn id=\"afn1\" fn-type=\"other\"><label>a</label><p>Authors of equal contribution.</p></fn></author-notes><pub-date publication-format=\"electronic\" date-type=\"pub\"><day>30</day><month>7</month><year>2020</year></pub-date><fpage>1</fpage><lpage>7</lpage><history><date date-type=\"received\"><day>01</day><month>5</month><year>2020</year></date><date date-type=\"rev-recd\"><day>16</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>21</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Society for Healthcare Epidemiology of America 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>The Society for Healthcare Epidemiology of America</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (<uri xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"S0899823X20003761a.pdf\"/><abstract abstract-type=\"normal\"><sec id=\"as1\"><title>Objectives:</title><p>We report our experience with an emergency room (ER) shutdown related to an accidental exposure to a patient with coronavirus disease 2019 (COVID-19) who had not been isolated.</p></sec><sec id=\"as2\"><title>Setting:</title><p>A 635-bed, tertiary-care hospital in Daegu, South Korea.</p></sec><sec id=\"as3\"><title>Methods:</title><p>To prevent nosocomial transmission of the disease, we subsequently isolated patients with suspected symptoms, relevant radiographic findings, or epidemiology. Severe acute respiratory coronavirus 2 (SARS-CoV-2) reverse-transcriptase polymerase chain reaction assays (RT-PCR) were performed for most patients requiring hospitalization. A universal mask policy and comprehensive use of personal protective equipment (PPE) were implemented. We analyzed effects of these interventions.</p></sec><sec id=\"as4\"><title>Results:</title><p>From the pre-shutdown period (February 10–25, 2020) to the post-shutdown period (February 28 to March 16, 2020), the mean hourly turnaround time decreased from 23:31 ±6:43 hours to 9:27 ±3:41 hours (P < .001). As a result, the proportion of the patients tested increased from 5.8% (N=1,037) to 64.6% (N=690) (P < .001) and the average number of tests per day increased from 3.8±4.3 to 24.7±5.0 (P < .001). All 23 patients with COVID-19 in the post-shutdown period were isolated in the ER without any problematic accidental exposure or nosocomial transmission. After the shutdown, several metrics increased. The median duration of stay in the ER among hospitalized patients increased from 4:30 hours (interquartile range [IQR], 2:17–9:48) to 14:33 hours (IQR, 6:55–24:50) (P < .001). Rates of intensive care unit admissions increased from 1.4% to 2.9% (P = .023), and mortality increased from 0.9% to 3.0% (P = .001).</p></sec><sec id=\"as5\"><title>Conclusions:</title><p>Problematic accidental exposure and nosocomial transmission of COVID-19 can be successfully prevented through active isolation and surveillance policies and comprehensive PPE use despite longer ER stays and the presence of more severely ill patients during a severe COVID-19 outbreak.</p></sec></abstract><counts><fig-count count=\"2\"/><table-count count=\"1\"/><ref-count count=\"25\"/><page-count count=\"7\"/></counts></article-meta></front><body><p>On March 11, 2020, the World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) a global pandemic.<sup><xref rid=\"r1\" ref-type=\"bibr\">1</xref></sup> The first patient in South Korea was reported on January 19, 2020.<sup><xref rid=\"r2\" ref-type=\"bibr\">2</xref></sup> Since the 31st Korean case, who was the first in Daegu, was diagnosed on February 18, 2020, the number of COVID-19 patients increased explosively because of a cluster infection among a religious group called Shincheonji, which accounted for ~70% of the Daegu cases.<sup><xref rid=\"r3\" ref-type=\"bibr\">3</xref></sup> As of March 14, 2020, the number of confirmed patients in the Daegu region accounted for ~74 % of all of the Korean cases (Fig. <xref ref-type=\"fig\" rid=\"f1\">1</xref>).<sup><xref rid=\"r4\" ref-type=\"bibr\">4</xref></sup> To cope with this major epidemic crisis, Daegu was designated a special disaster area on March 15, 2020. Many emergency centers in Daegu were consecutively and repeatedly closed, and medical staff on duty and inpatients were quarantined because of accidental exposure to a COVID-19 patient who had not been isolated.<sup><xref rid=\"r5\" ref-type=\"bibr\">5</xref></sup> Our emergency room (ER), which has ~30,000 patient visits annually, is a regional emergency center designated by the Ministry of Health and Welfare and 1 of 6 major ERs in Daegu. As of 2018, 13.5% of all ER patients in Daegu city had visited our ER.<sup><xref rid=\"r6\" ref-type=\"bibr\">6</xref></sup>\n</p><p>\n<fig id=\"f1\" orientation=\"portrait\" position=\"float\"><label>Fig. 1.</label><caption><p>The daily number of patients confirmed with COVID-19 in South Korea and Daegu city and the daily number of SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR) and patients with positive results in our emergency room (ER). The daily number of patients confirmed with COVID-19 in South Korea (blue line) and Daegu city (orange line) had reached the peak just after our ER shutdown. The daily number (gray bars) of SARS-CoV-2 RT-PCR and positive results (yellow bars) in our ER increased from the pre-shutdown period to the post-shutdown period.</p></caption><graphic xlink:href=\"S0899823X20003761_fig1\"/></fig>\n</p><p>On February 23, 2020, a 77-year-old male patient visited our ER in an ambulance. He presented with gradual deterioration of mental status, cough, sputum production, and vomiting for 3 days. He was not isolated and was closely monitored in the ER for 31 hours until a severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) reverse-transcriptase polymerase chain reaction (RT-PCR) test was performed. Subsequently, his wife and he were confirmed to have COVID-19. Among a total of 110 persons (47 patients or guardians and 63 healthcare workers [HCWs]) who were shown to have had contact with them by epidemiological investigation and closed-circuit television (CCTV) monitoring, we determined that 5 people (1 patient and 4 HCWs) needed to be quarantined for 14 days because of inappropriate personal protective equipment (PPE). The ER was closed for 55 hours from 3:50 <sc>p.m.</sc> on February 25 to 10:30 <sc>p.m.</sc> on February 27, 2020, while we investigated the close contacts, decontaminated the area, and established new protocols to ensure the safety of the ER.</p><p>Protecting HCWs and patients from SARS-CoV-2 while maintaining functional emergency medical care were critical in responding properly to this outbreak.<sup><xref rid=\"r7\" ref-type=\"bibr\">7</xref></sup> We implemented new interventions, including active isolation, surveillance, and comprehensive use of PPE in the ER, to prevent recurrence of an ER shutdown and nosocomial transmission of COVID-19. We performed this research to analyze the effects of our interventions during this outbreak.</p><sec sec-type=\"materials\" id=\"s1\"><title>Materials and methods</title><sec id=\"s1-1\" sec-type=\"other\"><title>Study population and design</title><p>This cross-sectional, observational study was conducted in a 635-bed, tertiary-care, academic hospital in Daegu, South Korea, from February 10 to March 16, 2020. The medical records for all patients visiting the ER during the study period were retrospectively reviewed. After the ER shutdown, we implemented following interventions in the ER: (1) Triage facilities were set up outside the ER (Fig. <xref ref-type=\"fig\" rid=\"f2\">2</xref>). (2) SARS-CoV-2 RT-PCR testing and chest X-ray were performed outside the ER for most patients who needed to be hospitalized, and these patients were admitted after their COVID-19 status was established. (3) Respiratory samples were obtained in the contaminated area (Fig. <xref ref-type=\"fig\" rid=\"f2\">2</xref>B) using drive-through or walk-through testing access for patients in stable condition. (4) Patients with respiratory symptoms, fever, abnormal chest x-ray findings, or any epidemiologic relevance to COVID-19 were isolated. (5) A portable negative-pressure isolation chamber was employed for COVID-19 patients and for patients whose COVID-19 status had not been identified but who needed to be moved inside the hospital beyond the ER. (6) A universal mask policy and a comprehensive use of PPE were established. (7) The number of doctors on duty was increased from 8 to 11 and from 23 to 34 for nurses. (8) Real-time communications were established between members of the COVID-19 patient management task force.</p><p>\n<fig id=\"f2\" orientation=\"portrait\" position=\"float\"><label>Fig. 2.</label><caption><p>Schematic illustrations of the emergency room structure changes between the pre-shutdown period and the post-shutdown period. (A) The structure of the emergency room (ER) in the pre-shutdown period. Before ER shutdown, there were 24 beds in 3 zones (A, B and C) and 2 nonairborne infection isolation rooms between entrance 1 and entrance 2. The 16 beds for adult patients were divided into zone A and B according to the severity of illness, and zone C contained 8 beds for children. The interbed distance was 1.5 m. (B) The structure of ER in the post-shutdown period. After the ER shutdown, we designated the clean area (blue letters) and the contaminated area (red letters) separated by entrance 2. We set up a triage including a reception area, a laboratory, a chest x-ray area, and a resuscitation room (isolation room 6 or 7) outside the ER using intermodal containers. We built airborne infection prevention systems in the isolation rooms 1, 2, 3, 4, 6 and 7 and x-ray 2 and laboratory rooms using mobile negative-air machines. We reduced the number of beds in zones A, B, and C to 14 and widened the interbed distance to 2 m. High-resolution closed-circuit televisions and portable patient monitors were installed in all of the isolation rooms to monitor vital signs, level of consciousness, and patient movement.</p></caption><graphic xlink:href=\"S0899823X20003761_fig2\"/></fig>\n</p><p>For this analysis, we defined the pre-shutdown period as February 10–25, 2020, and the post-shutdown period as February 28 to March 16, 2020. We compared the patient outcomes and durations of ER stay from both periods.</p></sec><sec id=\"s1-2\" sec-type=\"other\"><title>SARS-CoV-2 RT-PCR</title><p>Before the ER shutdown, SARS-CoV-2 RT-PCR was performed by an outside laboratory; after the ER shutdown, it was performed in our laboratory in the hospital. We expanded the regular working shifts of laboratory personnel from the usual 3 shifts to 4 shifts to shorten the turnaround time from sampling to obtaining a result. RNA was extracted from clinical samples with an automated nucleic acid extraction platform Libex (Xian Tianlong Science &Technology, Xi’an, China). SARS-CoV-2 was detected by RT-PCR using a PowerChekTM 2019 nCoV Real-Time PCR Kit (KogeneBiotech, Seoul, Korea) and a CFX96 real-time PCR detection system (Bio-Rad, Berkeley, CA). The statistics for these RT-PCR tests were analyzed, including the total number of tests, average number of tests per day, and turnaround time of tests in the ER between the pre-shutdown period and the post-shutdown period. This study was exempt from review by the institutional review board of the Kyungpook National University Chilgok Hospital (no. KNUCH 2020-03-034).</p></sec><sec id=\"s1-3\" sec-type=\"other\"><title>Statistical analysis</title><p>Continuous variables were expressed as the means ± standard deviation or median (IQR) and were compared using the Student <italic>t</italic> test or the Mann–Whitney U test. Categorical variables were compared with the Pearson χ<sup><xref rid=\"r2\" ref-type=\"bibr\">2</xref></sup> test or the Fisher exact test. The time lengths are expressed as HH:MM (ie, hours and minutes). All tests of significance were 2-tailed; <italic>P</italic> ≤ .05 was considered significant. The results were analyzed using SPSS version 21.0 software (IBM, Armonk, NY).</p></sec></sec><sec sec-type=\"results\" id=\"s2\"><title>Results</title><sec id=\"s2-1\" sec-type=\"other\"><title>COVID-19 RT-PCR test</title><p>In total, 1,727 patients were treated in the ER during entire study period (pre-shutdown, n = 1,037; post-shutdown, n = 690) (Table <xref rid=\"tbl1\" ref-type=\"table\">1</xref>). The proportions of the patients in whom SARS-CoV-2 RT-PCR was performed increased from 5.8% to 64.6% (<italic>P</italic> < .001), and the average number of tests per day increased from 3.8±4.3 to 24.7±5.0 (<italic>P</italic> < .001) from the pre-shutdown period to the post-shutdown period (Table <xref rid=\"tbl1\" ref-type=\"table\">1</xref>) (Fig. <xref ref-type=\"fig\" rid=\"f1\">1</xref>). Among the 690 patients in the post-shutdown period, 245 (35.4%) patients were not tested because they had already been tested (n = 85); they were discharged directly from the ER after asymptomatic short ER stays (n = 153). Also, 6 patients died in the ER after short ER stays. The mean turnaround time decreased from 23:31 ±6:43 hours to 9:27±3:41 hours (<italic>P</italic> < .001) from the pre-shutdown period to the post-shutdown period.</p><p>\n<table-wrap id=\"tbl1\" orientation=\"portrait\" position=\"float\"><label>Table 1.</label><caption><p>Changes in General Characteristics and Outcomes Before and After the Shutdown Period</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th colspan=\"1\" rowspan=\"1\">Variables</th><th align=\"center\" colspan=\"1\" rowspan=\"1\">Before the Shutdown<break/>(2/10/20–2/25/20)</th><th align=\"center\" colspan=\"1\" rowspan=\"1\">After the Shutdown<break/>(2/28/20–3/16/20)</th><th colspan=\"1\" rowspan=\"1\">\n<italic>P</italic> Value</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Total no. of patients</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1,037</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">690</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>Gender, no. (%)</bold>\n</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td rowspan=\"1\" colspan=\"1\" align=\"center\">.115</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Male</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">543 (52.4)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">388 (56.2)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Female</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">494 (47.6)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">302 (43.8)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Age, mean y ±SD</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">44.0±27.6</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">52.0±26.3</td><td rowspan=\"1\" colspan=\"1\" align=\"center\"><.001</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>Visit type, no. (%)</bold>\n</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td rowspan=\"1\" colspan=\"1\" align=\"center\"><.001</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Direct visit</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">664 (64.0)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">399 (57.8)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> 119 rescue</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">162 (15.6)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">176 (25.6)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Transfer</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">178 (17.2)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">79 (11.4)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Outpatient</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">32 (3.1)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">31 (4.5)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Others</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1 (0.1)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5 (0.7)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>KTAS, no. (%)</bold>\n</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td rowspan=\"1\" colspan=\"1\" align=\"center\">.157</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Level 1</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">12 (1.2)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">12 (1.7)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Level 2</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">25 (2.4)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">17 (2.5)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Level 3</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">361 (34.8)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">210 (30.4)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Level 4</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">622 (60.0)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">445 (64.5)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Level 5</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">17 (1.6)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">6 (0.9)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>Performance of RT-PCR</bold>\n</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Total no. (%)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">60 (5.8)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">445 (64.6)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\"><.001</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Average no. per day (mean±SD)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">3.8±4.3</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">24.7±5.0</td><td rowspan=\"1\" colspan=\"1\" align=\"center\"><.001</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Turnaround time (mean±SD)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">23:31±6:43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">9:27±3:41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\"><.001</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>No. of RT-PCR results, no. (%)</bold>\n</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Positive</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">7 (11.7)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">23 (5.2)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Negative</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">53 (88.3)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">422 (94.8)</td><td colspan=\"1\" rowspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>Outcome</bold>\n</td><td colspan=\"1\" rowspan=\"1\"/><td colspan=\"1\" rowspan=\"1\"/><td rowspan=\"1\" colspan=\"1\" align=\"center\">.004</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Admission to GW (%)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">268 (25.8)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">196 (28.4)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.239</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Admission to ICU (%)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">14 (1.4)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">20 (2.9)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.023</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Transfer to other hospital (%)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">13 (1.3)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">13 (1.9)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.292</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Discharge (%)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">733 (70.6)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">440 (63.8)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.003</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Death (DOA) (%)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">9 (1) (0.9)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">21 (0) (3.0)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.001</td></tr><tr><td colspan=\"4\" rowspan=\"1\">\n<bold>Median duration of ER stay (IQR)</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Total cases</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2:52 (1:21–5:25)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2:55 (1:06–11:05)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.012</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Admission to GW</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">4:44 (2:33–10:00)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">17:18 (7:34–25:21)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.000</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Admission to ICU</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1:41 (0:59–2:56)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">8:38 (1:50–14:52)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.033</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Transfer to other hospital</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">3:01 (1:19–12:00)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10:44 (3:48–23:59)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.038</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Discharge</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2:28 (1:08–4:12)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1:51 (0:50–3:47)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.002</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\"> Death (including DOA)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2:08 (0:57–11:43)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">3:08 (1:23–15:26)</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">.512</td></tr></tbody></table><graphic xlink:href=\"S0899823X20003761_tab1\"/></alternatives><table-wrap-foot><p>Note. SD, standard deviation; KTAS, Korean triage and acuity scale; level 1 (resuscitation), level 2 (emergency), level 3 (urgency), level 4 (less urgency), level 5 (nonurgent); RT-PCR, reverse-transcriptase polymerase chain reaction; GW, general ward; ICU, intensive care unit; DOA, death on arrival; ER, emergency room; IQR, interquartile range.</p></table-wrap-foot></table-wrap>\n</p></sec><sec id=\"s2-2\" sec-type=\"other\"><title>Screening and monitoring for nosocomial spread</title><p>From February 10 to June 14, a total of 9,177 SARS-CoV-2 RT-PCR tests were performed in our hospital. These included 934 tests for HCWs who had symptoms or any accidental exposure to the COVID-19 patients or who were taking care of COVID-19 patients. Also, 3,585 RT-PCR tests were performed for all ER patients who were hospitalized; 641 RT-PCR tests were performed for inpatients who had symptoms or were quarantined; 1,033 tests were preoperative screening tests; 508 tests were performed for preadmission screening; and 1,782 tests were performed for outpatients. During the outbreak, tests for all HCWs taking care of COVID-19 patients had been routinely performed every 2–4 weeks. In addition, all HCWs, inpatients, and their guardians were monitored daily for their symptoms and had screening tests any time they had symptoms. Through those tests and symptom monitoring, no evidence of person-to-person transmission of SARS-CoV-2 was detected in our hospital from February 10 to June 14.</p></sec><sec id=\"s2-3\" sec-type=\"other\"><title>Outcomes and durations of ER stays</title><p>The number of patients (7 versus 23) confirmed with COVID-19 in the ER increased from the pre-shutdown period to the post-shutdown period (Table <xref rid=\"tbl1\" ref-type=\"table\">1</xref>) (Fig. <xref ref-type=\"fig\" rid=\"f1\">1</xref>). Among 7 patients confirmed in the pre-shutdown period, 3 patients were admitted to the COVID-19 general care ward, and 4 patients were diagnosed after discharge. In total, 23 COVID-19 patients in the post-shutdown period were isolated in the ER without any problematic accidental exposure and nosocomial transmission. Among them, 10 patients were admitted to the COVID-19 general care ward, 2 patients were admitted to the COVID-19 intensive care unit (ICU), 6 patients were discharged from ER, 3 patients were transferred to other hospitals, and 2 patients who came to the ER in cardiac arrest died and were confirmed positive for COVID-19 posthumously.</p><p>The rates of ICU admission (1.4% vs 2.9%, <italic>P</italic> = .023) and mortality (0.9% vs 3.0%; <italic>P</italic> = .001) in the ER increased from the pre-shutdown period to the post-shutdown period (Table <xref rid=\"tbl1\" ref-type=\"table\">1</xref>). Among 9 deceased patients in the pre-shutdown period, 2 patients died after CPR, 3 patients in cardiac arrest died after CPR, and 3 patients died with a do-not-resuscitate (DNR) order. Among 21 deceased patients in the post-shutdown period, 3 patients died after CPR, 8 patients in cardiac arrest died after CPR, and 10 patients died with a DNR order. The 30-day mortality rates among patients admitted to the ICU were not different between the pre-shutdown period and the post-shutdown period: 21.4% (3 of 14) versus 30.0% (6 of 20) (<italic>P</italic> = .577).</p><p>The median duration of stay in the ER among hospitalized (general care ward and ICU) patients increased between the pre-shutdown period and the post-shutdown period: 4:30 hours (IQR, 2:17–9:48) versus 14:33 hours (IQR, 6:55–24:50) (<italic>P</italic> < .001). The median duration of stay outside the ER for tests and waiting in the post-shutdown period was 00:44 hours (IQR, 00:17–01:33).</p></sec></sec><sec sec-type=\"discussion\" id=\"s3\"><title>Discussion</title><p>In 2015, South Korea experienced the largest outbreak (186 cases and 38 deaths) of Middle East respiratory syndrome (MERS) outside the Middle East because of massive transmissions from a single, nonisolated patient in an overcrowded ER.<sup><xref rid=\"r8\" ref-type=\"bibr\">8</xref>,<xref rid=\"r9\" ref-type=\"bibr\">9</xref></sup> This experience caused hospitals in the city of Daegu, which had the first large outbreak of COVID-19 outside China, to respond actively and promptly to the accidental exposure to COVID-19 patients who were not identified for isolation at triage in the ER. In Daegu, 40 temporary ER closures took place, and 6 level-1 or level-2 ERs were shut down 27 times for 769 hours from February 18 to March 26, 2020.<sup><xref rid=\"r5\" ref-type=\"bibr\">5</xref></sup> To prevent ER shutdown and nosocomial transmission of COVID-19, many ERs in Daegu revised triage procedures and performed active surveillance and isolation and implemented a universal mask policy and comprehensive use of PPE, similar to our hospital. Consequently, these ERs could operate successfully, even amid a severe COVID-19 outbreak.<sup><xref rid=\"r5\" ref-type=\"bibr\">5</xref>,<xref rid=\"r10\" ref-type=\"bibr\">10</xref></sup> However, performing triage procedures, testing (laboratory and chest x-ray), and resuscitation outside the ER can increase the duration of stay in the ER and can affect patient outcomes. In fact, overcrowding and long duration of stay in the ER in general hospitals have been a constant problem in Korea. According to the 2015 nationwide survey of ERs, the average duration of stay among 414 ERs in Korea was 6 hours and 45 minutes; the average duration of stay at 20 ERs listed in the order of long stay was 14 hours.<sup><xref rid=\"r11\" ref-type=\"bibr\">11</xref></sup> Durations were becoming shorter through much effort but became longer again in the COVID-19 outbreak. The rates of ICU admission and mortality were higher after the interventions were implemented. The patients who came to the ER in cardiac arrest and died after CPR and those who died with DNR order comprised the majority of mortality cases. Therefore, we suspect think that patients with severe conditions could not come to the ER as easily as before because of the saturation of healthcare facilities associated with the COVID-19 outbreak in Daegu, or they were reluctant to come to the ER promptly for fear of being infected with COVID-19. For example, 3 COVID-19 patients in Daegu died at home while waiting for hospitalization.<sup><xref rid=\"r10\" ref-type=\"bibr\">10</xref></sup>\n</p><p>Early identification and rapid isolation of patients with COVID-19 are crucial to interrupting the spread of this virus.<sup><xref rid=\"r12\" ref-type=\"bibr\">12</xref>,<xref rid=\"r13\" ref-type=\"bibr\">13</xref></sup> The World Health Organization (WHO) also emphasized that countries need to implement strong measures to detect and achieve laboratory confirmation of their cases early.<sup><xref rid=\"r14\" ref-type=\"bibr\">14</xref></sup> In Korea, the Ministry of Food and Drug Safety urgently approved a diagnostic kit for SARS-CoV-2 RT-PCR and required certified private hospitals to use that kit beginning in February 2020.<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref></sup> The high level of test performance made it possible for us to test most patients to be hospitalized and for these patients to wait in the isolation room until the test results were obtained. When they needed to be moved inside the hospital for emergency operations or procedures before test results were obtained, we used a portable negative-pressure isolation chamber and comprehensive PPE. We previously reported on a patient undergoing appendectomy in a negative-pressure operating room with medical personnel wearing comprehensive PPE and including a powered air-purifying respirator.<sup><xref rid=\"r16\" ref-type=\"bibr\">16</xref></sup> He had a positive SARS-CoV-2 result after surgery but did not cause any nosocomial transmission of the virus. The drive-through screening system, which was first implemented at our hospital on February 23, 2020, was of great help in speeding up safe respiratory sample collection.<sup><xref rid=\"r17\" ref-type=\"bibr\">17</xref></sup>\n</p><p>SARS-CoV-2 transmission occurs mainly through respiratory droplets and contact, and airborne transmission may be possible during aerosol-generating procedures (AGPs). In this context, the WHO currently recommends droplet and contact precautions for suspected or confirmed COVID-19 patients and airborne precautions for AGPs.<sup><xref rid=\"r18\" ref-type=\"bibr\">18</xref></sup> However, appropriate selection and use of respiratory PPE during the COVID-19 crisis remains controversial.<sup><xref rid=\"r19\" ref-type=\"bibr\">19</xref></sup> The Korean Centers for Disease Control and Prevention (KCDC) recommended airborne and contact precautions in any situation involving contact with a suspected or confirmed patient, based on the experience of the 2015 MERS outbreak.<sup><xref rid=\"r20\" ref-type=\"bibr\">20</xref></sup> The KCDC initially recommended coveralls with shoe covers and double gloves for contact precautions; eye shield, face shield, and goggles for eye protection; N95 respirators or equivalent for respiratory protection; and powered air-purifying respirators when AGPs are performed.<sup><xref rid=\"r20\" ref-type=\"bibr\">20</xref></sup> Long-sleeved, water-resistant gowns and KF94 masks are recommended in the revision of previous recommendations. Following this KCDC guideline, we strengthened the level of the required PPEs in the ER to ensure safety in the events of accidental SARS-CoV-2 exposure. We think the strengthened PPE and universal mask policies played a crucial role in protecting HCWs and patients and guardians from accidental exposure to SARS-CoV-2 in the ER. Although PPE was difficult to obtain in the early stages of this outbreak, similar to the situation in other large cities, the supply was never exhausted. The Korean government and local city authorities controlled the consumption and supply of this critical element of care.<sup><xref rid=\"r10\" ref-type=\"bibr\">10</xref></sup> Healthcare facilities and HCWs had the highest priority for obtaining PPE. The role of the government and local city authorities was crucial for controlling the supply and demand of PPE during the outbreak.</p><p>The ER, which serves as a gatekeeper for hospitals, is expected to be the area most exposed to SARS-CoV-2. If healthcare facilities fail to organize an effective system for screening, isolating, and testing suspected cases, an increased number of patients and confusion in the ER can turn an ER into the epicenter of a hospital-associated outbreak.<sup><xref rid=\"r21\" ref-type=\"bibr\">21</xref>,<xref rid=\"r22\" ref-type=\"bibr\">22</xref></sup> The value of intermodal containers used for extra space outside the ER (Fig. <xref ref-type=\"fig\" rid=\"f2\">2</xref>B) and mobile negative-air machines used in the AIIRs was demonstrated in Korea during the MERS outbreak.<sup><xref rid=\"r21\" ref-type=\"bibr\">21</xref></sup> The temporary AIIRs in our ICU using mobile negative-air machines has played a crucial role in managing critically ill COVID-19 patients.<sup><xref rid=\"r23\" ref-type=\"bibr\">23</xref></sup> However, intermodal containers and mobile negative-air machines are only temporary equipment. Conventional or mobile telephone communication in the contaminated area was used as much as possible to reduce contact between HCWs and patients. Telemedicine can be useful for improving infection control during the COVID-19 pandemic.<sup><xref rid=\"r17\" ref-type=\"bibr\">17</xref>,<xref rid=\"r24\" ref-type=\"bibr\">24</xref>,<xref rid=\"r25\" ref-type=\"bibr\">25</xref></sup> To smooth the flow of patients, key personnel from the various departments (eg, administration, infectious diseases, respiratory diseases, emergency medicine, COVID-19 general care and ICU nursing teams, and the infection control team) conducted real-time communication using a mobile messaging application to assess the availability of beds, patient acceptance capabilities, and hospitalization process. The integrated response between our team representative and the out-of-hospital emergency system operated by the local government was critical in managing COVID-19 patients properly and preventing accidental SARS-CoV-2 exposure in each ER.</p><p>This study has several limitations. First, this study describes the experience of only 1 hospital, and the results may not be generalizable. However, our successful experience could be modified as a suitable model for ER operation in other areas during the COVID-19 crisis. We have provided detailed information for the measures we implemented. Second, this study is a retrospective, observational study. Because multiple interventions were implemented simultaneously, it is difficult to clearly determine which intervention worked significantly. However, a controlled experimental trial was not realistically possible during this swift-moving outbreak.</p><p>In conclusion, problematic accidental exposure and nosocomial transmission of the COVID-19 can be successfully prevented through active isolation and surveillance polices and comprehensive PPE use despite longer ER stays and the presence of more severely ill patients during a COVID-19 outbreak.</p></sec></body><back><ack><title>Acknowledgments</title><p>We appreciate all of the staff members who participated in coping with the COVID-19 outbreak in the Kyungpook National University Chilgok Hospital.</p></ack><sec id=\"s4\" sec-type=\"other\"><title>Financial support</title><p>This study was supported by a research grant from the Daegu Medical Association COVID-19 scientific committee.</p></sec><sec id=\"s5\" sec-type=\"other\"><title>Conflicts of interest</title><p>All authors report no conflicts of interest relevant to this study.</p></sec><ref-list id=\"reflist1\"><title>References</title><ref id=\"ref1\"><label>1.</label><mixed-citation publication-type=\"journal\" id=\"r1\">\n<string-name>\n<surname>Cucinotta</surname>\n<given-names>D</given-names>\n</string-name>, <string-name>\n<surname>Vanelli</surname>\n<given-names>M.</given-names>\n</string-name>\n<article-title>WHO declares COVID-19 a pandemic</article-title>. <source>Acta Biomed</source>\n<year>2020</year>;<volume>91</volume>:<fpage>157</fpage>–<lpage>160</lpage>.<pub-id pub-id-type=\"pmid\">32191675</pub-id></mixed-citation></ref><ref id=\"ref2\"><label>2.</label><mixed-citation publication-type=\"journal\" id=\"r2\">\n<string-name>\n<surname>Kim</surname>\n<given-names>JY</given-names>\n</string-name>, <string-name>\n<surname>Choe</surname>\n<given-names>PG</given-names>\n</string-name>, <string-name>\n<surname>Oh</surname>\n<given-names>Y</given-names>\n</string-name>, <etal>et al.</etal>\n<article-title>The first case of 2019 novel coronavirus pneumonia imported into Korea from Wuhan, China: implication for infection prevention and control measures</article-title>. <source>J Korean Med Sci</source>\n<year>2020</year>;<volume>35</volume>:<fpage>e61</fpage>.<pub-id pub-id-type=\"pmid\">32030925</pub-id></mixed-citation></ref><ref id=\"ref3\"><label>3.</label><mixed-citation publication-type=\"journal\" id=\"r3\">\n<collab>Korean Society of Infectious Diseases</collab>, <article-title>Korean Society of Pediatric Infectious Diseases, Korean Society of Epidemiology, Korean Society for Antimicrobial Therapy, Korean Society for Healthcare-associated Infection Control and Prevention, and Korea Centers for Disease Control and Prevention. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807888</article-id><article-id pub-id-type=\"pmc\">PMC7431853</article-id><article-id pub-id-type=\"publisher-id\">70850</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70850-0</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Qin</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Walker</surname><given-names>Douglas</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Uppal</surname><given-names>Karan</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Zihe</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ma</surname><given-names>Chunyu</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tran</surname><given-names>ViLinh</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Shuzhao</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Jones</surname><given-names>Dean P.</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Yu</surname><given-names>Tianwei</given-names></name><address><email>yutianwei@cuhk.edu.cn</email></address><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.24516.34</institution-id><institution-id institution-id-type=\"ISNI\">0000000123704535</institution-id><institution>School of Software Engineering, </institution><institution>Tongji University, </institution></institution-wrap>Shanghai, 201804 China </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.59734.3c</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0670 2351</institution-id><institution>Department of Environmental Medicine and Public Health, </institution><institution>Icahn School of Medicine at Mount Sinai, </institution></institution-wrap>New York, NY 10029 USA </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.189967.8</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0941 6502</institution-id><institution>Department of Medicine, School of Medicine, </institution><institution>Emory University, </institution></institution-wrap>Atlanta, GA 30322 USA </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.249880.f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0374 0039</institution-id><institution>The Jackson Laboratory, </institution></institution-wrap>Farmington, CT 06032 USA </aff><aff id=\"Aff5\"><label>5</label>School of Data Science, The Chinese University of Hong Kong – Shenzhen, Shenzhen, 518172 Guangdong Province China </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13856</elocation-id><history><date date-type=\"received\"><day>3</day><month>2</month><year>2020</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">With the growth of metabolomics research, more and more studies are conducted on large numbers of samples. Due to technical limitations of the Liquid Chromatography–Mass Spectrometry (LC/MS) platform, samples often need to be processed in multiple batches. Across different batches, we often observe differences in data characteristics. In this work, we specifically focus on data generated in multiple batches on the same LC/MS machinery. Traditional preprocessing methods treat all samples as a single group. Such practice can result in errors in the alignment of peaks, which cannot be corrected by post hoc application of batch effect correction methods. In this work, we developed a new approach that address the batch effect issue in the preprocessing stage, resulting in better peak detection, alignment and quantification. It can be combined with down-stream batch effect correction methods to further correct for between-batch intensity differences. The method is implemented in the existing workflow of the apLCMS platform. Analyzing data with multiple batches, both generated from standardized quality control (QC) plasma samples and from real biological studies, the new method resulted in feature tables with better consistency, as well as better down-stream analysis results. The method can be a useful addition to the tools available for large studies involving multiple batches. The method is available as part of the apLCMS package. Download link and instructions are at <ext-link ext-link-type=\"uri\" xlink:href=\"https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/\">https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/</ext-link>.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Data processing</kwd><kwd>Scientific data</kwd><kwd>Computational biology and bioinformatics</kwd></kwd-group><funding-group><award-group><funding-source><institution>National Key R</institution></funding-source></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">http://dx.doi.org/10.13039/100000002</institution-id><institution>National Institutes of Health</institution></institution-wrap></funding-source><award-id>U01CA235493</award-id></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Metabolomics using liquid chromatography-mass spectrometry (LC/MS) is widely used in identifying disease biomarkers, finding drug targets and unravelling complex biological networks. A high-resolution LC/MS profile from a complex biological sample contains thousands of features, and different LC/MS platforms yield profiles of different characteristics. There are a number of computational pipelines that conduct the necessary steps to preprocess LC/MS data, including peak detection, peak quantification, retention time (RT) correction, feature alignment, and weak signal recovery<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. Some methods provide utilities to group features that are potentially derived from the same metabolite<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref>–<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. Other data servers and packages are available to annotate features to known metabolites based on m/z and RT information<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref>–<xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>.\n</p><p id=\"Par3\">When the sample size is large, it is often necessary for the samples to be processed in batches. Across the batches, even if the data are generated from the same machine, we often observe different data characteristics. Using traditional data preprocessing approaches, we either treat all the samples as a single batch, or preprocess different batch individually, and then seek to merge the feature tables. As we discuss in the following, both of the approaches have some issues.</p><p id=\"Par4\">If we treat all samples as a single batch, the between-batch data characteristic changes will be considered as random noise. More lenient thresholds have to be used in feature alignment and weak signal recovery, in order to tolerate the between-batch differences. This can result in distinct features being artificially merged as a single feature. On the other hand, if a feature has a large drift in RT across batches, it may be artificially split into two features. The issue of misalignment caused by batch effect has been discussed in more detail by Brunius et al.<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>.</p><p id=\"Par5\">An alternative approach is to preprocess each batch individually, followed by alignment of features between the feature tables from separate batches<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. This approach allows optimal alignment within each batch. However, without between-batch RT correction and weak signal recovery across batches, low intensity features that are initially identified in a subset of batches cannot be accurately quantified in the remaining batches.</p><p id=\"Par6\">Applying batch effect removal methods after preprocessing can alleviate some of the issues. They include methods that use quality control data to adjust for signal drift and inter-batch and intra-batch variations<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref>–<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>, and some methods that use data characteristics without the need for quality control, mainly for between-batch adjustments<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref>–<xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>. However, such approaches can only adjust signal intensity. They cannot address issues such as misalignment of features across batches<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>, or the incomplete weak signal recovery from the original data.</p><p id=\"Par7\">To tackle the afore-mentioned problems, we propose a new approach that preprocess the data in a two-stage manner. The method directly uses the batch information to allow optimal within-batch and between-batch alignments. Within each batch, every sample contains a small amount of nonlinear RT drift, which is typically addressed by nonlinear curve fitting<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. Between batches, there may exist systematic RT drift. Both levels need to be adjusted for in the final data matrix. Another major issue is weak signal recovery across batches, as some peaks are too weak to pass the initial detection threshold, but can be later recovered based on the information of their counterparts in other samples. When such information come from other batches, accurate RT correction is critical for the faithful recovery of the weak signal. In our two-stage approach, the RT adjustment is based on cumulative nonlinear curve-fitting, which allows weak signal recovery across batches. Using a dataset from a quality control sample, a yeast cell line dataset, and a dataset generated from healthy human plasma samples, we show the method offers higher consistency in feature quantification for studies involving multiple batches, yielding better results in down-stream analyses.</p></sec><sec id=\"Sec2\"><title>Materials and methods</title><sec id=\"Sec3\"><title>The overall workflow</title><p id=\"Par8\">Different from the traditional workflow, the proposed method includes a two-stage procedure (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a). In the traditional workflow used by XCMS<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup> and apLCMS<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>, peaks are first identified in the individual profiles based on certain filters, and quantified using certain mathematical peak shape models. Then RT correction is conducted between the profiles, and peaks from different profiles are aligned into features. Then a weak signal recovery step is conducted, in order to capture feature signals that are not strong enough to pass the initial peak detection threshold.<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Illustration of the two-stage preprocessing approach. (<bold>a</bold>) The overall workflow. (<bold>b</bold>) Illustration of the calculation of RT shift for individual samples. (<bold>c</bold>) Example between-batch RT shift calculated from a real dataset.</p></caption><graphic xlink:href=\"41598_2020_70850_Fig1_HTML\" id=\"MO1\"/></fig></p><p id=\"Par9\">The new approach is divided into two stages. In the first stage (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, step 1), the method processes each batch individually by using the common preprocessing workflow that consists of peak detection/quantification, RT adjustment, peak alignment and weak signal recovery. The nonlinear curves for RT adjustment is recorded for each sample.</p><p id=\"Par10\">In the second stage (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, step 2), we generate a batch-level feature matrix for each batch. It is in the same format as the feature matrix from a single sample. For each feature detected in the batch, we keep the m/z value, and take the average RT value in the batch, and the average intensity value in the batch. Then across all the batch-level feature matrices, we conduct another round of RT adjustment and feature alignment (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, step 3). As each batch-level feature matrix is in the same structure as a single sample feature matrix, the RT adjustment and feature alignment can be easily achieved by calling the existing routines. At this stage, tolerance levels different than stage 1 can be used. Then the aligned batch-level features are mapped back to the original within-batch features, and weak signal recovery can be conducted across batches.</p><p id=\"Par11\">There are some challenges in this process. The major challenge is the second-round RT adjustment is conducted on the average RT values from each batch. We need to trace the adjustment back to each single sample in order to conduct weak signal recovery, which we address in the next subsection. The second and smaller challenge is the feature alignments across batch might result in the merging of two features from a batch, in which case we trace back to the feature matrix of the corresponding batch, merge the signal intensities of the corresponding features, and take the mean RT of the corresponding features.</p></sec><sec id=\"Sec4\"><title>The RT correction procedure</title><p id=\"Par12\">In the regular preprocessing procedure, RT adjustment is conducted once by nonlinear curve fitting<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. However in the two-stage procedure, there are two levels of RT deviation to be considered. One is within batch, and the other is between batch. In our new procedure, for each LC/MS profile, both levels of RT deviations are computed and added together, to create an overall RT correction at the profile level (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>b).</p><p id=\"Par13\">First within batch (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, step 1), the sample with the largest number of detected features is selected as the reference. The peak RTs of other samples are adjusted based on this reference sample. For each of the other samples, first a unique match between peaks in the sample and peaks in the reference sample is established based on certain m/z and RT tolerance levels. In the current study, to simplify the comparison between the two-stage and traditional apLCMS, we specified the same tolerance levels for them. Then a nonlinear curve is fitted between the RT difference and the observed RT in the sample to be corrected.</p><p id=\"Par14\">Within the <italic>k</italic>th batch, for the <italic>j</italic>th sample to be corrected, we denote the RTs of the uniquely matched peaks as <inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\\{ {t_{m}^{{\\left( {k,j} \\right)}} } \\right\\}_{m = 1, \\ldots ,M}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:msub><mml:mfenced close=\"}\" open=\"{\"><mml:msubsup><mml:mi>t</mml:mi><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msubsup></mml:mfenced><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>M</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70850_Article_IEq1.gif\"/></alternatives></inline-formula>, and the RT of the corresponding peaks in the reference sample as <inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\\{ {t_{m}^{{\\left( {k,0} \\right)}} } \\right\\}_{m = 1, \\ldots ,M}$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:msub><mml:mfenced close=\"}\" open=\"{\"><mml:msubsup><mml:mi>t</mml:mi><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:mfenced></mml:msubsup></mml:mfenced><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>M</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70850_Article_IEq2.gif\"/></alternatives></inline-formula>. We obtain a nonlinear curve fit for the deviation, represented by function <italic>f</italic>(),<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$ \\Delta t^{{\\left( {k,j} \\right)}} = t^{{\\left( {k,j} \\right)}} - t^{{\\left( {k,0} \\right)}} = f_{k,j} \\left( {t^{{\\left( {k,j} \\right)}} } \\right) + \\varepsilon $$\\end{document}</tex-math><mml:math id=\"M6\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msup><mml:mi>t</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msup><mml:mo>=</mml:mo><mml:msup><mml:mi>t</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msup><mml:mo>-</mml:mo><mml:msup><mml:mi>t</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:mfenced></mml:msup><mml:mo>=</mml:mo><mml:msub><mml:mi>f</mml:mi><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:msup><mml:mi>t</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msup></mml:mfenced><mml:mo>+</mml:mo><mml:mi>ε</mml:mi></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70850_Article_Equa.gif\" position=\"anchor\"/></alternatives></disp-formula>using kernel smoothing, and correct the RT of all the peaks in the <italic>j</italic>th sample to <inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\\{ {t_{m}^{{\\left( {k,j} \\right)}} - \\hat{f}_{k,j} \\left( {t_{m}^{{\\left( {k,j} \\right)}} } \\right)} \\right\\}_{m = 1, \\ldots ,N}$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:msub><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:msubsup><mml:mi>t</mml:mi><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msubsup><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:msubsup><mml:mi>t</mml:mi><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msubsup></mml:mfenced></mml:mrow></mml:mfenced><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70850_Article_IEq3.gif\"/></alternatives></inline-formula>, where <italic>N</italic> is the number of all the peaks in sample <italic>j</italic>.</p><p id=\"Par16\">After processing each batch, we obtain a batch-level feature table for each batch (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, step 2). In the feature table is the average RT value for each of the features in the batch. Between batches, we conduct a similar curve fit using the average feature RTs within each batch, against a reference batch (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, step 3). The batch with the largest number of aligned features is taken as the reference batch. For the <italic>k</italic>th batch, we denote the average RTs of the uniquely matched features as <inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\\{ {\\tau_{n}^{\\left( k \\right)} } \\right\\}_{n = 1, \\ldots ,P}$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:msub><mml:mfenced close=\"}\" open=\"{\"><mml:msubsup><mml:mi>τ</mml:mi><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mi>k</mml:mi></mml:mfenced></mml:msubsup></mml:mfenced><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>P</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70850_Article_IEq4.gif\"/></alternatives></inline-formula>, and the average RTs of the corresponding features in the reference batch <inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\\{ {\\tau_{n}^{\\left( 0 \\right)} } \\right\\}_{n = 1, \\ldots ,P}$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:msub><mml:mfenced close=\"}\" open=\"{\"><mml:msubsup><mml:mi>τ</mml:mi><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mn>0</mml:mn></mml:mfenced></mml:msubsup></mml:mfenced><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>P</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70850_Article_IEq5.gif\"/></alternatives></inline-formula>. We obtain a nonlinear curve fit for the deviation, represented by function <italic>g(),</italic><disp-formula id=\"Equb\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$ \\Delta \\tau^{\\left( k \\right)} = \\tau^{\\left( k \\right)} - \\tau^{\\left( 0 \\right)} = g_{k} \\left( {\\tau^{\\left( k \\right)} } \\right) + \\varepsilon $$\\end{document}</tex-math><mml:math id=\"M14\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msup><mml:mi>τ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>k</mml:mi></mml:mfenced></mml:msup><mml:mo>=</mml:mo><mml:msup><mml:mi>τ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>k</mml:mi></mml:mfenced></mml:msup><mml:mo>-</mml:mo><mml:msup><mml:mi>τ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>0</mml:mn></mml:mfenced></mml:msup><mml:mo>=</mml:mo><mml:msub><mml:mi>g</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:msup><mml:mi>τ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>k</mml:mi></mml:mfenced></mml:msup></mml:mfenced><mml:mo>+</mml:mo><mml:mi>ε</mml:mi></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70850_Article_Equb.gif\" position=\"anchor\"/></alternatives></disp-formula>using kernel smoothing. Some example between-batch RT correction curves from real data (the CHDWB data described later) are shown in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>c. In the batch-level feature table, the RT is then corrected to <inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\\{ {\\tau_{n}^{\\left( k \\right)} - \\hat{g}_{k} \\left( {\\tau_{n}^{\\left( k \\right)} } \\right)} \\right\\}_{n = 1, \\ldots ,N}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:msub><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:msubsup><mml:mi>τ</mml:mi><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mi>k</mml:mi></mml:mfenced></mml:msubsup><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>g</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:msubsup><mml:mi>τ</mml:mi><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mi>k</mml:mi></mml:mfenced></mml:msubsup></mml:mfenced></mml:mrow></mml:mfenced><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70850_Article_IEq6.gif\"/></alternatives></inline-formula>. Feature alignment are then conducted using the corrected batch-level RT, and then mapped back to the within-batch feature tables. As all batches share the same RT range, the parameter setting for the kernel smoother is the same for within-batch and cross-batch curve fitting.</p></sec><sec id=\"Sec5\"><title>Weak signal recovery procedure</title><p id=\"Par18\">Some features pass the detection threshold in only a subset of the batches. For such features, cross-batch weak signal recovery is needed after alignment. However, in the final data table, the RT is corrected across all batches. We need to adjust the RT points in the original data in order for the weak signal recovery to be reliable. Hence an RT correction is conducted for every LC/MS profile in every batch (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, step 3). For the <italic>j</italic>th profile in the <italic>k</italic>th batch, the corrected RT is obtained by:<disp-formula id=\"Equc\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$ t_{m, corrected}^{{\\left( {k,j} \\right)}} = t_{m}^{{\\left( {k,j} \\right)}} - \\hat{f}_{k,j} \\left( {t_{m}^{{\\left( {k,j} \\right)}} } \\right) - \\hat{g}_{k} \\left( {t_{m}^{{\\left( {k,j} \\right)}} - \\hat{f}_{k,j} \\left( {t_{m}^{{\\left( {k,j} \\right)}} } \\right)} \\right), $$\\end{document}</tex-math><mml:math id=\"M18\" display=\"block\"><mml:mrow><mml:msubsup><mml:mi>t</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msubsup><mml:mo>=</mml:mo><mml:msubsup><mml:mi>t</mml:mi><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msubsup><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:msubsup><mml:mi>t</mml:mi><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msubsup></mml:mfenced><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>g</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msubsup><mml:mi>t</mml:mi><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msubsup><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">^</mml:mo></mml:mover><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:msubsup><mml:mi>t</mml:mi><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mfenced></mml:msubsup></mml:mfenced></mml:mrow></mml:mfenced><mml:mo>,</mml:mo></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70850_Article_Equc.gif\" position=\"anchor\"/></alternatives></disp-formula>where <italic>m</italic> indexes the RT points (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>b). After changing the RT, the weak signal recovery can be conducted as previously described<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. Briefly, to recover the weak signal for a target m/z and RT pair in an LC/MS profile, a loose tolerance level in m/z and RT is first used to select a local region. Then two-dimensional kernel smoothing is conducted in the region to detect weak peaks. If a weak peak is close enough to the target m/z and RT pair (threshold determined by the peak detection tolerance levels), and the local point density passes a threshold, it is considered the recovered signal of the feature. More details can be found in<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>.</p></sec><sec id=\"Sec6\"><title>Datasets</title><p id=\"Par20\">We use three datasets for methods comparison. The first was a standard sample (QSTD) constructed from pooled human plasma which was run repeatedly with different batches of samples for quality control purposes. In this analysis, we took the QSTD sample profiles from 10 batches, each containing 10 runs of the same sample. The data were generated using a C18 column combined with the Thermo Fisher Q Exactive Orbitrap Mass Spectrometer, in negative ion mode.</p><p id=\"Par21\">The second dataset was the ST000868 dataset<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>, downloaded from Metabolomics Workbench<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>. The study compared the metabolomic profile of oak and wine yeast strains. The data were collected in three batches. Each yeast strain was measured 3–6 times in every batch.</p><p id=\"Par22\">The third dataset was a subset of the untargeted metabolomics data from Emory/Georgia Tech Center for Health Discovery and Well Being (CHDWB). The CHDWB metabolomics data was collected on healthy individuals that received preventive care, and the metabolomics data can be requested by submitting a request form to the CHDWB (<ext-link ext-link-type=\"uri\" xlink:href=\"https://predictivehealth.emory.edu/research/resources.html\">https://predictivehealth.emory.edu/research/resources.html</ext-link>)<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. The study is a prospective longitudinal cohort study. Biological specimen, including blood samples, are collected every year for each participant. Metabolomics was measured on all subjects at baseline. We focused on the baseline metabolomics data and its relation with baseline body mass index (BMI) in this analysis. There were a total of 25 batches in the entire dataset. Within each batch, roughly 20 subjects were measured. The plasma sample from each subject was measured 3 times consecutively. We refer to them as triplets in the following text. The data were generated using a HILIC column combined with the Thermo Fisher Q Exactive Orbitrap Mass Spectrometer, in positive ion mode.</p></sec><sec id=\"Sec7\"><title>Packages and parameters</title><p id=\"Par23\">We used apLCMS version 6.6.8 and xcms version 3.10.1, in the environment of R version 4.0.0. The apLCMS package and tutorial is available through <ext-link ext-link-type=\"uri\" xlink:href=\"https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/\">https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/</ext-link>, and XCMS is downloaded from Bioconductor.</p><p id=\"Par24\">There are three main parameters for this new approach. For the initial detection of peaks in each batch (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, step 1), <italic>p</italic><sub><italic>within_detect</italic></sub> controls the proportion of profiles a feature needs to be detected from, for it to be considered for the next step; <italic>p</italic><sub><italic>within_report</italic></sub> controls the proportion of profiles a feature need to be present after weak signal recovery, for it to be included in the final feature table from the batch. Between the batches (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, step 3), <italic>p</italic><sub><italic>batches</italic></sub> controls the proportion of batches the feature needs to be present, for it to be included in the overall feature table.</p><p id=\"Par25\">For apLCMS, the peak detection and quantification procedure for single LC/MS profile follows the existing method<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. In this study, the major parameters include min.run = 12, min.pres = 0.5, mz.tol = 1e-5, baseline.correct = 0, min.bw = NA, max.bw = NA, shape.model = \"bi-Gaussian\", sd.cut = c(0.125, 60), sigma.ratio.lim = c(0.2, 5), moment.power = 1. Other parameters are listed in the R codes in the <xref rid=\"MOESM1\" ref-type=\"media\">Supplementary Material</xref>.</p><p id=\"Par26\">For XCMS, four combinations of peak detection and RT correction methods were used. The parameters were optimized by the method IPO in an objective and dataset-specific manner<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>. XCMS IPO_1 uses optimal parameters found by IPO combining matched filter and orbiwarp. XCMS IPO_2 uses optimal parameters found by IPO combining matched filter and loess smoothing. XCMS IPO_3 uses optimal parameters found by IPO combining centWave and orbiwarp. XCMS IPO_4 uses optimal parameters found by IPO combining centWave and loess smoothing. As the parameters are dataset-specific, their values are listed in the Results and Discussions section.</p></sec></sec><sec id=\"Sec8\"><title>Results and discussions</title><p id=\"Par27\">We implemented the method in the existing workflow of the apLCMS package<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>, which conducts both untargeted and hybrid (untargeted/targeted) feature detection<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. To evaluate the feature detection performance of the proposed two-stage approach, we conducted comparison experiments with the traditional apLCMS approach, as well as the popular preprocessing method XCMS<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>, on three real datasets.</p><sec id=\"Sec9\"><title>Results from standard sample (QSTD) data</title><p id=\"Par28\">Using the QSTD data, we compared the performance of the new two-stage apLCMS with tradition apLCMS and XCMS in feature detection and quantification. For apLCMS, we first selected optimal parameter settings for peak detection and kept the parameters the same for both the two-stage and the traditional methods.</p><p id=\"Par29\">For the two-stage approach, we tested two scenarios for within-batch proportion parameters, <italic>p</italic><sub><italic>within_detect</italic></sub> = <italic>p</italic><sub><italic>within_report</italic></sub><italic>, </italic>and 2<italic>p</italic><sub><italic>within_detect</italic></sub> = <italic>p</italic><sub><italic>within_report</italic></sub> . We found the results to be similar with regard to the criteria we used to assess the performance. Thus in the following sections, we report results from using the same values for <italic>p</italic><sub><italic>within_detect</italic></sub> (before weak signal recovery) and <italic>p</italic><sub><italic>within_report</italic></sub> (after weak signal recovery). We used values of 0.2, 0.3, 0.4, 0.6, 0.8 and 1. The second parameter was between-batch detection proportion threshold <italic>p</italic><sub><italic>batches</italic></sub>, i.e. the proportion of batches a feature needed to be present in. We used values of 0.1, 0.2, 0.3, 0.5, 0.7, and 0.9. For the traditional apLCMS procedure, the detection threshold (number of profiles the feature needed to be present in) was set as 5, 10, 15, …., and 95.</p><p id=\"Par30\">For XCMS, we used the IPO package to optimize its parameters under 4 different method combinations. Below are the parameter combinations in each of the 4 settings:</p><p id=\"Par31\">XCMS IPO_1: matched filter parameters: fwhm = 15, snthresh = 1, step = 0.0805, steps = 2, sigma = 6.369, max = 5, mzdiff = 0.639, index = FALSE; peak grouping parameters: method = \"density\", bw = 0.879999, mzwid = 0.0614; Orbiwarp parameters: method = \"obiwarp\", plottype = \"none\", distFunc = \"cor_opt\", profStep = 1, center = 6, response = 1, gapInit = 0.78, gapExtend = 2.7, factorDiag = 2, factorGap = 1, localAlignment = 0.</p><p id=\"Par32\">XCMS IPO_2: matched filter parameters: same as XCMS IPO_1; peak grouping parameters: method = \"density\", bw = 0.879999, mzwid = 0.0362; Loess parameters: missing = 3, extra = 3, span = 0.221, smooth = \"loess\", family = \"gaussian\".</p><p id=\"Par33\">XCMS IPO_3: centWave parameters: peakwidth = c(3, 129.97), ppm = 10, noise = 0, snthresh = 1, mzdiff = -0.0109, prefilter = c(3,100), mzCenterFun = \"wMean\", integrate = 1, fitgauss = FALSE, verbose.columns = FALSE; peak grouping parameters: method = \"density\", bw = 12.4, mzwid = 0.01; Orbiwarp parameters: distFunc = \"cor_opt\", profStep = 1, center = 7, response = 1, gapInit = 0.54, gapExtend = 2.7, factorDiag = 2, factorGap = 1, localAlignment = 0.</p><p id=\"Par34\">XCMS IPO_4: centWave parameters: same as XCMS IPO_3; peak grouping parameters: bw = 0.25, mzwid = 0.0081; Loess parameters: missing = 5, extra = 1, span = 0.326, smooth = \"loess\", family = \"gaussian\".</p><p id=\"Par35\">To achieve different number of features detected by XCMS, while keeping the above parameters fixed, we varied the “minsamp” parameter, which controls the minimum number of samples necessary for a peak group to be detected. We used values of 5, 10, 20, 30, 40, 50, 60, 70, 80, 90.</p><p id=\"Par36\">To evaluate the results, we recorded the total number of zeros in the final data matrix (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>a), number of features with m/z matched to known KEGG metabolites using xMSAnnotator<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup> allowing adduct ions [M–H]<sup>−</sup>, [M–2H]<sup>2−</sup>, [M–2H + Na]<sup>−</sup>, [M–2H + K]<sup>−</sup>, [M–2H + NH4]<sup>−</sup>, [M–H<sub>2</sub>O–H]<sup>−</sup>, [M–H + Cl]<sup>2−</sup>, [M + Cl]<sup>−</sup>, [M + 2Cl]<sup>2−</sup> (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>b), coefficient of variation (CV) in the final data matrix without considering batches with and without considering the zero values (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>c,d), and the coefficient of variation (CV) after merging the repeated measurements in each batch to generate a single measurement from each batch, with and without considering the zero values (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>e,f). In the calculation of CV, including zero values can reflect feature detection consistency in the CV results, while excluding zero values can reflect feature quantification consistency.<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Comparison of the two-stage preprocessing approach with traditional apLCMS and XCMS using standard sample. Each dot represents a parameter setting. (<bold>a</bold>) Total number of zeros in the final data matrix; (<bold>b</bold>) proportion of features with m/z matched to known metabolites using xMSAnnotator; (<bold>c</bold>) level of variation as measured by coefficient of variation (CV) in the final data matrix without considering batches; (d) level of variation as measured by coefficient of variation (CV) in the final data matrix without considering batches, considering only non-zero values; (e) level of variation as measured by CV after merging each batch; (f) level of variation as measured by CV after merging each batch, considering only non-zero values. In all CV plots, the point is median; vertical bars represent 10th to 90th percentile.</p></caption><graphic xlink:href=\"41598_2020_70850_Fig2_HTML\" id=\"MO2\"/></fig></p><p id=\"Par37\">In untargeted metabolomics data measured by LC/MS, zeros in the final data matrix represent a mixture of true non-presence of the metabolic feature and missing values. It is still a difficult issue to address. Given the measurements here were taken on the same sample, we expect a better method to yield less zeros in the data matrix. However, the proportions of zero also depends on how consistent the LC/MS machinery generates the data, and how aggressive the weak signal recovery is conducted. Thus the results need to be considered together with the level of variation in the CV plots. When weak signal recovery is conducted in an overly aggressive manner taking noise ask signal, although the proportion of zeros may be lower, the inclusion of noise as signal will also worsen the quantification consistency. As shown in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>a, when the number of features were large, the two-stage approach (orange) tended to yield smaller proportions of zeros compared to the traditional apLCMS approach (blue) and XCMS (green).</p><p id=\"Par38\">The proportion of features that could be matched were similar for the three methods (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>b). Traditional apLCMS was slightly better, and XCMS was slightly inferior. When the detection threshold was loosened, some noise data points were expected to be mis-identified as features. At the same time, some low-abundance metabolites were detected. Thus we expected a higher false-positive rate in the metabolite mapping, which was a trade-off with a higher detection rate over all metabolites in the sample.</p><p id=\"Par39\">In the measurement of the coefficient of variation (CV) before and after merging within batches, as illustrated in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>c–f, the two-stage method (orange diamonds) yielded less variation compared to the traditional apLCMS (blue dots) and XCMS (green triangles) when zero was included in the calculation of CV (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>c,e). When zero values were excluded, XCMS with matched filter approach yielded better quantification consistency as evidenced by lower CV values (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>d). The advantage disappeared when the data from each batch was merged (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>f). However with regard to detection consistency, XCMS with matched filter resulted in much higher proportion of zeros (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>a). Given the data was collected on the same sample, we expect a feature’s presence should vary little across the files. Overall, the two-stage approach outperformed the traditional apLCMS and XCMS in terms of measurement stability.</p></sec><sec id=\"Sec10\"><title>Results from ST000868 dataset</title><p id=\"Par40\">For apLCMS, we used <italic>p</italic><sub><italic>within_detect</italic></sub> = <italic>p</italic><sub><italic>within_report</italic></sub> = <italic>0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and p</italic><sub><italic>batches</italic></sub> = <italic>0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9.</italic> All other parameter setting were the same as the previous section except min.run = 0.8 and min.pres = 0.4, given the shorter RT range of the data. We note some of the above parameter combinations may result in identical results given the small batch size. For traditional apLCMS, while keeping all other parameters the same as the two-stage approach, we used the detection threshold (number of profiles the feature needed to be present in) of 2, 4, 6, …, 28.</p><p id=\"Par41\">For XCMS, we again used the IPO package to optimize its parameters under 4 different method combinations. Below are the parameter combinations in each of the 4 settings:</p><p id=\"Par42\">XCMS IPO_1: Matched Filter parameters: fwhm = 25, snthresh = 3, step = 0.05, steps = 1, sigma = 10.617, max = 5, mzdiff = 0.75, index = FALSE; peak grouping parameters: method = \"density\", bw = 38, mzwid = 0.015; Orbiwarp parameters: method = \"obiwarp\", plottype = \"none\", distFunc = \"cor_opt\", profStep = 1, center = 3, response = 1, gapInit = 0, gapExtend = 2.7, factorDiag = 2, factorGap = 1, localAlignment = 0.</p><p id=\"Par43\">XCMS IPO_2: matched Filter parameters: same as XCMS IPO_1; peak grouping parameters: method = \"density\", bw = 12.4, mzwid = 0.027; Loess parameters: missing = 3, extra = 3, span = 0.22, smooth = \"loess\", family = \"gaussian\".</p><p id=\"Par44\">XCMS IPO_3: CentWave parameters: peakwidth = c(10, 50), ppm = 5, noise = 0, snthresh = 1, mzdiff = -0.01, prefilter = c(1, 100), mzCenterFun = \"wMean\", integrate = 1, fitgauss = FALSE, verbose.columns = FALSE; peak grouping parameters: method = \"density\", bw = 37.68, mzwid = 0.0001; Orbiwarp parameters: distFunc = \"cor_opt\", profStep = 1, center = 3, response = 1, gapInit = 0, gapExtend = 2.7, factorDiag = 2, factorGap = 1, localAlignment = 0.</p><p id=\"Par45\">XCMS IPO_4: CentWave parameters: same as XCMS IPO_3; peak grouping parameters: bw = 12.4, mzwid = 0.0001; Loess parameters: missing = 1, extra = 2, span = 0.42, smooth = \"loess\", family = \"gaussian\".</p><p id=\"Par46\">To achieve different number of features detected by XCMS, while keeping the above parameters fixed, we varied the “minsamp” parameter, which controls the minimum number of samples necessary for a peak group to be detected. We used values of of 2, 4, 6, …, 28.</p><p id=\"Par47\">To compare the results from the three methods, we compared detection/quantification consistency, matching to known metabolites, and testing results by contrasting the two cell types, as the original study was designed to find the metabolic differences between the genetically different cell types.</p><p id=\"Par48\">Similar to the QSTD data, the two-stage method resulted in smaller proportion of zeros (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>a). In the m/z matching to KEGG metabolites using adduct ions [M–H]<sup>−</sup>, [M–2H]<sup>2−</sup>, [M–2H + Na]<sup>−</sup>, [M–2H + K]<sup>−</sup>, [M–2H + NH4]<sup>−</sup>, [M–H<sub>2</sub>O–H]<sup>−</sup>, [M–H + Cl]<sup>2−</sup>, [M + Cl]<sup>−</sup>, [M + 2Cl]<sup>2−</sup>, the methods performed similarly, with XCMS with centWave peak detection yielding slightly higher rate of matching (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>b). With regard to CV values after adjusting for cell type and batch, i.e. the variation for each cell type within each batch, the two-stage approach resulted in lower CVs (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>c), indicating better detection and quantification consistency.<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Comparison of the two-stage preprocessing approach with traditional apLCMS and XCMS using the ST000868 dataset. Each dot represents a parameter setting. (<bold>a</bold>) Proportion of zeros in the final data matrix before merging triplets for each subject; (<bold>b</bold>) Proportion of features with m/z matched to known metabolites by xMSAnnotator; (<bold>c</bold>) Within-triplet coefficient of variation (CV). Point is median; vertical bars represent 10th to 90th percentile. (<bold>d</bold>) Number of significant features at FDR ≤ 0.2, without batch effect correction; (<bold>e</bold>) Number of significant features at FDR ≤ 0.2, after batch effect correction by ComBat; (<bold>f</bold>) Number of significant features at FDR ≤ 0.2, after batch effect correction by WaveICA.</p></caption><graphic xlink:href=\"41598_2020_70850_Fig3_HTML\" id=\"MO3\"/></fig></p><p id=\"Par49\">We then conducted testing between the two cell types using t-test. All tests were first conducted at the single metabolic feature level, and then the <italic>p</italic> values from all features were subjected to False Discovery Rate (FDR) correction<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. The tests were limited to features with ≤ 33% zeros in at least one of the cell types. Without batch effect correction, all method yielded relatively few significant metabolites at FDR ≤ 0.2, while the two-stage method tended to detect more significant feature (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>d). We then applied two batch effect correction methods. The first was the popular method ComBat<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>, which was originally developed for microarray data, and was later widely used in RNA-seq and metabolomics data. After applying ComBat to each of the data matrices, testing was conducted on the adjusted data. All methods detected more significant metabolic features after the adjustment (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>e). The two-stage approach, when combined with ComBat, resulted in more significant metabolic features than the other two methods (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>e). Among the four combinations of XCMS, matched filter appeared to result in more significant metabolic features. We notice that the number of significant metabolic features from XCMS-processed data tended to fall close to a horizontal line. This is due to the fact that in XCMS, features detected using a more restrictive minsamp setting are a strict subset of those detected using a looser minsamp setting, when other parameters stay the same. When the threshold ≤ 33% zeros in at least one cell type was applied to the data matrix, some matrices obtained with different minsamp settings yielded similar matrices after filtration.</p><p id=\"Par50\">We applied another recent batch effect correction method that was specifically developed for metabolomics data – WaveICA, which has shown excellent performance when compared to some other existing methods<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. After applying WaveICA to all the data matrices, the results were similar to ComBat. Again the two-stage approach detected more significant features (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>f). Overall, when applied to the ST000868 dataset, the new two-stage approach resulted in more consistent peak detection and better between-cell type testing results.</p></sec><sec id=\"Sec11\"><title>Results from the CHDWB data</title><p id=\"Par51\">In this study, we selected six batches from the CHDWB data that evenly spanned the entire dataset: batches 1, 5, 10, 15, 20, 25, which included 115 subjects in total. Between the traditional apLCMS and the new two-stage approach, we kept all other parameters the same, except the detection proportion threshold values. In the two-stage procedure, we applied within-batch detection proportion threshold values 0.2, 0.3, 0.4, 0.6, 0.8, and 1, and between-batch detection proportions 0.15, 0.3, 0.45, 0.6, 0.75, and 0.9. Given there were six batches, the between-batch detection proportions meant we required a feature to be initially detected in at least 1, 2, 3, 4, 5, or 6 batches, respectively. For the traditional apLCMS procedure, we set the detection threshold (number of samples) at 30, 60, 90, 120, 180, 240, and 300. For XCMS, we used the IPO package to optimize its parameters under 4 different method combinations. Below are the parameter combinations in each of the 4 settings:</p><p id=\"Par52\">XCMS IPO_1: Matched Filter parameters: fwhm = 27, snthresh = 1, step = 0.015, steps = 2, sigma = 11.4659, max = 5, mzdiff = 0.77, index = FALSE; peak grouping parameters: method = \"density\", bw = 0.879999, mzwid = 0.0265; Orbiwarp parameters: method = \"obiwarp\", plottype = \"none\", distFunc = \"cor_opt\", profStep = 1, center = 5, response = 1, gapInit = 0.928, gapExtend = 2.7, factorDiag = 2, factorGap = 1, localAlignment = 0.</p><p id=\"Par53\">XCMS IPO_2: Matched Filter parameters: same as XCMS IPO_1; peak grouping parameters: method = \"density\", bw = 0.879999, mzwid = 0.0265; Loess parameters: missing = 4, extra = 1, span = 0.05575, smooth = \"loess\", family = \"gaussian\".</p><p id=\"Par54\">XCMS IPO_3: CentWave parameters: peakwidth = c(3,110), ppm = 10, noise = 0, snthresh = 1, mzdiff = -0.0175, prefilter = c(3,100), mzCenterFun = \"wMean\", integrate = 1, fitgauss = FALSE, verbose.columns = FALSE; peak grouping parameters: method = \"density\", bw = 12.4, mzwid = 0.003; Orbiwarp parameters: distFunc = \"cor_opt\", profStep = 1, center = 2, response = 1, gapInit = 0.08, gapExtend = 2.7, factorDiag = 2, factorGap = 1, localAlignment = 0.</p><p id=\"Par55\">XCMS IPO_4: CentWave parameters: same as XCMS IPO_3; peak grouping parameters: bw = 22, mzwid = 0.018; Loess parameters: missing = 1, extra = 3, span = 0.2, smooth = \"loess\", family = \"gaussian\".</p><p id=\"Par56\">To achieve different number of features detected by XCMS, we varied the “minsamp” parameter, which controls the minimum number of samples necessary for a peak group to be detected. We used values 10, 20, 30, 50, 70, 90, 120, 180, 240, 300.</p><p id=\"Par57\">Some settings resulted in data matrices with more than 10,000 features, which is out of the range a regular untargeted analysis would consider. Thus we limited the following discussion to data matrices with 10,000 features or less. We assessed the results based on following criteria for consistency: Total number of zeros in the final data matrix (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>a), features with m/z matched to known metabolites with KEGG IDs using xMSAnnotator, allowing adduct ions [M + H]<sup>+</sup>, [M + NH4]<sup>+</sup>, [M + Na]<sup>+</sup>, [M + ACN + H]<sup>+</sup>, [M + ACN + Na]<sup>+</sup>, [M + 2Na–H]<sup>+</sup>, and [M + K]<sup>+</sup> (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>b), and coefficient of variation within the triplet that measured the same sample (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>c). As shown in Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>a, when the total number of features was below 4,000, the two-stage approach and traditional apLCMS yielded smaller proportion of zeros. When the total number of features went larger, the XCMS with centWave and orbiwap combination and the two-stage approach yielded data matrices that tended to have smaller proportions of zeros. Although the data were generated from different subjects, we still expected the core metabolism to be similar across the subjects, and a better method would conduct more consistent feature alignment between samples/batches, resulting in less zeros in the final data matrix. This should be true especially when smaller number of metabolic features are detected, which are more concentrated in core metabolism.<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Comparison of the two-stage approach with traditional apLCMS and XCMS using CHDWB samples. Each dot represents a parameter setting. (<bold>a</bold>) Proportion of zeros in the final data matrix before merging triplets for each subject; (<bold>b</bold>) proportion of features with m/z matched to known metabolites by xMSAnnotator; (<bold>c</bold>) average within-triplet coefficient of variation (CV). Point is median; vertical bars represent 10th–90th percentile. (<bold>d</bold>) Number of significant features at FDR ≤ 0.2, without batch effect correction; (<bold>e</bold>) Number of significant features at FDR ≤ 0.2, after batch effect correction by ComBat; (<bold>f</bold>) Number of significant features at FDR ≤ 0.2, after batch effect correction by WaveICA.</p></caption><graphic xlink:href=\"41598_2020_70850_Fig4_HTML\" id=\"MO4\"/></fig></p><p id=\"Par58\">With regard to features matched to known metabolites, the three methods performed similarly, with the two-stage approach having a slight edge when the number of features detected were smaller, and XCMS with matched filter having slightly more matched features when the number of features went larger (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>b). We computed the coefficient of variation (CV) over all the metabolic features within each triplet (subject). As shown in Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>c, the median CV level tended to be similar for all the approaches when the number of features were smaller (< 4,000), while the two-stage approach had an edge when the number of features were larger. In addition, the distribution of CV tended to be wider for XCMS, indicating part of the metabolic features showed larger variation within triplets.</p><p id=\"Par59\">Next we merged the triplet measures for each subject. The merging was done by taking the average non-zero values in the triplet for each feature. When all three measurements for a feature were zero, the resulting merged measurement was also zero. Using each of the feature table, we first filtered the features using a threshold of < 25% zeros, and then conducted down-stream analysis using the body mass index (BMI) as the outcome variable, while adjusting for age, gender and race. It is well known that BMI is associated with changes in metabolic patterns<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>. We fitted a linear model for each individual metabolic feature (denoted M):<disp-formula id=\"Equd\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$ BMI = \\mu + \\beta_{1,i} M_{i} + \\beta_{2} Age + \\beta_{3} Age^{2} + \\beta_{4} Gender + \\beta_{5} Race + \\varepsilon $$\\end{document}</tex-math><mml:math id=\"M20\" display=\"block\"><mml:mrow><mml:mi>B</mml:mi><mml:mi>M</mml:mi><mml:mi>I</mml:mi><mml:mo>=</mml:mo><mml:mi>μ</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mi>M</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mi>A</mml:mi><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mi>A</mml:mi><mml:mi>g</mml:mi><mml:msup><mml:mi>e</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mi>G</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mi>R</mml:mi><mml:mi>a</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mo>+</mml:mo><mml:mi>ε</mml:mi></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70850_Article_Equd.gif\" position=\"anchor\"/></alternatives></disp-formula></p><p id=\"Par60\">Here the subscript <italic>i</italic> indexes the metabolic feature. The <italic>p</italic> value associated with <italic>β</italic><sub>1,<italic>i</italic></sub> was recorded. Then the <italic>p</italic> values from all features were subjected to False Discovery Rate (FDR) correction<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>.</p><p id=\"Par61\">Without batch effect correction, the two-stage approach yielded higher number of significant features over the entire range of number of features detected (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>d). We then applied ComBat<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup> to adjust for batch effect in each data matrix before applying the above testing procedure. After the application of ComBat, the two-stage approach showed a trend of increasing number of significant features with the increase of total number of features in the matrix (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>e). It was again the method that detected the highest number of significant features across the range of total number of features. Applying the batch effect correction method WaveICA, the results were more mixed. When the number of features were low to moderate (< 5,000), the two-stage approach detected more significant features. When considering larger number of features, two settings of XCMS with centWave+ orbiwarp resulted in higher number of significant features. Overall, the new two-stage approach again resulted in more consistent peak detection and quantification, as well as better down-stream testing result.</p><p id=\"Par62\">Next we considered the biological interpretability of the testing results. For this purpose, we conducted pathway analyses using Mummichog<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. As Mummichog needed to be conducted manually, we selected a subset of the results for this analysis. We selected four groups of data matrices with ~ 5,000, ~ 4,000, ~ 3,000, and ~ 2000 features, respectively. Because pathway analysis requires a reasonable number of significant features, instead of using FDR, we used features with raw <italic>p</italic> value < 0.05.</p><p id=\"Par63\">As shown in Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>, in the two groups with lower feature counts (~ 2000 and ~ 3,000), the two-stage approach yielded more significant pathways with at least 5 significant metabolic features (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>, last column). In the group of ~ 4,000 features, the two-stage approach tied with traditional apLCMS at 8 significant pathways. In the group with ~ 5,000 features, traditional apLCMS had a slight edge over the two-stage approach. XCMS with centWave+ loess resulted in 5 significant pathways, which was only slightly worse.<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Comparison of feature selection and pathway analysis results.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Method</th><th align=\"left\">Total # features</th><th align=\"left\"># Significant pathways with 5 or more matched significant metabolites</th></tr></thead><tbody><tr><td align=\"left\">Two-stage, P<sub>within.detect</sub> = 0.3 p<sub>batches</sub> = 0.3</td><td align=\"left\">5,024</td><td align=\"left\">6</td></tr><tr><td align=\"left\">Two-stage, P<sub>within.detect</sub> = 0.6 p<sub>batches</sub> = 0.15</td><td align=\"left\">4,988</td><td align=\"left\">5</td></tr><tr><td align=\"left\">Traditional apLCMS, min.profiles = 50</td><td align=\"left\">5,097</td><td align=\"left\"><bold><italic>7</italic></bold></td></tr><tr><td align=\"left\">XCMS matched filter + orbiwarp, minsamp 30</td><td align=\"left\">5,004</td><td align=\"left\">0</td></tr><tr><td align=\"left\">XCMS centWave + orbiwarp, minsamp 300</td><td align=\"left\">5,064</td><td align=\"left\">5</td></tr><tr><td align=\"left\">XCMS centWave + loess, minsamp 240</td><td align=\"left\">5,201</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Two-stage, p<sub>within.detect</sub> = 0.2 p<sub>batches</sub> = 0.45</td><td align=\"left\">4,034</td><td align=\"left\"><bold><italic>8</italic></bold></td></tr><tr><td align=\"left\">Traditional apLCMS, min.profiles = 90</td><td align=\"left\">4,129</td><td align=\"left\"><bold><italic>8</italic></bold></td></tr><tr><td align=\"left\">XCMS centWave + loess, minsamp 300</td><td align=\"left\">4,165</td><td align=\"left\">2</td></tr><tr><td align=\"left\">XCMS matched filter + orbiwarp, minsamp 50</td><td align=\"left\">3,928</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Two-stage, p<sub>within.detect</sub> = 0.3 p<sub>batches</sub> = 0.6</td><td align=\"left\">2,837</td><td align=\"left\"><bold><italic>5</italic></bold></td></tr><tr><td align=\"left\">Traditional apLCMS, Min.profiles = 180</td><td align=\"left\">2,874</td><td align=\"left\">3</td></tr><tr><td align=\"left\">XCMS matched filter + orbiwarp, minsamp 90</td><td align=\"left\">2,789</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Two-stage, p<sub>within.detect</sub> = 0.3 p<sub>batches</sub> = 0.9</td><td align=\"left\">1667</td><td align=\"left\"><bold><italic>5</italic></bold></td></tr><tr><td align=\"left\">Traditional apLCMS, Min.profiles = 300</td><td align=\"left\">1725</td><td align=\"left\">3</td></tr><tr><td align=\"left\">XCMS matched filter + orbiwarp, minsamp 180</td><td align=\"left\">1704</td><td align=\"left\">0</td></tr></tbody></table><table-wrap-foot><p>BMI was used as the outcome variable. Age, age<sup>2</sup>, gender, and race were adjusted for in the model. Metabolic feature selection was conducted using features with < 25% zeros. Pathway analysis was conducted using Mummichog, using metabolic features with <italic>p</italic> < 0.05.</p><p>The bold italic font represents the biggest number of significant pathways in the comparison group</p></table-wrap-foot></table-wrap></p><p id=\"Par64\">Given the settings with ~ 4,000 features yielded the most significant pathways, we further examined the selected pathways by the three methods in this group (Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>). The two-stage approach and traditional apLCMS each yielded 8 significant pathways with at least 5 matched significant metabolites. Their results largely agreed with each other. The significant pathways tended to be focused on amino acid metabolism, which was expected to be highly relevant to BMI status. The top pathway selected by the two-stage approach also included “Phosphatidylinositol phosphate metabolism”, which is known to be involved in the activation of various pathways. Dysregulation of the metabolism of phosphatidylinositol-3,4,5-triphosphate mediates insulin resistance<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>, which is highly relevant to BMI. The XCMS yielded much fewer significant pathways. The urea cycle pathway was shared with the other two approaches.<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Significant pathways with at least 5 matched significant metabolic features for parameter settings where ~ 4,000 features were detected.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Pathways</th><th align=\"left\">Overlap_size</th><th align=\"left\">Pathway_size</th><th align=\"left\"><italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"4\">Two-stage apLCMS (within-batch proportion 0.3, initially detected in at least 4 batches), 4,034 features</td></tr><tr><td align=\"left\"> Lysine metabolism</td><td align=\"left\">6</td><td align=\"left\">19</td><td char=\".\" align=\"char\">0.00185</td></tr><tr><td align=\"left\"> Phosphatidylinositol phosphate metabolism</td><td align=\"left\">5</td><td align=\"left\">16</td><td char=\".\" align=\"char\">0.00479</td></tr><tr><td align=\"left\"> Butanoate metabolism</td><td align=\"left\">5</td><td align=\"left\">17</td><td char=\".\" align=\"char\">0.00681</td></tr><tr><td align=\"left\"> Glycine, serine, alanine and threonine metabolism</td><td align=\"left\">8</td><td align=\"left\">38</td><td char=\".\" align=\"char\">0.00798</td></tr><tr><td align=\"left\"> Aspartate and asparagine metabolism</td><td align=\"left\">9</td><td align=\"left\">52</td><td char=\".\" align=\"char\">0.01899</td></tr><tr><td align=\"left\"> Urea cycle/amino group metabolism</td><td align=\"left\">7</td><td align=\"left\">40</td><td char=\".\" align=\"char\">0.02756</td></tr><tr><td align=\"left\"> Pyrimidine metabolism</td><td align=\"left\">5</td><td align=\"left\">27</td><td char=\".\" align=\"char\">0.0463</td></tr><tr><td align=\"left\"> Glycerophospholipid metabolism</td><td align=\"left\">8</td><td align=\"left\">53</td><td char=\".\" align=\"char\">0.04966</td></tr><tr><td align=\"left\" colspan=\"4\">Traditional apLCMS (minimum samples detected 90), 4,129 features</td></tr><tr><td align=\"left\"> Butanoate metabolism</td><td align=\"left\">5</td><td align=\"left\">15</td><td char=\".\" align=\"char\">0.00387</td></tr><tr><td align=\"left\"> Glycine, serine, alanine and threonine metabolism</td><td align=\"left\">8</td><td align=\"left\">37</td><td char=\".\" align=\"char\">0.00689</td></tr><tr><td align=\"left\"> Arachidonic acid metabolism</td><td align=\"left\">6</td><td align=\"left\">24</td><td char=\".\" align=\"char\">0.00748</td></tr><tr><td align=\"left\"> Lysine metabolism</td><td align=\"left\">5</td><td align=\"left\">18</td><td char=\".\" align=\"char\">0.0079</td></tr><tr><td align=\"left\"> Vitamin B3 (nicotinate and nicotinamide) metabolism</td><td align=\"left\">5</td><td align=\"left\">18</td><td char=\".\" align=\"char\">0.0079</td></tr><tr><td align=\"left\"> Glycerophospholipid metabolism</td><td align=\"left\">9</td><td align=\"left\">52</td><td char=\".\" align=\"char\">0.01681</td></tr><tr><td align=\"left\"> Urea cycle/amino group metabolism</td><td align=\"left\">7</td><td align=\"left\">43</td><td char=\".\" align=\"char\">0.041</td></tr><tr><td align=\"left\"> Aspartate and asparagine metabolism</td><td align=\"left\">8</td><td align=\"left\">53</td><td char=\".\" align=\"char\">0.04899</td></tr><tr><td align=\"left\" colspan=\"4\">XCMS (centWave + loess, IPO optimized, minimum samples detected 90), 4,165 features</td></tr><tr><td align=\"left\"> C21-steroid hormone biosynthesis and metabolism</td><td align=\"left\">6</td><td align=\"left\">24</td><td char=\".\" align=\"char\">0.00395</td></tr><tr><td align=\"left\"> Urea cycle/amino group metabolism</td><td align=\"left\">5</td><td align=\"left\">30</td><td char=\".\" align=\"char\">0.04353</td></tr></tbody></table></table-wrap></p><p id=\"Par65\">Overall, with this larger dataset generated from real biological subjects, we again demonstrated that the two-stage approach generated data with higher consistency, as compared to the traditional apLCMS and XCMS that treated all the data as a single group.</p></sec><sec id=\"Sec12\"><title>Discussions</title><p id=\"Par66\">The two-stage approach is built on top of the existing apLCMS method. It first conducts the entire workflow of within-batch feature detection, RT correction, and feature alignment. Then it conducts between-batch feature alignment, RT correction, and weak signal recovery across batches. The RT correction is conducted in a two-stage manner, by adding together two smooth curves for each LC/MS profile. One curve is within-batch RT deviation, and the other curve is between-batch RT deviation.</p><p id=\"Par67\">The method has a few important parameters. The tuning of the parameters is somewhat heuristic. The situation is similar to the tuning of other parameters in the apLCMS, XCMS, or packages. Different studies may have different purposes. Some studies focus more on the core metabolic network, while others aim at identifying low-abundance metabolites and environmental chemicals. Hence there isn’t a globally optimal choice of the parameters. However, the newly added parameters for two-stage processing have straight-forward interpretations. They are proportions of samples from which the features are detected, either in each batch, or across the batches. The higher the value of <italic>p</italic><sub><italic>within_detect</italic></sub>, the more stringent the within-batch peak detection, the less features detected within each batch. Similarly, <italic>p</italic><sub><italic>within_report</italic></sub> tunes the stringency after within-batch weak signal recovery. A higher <italic>p</italic><sub><italic>within_report</italic></sub> value results in less features reported from each batch. The parameter <italic>p</italic><sub><italic>batch</italic></sub> controls between-batch stringency. A higher <italic>p</italic><sub><italic>batch</italic></sub> value requires an aligned feature to be detected in more batches. Thus increasing the value of <italic>p</italic><sub><italic>batch</italic></sub> results in lower number of features. Given their interpretability, the tuning would be a guided effort by the user.</p><p id=\"Par68\">By combining the two-stage method with batch-effect correction methods ComBat and WaveICA, we found that at least in some datasets, the application of batch-effect correction can further improve the data quality. After the application of the batch-effect correction methods, the two-stage approach still outperformed traditional apLCMS and XCMS. This indicates that addressing batch effect in data preprocessing is important.</p><p id=\"Par69\">Given the total number of samples, the computing time is influenced by the batch size. We examined the computing time using the 100 QC profiles, using an old HP workstation with dual first-generation Xeon E5-2660 CPU. We utilized 10 CPU cores. The computing time was ~ 70 min.</p><p id=\"Par70\">Besides de novo feature detection, a hybrid feature detection method is available in apLCMS, in which a pre-existing database of known feature is used to improve weak signal detection<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. In the current study, for fairness of comparison, we did not use known feature database. Nonetheless, besides conducting untargeted feature detection, the new two-stage procedure is also adapted to the hybrid feature detection procedure. It is capable of incorporating prior knowledge to boost feature detection.</p><p id=\"Par71\">There are some limitations to the method. The current implementation is limited to apLCMS, and thus limited to high-resolution LC/MS data. We believe the same strategy can be implemented in other packages for wider application, such as GC/MS data. This work was focused on data generated in multiple batches from the same machine. In the CHDWB dataset, we picked batches that were not consecutively collected, and the method worked well. Nonetheless, although there can be some batch effects, we still assume different batches cannot have drastically different characteristics, as reliable feature alignment is necessary for batch effect correction. The issue of combining data from multiple machines is a much more difficult one. We will try to address such issues in future studies.</p></sec></sec><sec id=\"Sec13\"><title>Conclusion</title><p id=\"Par72\">We presented a two-stage approach for LC/MS metabolomics data generated in multiple batches. By analyzing data with multiple batches, both generated from a standardized plasma sample and from real biological samples, we showed that the new method improved the consistency of feature detection and quantification. The method is available as part of the apLCMS package. The package can be downloaded at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/tianwei-yu/apLCMS\">https://github.com/tianwei-yu/apLCMS</ext-link>. The instructions are at <ext-link ext-link-type=\"uri\" xlink:href=\"https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/\">https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/</ext-link>.</p></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec14\"><p>\n<supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41598_2020_70850_MOESM1_ESM.zip\"><caption><p>Supplementary information</p></caption></media></supplementary-material></p></sec></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p> is available for this paper at 10.1038/s41598-020-70850-0.</p></sec><ack><title>Acknowledgements</title><p>This work was partially supported by NIH grants R01GM124061 and U01CA235493, National Key R&D Program of China Grant No. 2018YFB0505000, Emory/Georgia Tech Center for Health Discovery and Well Being (CHDWB), and a grant from the University Development Fund of CUHK-Shenzhen.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>T.Y. designed the method. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807869</article-id><article-id pub-id-type=\"pmc\">PMC7431854</article-id><article-id pub-id-type=\"publisher-id\">70868</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70868-4</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Spectral dependence of third-order susceptibility of Au triangular nanoplates</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Zhang</surname><given-names>Boyi</given-names></name><address><email>zhang.boyi@nims.go.jp</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sato</surname><given-names>Rodrigo</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tanaka</surname><given-names>Miyoko</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Takeda</surname><given-names>Yoshihiko</given-names></name><address><email>takeda.yoshihiko@nims.go.jp</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.20515.33</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2369 4728</institution-id><institution>School of Pure and Applied Sciences, </institution><institution>University of Tsukuba, </institution></institution-wrap>Tsukuba, Ibaraki 305-8577 Japan </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.21941.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0789 6880</institution-id><institution>Center for Green Research on Energy and Environmental Materials, </institution><institution>National Institute of Materials Science (NIMS), </institution></institution-wrap>Tsukuba, Ibaraki 305-0003 Japan </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13855</elocation-id><history><date date-type=\"received\"><day>22</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">We experimentally investigated the spectral dependence of the third-order susceptibility <inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq1.gif\"/></alternatives></inline-formula> of Au triangular nanoplates in a broad wavelength region (400–1,000 nm). Complex shaped plasmonic nanoparticles provide a promising route to achieve control of their optical properties at the nanoscale. However, little is known about the effects of geometrical parameters to the optical nonlinearities and underlying mechanisms of the plasmon modes. Here, we obtained the <inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq2.gif\"/></alternatives></inline-formula> of Au triangular nanoplates featuring a narrow plasmon resonance that is tunable in the visible and near-IR regions. This work demonstrates that the plasmonic triangular nanoplates simultaneously shows self-focusing and -defocusing, and saturable and reverse-saturable absorption properties at specific wavelength regions. Maximum amplitudes of real and imaginary components are − 6.8 × 10<sup>−18</sup> m<sup>2</sup>/V<sup>2</sup> at 668 nm and − 6.7 × 10<sup>−18</sup> m<sup>2</sup>/V<sup>2</sup> at 646 nm, respectively. Spectral dependence of the quantity <inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq3.gif\"/></alternatives></inline-formula> enables comparison between different shaped plasmonic NPs to boost active plasmonic applications performance.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Nanophotonics and plasmonics</kwd><kwd>Nonlinear optics</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Metallic nanoparticles (NPs) have attracted extensive interests because of their unique plasmonic properties<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. The light-driven collective motion of electrons within the NP is known as localized surface plasmon resonance (LSPR)<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. This resonance can boost both linear and nonlinear optical (NLO) properties<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. Thus, a plasmonic NP can feature large optical nonlinearity while exhibiting ultrafast response<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. These unique properties have been applied to various nonlinear optical functionalities, such as surface-enhanced Raman scattering (SERS), photonic waveguides and ultrafast all-optical switching<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR3\">3</xref>–<xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Among various plasmonic NPs, Au has been one of the best and common studied materials in nonlinear photonics due to its large optical nonlinearity and chemical stability<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>.</p><p id=\"Par3\">Triangular Au nanoplates is promising for strong local field enhancement expected at their sharp tips. Also, the LSPR of triangular nanoplates can be easily tuned from visible to near-IR regions by controlling their thickness and edge length<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Besides, Au nanoplates have been reported with a large wavelength dependence of nonlinear refraction and a tiny change of nonlinear absorption<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. These results imply that Au nanoplate is a good candidate for optical switches<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref>,<xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. However, previous studies on spherical and elongated Au NPs have shown that third-order susceptibility <inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq4.gif\"/></alternatives></inline-formula> exhibits a strong wavelength-dependent dispersion at LSPR region<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. Real and imaginary components of <inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq5.gif\"/></alternatives></inline-formula> consist of successively positive and negative peaks. This result indicate that Au simultaneously exhibit nonlinear absorption (saturable absorption SA and reversed saturable absorption RSA) and refraction (self-focusing and self-defocusing) at different wavelengths. To clarify and understand the reported abnormal optical nonlinearity on Au nanoplates, a spectral dependent dispersion of <inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq6.gif\"/></alternatives></inline-formula> is necessary. To the best of our knowledge, previously reported investigations on Au nanoplates were performed by the single-wavelength Z-scan method<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref>,<xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. The results concentrated only on very limited wavelengths. Therefore, current scattered Z-scan results are not sufficient to give a comprehensive description to NLO properties of Au triangular plates and still require further research.</p><p id=\"Par4\">In this manuscript we investigated the spectral dependence of <inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq7.gif\"/></alternatives></inline-formula> of Au triangular nanoplates by a combined analysis of pump-probe spectroscopy and spectroscopic ellipsometry. This method allows us to elucidate the complex dispersion of <inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq8.gif\"/></alternatives></inline-formula> over a broad wavelength range (400–1,000 nm). Different from previous reports, real and imaginary components of <inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq9.gif\"/></alternatives></inline-formula> of Au nanoplates show strong wavelength dependence in the vicinity of the plasmon modes. SA and RSA, self-focusing and self-defocusing simultaneously take place at different wavelengths. This negative and positive dispersion of <inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq10.gif\"/></alternatives></inline-formula> originates from the interaction between intrinsic nonlinearity of Au and LSPR. The significant optical switching properties from previous reports only occurs at a specific wavelength region. Moreover, the maximum saturable absorption and <inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq11.gif\"/></alternatives></inline-formula> locates at off-resonance wavelength, which shows a significant shift compared with linear LSPR. This result is beneficial for understanding the nonlinear behavior of plasmonic triangular nanoplates and optimizing the nonlinear plasmonic devices at desired wavelengths.</p></sec><sec id=\"Sec2\"><title>Results and discussion</title><p id=\"Par5\">Linear optical properties of Au nanoplates are shown in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>. Figure <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a shows the extinction spectrum from ellipsometry measurement. The spectral signatures can be understood by using discrete dipole approximation (DDA) simulation and simulated extinction efficiency is shown in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>b. The spectrum is composed of a strong absorption band at 655 nm. This peak originates from the dipole plasmon mode of nanoplates corresponding to electric filed distribution shown on the right side of Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>b<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. A large field enhancement is expected to appear at the tips. At shorter wavelengths, another absorption peak with lower amplitude can be ascribed to the quadrupole plasmon mode<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>. Compared with DDA simulation, quadrupole mode absorption from experiments gets broader and shifts to 540 nm. This indicates the contribution from minimal impurities (spherical NPs). Interband transitions from 4d band to 5sp band contributes to the absorption band below 540 nm<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. In all, consistence between DDA simulation and experimental results suggests that Au nanoplates are well dispersed in PVA and can be considered as the ensemble of single Au nanoplates. The Au/PVA composite behaves as an ensemble of single nanoplates without aggregation. Dielectric function of the Au nanoplates/PVA composite was evaluated by ellipsometry and plotted in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>c. The complex dielectric function was described by three Lorentz oscillators and fitted to ellipsometric data. The mean squared error (MSE) of our ellipsometric model was 17.0, which indicates a good fitting quality to evaluate the optical properties of the composite<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>. This minimized MSE allows to consider the permittivity in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>c adequate to extract the third-order susceptibility from pump-probe measurements.<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Linear optical properties of Au nanoplates: (<bold>a</bold>) extinction spectrum from ellipsometry measurement and ellipsometric model. (<bold>b</bold>) Extinction ecoefficiency from DDA simulation and corresponding dipole electric filed distribution at 655 nm. (<bold>c</bold>) Real (blue) and imaginary (red) components of dielectric function of nanoplates evaluated by ellipsometry.</p></caption><graphic xlink:href=\"41598_2020_70868_Fig1_HTML\" id=\"MO1\"/></fig></p><p id=\"Par6\">To clarify the NLO properties, ∆T/T of the composites were measured by ultrafast pump and probe spectroscopy and shown in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>. This transient SA and RSA reflect the laser-induced modulation on NLO properties. The spectrum consists of several successively SA and RSA peaks. The small RSA locates below 450 nm originating from the interband transitions of Au and shares a similar line shape compared with bulk Au<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. The strongest dipole polarization contributes to the sharp SA located at 647 nm and follows a broad RSA extending towards infrared region. As a comparison, the linear extinction was taken from Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a and plotted together with ∆T/T. Note that, the wavelength of maximum ∆T/T shows a blue shift compared with linear LSPR. Following the wavelength of linear LSPR, the measurement of nonlinearity will return a weaker value or even opposite sign of nonlinearity.<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Transient transmission changes of Au nanoplates composite measured by ultrafast pump-probe spectroscopy. The grey dashed line represents the linear extinction spectrum extracted from Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>a. for comparison.</p></caption><graphic xlink:href=\"41598_2020_70868_Fig2_HTML\" id=\"MO2\"/></fig></p><p id=\"Par7\">By the combined analysis of ∆T/T and the dielectric function from ellipsometry, real and imaginary components of <inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq12.gif\"/></alternatives></inline-formula> can be derived by<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>:<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Delta {\\upvarepsilon }\\left( {\\omega_{probe} } \\right) = \\frac{3}{4}\\chi_{{Au{ }NPL}}^{\\left( 3 \\right)} \\left( {\\omega_{probe} } \\right)I,$$\\end{document}</tex-math><mml:math id=\"M26\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:mi mathvariant=\"normal\">ε</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:msub><mml:mi>ω</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">probe</mml:mi></mml:mrow></mml:msub></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mn>3</mml:mn><mml:mn>4</mml:mn></mml:mfrac><mml:msubsup><mml:mi>χ</mml:mi><mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mi>u</mml:mi><mml:mrow/><mml:mi>N</mml:mi><mml:mi>P</mml:mi><mml:mi>L</mml:mi></mml:mrow></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:msub><mml:mi>ω</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">probe</mml:mi></mml:mrow></mml:msub></mml:mfenced><mml:mi>I</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70868_Article_Equ1.gif\" position=\"anchor\"/></alternatives></disp-formula>where <inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{3}{4}$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:mfrac><mml:mn>3</mml:mn><mml:mn>4</mml:mn></mml:mfrac></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq13.gif\"/></alternatives></inline-formula> is the K factor for intensity dependent refractive index<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup> and <italic>I</italic> the pump laser intensity. <inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Delta {\\upvarepsilon }$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:mi mathvariant=\"normal\">ε</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq14.gif\"/></alternatives></inline-formula> stands for the dielectric function modulation for Au/PVA composite and calculated from the difference of dielectric function at excited state (<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\upvarepsilon } + \\Delta {\\upvarepsilon }$$\\end{document}</tex-math><mml:math id=\"M32\"><mml:mrow><mml:mi mathvariant=\"normal\">ε</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:mi mathvariant=\"normal\">ε</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq15.gif\"/></alternatives></inline-formula>) and steady state (<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\upvarepsilon }$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:mi mathvariant=\"normal\">ε</mml:mi></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq16.gif\"/></alternatives></inline-formula>). <inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\upvarepsilon } + \\Delta {\\upvarepsilon }$$\\end{document}</tex-math><mml:math id=\"M36\"><mml:mrow><mml:mi mathvariant=\"normal\">ε</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:mi mathvariant=\"normal\">ε</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq17.gif\"/></alternatives></inline-formula> was evaluated by fitting the linear SE model to nonlinear transmission <inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{ T}} + \\Delta {\\text{T}}$$\\end{document}</tex-math><mml:math id=\"M38\"><mml:mrow><mml:mrow><mml:mspace width=\"0.333333em\"/><mml:mtext>T</mml:mtext></mml:mrow><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:mtext>T</mml:mtext></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq18.gif\"/></alternatives></inline-formula> as previously reported. Structural information (thickness, surface roughness and so on) remained same and only dielectric function (parameters of Lorentz oscillators) was modified in this process. The evaluated real and imaginary components of <inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M40\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq19.gif\"/></alternatives></inline-formula> were plotted in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>. Both components have shown distinctive dispersions consisted of positive and negative peaks, which indicates the nonlinear absorption and refraction, respectively. The maximum amplitude of <inline-formula id=\"IEq20\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)^{\\prime\\prime}}$$\\end{document}</tex-math><mml:math id=\"M42\"><mml:msup><mml:mi>χ</mml:mi><mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced><mml:mo>″</mml:mo></mml:msup></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq20.gif\"/></alternatives></inline-formula> locates at 646 nm with a magnitude of − 6.7 × 10<sup>–18</sup> m<sup>2</sup>/V<sup>2</sup>. This peak reflects the SA observed in ∆T/T spectrum. At wings, two positive band indicates the interband contribution at lower wavelength and LSPR contribution towards further IR region. For real components, maximum amplitude of <inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)^{\\prime}}$$\\end{document}</tex-math><mml:math id=\"M44\"><mml:msup><mml:mi>χ</mml:mi><mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced><mml:mo>′</mml:mo></mml:msup></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq21.gif\"/></alternatives></inline-formula> appears at 668 nm with a magnitude of -6.8 × 10<sup>–18</sup> m<sup>2</sup>/V<sup>2</sup>. One can note that <inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M46\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq22.gif\"/></alternatives></inline-formula> of nanoplates did not show several orders intense enhancement compared with Au spheres<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. One possible assumption is that the tips of fabricated nanoplates is not as sharp as simulation. Another assumption could be due to the average local field among whole nanoplates is not as strong as it at the sharp tips, which is usually considered by most theoretical calculations<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. Also, linear LSPR peak (straight dashed line) locates at 655 nm, in between of the maximum aptitude of real and imaginary components. The off-resonance location have been also been observed in Au nanorods<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>. For spherical nanoparticle, both linear and nonlinear peak locates around 540 nm. However, with tuning the LSPR towards infrared region by geometry modification, the nonlinear LSPR starts to show this wavelength shift compared linear one. This off-resonance shift could be significant when taking use of nanoparticles around LSPR wavelength.<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Real (blue) and imaginary (blue) components of third-order susceptibility of Au triangular nanoplates embed in PVA matrix. The straight dashed line represents the wavelength of linear LSPR.</p></caption><graphic xlink:href=\"41598_2020_70868_Fig3_HTML\" id=\"MO3\"/></fig></p><p id=\"Par8\">Shown in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>, <inline-formula id=\"IEq23\"><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M48\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq23.gif\"/></alternatives></inline-formula> of nanoplates displays a positive–negative dispersion and a large wavelength dependence around LSPR. Real and imaginary components have similar maximum amplitudes at different wavelength, which indicates that Au nanoplates own considerable nonlinear refraction and absorption properties simultaneously. Notice that due to the differential nature describing by Kronig–Kramers relationship<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>, the maximum amplitude of the real (imaginary) component accompanies with a crossover of the imaginary (real) component at the same wavelength. Thus, functionalities taking use of nonlinear refraction or absorption should be specified at a certain wavelength. These spectral signatures originate from interactions between intrinsic Au nonlinearity and LSPR<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR22\">22</xref>,<xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>. The intrinsic nonlinearity of Au has already been well investigated and described by laser-induced electron distribution change<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. While exhibiting same intrinsic nonlinearity, modification of geometries results in the different polarization of LSPR<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>. The key feature of nanoplates is the near-infrared dipole polarization and higher order plasmon modes close to interband region. Tuning LSPR towards IR region with intense local field enhancement can be easily achieved by controlling geometry of nanoplates rather than NPs. Another candidate for IR applications is elongated NPs, such as nanorods or nanobipyramids<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>. Those particles have two strong dipole polarization simultaneously at different wavelength. The transverse mode is similar to spherical NPs and the longitudinal mode locates at IR region. Compared with elongated NPs, nanoplates have the advantage from higher ratio of surface to bulk atoms and the ability to support multipole plasmon modes, such as higher-order eigenmodes or out-of-plane mode. In all, nanoplates supports LSPR modes at longer wavelength compared with spherical nanoparticles with complex valued and wavelength dependent <inline-formula id=\"IEq24\"><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M50\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq24.gif\"/></alternatives></inline-formula>. <inline-formula id=\"IEq25\"><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M52\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq25.gif\"/></alternatives></inline-formula> of nanoplates shows a significant shift compared with linear LSPR peak.</p><p id=\"Par9\">Having understood with the dispersion of the real and imaginary components of <inline-formula id=\"IEq26\"><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M54\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq26.gif\"/></alternatives></inline-formula>, abnormal results reported by Li et al<italic>.</italic><sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup> could be clarified. Direct comparison of the intensity of <inline-formula id=\"IEq27\"><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M56\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq27.gif\"/></alternatives></inline-formula> at same wavelength is not feasible due to different LSPR modes exhibiting from different sizes of nanoplates. Li’s group has measured a nanoplate solution with the edge length of 90 nm. The LSPR locates around 1,200 nm and the peak is broad due to the impurities and broad size distribution. The reported values of <inline-formula id=\"IEq28\"><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M58\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq28.gif\"/></alternatives></inline-formula> are (6.37 + i1.21) × 10<sup>–13</sup> esu at 800 nm and (− 125 + i1.03) × 10<sup>–13</sup> esu at 1,240 nm, respectively. (The reported value of <inline-formula id=\"IEq29\"><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{{\\left( 3 \\right){^{\\prime}}}}$$\\end{document}</tex-math><mml:math id=\"M60\"><mml:msup><mml:mi>χ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq29.gif\"/></alternatives></inline-formula> at 1,240 nm is 125 × 10<sup>–13</sup> esu. However, we believe that the authors have applied an incorrect conversion formula in converting closed aperture measurement to <inline-formula id=\"IEq30\"><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{{\\left( 3 \\right){^{\\prime}}}}$$\\end{document}</tex-math><mml:math id=\"M62\"><mml:msup><mml:mi>χ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq30.gif\"/></alternatives></inline-formula>. The positive–negative dispersion in closed-aperture measurements should be converted to a negative sign of <inline-formula id=\"IEq31\"><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{{\\left( 3 \\right){^{\\prime}}}}$$\\end{document}</tex-math><mml:math id=\"M64\"><mml:msup><mml:mi>χ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq31.gif\"/></alternatives></inline-formula>.) The huge <inline-formula id=\"IEq32\"><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{{\\left( 3 \\right){^{\\prime}}}}$$\\end{document}</tex-math><mml:math id=\"M66\"><mml:msup><mml:mi>χ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq32.gif\"/></alternatives></inline-formula> at 1,240 nm was ascribed to the dipole resonance of nanoplates while <inline-formula id=\"IEq33\"><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{{\\left( 3 \\right)^{{^{\\prime\\prime}}} }}$$\\end{document}</tex-math><mml:math id=\"M68\"><mml:msup><mml:mi>χ</mml:mi><mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced><mml:msup><mml:mrow/><mml:mo>″</mml:mo></mml:msup></mml:msup></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq33.gif\"/></alternatives></inline-formula> is not sensitive to wavelength. First of all, clarification of the wavelength regime of their measurement is necessary. They simply assumed that the peak of T and ∆T/T locate at the same wavelength. However, the open-aperture Z-scan measurement at 1,240 nm shows negative sign of ∆T/T, which correspond. Shown in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>, the linear LSPR locates at a longer wavelength than SA peak of ∆T/T, where ∆T/T decreases rapidly and shows a crossover from SA to RSA. The possible Z-scan measurement region was marked together with Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref> and can be found as Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S1</xref> online. Next, the corresponding spectral dependence at this region can be achieved. As shown in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>, the maximum amplitude of <inline-formula id=\"IEq34\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{{\\left( 3 \\right){^{\\prime}}}}$$\\end{document}</tex-math><mml:math id=\"M70\"><mml:msup><mml:mi>χ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:mrow></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq34.gif\"/></alternatives></inline-formula> locates around this crossover while <inline-formula id=\"IEq35\"><alternatives><tex-math id=\"M71\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{{\\left( 3 \\right){^{\\prime\\prime}}}}$$\\end{document}</tex-math><mml:math id=\"M72\"><mml:msup><mml:mi>χ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced><mml:msup><mml:mrow/><mml:mo>″</mml:mo></mml:msup></mml:mrow></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq35.gif\"/></alternatives></inline-formula> increase rapidly with a sign reversion form negative (SA) to positive (RSA). Hence, we can summarize that, Li et al<italic>.</italic>, have conducted their measurement at linear LSPR wavelength, several tenth nanometers red-shifted compared with SA of ∆T/T. Consequently, a strong wavelength dependence of nonlinear refraction and a tiny change on nonlinear absorption was observed. Limited by the scattered data at 800 nm and 1,240 nm, they concluded that Au nanoplates as a promising candidate for broadband optical-switching. Shown in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>, this conclusion is only valid at a specific wavelength region. Finally, indicated by this comparison with scattered Z-scan measurements, the dispersion of <inline-formula id=\"IEq36\"><alternatives><tex-math id=\"M73\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M74\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq36.gif\"/></alternatives></inline-formula> can be crucially important to comprehensively interpret the NLO properties. The strong spectral dependence and off-resonance located <inline-formula id=\"IEq37\"><alternatives><tex-math id=\"M75\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M76\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq37.gif\"/></alternatives></inline-formula> provides insight into the design of plasmonic metamaterials at an optimized wavelength.</p></sec><sec id=\"Sec3\"><title>Conclusion</title><p id=\"Par10\">We investigated the dispersion of <inline-formula id=\"IEq38\"><alternatives><tex-math id=\"M77\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M78\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq38.gif\"/></alternatives></inline-formula> of Au nanoplates/PVA composite in a broad wavelength region (400–1,000 nm) through a combined analysis of spectroscopic ellipsometry and pump-probe spectroscopy. <inline-formula id=\"IEq39\"><alternatives><tex-math id=\"M79\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M80\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq39.gif\"/></alternatives></inline-formula> of nanoplates shows a complex wavelength dependence resulting from intrinsic nonlinearity of Au and plasmon modes. Both real and imaginary components of <inline-formula id=\"IEq40\"><alternatives><tex-math id=\"M81\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M82\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq40.gif\"/></alternatives></inline-formula> have a strong negative peak around LSPR wavelength and two opposite band at the wings. Maximum amplitudes of real and imaginary components are − 6.8 × 10<sup>–18</sup> m<sup>2</sup>/V<sup>2</sup> at 668 nm and − 6.7 × 10<sup>–18</sup> m<sup>2</sup>/V<sup>2</sup> at 646 nm, respectively. In particular, the real (imaginary) component shows a significant shift towards longer (shorter) wavelength compared with linear LSPR. The dispersion offers a comprehensive understanding compared with previous reported nonlinearity of Au nanoplates. This result indicates the importance of the dispersion of the quantity <inline-formula id=\"IEq41\"><alternatives><tex-math id=\"M83\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi^{\\left( 3 \\right)}$$\\end{document}</tex-math><mml:math id=\"M84\"><mml:msup><mml:mi>χ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mn>3</mml:mn></mml:mfenced></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70868_Article_IEq41.gif\"/></alternatives></inline-formula> to optimize the nonlinear absorption or refraction properties at a desired wavelength.</p></sec><sec id=\"Sec4\"><title>Methods</title><p id=\"Par11\">Au triangular nanoplates dispersed in water with ~ 45 nm edge length were bought from Dai Nippon Toryo Co., Ltd. The TEM measurements was carried and shown in Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>. TEM observation is performed using JEM-2100F(JEOL) and operating at 200 kV. The solution of Au nanoplates was diluted with equal amount of ethanol, treated with ultrasonication for 15 min, and then dropped onto a TEM carbon grid. Those NPs were used without any further treatment and had been embedded in a poly(vinyl alcohol)(PVA) matrix by a spin coating method as reported by Sato <italic>et al</italic><sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. Nanoplates were separated from the mother liquid by centrifugation. The precipitated NPs was redispersed into PVA solution (20 g/l). 15 μl of this NPs/PVA suspension was then spin-coated (800 RPM, 10 min) on an amorphous silica glass substrate. The substrates were precleaned by an ozone cleaner before spin coating. The computational simulations of the extinction efficiency and electric field distributions were performed by DDA method using DDSCAT<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>. The edge length of the Au NPLs was set to be 45 nm and the thickness was chosen as 30 nm to fit the LSPR wavelength. The refractive index of the medium was approximately 1.5 at all wavelength (extracted from a pure PVA thin film by spectroscopic ellipsometry) and dielectric function of Au was taken from Palik’s report<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>. All the simulations were performed with an incident filed perpendicular to the triangular plane of the nanoplates so that only in-plane polarization mode was excited.<fig id=\"Fig4\"><label>Figure 4</label><caption><p>TEM image of Au nanoplates with an edge length of 45 nm.</p></caption><graphic xlink:href=\"41598_2020_70868_Fig4_HTML\" id=\"MO4\"/></fig></p><p id=\"Par12\">Linear optical properties and thicknesses of those Au/PVA composites were measured by a variable angle spectroscopic ellipsometry (SE) (J.A. Woollam, VASE). Parameters including phase and polarization changes of reflected light were collected at incident angles of 50°, 60°, and 70° from 300 to 1,000 nm. Extinction (− log (T/T<sub>0</sub>)) of the composite was measured at incident angle of 90° from 300 to 1,000 nm, where T represents the transmittance of nanoplate composite on silica substrates and T<sub>0</sub> is the transmittance of baseline (air). Several Lorentz oscillators were applied to fit the reflection and extinction data for the evaluation of in-plane dielectric function <italic>ε</italic><sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. The parameters of each Lorentz oscillator can be found as Supplementary <xref rid=\"MOESM1\" ref-type=\"media\">S2</xref> online. The thickness of the composite was evaluated as 590 nm. The nonlinear response of composites was measured by a custom-made pump and probe spectroscopy. The scheme of pump and probe spectroscopy can be found as Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S3</xref> online. The quantities measured were transient transmission changes (∆T/T), which were defined as the ration between transmitted light with and without laser excitation. Fundamental laser source was supplied by a Ti:sapphire regenerative amplifier (Spitfire, Spectra-Physics) seeded with an oscillator (Mai Tai, Spectra-Physics) and pumped by a diode-pumped laser (Empower, Spectra-Physics). The fundamental beam with an output pulse of 130 fs at 800 nm and 1 kHz repetition was divided into two portions: pump beam and probe beam. Pump beam at 400 nm was generated by a BBO crystal through second harmonic generation. Also, the repetition rate of pump beam was converted to 0.5 kHz by an optical chopper. The sample was illuminated with a peak intensity of 1 GW/cm<sup>2</sup>. The supercontinuum probe beam was generated using Al<sub>2</sub>O<sub>3</sub> and YAG crystals for visible (400–800 nm) and near infrared region (800–1,000 nm), respectively. Group velocity correction of the raw data was done according to chirping effect using the Kerr gate technique<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>.</p></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec5\"><p>\n<supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41598_2020_70868_MOESM1_ESM.docx\"><caption><p>Supplementary information.</p></caption></media></supplementary-material></p></sec></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p>is available for this paper at 10.1038/s41598-020-70868-4.</p></sec><ack><title>Acknowledgements</title><p>The authors would like to thank the fruitful discussions with Rang Li concerning the DDSCAT simulation and interpretation. Also, thanks are given to Junwei Cheng, Zhaoyan Li and Shuo Qi for the help about DDA modelling. A part of this work was supported by using the facility of NIMS TEM Station.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>B.Z. and R.S. performed all the experiments. R. S. and Y.T. designed the experiments. M.T. helped with TME measurements. All the authors discussed the results and edited the manuscript.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par13\">The authors declare no competing interests.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Kauranen</surname><given-names>M</given-names></name><name><surname>Zayats</surname><given-names>AV</given-names></name></person-group><article-title>Nonlinear plasmonics</article-title><source>Nat. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Transl Psychiatry</journal-id><journal-id journal-id-type=\"iso-abbrev\">Transl Psychiatry</journal-id><journal-title-group><journal-title>Translational Psychiatry</journal-title></journal-title-group><issn pub-type=\"epub\">2158-3188</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807799</article-id><article-id pub-id-type=\"pmc\">PMC7431855</article-id><article-id pub-id-type=\"publisher-id\">974</article-id><article-id pub-id-type=\"doi\">10.1038/s41398-020-00974-4</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The association of PTSD symptom severity with amygdala nuclei volumes in traumatized youths</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-0728-3141</contrib-id><name><surname>Ousdal</surname><given-names>Olga Therese</given-names></name><address><email>olgatherese.ousdal@gmail.com</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Milde</surname><given-names>Anne Marita</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hafstad</surname><given-names>Gertrud Sofie</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hodneland</surname><given-names>Erlend</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Dyb</surname><given-names>Grete</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Craven</surname><given-names>Alexander R.</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref><xref ref-type=\"aff\" rid=\"Aff6\">6</xref><xref ref-type=\"aff\" rid=\"Aff7\">7</xref></contrib><contrib contrib-type=\"author\"><name><surname>Melinder</surname><given-names>Annika</given-names></name><xref ref-type=\"aff\" rid=\"Aff8\">8</xref><xref ref-type=\"aff\" rid=\"Aff9\">9</xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-4403-6887</contrib-id><name><surname>Endestad</surname><given-names>Tor</given-names></name><xref ref-type=\"aff\" rid=\"Aff9\">9</xref><xref ref-type=\"aff\" rid=\"Aff10\">10</xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-0008-4326</contrib-id><name><surname>Hugdahl</surname><given-names>Kenneth</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref><xref ref-type=\"aff\" rid=\"Aff6\">6</xref><xref ref-type=\"aff\" rid=\"Aff11\">11</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412008.f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9753 1393</institution-id><institution>Department of Radiology, </institution><institution>Haukeland University Hospital, </institution></institution-wrap>Bergen, Norway </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.7914.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 7443</institution-id><institution>Department of Biological and Medical Psychology, </institution><institution>University of Bergen, </institution></institution-wrap>Bergen, Norway </aff><aff id=\"Aff3\"><label>3</label>NORCE Norwegian Research Centre AS, Bergen, Norway </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.504188.0</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0460 5461</institution-id><institution>Norwegian Centre for Violence and Traumatic Stress Studies, </institution></institution-wrap>Oslo, Norway </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.5510.1</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 8921</institution-id><institution>Child and Adolescent Psychiatry Unit, Division of Mental Health and Addiction, Institute of Clinical Medicine, Faculty of Medicine, </institution><institution>University of Oslo, </institution></institution-wrap>Oslo, Norway </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.7914.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 7443</institution-id><institution>NORMENT-Norwegian Center for Mental Disorders Research, </institution><institution>University of Bergen, </institution></institution-wrap>Bergen, Norway </aff><aff id=\"Aff7\"><label>7</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412008.f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9753 1393</institution-id><institution>Department of Clinical Engineering, </institution><institution>Haukeland University Hospital, </institution></institution-wrap>Bergen, Norway </aff><aff id=\"Aff8\"><label>8</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.55325.34</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0389 8485</institution-id><institution>Department of Child and Adolescent Mental Health, </institution><institution>Oslo University hospital, </institution></institution-wrap>Oslo, Norway </aff><aff id=\"Aff9\"><label>9</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.5510.1</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 8921</institution-id><institution>Institute of Psychology, </institution><institution>University of Oslo, </institution></institution-wrap>Oslo, Norway </aff><aff id=\"Aff10\"><label>10</label>Division of Neuropsychology, Helgeland Hospital, Mosjøen, Norway </aff><aff id=\"Aff11\"><label>11</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412008.f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9753 1393</institution-id><institution>Division of Psychiatry, </institution><institution>Haukeland University Hospital, </institution></institution-wrap>Bergen, Norway </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>288</elocation-id><history><date date-type=\"received\"><day>25</day><month>10</month><year>2019</year></date><date date-type=\"rev-recd\"><day>3</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>3</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">The amygdala is a core component in neurobiological models of stress and stress-related pathologies, including post-traumatic stress disorder (PTSD). While numerous studies have reported increased amygdala activity following traumatic stress exposure and in PTSD, the findings regarding amygdala volume have been mixed. One reason for these mixed findings may be that the amygdala has been considered as a homogenous entity, while it in fact consists of several nuclei with unique cellular and connectivity profiles. Here, we investigated amygdala nuclei volumes of the basolateral and the centrocorticomedial complex in relation to PTSD symptom severity in 47 young survivors from the 2011 Norwegian terror attack 24–36 months post-trauma. PTSD symptoms were assessed 4–5, 14–15 and 24–36 months following the trauma. We found that increased PTSD symptom severity 24–36 months post-trauma was associated with volumetric reductions of all basolateral as well as the central and the medial nuclei. However, only the lateral nucleus was associated with longitudinal symptom development, and mediated the association between 4–5 months and 24–36 months post-trauma symptoms. The results suggest that the amygdala nuclei may be differentially associated with cross-sectional and longitudinal measures of PTSD symptom severity. As such, investigations of amygdala total volume may not provide an adequate index of the association between amygdala and stress-related mental illness.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Psychiatric disorders</kwd><kwd>Neuroscience</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/501100004257</institution-id><institution>Helse Vest (Western Norway Regional Health Authority)</institution></institution-wrap></funding-source><award-id>911780</award-id><award-id>911813</award-id><award-id>912045</award-id><award-id>911780</award-id><award-id>911813</award-id><award-id>912045</award-id><award-id>911780</award-id><award-id>911813</award-id><award-id>912045</award-id><award-id>911780</award-id><award-id>911813</award-id><award-id>912045</award-id><award-id>911780</award-id><award-id>911813</award-id><award-id>912045</award-id><award-id>911780</award-id><award-id>911813</award-id><award-id>912045</award-id><award-id>911780</award-id><award-id>911813</award-id><award-id>912045</award-id><award-id>911780</award-id><award-id>911813</award-id><award-id>912045</award-id><principal-award-recipient><name><surname>Ousdal</surname><given-names>Olga Therese</given-names></name><name><surname>Milde</surname><given-names>Anne Marita</given-names></name><name><surname>Hafstad</surname><given-names>Gertrud Sofie</given-names></name><name><surname>Hodneland</surname><given-names>Erlend</given-names></name><name><surname>Dyb</surname><given-names>Grete</given-names></name><name><surname>Craven</surname><given-names>Alexander R.</given-names></name><name><surname>Endestad</surname><given-names>Tor</given-names></name><name><surname>Hugdahl</surname><given-names>Kenneth</given-names></name></principal-award-recipient></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par2\">Experiencing an extremely traumatic event, like combat or violent assault, poses a significant threat to mental well-being. For the majority of individuals, stress reactions are transitory, however in a significant number of individuals they can endure, causing distress and mental illness, like post-traumatic stress disorder (PTSD)<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. PTSD is a severe psychiatric disorder leading to tremendous personal suffering, and with current treatments being only modestly effective<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>. As such, identifying structural and functional brain changes associated with PTSD is of major interest, as these may yield important clues to the pathophysiology of this disease, and ultimately inform new treatments.</p><p id=\"Par3\">Current neurocircuit models of stress and PTSD focus on the amygdala<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. The amygdala is an evolutionally conserved brain structure with multiple functions among which the best known is to encode and extinguish memory of fearful stimuli<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>, so as to direct physiological and behavioral responses when such stimuli are encountered. In addition to its role in fear acquisition and extinction, the amygdala plays an essential role in fear generalization<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>, arousal<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup> and processing of rewards<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>, all of which may be disrupted in PTSD. Exaggerated amygdala activity in response to trauma-related and more generic stimuli is a frequent finding in functional magnetic resonance imaging (fMRI) studies of PTSD<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. The evidence from structural MRI studies are, however, inconclusive, as both volumetric increases<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup> and volumetric reductions<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup> have been reported.</p><p id=\"Par4\">One reason for the mixed findings may be that most studies have considered the amygdala as a homogenous entity, without taking its specific nuclei into account. The amygdala has historically been divided into two main complexes based on their distinct cellular architecture and connectivity<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>. The evolutionarily primitive centrocorticomedial complex (CMA), consisting of the central, medial and the cortical nuclei, is densely interconnected with the striatum, brainstem and the hypothalamus. In contrast, the evolutionarily newer basolateral complex (BLA), comprising the basal, accessory basal and the lateral nuclei, is extensively interconnected with sensory as well as prefrontal cortical areas, thalamus and the hippocampus<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>.</p><p id=\"Par5\">The results from animal models indicate distinct responses of the BLA<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup> and CMA<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup> complex to chronic or severe stress, and functional MRI studies in humans suggest that the BLA and the CMA differ in terms of activity<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup> and functional connectivity<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup> in PTSD. This renders it likely that the nuclei of the two amygdala complexes may be differently associated with PTSD. However, due to the small size of the amygdala nuclei, investigations in humans using non-invasive imaging methods have been difficult. With the recent development of automatic segmentation algorithms of amygdala nuclei and hippocampus subfields, it is now possible to look beyond overall volumetric changes and to assess specific subregions of these brain areas<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. Of importance, these procedures yield reproducible measurements, which also correlate well with the manual delineation of amygdala nuclei and hippocampal subfields<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>. We availed ourselves of this methodology to investigate the association between long-term PTSD symptom load and amygdala nuclei volumes in 47 survivors of the 2011 Norwegian terror attack at Utøya. Rather than traditionally dividing the survivors by PTSD diagnostic status, we chose to employ a single group dimensional approach to capture a continuous spectrum of PTSD symptoms as suggested by others<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. Furthermore, repeated measurements of PTSD symptom severity were obtained in 31 of the participants, thus we were also able to investigate whether PTSD symptom development or average PTSD symptom load were associated with the amygdala nuclei volumes.</p></sec><sec id=\"Sec2\" sec-type=\"materials|methods\"><title>Materials and methods</title><sec id=\"Sec3\"><title>Participants</title><p id=\"Par6\">The present study was part of a larger project investigating the effects of traumatic stress on cognitive and brain measures<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref>,<xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>, and included MRI and clinical data collected from 47 survivors of the 2011 Norwegian terrorist attack at 24–36 months post-trauma. Data were collected at two sites; the University of Bergen (UiB, site 1) and the University of Oslo (UiO, site 2), Norway. Both studies were approved by the Norwegian Regional Committees for Medical and Health Research Ethics (#2012/1464 and #2011/2507) and all participants provided written informed consent before participation. For comparison purposes, we also recruited 60 age-, sex- and education-matched control subjects. General exclusion criteria were a history of neurological or severe somatic disorder, head trauma and MRI-incompatibility. In order to obtain information concerning participants’ mental status, the Mini International Neuropsychiatric Interview (MINI, 6.0.0<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>) was utilized at site 1. At site 2, all participants completed the PTSD Checklist- civilian version (PCL-C)<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>, Beck depression inventory (BDI), and the Beck anxiety inventory (BAI). Six control participants fulfilling the criteria of ongoing depression or an anxiety disorder were excluded. In addition, one person from the trauma group with incidental brain pathology discovered during the MRI exam and another trauma survivor with incomplete data were excluded. The final sample thus comprised 45 trauma survivors (mean age ± SD = 20.22 ± 2.08, 51.1% females) and 54 controls (mean age ± SD = 20.76 ± 2.71, 55.6% females).</p><p id=\"Par7\">Thirty-eight of the trauma survivors also took part in a prospective, longitudinal study on neuropsychiatric sequela of the attacks; a study lead by the Norwegian Centre for Violence and Traumatic Stress Studies (NKVTS, site 3) and approved by the Norwegian Regional Committees for Medical and Health Research Ethics (#2011/1625). Although data were collected at several time points, only data acquired 4–5 months and 14–15 months post-trauma were used in the present study. Participation included semi-structured interviews performed by professional health personnel. The interviews assessed traumatic exposures, peri-traumatic reactions, PTSD symptom scores, degree of social support, functional impairments as well as more general measures of mental health and sociodemographics<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. PTSD symptom load was assessed using the University of California at Los Angeles PTSD Reaction Index (PTSD-RI)<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. Combining data from the three projects was approved by the Norwegian Regional Committees for Medical and Health Research Ethics (#2017/1293).</p></sec><sec id=\"Sec4\"><title>PTSD symptom load assessments</title><p id=\"Par8\">At site 1, PTSD symptom scores were assessed using the MINI 6.0.0<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. The MINI is a short diagnostic structured interview that explores psychiatric diagnosis according to the DSM-IV (Axis I) and ICD-10<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. Each question has only two response options (No = 0, Yes = 1). The PTSD diagnostic section started out by using three screening questions (i.e., H1: experienced or witnessed a significant trauma, H2: reaction to trauma and H3: re-experiencing symptoms over the last month), and if answered positively, 12 follow-up questions were asked in order to examine the presence of symptoms needed to fulfill the diagnostic criteria. PTSD load was calculated based on the number of positively answered questions for the PTSD diagnostic section (Part H in MINI 6.0.0). At site 2, subjects completed the PCL-C<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>, which is a DSM-IV based 17-item rating scale with self-report ratings ranging from 1 (not at all) to 5 (extremely) for each item. At site 3, post-traumatic stress reactions were measured using the PTSD-RI<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. The PTSD-RI is a DSM-IV based 20-item scale in which responses are recorded on a 5-point scale, ranging from 0 (never) to 4 (most of the time). Since three of the items have two alternative formulation, only the formation leading to the highest score was utilized, resulting in 17 items being used for the total symptom scale score calculation. The PTSD symptom scores were z-standardized within each site before entering any analyses<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>.</p></sec><sec id=\"Sec5\"><title>Imaging data acquisition and analysis</title><p id=\"Par9\">MRI data were collected 24–36 months post-trauma at sites 1 and 2. At site 1, images were acquired with a GE Signa HDx, 3 T MR scanner with an 8-channel head coil, and included a whole-brain T1 structural FSPGR sequence with a voxel size of 1 × 1 × 1 mm<sup>3</sup>,180 sagittal slices, TR = 7.8 ms, TE = 3.0 ms, FOV = 256 × 256 and flip angle 14°. At site 2, images were acquired with a Philips Achieva whole-body 3 T MR scanner with an 8-channel head coil, and included a whole-brain T1-weighted structural sequence with a voxel size of 1 × 1 × 1.2 mm<sup>3</sup>, 180 sagittal slices, TR = 6.6 ms; TE = 3.06 ms, FOV = 256 × 256 and flip angle = 8°.</p><p id=\"Par10\">All data were analysed within the same analysis pipeline at site 1, and were processed using FreeSurfer v6.0 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://surfer.nmr.mgh.harvard.edu/\">https://surfer.nmr.mgh.harvard.edu/</ext-link>) software, which enables fully automated volumetric segmentation of neuroanatomical structures including both bilateral hippocampus and amygdala. The segmentation procedure included the following: (a) removal of non-brain tissue using hybrid watershed/surface deformation procedures, (b) automated Talairach transformation, (c) segmentation of the subcortical white matter and deep gray matter volumetric structures, (d) tessellation of the gray/white matter boundary, (e) automated topology correction, and (f) surface deformation following intensity gradients to optimally place the gray/white and gray/CSF borders at the location where the greatest shift in intensity defines the transition to the other tissue class. After completing the fully automated-brain segmentation, we segmented bilateral amygdala into its respective nuclei, using a newly developed software extension<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. The nuclei included the basal, the lateral, the accessory basal, the cortical, the central, the medial, the paralaminar, the corticoamygdala transition zone and the anterior amygdala area (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a, b</xref>). All segmented data were visually inspected by a radiologist to assure the accuracy of the whole-brain segmentation. None of the subjects had to be excluded based on the visual inspection.<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Amygdala nuclei and hippocampal subfield segmentation.</title><p>The result of an amygdala nuclei and hippocampus subfield segmentation from a representative individual. A: Sagittal and B: Axial view of the color coded subfields and nuclei. Yellow: anterior amygdala area, dark blue = corticoamygdala transition zone, orange = accessory basal nucleus, pink: basal nucleus, pale blue: lateral nucleus, light green = Hippocampus amygdala transition zone; red = CA1, dark green = CA3, beige = CA4, bright blue = subiculum, dark purple = parasubiciulum, light purple = hippocampal tail, pink = fimbria.</p></caption><graphic xlink:href=\"41398_2020_974_Fig1_HTML\" id=\"d30e649\"/></fig></p></sec><sec id=\"Sec6\"><title>Statistical analyses</title><p id=\"Par11\">Statistical analyses were performed using IBM SPSS, version 25 (IBC Corp, Armonk, New York) and R (version 3.5.0). Standardized residuals were estimated for all multiple linear regression models, and the data were reanalyzed after exclusion of all subjects with residual values >3.0 or <−3.0. Covariates that did not show at least a modest relationship with the dependent variable (<italic>p</italic> < 0.2) were dropped from the statistical models<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref>,<xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. For the nuclei analyses, Bonferroni correction for the number of amygdala nuclei tested (<italic>N</italic> = 6, <italic>p</italic> = 0.008) was used to account for multiple comparisons.</p><p id=\"Par12\">Group differences in left and right total amygdala volume were tested using analyses of covariance (ANCOVA) with the amygdala volume as the dependent variable, group as fixed factor, and age, sex, site and total intracranial volume (ICV) as covariates. Next, we examined the relationship between total left and right amygdala volume and severity of PTSD symptoms 24–36 months post-trauma in the trauma survivors using multiple linear regression analyses while covarying for age, sex, ICV and site. Preliminary analyses were conducted to assess potential violations of the assumptions of normality of residuals and homoscedasticity. Moreover, we tested for multicollinearity among the independent variables. In some of the models, the assumption of homoscedasticity was not met (based on inspection of the standardized residuals plot), thus in these models, the dependent variable and the predictor of interest were log-transformed before entering any analyses. Finally, the PTSD symptom scores were z-standardized within each site<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>.</p><p id=\"Par13\">Traditionally, the amygdala has been thought of as consisting of two broad complexes, i.e. the basolateral (BLA) division and the centrocorticomedial (CMA) division<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>. While the BLA is composed of the basal, the lateral and the accessory basal nuclei, the CMA consists of the central, the medial and the cortical nuclei. To explore the impact of PTSD symptom load 24–36 months post-trauma on the nuclei of the BLA and the CMA, we performed separate multiple linear regression analyses with the amygdala nuclei volume as the dependent and PTSD symptoms, age, sex, site and ICV as the independent variables. Based on the results of the total amygdala volume analyses, only the nuclei of the right amygdala were investigated.</p></sec><sec id=\"Sec7\"><title>Longitudinal symptom assessments</title><p id=\"Par14\">In 31 of the trauma survivors, PTSD symptom load was assessed at three time points, i.e., 4–5 months, 14–15 months and 24–36 months following the trauma. The third observation corresponded in time with the MRI scan. This gave us a unique opportunity to investigate the association between amygdala nuclei volumes and longitudinal PTSD symptom load. We first calculated the average symptom load (AUC/time) by estimating the area under the curve (AUC) and dividing this by time between the first and the last assessment:<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{AUC/time}} = \\frac{{{\\Delta}t_{01}\\frac{{\\left( {\\mathrm{PTSD}_0 + \\mathrm{PTSD}_1} \\right)}}{2} + {\\Delta}t_{12}\\frac{{\\left( {\\mathrm{PTSD}_1 + \\mathrm{PTSD}_2} \\right)}}{2}}}{{t_{02}}}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mrow><mml:mi mathvariant=\"normal\">AUC/time</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msub><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mn>01</mml:mn></mml:mrow></mml:msub><mml:mfrac><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">PTSD</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">PTSD</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msub><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mn>12</mml:mn></mml:mrow></mml:msub><mml:mfrac><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">PTSD</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">PTSD</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mn>02</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math><graphic xlink:href=\"41398_2020_974_Article_Equa.gif\" position=\"anchor\"/></alternatives></disp-formula>where Δ<italic>t</italic><sub>01</sub> represents time between the 4–5 months and the 14–15 months assessments, Δ<italic>t</italic><sub>12</sub> represents time between the 14–15 and the 24–36 months assessments and t<sub>02</sub> is the total time between the 4–5 and the 24–36 months assessments. PTSD<sub>0</sub> is the PTSD symptom load 4–5 months after trauma, while PTSD<sub>1</sub> and PTSD<sub>2</sub> represent symptom load 14–15 and 24–36 months post-trauma, respectively. The majority of subjects experienced a symptom reduction from the first to the last assessment. By regressing each subjects’ PTSD symptom score against time point of assessment, we estimated an intercept and a linear slope, where the slope represents the individual symptom reduction from the first to the last assessment:<disp-formula id=\"Equb\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{y}} = \\beta _0 + \\beta _1t$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:mi mathvariant=\"normal\">y</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mi>t</mml:mi></mml:mrow></mml:math><graphic xlink:href=\"41398_2020_974_Article_Equb.gif\" position=\"anchor\"/></alternatives></disp-formula>where <italic>β</italic><sub>0</sub> is the intercept, <italic>β</italic><sub>1</sub> is the individual linear slope and <italic>t</italic> is the assessments. The association between average symptom load (AUC/time) and right amygdala nuclei volumes were investigated in separate multiple linear regression models covarying for age, sex, ICV and site. Equivalent statistical models were used to test the associations between the individual symptom development (<italic>β</italic><sub>1</sub>) and right amygdala nuclei volumes, while additionally covarying for the individual intercepts.</p><p id=\"Par15\">Based on the results from the above analyses, we finally tested if the right lateral nucleus mediated the association between PTSD symptom scores acquired 4–5 months and 24–36 months post-trauma using hierarchical linear regression as outlined in Baron and Kenny<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>. To estimate the indirect effects in the mediation model, we used the INDIRECT software as implemented in SPSS<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. The analyses controlled for age, sex, ICV and site. Indirect effects were considered significant if the 95% confidence interval did not overlap zero<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>.</p></sec></sec><sec id=\"Sec8\" sec-type=\"results\"><title>Results</title><sec id=\"Sec9\"><title>Demographics</title><p id=\"Par16\">The demographic and clinical data of participants are presented in Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>. Information regarding each site’s demographic and clinical characteristics are provided in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>. None of the trauma survivors were prescribed any antidepressants. However, five trauma survivors occasionally used a benzodiazepine not further specified.<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of the subjects.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Trauma survivors (<italic>N</italic> = 45)</th><th>Controls (<italic>N</italic> = 54)</th><th><italic>p</italic>-value</th></tr></thead><tbody><tr><td>Age (mean ± SD)<sup>a</sup></td><td>20.22 ± 2.08</td><td>20.76 ± 2.71</td><td>0.24</td></tr><tr><td>Sex (females)</td><td>23</td><td>30</td><td>0.66</td></tr><tr><td>Traumatic exposure (mean ± SD)<sup>b</sup></td><td>0.67 ± 0.14</td><td>NA</td><td/></tr><tr><td>PTSD<sup>c</sup></td><td>14</td><td>0</td><td><0.001</td></tr><tr><td>Major depression<sup>d</sup></td><td>9</td><td>0</td><td>0.001</td></tr><tr><td>Anxiety disorder<sup>e</sup></td><td>17</td><td>0</td><td><0.001</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>Age at the time of the MRI scan.</p><p><sup>b</sup>A checklist developed by NKVTS (please see<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup> for details) to assess 14 characteristics of potential traumatic exposure events (“Yes” or “No” answers). A sum (z-standardized) based on number of “yes” answers was calculated.</p><p><sup>c</sup>The presence of Post-traumatic stress disorder (PTSD) was assessed by using the Mini International Neuropsychiatric Interview (M.I.N.I 6.0.0) at site 1 and the PTSD Checklist civilian version (cut-off ≥45) at site 2.</p><p><sup>d</sup>The presence of a major depressive episode was assessed using M.I.N.I at site 1 and Beck Depression Inventory (BDI) (cut-off ≥18) at site 2.</p><p><sup>e</sup>Site 1 utilized the M.I.N.I, and anxiety disorder refers to the presence of Generalized Anxiety disorder and/or Panic disorder. Site 2 only measured anxiety symptoms in general using the Beck Anxiety Inventory (BAI) (anxiety disorder cut-off ≥16), thus these subjects cannot be further characterized. A two-sample <italic>t</italic>-test was used for age comparisons between the two groups, while the <italic>χ</italic><sup>2</sup> test was used for sex and psychopathology comparisons.</p></table-wrap-foot></table-wrap></p></sec><sec id=\"Sec10\"><title>Total amygdala and amygdala nuclei volumes</title><p id=\"Par17\">The amygdala nuclei volumes divided by group and site are presented in Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>. Two ANCOVAs assessing the group (i.e. trauma survivors vs. controls) difference in right (<italic>F</italic><sub>1,92</sub> = 0.07, <italic>p</italic> = 0.79, partial eta squared = 0.001) and left (<italic>F</italic><sub>1</sub><sub>,92</sub> = 1.88, <italic>p</italic> = 0.17, partial eta squared = 0.02) amygdala volumes while controlling for age, sex, ICV and site were not significant. A multiple linear regression analysis using the right amygdala volumes as dependent variables and the PTSD symptom load, age, sex, ICV and site as predictors, revealed a negative association between right amygdala volume and symptom load 24–36 months post-trauma (<italic>β</italic> = −0.34, <italic>t</italic> = −4.04, <italic>p</italic> = 0 < 0.001). An equivalent analysis for the left amygdala showed a trend towards significance (<italic>β</italic> = −0.17, <italic>t</italic> = −2.01, <italic>p</italic> = 0.051).<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Amygdala volumes divided by group and site.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th colspan=\"4\">Trauma survivors</th><th colspan=\"4\">Controls</th></tr><tr><th>Site</th><th colspan=\"2\">Site 1</th><th colspan=\"2\">Site 2</th><th colspan=\"2\">Site 1</th><th colspan=\"2\">Site 2</th></tr><tr><th/><th>Mean (SD)</th><th>Range</th><th>Mean (SD)</th><th>Range</th><th>Mean (SD)</th><th>Range</th><th>Mean (SD)</th><th>Range</th></tr></thead><tbody><tr><td colspan=\"9\">Left</td></tr><tr><td> Lateral nucleus</td><td>651 (90)</td><td>545–957</td><td>672 (106)</td><td>493–896</td><td>659 (61)</td><td>511–837</td><td>654 (53)</td><td>567–778</td></tr><tr><td> Basal nucleus</td><td>454 (58)</td><td>377–622</td><td>468 (71)</td><td>332–609</td><td>457 (43)</td><td>366–545</td><td>473 (41)</td><td>400–597</td></tr><tr><td> Accessory basal nucleus</td><td>264 (33)</td><td>210–339</td><td>282 (44)</td><td>197–384</td><td>264 (28)</td><td>211–326</td><td>289 (28)</td><td>251–387</td></tr><tr><td> Central nucleus</td><td>41 (8)</td><td>25–60</td><td>47 (9)</td><td>33–63</td><td>41 (7)</td><td>28–59</td><td>47 (7)</td><td>36–69</td></tr><tr><td> Medial nucleus</td><td>18 (4)</td><td>11–25</td><td>21 (6)</td><td>10–34</td><td>17 (3)</td><td>11–24</td><td>21 (5)</td><td>15–33</td></tr><tr><td> Cortical nucleus</td><td>23 (3)</td><td>17–31</td><td>27 (5)</td><td>16–37</td><td>23 (3)</td><td>16–29</td><td>28 (4)</td><td>22–38</td></tr><tr><td colspan=\"9\">Right</td></tr><tr><td> Lateral nucleus</td><td>699 (90)</td><td>587–934</td><td>724 (103)</td><td>521–943</td><td>691 (67)</td><td>587–867</td><td>695 (52)</td><td>606–793</td></tr><tr><td> Basal nucleus</td><td>490 (60)</td><td>387–661</td><td>497 (76)</td><td>342–668</td><td>474 (37)</td><td>409–583</td><td>499 (36)</td><td>418–600</td></tr><tr><td> Accessory basal nucleus</td><td>291 (41)</td><td>223–387</td><td>296 (46)</td><td>201–392</td><td>281 (25)</td><td>243–344</td><td>303 (22)</td><td>258–354</td></tr><tr><td> Central nucleus</td><td>50 (11)</td><td>28–73</td><td>51 (12)</td><td>31–76</td><td>47 (7)</td><td>31–58</td><td>51 (8)</td><td>38–67</td></tr><tr><td> Medial nucleus</td><td>22 (8)</td><td>11–46</td><td>22 (9)</td><td>13–50</td><td>19 (4)</td><td>11–27</td><td>22 (4)</td><td>16–34</td></tr><tr><td> Cortical nucleus</td><td>26 (5)</td><td>18–40</td><td>28 (5)</td><td>19–40</td><td>25 (3)</td><td>20–32</td><td>29 (3)</td><td>23–34</td></tr></tbody></table></table-wrap></p><p id=\"Par18\">Explorative multiple regression analyses of the right amygdala nuclei revealed a significant negative association between PTSD symptom load 24–36 months post-trauma and the lateral (<italic>β</italic> = −0.42, <italic>t</italic> = −3.87, <italic>p</italic> < 0.001, Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>), the basal (<italic>β</italic> = −0.30, <italic>t</italic> = −3.63, <italic>p</italic> = 0.001, Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>) as well as the accessory basal (<italic>β</italic> = −0.32, <italic>t</italic> = −2.95, <italic>p</italic> = 0.005, Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>) nuclei volume when co-varying for age, sex, ICV and site. Additional negative associations emerged for the right central (<italic>β</italic> = −0.37, <italic>t</italic> = −3.75, <italic>p</italic> = 0.001, Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>) and the right medial (<italic>β</italic> = −0.37, <italic>t</italic> = −2.90, <italic>p</italic> = 0.006, Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>) nuclei. To further ensure that the effects were not driven by one site only, we also investigated the association between amygdala nuclei volumes and PTSD symptom load for each site separately. Additional analyses were performed to test whether the choice of PTSD symptom assessment instrument influenced our results. The results of these analyses can be found in the Supplemental Materials.<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Right amygdala nuclei volumes and PTSD symptom scores.</title><p>The association between the individual amygdala nuclei volumes and PTSD symptom scores (z-standardized) 24–36 months post-trauma. The regression lines represent the relationship between the dependent variable and the predictor of interest calculated without covariates. The gray shadings represent the 95% confidence interval. Outliers (residual values > 3.0 or <−3.0) are indicated by a red dot color.</p></caption><graphic xlink:href=\"41398_2020_974_Fig2_HTML\" id=\"d30e1565\"/></fig></p></sec><sec id=\"Sec11\"><title>Longitudinal PTSD symptom load</title><p id=\"Par19\">Separate multiple linear regression analyses were performed to assess the associations between average symptom load (AUC/time) and the right amygdala nuclei volumes. Although nominally significant associations emerged for the right lateral (<italic>β</italic> = −0.45, <italic>t</italic> = −2.83, <italic>p</italic> = 0.009), the right basal (<italic>β</italic> = −0.35, <italic>t</italic> = −2.29, <italic>p</italic> = 0.03) and the right central (<italic>β</italic> = −0.34, <italic>t</italic> = −2.62, <italic>p</italic> = 0.01) nuclei, none of these associations remained significant after correction for multiple comparisons. Next, we used the same statistical framework to assess the associations between individual symptom development (<italic>β</italic><sub>1</sub>) and the volumes of the right amygdala nuclei. Interestingly, the analyses revealed a significant negative association for the right lateral nucleus (<italic>β</italic> = −0.52, <italic>t</italic> = −2.87, <italic>p</italic> = 0.008), implying that individuals experiencing less symptom reduction also had a smaller lateral nucleus volume. No other significant associations emerged after correcting for multiple comparisons (all <italic>p</italic>’s > 0.05).</p><p id=\"Par20\">The analyses so far have demonstrated an association between the right lateral nucleus and longitudinal symptom development. As such, it is possible that post-traumatic symptoms in the early phase following a trauma may influence long-term symptoms through an impact on the lateral nucleus. To further test this hypothesis, we examined whether the right lateral nucleus mediated the association between immediate- and long-term PTSD symptom load. Using hierarchical regression, we first demonstrated that PTSD symptoms 4–5 months post-trauma predicted PTSD symptoms 24–36 months post-trauma (<italic>B</italic> = 0.64, <italic>t</italic> = 3.17, <italic>p</italic> = 0.003, Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>). A second regression showed that the PTSD symptoms 4–5 months post-trauma was associated with right lateral nucleus volume 24–36 months post-trauma (<italic>B</italic> = −51,83, <italic>t</italic> = −3.12, <italic>p</italic> = 0.004, Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>). Right lateral nucleus volume was also associated with concurrent PTSD symptoms (i.e. 24–36 months following trauma) (<italic>B</italic> = −0.004, <italic>t</italic> = −2.18, <italic>p</italic> = 0.04, Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>). Importantly, adding the right lateral nucleus volume as a second predictor for PTSD symptom load 24–36 after trauma moderated the effect of PTSD symptom load 4–5 months post-trauma (<italic>B</italic> = 0.41, <italic>t</italic> = 1.88, <italic>p</italic> = 0.07, Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>), and the indirect effect of the right lateral nucleus volume on long-term PTSD symptoms was significant (bootstrap results for indirect effect; 95% CI [0.03, 0.57]), consistent with a mediating role.<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Mediation analysis.</title><p>The right lateral nucleus volume mediated the relationship between PTSD symptom load acquired 4–5 and 24–36 months following the trauma. *<italic>p</italic> < 0.05, **<italic>p</italic> < 0.005. Standardized coefficients in parenthesis.</p></caption><graphic xlink:href=\"41398_2020_974_Fig3_HTML\" id=\"d30e1685\"/></fig></p></sec></sec><sec id=\"Sec12\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par21\">We provide converging evidence of long-term effects of a traumatic event during adolescence on amygdala volume. More specifically, traumatized youths showed reduction of amygdala volume with increase in PTSD symptom severity 24–36 months post-trauma, which is in line with previous studies in PTSD patients<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Perhaps more interestingly, the subdivision analyses revealed that the negative association between amygdala volume and symptom severity could be ascribed to the nuclei of the BLA complex as well as the right central and medial nuclei. However, only the lateral nucleus was associated with individual PTSD symptom development, and mediated the association between short- and long-term PTSD symptoms. The results indicate that the various amygdala nuclei may be differentially associated with cross-sectional and longitudinal measures of PTSD symptom load. Future studies may therefore benefit from considering the amygdala as a heterogeneous brain area, when understanding the relationship between amygdala structure and PTSD.</p><p id=\"Par22\">One possible explanation for the conflicting amygdala volumetric findings in PTSD may be that previous studies have treated the amygdala as a homogeneous entity, and not taken its structural and functional heterogeneity into account<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. The nuclei of the BLA and the CMA have unique cellular architectures and structural connections<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>, which is reflected in their distinct roles in fear learning- and regulation<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref>,<xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. In line with this notion, the volume of the individual amygdala nuclei may be uniquely affected in disorders altering fear sensation<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>. Moreover, whereas increased spinogenesis and dendritic growth of principal and stellate neurons have been reported in the BLA following severe stress<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>, a loss of stellate neuron spines may occur in the CMA nuclei<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. Finally, preliminary findings from human functional imaging studies suggest that the BLA and the CMA differ in terms of activity<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup> and functional connectivity<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup> in PTSD, further suggesting that these complexes should be considered separately in trauma- and stress-related disorders<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref>,<xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>.</p><p id=\"Par23\">We here report a negative association between long-term (i.e. 24–36 months) PTSD symptom severity and the nuclei of the BLA complex. The results are corroborated by findings of unique structural alterations of the BLA in animals exposed to repeated restraint stress<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref>,<xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. Although stress-dependent structural changes in animals are mainly trophic<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref>,<xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>, it has been suggested that the initial volumetric expansion may be followed by a long-term volumetric reduction in humans<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. This is plausible, given that the BLA contains abundant glucocorticoid receptors<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>, and thus stress and excessive amounts of glucocorticoids may have direct and indirect neurotoxic effects on the BLA complex, inhibiting dendritic expansion and even causing neuronal loss. Furthermore, other stress-related mental illnesses like depression are also associated with initial amygdala volumetric increases<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup> followed by volumetric reductions upon recurrent depressive episodes<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>. Of note, rodents with smaller BLA show stronger conditioned fear responses and corticosteroid responses to stress<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>, and humans with a genetically rare disease (Urbach-Wiethe) damaging the BLA show increased vigilance in response to threat cues<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup>. As such, the increased arousal and vigilance, which is part of the PTSD symptom complex may be at least partially mediated by structural changes in BLA. This is further suggested by an inverse relationship between total amygdala volume and amygdala activity<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>, providing a link between our findings and the more frequently reported amygdala hyperactivity in PTSD<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>.</p><p id=\"Par24\">We also found evidence for an association between long-term PTSD symptom severity and concurrent volumes of the central and the medial nuclei. A recent study using vertex-based neuroimaging identified specific abnormalities in the morphology of the CMA which scaled with PTSD load<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. In addition, a study in young PTSD patients found altered gray matter density and intrinsic connectivity of both the BLA and CMA complexes<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. The central nucleus of the CMA is essential for fear expression and autonomic arousal in response to threat cues, and receives numerous connections from the lateral and basal nuclei<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>. Interestingly, the communication between the lateral and the central nucleus is regulated by prefrontal inputs<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>. As such, aberrant medial prefrontal—BLA connectivity in PTSD patients<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup> may facilitate signaling through the lateral—central nuclei route, with potential consequences for the central nucleus structure. Nevertheless, a combined effect on both the BLA and CMA could explain why PTSD is likely to affect both fear learning and expression, and also why extinguishing fear is so difficult in this disorder<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref>,<xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup>.</p><p id=\"Par25\">We had the unique opportunity to investigate the association between PTSD symptom severity acquired at several time-points (i.e. 4–5, 14–15 and 24–36 months) post-trauma and long-term amygdala nuclei volumes. Interestingly, we found that the individual PTSD symptom development was closely related to the lateral nucleus volume 24–36 months post-trauma. Moreover, the right lateral nucleus volume mediated the association between short- and long-term PTSD symptoms. The findings are in line with a recent study showing that amygdala reactivity immediately following the index trauma is related to PTSD symptoms months post-trauma<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. In addition, previous studies have reported that amygdala reactivity to affective stimuli pre-deployment positively predicted post-deployment PTSD symptoms in military samples<sup><xref ref-type=\"bibr\" rid=\"CR52\">52</xref>,<xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup>, and that post-traumatic stress symptoms in the aftermath of an index trauma were negatively associated with total amygdala volume 24 years later<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>. The present study extends these findings by showing that the long-term lateral nucleus volume is associated with early symptom development, and indeed may mediate the association between short-and long-term outcome. As such, nuclei of the BLA may be an essential target of early interventions including pharmacological or psychological treatments following trauma, to prevent the development of chronic PTSD.</p><p id=\"Par26\">Although our study may add novel insight into the association between amygdala volume and PTSD, several questions remain unanswered. One important question relates to whether lower amygdala nuclei volumes are a consequence of the extreme stress exposure per se or represent a preexisting vulnerability for developing PTSD. Findings of altered amygdala morphology in animals exposed to stress<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>–<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup> as well as altered structure and function in humans exposed to early life adversity<sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref>,<xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup> may suggest an effect of stress per se. In contrast, the observation of reduced amygdala volume in PTSD patients relative to trauma-exposed control subjects without PTSD<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup> is comparable with the hypothesis that lower amygdala volume is a heritable risk factor for developing or a consequence of having PTSD. However, to directly answer this question would require research studies using prospective, longitudinal designs and twin studies.</p><p id=\"Par27\">We acknowledge that a potential limitation of the present study rests in the heterogeneity related to the trauma group. This is always likely to be a challenge in these types of studies given the variability of response to stressors. A second limitation is the use of different MRI scanners and PTSD symptom assessment instruments which may have influenced the results. In addition to the use of different PTSD instruments, the majority of subjects had PTSD symptom scores in the lower ranges of the continuum. It is not clear whether the findings would be similar if more subjects with increased PTSD severity had been recruited. Therefore, future potential replication studies should be conducted in larger samples with a more even distribution of subjects across PTSD symptom severity. Although PTSD symptoms were assessed at multiple time-points, none of the subjects were assessed prior to the trauma, and thus the study cannot disentangle pre-existing aberrations from trauma-induced changes. Furthermore, traumatic experiences was not an exclusion criteria for the control subjects, which may have influenced the group comparison (i.e. trauma survivors vs controls). Moreover, our analyses were quite selective, as only the right lateral nucleus was subjected to a mediation analysis. Finally, we acknowledge that the FreeSurfer v6.0 extension used to segment the amygdala nuclei is a developmental version, and thus the results warrant replication in future samples with a greater diversity of PTSD symptom severity.</p><p id=\"Par28\">The present findings indicate that long-term PTSD symptom severity in the aftermath of trauma is associated with concurrent volumetric reduction of all basolateral nuclei as well as the central and the medial nuclei. However, only the lateral nucleus volume predicted the individual longitudinal PTSD symptom development and mediated the association between 4–5 months and 24–36 months PTSD symptom load. Our findings suggest that the amygdala nuclei may be differentially associated with cross-sectional and longitudinal measures of PTSD symptom severity. Accordingly, total amygdala volume alone may not provide a reliable index of the association between amygdala and stress-related mental illness.</p></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec13\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41398_2020_974_MOESM1_ESM.docx\"><caption><p>Supplemental Material_Amygdala_nuclei_volumes_in_traumatized_youths</p></caption></media></supplementary-material></sec></sec></body><back><fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Tor Endestad, Kenneth Hugdahl</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary Information</bold> accompanies this paper at (10.1038/s41398-020-00974-4).</p></sec><ack><title>Acknowledgements</title><p>This work was supported by the Western Norway Regional Health Authority (OTO #911780, #911813 and #912045) and the Norwegian Directorate of Health. The authors would like to thank Eva Øksnes, Roger Barndon, Turid I. Randa, Christel Jansen and Trond M. Øvreaas for their technical support during data acquisition. Furthermore, the authors thank Lena Matre, Silje Haukenes Stavestrand, Maja Holmeng and Kristine Lorentzen for administrative support during data acquisition.</p></ack><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Conflict of interest</title><p id=\"Par29\">K.H. and A.R.C. hold shares in the Nordic NeuroLab Inc. which produces add-on functional MRI equipment. They do not declare any conflict of interest. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807801</article-id><article-id pub-id-type=\"pmc\">PMC7431856</article-id><article-id pub-id-type=\"publisher-id\">69410</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-69410-3</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Characterization of native plant growth promoting rhizobacteria and their anti-oomycete potential against <italic>Phytophthora capsici</italic> affecting chilli pepper (<italic>Capsicum annum</italic> L.)</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-6094-5650</contrib-id><name><surname>Hyder</surname><given-names>Sajjad</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0003-3701-0897</contrib-id><name><surname>Gondal</surname><given-names>Amjad Shahzad</given-names></name><address><email>amjadshahzad@live.com</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rizvi</surname><given-names>Zarrin Fatima</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-9673-8610</contrib-id><name><surname>Ahmad</surname><given-names>Raees</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Alam</surname><given-names>Muhammad Mohsin</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hannan</surname><given-names>Abdul</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ahmed</surname><given-names>Waqas</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fatima</surname><given-names>Nida</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Inam-ul-Haq</surname><given-names>M.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.440552.2</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9296 8318</institution-id><institution>Department of Plant Pathology, </institution><institution>PMAS Arid Agriculture University, </institution></institution-wrap>Rawalpindi, Pakistan </aff><aff id=\"Aff2\"><label>2</label>Department of Botany, G.C Women University, Sialkot, Pakistan </aff><aff id=\"Aff3\"><label>3</label>Department of Plant Pathology, Bahauddin Zakriya University, Multan, Pakistan </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.448869.f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 6362 6107</institution-id><institution>Department of Botany, </institution><institution>Ghazi University, </institution></institution-wrap>D. G. Khan, Pakistan </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.440552.2</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9296 8318</institution-id><institution>Department of Soil Science and SWC, </institution><institution>PMAS Arid Agriculture University, </institution></institution-wrap>Rawalpindi, Pakistan </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13859</elocation-id><history><date date-type=\"received\"><day>9</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>6</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\"><italic>Phytophthora capsici</italic> is a notorious fungus which infects many crop plants at their early and late growth stages. In the present study, twelve <italic>P. capsici</italic> isolates were morphologically characterized, and based on pathogenicity assays; two highly virulent isolates causing post-emergence damping-off on locally cultivated chilli pepper were screened. Two <italic>P. capsici</italic> isolates, HydPak1 (MF322868) and HydPk2 (MF322869) were identified based on internal transcribed spacer (ITS) sequence homology. Plant growth promoting rhizobacteria (PGPR) play a significant role in disease suppression and plant growth promotion in various crops. Out of fifteen bacterial strains recovered from chilli rhizosphere, eight were found potential antagonists to <italic>P. capsici </italic>in vitro. Bacterial strains with strong antifungal potential were subjected to biochemical and molecular analysis. All tested bacterial strains, were positive for hydrogen cyanide (HCN), catalase production and indole-3-acetic acid (IAA) production (ranging from 6.10 to 56.23 µg ml<sup>−1</sup>), while siderophore production varied between 12.5 and 33.5%. The 16S rRNA sequence analysis of tested bacterial strains showed 98–100% identity with <italic>Pseudomonas putida</italic>, <italic>P. libanensis</italic>, <italic>P. aeruginosa</italic>, <italic>Bacillus subtilis</italic>, <italic>B. megaterium</italic>, and <italic>B. cereus</italic> sequences available in the National Center for Biotechnology Information (NCBI) GenBank nucleotide database. All sequences of identified bacteria were submitted to GenBank for accessions numbers (MH796347-50, MH796355-56, MH801129 and MH801071). Greenhouse studies concluded that all tested bacterial strains significantly suppressed the <italic>P. capsici</italic> infections (52.3–63%) and enhanced the plant growth characters in chilli pepper. Efficacy of many of these tested rhizobacteria is being first time reported against <italic>P. capsici</italic> from Pakistan. Plant growth promoting rhizobacteria (PGPR) exhibiting multiple traits may be used in the development of new, eco-friendly, and effective bioformulations as an alternative to synthetic fungicides.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Biotic</kwd><kwd>Microbe</kwd><kwd>Biotic</kwd><kwd>Microbe</kwd></kwd-group><funding-group><award-group><funding-source><institution>Higher Education Commission, Pakistan (HEC)</institution></funding-source></award-group></funding-group><funding-group><award-group><funding-source><institution>Punjab Agriculture Research Board (PARB)</institution></funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Chilli, also red pepper or chilli pepper (<italic>Capsicum annuum</italic> L.) is among the extensively grown spice crop in Pakistan like many other countries around the globe. Quality and quantity of the crop are adversely affected by numerous soil-borne and areal pathogens of which <italic>Phytophthora capsici</italic> is one of the most devastating oomycete pathogens, resulting into damping-off and blight diseases<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. This pathogen causes complete crop failure under favorable environmental conditions<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. Synthetic pesticides are frequently applied to attain a high yield of the produce however, this disease management strategy comes with potential risks to the environment and human health<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. Soil-borne nature of oomycete agents makes them more difficult to control due to longterm surviving potential in the soil<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>.</p><p id=\"Par3\">In the rhizosphere, bacteria are abundant microbes within the soil, and many of them have plant growth promotion traits known as PGPRs<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Their mechanism of action includes the production of indole-3-acetic acid (IAA), nitrogen fixation, soil phosphorus solubilization and various nutrients, and antagonism against pathogens by siderophores, cellulose, protease, antibiotics and cyanide production<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. Many scientists have reported the growth promotion in cereal crops<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>, fruits<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>, vegetables<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup> including pepper<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup> by the application of rhizobacteria.</p><p id=\"Par4\">Bacterial strains belonging to Actinobacteria, <italic>Bacillus</italic>, <italic>Pseudomonas</italic>, and <italic>Streptomyces</italic> spp. are declared as biological control agents of <italic>P. capsici</italic> and suppress damping-off disease in various crops<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>–<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. Because of the undesirable features of synthetic agrochemicals and increasing threat to the environment, utilization of naturally occurring biological control agents for disease suppression and plant growth enhancement is one of the possible alternatives. The present study is designed to isolate, characterize, to test the disease suppressiveness and PGP effects of native rhizobacterial strains, recovered from chilli rhizosphere. This study will help to explore the potential of these bacterial strains against other soil-borne pathogens and bio-pesticide development.</p></sec><sec id=\"Sec2\"><title>Materials and methods</title><sec id=\"Sec3\"><title>Isolation and pathogenicity of <italic>Phytophthora capsici</italic></title><p id=\"Par5\">During two years survey in chilli paper growing fields in February–November, 2016–2017, young symptomatic seedlings (15–30 days old) showing post-emergence damping-off symptoms were collected. Damping-off was confirmed by carefully observing the rotten roots<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup> and symptomatic roots showing typical browning and necrosis were collected in zip polythene bags, properly labelled, and kept in an icebox. All the collected samples were brought to the Department of Plant Pathology, PMAS Arid Agriculture University for further processing. Infected roots were surface disinfected in 0.1% sodium hypochlorite for 3 min followed by three consecutive washings in sterilized distilled water (SDW). Root tissues were cut into small slices (0.5 mm), and were placed aseptically on Petri plates containing <italic>P. capsici</italic> selective CMA-PARPH medium (Corn meal agar, 17 g; pimaricin, 10 mg; ampicillin, 250 mg; rifampicin, 10 mg; PCNB, 100 mg; hymexazol, 50 mg; and distilled water, 1,000 ml)<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>. All the Petri dishes were sealed with Parafilm tape, labelled with isolate code, data of isolation, and were incubated at 28 ± 2 °C for 7 days. A total of 12 <italic>P. capsici</italic> isolates were recovered and maintained on PDA medium amended with rifamycin (5 mg l<sup>−1</sup>).</p><p id=\"Par6\">Pathogenicity assay was conducted on seeds of two locally available chilli varieties (Long Green and Neelam) in vitro. Prior to seed sowing, already disinfected soil was flooded with 20 ml sporangial suspension (1 × 10<sup>3</sup> sporangia ml<sup>−1</sup>) of <italic>P. capsici</italic> in 1.5 l capacity plastic pots. Seeds of both the varieties were surface sterilized and sown in infected soil (10 seeds/pot) in five repeats for each test fungal strain in a repeated experiment and un-inoculated pots were served as control. All the pots were kept 25 ± 2 °C up to 20 days. Seedling mortality percentage was observed 15 days after sowing. Re-isolation of <italic>P. capsici</italic> from the infected root samples confirmed the association of the pathogen with the chilli damping off disease.</p></sec><sec id=\"Sec4\"><title>Characterization of <italic>Phytophthora capsici</italic></title><p id=\"Par7\"><italic>Phytophthora capsici</italic> was identified based on morphological characteristics. For sporangial production, small discs (5-mm) from actively growing mycelia were cultured on V8-agar medium containing Petri plates under white fluorescent light at 26 ± 2 °C for 7 days<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. Sterile distilled water (SDW) was added to each Petri plate, shaken to detach sporangia, and poured on a glass slide. Glass slide was covered with a coverslip and was examined under a compound microscope. Sporangial shape and size were measured at 200 × magnification while pedicle length was measured at 100 × magnification<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup> for 20 randomly chosen sporangia from each isolate. Chlamydospores production was studied in accordance with Ristaino<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. Briefly, actively growing <italic>P. capsici</italic> was aseptically transferred to 25 ml of clarified V8 broth (C-V8, 163 ml clarified V8 juice, 3 g CaCO<sub>3</sub>, 1,000 ml distilled water, 86 mg ampicillin, 26 mg rifampicin) in a sterilized 50 ml lid containing tube, and incubated in the dark at 26 ± 2 °C for 24 h. After incubation, tubes were shaken for 5 min and were incubated in the dark for 5 days. C-V8 broth was replaced with 45 ml of SDW, followed by incubation in the dark at 18 ± 2 °C for 72 h. Chlamydospores were detached from mycelium using sterilized forceps and a dissecting needle and were observed under 100X magnification.</p><p id=\"Par8\">Two highly virulent <italic>P. capsici</italic> strains (HydPk1 and HydPk2) confirmed in Pathogenicity assays were subjected to molecular based identification. Genomic DNA was extracted by using the standard protocol of Cetyl Trimethylammonium Bromide (CTAB)<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>,<xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. The internal transcribed spacer regions (ITS1 and ITS2) of the genomic DNA were amplified by polymerase chain reaction (PCR) using universal sense ITS1 (5′-TCCGTAGGTGAACCTGCGG-3′) and ITS4 (5′-TCCTCCGCTTATTGATATGC-3′) encoding ITS-1–5.8S-ITS-2<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>. Reaction was carried out in 50 μl total reaction mixture volumes containing 33·25 μl grade water, 5 μl PCR (10X) buffer, 4 μl dNTPs (5 Mm each), 1·5 μl MgCl<sub>2</sub> (25 mM), 2 μl each of ITS1 and IT4 primers (20 pmol), 0·25 μl (0·5 U) Taq DNA polymerase and 2 μl DNA as a template. PCR conditions were 94 °C for 5 min, followed by 30 cycles of 94 °C for 1 min, 55 °C for 1 min, and 72 °C for 2 min and a final elongation at 72 °C for 7 min. The PCR amplified products were analyzed in 2% agarose gel (high resolution agarose, Q-BIOgen) in TAE buffer containing 40 mmol l<sup>−1</sup> Tris–HCl (pH 7.9), 4 mmol l<sup>−1</sup> sodium acetate, and 1 mmol l<sup>−1</sup> EDTA (pH 7.9). The purified PCR products were sequenced in both directions and sequences were assembled to create a final sequence for each tested isolate. Basic local alignment search tool (BLAST) analysis was performed to check the sequence similarities of the tested sequences with those already deposited sequences in the National Center for Biotechnology Information (NCBI). All the retrieved and tested sequences were aligned using ClustalW program and subjected to phylogenetic analysis. The phylogenetic tree was constructed using Neighbor-Joining (NJ) method in MEGA-X version 10.1.7 with 1,000 bootstrap replications and the evolutionary distances were calculated by using Jukes–Cantor model. Sequences were submitted to NCBI and accession numbers were obtained.</p></sec></sec><sec id=\"Sec5\"><title>Isolation of PGPR strains</title><sec id=\"Sec6\"><title>Soil sampling</title><p id=\"Par9\">Rhizospheric soil samples along with healthy roots were collected from the chilli pepper fields in Rawalpindi division, Pakistan (33.5651° N, 73.0169° E). Samples were labelled properly and shifted to the icebox for transportation to Plant Bacteriology Laboratory, Department of Plant Pathology, PMAS Arid Agriculture University Rawalpindi and preserved at 4 °C till further use for the isolation of rhizobacterial strains.</p></sec><sec id=\"Sec7\"><title>Bacterial isolation and preservation</title><p id=\"Par10\">The Serial dilution procedure described by Xu and Kim<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup> was adopted for the isolation of rhizobacterial strains. One gram (1 g) of the rhizosphere soil from each sample, strongly adhering to the roots was poured separately in a test tube containing 9 ml sterile distilled water (10<sup>−1</sup>) and vortexed vigorously for 10 min. Subsequently, serial dilutions (from 10<sup>−1</sup> to 10<sup>−8</sup>) were made. For the isolation of <italic>Bacillus</italic> spp., 100 μl of each dilution was poured on Nutrient agar (NA, Difco, USA) plates, while <italic>Pseudomonas</italic> spp. were isolated on Kings’ B medium incubated at 28 ± 2 °C for 24 h. Pure bacterial cultures were obtained by picking up a single discrete bacterial colony on freshly prepared NA medium plates and a total of fifteen bacterial isolates were recovered (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>). Obtained isolates were stored at − 80 °C in 40% glycerol and distilled water solution<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup> until further use in experiments.<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>List of rhizobacterial strains isolated from chilli pepper fields from three different locations in Rawalpindi District, Pakistan.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">S. no.</th><th align=\"left\">Isolates code</th><th align=\"left\">No. of strains</th><th align=\"left\">Crop</th><th align=\"left\">Location</th><th align=\"left\">Coordinates</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">AJ-RB22, AJ-RB13, JHL 3, JHL 4, JHL 8, JHL-12</td><td align=\"left\">6</td><td align=\"left\">Chilli pepper</td><td align=\"left\">Adiiyala Jhamra</td><td align=\"left\">33.4573° N, 72.9948° E</td></tr><tr><td align=\"left\">2</td><td align=\"left\">KSL-8T, KSL-24, RWPRB03, 4a2, RH-87, 5C</td><td align=\"left\">6</td><td align=\"left\">Chilli pepper</td><td align=\"left\">Kasala</td><td align=\"left\">15.4581° N, 36.4040° E</td></tr><tr><td align=\"left\">3</td><td align=\"left\">DKB53, RB09, RBT7</td><td align=\"left\">3</td><td align=\"left\">Chilli pepper</td><td align=\"left\">Dhok Bawa</td><td align=\"left\">33.4739° N, 73.0206° E</td></tr></tbody></table></table-wrap></p></sec><sec id=\"Sec8\"><title>Antagonism assay against <italic>P. capsici </italic>in vitro</title><p id=\"Par11\">Bacterial strains were screened in vitro for antagonism against <italic>P. capsici</italic>. Rhizobacterial cultures were re-streaked on NA medium 48 h before testing their antagonistic potential in dual culture<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. A seven days old culture of HydPk2—<italic>P. capsici</italic> (accession MF322869) on potato dextrose agar (PDA) was used in this experiment. Briefly, 5 mm fungal mycelial plugs were cultured in the center of PDA Petri plates and bacterial cultures were streaked on both sides of the fungal plugs at a 2 cm of distance. Controls consisted of single cultures of the tested pathogen strain/s. Bacterial antagonism was tested in triplicate and plates were incubated at 28 ± 2 °C for about 96 h. The antagonistic potential was evaluated as inhibition of the mycelial radial growth of <italic>P. capsici</italic> against each bacterial strain tested. The experiment was carried out under a complete randomized design (CRD) with three replications.<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{Mycelial}}\\,{\\text{inhibition}}\\,\\left( \\% \\right) = \\left[ {\\frac{{{\\text{R}} - {\\text{r}}}}{{\\text{R}}}} \\right] \\times 100$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mtext>Mycelial</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mtext>inhibition</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mo>%</mml:mo></mml:mfenced><mml:mo>=</mml:mo><mml:mfenced close=\"]\" open=\"[\"><mml:mfrac><mml:mrow><mml:mtext>R</mml:mtext><mml:mo>-</mml:mo><mml:mtext>r</mml:mtext></mml:mrow><mml:mtext>R</mml:mtext></mml:mfrac></mml:mfenced><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_69410_Article_Equa.gif\" position=\"anchor\"/></alternatives></disp-formula>where R and r is the radius of fungal mycelial growth in control and treatment, respectively.</p></sec></sec><sec id=\"Sec9\"><title>Biochemical features of the obtained rhizobacterial strains</title><sec id=\"Sec10\"><title>Ammonia (NH<sub>3</sub>) production test</title><p id=\"Par12\">The Ammonia (NH3) production test was performed in accordance with Dye<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>. In particular, the test tubes containing peptone water (10.0 g peptone; 5.0 g NaCl; 1,000 ml distilled water; 7.0 pH) were inoculated with 100 μl of 24 h grown bacterial cultures in nutrient broth medium and incubated at 28 ± 2 °C for 48–72 h. The ammonia production was detected by adding Nessler's reagent (0.5 ml) in each test tube. The production of a brown to the deep yellow colour indicated NH<sub>3</sub> production.</p></sec><sec id=\"Sec11\"><title>Hydrogen cyanide production</title><p id=\"Par13\">Hydrocyanic acid (HCN) production was evaluated according to<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup> with a modification in the procedure. In particular, 24 h bacterial cultures grown in nutrient broth medium were inoculated on TSA medium amended with 4.4 g l<sup>−1</sup> glycine in Petri plates. Filter papers soaked in the picric acid solution (0.5% picric acid, 2% sodium carbonate) were put into lids of each Petri plate. These Petri plates were properly sealed with Parafilm and inoculated for 2 to 4 days at 28 ± 2 °C. After incubation for 48 h at 28 ± 2 °C, filter papers were observed for colour changes from weak yellow to reddish brown for each of the bacterial strain; indicating the positive test results.</p></sec><sec id=\"Sec12\"><title>Catalase test</title><p id=\"Par14\">Catalase test was carried out following Hayward<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. In particular, freshly grown (24 h old) bacterial culture on NA medium was placed on a dry glass slide and one drop of 3% H<sub>2</sub>O<sub>2</sub> was dropped on the bacterial colony. Rapid gas bubbles formation indicated the positive test results for the tested bacterial strains.</p></sec><sec id=\"Sec13\"><title>Potassium hydroxide (KOH) solubility test</title><p id=\"Par15\">Potassium hydroxide (KOH) solubility test was determined by following the procedure adopted by KIrsop and Doyle<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>. A loopful of 24 h old bacterial culture grown on NA medium was mixed with 3% Potassium Hydroxide solution on a dry glass slide till even suspension is formed. Formation of mucoid thread (loop) confirmed the positive reaction for the tested bacterial strains.</p></sec><sec id=\"Sec14\"><title>IAA production assay</title><p id=\"Par16\">Indole-3-acetic acid (IAA) production in rhizobacteria was tested in accordance with Dasri, et al.<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>. Each rhizobacterial strain was suspended in distilled water at (10<sup>8</sup> CFU ml<sup>−1</sup>) (OD620 = 0.1), then 0.5 ml aliquots were inoculated into 50 ml of King's B broth amended with 0.1% <sc>l</sc>-tryptophan followed by incubation at 28 °C for 72 h. Cultures were centrifuged at 12,000 rpm for 10 min and then 2 ml supernatant was thoroughly mixed with 4 ml of Salkowski reagent (1 ml of 0.5 M FeCl<sub>3</sub> solution in 50 ml of 35% perchloric acid). Change in the colour pink to red indicated the production of IAA. Optical density (OD) was measured at 530 nm by using a spectrophotometer, and IAA concentration was determined in comparison to 10–100 µg ml<sup>−1</sup> IAA standard curve. There were three replications for each bacterial strain to confirm the test results, and mean values were statistically analyzed.</p></sec><sec id=\"Sec15\"><title>Siderophore production</title><p id=\"Par17\">Siderophore production was assessed by culturing the bacterial strains on Chrome azurol S (CAS) agar. All the tested bacterial cultures were incubated 28 ± 2 °C for 5–7 days and the siderophore production was detected by the development of a yellow-orange halo around the bacterial colonies<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. For each strain, the siderophore production was quantified by CAS-liquid assay by mixing the bacterial culture supernatant (0.5 ml) with 0.5 ml of CAS reagent. Absorption was measured at 630 nm over a control treatment made of 0.5 ml of non-inoculated broth medium mixed with 0.5 ml of CAS reagent. There were three replications for each bacterial strain to confirm the test results, mean values were calculated, and data was statistically analyzed.</p></sec><sec id=\"Sec16\"><title>Molecular-based identification of rhizobacterial strains</title><p id=\"Par18\">Total genomic DNA of rhizobacterial strains was isolated using GeneJet Genomic DNA purification Kit (Thermo Scientific), following the manufacturer’s instructions. Bacterial strains were identified by amplifying 16S rRNA region by PCR using a set of universal primers; 27F (5′-AGAGTTTGATCMTGGCTCAG-3) and 1492R (5′-TACGGYTACCTTGTTACGACTT-3)<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref>,<xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. Polymerase chain reaction (PCR) reaction was carried out by using the standard reaction mixture (100 µl) containing: 5 × PCR buffer, 25 mM Mgcl<sub>2</sub>, 10 mM of each dNTPs, 4 µl of each primer (0.5 µM), 1 µl of Taq Polymerase enzyme (0.5 U µl<sup>−1</sup>), and 2 µl of bacterial DNA template. PCR conditions for 16S rRNA gene amplification were: initial denaturation of DNA template at 95 °C for 1 min per cycle, 35 cycles of denaturation at 95 °C for 15 s, annealing at 55 °C for 15 s, extension at 72 °C for 1 min and final elongation at 72 °C for 7 min. PCR amplified products were examined by separating the product on 1% agarose gel and visualized under UV transilluminator. Gel photographs were taken by using a gel documentation system. Amplified PCR product (1.5 kb) was purified using Gel and PCR Clean-Up System (Promega) and DNA quantification was carried out using NanoDrop. The amplified products were then sent to the Crop Science Department, University of Illinois Urbana-Champaign, USA for sequencing. Final sequences were obtained by joining both the reverse and forward sequences, and the 16S rRNA sequences of the bacterial strains were submitted in the GenBank database to obtain accession numbers (Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>). The similarity between the sequences was checked by aligning test sequences, and their best-matched sequences (on average 4 to 5 sequences) available in National Center for Biotechnology Information (NCBI) GenBank nucleotide database (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov\">www.ncbi.nlm.nih.gov</ext-link>) using ClusterW program. The nucleotide sequence homology was also cross verified from the DNA sequences data deposited in the DNA Data Bank of Japan-DDBJ (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ddbj.nig.ac.jp\">https://www.ddbj.nig.ac.jp</ext-link>). Evolutionary relatedness between test sequences and retrieved sequences was determined by constructing a Maximum-Likelihood phylogenetic tree in molecular evolutionary genetics analysis (MEGA 6) software, using a Kimura 2-parameter model with Gamma distribution (K2 + G), and 1,000 bootstrap replicates.<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Sequence analysis based on 16S rRNA and identity with accessions available on NCBI database “16S ribosomal RNA sequences (Bacteria and Archaea).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Isolates</th><th align=\"left\">Identified as</th><th align=\"left\">Accessions</th><th align=\"left\">% similarity</th><th align=\"left\">Accessions (NCBI)</th></tr></thead><tbody><tr><td align=\"left\">RWPRB03</td><td align=\"left\"><italic>P. putida</italic></td><td align=\"left\">MH801129</td><td char=\".\" align=\"char\">99</td><td align=\"left\">HM486417</td></tr><tr><td align=\"left\">RBT7</td><td align=\"left\"><italic>P. putida</italic></td><td align=\"left\">MH801071</td><td char=\".\" align=\"char\">99</td><td align=\"left\">KY982927</td></tr><tr><td align=\"left\">RB09</td><td align=\"left\"><italic>P. libanensis</italic></td><td align=\"left\">MH796355</td><td char=\".\" align=\"char\">98</td><td align=\"left\">DQ095905</td></tr><tr><td align=\"left\">AJ-RB13</td><td align=\"left\"><italic>P. aeruginosa</italic></td><td align=\"left\">MH796356</td><td char=\".\" align=\"char\">99</td><td align=\"left\">KF420126</td></tr><tr><td align=\"left\">DKB53</td><td align=\"left\"><italic>B. subtilus</italic></td><td align=\"left\">MH796349</td><td char=\".\" align=\"char\">100</td><td align=\"left\">KX061099</td></tr><tr><td align=\"left\">AJ-RB22</td><td align=\"left\"><italic>B. megaterium</italic></td><td align=\"left\">MH796350</td><td char=\".\" align=\"char\">100</td><td align=\"left\">MG544100</td></tr><tr><td align=\"left\">KSL-24</td><td align=\"left\"><italic>B. cereus</italic></td><td align=\"left\">MH796347</td><td char=\".\" align=\"char\">100</td><td align=\"left\">KP236185</td></tr><tr><td align=\"left\">KSL-8T</td><td align=\"left\"><italic>B. cereus</italic></td><td align=\"left\">MH796348</td><td char=\".\" align=\"char\">99</td><td align=\"left\">MF375116</td></tr></tbody></table></table-wrap></p></sec><sec id=\"Sec17\"><title>Seed germination assay</title><p id=\"Par19\">Rhizobacterial strains were tested for their effect on seed germination before proceeding pot experiments. Chilli pepper seeds (variety; Long green) were surface sterilized by dipping in 1% sodium hypochlorite for 3–5 min and washed three times in dH<sub>2</sub>O. Disinfected chilli seeds were soaked in 25 ml prepared concentrations of each rhizobacterial suspensions in three concentrations (10<sup>8</sup> CFU ml<sup>−1</sup>) by gently shaking for 2 h on the shaker followed by surface drying the bacteria treated seeds on blotter paper. Ten seeds/Petri plates were placed on wet Whatman filter paper No. 41 in each Petri plate, and were incubated at 26 ± 2 °C. Filter papers were kept moist with autoclaved distilled water. Seeds soaked in autoclaved distilled water were kept as control. There were three replications for each treatment and data on seed germination percentage was recorded after 15 days of incubation. The experiment was carried out under a completely randomized design (CRD). Mean values for seed germination were calculated, and data was statistically analyzed. Seed germination percentage was calculated by the following formula:<disp-formula id=\"Equb\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{Seed}}\\,{\\text{germination}}\\,\\left( \\% \\right) = \\frac{{{\\text{No}}{.}\\,{\\text{of}}\\,{\\text{germinated}}\\,{\\text{seeds}}}}{{{\\text{total}}\\,{\\text{no}}{.}\\,{\\text{of}}\\,{\\text{seeds}}}} \\times 100.$$\\end{document}</tex-math><mml:math id=\"M4\" display=\"block\"><mml:mrow><mml:mtext>Seed</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mtext>germination</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mo>%</mml:mo></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mtext>No</mml:mtext><mml:mo>.</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mtext>of</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mtext>germinated</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mtext>seeds</mml:mtext></mml:mrow><mml:mrow><mml:mtext>total</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mtext>no</mml:mtext><mml:mo>.</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mtext>of</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mtext>seeds</mml:mtext></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mn>100</mml:mn><mml:mo>.</mml:mo></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_69410_Article_Equb.gif\" position=\"anchor\"/></alternatives></disp-formula></p></sec><sec id=\"Sec18\"><title>Greenhouse testing of PGPR for damping-off suppression and growth promotion</title><p id=\"Par20\">Identified rhizobacteria were subjected to check their effect on disease suppression and various plant growth parameters. Pot experiments were performed in the greenhouse located at the Department of Plant Pathology, PMAS Arid Agriculture University, Rawalpindi. Chilli (variety; Long Green) seeds were surface sterilized with 10% (v/v) sodium hypochlorite for 5 min followed by six consecutive washings in dH<sub>2</sub>O. Plastic pots (1.5 l) were filled with autoclaved sandy loam texture soil having physical and chemical properties as; Cation exchange capacity (18 cmol kg<sup>−1</sup>), pH (7.9), Organic matter (4.3 g kg<sup>−1</sup>), CaCO<sub>3</sub> (76 g kg<sup>−1</sup>), Electrical conductivity, extract (0.48 dS m<sup>−1</sup>) Total N (0.2 g kg<sup>−1</sup>), Total P (267 mg kg<sup>−1</sup>) and Total K (198 mg kg<sup>−1</sup>). The soil was flooded with 20 ml sporangial suspension (1 × 10<sup>3</sup> sporangia ml<sup>−1</sup>) of <italic>P. capsici</italic>. Surface sterilized seeds were dipped for 2 h in the bacterial suspension of 10<sup>8</sup> CFU ml<sup>−1</sup> and ten seeds per pot were sown. The control treatment consisted of pots containing soil infested with <italic>P. capsici</italic> inoculum and distilled water-treated chilli seeds (without bacterial inoculation). All the pots were arranged in complete randomized designed (CRD) in five replications and were placed in the greenhouse. Disease severity was taken on the disease rating scale (DRS) representing 0 = healthy plant; 1 = 1 to 30% wilting; 2 = 31 to 50% wilting; 3 = 51 to 70% wilting; 4 = 71 to 90% wilting; 5 = more than 90% wilting or dead plant<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. Data on damping-off disease suppression was recorded 15 days after seed sowing while plant growth parameters were recorded 30 days after sowing.</p><sec id=\"Sec19\"><title>Statistical analysis</title><p id=\"Par21\">Statistical analysis was performed using Statistix 8.1 software and Microsoft Office Excel 2010. A completely randomized design (CRD) was used for all experiments with replicated treatments. Mean values for each treatment were calculated, and all the treatment means were compared via Analysis of variance (ANOVA) using the least significant differences test (LSD) at 5% (<italic>P</italic> ≤ 0.05) probability level.</p></sec></sec></sec><sec id=\"Sec20\"><title>Results</title><sec id=\"Sec21\"><title><italic>Phytophthora capsici</italic> and pathogenicity assay</title><p id=\"Par22\">A total of twelve <italic>P. capsici</italic> isolates were recovered from infected chilli pepper root samples, collected during a survey in Rawalpindi region (33.5651° N, 73.0169° E). Pure culture of <italic>P. capsici</italic> and the microscopic images are shown (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>). Two separate experiments under the same environmental conditions were conducted to check the pathogen virulence on the chilli pepper. All the tested isolates varied in their virulence to chilli seedlings and mortality percentage was recorded which ranged from 23.3 to 61.7% on <italic>cv.</italic> Long Green and 26.7–65.7% on <italic>cv.</italic> Neelam ( Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>). Out of 12 tested isolates, HydPk1 and HydPk2 showed the highest percentage seedling mortality in both the chilli varieties were ranged 46–56% and 62–66% respectively (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>) and were highly virulent. The isolate HydPk2 showed maximum seedling mortality on both cultivars; 62% and 66%, it was selected to test in management trials in vitro and in greenhouse.<fig id=\"Fig1\"><label>Figure 1</label><caption><p>(<bold>a</bold>) Pure culture of <italic>P. capsici</italic> on CMA-PARPH medium, (<bold>b</bold>) microscopic image of <italic>P. capsici</italic>.</p></caption><graphic xlink:href=\"41598_2020_69410_Fig1_HTML\" id=\"MO1\"/></fig><fig id=\"Fig2\"><label>Figure 2</label><caption><p>Pathogenicity assay to test seedling mortality percentage in chilli pepper by <italic>P. capsici</italic>. Long Green and Neelam are the two chilli pepper varieties. Pathogenicity assay was carried out in five replications for each treatment and data on seedling mortality percentage was recorded 15 days after treatment. Mean values were calculated and statistical analysis was performed using Statistix 8.1. All the mean values were subjected to analysis of variance, and means were separated by LSD test at 5% probability. Error bars represent the standard error values of the means.</p></caption><graphic xlink:href=\"41598_2020_69410_Fig2_HTML\" id=\"MO2\"/></fig></p></sec><sec id=\"Sec22\"><title>Characterization of <italic>Phytophthora capsici</italic></title><p id=\"Par23\">All the isolates produced ovoid, papillate sporangia except RWP14 and GU43 which produced spherical, papillated sporangia. The mean sporangial length among the isolates ranged from 44.7 to 53.1 µm while the sporangial width varied from 24 to 38.4 µm. All the isolates showed pedicle length ranged from 30.6 to 75.5 µm. Maximum pedicle length of 75.5 µm was shown by RWP11 while minimum pedicle length 30.6 µm was observed in HydPk1. Chlamydospores diameter was also recorded in the range of 20.7–29.7 µm (Table <xref rid=\"Tab3\" ref-type=\"table\">3</xref>). Two highly virulent <italic>P. capsici</italic> (HydPk1 and HydPk2) were subjected to molecular characterization. Internal transcribed spacer regions ITS1 and ITS2 were amplified in both forward and reverse directions and BLAST analysis confirmed 99% identity with those already deposited ITS sequences of <italic>P. capsici</italic> (KM369964 (Mexico) and KU518782 (India). Evolutionary relationship of all the sequence was determined by constructing a phylogenetic tree (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>). Sequences of HydPk1 and HydPk2 were submitted to GenBank nucleotide database and accessions MF322868 and MF322869 were obtained.<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Morphological features of isolates of <italic>Phytophthora capsici</italic> isolated from chilli pepper fields.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Isolate code</th><th align=\"left\">Sporangial shape</th><th align=\"left\">Sporangial length (µm)</th><th align=\"left\">Sporangial width (µm)</th><th align=\"left\">Pedicle length (µm)</th><th align=\"left\">Chlamydospores diameter (µm)</th></tr></thead><tbody><tr><td align=\"left\">Pyt JHL07</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">45.8 ± 3.1<sup>ab</sup></td><td align=\"left\">35.7 ± 6.3<sup>abc</sup></td><td align=\"left\">71.9 ± 4.4<sup>ab</sup></td><td align=\"left\">20.7 ± 1.1<sup>b</sup></td></tr><tr><td align=\"left\">Pyt JHL09</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">50.0 ± 2.5<sup>ab</sup></td><td align=\"left\">32.0 ± 3.7<sup>abcd</sup></td><td align=\"left\">70.8 ± 4.2<sup>ab</sup></td><td align=\"left\">29.7 ± 1.7<sup>a</sup></td></tr><tr><td align=\"left\">RWP11</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">52.1 ± 2.3<sup>ab</sup></td><td align=\"left\">36.6 ± 4.9<sup>ab</sup></td><td align=\"left\">75.5 ± 4.7<sup>a</sup></td><td align=\"left\">24.3 ± 2.9<sup>ab</sup></td></tr><tr><td align=\"left\">RWP14</td><td align=\"left\">Spherical-papillate</td><td align=\"left\">53.1 ± 4.4<sup>a</sup></td><td align=\"left\">24.0 ± 1.3<sup>d</sup></td><td align=\"left\">65.8 ± 2.5<sup>ab</sup></td><td align=\"left\">29.0 ± 1.6<sup>a</sup></td></tr><tr><td align=\"left\">Pyt GR14</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">46.4 ± 1.8<sup>ab</sup></td><td align=\"left\">38.4 ± 3.2<sup>a</sup></td><td align=\"left\">39.6 ± 3.1<sup>cd</sup></td><td align=\"left\">24.8 ± 2.7<sup>ab</sup></td></tr><tr><td align=\"left\">Pyt GR17</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">45.8 ± 2.8<sup>ab</sup></td><td align=\"left\">34.2 ± 3.4<sup>abcd</sup></td><td align=\"left\">69.9 ± 5.7<sup>ab</sup></td><td align=\"left\">23.6 ± 2.0<sup>ab</sup></td></tr><tr><td align=\"left\">Pyt GR22</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">49.0 ± 2.5<sup>ab</sup></td><td align=\"left\">29.8 ± 2.6<sup>abcd</sup></td><td align=\"left\">61.2 ± 3.3<sup>b</sup></td><td align=\"left\">25.3 ± 3.5<sup>ab</sup></td></tr><tr><td align=\"left\">HydPk1</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">45.8 ± 2.2<sup>ab</sup></td><td align=\"left\">27.3 ± 2.9<sup>bcd</sup></td><td align=\"left\">30.6 ± 2.1<sup>d</sup></td><td align=\"left\">24.7 ± 1.0<sup>ab</sup></td></tr><tr><td align=\"left\">HydPk2</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">44.7 ± 2.2<sup>b</sup></td><td align=\"left\">26.1 ± 2.5<sup>cd</sup></td><td align=\"left\">37.8 ± 4.5<sup>cd</sup></td><td align=\"left\">25.3 ± 1.4<sup>ab</sup></td></tr><tr><td align=\"left\">Gu33</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">50.6 ± 3.0<sup>ab</sup></td><td align=\"left\">29.7 ± 2.0<sup>abcd</sup></td><td align=\"left\">74.9 ± 3.8<sup>a</sup></td><td align=\"left\">24.2 ± 2.1<sup>ab</sup></td></tr><tr><td align=\"left\">GU43</td><td align=\"left\">Spherical-papillate</td><td align=\"left\">49.1 ± 4.0<sup>ab</sup></td><td align=\"left\">35.6 ± 4.1<sup>abc</sup></td><td align=\"left\">42.5 ± 3.6<sup>c</sup></td><td align=\"left\">26.3 ± 1.6<sup>ab</sup></td></tr><tr><td align=\"left\">GU53</td><td align=\"left\">Ovoid-papillate</td><td align=\"left\">47.3 ± 2.6 <sup>ab</sup></td><td align=\"left\">35.1 ± 3.9<sup>abc</sup></td><td align=\"left\">41.0 ± 3.3<sup>cd</sup></td><td align=\"left\">27.9 ± 2.2<sup>ab</sup></td></tr><tr><td align=\"left\" colspan=\"2\">LSD</td><td align=\"left\">8.1734</td><td align=\"left\">10.310</td><td align=\"left\">11.097</td><td align=\"left\">6.0214</td></tr></tbody></table><table-wrap-foot><p>All the presented values are means of twenty replicates. All the means were subjected to analysis of variance and means were separated by LSD test. Letters represent the significant difference among the mean values and ± are standard error values of the means.</p></table-wrap-foot></table-wrap><fig id=\"Fig3\"><label>Figure 3</label><caption><p>Phylogenetic relationship between the identified strains and representative <italic>P. capsici</italic> sequences. ITS1 and ITS2 regions of the tested <italic>P. capsici</italic> isolates were amplified, and all the retrieved and tested sequences were aligned using the CLUSTAL W program. The phylogenetic tree was constructed using Neighbor-Joining (NJ) method in MEGA-X version 10.1.7 with 1,000 bootstrap replications and the evolutionary distances were calculated by using Jukes–Cantor model.</p></caption><graphic xlink:href=\"41598_2020_69410_Fig3_HTML\" id=\"MO3\"/></fig></p></sec><sec id=\"Sec23\"><title>Antagonism assay against <italic>P. capsici </italic>in vitro</title><p id=\"Par24\">Antagonism assay was performed to check the potential of rhizobacterial strains in mycelial growth inhibition against <italic>P. capsici </italic>in vitro. All the tested bacterial strains showed significant antagonism activity against <italic>P. capsici</italic> in dual culture assay on PDA (Table <xref rid=\"Tab4\" ref-type=\"table\">4</xref>). Out of 15 tested agents, eight bacterial strains were found potential agents against <italic>P. capsici</italic> and significantly inhibited the mycelial growth 96 h after incubation. Fungal growth inhibition (cm) was ranged 61.7–88.3% over un-inoculated control. Maximum fungal mycelial growth inhibition was done by KSL-8T—<italic>Bacillus cereus</italic> (88.3%), AJ-RB13—<italic>Pseudomonas aeruginosa</italic> (85.7%) and RWPRB03—<italic>Pseudomonas putida</italic> (81.1%) while AJ-RB22—<italic>Bacillus megatarium</italic> was least effective (61.7%) among all the tested bacterial agents over untreated control.<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>In vitro antifungal activity of rhizobacteria strains on <italic>Phytophthora capsici</italic>.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">S. no.</th><th align=\"left\">Bacterial strains</th><th align=\"left\">Fungal mycelia growth 96 h after incubation</th><th align=\"left\">Percentage inhibition</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">RWPRB03—<italic>Pseudomonas putida</italic></td><td align=\"left\">0.9 ± 0.08<sup>de</sup></td><td align=\"left\">81.1</td></tr><tr><td align=\"left\">2</td><td align=\"left\">RBT7—<italic>Pseudomonas putida</italic></td><td align=\"left\">1.3 ± 0.08<sup>cd</sup></td><td align=\"left\">74</td></tr><tr><td align=\"left\">3</td><td align=\"left\">RB09—<italic>Pseudomonas libansis</italic></td><td align=\"left\">1.6 ± 0.19<sup>bc</sup></td><td align=\"left\">68.2</td></tr><tr><td align=\"left\">4</td><td align=\"left\">AJ-RB13—<italic>Pseudomonas aeruginosa</italic></td><td align=\"left\">0.7 ± 0.08<sup>e</sup></td><td align=\"left\">85.7</td></tr><tr><td align=\"left\">5</td><td align=\"left\">DKB53—<italic>Bacilus subtilus</italic></td><td align=\"left\">1.6 ± 0.21<sup>bc</sup></td><td align=\"left\">68.8</td></tr><tr><td align=\"left\">6</td><td align=\"left\">AJ-RB22—<italic>Bacilus megatarium</italic></td><td align=\"left\">1.97 ± 0.12<sup>b</sup></td><td align=\"left\">61.7</td></tr><tr><td align=\"left\">7</td><td align=\"left\">KSL-24—<italic>Bacilus cereus</italic></td><td align=\"left\">1.3 ± 0.18<sup>cd</sup></td><td align=\"left\">75.3</td></tr><tr><td align=\"left\">8</td><td align=\"left\">KSL-8T—<italic>Bacilus cereus</italic></td><td align=\"left\">0.6 ± 0.15<sup>e</sup></td><td align=\"left\">88.3</td></tr><tr><td align=\"left\">9</td><td align=\"left\">Control</td><td align=\"left\">5.1 ± 0.12<sup>a</sup></td><td align=\"left\">0</td></tr><tr><td align=\"left\" colspan=\"2\">LSD value</td><td align=\"left\">0.4253</td><td align=\"left\"/></tr></tbody></table><table-wrap-foot><p>All the presented values are means of three replicates. Means were subjected to analysis of variance and were separated by LSD test. Letters represent the significant difference among the mean values and ± are standard error values of the means.</p></table-wrap-foot></table-wrap></p></sec><sec id=\"Sec24\"><title>Biochemical analysis</title><p id=\"Par25\">Bacterial strains with promising antifungal potential; RWPRB03—<italic>Pseudomonas putida</italic>, RBT7—<italic>Pseudomonas putida,</italic> RB09—<italic>Pseudomonas libansis</italic>, AJ-RB13—<italic>Pseudomonas aeruginosa</italic>, DKB53—<italic>Bacillus subtilus</italic>, AJ-RB22—<italic>Bacillus megatarium</italic>, KSL-24—<italic>Bacillus cereus</italic> and KSL-8T—<italic>Bacillus cereus</italic> were subjected to biochemical analysis and plant growth promoting (PGP) traits. All the rhizobacteria produced ammonia except strain AJ-RB13, while HCN and catalase test results were positive for all the bacterial strains. For Potassium hydroxide (KOH) test, bacteria belonging to <italic>Pseudomonas</italic> spp. showed positive response while bacteria from <italic>Bacillus</italic> spp. showed a negative test results. All the bacterial strains significantly produced IAA, and IAA production was quantified ranging (6.1–56.2 µg ml<sup>−1</sup>). Maximum IAA was produced by KSL-24—<italic>Bacillus cereus</italic> (56.2 ± 2.58 µg ml<sup>−1</sup>) followed by RWPRB03—<italic>Pseudomonas putida</italic> (35.9 µg ml<sup>−1</sup>) and KSL-8T—<italic>Bacillus cereus</italic> (29.7 µg ml<sup>−1</sup>). Siderophore production was exhibited by all the tested rhizobacterial strains ranging (12.5—33.5%). Highest siderophore production percentage was produced by RWPRB03—<italic>Pseudomonas putida</italic> (33.5%) followed by AJ-RB13—<italic>Pseudomonas aeruginosa</italic> (31.6%) and AJ-RB22—<italic>Bacillus megatarium</italic> (29.6%) while this activity was observed least in KSL-8T—<italic>Bacillus cereus</italic> (12.5%) and the results are presented in (Table <xref rid=\"Tab5\" ref-type=\"table\">5</xref>).<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Biochemical and plant growth promoting traits of the tested bacterial isolated from chilli pepper.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Rhizobacterial isolate</th><th align=\"left\" rowspan=\"2\">AP</th><th align=\"left\" rowspan=\"2\">HCP</th><th align=\"left\" rowspan=\"2\">CT</th><th align=\"left\" rowspan=\"2\">KT</th><th align=\"left\" colspan=\"2\">IAA production (µg ml<sup>−1</sup>)</th><th align=\"left\" rowspan=\"2\">Siderophore production (%)</th></tr><tr><th align=\"left\">Without tryptophan</th><th align=\"left\">With tryptophan</th></tr></thead><tbody><tr><td align=\"left\">RWPRB03—<italic>Pseudomonas putida</italic></td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">5.6 ± 0.52<sup>a</sup></td><td align=\"left\">35.9 ± 3.10<sup>b</sup></td><td align=\"left\">33.5 ± 0.9<sup>a</sup></td></tr><tr><td align=\"left\">RBT7—<italic>P. putida</italic></td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">2.4 ± 0.09<sup>de</sup></td><td align=\"left\">16.9 ± 3.10<sup>e</sup></td><td align=\"left\">26.8 ± 1.7<sup>c</sup></td></tr><tr><td align=\"left\">RB09—<italic>P. libansis</italic></td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">3.6 ± 0.60<sup>bcd</sup></td><td align=\"left\">19.9 ± 1.65<sup>de</sup></td><td align=\"left\">21.2 ± 1.4<sup>d</sup></td></tr><tr><td align=\"left\">AJ-RB13—<italic>P. aeruginosa</italic></td><td align=\"left\">−</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">2.9 ± 0.54<sup>cd</sup></td><td align=\"left\">6.1 ± 1.03<sup>f</sup></td><td align=\"left\">31.6 ± 1.6<sup>ab</sup></td></tr><tr><td align=\"left\">DKB53—<italic>Bacilus subtilus</italic></td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">−</td><td align=\"left\">5.4 ± 1.27<sup>a</sup></td><td align=\"left\">17.3 ± 1.10<sup>e</sup></td><td align=\"left\">16.7 ± 1.7<sup>e</sup></td></tr><tr><td align=\"left\">AJ-RB22—<italic>B. megatarium</italic></td><td align=\"left\"> ± </td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">−</td><td align=\"left\">1.6 ± 0.52<sup>e</sup></td><td align=\"left\">22.9 ± 3.19<sup>d</sup></td><td align=\"left\">29.6 ± 0.8<sup>b</sup></td></tr><tr><td align=\"left\">KSL-24—<italic>B. cereus</italic></td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">−</td><td align=\"left\">3.7 ± 0.60<sup>bc</sup></td><td align=\"left\">56.2 ± 2.58<sup>a</sup></td><td align=\"left\">16.4 ± 1.0<sup>e</sup></td></tr><tr><td align=\"left\">KSL-8T—<italic>B. cereus</italic></td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">−</td><td align=\"left\">4.2 ± 0.65<sup>b</sup></td><td align=\"left\">29.7 ± 3.52<sup>c</sup></td><td align=\"left\">12.5 ± 0.3<sup>f</sup></td></tr><tr><td align=\"left\" colspan=\"5\">LSD value</td><td align=\"left\">1.1588</td><td align=\"left\">4.4717</td><td align=\"left\">2.1883</td></tr></tbody></table><table-wrap-foot><p>All the presented values are means of three replicates. Means were subjected to analysis of variance and were separated by LSD test. Letters represent the significant difference among the mean values and ± are standard error values of the means.</p><p><italic>AP</italic> ammonia production, <italic>HCP</italic> hydrogen cyanide production, <italic>CT</italic> catalase test, <italic>KT</italic> KOH test.</p></table-wrap-foot></table-wrap></p></sec><sec id=\"Sec25\"><title>Molecular-based identification of rhizobacterial strains</title><p id=\"Par26\">Bacterial strains with promising antagonistic potential and biochemical traits were identified based on 16S rRNA sequence analysis. Phylogenetic trees constructed from 16S rRNA sequences showed that tested bacterial strains were belonging to <italic>Pseudomonas</italic> and <italic>Bacillus</italic> genus (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). The Maximum-Likelihood tree indicated that RWPRB03 and RBT7 clustered with <italic>P. putida</italic>, and showed 99% identity with accessions; HM486417 and KY982927. Bacterial strain RB09 was 98% identical to <italic>P. libanensis</italic> (DQ095905), while AJ-RB13 clustered with <italic>P. aeruginosa</italic> and showed 99% sequence homology with accession KF420126. The other four strains viz., DKB53, AJ-RB22, KSL-24 and KSL-8T were closely related to <italic>B. subtilus</italic>, <italic>B. megatarium</italic> and <italic>B. cereus</italic> and showed 99 to 100% identity with accessions: KX061099, MG544100, KP236185 and MF375116 respectively (Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>).<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Phylogenetic relationship between the identified <italic>Bacillus</italic> and <italic>Pseudomonas</italic> strains and representative bacterial species based on 16S rRNA gene sequences developed with the ClustarW program in MEGA6 and constructed using Maximum-Likelihood method with 1,000 bootstrap replicates. The values indicate the percentage of clustering matches. Sequence closest matches were based on the NCBI database “16S ribosomal RNA sequences. The scale bar indicates the number of differences in base composition among sequences.</p></caption><graphic xlink:href=\"41598_2020_69410_Fig4_HTML\" id=\"MO4\"/></fig></p></sec><sec id=\"Sec26\"><title>Seed germination assay</title><p id=\"Par27\">Seed germination assay was performed to investigate any positive or negative impact of bacterial strains on chilli seeds at germination stage. The effect of bacterial seed treatment upon seed germination varied with different strains. All bacterial treatments showed a significant effect on the seed germination percentage as compared to control. Seed germination percentage (%) was observed ranging from 73.3 to 93.3% in all the treatments and no stress on seed germination was observed in any treatment. Maximum seed germination was observed in chilli seeds dressed with <italic>P. putida</italic> (93.3%) followed by <italic>P. libanensis</italic> and <italic>B. subtilus</italic> (86.7%). Germination was observed slightly low (73.3%) in <italic>P. aeruginosa</italic> treated seeds among all the treatments compared to untreated control (Table <xref rid=\"Tab6\" ref-type=\"table\">6</xref>). These results showed that rhizobacterial seed treatment could improve the chilli seed germination without posing any negative impact.<table-wrap id=\"Tab6\"><label>Table 6</label><caption><p>Effect of antagonistic bacterial strains on chilli pepper seed germination in vitro.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Bacterial isolates (10<sup>8</sup> cfu ml<sup>−1</sup>)</th><th align=\"left\">Chilli pepper seed germination (%)</th></tr></thead><tbody><tr><td align=\"left\">RWPRB03—<italic>Pseudomonas putida</italic></td><td align=\"left\">93.3 ± 5.77a</td></tr><tr><td align=\"left\">RBT7—<italic>Pseudomonas putida</italic></td><td align=\"left\">83.3 ± 5.77abc</td></tr><tr><td align=\"left\">RB09—<italic>Pseudomonas libanensis</italic></td><td align=\"left\">86.7 ± 5.77ab</td></tr><tr><td align=\"left\">AJ-RB13—<italic>Pseudomonas aeruginosa</italic></td><td align=\"left\">73.3 ± 5.77c</td></tr><tr><td align=\"left\">DKB53—<italic>Bacillus subtilis</italic></td><td align=\"left\">86.7 ± 5.77ab</td></tr><tr><td align=\"left\">AJ-RB22—<italic>Bacillus megatarium</italic></td><td align=\"left\">80.0 ± 10.0bc</td></tr><tr><td align=\"left\">KSL-24—<italic>Bacillus cereus</italic></td><td align=\"left\">83.3 ± 11.6abc</td></tr><tr><td align=\"left\">KSL-8T—<italic>Bacillus cereus</italic></td><td align=\"left\">76.7 ± 5.77bc</td></tr><tr><td align=\"left\">Control</td><td align=\"left\">86.7 ± 5.77ab</td></tr><tr><td align=\"left\">LSD</td><td align=\"left\">12.352</td></tr></tbody></table><table-wrap-foot><p>All the presented values are means of three replicates. Means were subjected to analysis of variance and were separated by LSD test. Letters represent the significant difference among the mean values and ± are standard error values of the means.</p></table-wrap-foot></table-wrap></p></sec><sec id=\"Sec27\"><title>Greenhouse testing of PGPR for damping-off suppression and PGP effect</title><p id=\"Par28\">Rhizobacteria with high antifungal potential were evaluated for disease suppression and plant growth promotion traits in pot trials under greenhouse conditions. All the tested bacterial strains significantly enhanced the seed germination ranged (75.6–91.1%) as compared to control treatment (57.8%) and reduced the seed mortality caused by <italic>P. capsici</italic>. However, maximum seed germination was done by DKB53—<italic>Bacillus subtilus</italic> (91.1%) followed by AJ-RB22—<italic>Bacillus megatarium</italic> (88.9%) and RWPRB03—<italic>Pseudomonas putida</italic> and KSL-8T—<italic>Bacillus cereus</italic> (86.7%). Plant growth characters viz., shoot and root length (cm), fresh and dry shoot and root weight (g) were significantly enhanced by the bacterial seed inoculation as compared to untreated control. All the rhizobacterial strains enhanced the shoot and root length in range (9.47–14.67 and 3.30–5.63 in cm) as compared to control where shoot and root length was 3.43 cm and 1.07 cm respectively. All the tested bacterial strains showed a strong ability to produce IAA, thus aided to enhance plant growth characters in chilli pepper. An increase in fresh shoot and root weight was also observed against untreated control treatment. Dry shoot and root weight were also significantly increased over untreated control (Table <xref rid=\"Tab7\" ref-type=\"table\">7</xref>).<table-wrap id=\"Tab7\"><label>Table 7</label><caption><p>Effect of bacterial isolates on disease suppressiveness and plant growth promotion in chilli pepper in greenhouse conditions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Rhizobacterial strains</th><th align=\"left\" rowspan=\"2\">GP</th><th align=\"left\" rowspan=\"2\">DRS</th><th align=\"left\" rowspan=\"2\">MP</th><th align=\"left\" colspan=\"6\">Plant growth parameters</th></tr><tr><th align=\"left\">SL (cm)</th><th align=\"left\">RL (cm)</th><th align=\"left\">FSW (g)</th><th align=\"left\">FRW (g)</th><th align=\"left\">DSW (g)</th><th align=\"left\">DRW (g)</th></tr></thead><tbody><tr><td align=\"left\">RWPRB03—<italic>Pseudomonas putida</italic></td><td align=\"left\">86.7 ± 3.8<sup>ab</sup></td><td align=\"left\">1</td><td align=\"left\">13.3 ± 3.8<sup>bc</sup></td><td align=\"left\">11.47 ± 0.58<sup>cd</sup></td><td align=\"left\">4.67 ± 0.27<sup>bc</sup></td><td align=\"left\">2.3 ± 0.17<sup>a</sup></td><td align=\"left\">1.8 ± 0.19<sup>ab</sup></td><td align=\"left\">0.51 ± 0.03<sup>d</sup></td><td align=\"left\">0.42 ± 0.02<sup>c</sup></td></tr><tr><td align=\"left\">RBT7—<italic>P. putida</italic></td><td align=\"left\">80.0 ± 3.8<sup>ab</sup></td><td align=\"left\">1</td><td align=\"left\">20 ± 3.8<sup>bc</sup></td><td align=\"left\">10.57 ± 0.52<sup>de</sup></td><td align=\"left\">4.27 ± 0.15<sup>bc</sup></td><td align=\"left\">2.1 ± 0.15<sup>ab</sup></td><td align=\"left\">1.3 ± 0.20<sup>bcd</sup></td><td align=\"left\">0.66 ± 0.05<sup>c</sup></td><td align=\"left\">0.51 ± 0.04<sup>bc</sup></td></tr><tr><td align=\"left\">RB09—<italic>P. libanensis</italic></td><td align=\"left\">84.4 ± 5.9<sup>ab</sup></td><td align=\"left\">1</td><td align=\"left\">15.6 ± 5.9<sup>bc</sup></td><td align=\"left\">13.97 ± 0.35<sup>ab</sup></td><td align=\"left\">5.03 ± 0.44<sup>ab</sup></td><td align=\"left\">1.5 ± 0.23<sup>c</sup></td><td align=\"left\">1.6 ± 0.17<sup>abcd</sup></td><td align=\"left\">1.08 ± 0.03<sup>a</sup></td><td align=\"left\">0.73 ± 0.03<sup>a</sup></td></tr><tr><td align=\"left\">AJ-RB13—<italic>P. aeruginosa</italic></td><td align=\"left\">75.6 ± 5.9<sup>b</sup></td><td align=\"left\">1</td><td align=\"left\">24.4 ± 5.9<sup>b</sup></td><td align=\"left\">11.37 ± 0.66<sup>cd</sup></td><td align=\"left\">3.30 ± 0.26<sup>d</sup></td><td align=\"left\">2.3 ± 0.22<sup>a</sup></td><td align=\"left\">1.2 ± 0.18<sup>d</sup></td><td align=\"left\">0.92 ± 0.11<sup>b</sup></td><td align=\"left\">0.44 ± 0.03<sup>bc</sup></td></tr><tr><td align=\"left\">DKB53—<italic>Bacillus subtilus</italic></td><td align=\"left\">91.1 ± 2.2<sup>a</sup></td><td align=\"left\">1</td><td align=\"left\">8.9 ± 2.2<sup>c</sup></td><td align=\"left\">9.47 ± 0.75<sup>e</sup></td><td align=\"left\">4.30 ± 0.21<sup>bc</sup></td><td align=\"left\">2.0 ± 0.21<sup>abc</sup></td><td align=\"left\">1.7 ± 0.18<sup>abc</sup></td><td align=\"left\">0.61 ± 0.03<sup>cd</sup></td><td align=\"left\">0.42 ± 0.04<sup>c</sup></td></tr><tr><td align=\"left\">AJ-RB22—<italic>B. megatarium</italic></td><td align=\"left\">88.9 ± 4.4<sup>a</sup></td><td align=\"left\">1</td><td align=\"left\">11.1 ± 4.4<sup>c</sup></td><td align=\"left\">14.67 ± 0.66<sup>a</sup></td><td align=\"left\">5.63 ± 0.20<sup>a</sup></td><td align=\"left\">1.8 ± 0.19<sup>abc</sup></td><td align=\"left\">1.8 ± 0.09<sup>a</sup></td><td align=\"left\">1.13 ± 0.05<sup>a</sup></td><td align=\"left\">0.78 ± 0.03<sup>a</sup></td></tr><tr><td align=\"left\">KSL-24—<italic>B. cereus</italic></td><td align=\"left\">80.0 ± 3.8<sup>ab</sup></td><td align=\"left\">1</td><td align=\"left\">20 ± 3.8<sup>bc</sup></td><td align=\"left\">9.60 ± 0.32<sup>e</sup></td><td align=\"left\">4.03 ± 0.38<sup>cd</sup></td><td align=\"left\">2.2 ± 0.20<sup>ab</sup></td><td align=\"left\">1.3 ± 0.09<sup>cd</sup></td><td align=\"left\">0.72 ± 0.05<sup>c</sup></td><td align=\"left\">0.47 ± 0.03<sup>bc</sup></td></tr><tr><td align=\"left\">KSL-8T—<italic>B. cereus</italic></td><td align=\"left\">86.7 ± 3.8<sup>ab</sup></td><td align=\"left\">1</td><td align=\"left\">13.3 ± 3.8<sup>bc</sup></td><td align=\"left\">12.5 ± 0.51<sup>bc</sup></td><td align=\"left\">4.43 ± 0.41<sup>bc</sup></td><td align=\"left\">1.6 ± 0.18<sup>bc</sup></td><td align=\"left\">1.2 ± 0.21<sup>cd</sup></td><td align=\"left\">0.59 ± 0.03<sup>cd</sup></td><td align=\"left\">0.52 ± 0.03<sup>b</sup></td></tr><tr><td align=\"left\">Control (untreated)</td><td align=\"left\">57.8 ± 2.2<sup>c</sup></td><td align=\"left\">3</td><td align=\"left\">42.2 ± 2.2<sup>a</sup></td><td align=\"left\">3.43 ± 0.45<sup>f</sup></td><td align=\"left\">1.07 ± 0.30<sup>e</sup></td><td align=\"left\">0.8 ± 0.19<sup>d</sup></td><td align=\"left\">0.5 ± 0.12<sup>e</sup></td><td align=\"left\">0.29 ± 0.03<sup>e</sup></td><td align=\"left\">0.2 ± 0.02<sup>d</sup></td></tr><tr><td align=\"left\">LSD value</td><td align=\"left\">12.454</td><td align=\"left\"/><td align=\"left\">12.454</td><td align=\"left\">1.6417</td><td align=\"left\">0.9119</td><td align=\"left\">0.5756</td><td align=\"left\">0.4863</td><td align=\"left\">0.1485</td><td align=\"left\">0.0902</td></tr></tbody></table><table-wrap-foot><p>All the presented values are means of five replicates. Means were subjected to analysis of variance, and means were separated by LSD test. Letters represent the significant difference among the mean values and ± are standard error values of the means. Germination percentage and disease data was recorded 15 days after treatment while data on growth promotion parameters was recorded 30 days after sowing. All the results were compared with untreated control where only <italic>P. capsici</italic> was applied with no bacterial seed inoculation.</p><p><italic>GP</italic> germination percentage, <italic>DRS</italic> disease rating scale, <italic>MP</italic> mortality percentage, <italic>SL</italic> shoot length, <italic>RL</italic> root length, <italic>FSW</italic> fresh shoot weight, <italic>FRW</italic> fresh root weight, <italic>DSW</italic> dry shoot weight, <italic>DRW</italic> dry root weight.</p></table-wrap-foot></table-wrap></p></sec></sec><sec id=\"Sec28\"><title>Discussion</title><p id=\"Par29\"><italic>Phytophthora capsici</italic> is the most destructive plant pathogen, that poses a serious threat by infecting the host plants at any growth stage and causes seedling death, crown rot, foliar blight, and fruit rot. The pathogen causes severe losses in many crops like cucurbits, eggplant, pepper, and tomato<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref>,<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. <italic>Phytophthora capsici</italic> is soil borne in nature and can survive for a long time in soil by forming oospores. Availability of free water support asexual reproduction and pathogen form sporangia and zoospores<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup> which are dispersed with soil, water, and air currents<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. In the present study, <italic>P. capsici</italic> was isolated from damping-off chilli root samples showing the characteristic symptoms of rotten roots, decay, wilt and necrosis<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup> and a total of 12 isolates were recovered and purified. Pathogenicity assay was performed to screen the most virulent isolates as different isolates have varied level of virulence. A research study suggested that <italic>P. capsici</italic> isolates from pepper and pumpkin differ in virulence levels<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Morphologically identification confirmed the production of papillate sporangia on long pedicels, and Chlamydospores and these findings are supported by a study of<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. According to<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>, Chamydospores production is not much common in <italic>Capsicum</italic> isolates but the formation of Chamydospores in <italic>P. capsici</italic> isolates depends on the cultural methods and the origin of the host<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. Two most aggressive isolates (HydPk1 and HydPak2) screened in pathogenicity assays were subjected to molecular identification by amplifying ITS1 and ITS2 regions<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>, and were identified as <italic>P. capsici</italic>. In another study, pathogen was identified as <italic>Phytophthora colocasiae</italic> on the basis of internal transcribed spacer (ITS) sequence homology<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>. Other researchers have also identified <italic>P. capsici</italic> by amplifying ITS regions of the isolates collected from chilli pepper blight samples<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>.</p><p id=\"Par30\">Rhizobacteria colonizing the rhizosphere interact with crops in various ways; by controlling the plant diseases by antagonism and by promoting plant growth parameters<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. The interaction of beneficial rhizobacteria and plant-phytopathogen could offer new strategies to enhance plant productivity in an environment friendly way<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. Rhizobacteria with plant growth promoting potential are used as an alternative to chemical pesticides in the agriculture industry<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. Various researches have proved that upon attack by soil-borne fungal pathogens, plants can exploit microbial consortia from the soil for protection against infections, restructuring bacterial communities associated with rhizosphere<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>. In our study, fifteen rhizobacterial strains were recovered from chilli rhizosphere, majorly belonged to the genera <italic>Bacillus</italic> and <italic>Pseudomonas</italic>. Our findings are comparable to various reports on biocontrol potential of bacteria belonging to genera <italic>Achromobacter</italic>, <italic>Arthrobacter</italic>, <italic>Azospirillum</italic>, <italic>Azotobacter</italic>, <italic>Bacillus</italic>, <italic>Clostridium</italic>, <italic>Enterobacter</italic>, <italic>Flavobacterium</italic>, <italic>Micrococcus</italic> and <italic>Pseudomonas</italic> being the most common bacterial groups prevailing in soil<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref>,<xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup>.</p><p id=\"Par31\">Biological control of <italic>P. capsici</italic> can be due to antagonistic ability of the bacterial strains<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref>,<xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. Initially, fifteen bacterial strains were screened for antagonism against <italic>P. capsici</italic>. All the tested bacterial strains showed varied levels of antagonistic potential. Results revealed that five bacterial isolates viz., RWPRB03—<italic>Pseudomonas putida</italic>, RBT7—<italic>Pseudomonas putida</italic>, AJ-RB13—<italic>Pseudomonas aeruginosa</italic>, KSL-24—<italic>Bacillus cereus</italic>, and KSL-8T—<italic>Bacillus cereus</italic> showed > 70% mycelial growth inhibition of <italic>P. capsici</italic>. In a research study, out of 811 tested rhizospheric bacteria, five bacterial strains showed highest antagonistic potential against three most prevalent strains of bacterial leaf blight (BLB) pathogen <italic>Xanthomonas oryzae</italic> pv. <italic>oryzae</italic><sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>. Biocontrol potential of tested bacterial isolates could be due to antibiosis and various antibiotics have been previously identified and reported by many researchers<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref>,<xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup>. It has been reported that production of various antimicrobial compounds are responsible for fungal growth inhibition as these compounds result into cytolysis, leakage of potassium ions, disruption of the structural integrity of membranes, inhibition of mycelial growth, spore germination inhibition and protein biosynthesis<sup><xref ref-type=\"bibr\" rid=\"CR52\">52</xref></sup>. Antifungal potential of <italic>Bacillus</italic> spp. attributed to the production of various levels of different lipopeptides against varying fungal phytopathogens<sup><xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup> while Dimethyl disulfide and other sulfur-containing compounds production by <italic>Pseudomonas</italic> spp. were studied to stop the mycelial growth of <italic>P. infestans</italic><sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup> and various other volatile compounds emitted by <italic>Pseudomonas</italic> spp., such as 1-Undecene, also reduce sporangia formation and release of zoospores in <italic>P. infestans</italic><sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref></sup>.</p><p id=\"Par32\">Most of the tested bacteria exhibited multiple PGP traits which aid to growth promotion and disease reduction capability of PGPR. Such multiple modes of action have been researched to be the prime reasons for the plant growth promotion and disease suppressing potential of PGPR<sup><xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>. It is important to explore the potential of native rhizobacterial strains for their PGP traits to get the desired benefits on disease control and plant growth promotions. In this study, eight bacterial strains with strong antagonistic potential were screened for biochemical and plant growth promotion characters. All tested bacterial strains showed a positive response to ammonia production, Hydrogen cyanide (HCN) production, catalase reaction, KOH test (<italic>Bacillus</italic> spp.), IAA and siderophore production. Ammonia (NH<sub>3</sub>) production is important feature of PGPR, which indirectly enhances the plant growth<sup><xref ref-type=\"bibr\" rid=\"CR57\">57</xref></sup>. It accumulates and supply nitrogen to their host plants and promotes plant growth<sup><xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>. Various studies have reported the ammonia production by rhizobacteria<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR59\">59</xref>,<xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>. In our studies, except one bacterial strain, all were positive for ammonia production. All the tested bacterial strains were positive for HCN production test. It was originally believed that HCN production play its role in plant growth promotion by suppressing the plant pathogens<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref>,<xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>. However, this concept has recently been changed. It has been believed that HCN production indirectly increases phosphorus availability by chelation and sequestration of metals, and indirectly increases the nutrient availability to the rhizobacteria and host plants<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. The production of HCN by PGPR are independent of their genus, thus they are used as biofertilizers or biocontrol to enhance crop production<sup><xref ref-type=\"bibr\" rid=\"CR64\">64</xref></sup> and these are being used as biofertilizer in growth promotion and yield enhancement. All the tested bacterial strains were catalase positive. Previous studies have reported the production of antioxidant enzymes like catalase by rhizobacteria<sup><xref ref-type=\"bibr\" rid=\"CR65\">65</xref></sup> which suppressed early blight disease in tomato<sup><xref ref-type=\"bibr\" rid=\"CR66\">66</xref></sup> and induced resistance against tomato yellow leaf curl virus<sup><xref ref-type=\"bibr\" rid=\"CR67\">67</xref></sup>. Indole-3-acetic acid (IAA) is secondary metabolites, and its production in bacterial agents is generally described based on their ability to use tryptophan supplemented in the growth medium, which is the major precursor of IAA biosynthesis via indole pyruvic acid (IPA) pathway<sup><xref ref-type=\"bibr\" rid=\"CR68\">68</xref></sup>. It supports root development, elongation and proliferation and help plants to take up water and nutrients from soil<sup><xref ref-type=\"bibr\" rid=\"CR69\">69</xref></sup>. All the tested bacterial strains produced IAA in various concentrations and this varying ability could be due to the difference in bacterial physiological characters, however<sup><xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup> reported that Indolepyruvic decarboxylase (IPDC) is the enzyme which determines IAA biosynthesis. All the tested strains belonging to <italic>Bacillus</italic> and <italic>Pseudomonas</italic> spp. produced IAA even in the growth medium without tryptophan as supplementary material. Siderophore was also produced by all the tested bacterial strains ranged from 12.5 to 33.5%. These findings are further supported by previous reports in which siderophore production was reported as an important mechanism involved in the suppression of bacterial leaf blight (BLB) disease<sup><xref ref-type=\"bibr\" rid=\"CR71\">71</xref></sup>. Siderophore is one of the major biocontrol mechanisms exhibited by various plant growth promoting rhizobacterial groups under iron-limiting condition. PGPR produces a wide range of siderophore which has a high affinity for iron thus, lowering the availability of iron to pathogenic agents including plant pathogenic fungi<sup><xref ref-type=\"bibr\" rid=\"CR72\">72</xref></sup>. Siderophore production by PGPR controls soil-borne pathogenic fungi by limiting iron availability to them<sup><xref ref-type=\"bibr\" rid=\"CR73\">73</xref></sup>. The antagonistic potential of the selected bacterial strains might be due to the siderophores and HCN production or synergistic interaction of these two or with other metabolites<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>.</p><p id=\"Par33\">The 16S rRNA sequences have been widely used in the classification and identification of Bacteria and Archaea<sup><xref ref-type=\"bibr\" rid=\"CR74\">74</xref></sup>. In our studies, 16S rRNA sequence based maximum-likelihood tree indicated that tested bacterial strains RWPRB03 and RBT7 showed 99% sequence homology with <italic>P. putida</italic> (accessions; HM486417 and KY982927), while RB09 was 98% identical to <italic>P. libanensis</italic> (DQ095905). AJ-RB13 clustered with <italic>P. aeruginosa</italic> and showed 99% homology with accession KF420126. The other four bacterial strains viz., DKB53, AJ-RB22, KSL-24 and KSL-8T showed 99 to 100% sequence homology with accessions: KX061099, MG544100, KP236185 and MF375116 respectively. Similarly, Ref<sup><xref ref-type=\"bibr\" rid=\"CR75\">75</xref></sup> reported that 16S rRNA sequence based characterization indicated that most of the bacterial strains isolated from cucumber rhizosphere belonged to <italic>Pseudomonas stutzeri</italic>, <italic>Bacillus subtilis</italic>, <italic>Stenotrophomonas maltophilia</italic>, and <italic>B. amyloliquefaciens</italic>.</p><p id=\"Par34\">Studies have proved that seed treatment is an effective strategy to enhance seedling emergence, seed vigor, and to prevent seed and soil borne pathogens<sup><xref ref-type=\"bibr\" rid=\"CR76\">76</xref></sup>. Chemical seed treatment is a major practice being followed to prevent damping off disease<sup><xref ref-type=\"bibr\" rid=\"CR77\">77</xref>,<xref ref-type=\"bibr\" rid=\"CR78\">78</xref></sup>. Many kinds of chemicals are used to remove the pathogen inoculum from seed coats<sup><xref ref-type=\"bibr\" rid=\"CR76\">76</xref></sup> but these chemicals could negatively affect seed germination, cause phytotoxicity<sup><xref ref-type=\"bibr\" rid=\"CR79\">79</xref></sup>, pose a negative impact on human health and environment<sup><xref ref-type=\"bibr\" rid=\"CR80\">80</xref></sup>. Results from our studies indicated that seed treatment with bacterial strains significantly enhanced the seed germination over untreated control, and no phytotoxicity was observed in any treatment and our results are supported by the finding of<sup><xref ref-type=\"bibr\" rid=\"CR75\">75</xref></sup>. In a study, high amylase activity during germination was observed in rice and legume inoculated with PGPR<sup><xref ref-type=\"bibr\" rid=\"CR79\">79</xref></sup> which support the root and shoot germination in young seedlings.</p><p id=\"Par35\">Results from our study indicate that seed treatment with PGPR significantly reduced the seedling mortality and disease severity of <italic>P. capsici</italic> in chilli pepper and studies have proved the role of rhizobacteria as biological control agents<sup><xref ref-type=\"bibr\" rid=\"CR81\">81</xref>,<xref ref-type=\"bibr\" rid=\"CR82\">82</xref></sup>. Various studies have reported the antifungal potential of rhizobacteria belonging to <italic>Pseudomonas</italic> and <italic>Bacillus</italic> spp. against <italic>P. infestans</italic> and <italic>P. capsici</italic><sup><xref ref-type=\"bibr\" rid=\"CR83\">83</xref>,<xref ref-type=\"bibr\" rid=\"CR84\">84</xref></sup>. Results from our study showed that eight bacterial strains significantly suppress the damping off disease, and enhanced the plant growth in pot experiments. The results from greenhouse trials suggested that the tested PGPR slightly differ in their effects on disease suppression and PGP traits in chilli, and all the tested bacterial strains performed better under greenhouse conditions compared to untreated control. Earlier researches have revealed that the PGP effects and disease reduction potential of rhizobacteria are attributed to multiple traits<sup><xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>. Our results on chilli disease suppression and PGP are supported by other studies on growth promotion by PGPR in common bean<sup><xref ref-type=\"bibr\" rid=\"CR85\">85</xref></sup> and tomato<sup><xref ref-type=\"bibr\" rid=\"CR86\">86</xref></sup>. In another study, strains of PGPR from cucumber plants rhizosphere were tested for their plant growth promoting traits and antifungal potential against Phytophthora crown rot of cucumber seedlings under in vitro and greenhouse conditions. Results revealed that tested rhizobacterial strains protected the plants by various modes of action including antibiosis against target pathogen, competitive colonization, and plant growth promotion<sup><xref ref-type=\"bibr\" rid=\"CR75\">75</xref></sup>. These findings support the utilization of these rhizobacterial strains for bioformulations development their commercial use as biocontrol agents in the open fields.</p><p id=\"Par36\">It has been reported that the field application of bacterial based products has been hampered because of their low performance due to various climatic and soil factors<sup><xref ref-type=\"bibr\" rid=\"CR87\">87</xref>,<xref ref-type=\"bibr\" rid=\"CR88\">88</xref></sup>. This is important to test the efficacy of native rhizobacteria under the same environmental condition where they would be used in plant growth promotion. It was concluded in various studies that environmental factors significantly influence the rhizobacterial colonization, biological activates, and disease suppressing potential<sup><xref ref-type=\"bibr\" rid=\"CR89\">89</xref>–<xref ref-type=\"bibr\" rid=\"CR91\">91</xref></sup>. In the majority of the cases, bacterial based formulations imported from the other counters bearing different climatic conditions failed to perform up to their maximum potential under warm environmental conditions prevailing in Pakistan. In this study, bacterial strains were isolated from the local fields located in Pakistan and were tested for their potential to suppress the soil-borne oomycetes and plant growth promotion in chilli pepper. Development of bioformulations on available carrier materials is undergoing. To find out optimized formulations dose level and appropriate application method, it requires a series of experiments with different soil types under greenhouse and open field conditions, and these studies are under progress.</p></sec><sec id=\"Sec29\"><title>Conclusions</title><p id=\"Par37\">Out of fifteen, eight bacterial strains efficiently suppressed the mycelial growth of pathogenic <italic>Phytophthora capsici</italic> in direct interactions-assays in vitro. Bacterial strains with strong anti-fungal potential were found positive for HCN, catalase test, Indole-3-acetic acid (IAA) and siderophore production. The 16S rRNA sequence analysis of bacterial strains showed 98 to 100% identity with close relatives belonging to <italic>Bacillus</italic> and <italic>Pseudomonas</italic> genera. Greenhouse studies revealed that bacterial strains suppressed the <italic>P. capsici</italic> and significantly enhanced the plant growth characters in chilli pepper. These results confirmed the significant role of native rhizobacteria for the control of soil-borne oomycetes and the potential use of <italic>Bacillus</italic> and <italic>Pseudomonas</italic> spp. in bio-fertilizers and bio-fungicides development.</p></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><ack><title>Acknowledgements</title><p>Financial support received from Punjab Education Commission (PHEC) and Punjab Agriculture Research Board (PARB), Pakistan for carrying out this research work is gratefully acknowledged. We also acknowledge Professor Youfu Frank Zhao, Department of Crop Sciences, the University of Illinois at Urbana-Champaign for providing bench space in his laboratory and helping in various research activities.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>S.H., A.S.G., N.F. Conceived the presented idea, planned and carried out research experiments and wrote the paper. M.M.A., R.A., Z.F.R. Helped authors in DNA sequence analysis. W.A. Contributed to sample preparation and helped in performing different tests. A.H. Helped authors in the interpretation of results. M.I. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807874</article-id><article-id pub-id-type=\"pmc\">PMC7431857</article-id><article-id pub-id-type=\"publisher-id\">70710</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70710-x</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Lithium chloride enhances serotonin induced calcium activity in EGFP-GnIH neurons</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Teo</surname><given-names>Chuin Hau</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Soga</surname><given-names>Tomoko</given-names></name><address><email>tomoko.soga@monash.edu</email></address><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Parhar</surname><given-names>Ishwar</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><aff id=\"Aff1\"><institution-wrap><institution-id institution-id-type=\"GRID\">grid.440425.3</institution-id><institution>Brain Research Institute, Jeffery Cheah School of Medicine and Health Sciences, </institution><institution>Monash University Malaysia, </institution></institution-wrap>47500 Bandar Sunway, Selangor Malaysia </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13876</elocation-id><history><date date-type=\"received\"><day>9</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>20</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Neurons synthesizing gonadotropin-inhibitory hormone (GnIH) have been implicated in the control of reproduction, food intake and stress. Serotonin (5-HT) receptors have been shown in GnIH neurons; however, their functional role in the regulation of GnIH neurons remains to be elucidated. In this study, we measured intracellular calcium ion levels following 5-HT treatment to hypothalamic primary cultures of enhanced fluorescent green protein-tagged GnIH (EGFP-GnIH) neurons from Wistar rat pups of mixed sex. Three days after initial seeding of the primary cultures, the test groups were pre-treated with lithium chloride to selectively inhibit glycogen synthase kinase 3 beta to promote intracellular calcium levels, whereas the control groups received culture medium with no lithium chloride treatment. 24 h later, the cultures were incubated with rhodamine-2AM (rhod-2AM) calcium indicator dye for one hour prior to imaging. 5-HT was added to the culture dishes 5 min after commencement of imaging. Analysis of intracellular calcium levels in EGFP-GnIH neurons showed that pre-treatment with lithium chloride before 5-HT treatment resulted in significant increase in intracellular calcium levels, two times higher than the baseline. This suggests that lithium chloride enhances the responsiveness of GnIH neurons to 5-HT.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Cell biology</kwd><kwd>Neuroscience</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Gonadotropin-inhibitory hormone (GnIH) was discovered in birds as an antagonist to gonadotropin-releasing hormone (GnRH)<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. Since then, GnIH has been identified in many mammalian and non-mammalian vertebrates including rodents, bovines and primates<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref>–<xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. The function of GnIH has been highly conserved across vertebrates, acting to inhibit GnRH-mediated gonadotrophin release with some exceptions found in teleosts<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR4\">4</xref>–<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Besides its role in reproduction and appetite<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>, GnIH has been implicated in depressive-like behaviour and stress<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref>,<xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>.\n</p><p id=\"Par3\">A number of hormonal factors (melatonin, glucocorticoids, estrogen and thyroid hormones)<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup> have been suggested to regulate GnIH neurons. However, the regulation of GnIH neurons by neurotransmitters is not well-identified, except for serotonin (5-HT). GnIH neurons co-express 5-HT receptors (5-HT1A, 1B, 1D, 1F, 2A, 2B, 3A, 5A, 5B, as well as 5-HT6 and 5-HT7)<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>; the selective serotonin reuptake inhibitor, citalopram (anti-depressant) increases GnIH neuronal numbers in the dorsomedial hypothalamus<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>. Social stress decreases 5-HT fiber innervations to GnIH neurons<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup> but increases the expression levels of beta-catenin within GnIH neurons, suggesting increased activity of the Wnt signalling pathway, which can result in transcriptional activation or repression of cellular machinery<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>. Impairment of 5-HT function under social stress<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup> indicates a potential connection between 5-HT regulation and beta-catenin expression. Indeed, 5-HT (5-HT1A, 5-HT1D, 5-HT2, 5-HT7) receptors are known to modulate beta-catenin activity<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref>–<xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup> and fluoxetine, a selective serotonin reuptake inhibitor, increases nuclear localization of beta-catenin<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. Translocation of beta-catenin into the nucleus is facilitated by GSK3β, which is part of the Wnt signaling pathway<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref>,<xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>; this promotes an increase in intracellular calcium<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>.</p><p id=\"Par4\">The 5-HT and the GnIH system in the brain have a strong association with stress and reproduction<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>, but how these two neuronal systems interact with each other is less known. Despite the presence of 5-HT receptors in GnIH neurons<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>, the effect of 5-HT on the internal dynamics of GnIH neurons remains to be investigated, which we hypothesize is through the GSK3β-Wnt signaling pathway. Therefore, the objective of this study was to treat rat EGFP-GnIH neurons in hypothalamic primary cultures with 5-HT and selectively inhibit GSK3β using lithium chloride (LiCl) to observe change in the activity of EGFP-GnIH neurons using calcium imaging. Calcium imaging measures the intensity of fluorescent dyes bound to free calcium ions in the cytosol<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>. This allows microscopic imaging of activity within the cell as an alternative to electrophysiology and patch clamp methods. Thus, the elucidation of GSK3β-Wnt signaling pathway, by which 5-HT regulates GnIH, will provide a new avenue for future studies to explore the complexity of stress-induced reproductive dysfunction.</p></sec><sec id=\"Sec2\"><title>Results</title><sec id=\"Sec3\"><title>Live cell calcium imaging</title><p id=\"Par5\">Neurons were observed at day 5 of the primary culture, with treatment being performed 1 day prior (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>). During imaging, basal fluorescence (F<sub>0</sub>) was determined at 4 min after commencement of imaging. 5-HT treatment was delivered at 5 min and peak fluorescence measured at any time post treatment.<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Flow-chart for primary cell culture and imaging process. (<bold>a</bold>) The flowchart depicts the process in the primary culture of hypothalamic neurons. (<bold>b</bold>) The steps involved in imaging the neurons.</p></caption><graphic xlink:href=\"41598_2020_70710_Fig1_HTML\" id=\"MO1\"/></fig></p><p id=\"Par6\">Cultured GnIH neurons (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>a) (defined as neurons with a measured green fluorescent intensity of > 50 during imaging), were successfully loaded with rhod-2AM (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>b) at 5 µM. Imaging of both dyes showed co-localization of the dye with GnIH neurons (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>c).<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Loading of neurons with Rhod-2AM at differing concentrations. (<bold>a</bold>) EGFP-GnIH fluorescent neuron (green), (<bold>b</bold>) Rhodamine-2AM (rhod-2AM) calcium indicator staining (red), (<bold>c</bold>) colocalization of rhod-2AM within EGFP-GnIH neurons. Scale bar = 25 µm.</p></caption><graphic xlink:href=\"41598_2020_70710_Fig2_HTML\" id=\"MO2\"/></fig></p><p id=\"Par7\">The change in fluorescent intensity was measured by calculating the change in intensity, which is defined as the difference between the peak fluorescence post-treatment and the basal fluorescence. This can be represented by the term ΔF. Treatment of GnIH neurons with water as a placebo failed to elicit a significant response (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>a–c). The change in intensity of the rhod-2AM dye in those neurons remained low (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>d). In GnIH neurons not incubated with LiCl but treated with 5-HT (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>e–g), a small response was also observed (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>h). GnIH neurons pre-treated with LiCl and treated with 5-HT tend to demonstrate an increase in intensity after the 5-HT treatment (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>i–k), with a hike in intensity (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>l) observed shortly after treatment.<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Calcium imaging of day-5 primary culture GnIH neurons from the dorsomedial hypothalamus. (<bold>a</bold>) Typical GnIH neuron incubated with LiCl (<bold>b</bold>) before treatment and (<bold>c</bold>) 10 min after placebo treatment (H<sub>2</sub>O). (<bold>d</bold>) Example of relative change intensity in neurons treated with placebo. (<bold>e</bold>) Typical GnIH neuron not incubated with LiCl (<bold>f</bold>) before treatment and (<bold>g</bold>) 10 min after 5-HT treatment. (<bold>h</bold>) Example of relative change intensity in neurons treated with 5-HT only. (<bold>i</bold>) Typical GnIH neuron incubated with LiCl (<bold>j</bold>) before treatment and (<bold>k</bold>) 10 min after 5-HT treatment. (<bold>l</bold>) Example of relative change intensity in neurons pre-treated with LiCl and treated with 5-HT. (<bold>m</bold>) Spectrum for the pseudo-colour representation used to represent calcium intensity. Blue indicates low and red indicates high calcium intensity. White arrow in images <bold>e</bold>–<bold>g</bold> indicates GnIH neuron used for measurement in <bold>h</bold>. *Shows a cell with high rhod-2AM fluorescent intensity before treatment (unused in 5-HT response calculations). Scale bar = 25 µm.</p></caption><graphic xlink:href=\"41598_2020_70710_Fig3_HTML\" id=\"MO3\"/></fig></p><p id=\"Par8\">A total of 82 GnIH neurons were observed across five independent experiments (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). The ΔF for all GnIH neurons counted in their respective populations were averaged together and grouped by treatment. One neuron exhibited a marked increase in fluorescent intensity in the control group after the placebo treatment. Of the neurons pre-treated with only LiCl, a single neuron exhibited a notably positive change in fluorescent intensity, with the average intensity not significantly different from the control. GnIH neurons treated with 5 mM 5-HT exhibited significant differences from the control on average, although the same was not observed when comparing 1 mM 5-HT treatment with the control (5-HT (5 mM): 24.94 ± 2.163 [n = 19] and control: 9.247 ± 0.9391 [n = 11], <italic>p</italic> < 0.05, Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). The 5 mM 5-HT treatment yielded a higher change in fluorescent intensity than 1 mM 5-HT on average, but the difference between the two was not found to be statistically significant.<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Effect of LiCl and 5HT treatment on calcium levels in GnIH neuron primary culture. Measurement of change in fluorescent intensity of each neuron charted as a scatterplot of relative change in intensity under LiCl pre-treatment with differing doses of 5-HT concentration. The treatments consist of control (average ΔF = 9.247, n = 11), LiCl only (average ΔF = 13.88, n = 10), 5-HT (1 mM) (average ΔF = 12.53, n = 13), LiCl + 5-HT (1 mM) (average ΔF = 29.38, n = 11), 5-HT (5 mM) (average ΔF = 24.94, n = 19), LiCl + 5-HT (5 mM) (average ΔF = 46.82, n = 18). Data in scatterplot is presented as means ± SEM for ΔF. Significance was set at *<italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"41598_2020_70710_Fig4_HTML\" id=\"MO4\"/></fig></p><p id=\"Par9\">Neurons pre-treated with LiCl followed by treatment with 5 mM 5-HT showed a significantly greater increase in fluorescent intensity compared to treatment with 5 mM 5-HT alone and the control group, suggesting heightened calcium activity. (LiCl + 5-HT (5 mM): 46.82 ± 6.867 [n = 18] and 5-HT (5 mM) 24.94 ± 2.163 [ n = 19], <italic>p</italic> < 0.05; LiCl + 5-HT (5 mM): 46.82 ± 6.867 [n = 18] and control: 9.247 ± 0.9391 [n = 11], <italic>p</italic> < 0.05; Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). Furthermore, a significant difference was also observed with neurons pre-treated with LiCl and 5-HT 1 mM treatment when compared to the control (LiCl + 5-HT (1 mM): 29.38 ± 8.859 [n = 11] and control: 9.247 ± 0.9391 [n = 11], <italic>p</italic> < 0.05; Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>).</p></sec></sec><sec id=\"Sec4\"><title>Discussion</title><p id=\"Par10\">Primary cultures treated with 5-HT alone or in combination with LiCl showed an increased percentage of activated GnIH neurons and an increase in intracellular calcium levels within those neurons. This shows that 5-HT can regulate GnIH neurons.</p><p id=\"Par11\">In this study, a higher concentration of 5-HT was utilized (5 mM) to elicit a more pronounced response from the neurons. Since a higher concentration of 5-HT has been utilized in primary cell cultures in calcium imaging experiments<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>, it is not likely to be adverse to the cells. GnIH neurons treated with only 5 mM 5-HT demonstrated increased fluorescence intensity, but the responsiveness of GnIH neurons to 5-HT treatment was not significant in the 1 mM in comparison to 5 mM treatment. This suggests that a small scale calcium elevation in GnIH neurons, in response to 5-HT, occurs even in the absence of LiCl treatment. The elevation in calcium was measured in the span of minutes, indicating a slower, global transient pattern involved in internal cellular signalling<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>.</p><p id=\"Par12\">It is possible that the measurement of intracellular calcium levels can be influenced by neuronal potentiation due to interaction between matured neurons after their 14th division<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref>–<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. However, in the present study, it is unlikely that 5-HT neuronal network activity could have affected our observations because we measured intracellular calcium levels in GnIH neurons in primary cultures that are unlikely to have synaptic transmission. Secondly, our primary cultures were at an immature stage, where the GnIH neurons are approximately at the 4<sup>th</sup> division and, therefore, could not be influenced by other neurons. However, the effect of 5-HT can differ depending on the developmental stage of the brain<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>, and as such the response of neurons in primary culture to 5-HT may depend on the age at which they are treated.</p><p id=\"Par13\">While lithium treatment has an attenuative effect on intracellular calcium signalling<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR34\">34</xref>,<xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>, here we observed that pre-treatment with LiCl enhances the responsiveness of GnIH neurons to 5-HT treatment, which further increased intracellular calcium levels. In particular, the calcium intensity in the 1 mM 5-HT treatment was bolstered by LiCl to a notable degree as the majority of the neurons in that group were not responsive to 5-HT in the absence of LiCl. Thus, it can be speculated that the selective inhibition of GSK3β by LiCl<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup> could have activated the Wnt canonical pathway and thereby facilitate the translocation of beta-catenin into the nucleus<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref>,<xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>, which promoted an increase in intracellular calcium. This is similar to lymphoblast, glioma cell lines<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup> and cortical neurons<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup> where lithium treatment increases intracellular calcium levels. Further investigations are needed to elucidate the nature of 5-HT induced Wnt signalling in GnIH neurons. Of particular importance would be the use of thapsigargin, which has been shown to block beta-catenin, part of the GSK3β-Wnt signalling pathway, that induces intracellular calcium release<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>.</p><p id=\"Par14\">In conclusion, this study shows that LiCl treatment enhances intracellular calcium response to 5-HT in GnIH neurons. It can be speculated that this enhancement is due to the inhibition of the GSK3β-Wnt signalling pathway by lithium. This finding will provide a new avenue for future studies to explore the role of 5-HT-GnIH in stress-induced reproductive dysfunction.</p></sec><sec id=\"Sec5\"><title>Methods</title><sec id=\"Sec6\"><title>Animals and housing conditions (primary culture)</title><p id=\"Par15\">In this experiment, we used 48 EGFP-GnIH Wistar rat pups of mixed sex on post-natal day 5. We created these transgenic rats using rat GnIH promoter (3 kbp upstream from the start codon)-driven EGFP expression plasmid and confirmed the genotyping and phenotype in the brain<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. A 12 h-long light/dark cycle (lights on from 12:00 am till 12:00 pm) was established for the rats. The room temperature was kept steady at ± 22 °C with humidity maintained at stable levels. This environment and light/dark cycle was continued for the duration of the housing. Food and water was made available ad libitum for the animals. Animal welfare and experimental ethics were in line with the authorized guidelines laid out by the Animal Ethics Committee of Monash University, which is in compliance with the Australian code for the care and use of animals for scientific purposes (2013). An approval code from the committee, AEC (MARP/2017/021 [2017–2020]), was obtained for the primary culture study. All experimental procedures were approved and performed under the guidelines given by the Monash University.</p></sec><sec id=\"Sec7\"><title>Primary culture</title><p id=\"Par16\">Extraction of brains from 48 postnatal day 5 EGFP-GnIH rat pups was conducted by first placing the pups in an air-tight polystyrene container with dry ice (solid CO<sub>2</sub>) in order to anesthetize them. The pups were then decapitated with scissors, the skulls opened and the brains harvested to be put into 25 mL of Krebs buffer (119 mM NaCl, 2.5 mM KCl, 1.0 mM NaH<sub>2</sub>PO<sub>4</sub>, 2.5 mM CaCl<sub>2</sub>·2H<sub>2</sub>O, 1.3 mM MgCl<sub>2</sub>·6H<sub>2</sub>O, 20 mM HEPES, 11 mM glucose) at 4 °C. The brains were quickly transferred to a dissection dish as soon as possible. The total number of independent observations in this experiment was five, taken from five batches of pups delivered from separate mothers.</p><p id=\"Par17\">The brains were dissected with Adult Mouse Brain Slicer Matrix with 1.0 mm coronal section slice intervals. The hypothalamus was removed from the brain slices and collected into 5 mL 37 °C Tryple Express (Gibco Tryple Express, Thermofisher Scientific, MA, USA) solution before being incubated for 20 min. The medium was removed slowly and carefully to avoid any bubbling. 5 mL of fresh Krebs buffer was then added and incubation performed at 37 °C for 5 min. The medium was removed once again, fresh 5 mL Krebs buffer added, and incubated at 37 °C for 5 min. The medium was slowly removed before adding fresh 2.5 mL of Krebs buffer and 5 µL of DNase I. The cells were triturated gently and seeded at 500 µL/dish to a coated 35 mm Eppendorf glass-bottomed dish (Eppendorf Cell Imaging Dish 145 µm, Eppendorf AG, Hamburg, Germany). Neurobasal A medium (Neurobasal-A medium minus phenol red, Thermo-Fisher Scientific, MA, USA) was supplemented with 2% serum-free B-2 (17,504,044, Gibco B-27 Supplement 50X, Thermo-Fisher Scientific, MA, USA) 0.5 mM <sc>l</sc>-glutamine (Gibco <sc>l</sc>-glutamine, Thermo-Fisher Scientific, MA, USA), 30 mM glucose (<sc>d</sc>-glucose anhydrous, Fisher Scientific UK, Loughborough, UK) and 14.2 uL/mL 100× antibiotic–antimycotic solution (Gibco Antibiotic–Antimycotic 100×, Thermo-Fisher Scientific, MA, USA). 1 mL of Neurobasal A medium was added to each dish and incubated at 37 °C for 2 h. The dishes were examined to ensure most of the cells have attached. Afterwards, the medium was replaced with 37 °C Neurobasal A medium (2 mL/well) and allowed to incubate at 37 °C for 18 h. Medium change was performed every 2 days using fresh Neurobasal A medium to replace 3/4ths (1.5 mL) of the existing medium.</p></sec><sec id=\"Sec8\"><title>Primary culture treatment</title><p id=\"Par18\">Treatment of the primary culture was performed on day 4 of the culture. 200 µL of 100 mM LiCl (Takara Bio, Japan) was added to the culture dish containing 2 mL of Neurobasal A medium for a final concentration of 10 mM LiCl. The dish was allowed to incubate for 24 h before imaging. Dishes assigned for no LiCl pre-treatment were instead treated with 200 µL of ultrapure miliQ H<sub>2</sub>O replacing the LiCl stock solution.</p></sec><sec id=\"Sec9\"><title>Calcium dye loading</title><p id=\"Par19\">Calcium dye loading was performed on day 5 of the culture and one hour before imaging. 10 µL of Stock Rhod-2AM calcium dye (1 mg/mL, AB142780, Abcam Inc, MA, USA) in DMSO was mixed with 10 µL of pluronic F-127 (20% DMSO) and vortexed. The resulting solution was pipetted directly on top of the dish in 990 µL Krebs buffer at room temperature (rhod-2AM 5 µM, DMSO v/v < 0.1%). The dish was wrapped in aluminium foil and allowed to incubate for 1 h at 37 °C. The cells were then washed in 37 °C 1 mL Krebs buffer twice for 10 min to remove the excess dye. 37 °C 2 mL Krebs buffer is then added prior to commencing with imaging.</p></sec><sec id=\"Sec10\"><title>Imaging</title><p id=\"Par20\">The Leica laser-scanning multiphoton microscope (Leica Microsystems, Germany) was used for measuring activity of GnIH neurons loaded with Rhod-2AM dye. The EGFP and rhod-2AM fluorophores were excited by lasers emitting at wavelengths of 488 nm and 581 nm respectively. Using the Leica Application Suite X (LAS X) software, the dish was observed live for 5 min. 100 µL of 100 mM serotonin creatinine sulfate complex (5-HT) was added to the glass bottom dish for a final concentration of 5 mM 5-HT before proceeding to record for another 15 min. Control dishes that would not undergo 5-HT treatment were instead treated with 100 µL of ultrapure miliQ H<sub>2</sub>O replacing the 5-HT solution. Activity of GnIH neurons (green fluorescence) was determined by the intensity of rhod-2AM dye (red fluorescence) which increases with calcium activity. While a threshold for intracellular calcium levels is normally set for measurement of action potentials<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>, the measurement of internal calcium signalling, particularly of rhodamine-2AM dye is performed without setting thresholds; instead, it is a direct measure of change in fluorescent intensity<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref>–<xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. Dosage response was measured by repeating the experiment with 20 µL of 100 mM 5-HT for a final concentration of 1 mM 5-HT.</p></sec><sec id=\"Sec11\"><title>Data analysis and statistics</title><p id=\"Par21\">Basal pre-treatment fluorescence (F<sub>0</sub>) was recorded at 4 min after imaging had commenced using the Leica Application Suite X (LAS X) software. A number of EGFP-GnIH neurons were already highly fluorescent prior to treatment. As such, all EGFP-GnIH neurons exhibiting rhodamine-2AM fluorescence at an initial intensity value of lower than 100 were chosen to observe the 5-HT response in order to omit neurons unsuitable for 5-HT response analysis. Peak fluorescence was selected from the highest fluorescent intensity readings at any time post-treatment. Change in fluorescent intensity (ΔF) was calculated by measuring the difference in intensity between current fluorescence and basal fluorescence. Data is presented as means ± SEM for comparison of relative change, and analysed using a one way ANOVA post hoc test in order to determine whether there were any statistically significant differences between the means of the different treatment categories. Significance was set as <italic>p</italic> < 0.05.</p></sec></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>T.C.H., I.S.P. and T.S. designed all experiments and T.C.H. conducted all experiments. T.C.H. and T.S. analyzed all data together. T.C.H. wrote the main manuscript text and prepared all figures. T.S., I.S.P. and T.C.H. edited the manuscript and all authors reviewed the manuscript.</p></notes><notes notes-type=\"data-availability\"><title>Data availability statement</title><p>The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par23\">The authors declare no competing interests.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Tsutsui</surname><given-names>K</given-names></name><etal/></person-group><article-title>A novel avian hypothalamic peptide inhibiting gonadotropin release</article-title><source>Biochem. Biophys. Res. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Br J Nutr</journal-id><journal-id journal-id-type=\"iso-abbrev\">Br J Nutr</journal-id><journal-id journal-id-type=\"publisher-id\">BJN</journal-id><journal-title-group><journal-title>The British Journal of Nutrition</journal-title></journal-title-group><issn pub-type=\"ppub\">0007-1145</issn><issn pub-type=\"epub\">1475-2662</issn><publisher><publisher-name>Cambridge University Press</publisher-name><publisher-loc>Cambridge, UK</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32703328</article-id><article-id pub-id-type=\"pmc\">PMC7431858</article-id><article-id pub-id-type=\"pii\">S0007114520002913</article-id><article-id pub-id-type=\"doi\">10.1017/S0007114520002913</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Full Papers</subject></subj-group><subj-group subj-group-type=\"section\"><subject>Microbiology</subject></subj-group></article-categories><title-group><article-title>The rationale for a multi-step therapeutic approach based on antivirals, drugs and nutrients with immunomodulatory activity in patients with coronavirus-SARS2-induced disease of different severities</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Fiorino</surname><given-names>Sirio</given-names></name><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"a2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"cor1\">*</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zippi</surname><given-names>Maddalena</given-names></name><xref ref-type=\"aff\" rid=\"a3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Gallo</surname><given-names>Claudio</given-names></name><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Sifo</surname><given-names>Debora</given-names></name><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Sabbatani</surname><given-names>Sergio</given-names></name><xref ref-type=\"aff\" rid=\"a4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Manfredi</surname><given-names>Roberto</given-names></name><xref ref-type=\"aff\" rid=\"a4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Rasciti</surname><given-names>Edoardo</given-names></name><xref ref-type=\"aff\" rid=\"a5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Rasciti</surname><given-names>Leonardo</given-names></name><xref ref-type=\"aff\" rid=\"a1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Giampieri</surname><given-names>Enrico</given-names></name><xref ref-type=\"aff\" rid=\"a6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Corazza</surname><given-names>Ivan</given-names></name><xref ref-type=\"aff\" rid=\"a6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Leandri</surname><given-names>Paolo</given-names></name><xref ref-type=\"aff\" rid=\"a2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-0609-8817</contrib-id><name><surname>de Biase</surname><given-names>Dario</given-names></name><xref ref-type=\"aff\" rid=\"a7\"><sup>7</sup></xref></contrib></contrib-group><aff id=\"a1\"><label>1</label>Medicine Department, Internal Medicine Unit, <institution>Budrio Hospital Azienda USL</institution>, Budrio, 40054 <city>Bologna</city>, <country>Italy</country></aff><aff id=\"a2\"><label>2</label>Medicine Department, Internal Medicine Unit C, <institution>Maggiore Hospital Azienda USL</institution>, 40100 Bologna, <country>Italy</country></aff><aff id=\"a3\"><label>3</label>Gastroenterology and Hepatology Department, Unit of Gastroenterology and Digestive Endoscopy, <institution>Sandro Pertini Hospital</institution>, 00100 <city>Rome</city>, <country>Italy</country></aff><aff id=\"a4\"><label>4</label>Gastroenterology and Hepatology Department, Infective Disease Unit, Policlinico S. Orsola-Malpighi, <institution>University of Bologna</institution>, 40100 <city>Bologna</city>, <country>Italy</country></aff><aff id=\"a5\"><label>5</label>Unit of Radiodiagnostics, Ospedale degli Infermi, 48018 <institution>Faenza, AUSL Romagna</institution>, <country>Italy</country></aff><aff id=\"a6\"><label>6</label>Experimental, Diagnostic and Specialty Medicine Department, <institution>University of Bologna</institution>, 40100 <city>Bologna</city>, <country>Italy</country></aff><aff id=\"a7\"><label>7</label>Department of Pharmacy and Biotechnology, <institution>University of Bologna</institution>, 40100 <city>Bologna</city>, <country>Italy</country></aff><author-notes><corresp id=\"cor1\"><label>*</label><bold>Corresponding author:</bold> Sirio Fiorino, fax + 39 51809034, email <email>sirio.fiorino@ausl.bologna.it</email></corresp></author-notes><pub-date date-type=\"pub\" publication-format=\"print\" iso-8601-date=\"2021-02-14\"><day>14</day><month>2</month><year>2021</year></pub-date><pub-date date-type=\"pub\" publication-format=\"electronic\" iso-8601-date=\"2020-07-24\"><day>24</day><month>7</month><year>2020</year></pub-date><volume>125</volume><issue>3</issue><fpage>275</fpage><lpage>293</lpage><history><date date-type=\"received\"><day>17</day><month>3</month><year>2020</year></date><date date-type=\"rev-recd\"><day>28</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>02</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Authors 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>The Authors</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (<uri xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"S0007114520002913a.pdf\"/><abstract abstract-type=\"normal\"><p>In December 2019, a novel human-infecting coronavirus, named Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), was recognised to cause a pneumonia epidemic outbreak with different degrees of severity in Wuhan, Hubei Province in China. Since then, this epidemic has spread worldwide; in Europe, Italy has been involved. Effective preventive and therapeutic strategies are absolutely required to block this serious public health concern. Unfortunately, few studies about SARS-CoV-2 concerning its immunopathogenesis and treatment are available. On the basis of the assumption that the SARS-CoV-2 is genetically related to SARS-CoV (about 82 % of genome homology) and that its characteristics, like the modality of transmission or the type of the immune response it may stimulate, are still poorly known, a literature search was performed to identify the reports assessing these elements in patients with SARS-CoV-induced infection. Therefore, we have analysed: (1) the structure of SARS-CoV-2 and SARS-CoV; (2) the clinical signs and symptoms and pathogenic mechanisms observed during the development of acute respiratory syndrome and the cytokine release syndrome; (3) the modification of the cell microRNome and of the immune response in patients with SARS infection; and (4) the possible role of some fat-soluble compounds (such as vitamins A, D and E) in modulating directly or indirectly the replication ability of SARS-CoV-2 and host immune response.</p></abstract><kwd-group><title>Key words:</title><kwd>SARS</kwd><kwd>COVID-19</kwd><kwd>Vitamins</kwd><kwd>Therapy</kwd></kwd-group><kwd-group kwd-group-type=\"abbreviations\"><title>Abbreviations:</title><compound-kwd><compound-kwd-part content-type=\"abbrev\">CRS</compound-kwd-part><compound-kwd-part content-type=\"expansion\">cytokine release syndrome</compound-kwd-part></compound-kwd><compound-kwd><compound-kwd-part content-type=\"abbrev\">HBV</compound-kwd-part><compound-kwd-part content-type=\"expansion\">hepatitis B virus</compound-kwd-part></compound-kwd><compound-kwd><compound-kwd-part content-type=\"abbrev\">NPS</compound-kwd-part><compound-kwd-part content-type=\"expansion\">non-structural proteins</compound-kwd-part></compound-kwd><compound-kwd><compound-kwd-part content-type=\"abbrev\">ORF</compound-kwd-part><compound-kwd-part content-type=\"expansion\">open reading frames</compound-kwd-part></compound-kwd><compound-kwd><compound-kwd-part content-type=\"abbrev\">RAR</compound-kwd-part><compound-kwd-part content-type=\"expansion\">nuclear RA receptors</compound-kwd-part></compound-kwd><compound-kwd><compound-kwd-part content-type=\"abbrev\">VA</compound-kwd-part><compound-kwd-part content-type=\"expansion\">vitamin A</compound-kwd-part></compound-kwd></kwd-group><counts><fig-count count=\"5\"/><ref-count count=\"114\"/><page-count count=\"19\"/></counts></article-meta></front><body><p>In December 2019, a novel human-infecting coronavirus, named SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2), emerged as a very serious public health concern, causing a pneumonia epidemic outbreak in Wuhan, Hubei Province in China with different degrees of severity<sup>(<xref rid=\"r1\" ref-type=\"bibr\">1</xref>)</sup>. This pathological condition has been defined as ‘coronavirus disease 2019’ (abbreviated ‘COVID-19’), and the most common clinical presentation in infected subjects is represented by flu-like symptoms in 80 % of cases. About 10–15 % of infected subjects develop a more serious respiratory form. It is characterised by an interstitial pneumonia with chest discomfort, severe dyspnoea, high fever and dry cough potentially evolving into acute respiratory failure with a severe respiratory distress syndrome in about 10 % of infected subjects. The mortality rate is about 7 % of affected patients<sup>(<xref rid=\"r2\" ref-type=\"bibr\">2</xref>)</sup>. However, patients may also present less common symptoms, like diarrhoea, headache, myalgia or arthralgia, chills, nausea or vomiting, nasal congestion and conjunctival congestion (0·8 %)<sup>(<xref rid=\"r3\" ref-type=\"bibr\">3</xref>)</sup>. The epidemic has been declared a ‘public health emergency of international concern’ by the International Health Regulations Emergency Committee of the WHO<sup>(<xref rid=\"r4\" ref-type=\"bibr\">4</xref>)</sup>. A dramatic situation is developing in Italy with a progressively increasing number of infected subjects, mainly rather old individuals. According to current data, about 15 % of patients with SARS-CoV-2 infection develop severe forms of pneumonia, radiological signs of interstitial involvement at the computerised axial tomography. These subjects require intensive care and they are at high risk of death. The need for intensive care beds also is progressively increasing, and this condition might lead to the collapse of the Italian Health System in a very short time (data from Ministero della Salute Italiano, <uri xlink:href=\"http://www.salute.gov.it/portale/nuovocoronavirus/homeNuovoCoronavirus.jsp?lingua=english\">http://www.salute.gov.it/portale/nuovocoronavirus/homeNuovoCoronavirus.jsp?lingua=english</uri>). Unfortunately, to date, neither a vaccine nor specific proved effective treatments against this virus are available worldwide. Therefore, new therapeutic strategies are strongly required to efficaciously counteract SARS-CoV-2 as soon as possible and to establish effective antiviral approaches. Unfortunately, it must be considered that this virus has been isolated only recently, and a few articles describing its structure and genome organisation have been published. To date, studies concerning immune response against SARS-CoV-2 and the alterations induced in cell structure by this pathogen have not been studied and are not well known yet<sup>(<xref rid=\"r5\" ref-type=\"bibr\">5</xref>)</sup>.</p><sec sec-type=\"other\" id=\"s1\"><title>Immunopathogenesis of Severe Acute Respiratory Syndrome Corona Virus 2 infection</title><p>In the last weeks, bioinformatics analysis has been carried out on a virus genome from a patient with SARS2019-nCoV infection to compare it with other related coronavirus genomes<sup>(<xref rid=\"r6\" ref-type=\"bibr\">6</xref>)</sup>. According to the results, the genome of SARS2019-nCoV (now known as SARS-CoV-2) presents around 89 % nucleotide identity with the bat SARS-like-CoVZXC21 viral genome and about 82 % with that of human SARS-CoV. A wide range of viruses and host factors mutually modulate their interaction, influence the antiviral immune response and contribute to determine the pathogenesis of SARS-CoV-2<sup>(<xref rid=\"r7\" ref-type=\"bibr\">7</xref>)</sup>. Therefore, on the basis of the assumption that the SARS-CoV-2 is genetically related to SARS-CoV, but that its characteristics are still poorly known, we have performed a literature search to identify the reports assessing these elements in patients with SARS-CoV-induced infection, a better-defined pathologic condition since several years ago. The SARS-CoV-mediated disease resembles the SARS-CoV-2 one, and then the SARS-CoV may be helpful to better understand COVID-19. As happened for the other ‘CoV severe acute lung injury’ (such as SARS-CoV or MERS-CoV)<sup>(<xref rid=\"r8\" ref-type=\"bibr\">8</xref>,<xref rid=\"r9\" ref-type=\"bibr\">9</xref>)</sup>, it has been hypothesised that an imbalance in the host immune response against the CoV-2 virus may cause either the severe distress respiratory syndrome or lead to an unfavourable outcome<sup>(<xref rid=\"r10\" ref-type=\"bibr\">10</xref>–<xref rid=\"r12\" ref-type=\"bibr\">12</xref>)</sup>.</p><p>The aim of this paper is to examine the possible aspects of the complex loop which can develop between host and SARS-CoV-2 in brief as well as the factors and mechanisms involved in this intricate process as well as the possible immunoregulatory role of some compounds in this life-threatening condition. According to a schematic representation, some distinct phases may be recognised during the clinical course of SARS. In the first one, a robust virus replication is detectable in these patients, and it is often characterised by the appearance of fever, sore throat and non-productive cough. These symptoms generally subside in a few days with illness resolution. Nevertheless, in some individuals, a second clinical phase develops. It is characterised by elevated fever, hypoxaemia and progression to pneumonia. This step is associated with an exuberant host inflammatory response and with the sharp and vigorous decrease in virus titers<sup>(<xref rid=\"r13\" ref-type=\"bibr\">13</xref>)</sup>. Following this phase, about 20 % of patients develop an ‘Acute Respiratory Distress Syndrome’ with a possible fatal outcome. Lung specimens obtained from patients who have died because of SARS show several histologic tissue modifications. In particular, the most frequent alterations are represented by extensive cellular infiltrates in the interstitium and alveoli, diffuse alveolar damage with alveolar haemorrhage/oedema, hyaline membrane formation, fibrin exudation, epithelial necrosis with thickening of alveolar septa in the earlier phases and the progression to fibrosis in septa and alveoli in later stages. In particular, diffuse alveolar damage represents a critical and prominent histological feature detectable in the lungs from individuals, who have developed a fatal SARS-CoV-induced infection<sup>(<xref rid=\"r14\" ref-type=\"bibr\">14</xref>)</sup>. Furthermore, SARS-CoV genome and antigens have been observed in airway and alveolar epithelial cells, vascular endothelial cells, neutrophils, macrophages, monocytes and lymphocytes in samples from humans as well as from animal models<sup>(<xref rid=\"r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"r15\" ref-type=\"bibr\">15</xref>)</sup>.</p></sec><sec sec-type=\"other\" id=\"s2\"><title>Severe Acute Respiratory Syndrome Corona Virus 2 genome organisation and viral proteins</title><p>SARS Cov-2 is a spherical-shaped enveloped virus, approximately 120 nm in diameter<sup>(<xref rid=\"r16\" ref-type=\"bibr\">16</xref>)</sup>, with the envelope consisting of a lipid bilayer derived from the host cell membrane and with spike proteins, protruding from the virion surface. These projections confer to the viral particles a crown-like morphology under electronic microscopy, so that the virus is also known as coronavirus. Each virion is composed of a positive 5’-capped and 3’-polyadenylated single-stranded RNA. The viral genome sequence is approximately 30 000 bases in length (<xref ref-type=\"fig\" rid=\"f1\">Fig. 1</xref>)<sup>(<xref rid=\"r17\" ref-type=\"bibr\">17</xref>)</sup>, and it encodes several proteins including:\n<list list-type=\"simple\"><list-item><label>(1)</label><p>Structural proteins, such as the nucleocapsid (N) protein, the matrix (M) protein, the small envelope (E) protein and the spike (S) glycoprotein. These proteins are in the 3’-terminus of the genome. The N protein (about 419 aminoacids in length) is detectable in the core of the viral particle and interacts with the viral RNA, generating a helical ribonucleocapsid. The N protein also binds M and nsp3 proteins, and it contributes to genome protection, viral RNA replication, virion assembly, nucleocapsid formation, generation of the mature virions and immune evasion, the E protein is a small membrane protein (about 75 amino acids in length), regulating viral particle assembly, budding and pathogenesis. It binds M, N, 3a and 7a proteins. The M protein is a membrane/matrix protein (around 222 amino acids in length), and it is involved in viral particles assembly and budding via the recruitment of other structural proteins. In particular, the M protein interacts with N protein for RNA packaging into virion and with some accessory proteins like 3a and 7a. The S, E and M proteins together create the viral envelope. The spike protein is synthesised as a precursor (around 1273 amino acids in length) and then it is cleaved into glycosylated subunits, S1 and S2. During virus infection, S1 allows the attachment of Sars-CoV-2 to a specific receptor of host’s cell, known as angiotensin-converting enzyme 2 receptor, while S2 mediates the fusion between virus and cell membrane.</p></list-item><list-item><label>(2)</label><p>Non-structural proteins (NSP), to date, sixteen NSP have been described, but, for most of them, the function is not yet known. Among the better characterised proteins there are:</p></list-item></list></p><p><fig id=\"f1\" orientation=\"portrait\" position=\"float\"><label>Fig. 1.</label><caption><p>Coronavirus genome and its structural and non-structural proteins. ORF, open reading frame; aa, amino acids; N, nucleocapsid protein; S, spike protein; M, matrix protein.</p></caption><graphic xlink:href=\"S0007114520002913_fig1\"/></fig></p><p>(a) NSP1 (about 180 amino acids), it modulates viral gene expression and immunoevasion by influencing interferon-mediated signalling, (b) NSP2 (about 638 amino acids), it perturbs intracellular microenvironment and alters intracellular signalling paths, (c) NSP4 (with NSP3) is needed for the assembly of the virion particles during the course of viral replication, (d) NSP7 and NSP8, they act as a cofactor for the RNA-dependent RNA polymerase (known as NSP12) activity with NSP9. These proteins regulate viral replication, (e) NSP12 (about 932 amino acids) represents the RNA-dependent RNA polymerase, and it is involved both in replication and in transcription of the SARS-CoV-2 genome and (f) NSP13, NSP14 and NSP15, they modulate viral replication.</p><p>Open reading frames (ORF), a variable number of (6–10) ORF have been described. The first two ORF at 5′ untranslated region code for polyprotein (ORF1a and ORF1b). The ORF1a produces a polypeptide 1a that is cleaved into 11 NSP and ORF-1b produces the polypeptide 1b that it is cleaved into fifteen proteins. The viral proteases NSP3 and NSP5 are involved in this process. These NSP are required for virus replication. Further, eight accessory proteins designated ORF-3a, 3b, 6, 7a, 7b, 8a, 8b and 9b have been described in the viral genome. These sequences are interposed among the structural genes (<xref ref-type=\"fig\" rid=\"f1\">Fig. 1</xref>)<sup>(<xref rid=\"r18\" ref-type=\"bibr\">18</xref>)</sup> and exert different and complex viral functions. Among the most important functions of the accessory proteins, ORF-3a binds to proteins 7a, M, S and E and activates cell inflammatory mediators and contributes to the generation of cytokine storm. ORF-6 acts as antagonist of type I interferons (IFN) and promotes viral escape from the host innate immune system. Following the entry into cells, the genomic RNA is translated to generate NSP from the ORF1a and ORF1b. The viral genome serves also as the template for replication and transcription, via the activation of NSP12, which exerts RNA dependent RNA polymerase activity. Furthermore, also negative-sense RNA intermediates are synthesised and function as templates for the generation of positive-sense genomic RNA (gRNA) and subgenomic RNA (sgRNA). The gRNA and the structural proteins are assembled and generate the viral progeny. The sgRNA serve as template for the structural proteins (spike-, envelope-, membrane- and nucleocapsid proteins) and accessory proteins<sup>(<xref rid=\"r19\" ref-type=\"bibr\">19</xref>)</sup>.</p></sec><sec sec-type=\"other\" id=\"s3\"><title>Defective and dysfunctional immune response in patients with Severe Acute Respiratory Syndrome Corona Virus 2-related infection</title><p>A comprehensive theory of the pathogenesis for SARS-CoV-2 infectious disease is still lacking, but it has been proposed for SARS-CoV in the past<sup>(<xref rid=\"r14\" ref-type=\"bibr\">14</xref>)</sup>, and some preliminary studies about SARS-CoV-2 have been published or are in progress<sup>(<xref rid=\"r20\" ref-type=\"bibr\">20</xref>)</sup>.</p><p>Therefore, taking into account all available data in SARS-CoV infection and considering SARS-CoV-2 as a virus with similar characteristics and immunopathogenic effects to SARS-Co-V, it may be hypothesised that the deleterious events in patients with the most severe forms of the COVID-19 are the results both of an excessive or inadequate immune response of the host<sup>(<xref rid=\"r14\" ref-type=\"bibr\">14</xref>,<xref rid=\"r21\" ref-type=\"bibr\">21</xref>,<xref rid=\"r22\" ref-type=\"bibr\">22</xref>)</sup>. According to Gu’s hypothesis, the SARS-CoV infects the human body through the respiratory tract, entering the epithelial cells of the trachea, bronchi, bronchioles and lungs<sup>(<xref rid=\"r14\" ref-type=\"bibr\">14</xref>)</sup> (<xref ref-type=\"fig\" rid=\"f2\">Fig. 2</xref>). In this context, the virus colonises also resident, infiltrating and circulating immune cells. Then, the virus disseminates to all human organs, being carried by the infected circulating immune cells and spread to different types of cells in other organs. The immune cells of the spleen, peripheral and central lymph nodes, other lymphoid tissues are colonised and damaged by the virus. Furthermore, the mucosa of the intestine, the epithelium of the renal distal tubules, the neurons of the brain and macrophages in different organs are also involved. According to this hypothesis, it may be assumed that infected circulating immune cells spread to the mucosa-associated lymphoid tissue, bronchus-associated lymphoid tissue and nasopharynx-associated lymphoid tissue. No data are available concerning the possible virus-mediated alterations in the function of these lymphoid compartments in patients with SARS-CoV-2 infection. The immune defence is significantly impaired and infected patients may develop pneumonia with different degrees of severity and experiment a rapid deterioration of clinical conditions. In particular, aged subjects with chronic diseases have often a compromised immune function, generally develop more severe clinical pictures and present a more elevated mortality in comparison with healthy subjects<sup>(<xref rid=\"r23\" ref-type=\"bibr\">23</xref>)</sup>. According to Gu’s study, the severity of the immune cell damage more than the extent of the lesions detectable in the lungs suggests that the patient’s immune status and his lymphocyte count probably represent the main predictor of his clinical evolution<sup>(<xref rid=\"r14\" ref-type=\"bibr\">14</xref>)</sup>. Viral load also may exert a crucial impact on the strength and efficacy of the patient’s immune response<sup>(<xref rid=\"r23\" ref-type=\"bibr\">23</xref>)</sup>. During the course of SARS-CoV and CoV2 diseases, an activation of the immune response progressively develops, leading to a self-maintaining and self-increasing inflammatory state. High serum levels of pro-inflammatory cytokines (IFN-<italic>γ</italic>, IL-1, IL-6, IL-12 and TGFβ)<sup>(<xref rid=\"r24\" ref-type=\"bibr\">24</xref>,<xref rid=\"r25\" ref-type=\"bibr\">25</xref>)</sup> and chemokines (CCL2, CXCL10, CXCL9 and IL-8) have been detected in SARS patients, who develop the most severe clinical forms of disease in comparison with subjects with a milder illness<sup>(<xref rid=\"r26\" ref-type=\"bibr\">26</xref>–<xref rid=\"r28\" ref-type=\"bibr\">28</xref>)</sup>. Furthermore, a strong pro-inflammatory Th1 and Th17 response has been observed in patients with MERS-CoV (Middle East respiratory syndrome Coronavirus) infection, with increased concentrations of IFN-<italic>γ</italic>, TNF-<italic>α</italic>, IL-15 and IL-17<sup>(<xref rid=\"r29\" ref-type=\"bibr\">29</xref>)</sup>. In humans, Th17 cells (T-helper 17) can be induced by IL-6 and IL-1<italic>β</italic><sup>(<xref rid=\"r30\" ref-type=\"bibr\">30</xref>)</sup>. Experimental research in <italic>in vitro</italic> models of cultured cells has examined the pattern of SARS-CoV proteins and has allowed to identify the potential pro-inflammatory role of some among them in the pathogenesis of SARS. In particular, nucleocapsid (N) and spike (S) SARS-CoV proteins possess direct binding sites on several specific DNA sequences, localised in the promoter region of a wide series of interleukins and cytokines<sup>(<xref rid=\"r31\" ref-type=\"bibr\">31</xref>,<xref rid=\"r32\" ref-type=\"bibr\">32</xref>)</sup>.</p><p><fig id=\"f2\" orientation=\"portrait\" position=\"float\"><label>Fig. 2.</label><caption><p>Gu’s hypothesis, concerning SARS-CoV infection<sup>(<xref rid=\"r14\" ref-type=\"bibr\">14</xref>)</sup>. A similar scheme may be considered with the purpose to explain the pathogenesis of SARS-CoV-2. The SARS-CoV infects the human body through the respiratory tract, entering the epithelial cells of the trachea, bronchi, bronchioles and lungs. In this context, the virus also colonises resident, infiltrating and circulating immune cells. Then, the virus disseminates to all human organs, being carried by the infected circulating immune cells and spread to different types of cells in other organs. The immune cells of the spleen, peripheral and central lymph nodes, other lymphoid tissues are colonised and damaged by the virus. Furthermore, the mucosa of the intestine, the epithelium of the renal distal tubules, the neurons of the brain and the macrophages in different organs are also involved. According to this hypothesis, it may be assumed that infected circulating immune cells spread to the mucosa-associated lymphoid tissue (MALT) and bronchus-associated lymphoid tissue (BALT) The immune defence is significantly impaired and infected patients may develop pneumonia with different degrees of severity and experiment a rapid deterioration of clinical conditions. Aged subjects with chronic diseases have often a compromised immune function, generally develop more severe clinical pictures and present a more elevated mortality in comparison with healthy subjects. The severity of the immune cell damage more than the extent of the lesions detectable in the lungs suggests the patient’s immune status, and his lymphocyte count probably represents the main predictor of his clinical evolution. Viral load also may exert a crucial impact on the strength and efficacy of the patient’s immune response. The possible action of fat-soluble vitamins in improving immune response activity is indicated. ARDS, acute respiratory distress syndrome.</p></caption><graphic xlink:href=\"S0007114520002913_fig2\"/></fig></p><p>It may be hypothesised that SARS-CoV-2-induced disease with severe clinical courses and with a fatal outcome is characterised by a massive release of a wide spectrum of cytokines, leading to the cytokine release syndrome (CRS)<sup>(<xref rid=\"r33\" ref-type=\"bibr\">33</xref>)</sup>. A more detailed discussion of this topic is beyond the scope of this work, and it will be the subject of a further paper. Therefore, on the basis of these concepts and observations, a proper modulation or control of the exuberant inflammatory response, developing in the course of SARS-CoV-2 infection, might be a key strategy for the treatment of the patients with severe forms of SARS-CoV-2 infections and, probably, it might also prevent the evolution of the illness towards an unfavourable outcome.</p></sec><sec sec-type=\"other\" id=\"s4\"><title>Factors involved in the inflammatory immune response in patients with Severe Acute Respiratory Syndrome Corona Virus 2</title><p>Multiple factors may contribute to explain the exuberant inflammatory response, detectable in this severe disease and should be considered in the strategy of treatment. Overall, these elements may contribute to determine the differences in clinical course and severity of illness in patients with COVID-19. The following points should be considered:\n<list list-type=\"simple\"><list-item><label>(i)</label><p>Rapidity of viral replication and load of viral proteins, mainly proteins causing the release of IL-1, IL-6, IL-8 and TNF-<italic>α</italic>;</p></list-item><list-item><label>(ii)</label><p>Anatomical human compartment or organ predominantly infected by the virus;</p></list-item><list-item><label>(iii)</label><p>Cytokine storm and antiviral impaired immune response.</p></list-item></list></p></sec><sec sec-type=\"other\" id=\"s5\"><title>Possible role of some drugs and nutrients in modulating directly or indirectly the replication ability of Severe Acute Respiratory Syndrome Corona Virus 2 and host immune response</title><p>On the basis of all these immunopathogenic and clinical observations and considerations, a potential useful therapeutic rescue strategy for the treatment of patients affected by severe forms of SARS-CoV-2 infection could include the following points:\n<list list-type=\"simple\"><list-item><label>(i)</label><p>Antiviral therapy with the currently available drugs, which have been demonstrated to be effective in reducing or in inhibiting replication of RNA-viruses (HCV, HIV and Ebola virus) in previous trials or of SARS-CoV-2 itself in very preliminary reports and anecdotal cases. This therapy should be administered as soon as possible to counteract SARS-CoV-2 replication with the main purpose to decrease the synthesis and the release of some crucial viral proteins (nucleocapsid and spike proteins) detectable in the cytoplasm and in the nucleus of the infected cells. The inhibition in the synthesis of these proteins should promote the decrease of their amounts and remove the persisting stimulus, which induce the transcription and the translation of the pro-inflammatory cytokines. This strategy may prevent the persistence of the self-maintaining and self-stimulating pro-inflammatory loop in the body tissues of infected individuals, mainly in the lung, associated with the release of the pro-inflammatory cytokines. The result of this therapy is the inhibition of the so called ‘cytokine storm’ and the block of its related deleterious effects (<xref ref-type=\"fig\" rid=\"f3\">Figs. 3</xref> and <xref ref-type=\"fig\" rid=\"f4\">4</xref>). A high viral replication in infected cells may be associated with the release of elevated N and S protein amounts. The binding to the promoters of the pro-inflammatory cytokines and enzymes may induce a hyper activation in the transduction and translation of these genes. As consequence, elevated amounts of pro-inflammatory cytokines are synthesised and secreted. The massive release of these mediators is associated with the development of the CRS. Subjects with an immune system dysregulation (e.g. aged individuals with chronic diseases and impaired immune system function) are particularly at risk to develop this life-threatening condition.</p></list-item></list></p><p><fig id=\"f3\" orientation=\"portrait\" position=\"float\"><label>Fig. 3.</label><caption><p>Pathogenetic mechanisms involved in the cytokine storm syndrome. N and S viral proteins possess some target sequences on the DNA in the nucleus of human cells. Some binding motifs are detectable in the promoter of some cell genes, encoding key cytokines or enzymes involved in inflammatory process, such as IL-1, IL-6, IL-8, TNF-<italic>α</italic> and cyclo-oxygenase (COX)-2. Subjects with an immune system dysregulation (e.g. aged individuals with chronic diseases and impaired immune system function) are particularly at risk to develop this life-threatening condition.</p></caption><graphic xlink:href=\"S0007114520002913_fig3\"/></fig></p><p><fig id=\"f4\" orientation=\"portrait\" position=\"float\"><label>Fig. 4.</label><caption><p>Possible or putative therapeutic targets potentially useful for the prevention or treatment of the cytokine release syndrome (CRS) by means of acetylsalicylic acid (although perplexity has been expressed about this treatment), monoclonal antibodies against the receptors of some interleukins like IL-6, IL-1 alone or in association with some fat-soluble vitamins (mainly vitamin D). This figure provides the conceptual hypothesis that multiple therapeutic targets may be considered. To date, there are no certainties on the efficacy of any therapies, alone or in combination, which may have some efficacy in the treatment of the CRS in patients with SARS-CoV-2 infection.</p></caption><graphic xlink:href=\"S0007114520002913_fig4\"/></fig></p><p>To date, some drugs have demonstrated potential efficacy in the treatment of SARS-CoV-2-infected individuals, including (a) approved nucleoside analogues (Favipiravir and Ribavirin) and experimental nucleoside analogues (Remdesivir and Galidesivir) able to inhibit the RNA-dependent RNA polymerase and to block viral RNA synthesis in a broad spectrum of RNA viruses, including human coronaviruses<sup>(<xref rid=\"r34\" ref-type=\"bibr\">34</xref>)</sup>; (b) approved protease inhibitors including disulfiram, lopinavir, indinavir, saquinavir, ritonavir, atazanavir and darunavir have been shown to have activity against SARS-CoV-2<sup>(<xref rid=\"r35\" ref-type=\"bibr\">35</xref>)</sup>.</p><p><list list-type=\"simple\"><list-item><label>(ii)</label><p>Immunomodulatory therapy, including (a) monoclonal antibodies against IL-6 (as suggested in preliminary reports) and eventually against IL-1 and/or IL-8 as well as against cyclo-oxygenase (COX) inhibitors, like aspirin or other non-steroidal anti-inflammatory drugs with the purpose to stop or to prevent the strong inflammatory response and the release of further cytokines and mediators of inflammation.</p></list-item></list></p><p>Very preliminary observation suggests that the block of IL-6 pathway cascade may have a beneficial effect in patients with severe forms of SARS. Tocilizumab is a humanised anti-IL-6 receptor subunit <italic>α</italic> (anti-IL-6 R) monoclonal antibody approved in numerous countries throughout the world, for the treatment of rheumatoid arthritis, with moderate to severe active rheumatoid arthritis, refractory to methotrexate<sup>(<xref rid=\"r36\" ref-type=\"bibr\">36</xref>)</sup>. In patients with rheumatoid arthritis, the inhibition of IL-6 leads to Th1 and Th17 suppression and Th2 expansion via activation of T-regulatory (T-reg) cells<sup>(<xref rid=\"r37\" ref-type=\"bibr\">37</xref>,<xref rid=\"r38\" ref-type=\"bibr\">38</xref>)</sup>.</p><p>It is conceivable that the observed improvement in clinical conditions of patients suffering from severe forms of SARS-CoV-2 infections depends on the attenuation of the CRS. Well-designed clinical trials are need in a very short time to test the efficacy and the safety of this potentially very promising therapeutic approach (unpublished observations). No data are available on the possible efficacy and safety of acetylsalicylic acid as well as the duration for an effective treatment. To date, the use of aspirin as an option for the treatment of acute respiratory distress syndrome, with the purpose to inhibit COX-2 activity, has been proposed<sup>(<xref rid=\"r39\" ref-type=\"bibr\">39</xref>)</sup>. Inhibition of COX-2 might attenuate the CRS, but only one experimental study in animals has tested a possible role of aspirin in acute lung injury. Aspirin has been reported to protect mice in a two-event model of transfusion-related acute lung injury<sup>(<xref rid=\"r40\" ref-type=\"bibr\">40</xref>)</sup>. The lack of studies on this topic makes it difficult to hypothesise the role of aspirin in the treatment of these patients and requires further studies.</p><p>Other possible, but, to date, not tested anti-SARS-CoV-2 compounds with potential usefulness against virus or against its related complications may be represented by some fat-soluble vitamins. Therapeutic regimens with fat-soluble vitamins’ administration (such as A, D and E) are based on their immunoregulatory activity, due to their ability to exert a protective role for the maintenance of a proper function of the immune response as well as on their antioxidant activities with potential beneficial effects in attenuating the oxidative stress, which emerges in cells and tissue, during both acute and persistent viral infections<sup>(<xref rid=\"r41\" ref-type=\"bibr\">41</xref>)</sup>. Oxidative stress represents one of the first events developing as defence mechanism, when a pathogen (bacteria, fungi or viruses) infects a host. In normal conditions, host’s cells in general and immune cells in particular produce reactive species, including reactive oxygen species and reactive nitrogen species, which act as mediators both in physiological and in pathological processes. The synthesis and release of these chemical compounds by immune cells, like macrophages, neutrophils and monocyte, are increased, following an infection<sup>(<xref rid=\"r42\" ref-type=\"bibr\">42</xref>)</sup>. Reactive species counteract the invading pathogens, contribute to hinder them and to control the infection via regulation of cellular signalling paths, cytokines release, growth factors transcription, proliferation, gene expression, adhesion, metabolism and apoptosis. Nevertheless, these chemical specimens also display harmful actions and their hyperproduction may lead to DNA, lipids and proteins oxidation resulting in their damage and in alteration of cellular integrity and homoeostasis<sup>(<xref rid=\"r43\" ref-type=\"bibr\">43</xref>)</sup>. Cells possess an antioxidant defence system to prevent oxidative injury, including enzymatic (superoxide dismutase, catalase and glutathione peroxidase) and non-enzymatic components (like vitamin E), This imbalance could result from a lack of antioxidant capacity or an overabundance of oxygen reactive species. When the abundance of reactive oxygen species overcomes the host’s antioxidant capacity, an unbalance of cell oxidant–antioxidant status results. This condition is defined ‘oxidative stress’ and may induce a potential cellular and tissue damage. Since several years ago, it is well known that a wide spectrum of viruses including hepatitis B virus (HBV)<sup>(<xref rid=\"r44\" ref-type=\"bibr\">44</xref>)</sup>, hepatitis C (HCV)<sup>(<xref rid=\"r45\" ref-type=\"bibr\">45</xref>)</sup>, delta (HDV)<sup>(<xref rid=\"r46\" ref-type=\"bibr\">46</xref>)</sup>, herpes viruses<sup>(<xref rid=\"r47\" ref-type=\"bibr\">47</xref>)</sup> and respiratory viruses<sup>(<xref rid=\"r48\" ref-type=\"bibr\">48</xref>,<xref rid=\"r49\" ref-type=\"bibr\">49</xref>)</sup> may affect cellular redox balance by increasing reactive species such as superoxide and nitric oxide and inhibit the synthesis of antioxidant enzymes such as superoxide dismutase, catalase and glutathione peroxidase<sup>(<xref rid=\"r50\" ref-type=\"bibr\">50</xref>)</sup>. Furthermore, although the available data are still partial, some studies have shown that patients with SARS-CoV-2 infection also present an increased production of reactive species, with an alteration of host’s antioxidant system, exerting a major role in the pathogenesis, progression and severity of this pathological condition<sup>(<xref rid=\"r51\" ref-type=\"bibr\">51</xref>,<xref rid=\"r52\" ref-type=\"bibr\">52</xref>)</sup>.</p><p>Previous studies have shown that vitamins A, D and E possess antioxidant effectiveness counteracting peroxidation of lipids incorporated in plasma membrane cells, in membranes of mitochondria, endoplasmic reticulum and lysosomes as well as the oxidative damage of DNA and of macromolecular protein structures inside the cytoplasm<sup>(<xref rid=\"r53\" ref-type=\"bibr\">53</xref>–<xref rid=\"r58\" ref-type=\"bibr\">58</xref>)</sup>.</p><p>The antioxidant effects and mechanisms of vitamins A, D and E will be discussed in detail in the section entitled: ‘Potential anti-SARS-Cov-2 biological activity of the vitamins A, D and E may be associated with their molecular structure’.</p><p>The rationale for the use of these compounds with the purpose to treat SARS-CoV-2 infection deserves a conceptual explanation. Fat-soluble vitamins possess numerous cellular targets and can modulate a wide variety of cell activities at various levels<sup>(<xref rid=\"r59\" ref-type=\"bibr\">59</xref>)</sup>. In this paper, we will consider in brief the regulatory activities of fat-soluble vitamins on the immune system functions and on the inflammatory response. These compounds possess pleiotropic effects and may exert a systemic direct antiviral- or immunomodulatory effects.</p><p>The following points must be considered:\n<list list-type=\"simple\"><list-item><label>(i)</label><p>A large series of clinical studies have shown that the serum concentrations of vitamins A, E and D are decreased in patients with some chronic viral infections, like HBV, HCV and HIV<sup>(<xref rid=\"r60\" ref-type=\"bibr\">60</xref>,<xref rid=\"r61\" ref-type=\"bibr\">61</xref>)</sup>, in comparison with uninfected individuals as well as in aged patients<sup>(<xref rid=\"r62\" ref-type=\"bibr\">62</xref>)</sup>.</p></list-item><list-item><label>(ii)</label><p>The deficiency of vitamins D, E and A is associated with higher levels of viral replication as well as with higher values of inflammatory cytokines, like IL-6 and TNF-<italic>α</italic><sup>(<xref rid=\"r63\" ref-type=\"bibr\">63</xref>–<xref rid=\"r65\" ref-type=\"bibr\">65</xref>)</sup>.</p></list-item></list></p><p>Vitamin E has been shown in several trials to enhance the immune response and resistance to infections<sup>(<xref rid=\"r66\" ref-type=\"bibr\">66</xref>)</sup>. All-trans retinoic acid is an active metabolite of vitamin A (VA), and it has been shown to modulate immunity. It induces the differentiation of CD4<sup>+</sup> T-cells into T-reg cells but inhibits the differentiation of Th17 cells, thereby it contributes to the maintenance of the Th17/T-reg cell balance<sup>(<xref rid=\"r67\" ref-type=\"bibr\">67</xref>)</sup>.</p><p>Some vitamins, like vitamins E, D and A, have been used in clinical trials for the treatment of patients with persistent viral infections, including HBV, HCV and HIV. These micronutrients have been demonstrated to enhance both the innate and the adaptive immunity against these pathogens<sup>(<xref rid=\"r61\" ref-type=\"bibr\">61</xref>,<xref rid=\"r68\" ref-type=\"bibr\">68</xref>–<xref rid=\"r73\" ref-type=\"bibr\">73</xref>)</sup> and to decrease susceptibility of CD4+ T lymphocytes to HIV-1 infection<sup>(<xref rid=\"r74\" ref-type=\"bibr\">74</xref>)</sup>. Furthermore, vitamins A, D and E have been suggested to improve innate and adaptive immune response against respiratory viruses, including influenza virus, rhinovirus and respiratory syncytial virus both <italic>in vivo</italic> and <italic>in vitro</italic> studies<sup>(<xref rid=\"r75\" ref-type=\"bibr\">75</xref>)</sup>. Possible antiviral role of vitamin E has been already suggested several years ago in patients with respiratory infections<sup>(<xref rid=\"r76\" ref-type=\"bibr\">76</xref>)</sup>, but very interesting and promising anti-HBV effects have been observed in clinical trials, involving a small number of children<sup>(<xref rid=\"r77\" ref-type=\"bibr\">77</xref>,<xref rid=\"r78\" ref-type=\"bibr\">78</xref>)</sup> and adult patients<sup>(<xref rid=\"r79\" ref-type=\"bibr\">79</xref>)</sup>, with HBeAg-positive and HBeAg-positive/negative chronic hepatitis. The possible rationale of vitamin E use in these patients and the potential targets of direct or indirect antiviral effects mediated by vitamin E have been widely discussed in a previous systematic review<sup>(<xref rid=\"r70\" ref-type=\"bibr\">70</xref>)</sup>.</p><p><list list-type=\"simple\"><list-item><label>(iii)</label><p>Fat-soluble vitamins possess well-known multiple nuclear and cytoplasmic targets in all the different types of mammalian cells, and they may modulate and regulate an elevated number of intra- and extracellular pathways via a direct binding to regulatory regions in a large series of genes critical for the maintenance of cell homoeostasis, via modulation of a wide series of cell functions<sup>(<xref rid=\"r70\" ref-type=\"bibr\">70</xref>,<xref rid=\"r80\" ref-type=\"bibr\">80</xref>)</sup>.</p></list-item></list></p></sec><sec sec-type=\"other\" id=\"s6\"><title>Possible mechanisms underlying the effects of fat-soluble vitamins in counteracting Severe Acute Respiratory Syndrome Corona Virus 2 infection</title><p>On the basis of this brief revision of the reported antiviral activities of vitamins A, D and E against different human viruses (both DNA and RNA viruses), reported in <italic>in vivo</italic> and in <italic>in vitro</italic> studies, it may be hypothesised that these micronutrients may have possible beneficial effects also in counteracting SARS-CoV-2 infection. Several elements may have a role in these events, and their accurate definition and understanding may contribute to increase our knowledge of SARS-CoV-2 pathogenesis and to improve the treatment of this pathogen.</p><sec id=\"s6-1\" sec-type=\"other\"><title>Potential anti-Severe Acute Respiratory Syndrome Corona Virus 2 biological activity of the vitamins A, D and E may be associated with their molecular structure</title><p>Several studies have underlined that a key event in the development of a productive viral infection is represented by the optimal interaction between some components of the host cell plasma membrane and some proteins of the virus envelope<sup>(<xref rid=\"r81\" ref-type=\"bibr\">81</xref>)</sup>. This process allows the entry of the pathogen into the cell and affects the infective ability of each virus as well as its tissue tropism, its local or diffuse replication and dissemination and, as further aspects, its virulence and its pathogenicity<sup>(<xref rid=\"r82\" ref-type=\"bibr\">82</xref>)</sup>. SARS-CoV-2 infects permissive host’s cells by means of its glycoprotein S (spike protein), which interacts with the angiotensin-converting enzyme 2 receptors on human cells.</p><p>Following the binding, spike protein divides into two subunits (S1 and S2). S1 protein includes a receptor sequence for the binding to the peptidase domain of angiotensin-converting enzyme 2, whereas S2 is involved in the process of fusion between plasma membranes and the envelope of viral particles<sup>(<xref rid=\"r83\" ref-type=\"bibr\">83</xref>)</sup>. Available data suggest that the lipid composition of cell plasmatic membranes may affect the entry into host’s cells of several viruses, and it may modulate their replication. In particular, some studies have shown that the entry of several viruses, including SARS-CoV-2, into the host’s cells is mediated by some specialised microdomains with specific constituents, detectable in plasmatic membrane cells<sup>(<xref rid=\"r84\" ref-type=\"bibr\">84</xref>)</sup>. These complexes have been defined lipid rafts, they are rich in cholesterol, sphingolipid and proteins and act as platforms that modulate the signals and the cascade pathways in cell membrane<sup>(<xref rid=\"r85\" ref-type=\"bibr\">85</xref>)</sup>. It has been suggested that lipid rafts facilitate the interaction between the spike protein and its ACE2 receptor and favour the entry of SARS-CoV into the cells via the fusion of the viral lipid envelope with the plasma membrane of the susceptible cells<sup>(<xref rid=\"r86\" ref-type=\"bibr\">86</xref>)</sup>. This event is followed by the endocytosis of virions. In particular, both cholesterol and fatty acids regulate these processes, and it has been shown that the pharmacological depletion of cholesterol activity may inhibit the attachment of several viruses, including SARS-CoV-2, to host’s membrane cells<sup>(<xref rid=\"r87\" ref-type=\"bibr\">87</xref>)</sup>. Furthermore, viruses themselves may modulate cell lipid metabolism and may induce a modification in the total specific lipid content of the cellular plasmatic membranes. It has been suggested that lipids in these structures may undergo an oxidative process via the activation of canonical lipase pathways. The changes in the lipid membrane composition are associated with an alteration in its fluidity and permeability. The variation of these physical parameters may have a crucial impact in the infectivity of viruses<sup>(<xref rid=\"r85\" ref-type=\"bibr\">85</xref>)</sup>. Taking advantage from all these studies and observations, it may be hypothesised that the biological actions of vitamins A, D and E against SARS-CoV-2 could depend on the ability of these vitamins to modulate the rigidity/fluidity of the plasmatic membrane cells. These effects may be explained by the structure of these micronutrients. <xref ref-type=\"fig\" rid=\"f5\">Fig. 5</xref> summarises the chemical structures of vitamins A, D and E.</p><p><fig id=\"f5\" orientation=\"portrait\" position=\"float\"><label>Fig. 5.</label><caption><p>Chemical structure, biological activities and use as antiviral treatments of vitamins A, D and E. AVT, antiviral therapy; BetaC, betacarotene; C, controls; CT, controlled trial; CHB, chronic hepatitis B; CHC, chronic hepatitis C; DB, double blind; F, female; FU, follow-up; HBV, hepatitis B virus; HCV, hepatitis C virus; I, Intervention group; IU, international units; M, male; NT, not treated; PC, placebo controlled; R, randomised; RBP, retinol-binding protein; SVR, sustained virological response; T, treated; y, years; TGF, transforming growth factor; VA, vitamin A; VC, vitamin C; VD, vitamin D; VE, vitamin E.</p></caption><graphic xlink:href=\"S0007114520002913_fig5\"/></fig></p><p>VA is a term indicating retinol and its derivatives, collectively defined ‘retinoids’. They are essential nutrients for all vertebrate animal species. Two dietary sources of VA exist in nature such as preformed retinoids and provitamin A (pro-VA) carotenoids. Among carotenoids, <italic>β</italic>-carotene represents the most important precursor of VA. Furthermore, retinol, retinal and retinoic acid are the forms of this micronutrient detectable in the body<sup>(<xref rid=\"r88\" ref-type=\"bibr\">88</xref>)</sup>. All these compounds are toxic at elevated concentration; therefore, they are bound to proteins both in the intracellular and extracellular microenvironments. Retinoic acid (RA) is the main biologically active form of this micronutrient. The structure of all forms of VA consists of a <italic>β</italic>-ionone ring which is attached to an isoprenoid chain (retinyl group). Both elements are essential for the biological activity of these micronutrients. The liver and adipose tissue act as deposits for the different forms of VA, which are stored as long-chain fatty esters and as provitamin carotenoids. The main functions of the biological active forms of these micronutrients include vision, immunity, cell differentiation, embryological development, cellular differentiation and proliferation as well as antioxidant activity<sup>(<xref rid=\"r89\" ref-type=\"bibr\">89</xref>)</sup>. The different forms of VA possess an antioxidant activity, due to the hydrophobic chain of polyene elements. They can quench singlet oxygen, neutralise thiyl radicals and decrease the generation of peroxyl radicals. In general, the peroxyl radical stabilising ability depends on the length of the polyene chain, the longer it is, the greater is the peroxyl radical stabilising activity. Furthermore, when O<sub>2</sub> tension increases, the different biological forms of VA can autoxidise, and this function depends on their structures. This activity is observed in human tissues, where low oxygen tensions exist physiologically. Therefore, retinoids are very effective antioxidants in this condition<sup>(<xref rid=\"r90\" ref-type=\"bibr\">90</xref>)</sup>. VA promotes the maintenance of levels and structure of tight junctions among the cells in the small intestine. Diets with restriction in VA in animal models cause an impairment in the architecture and tight junctions barrier in the cells of the small intestine. This damage involves villi and it is characterised by a decrease in amount of tight junction proteins, such as Zonula Occludens-1, occludin and claudin-1<sup>(<xref rid=\"r91\" ref-type=\"bibr\">91</xref>)</sup>. It is well known that retinoic acid modulates the expression of several cellular gene programmes via the activation of the nuclear RA receptors (RAR). They are represented by three subtypes (RAR<italic>α</italic>, RAR<italic>β</italic> and RAR<italic>γ</italic>). These elements are ligand-inducible transcriptional regulators and heterodimerise with retinoid X receptors (RXR). RAR possess a domain for the binding to nuclear DNA. Interestingly, a fraction of RAR<italic>α</italic> is in lipid rafts. In these specialised structures, there are some signal-transducing molecules, like protein kinase. To date, it is not known whether the binding of RA to RAR <italic>α</italic> may induce modification in fluidity of plasma cell membranes and whether this event may influence viral infectivity. Further studies are needed to clarify this point<sup>(<xref rid=\"r92\" ref-type=\"bibr\">92</xref>)</sup>.</p><p>The term vitamin D indicates a spectrum of fat-soluble micronutrients with multiple biological effects. In humans, the most important members of this group are represented by vitamin D<sub>2</sub> (VD<sub>2</sub>) (ergocalciferol) and by vitamin D<sub>3</sub> (VD<sub>3</sub>) (cholecalciferol)<sup>(<xref rid=\"r93\" ref-type=\"bibr\">93</xref>)</sup>. Vitamin D<sub>3</sub> is the most relevant form of vitamin D. It is synthesised from 7-dehydrocholesterol through a chemical reaction that is dependent on sun exposure (specifically UVB radiation). During this process, the B ring of this chemical compound opens and becomes a less rigid structure. This event occurs in the lipid bilayer of the plasma membranes inside the cells, which are localised in the lower layers of skin epidermis. Alternatively, vitamin D<sub>3</sub> can be acquired with the diet. Vitamin D<sub>3</sub>, which is introduced with the diet or is synthesised in the skin, is biologically inactive. It undergoes two enzymatic hydroxylation steps, the first occurs in the liver and the second in the kidneys. In particular, cholecalciferol is turned into calcifediol (25-hydroxycholecalciferol) and ergocalciferol into 25-hydroxyergocalciferol in the liver. Calcifediol is converted into calcitriol, known as 1,25-dihydroxycholecalciferol, via a further hydroxylation in the kidneys<sup>(<xref rid=\"r93\" ref-type=\"bibr\">93</xref>)</sup>. This is the biologically active form of vitamin D. Calcitriol has a major role in regulating the concentration of Ca and P, and it is involved in remodelling of bone. Furthermore, it also has other effects, including some on cell growth, neuromuscular and immune functions, and down-regulation of inflammation. Geometry of the rings A and C and side chain in its structure can affect some biological activities of vitamin D<sub>3</sub>, like its differentiative and antiproliferative abilities as well as its resistance to catabolism. Since several years ago, vitamin D<sub>3</sub> has been shown to possesses <italic>in vitro</italic> and <italic>in vivo</italic> antioxidant properties. In particular, vitamin D<sub>3</sub> acts as a membrane antioxidant with inhibitory activity on iron-induced lipid peroxidation of brain liposomes membrane<sup>(<xref rid=\"r55\" ref-type=\"bibr\">55</xref>)</sup>, or it has been able to suppress the process of lipid peroxidation in rats with deficiency in vitamin D<sub>3</sub><sup>(<xref rid=\"r94\" ref-type=\"bibr\">94</xref>)</sup>. Furthermore, this micronutrient has been reported to reduce OS by up-regulating antioxidative defence systems, including glutathione content, glutathione peroxidase and superoxide dismutase in cultured astrocytes and in hepatic cells<sup>(<xref rid=\"r53\" ref-type=\"bibr\">53</xref>)</sup>. Furthermore, vitamin D promotes the maintenance of tight junctions, gap junctions and adherens junctions in the cells (e.g. by E-cadherin)<sup>(<xref rid=\"r95\" ref-type=\"bibr\">95</xref>,<xref rid=\"r96\" ref-type=\"bibr\">96</xref>)</sup>. 1,25-Dihydroxycholecalciferol is not detectable inside the lipid bilayer in cellular plasma membranes, but it exerts its modulatory activities by stimulation of two receptors: a nuclear vitamin D receptor and a membrane receptor ERp60. Vitamin D<sub>3</sub> binding to these receptors induces the activation of several cytoplasmic pathways, including the activation of several protein kinases. Both receptors are incorporated into the lipid rafts in plasma membrane cells, and this evidence suggests the hypothesis that these microdomains have a major role in the mechanism of 1<italic>α</italic>,25(OH)<sub>2</sub>D<sub>3</sub> action. It is conceivable that vitamin D<sub>3</sub>, by binding to its cognate receptors, may modulate the rigidity/fluidity of membrane cells and may modulate viral infectivity<sup>(<xref rid=\"r97\" ref-type=\"bibr\">97</xref>)</sup>. The term vitamin E indicates a series of related compounds, each of these is composed by a 6-chromanol ring and by a polyisopentenyl side chain<sup>(<xref rid=\"r98\" ref-type=\"bibr\">98</xref>)</sup>. This chain is either saturated (tocopherols) or unsaturated with three double bonds, detectable at positions 3’, 7’ and 11’ (tocotrienols). Tocopherols and tocotrienols include four isomers (<italic>α</italic>, <italic>β</italic>, <italic>γ</italic> and <italic>δ</italic>); each of them is defined on the basis of the number and localisation of the methyl groups on the phenol ring. Vitamin E (<italic>α</italic>-tocopherol) has a hydrophobic structure, and it is distributed in all membrane cells including plasmatic and mitochondrial membranes. It has been suggested that <italic>α</italic>-tocopherol is not randomly incorporated in the phospholipid bilayer, but it is segregated in specialised membrane complexes, like lipid rafts, where it is associated with PUFA present in phosphatidylcholine. The effect of this interaction is the decrease of the membrane cell fluidity and the increase of its rigidity. This event may change the activity of enzymes associated with lipid rafts in cell membranes<sup>(<xref rid=\"r99\" ref-type=\"bibr\">99</xref>)</sup>. Furthermore, this micronutrient represents the major lipid soluble chain-breaking antioxidant and it traps peroxyl-radicals and reactive oxygen species, which are produced during peroxidative reactions, by means of its chromanol ring<sup>(<xref rid=\"r100\" ref-type=\"bibr\">100</xref>)</sup>. Therefore, <italic>α</italic>-tocopherol modulates the action of free radicals and contributes to prevent the damage of cellular macromolecules end microrganelles, induced by the OS.</p><p>Overall, it may be hypothesised that these fat-soluble vitamins might directly or indirectly regulate the physical characteristic of the lipid rafts and modulate the fluidity plasmatic cell membranes, increasing the rigidity of these structures. A large series of the enzymes regulated by fat-soluble vitamins, such as tocopherol, are associated with lipid rafts and can change protein–lipid and protein–protein interactions and influence raft-embedded signal transduction pathways. These modifications may contribute to decrease the infective ability of the viruses, including SARS-CoV<sup>(<xref rid=\"r101\" ref-type=\"bibr\">101</xref>)</sup>.</p></sec><sec id=\"s6-2\" sec-type=\"other\"><title>Modulation of immune response function</title><p>The modulation of immune response leading to the improvement of antiviral response derives the conceptual rationale for the inclusion of vitamins A, D and E in a possible multitherapeutic protocol for the treatment of patients with SARS-CoV-2-related infection.</p><p>These vitamins may contribute to improve normal immune response, by restoring the normal immune system activity, mainly by counteracting Th1/Th2/Th17 unbalance and modulating the amounts and the ratio among the pro-inflammatory and anti-inflammatory cytokines. As reported in the studies, vitamin D alone or in association with Tocilizumab is able to block the activity of IL-6 receptor and to promote the generation of Foxp3<sup>+</sup> T-cells and to counteract IL-17 production. These cells modulate the immune response and contribute to turn off the production of pro-inflammatory cytokines. Furthermore, vitamin E also is able to prevent IL-6 release. A very recent report has shown that SARS-CoV-2 viral load (RNAemia) in serum is closely associated with drastically elevated IL-6 level in patients with severe disease (data not published). The combined use of fat-soluble vitamins might exert an even more beneficial effect in elderly patients, who are characterised by an impairment of immune system function. These individuals are characterised by a very high mortality in Italy during this epidemic outbreak (unpublished data)<sup>(<xref rid=\"r102\" ref-type=\"bibr\">102</xref>–<xref rid=\"r104\" ref-type=\"bibr\">104</xref>)</sup>.</p><p>Furthermore, these compounds present an additional anti-inflammatory activity mediated by the production of microRNA-122. These elements are short-cell RNAs which exert a wide series of regulatory cell activities and modulate also antiviral immune response.</p><p>According to Gu’s hypothesis, the immune system dysfunction is the most important cause of clinical deterioration and possible unfavourable outcome in the individuals with CoV disease<sup>(<xref rid=\"r14\" ref-type=\"bibr\">14</xref>)</sup>. Therefore, the possible usefulness of immune system restoration by using these fat-soluble vitamins might represent a crucial strategy with the purpose to prevent or to progressively inhibit the CRS. However, in their use with this indication, fat-soluble vitamins A, D, E should be considered not only as nutrients but also as real drugs with potential useful or dangerous effects. Unfortunately, to date, no studies have assessed the blood concentration of these fat-soluble vitamins in patients with SARS-CoV-2 as well as it is unknown whether deficiency in these micronutrients may be associated with a more severe course and outcome of this disease. Therefore, trials evaluating blood concentration of these compounds should be performed as soon as possible and the possible inclusion of fat-soluble vitamins in the treatment schedules of COVID-19 patients should be considered. However, the possible side effects of these compounds should be considered, and the dosage of blood fat-soluble vitamins should be provided. Based on all these pathogenic considerations, a possible protocol proposal for the treatment of patients with SARS-CoV-2 should consist of the following schedule already in the early phase of the disease:\n<list list-type=\"simple\"><list-item><label>(i)</label><p>antiviral drugs to block viral replication and, mainly, the release of high amounts of viral proteins able to trigger a robust pro-inflammatory response;</p></list-item><list-item><label>(ii)</label><p>immunomodulatory compounds with the purpose of restoring the unbalanced and dysregulated immune system function, including fat-soluble vitamins in association with Tocilizumab.</p></list-item></list></p><p>The early administration of these drugs could prevent the development of CRS with the subsequent clinical deterioration and deaths as well as it could decrease the need of intensive care beds.</p><p>On the basis of the available data concerning the dosage of fat-soluble vitamins as treatment of viral infections (HBV, HCV, HIV, etc.), it may be suggested that these micronutrients should be used as drugs and not as simple dietary supplements, with the purpose to obtain proper serum and tissue concentration<sup>(<xref rid=\"r78\" ref-type=\"bibr\">78</xref>,<xref rid=\"r79\" ref-type=\"bibr\">79</xref>,<xref rid=\"r103\" ref-type=\"bibr\">103</xref>,<xref rid=\"r105\" ref-type=\"bibr\">105</xref>)</sup>. To date, the possible effective dosage of these micronutrients for the therapy of the acute infection caused by SARS-CoV-2 is unknown, as no trials have been concluded in these patients with this purpose. Therefore, it may be conceivable to take into account the dose of vitamins A, D and E in the studies performed in patients with HBV/HCV/HIV persistent infection as well as in patients with autoimmune diseases, like rheumatoid arthritis<sup>(<xref rid=\"r103\" ref-type=\"bibr\">103</xref>)</sup>. The potential doses are indicated in <xref ref-type=\"fig\" rid=\"f5\">Fig. 5</xref><sup>(<xref rid=\"r77\" ref-type=\"bibr\">77</xref>–<xref rid=\"r79\" ref-type=\"bibr\">79</xref>,<xref rid=\"r105\" ref-type=\"bibr\">105</xref>–<xref rid=\"r114\" ref-type=\"bibr\">114</xref>)</sup>. In elderly people with moderate/severe deficiency in these micronutrients, it may be useful to consider schedules for the supplementation of all these vitamins with the purpose to reach normal tissue and serum concentrations of these fat-soluble vitamins. It may be hypothesised that this strategy, in the current absence of an effective vaccine against SARS-CoV-2, might improve the activity of immune system. This approach might both preventively attenuate the risk of the Th17-mediated pro-inflammatory response with potential deleterious effects and stimulate a regulatory T cell immune response leading to the prevention or to the reduction of ‘cytokine storm’ syndrome. In conclusion, in this paper, we have provided a rapid excursus on available data about a very life-threatening disease worldwide, known as SARS-CoV 2, then we have examined the crucial mechanisms potentially involved in the development of this severe illness. Since our research, we have identified the possible viral and host cell targets and suggested a rationale for an early poly-therapeutic approach. Unfortunately, several problems are also evident, including the dosage of antiviral drugs, of fat-soluble vitamins and Tocilizumab as well as the potential side effects of these treatments. Well-designed and well-sized protocols are needed to improve our knowledge in the immunopathogenesis of this complex disease, with the purpose to contribute to the control of this public health emergency.</p></sec></sec></body><back><ack><title>Acknowledgements</title><p>The authors thank Dr Simonetta Righi, Biblioteca Centralizzata, Policlinico S. Orsola-Malpighi, Università di Bologna, Bologna, Italy for her support in the search of scientific bibliography.</p><p>This research received no specific grant from any funding agency, commercial or not-for-profit sectors.</p><p>S. F. designed the study. S. F., M. Z., P. L., D. S., S. S., R. M. and D. B. performed the literature search. S. F., M. Z., C. G., D. S., E. R., L. R., P. L., E. G., I. C., S. S., R. M. and D. B. collected the data. S. F., C. G., E. G., I. C., E. R., L. R., P. L., D. S., S. S., R. M. and D. B. interpreted the data. S. F., C. G., M. Z., E. G., I. C. and D. B. prepared the manuscript. S. F., M. Z., C. G., D. S., E. R., L. R., P. L., E. G., I. C. and D. 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pub-id-type=\"doi\">10.1038/s41467-020-17981-0</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Strength of immune selection in tumors varies with sex and age</article-title></title-group><contrib-group><contrib contrib-type=\"author\" equal-contrib=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-5873-2496</contrib-id><name><surname>Castro</surname><given-names>Andrea</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Pyke</surname><given-names>Rachel Marty</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Xinlian</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Thompson</surname><given-names>Wesley Kurt</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0001-5200-2372</contrib-id><name><surname>Day</surname><given-names>Chi-Ping</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0003-3596-4515</contrib-id><name><surname>Alexandrov</surname><given-names>Ludmil B.</given-names></name><xref ref-type=\"aff\" rid=\"Aff6\">6</xref><xref ref-type=\"aff\" rid=\"Aff7\">7</xref><xref ref-type=\"aff\" rid=\"Aff8\">8</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zanetti</surname><given-names>Maurizio</given-names></name><xref ref-type=\"aff\" rid=\"Aff8\">8</xref><xref ref-type=\"aff\" rid=\"Aff9\">9</xref><xref ref-type=\"aff\" rid=\"Aff10\">10</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-1729-2463</contrib-id><name><surname>Carter</surname><given-names>Hannah</given-names></name><address><email>hkcarter@health.ucsd.edu</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref><xref ref-type=\"aff\" rid=\"Aff8\">8</xref><xref ref-type=\"aff\" rid=\"Aff11\">11</xref><xref ref-type=\"aff\" rid=\"Aff12\">12</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>Department of Medicine, Division of Medical Genetics, </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>Bioinformatics and Systems Biology Program, </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>Health Science, Department of Biomedical Informatics, School of Medicine, </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>Department of Family Medicine and Public Health, Division of Biostatistics & Bioinformatics, </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.94365.3d</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2297 5165</institution-id><institution>Laboratory of Cancer Biology and Genetics, National Cancer Institute, </institution><institution>National Institutes of Health, </institution></institution-wrap>Bethesda, MD 20892 USA </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>Department of Cellular and Molecular Medicine, </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff7\"><label>7</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>Department of Bioengineering, </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff8\"><label>8</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>Moores Cancer Center, </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff9\"><label>9</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>The Laboratory of Immunology, </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff10\"><label>10</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>Department of Medicine, Division of Hematology-Oncology, </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff11\"><label>11</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.266100.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 4242</institution-id><institution>Cancer Cell Map Initiative (CCMI), </institution><institution>University of California San Diego, </institution></institution-wrap>La Jolla, CA 92093 USA </aff><aff id=\"Aff12\"><label>12</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.440050.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0408 2525</institution-id><institution>CIFAR, </institution></institution-wrap> MaRS Centre, West Tower, 661 University Ave., Suite 505, Toronto, ON Canada </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>4128</elocation-id><history><date date-type=\"received\"><day>31</day><month>12</month><year>2019</year></date><date date-type=\"accepted\"><day>28</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Individual MHC genotype constrains the mutational landscape during tumorigenesis. Immune checkpoint inhibition reactivates immunity against tumors that escaped immune surveillance in approximately 30% of cases. Recent studies demonstrated poorer response rates in female and younger patients. Although immune responses differ with sex and age, the role of MHC-based immune selection in this context is unknown. We find that tumors in younger and female individuals accumulate more poorly presented driver mutations than those in older and male patients, despite no differences in MHC genotype. Younger patients show the strongest effects of MHC-based driver mutation selection, with younger females showing compounded effects and nearly twice as much MHC-II based selection. This study presents evidence that strength of immune selection during tumor development varies with sex and age, and may influence the availability of mutant peptides capable of driving effective response to immune checkpoint inhibitor therapy.</p></abstract><abstract id=\"Abs2\" abstract-type=\"web-summary\"><p id=\"Par2\">Here the authors show that stronger immune selection and immune editing in females and younger patients lead to the accumulation of poorly presented driver mutations in tumors. These results may explain why young and female patients are characterized by lower response rates to immune checkpoint blockade therapies.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Cancer genomics</kwd><kwd>Tumour immunology</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/100000092</institution-id><institution>U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM)</institution></institution-wrap></funding-source><award-id>T15LM011271</award-id><principal-award-recipient><name><surname>Castro</surname><given-names>Andrea</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/100000001</institution-id><institution>National Science Foundation (NSF)</institution></institution-wrap></funding-source><award-id>2015205295</award-id><principal-award-recipient><name><surname>Pyke</surname><given-names>Rachel Marty</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/100000002</institution-id><institution>U.S. Department of Health & Human Services | National Institutes of Health (NIH)</institution></institution-wrap></funding-source><award-id>DP5-OD017937</award-id><award-id>CA220009</award-id><principal-award-recipient><name><surname>Carter</surname><given-names>Hannah</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/100007631</institution-id><institution>Canadian Institute for Advanced Research (L'Institut Canadien de Recherches Avancées)</institution></institution-wrap></funding-source></award-group></funding-group><funding-group><award-group><funding-source><institution>U.S. Department of Health & Human Services | National Institutes of Health (NIH)</institution></funding-source></award-group></funding-group><funding-group><award-group><funding-source><institution>Mark Foundation for Cancer Research (grant number 18-022-ELA)</institution></funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par3\">The major histocompatibility complex (MHC) exposes protein content on the cell surface to allow detection of antigens by the immune system. This applies to nonself antigens such as viral proteins, and self-proteins that include tumor antigens. Tumor cells harbor oncogenic alterations that can be presented to the immune system by the MHC, causing immune recognition and elimination (immune surveillance)<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. However, in order to grow, invade, and spread, tumors must evade immune surveillance. Common mechanisms of immune evasion include loss of the MHC molecules and the upregulation of immune checkpoint molecules on cell surfaces that normally regulate the amplitude and duration of a T-cell response<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. Immune checkpoint blockade (ICB) uses antibodies to block these immune checkpoint molecules, and can invigorate inactive and/or exhausted T cells to produce antitumor effects that confer long-term survival benefits in certain types of cancer<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. However, ICB is effective in only 10–40% of patients for reasons that remain unclear. Meta-analyses of clinical trials in multiple cancer types treated with ICB suggest that young and female patients are characterized by low response rates<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>–<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. The reason(s) for the poor response of these two populations remains elusive.</p><p id=\"Par4\">An accumulating body of literature points to sexual dimorphism in immune responses<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. Moderated by genetic and hormonal factors, females have twice the antibody response to influenza vaccines<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup> and higher CD4<sup>+</sup> T-cell counts than males<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. Moreover, females are far more susceptible to autoimmune diseases<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>, demonstrating a stark imbalance in the way the immune response causes diseases in the two sexes. Immunosequencing of over 800 individuals revealed sex associated differences in the extent to which HLA molecules propagate selection and expansion of CD8+ T cells<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. Interestingly, a stronger immune response in females has been observed across several species<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref>–<xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>, and sexual dimorphism has been demonstrated in immune selection and restriction of intratumor genetic heterogeneity in a mouse model of B-cell lymphoma<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. In addition, a recent study has found sex-based differences in molecular biomarkers and immune checkpoint expression in multiple tumor types treated with ICB<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Altogether, these studies suggest that these differences are sex-specific and not lifestyle dependent.</p><p id=\"Par5\">Studies have demonstrated age-related changes in immune response as well. As humans age, there is a decrease of general immune function including production of IL-2, a pivotal growth factor for T cells<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. Reduced thymic output, lower numbers of naive T cells, and overall reshaping of the size and specificity of the T-cell repertoire by microbial pathogens may explain why, for example, about 90% of excess deaths during flu season occur in patients greater than 65 years of age<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. In addition, elderly people have reduced phagocytic function and HLA-II expression on antigen presenting cells<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>. Collectively, these factors render elderly individuals less able to mount a T-cell response to new antigens and respond to vaccination.</p><p id=\"Par6\">Recently, we developed the Patient Harmonic-mean Best Rank (PHBR) score that quantifies patients’ ability to present somatic mutations in their tumor by their specific MHC-I and MHC-II haplotypes<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref>,<xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. PHBR-I and PHBR-II scores aggregate predicted peptide-MHC molecule binding affinities from established tools<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref>,<xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup> to produce a mass spectrometry-validated, residue-centric, and patient-specific presentation score that captures a mutant peptide’s visibility to the immune system. In previous publications we used PHBR scores to assess the role of MHC genotype in shaping mutation accumulation during tumorigenesis<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref>,<xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. We found that patients tend to accumulate driver mutations that cannot be effectively presented by their own MHC molecules, likely a consequence of immune-based elimination of tumor cells harboring well-presented driver mutations, a selective process referred to as immunoediting<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. This analysis revealed that thyroid carcinoma and low-grade glioma patients experience the highest MHC-based selective pressure on driver mutations<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref>,<xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. Interestingly, these tumor types also had the youngest average age at diagnosis compared to all studied tumor types. In light of these observations, we reasoned that younger and female patients may experience stronger immunoediting early in their tumor history, accumulating mutations that are less favorably presented by their MHC, i.e., mutations more invisible to their immune system, at the time of diagnosis. Predictably, a depletion of potentially immunogenic mutant peptides would cause ICB to be ineffective. At first approximation we ruled out an effect due to sex-specific (MHC-I Pearson R = 0.99, MHC-II Pearson R = 0.99) or age-specific (MHC-I Pearson R = 0.98, MHC-II Pearson R = 0.99) imbalances in MHC genotype frequencies. Therefore, we sought to test the hypothesis that sex- and age-specific differences in driver mutation presentation are the result of differential immunoediting.</p><p id=\"Par7\">In this study we find that female and younger patients exhibit stronger immune selection in their tumors, measured by the affinity of their observed, expressed driver mutations compared to male and older patients. MHC-II appears to have a stronger effect compared to MHC-I. Our findings, based on TCGA samples, are validated in an independent validation cohort.</p></sec><sec id=\"Sec2\" sec-type=\"results\"><title>Results</title><sec id=\"Sec3\"><title>Fewer presentable drivers in female and younger patients</title><p id=\"Par8\">We focused on a set of 1018 driver mutations, defined in<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>, as driver mutations are more prevalent in the clonal architecture of an individual’s cancer and confer a selective growth advantage. We assigned MHC-I and MHC-II types using PolySolver and HLA-HD, two exome-based calling methods<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref>,<xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup> and considered only microsatellite-stable TCGA tumors. After excluding 515 patients from class I and 1064 patients from class II analyses due to HLA genotype incompatibility with NetMHCpan affinity prediction software, 9913 patients with MHC-I calls and 7174 patients with MHC-II calls remained. These patients were diverse in sex, with more males than females (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1A</xref>), and a broad distribution of age at diagnosis (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1B</xref>). PHBR-I and -II scores were calculated for all patients across the 1018 driver events by taking the harmonic mean of each allele’s best NetMHCpan percentile rank affinity score, providing an estimate of each patient’s potential to present each mutation via MHC-I and MHC-II, respectively. Importantly, the PHBR-I and PHBR-II scores aggregate percentile rank scores of mutated peptides relative to large numbers of random peptide provided by NetMHCpan-4.0 and NetMHCIIpan3.2. For single peptide-HLA pairs, percentile rank scores of 0.5% and 2% for MHC-I and 2% and 10% for MHC-II have been used to represent strong and weak binding cutoffs respectively<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref>,<xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>.</p><p id=\"Par9\">To rule out other covariates, we performed a series of control analyses. We categorized patients into subgroups according to sex (male versus female) and age (younger versus older based on pan-cancer 30th and 70th percentiles at age of diagnosis for categorical analyses). For sex-specific analyses, we further excluded seven sex-specific tumor types (breast, cervical, ovarian, uterine, prostate, and testicular cancer). First, we established that there were similar average numbers of driver mutations across sex and age patient groups (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>). We previously found that TCGA patients with somatic MHC-I mutations had altered mutational landscapes, with a higher fraction of binding mutant peptides than patients without MHC-I mutations<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. To ensure that somatic MHC-I mutations would not skew the driver mutation PHBR-I score distributions, we compared scores for patients with and without MHC-I mutations grouped by sex and age and found no significant differences (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3</xref>). We then compared the distributions of patient PHBR-I and PHBR-II scores across the 1018 driver mutations (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4A–D</xref>) and found significant <italic>p</italic> values, but very small effect sizes between groups. To ensure that the potential to present driver mutations was consistent across sex and age, we compared the fraction of presented drivers at various score thresholds, and found no significant differences (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4E, F</xref>). The overall similarity of MHC presentation suggests that patients of both sexes and various ages at diagnosis present driver mutations with roughly equivalent efficacy, implying that specificity of MHC presentation resulting from specific allele combinations is not a mechanism causing differences in ICB response rate.</p><p id=\"Par10\">We therefore reasoned that the discrepancy might be due to differences in the strength of immune selection, e.g., tumors with stronger immunoediting should retain fewer driver mutations that are presentable to T cells by the patient’s own MHC molecules. For sex- and age-specific groups in each cohort, we compared the PHBR-I and PHBR-II score distributions for observed, RNA-expressed driver mutations observed in patient tumors, excluding 4782 patients with no drivers from the list of 1018. While the number of observed drivers was not significantly different between sex and age groups (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>), younger female patients were overrepresented in the group with no observed driver mutations (Fisher’s exact test: class I: OR = 1.12, <italic>p</italic> < 0.12; class II: OR = 1.28, <italic>p</italic> < 0.015). We note this group had an overrepresentation of thyroid cancer cases, a disease associated with low mutational burden and that typically only has a single driver mutation<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. We therefore performed sex-specific analysis for unique 2900 patients and age-specific analysis for 3928 unique patients.</p><p id=\"Par11\">Across pan-cancer cohorts, females were at a significant disadvantage (higher PHBR scores) in presenting their driver mutations by both their MHC-I and MHC-II molecules (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a, b</xref>, <italic>p</italic> < 2.6e−04 and <italic>p</italic> < 1.2e−07, respectively). Younger patients also tended to have worse presentation of driver mutations by both MHC-I and MHC-II molecules (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c, d</xref>, <italic>p</italic> < 2.4e−5 and <italic>p</italic> < 7.3e−04, respectively). Notably, the shift in PHBR score distributions between groups occurs near the threshold for weak binding. Given that a limited number of somatic mutations generate mutant peptides and not all of these are immunogenic, this small shift may translate to significantly less opportunity to generate a host antitumor response upon ICB. Importantly, we found that these observed between-group differences in PHBR scores were far greater (falling outside the 99% confidence interval) than differences when we randomly reassigned mutations across patients and recalculated patient-specific PHBR scores (Methods; Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5</xref>), and were an order of magnitude greater than the effect sizes observed when comparing score distributions independent of mutation occurrence (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S4</xref>). We also found differences in affinity independent of the PHBR score, using median NetMHCpan affinity scores across all alleles (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">6</xref>). Altogether this suggests that score differences do indeed result from the interaction of inherited MHC genotype with the observed mutations. Interestingly, the mutation-specific fraction of RNA reads mapping to these driver mutations was significantly lower for females and younger patients (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7</xref>), further supporting sex- and age-based differential strength in immune selection.<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Sex- and age-specific MHC presentation of observed, RNA-expressed driver mutations.</title><p><bold>a</bold>, <bold>b</bold> Box plots denoting the distribution of (<bold>a</bold>) PHBR-I and (<bold>b</bold>) PHBR-II scores for expressed driver mutations in female and male pan-cancer patients. <bold>c</bold>, <bold>d</bold> Box plots denoting the distribution of (<bold>c</bold>) PHBR-I and (<bold>d</bold>) PHBR-II scores for expressed driver mutations in younger and older pan-cancer patients. <italic>P</italic> values were calculated using the one-tailed Mann–Whitney <italic>U</italic> test. Median values are shown in each boxplot. All box plots include the median line, the box denotes the interquartile range (IQR), whiskers denote the rest of the data distribution and outliers are denoted by points greater than ±1.5 × IQR. The following effect sizes were calculated using Cliff’s d: (<bold>a</bold>) <italic>r</italic> = −0.0654, (<bold>b</bold>) −0.104, (<bold>c</bold>) −0.081, (<bold>d</bold>) −0.0734.</p></caption><graphic xlink:href=\"41467_2020_17981_Fig1_HTML\" id=\"d30e753\"/></fig></p><p id=\"Par12\">We next examined evidence for sex and age differences in specific tumor types, adjusting age thresholds according to tumor type. There was a general trend for female and younger patients’ tumors to have higher median PHBR-I and II scores across tumor types, although the difference was only statistically significant in melanoma (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8A</xref>). We observed more variability in the trends across tumor types by age. Younger individuals trended toward higher median PHBR-I and II scores in tumors where the 30th/70th percentile was associated with a large age gap and the younger age threshold was under 55, with some notable exceptions that included rectal cancer, thyroid cancer, stomach cancer, and liver (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8B</xref>). Overall these trends suggest that stronger pan-cancer immune selection in younger and female patients results broadly from effects observed across multiple tumor types.</p><p id=\"Par13\">Next, we explored the effect of age and sex in the context of the immune system’s ability to eliminate effectively-presented mutations by modeling the relationship between mutation occurrence and immune visibility as modeled by PHBR-I and II scores. We constructed sex- and age-specific generalized additive models with random effects to account for variation in mutation rate across individuals, and examined the coefficients corresponding to independent and interaction effects for PHBR-I, PHBR-II, and sex or age to assess their contribution to immune selection for expressed mutations observed ≥2 times in the cohort, excluding patients with no observed, expressed driver mutations. To control for the fact that some driver mutations occurred in the same tumor, and thus are not completely independent events, we included patient ID as a random effect in our linear model. In both models, we found that PHBR-I and PHBR-II scores alone had significant effects on the probability of a mutation to be a target of immune selection (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>). Positive coefficients for both PHBR scores indicate that the higher the PHBR score (i.e., poorer presentation), the higher the probability of mutation. Furthermore, when we quantified the influence of both scores on probability of mutation using odds ratios between respective 25th and 75th percentiles, we found that PHBR-II (OR: 3.4, CI [3.19, 3.6]) has a much larger impact on probability of mutation than PHBR-I (OR: 1.27, CI [1.26, 1.29]), echoing the larger effect sizes seen in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>. As expected, sex and age alone did not influence the probability of mutation; however, of particular interest are the interaction terms that indicate the influence of PHBR scores on probability of mutation within the context of sex and age. Both the PHBR-I:sex and PHBR-I:age interactions as well as the PHBR-II:sex and PHBR-II:age interactions were significant. The negative PHBR:age estimates indicate stronger effects of PHBR-I as well as PHBR-II contribution to the probability of mutation in younger patients. On the other hand, positive PHBR:sex estimates indicate stronger effects of PHBR-I and PHBR-II contributing to probability of mutation in females according to the model formulation (Methods). Collectively, these results suggest stronger immune selection in females and younger patients.<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Quantitative estimate of the association between PHBR score and mutation occurrence in sex- and age-specific cohorts.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Parametric coefficients</th><th>Estimate</th><th>Pr(>|z|)</th></tr></thead><tbody><tr><td rowspan=\"5\">Sex analysis</td><td><bold>PHBR-I</bold></td><td><bold>0.048</bold></td><td><bold>0.0035</bold></td></tr><tr><td><bold>PHBR-II</bold></td><td><bold>0.31</bold></td><td><bold>1.66e−56</bold></td></tr><tr><td>Sex</td><td>−0.02</td><td>0.59</td></tr><tr><td><bold>PHBR-I:sex</bold></td><td><bold>0.07</bold></td><td><bold>0.02</bold></td></tr><tr><td><bold>PHBR-II:sex</bold></td><td><bold>0.15</bold></td><td><bold>0.00035</bold></td></tr><tr><td rowspan=\"5\">Age analysis</td><td><bold>PHBR-I</bold></td><td><bold>0.043</bold></td><td><bold>0.0078</bold></td></tr><tr><td><bold>PHBR-II</bold></td><td><bold>0.31</bold></td><td><bold>1.01e−54</bold></td></tr><tr><td>Age</td><td>−0.0025</td><td>0.06</td></tr><tr><td><bold>PHBR-I:age</bold></td><td><bold>−0.0029</bold></td><td><bold>0.005</bold></td></tr><tr><td><bold>PHBR-II:age</bold></td><td><bold>−0.0035</bold></td><td><bold>0.007</bold></td></tr></tbody></table><table-wrap-foot><p>Estimates and <italic>p</italic> values are shown for a generalized additive model with random effects relating PHBR scores to the set of expressed driver mutations observed ≥2 times in this cohort. <italic>P</italic> values were calculated via Wald tests using the Bayesian covariance matrix for the coefficients. Variables and their respective estimates and <italic>p</italic> values have been bolded if significant (<italic>p</italic> < 0.05).</p></table-wrap-foot></table-wrap></p><p id=\"Par14\">As females and younger patients both demonstrated stronger immune selection compared to males and older patients, we further partitioned the cohorts simultaneously by sex and age, and investigated the distribution of PHBR-I and -II scores for these groups. We found that sex and age effects are cumulative, with tumors in younger females exhibiting significantly higher selective pressure by MHC than those in the other three groups (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>). We noticed a profound difference between PHBR score distributions between younger females and older males. Because younger males had worse presentation of their driver mutations compared to older females (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>), we sought to ensure that sex had an effect on immune selection independent of age. In two models incorporating sex, age, and PHBR-I and PHBR-II scores, respectively, both PHBR:sex and PHBR:age were independently significant for both class I and class II (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). These results demonstrate that more aggressive immune selection in younger females selects for tumors with driver mutations that are less visible to the immune system.<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Integrated sex- and age-specific analysis.</title><p><bold>a</bold> PHBR-I and <bold>b</bold> PHBR-II scores for the observed driver mutations in pan-cancer integrated sex- and age-specific patient cohorts. One asterisk indicates <italic>p</italic> values < 0.05 and two asterisks indicates <italic>p</italic> values < 0.001. All <italic>p</italic> values were calculated using a one-tailed Mann–Whitney <italic>U</italic> test. The Benjamini–Hochberg method was used to adjust for multiple comparisons for (<bold>a</bold>, <bold>b</bold>). Median values are shown in each boxplot. Exact <italic>p</italic> values for (<bold>a</bold>) include: YF, OM: 0.7e−05; YF, OF: 0.005; YF, YM: 0.008; YM, OM: 0.008; OF, OM: 0.08; OF, YM: 0.22. Exact <italic>p</italic> values for (<bold>f</bold>) include: YF, OM: 5.51e−07; YF, YM: 0.0003; YM, OM: 0.035; YF, OF: 0.038; OF, YM: 0.17. Y = younger, O = older, F = female, M = male. All box plots include the median line, the box denotes the interquartile range (IQR), whiskers denote the rest of the data distribution and outliers are denoted by points greater than ±1.5 × IQR.</p></caption><graphic xlink:href=\"41467_2020_17981_Fig2_HTML\" id=\"d30e997\"/></fig></p></sec><sec id=\"Sec4\"><title>Mutational signatures do not explain differential selection</title><p id=\"Par15\">We next explored whether sex- and age-specific effects could be driven by differences in environmental exposure rather than the strength of immune selection. Mutational signatures assign specific mutations to different mutagenic processes, allowing the exploration of differences in environmental exposure across sex and age. We compared the sex-specific occurrence of mutational signatures in each tumor type and found only a minority of instances where signature strength was weakly but significantly associated with sex (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a</xref>). Importantly, only three of the signatures (01, 02, and 05) where we observed significant sex-specific differences contribute to the set of driver mutations used for this analysis (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>). Since signatures 01 and 05 are endogenous rather than exposure associated signatures, this suggests a very low impact of environmental exposures on sex-specific effects of immune selection on drivers. Furthermore, when we excluded the tumor types with significant signature differences (glioblastoma multiforme, GBM and liver hepatocellular carcinoma, LIHC), we still observed sex- and age-related differences (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>). In addition, only two signatures correlated with age, both of which have known association with aging<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. We examined C>T and T>C mutations, which are hallmarks of signature 01 and 05, respectively, and found that observed driver mutations in these categories were broadly distributed across age at diagnosis. To explain weaker immune selection in older individuals, age-related mutations would have to be better presented (have lower PHBR scores) than other mutations. Instead, we found that C>T and T>C mutations were significantly more poorly presented (had slightly higher PHBR scores) than other mutations across all possible MHC-I and MHC-II alleles, suggesting that these mutations, and by extension, signatures 01 and 05, could not drive the apparent age-associated difference in immune selection (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3c</xref>). Thus, we conclude that the sex- and age-specific effects on immune selection are not likely due to environmental exposure differences<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref>,<xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>.<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Sex-specific exposure analysis with mutational signatures.</title><p><bold>a</bold> Heatmap of log2 male (blue) to female (pink) ratios of mutational signatures for each tumor type with asterisks denoting a significantly different ratio between male and female sexes. <bold>b</bold> The percentage of mutations in the set of driver mutations that are part of each mutational signature. <bold>c</bold> Boxplot comparing MHC-I and MHC-II presentation scores across all possible alleles for C>T or T>C driver mutations (green) versus driver mutations resulting from other base substitutions (yellow); 1,063,975 and 2,051,300 affinity scores were evaluated for C>T or T>C mutations for class I and II, respectively; and 1,851,025 and 3,568,700 affinity scores were evaluated for other mutations for class I and II, respectively. Exact <italic>p</italic> values were calculated using a one-tailed Mann–Whitney <italic>U</italic> test: (<bold>c</bold>) 2.2e−308 and (<bold>d</bold>) 1.4e−86. Median values are denoted in each boxplot. All box plots include the median line, the box denotes the interquartile range (IQR), whiskers denote the rest of the data distribution and outliers are denoted by points greater than ±1.5 × IQR.</p></caption><graphic xlink:href=\"41467_2020_17981_Fig3_HTML\" id=\"d30e1058\"/></fig></p></sec><sec id=\"Sec5\"><title>Validation in an independent non-TCGA cohort</title><p id=\"Par16\">We sought validation of our findings in a cohort of 342 patients (309 with compatible MHC-I type calls and 277 with MHC-II type calls) compiled from published dbGaP studies and non-TCGA samples in the International Cancer Genome Consortium (ICGC) database<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup> and filtered to exclude tumor types not represented in TCGA. While fewer tumor types were represented relative to the discovery cohort, these patients were diverse with respect to sex and age at diagnosis, with slightly more males than females, and similar average numbers of driver mutations. As in the discovery cohort, we found some significant differences in patient PHBR score distributions across the 1018 driver mutations, also with very small effect sizes between groups. Likewise, there was no difference in the fraction of presented drivers at various score thresholds (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">9</xref>). The majority of our validation cohort did not have expression data, so we predicted RNA expression using a logistic regression classifier trained on the TCGA cohort (Methods).</p><p id=\"Par17\">We found, as in the discovery cohort, that effectively-presented driver mutations were significantly depleted in younger and female patients compared to older and male patients (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a–d</xref>). These differences were an order of magnitude greater than the effect sizes observed when comparing score distributions independent of mutation occurrence (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S9E–H</xref>). When we examined the simultaneous effects of sex and age (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4e, f</xref>), younger females once again had significantly worse presentation of their driver mutations than older males across both MHC-I and MHC-II (<italic>p</italic> < 0.001, <italic>p</italic> < 0.007). We repeated the sex- and age-specific analyses using the generalized additive models and found that, for both sex and age, PHBR-II scores alone significantly influenced the probability of mutation, with higher PHBR scores (i.e., worse presentation) leading to higher probability of mutation (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">3</xref>). While PHBR-II:sex and PHBR-II:age coefficients trended in the same direction, with stronger effects in females and younger patients, they did not reach significance, likely due to sample size.<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Sex- and age-specific MHC presentation of observed driver mutations in the validation cohort.</title><p><bold>a</bold>, <bold>b</bold> Box plots denoting the distribution of (<bold>a</bold>) PHBR-I and (<bold>b</bold>) PHBR-II scores for driver mutations in female and male pan-cancer patients. Exact <italic>p</italic> values were calculated using a one-tailed Mann–Whitney <italic>U</italic> test: (<bold>a</bold>) 0.027 and (<bold>b</bold>) 0.024, and effect sizes were calculated using Cliff’s d: (<bold>a</bold>) <italic>r</italic> = −0.154, (<bold>b</bold>) <italic>r</italic> = −0.164. <bold>c</bold>, <bold>d</bold> Box plots denoting the distribution of (<bold>c</bold>) PHBR-I and (<bold>d</bold>) PHBR-II scores for driver mutations in younger and older pan-cancer patients. Exact <italic>p</italic> values were calculated using a one-tailed Mann–Whitney <italic>U</italic> test: (<bold>c</bold>) 0.022 and (<bold>d</bold>) 7.9e−04, and effect sizes were calculated using Cliff’s d: (<bold>c</bold>) <italic>r</italic> = −0.207, (<bold>d</bold>) −0.346. <bold>e</bold>, <bold>f</bold> Box plots denoting the distribution of (<bold>e</bold>) PHBR-I and (<bold>f</bold>) PHBR-II scores for driver mutations among integrated sex- and age-specific pan-cancer patient cohorts. One asterisk indicates <italic>p</italic> values < 0.05 and two asterisks indicates <italic>p</italic> values < 0.001. <italic>P</italic> values were calculated using a one-tailed Mann–Whitney <italic>U</italic> test. The Benjamini–Hochberg method was used to adjust for multiple comparisons for (<bold>e</bold>, <bold>f</bold>). Median values are shown in each boxplot. Exact <italic>p</italic> values for (<bold>e</bold>) include: YM, OM: 0.024; YF, OM: 0.028; OF, OM: 0.070; YF, OF: 0.56; YF, YM: 0.49; OF, YM: 0.50. Exact <italic>p</italic> values for (<bold>f</bold>) include: YF, OF: 0.0083; YF, OM: 0.013; OF, YM: 0.023; YM, OM: 0.045; YF, YM: 0.24; OF, OM: 0.34. Y = younger, O = older, F = female, M = male. All box plots include the median line, the box denotes the interquartile range (IQR), whiskers denote the rest of the data distribution and outliers are denoted by points greater than ±1.5 × IQR.</p></caption><graphic xlink:href=\"41467_2020_17981_Fig4_HTML\" id=\"d30e1218\"/></fig></p></sec></sec><sec id=\"Sec6\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par18\">Here, we present evidence that both sex and age impact the driver mutations that arise and persist during tumorigenesis. We found that younger and female patients accumulate driver mutations in their tumors that are less readily presented by their MHC molecules (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>), suggesting a stronger toll by immune selection early in tumorigenesis. This finding is consistent with recent meta-analyses across multiple tumors showing sex- and age-dependent differences in response to ICB<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>–<xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>. We also observed the strongest effects in MHC-II based selection, in agreement with the fact that females have higher CD4<sup>+</sup> T-cell counts than males<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>. A prevalent role of MHC-II driven immune selection can be explained by the fact that CD4<sup>+</sup> T cells, besides direct effector function comparable to that of CD8<sup>+</sup> T cells, also play a deep-rooted regulatory role in cooperating with CD8<sup>+</sup> T cells via associative recognition of antigen<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref>,<xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>. Their function in orchestrating T-cell immunity, in general terms, makes them privileged actors, hence targets of immune selection as revealed herein. In older individuals, immune selection effects by MHC-II presentation of driver mutations are mitigated by a reduced CD4<sup>+</sup>/CD8<sup>+</sup> ratio<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup> and greater telomere attrition in CD4<sup>+</sup> T cells than in CD8<sup>+</sup> T cells<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup> leading to accelerated senescence. Taken together, the evidence suggests that tumors developing in younger and female patients are prone to stronger immunoediting than those in older and male patients.<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Proposed model of the relationship between immune selection and immunotherapy in cancer patients.</title><p>Young females experience the strongest immune response, rendering their diagnosed tumors more invisible to the immune system and difficult to treat with ICB. On the other extreme, old males experience the weakest immune response, leaving their diagnosed tumors more visible to the immune system and open to attack when stimulated with ICB. Blue dots indicate immunologically visible driver mutations while red dots indicate immunologically invisible driver mutations at various time points.</p></caption><graphic xlink:href=\"41467_2020_17981_Fig5_HTML\" id=\"d30e1289\"/></fig></p><p id=\"Par19\">Our findings based on the TCGA were reproduced in the smaller validation cohort where we once again observed poorer MHC-based presentation of driver mutations in females versus males and younger versus older patients, with presentation being worse in younger and female patients. When modeling the influence of MHC genotype on the probability of observing driver mutations, the estimated effect sizes are modest, although relatively large compared to effects detected by genome wide association studies where odds ratios are often <1.2<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. Several sources of uncertainty, including errors in patient genotyping, prediction of the peptide-HLA binding affinities used to calculate the PHBR score, and errors in somatic mutation calling could obscure the true effects<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>. More accurate estimates will likely require larger sample sizes, and ideally availability of expression data as non-expressed mutations should not reflect the effects of immune selection.</p><p id=\"Par20\">In this analysis, we focused on a set of recurrent missense and indel mutations in established driver genes developed in our previous work. This is motivated by the assumption that these are more likely to occur early during tumorigenesis, and may thus provide a view of immune selection before various mechanisms of immune evasion occur<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. However it is unlikely that immune selection operates differently on different categories of mutation, and nondriver mutation-derived neoantigens should be equally capable of triggering a T-cell response. Whether tumor cells can evade T-cell responses more easily when they are targeted against nonessential nondriver mutations remains an important question. It has been suggested that ICB responses are most effective when a clonal driver neoantigen is present<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. While we did not observe large sex or age bias in the mutational signatures associated with the 1018 driver mutations, we speculate that it is possible nondriver mutations could show differences in their potential to serve as neoantigens if the underlying mutational processes are active at different times or are biased to generate mutations in expressed protein coding sequences with characteristics that bias their presentation.</p><p id=\"Par21\">Notwithstanding some limitations, our analysis provides a compelling case for the paradigm that immune selection exerts its toll differently with respect to sex and age, with a greater effect in younger females. Of note, the younger female cohort had the poorest driver mutation presentation across both the discovery and validation cohorts, suggesting that these effects are strong and complementary. Although our analysis suggests that younger age is associated with stronger antitumor immune responses, we strongly suggest caution in considering whether this trend could generalize to pediatric tumors. The genomic landscape of pediatric tumors is distinct from that of adulthood tumors, with lower mutation burdens, different driver events and more germline factors and the characteristics of the pediatric immune system differ greatly from those of an adult<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. Furthermore, we are unable to control for other sex- and age-related factors beyond predicted MHC presentation of driver mutation-derived peptides. These possibilities may include (a) differences in the antigen processing machinery preceding surface exposure of MHC-peptide complexes, and (b) genetic and epigenetic factors causing preferential mutation accumulation in the cohorts for reasons other than immunoediting.</p><p id=\"Par22\">In conclusion, this study indicates that immune selection exerts its toll differently with respect to sex and age, with a greater effect in younger females. As such, the response rate to ICB may be dependent on the strength of immune selection occurring early in tumorigenesis. Methods to accurately predict the impact of immunoediting on a patient-specific basis may lead to better predictive algorithms for response to therapy. As a corollary, we posit that ICB treatment is likely to have a reduced effect in younger female patients since this treatment will attempt to reactivate T cells for immunologically invisible neoantigens. Rather, adaptive T-cell therapy against patient-validated neoantigens or therapeutic vaccination against conserved antigens will likely be more beneficial in these patients. Notably prior to treatment with ICB, male sex (and less consistently older age) are associated with higher risk of recurrence and death in melanoma and may stand to benefit more from ICB<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref>,<xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>, thus it is also possible that overall stronger immune surveillance could prove advantageous in the context of ICB despite differences in the quality of neoantigens. Finally, these findings shed light on the role of immune surveillance in cancer progression.</p></sec><sec id=\"Sec7\"><title>Methods</title><sec id=\"Sec8\"><title>HLA typing</title><p id=\"Par23\">HLA genotyping was performed for class I genes <italic>HLA-A, HLA-B, HLA-C</italic>, and class II genes <italic>HLA-DRB1</italic>, <italic>HLA-DPA1, HLA-DPB1, HLA-DQA1</italic>, and <italic>HLA-DQB1</italic>, which encode three protein determinants of MHC-I peptide binding specificity, <italic>HLA-DR</italic>, <italic>HLA-DP</italic>, and <italic>HLA-DQ</italic>. TCGA samples were typed with Polysolver<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>, with default parameters, for class I and typed with HLA-HD<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>, using default parameters, for class II. Both tools require germline (whole blood or tissue matched) whole exome sequenced samples. Samples with very low coverage on specific genes are left untyped by HLA-HD. Patients were assigned an <italic>HLA-DR</italic> type if they were successfully typed for <italic>HLA-DRB1</italic>. Patients were assigned <italic>HLA-DP</italic> and <italic>-DQ</italic> types if they had successful typing for <italic>HLA-DPA1/HLA-DPB1</italic> and <italic>HLA-DQA1/HLA-DQB1</italic>, respectively. Class I and class II types were validated by xHLA<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>, run with default parameters, and only patients where all alleles agreed in both classes were included in the analysis.</p></sec><sec id=\"Sec9\"><title>Presentation score assignment</title><p id=\"Par24\">We used patient presentation scores, as defined in<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>, to represent a particular patient’s ability to present a residue given their distinct set of HLA types. For class I, 6 HLA alleles were considered (<italic>HLA-A, HLA-B</italic>, and <italic>HLA-C)</italic>. For class II, 12 HLA-encoded MHC-II molecules (4 combinations of <italic>HLA-DPA1</italic>/<italic>DPB1</italic> and <italic>HLA-DQA1</italic>/<italic>DQB1</italic>; 2 alleles of <italic>HLA-DRB1</italic> considered twice each—since <italic>HLA-DRA1</italic> is invariant—for consistency between resulting molecules). NetMHCpan4.0<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup> and NetMHCIIpan3.2<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup> were used to calculate binding affinities. The PHBR score was assigned as the harmonic mean of the best residue presentation scores for each group of MHC-I and MHC-II molecules. A lower patient presentation score indicates that the patient’s MHC molecules are more likely to present a residue on the cell surface.</p></sec><sec id=\"Sec10\"><title>Set of driver mutations</title><p id=\"Par25\">Somatic mutations were considered to be recurrent and oncogenic if they occurred in one of the 100 most highly ranked oncogenes or tumor suppressors described by Davoli et al.<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup> and were observed in at least three TCGA samples. Among these, we retained only mutations that would result in predictable protein sequence changes that could generate neoantigens, including missense mutations and inframe indels. A total of 1018 mutations (512 missense mutations from oncogenes, 488 missense mutations from tumor suppressors, 11 indels from oncogenes and 7 indels from tumor suppressors) were obtained<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>.</p></sec><sec id=\"Sec11\"><title>Modeling the effects of PHBR score on mutation probability</title><p id=\"Par26\">We built two matrices, for PHBR-I scores and PHBR-II scores, from the 1018 mutations and the 1912 patients with both PHBR-I and -II calls. Next, we built a binary mutation matrix <italic>y</italic><sub><italic>ij</italic></sub> ∈ {0,1} indicating whether patient <italic>i</italic> has a specific mutation <italic>j</italic>. We evaluated the relationship between this binary matrix, the matched 1912 × 1018 matrices with log PHBR-I and -II scores, <italic>x</italic>1<sub><italic>ij</italic></sub> and <italic>x</italic>2<sub><italic>ij</italic></sub>, respectively, and the variable of interest (sex or age) for patient <italic>i</italic> and mutation <italic>j</italic>. We fit a generalized additive model for the centered log PHBR-I, centered log PHBR-II scores, centered sex (coded 0/1 for males/females) or centered age, and mutation probability with the GAM function in the MGCV R package<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>. To estimate the effects of PHBR and sex or age on probability of mutation, we considered the following random effects models:<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{Logit}}\\left( {{\\mathrm{P}}\\left( {{{y}}_{ij} \\,=\\, 1} \\right)} \\right) \t\\,= \\, {\\upbeta}_{\\mathrm{1}}{{x1}}_{ij} \\,+\\, {\\upbeta}_{\\mathrm{2}}{{x2}}_{ij} \\,+\\, {\\upbeta}_{\\mathrm{3}}{\\mathrm{Sex}}_i \\,+\\, {\\upbeta}_4\\left( {{{x}}1_{ij} \\,\\times\\, {\\mathrm{Sex}}_i} \\right) \\\\ \t\\quad\\, +\\, {\\upbeta}_{\\mathrm{5}}\\left( {{{x}}2_{ij} \\,\\times\\, {\\mathrm{Sex}}_i} \\right) + {\\upeta}_i,$$\\end{document}</tex-math><mml:math 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mathvariant=\"normal\">η</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:math><graphic xlink:href=\"41467_2020_17981_Article_Equ1.gif\" position=\"anchor\"/></alternatives></disp-formula><disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{Logit}}\\left( {{\\mathrm{P}}\\left( {{{y}}_{ij} \\,=\\, 1} \\right)} \\right) \t\\,=\\, {\\upbeta}_{\\mathrm{1}}{{x1}}_{ij} \\,+\\, {\\upbeta}_{\\mathrm{2}}{{x2}}_{ij} \\,+\\, {\\upbeta}_{\\mathrm{3}}{\\mathrm{Age}}_i \\,+\\, {\\upbeta}_4\\left( {{{x}}1_{ij} \\,\\times\\, {\\mathrm{Age}}_i} \\right) \\\\ \t\\, \\quad +\\, {\\upbeta}_{\\mathrm{5}}\\left( {{{x}}2_{ij} \\,\\times\\, {\\mathrm{Age}}_i} \\right) \\,+\\, {\\upeta}_i.$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mi mathvariant=\"normal\">Logit</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi mathvariant=\"normal\">P</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.25em\"/><mml:mn>1</mml:mn></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">1</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>x</mml:mi><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">2</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>x</mml:mi><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">3</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Age</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>x</mml:mi><mml:msub><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Age</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mspace width=\"1.0em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">5</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>x</mml:mi><mml:msub><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Age</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">η</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>.</mml:mo></mml:math><graphic xlink:href=\"41467_2020_17981_Article_Equ2.gif\" position=\"anchor\"/></alternatives></disp-formula></p><p id=\"Par27\">And PHBR-I and PHBR-II specific models (results in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>):<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{Logit}}\\left( {{\\mathrm{P}}\\left( {{{y}}_{ij} \\,=\\, 1} \\right)} \\right) \t\\,=\\, {\\upbeta}_{\\mathrm{1}}{{x1}}_{ij} \\,+\\, {\\upbeta}_{\\mathrm{2}}{\\mathrm{Age}}_i \\,+\\, {\\upbeta}_{\\mathrm{3}}{\\mathrm{Sex}}_i \\,+\\, {\\upbeta}_4\\left( {{{x}}1_{ij} \\,\\times\\, {\\mathrm{Sex}}_i} \\right) \\\\ \t\\, \\quad +\\, {\\upbeta}_{\\mathrm{5}}\\left( {{{x}}1_{ij} \\,\\times\\, {\\mathrm{Age}}_i} \\right) \\,+\\, {\\upeta}_i,$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:mi mathvariant=\"normal\">Logit</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi mathvariant=\"normal\">P</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.25em\"/><mml:mn>1</mml:mn></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">1</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>x</mml:mi><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">2</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Age</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">3</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Sex</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>x</mml:mi><mml:msub><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Sex</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mspace width=\"1.0em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">5</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>x</mml:mi><mml:msub><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Age</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">η</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:math><graphic xlink:href=\"41467_2020_17981_Article_Equ3.gif\" position=\"anchor\"/></alternatives></disp-formula><disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathrm{Logit}}\\left( {{\\mathrm{P}}\\left( {{{y}}_{ij} \\,=\\, 1} \\right)} \\right) \t\\,=\\, {\\upbeta}_{\\mathrm{1}}{{x2}}_{ij} \\,+\\, {\\upbeta}_{\\mathrm{2}}{\\mathrm{Age}}_i \\,+\\, {\\upbeta}_{\\mathrm{3}}{\\mathrm{Sex}}_i \\,+\\, {\\upbeta}_4\\left( {{{x}}2_{ij} \\,\\times\\, {\\mathrm{Sex}}_i} \\right) \\\\ \t\\, \\quad +\\, {\\upbeta}_{\\mathrm{5}}\\left( {{{x}}2_{ij} \\,\\times\\, {\\mathrm{Age}}_i} \\right) \\,+\\, {\\upeta}_i.$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mi mathvariant=\"normal\">Logit</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi mathvariant=\"normal\">P</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.25em\"/><mml:mn>1</mml:mn></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">1</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>x</mml:mi><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">2</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Age</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">3</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Sex</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>x</mml:mi><mml:msub><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Sex</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mspace width=\"1.0em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">β</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">5</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>x</mml:mi><mml:msub><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>×</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">Age</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">η</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>.</mml:mo></mml:math><graphic xlink:href=\"41467_2020_17981_Article_Equ4.gif\" position=\"anchor\"/></alternatives></disp-formula>where η<sub><italic>i</italic></sub> ~ N(0, θ<sub>η</sub>) are random effects capturing different mutation propensities among patients, using patient IDs. In these models β<sub><italic>n</italic></sub> measures the effect of the log-PHBR-I, log-PHBR-II, and sex or age. This analysis was repeated for the validation cohort.</p></sec><sec id=\"Sec12\"><title>Mutational signature analysis</title><p id=\"Par28\">Mutational signatures analysis was performed using a previously developed computational framework SigProfiler<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. A detailed description of the workflow of the framework can be found in ref. <sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>, while the code can be downloaded freely from: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.mathworks.com/matlabcentral/fileexchange/38724-sigprofiler\">https://www.mathworks.com/matlabcentral/fileexchange/38724-sigprofiler</ext-link>.</p></sec><sec id=\"Sec13\"><title>Predicting RNA expression from DNA variant allelic fraction</title><p id=\"Par29\">To predict binary RNA expression (≥5 reads at the mutant allele), we used the LogisticRegressionCV function from the Python sklearn v0.20.3 package to train a logistic classifier on the TCGA discovery cohort, using DNA variant allelic fraction (VAF), VAF percentile rank within the patient, and mutated gene as features. We conducted 10-fold cross-validation, achieving a mean 72% area under the receiver operating curve.</p></sec><sec id=\"Sec14\"><title>Statistical analysis</title><p id=\"Par30\">All box plots were evaluated using the default one-tailed Mann–Whitney <italic>U</italic> statistical test, via the scipy.stats Python package. Mutational signature sex-specific distributions were also compared using the one-tailed Mann–Whitney <italic>U</italic> test, and <italic>p</italic> values were adjusted using the Benjamini–Hochberg Procedure. All boxplot figures include the median line, the box denotes the interquartile range (IQR), whiskers denote the rest of the data distribution and outliers are denoted by points determined by ±1.5 × IQR. Effect sizes were calculated using Cliff’s d (Cliff 1993).</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec15\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41467_2020_17981_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41467_2020_17981_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"41467_2020_17981_MOESM3_ESM.pdf\"><caption><p>Description of Additional Supplementary Files</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM4\"><media xlink:href=\"41467_2020_17981_MOESM4_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media></supplementary-material></sec></sec></body><back><fn-group><fn><p><bold>Peer review information</bold>\n<italic>Nature Communications</italic> thanks Joshua Rubin and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Peer review reports are available.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Andrea Castro, Rachel Marty Pyke.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> is available for this paper at 10.1038/s41467-020-17981-0.</p></sec><ack><title>Acknowledgements</title><p>We would like to thank T. Cameron Waller, Tina Wang, and Trey Ideker for scientific discussion. This work was supported by an NIH National Library of Medicine Training Grant T15LM011271 to A.C. an NSF graduate fellowship #2015205295 to R.M.P., NIH grants DP5-OD017937, an Emerging Leader Award from The Mark Foundation for Cancer Research, grant #18-022-ELA and a CIFAR fellowship to H.C. and RO1 CA220009 to M.Z. and H.C., P41-GM103504 for computing resources provided by the National Resource for Network Biology (NRNB). We would like to thank the TCGA research network for providing data used in the analyses, the ICGC database, as well as the following studies used in the validation cohort. <italic>phs001493.v1.p1.c2 and phs001451.v1.p1.c1</italic> We would also like to thank the Blavatnik Family Foundation, grants from the Broad Institute SPARC program, the National Institutes of Health (NCI-5R01CA155010-02, NHLBI-5R01HL103532-03, NCI-SPORE-2P50CA101942-11A1, NCI-R50-RCA211482A), the Francis and Adele Kittredge Family Immuno-Oncology and Melanoma Research Fund, the Faircloth Family Research Fund, and the DFCI Center for Cancer Immunotherapy Research fellowship and Leukemia and Lymphoma Society. <italic>phs001041.v1.p1.c1</italic>. We thank Martin Miller at Memorial Sloan Kettering Cancer Center (MSKCC) for his assistance with the NetMHC server, Agnes Viale and Kety Huberman at the MSKCC Genomics Core, Annamalai Selvakumar and Alice Yeh at the MSKCC HLA typing laboratory for their technical assistance, and John Khoury for assistance in chart review. <italic>phs001425.v1.p1.c1</italic> Christine N. Spencer, Pei-Ling Chen, Michael T. Tetzlaff, Michael A. Davies, Jeffrey E. Gershenwald, Sapna P. Patel, Adi Diab, Isabella C. Glitza, Hussein Tawbi, Alexander J. Lazar, Patrick Hwu, Wen-Jen Hwu, Scott E. Woodman, Rodabe N. Amaria, Victor G. Prieto, and Jennifer A. Wargo enrolled subjects and contributed samples. <italic>phs001493.v1.p1.c1</italic> This study was supported by an AACR KureIt grant. <italic>phs000980.v1.p1.c1</italic>. We thank the members of the Thoracic Oncology Service and the Chan and Wolchok labs at MSKCC for helpful discussions, as well as the Immune Monitoring Core at MSKCC, including L. Caro, R. Ramsawak, and Z. Mu, for exceptional support with processing and banking peripheral blood lymphocytes. We thank P. Worrell and E. Brzostowski for help in identifying tumor specimens for analysis. We thank A. Viale for superb technical assistance. We thank D. Philips, M. van Buuren, and M. Toebes for help performing the combinatorial coding screens. This work was supported by the Geoffrey Beene Cancer Research Center (MDH, NAR, TAC, JDW, AS), the Society for Memorial Sloan Kettering Cancer Center (MDH), Lung Cancer Research Foundation (WL), Frederick Adler Chair Fund (TAC), The One Ball Matt Memorial Golf Tournament (EBG), Queen Wilhelmina Cancer Research Award (TNS), The STARR Foundation (TAC, JDW), the Ludwig Trust (JDW), and a Stand Up To Cancer-Cancer Research Institute Cancer Immunology Translational Cancer Research Grant (JDW, TNS, TAC). Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. <italic>phs001469.v1.p1.c1</italic>. This work was supported by NIH grants R35CA197633, P01CA168585, 5P50CA168536, and GM08042. A comprehensive description of the dataset can be found at PMID:29320474. <italic>phs001519.v1.p1.c1</italic>. We thank the Ben and Catherine Ivy Foundation, the Blavatnik Family Foundation, the Broad Institute SPARC program, and NIH (NCI-1RO1CA155010-02 (to C.J.W.)), NHLBI-5R01HL103532-03 (to C.J.W.), Francis and Adele Kittredge Family Immuno-Oncology and Melanoma Research Fund (to P.A.O.), Faircloth Family Research Fund (to P.A.O.), NIH/NCI R21 CA216772-01A1 (to D.B.K.), NCI-SPORE-2P50CA101942-11A1 (to D.B.K.); NHLBI-T32HL007627 (to J.B.I.); NCI (R50CA211482) (to S.A.S.), Zuckerman STEM Leadership Program (to I.T.); Benoziyo Endowment Fund for the Advancement of Science (to I.T.); P50 CA165962 (SPORE) and P01 CA163205 (to K.L.L.); DFCI Center for Cancer Immunotherapy Research fellowship (to Z.H.); Howard Hughes Medical Institute Medical Research Fellows Program (to A.J.A.); and American Cancer Society PF-17-042-01–LIB (to N.D.M.). C.J.W. is a scholar of the Leukemia and Lymphoma Society. We thank the Center for Neuro-Oncology, J. Russell and Dana-Farber Cancer Institute (DFCI) Center for Immuno-Oncology (CIO) staff; B. Meyers, C. Harvey and S. Bartel (Clinical Pharmacy); M. Severgnini, K. Kleinsteuber, and E. McWilliams, (CIO laboratory); M. Copersino (Regulatory Affairs); T. Bowman (DFHCC Specialized Histopathology Core Laboratory); A. Lako (CIO); M. Seaman and D. H. Barouch (BIDMC); the Broad Institute’s Biological Samples, Genetic Analysis and Genome Sequencing Platforms; J. Petricciani and M. Krane for regulatory advice; B. McDonough (CSBio), I. Javeri and K. Nellaiappan (CuriRx) for peptide development. <italic>phs001565.v1.p1.c1</italic> The research reported in this article was supported by BroadIgnite, BroadNext10, NIH K08CA188615, the Howard Hughes Medical Institute, and Stand Up To Cancer—American Cancer Society Lung Cancer Dream Team Translational Research Grant (grant number: SU2C-AACR-DT17-15). Stand Up To Cancer is a program of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the scientific partner of SU2C.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>Original concept, R.M.P.; project supervision, H.C. and M.Z.; project planning and experimental design, A.C., R.M.P., C.P.D., M.Z., and H.C.; statistical advising, X.Z., W.K.T.; data acquisition, processing, and analysis, A.C. and R.M.P.; mutational signature analysis, L.A.; preparation of paper, A.C., R.M.P., M.Z., and H.C.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>Discovery cohort: data were obtained from publicly available sources including The Cancer Genome Atlas (TCGA) Research Network [<ext-link ext-link-type=\"uri\" xlink:href=\"http://cancergenome.nih.gov/\">http://cancergenome.nih.gov/</ext-link>]. TCGA normal exome sequences and TCGA clinical data were downloaded from the GDC on June 23–26th, 2018 and April 25th, 2017, respectively, using the gdc-client v1.3.0. Furthermore, TCGA somatic mutations were accessed from the NCI Genomic Data Commons [<ext-link ext-link-type=\"uri\" xlink:href=\"https://portal.gdc.cancer.gov/\">https://portal.gdc.cancer.gov/</ext-link>] on May 14th, 2017. Validation cohort: dbGaP studies (accession numbers: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001493.v1.p1\">phs001493.v1.p1.c2</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001041.v1.p1\">phs001041.v1.p1.c1</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001425.v1.p1\">phs001425.v1.p1.c1</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001493.v1.p1\">phs001493.v1.p1.c1</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000980.v1.p1\">phs000980.v1.p1.c1</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001469.v1.p1\">phs001469.v1.p1.c1</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000452.v2.p1\">phs000452.v2.p1.c1</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001451.v1.p1\">phs001451.v1.p1.c1</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001519.v1.p1\">phs001519.v1.p1.c1</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001565.v1.p1\">phs001565.v1.p1.c1</ext-link>) were obtained from the dbGaP database using the ascp tool from AsperaConnect v3.9.5.172984 and WXS/WGS data obtained from the Sequence Read Archive (SRA)<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup> using the SRA toolkit v2.9.2. Somatic mutation files were obtained from the respective papers associated with each study. Additional non-TCGA patients’ WXS/WGS data was obtained from the ICGC using the EGA download client v2.2.2 and icgc-get v0.6.1 and somatic mutation data from the ICGC DCC Data Release [<ext-link ext-link-type=\"uri\" xlink:href=\"https://dcc.icgc.org/\">https://dcc.icgc.org/</ext-link>] on (April 2, 2019 (PCAWG), March 18, 2019 (THCA-SA)) (Supplementary Dataset <xref rid=\"MOESM4\" ref-type=\"media\">1</xref>). The validation cohort’s MHC-I and -II genotypes were typed using HLA-HD<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup> and PHBR scores calculated using the method described in “Presentation score assignment”. All remaining relevant data are available in the article, <xref rid=\"MOESM1\" ref-type=\"media\">Supplementary Information</xref>, or from the corresponding author upon reasonable request.</p></notes><notes notes-type=\"data-availability\"><title>Code availability</title><p>Code to reproduce findings and figures can be freely accessed at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/CarterLab/HLA-immunoediting\">https://github.com/CarterLab/HLA-immunoediting</ext-link>.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par31\">R.M.P. is an employee and holds stock in Personalis. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Nat Commun</journal-id><journal-id journal-id-type=\"iso-abbrev\">Nat Commun</journal-id><journal-title-group><journal-title>Nature Communications</journal-title></journal-title-group><issn pub-type=\"epub\">2041-1723</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807785</article-id><article-id pub-id-type=\"pmc\">PMC7431860</article-id><article-id pub-id-type=\"publisher-id\">17768</article-id><article-id pub-id-type=\"doi\">10.1038/s41467-020-17768-3</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Photothermogenetic inhibition of cancer stemness by near-infrared-light-activatable nanocomplexes</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-7620-6078</contrib-id><name><surname>Yu</surname><given-names>Yue</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Xi</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0001-5132-5196</contrib-id><name><surname>Reghu</surname><given-names>Sheethal</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-0046-3916</contrib-id><name><surname>Kaul</surname><given-names>Sunil C.</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wadhwa</surname><given-names>Renu</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-1157-6174</contrib-id><name><surname>Miyako</surname><given-names>Eijiro</given-names></name><address><email>e-miyako@jaist.ac.jp</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.444515.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1762 2236</institution-id><institution>Graduate School of Advanced Science and Technology, </institution><institution>Japan Advanced Institute of Science and Technology, </institution></institution-wrap>1-1 Asahidai, Nomi, Ishikawa 923-1292 Japan </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.208504.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2230 7538</institution-id><institution>AIST-INDIA DAILAB, DBT-AIST International Center for Translational & Environmental Research (DAICENTER), </institution><institution>Cellular and Molecular Biotechnology Research Institute, AIST, </institution></institution-wrap>Tsukuba, 305-8565 Japan </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.208504.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2230 7538</institution-id><institution>Present Address: Biomedical Research Institute, </institution><institution>National Institute of Advanced Industrial Science & Technology (AIST), </institution></institution-wrap>Ikeda, 563-8577 Japan </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>4117</elocation-id><history><date date-type=\"received\"><day>10</day><month>9</month><year>2019</year></date><date date-type=\"accepted\"><day>17</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Strategies for eradicating cancer stem cells (CSCs) are urgently required because CSCs are resistant to anticancer drugs and cause treatment failure, relapse and metastasis. Here, we show that photoactive functional nanocarbon complexes exhibit unique characteristics, such as homogeneous particle morphology, high water dispersibility, powerful photothermal conversion, rapid photoresponsivity and excellent photothermal stability. In addition, the present biologically permeable second near-infrared (NIR-II) light-induced nanocomplexes photo-thermally trigger calcium influx into target cells overexpressing the transient receptor potential vanilloid family type 2 (TRPV2). This combination of nanomaterial design and genetic engineering effectively eliminates cancer cells and suppresses stemness of cancer cells in vitro and in vivo. Finally, in molecular analyses of mechanisms, we show that inhibition of cancer stemness involves calcium-mediated dysregulation of the Wnt/β-catenin signalling pathway. The present technological concept may lead to innovative therapies to address the global issue of refractory cancers.</p></abstract><abstract id=\"Abs2\" abstract-type=\"web-summary\"><p id=\"Par2\">Cancer stem cells (CSCs) are known to induce chemotherapy resistance, and cause tumour relapse and metastasis. Here, the authors develop photoactive nanocarbon complexes with second near-infrared photothermal ability to target cancer cells overexpressing the receptor TRPV2 and show it to suppress CSCs through dysregulation of the Wnt/β-catenin signalling pathway.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Biomedical materials</kwd><kwd>Nanotechnology in cancer</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/501100001691</institution-id><institution>MEXT | Japan Society for the Promotion of Science (JSPS)</institution></institution-wrap></funding-source><award-id>19H00857</award-id><award-id>16H03834</award-id><award-id>16KK0117</award-id><principal-award-recipient><name><surname>Miyako</surname><given-names>Eijiro</given-names></name></principal-award-recipient></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par3\">Chemotherapy is a principal medical remedy for cancer, but resistance to anticancer drugs is a hallmark of malignant tumours that limits the efficacy of cancer treatments. In addition, with disadvantages of cancer selectivity, toxicity and dosage complications of anticancer drugs, the drug design has become less attractive as a long-term solution for cancer. Most anticancer drugs are also limited to natural passive diffusion in the body and are therefore poorly targeted to their sites of action. Moreover, cancer stem cells (CSCs), also known as tumour-initiating cells, are considered responsible for drug resistance and cancer relapse due in large part to their ability to self-renew and promote metastasis<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. There are almost no effective ways to eliminate CSCs except for molecular inhibitors of cancer stemness<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>–<xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. Yet this still lack enough efficacy, and the design of strategic technologies for inhibiting CSCs remains a major challenge.</p><p id=\"Par4\">Nanomaterials have the potential to control cellular activities with spatial and temporal selectivity through the use of physical treatments, such as magnetic, acoustic and optical excitations<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. However, these approaches have largely been developed independently of the molecular genetics and fail to target the specific cellular activities. Optogenetics employing genetically encoded light-sensitive ion channels or opsins to selectively activate or inhibit neurons could be a possible answer, but its clinical applications are hampered by the limited tissue penetration of visible light<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref>,<xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. Only a few research groups reported strategies using functional nanomaterials for cellular stimulations. Stanley et al. showed that radio wave-induced iron oxide magnetic nanoparticles can regulate transient receptor potential of vanilloid family type 1 (TRPV1) channels, and demonstrated control over insulin activity and cellular glucose levels in mice<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. Gao et al. showed that copper sulfide nanoparticles ameliorate atherosclerosis by photo-thermally modulating TRPV1 signalling activities<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. Cho et al. achieved remote control of apoptosis by manipulating magnetic nanoparticles with a targeting antibody for death receptor 4 in colon cancer cells<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>. To overcome the challenges of clinical application by regulating cellular activities, we developed biopermeable near-infrared (NIR) light-driven exothermic nanomaterials, including carbon nanotubes (CNTs)<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>, carbon nanohorn (CNH)<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>–<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>, liquid metal nanocomplexes<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>, and semiconducting polymer nanoparticles<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>. In these studies, we demonstrated the use of these nanomaterials for photo-thermally controlling heat-sensitive TRPV1 or TRPV2 ion channels on cell membrane to stimulate cells that we call “photothermogenetics”. We believe that this photothermogenetic approach has the potential to impose gain or loss of function of defined cellular processes by rational molecular design and would be an excellent candidate for developing innovative biomedical applications to the regulation of cancer cell stemness.</p><p id=\"Par5\">In the current study, we develop a photothermogenetics approach using NIR light-activatable CNH complexes, in which tissue-penetrating NIR light is locally converted to thermal energy at levels that are sufficient to stimulate TRPV2 overexpressing cancer cells. NIR light-driven CNHs-mediated photothermogenetics disrupt intracellular Ca<sup>2+</sup> homoeostasis, suppress oncogenic Wnt/β-catenin signalling through Ca<sup>2+</sup> dependent degradation of β-catenin, and effectively eliminate cancer cells and inhibit cancer stemness in vitro and in vivo. Our findings contribute to the design of next-generation cancer remedies with various biomedical applications.</p></sec><sec id=\"Sec2\" sec-type=\"results\"><title>Results</title><sec id=\"Sec3\"><title>Preparation and characterisation of CNH complexes</title><p id=\"Par6\">We selected CNHs as model photo-exothermic nanomaterials due to their strong photothermal conversion effect<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>–<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup> and high in vitro and in vivo biocompatibility<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref>–<xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. To develop CNHs as photothermogenetic tools for use under physiological conditions, we improved water dispersibility using a PEGylation technique. Because CNHs are hydrophobic, the hydrocarbon chains of phospholipid–PEG (DSPE–PEG) were adsorbed onto the surfaces of CNH agglomerations via van der Waals and hydrophobic interactions, and the hydrophilic PEG chains extend into the aqueous phase to render water solubility. N-Hydroxysuccinimide (NHS) moieties of PEG chain ends were then conjugated with TRPV2 antibody via carbodiimide condensation reactions, therefore enabling the PEGylated CNH (PCNH) to target TRPV2 ion channels selectively (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>). TRPV2<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup> was chosen as a model target because it can be stimulated at temperatures over 52 °C, at which cancer cell growth is abrogated. TRPV2 receptors also have sufficient heat sensitivity threshold for future clinical applications, because their activation temperature is not reached under conditions of fever or intense physical exercise. Thus, TRPV2-triggered cell activation is controllable by photo-exothermic applications without misregulation. The success of antibody immobilisation was examined using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS–PAGE). After electrophoresis, immunoglobulin (IgG) antibody (isotype control of the TRPV2 antibody)-modified PCNH (IgG–PCNH) clearly showed bands of heavy and light IgG chains (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>). Transmission electron microscope (TEM) observations and dynamic light scattering (DLS) analyses also revealed that TRPV2 antibody-modified PCNH (TRPV2–PCNH) contained particles of homogeneous spherical morphology and size, ranging from 80 to 150 nm (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c, d</xref>). The diameter of TRPV2–PCNH was slightly greater than that of PCNH, further confirming successful conjugation. In addition, the nanoparticles were well dispersed in phosphate-buffered saline (PBS) and remained stable for at least 1 week (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1a</xref>).<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Syntheses and characterisation TRPV2-PCNH.</title><p><bold>a</bold> Carbon nanohorn (CNH) agglomerations were self-coated with amphiphilic block phospholipid–PEG copolymers (DSPE–PEG) and TRPV2 antibody was then conjugated to the surface. <bold>b</bold> Sodium dodecyl sulfate-polyacrylamide gele electrophoresis (SDS–PAGE) analyses show successful immobilisation of IgG antibody on PCNH. <bold>c</bold> Representative negative staining transmission electron microscope (TEM) image (a magnified image is shown in the top left corner) and <bold>d</bold> dynamic light scattering (DLS) analyses of TRPV2–PCNH show homogeneous shapes and sizes ranging from 80 to 150 nm. <bold>e</bold> Ultraviolet–visible–near-infrared (UV–Vis–NIR) absorption spectra of TRPV2–PCNH shows a wide absorption range in the NIR region. <bold>f</bold> Thermographic images of phosphate-buffered saline (PBS; 2 ml) and TRPV2–PCNH aqueous dispersions (2 ml, 100 µg ml<sup>−1</sup>) after irradiation with a 1064-nm NIR laser at 1 W (~50 mW mm<sup>−2</sup>). <bold>g</bold> Time-dependent temperature elevation profiles of NIR laser-induced TRPV2–PCNH aqueous dispersions (200 µl) of varying concentrations. Identical volumes of PBS were used as a negative control. Laser power was applied at 1 W (~50 mW mm<sup>−2</sup>). Data are represented as means ± standard deviation (s.d.); <italic>n</italic> = 3 independent experiments.</p></caption><graphic xlink:href=\"41467_2020_17768_Fig1_HTML\" id=\"d30e457\"/></fig></p><p id=\"Par7\">NIR light has been extensively applied in various sensing, imaging, biological diagnosis, and therapeutic applications, reflecting advantages of remote manipulation, minimal invasion, and high transparency in the range of optical wavelengths for biological tissues<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>. Among NIR regions, the second NIR optical window (NIR-II, 1000–1700 nm) has advantages of deeper tissue penetration and higher maximum permissible light exposure<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. CNHs have been shown to be capable of absorbing light in NIR-II region<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>–<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. We anticipated that the utilisation of NIR-II light-induced CNHs will lead to promising applications in treating deep-tissue-cancer. Indeed, TRPV2–PCNH has high optical absorbance over a wide range of wavelengths, including the NIR-II window (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e</xref>). Thus, its photothermal properties were studied using a 1064 nm laser. Following irradiation in the NIR range, we envisioned that since nanoparticles transduce electromagnetic radiation into heat, they may therefore be used as in vivo photothermal probes. Thermal images showed that laser irradiation of PCNH dispersions led to remarkable increases in temperature, compared with those with the PBS vehicle control (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f</xref>). Temperature changes (∆T) of 1-mg ml<sup>−1</sup> PCNH induced by irradiation at 1 W (~50 mW mm<sup>−2</sup>) were evaluated; these showed increase by ca. 50 °C after 5 min of irradiation (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g</xref>). Of note, these temperature changes were irradiation time- and PCNH concentration-dependent, offering ease of temperature control. Moreover, photothermal stability was studied by heating and cooling PCNH nanoparticle solutions. The maximum temperatures of PCNH were stable for at least five cycles (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1b</xref>). The photothermal conversion efficiency of PCNH at 1064 nm was 59.4%; better than that shown for NIR-II activated competitive gold and polymer nanoparticles<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref>,<xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. These results indicated excellent photothermal properties and suggest potential of our nanocomplexes as activators of thermosensitive ion channels such as TRPV2.</p></sec><sec id=\"Sec4\"><title>Targeted photoactivation of TRPV2 by CNHs</title><p id=\"Par8\">To determine whether TRPV2–PCNH nanoparticles can target cellular TRPV2, we generated mCherry-tagged TRPV2 plasmids and established stably transfected cell lines that overexpress TPRV2 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2a,b</xref>). Since the endogenous TRPV2 expression was nearly absent before transfection (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2b</xref>), non-transfected cells were used as negative control for TRPV2 overexpression. Transgenic cells were then incubated with fluorescein isothiocyanate (FITC)-labelled TRPV2–PCNH (TRPV2<sup>FITC</sup>–PCNH). Subsequent fluorescence microscopy analyses showed that U2OS cells overexpressing TRPV2 (U2OS–TRPV2) more efficiently accumulated TRPV2<sup>FITC</sup>–PCNH than non-transfected cells. Green fluorescence of TRPV2<sup>FITC</sup>–PCNH was detected all along the cell membranes and was localised to TRPV2, indicating good surface labelling and targeting (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>).<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Targeted activation of TRPV2 ion channel by laser-induced TRPV2–PCNH.</title><p><bold>a</bold> Fluorescence microscopy imaging of U2OS control and TRPV2-transfected cells incubated with FITC–TRPV2–PCNH (100 µg ml<sup>−1</sup>) for 24 h; TRPV2-PCNH accumulated more efficiently on transfected cells. Nuclei were stained with Hoechst. <bold>b</bold> The schematic illustrates photothermal activation of TRPV2-mediated calcium influx in cancer cells. Internalised Ca<sup>2+</sup> was monitored in real-time using a Ca<sup>2+</sup> binding fluorescence indicator. <bold>c</bold> Live cell imaging of U2OS control and nanocomplexes-treated cells shows that TRPV2–PCNH selectively activates Ca<sup>2+</sup> influx in laser-irradiated TRPV2-transfected cells. Cells were incubated with TRPV2–PCNH (100 µg ml<sup>−1</sup>) for 24 h and were then loaded with the Ca<sup>2+</sup> indicator (Green) for 30 min before laser stimulation at 0.7 W and ~97 mW mm<sup>−2</sup> for 1 s. Red crosses indicate irradiated spots. <bold>d</bold> Real-time fluorescence intensities of cells treated with or without TRPV2–PCNH and laser irradiation.</p></caption><graphic xlink:href=\"41467_2020_17768_Fig2_HTML\" id=\"d30e568\"/></fig></p><p id=\"Par9\">We next attempted to activate TRPV2 ion channels using laser-stimulated TRPV2–PCNH. We expected that the heat from membrane-bound particles would open TRPV2 channels to allow Ca<sup>2+</sup> influx into the cytosol, and hence monitored the fluorescence of a Ca<sup>2+</sup> indicator in real-time (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). U2OS cells and their TRPV2 overexpressing counterparts were treated with TRPV2–PCNH nanoparticles for 24 h and were then exposed to 1064-nm laser irradiation for 1 s in fresh medium containing the Ca<sup>2+</sup> indicator. As shown in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c</xref> and Supplementary Movies <xref rid=\"MOESM3\" ref-type=\"media\">1</xref> and <xref rid=\"MOESM4\" ref-type=\"media\">2</xref>, laser irradiation did not alter the fluorescence intensity of non-transfected U2OS cells, regardless of nanoparticle treatments. In contrast, nanoparticle-treated U2OS–TRPV2 cells showed significant increase in fluorescence intensity upon stimulation (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c</xref> and Supplementary Movie <xref rid=\"MOESM6\" ref-type=\"media\">4</xref>), indicating induction of Ca<sup>2+</sup> influx. TPRV2-transfected cells without nanoparticle treatments did not show the similar response (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c</xref> and Supplementary Movie <xref rid=\"MOESM5\" ref-type=\"media\">3</xref>), confirming that the observed fluorescence is attributable to photothermal activation of TRPV2. The fact that Ca<sup>2+</sup> influx occurred within 2 s of laser exposure reflected the favourable photothermal sensitivity of TPRV2–PCNH (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2d</xref>). In further experiments, TRPV2–PCNH efficiently accumulated on TRPV2-transfected C6 glioma cells (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3a</xref>) and led to Ca<sup>2+</sup> influx following NIR laser irradiation. Interestingly, increase in intracellular Ca<sup>2+</sup> was also propagated from directly irradiated cells to adjacent cells over time, even after switching off the laser (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3b</xref> and Supplementary Movie <xref rid=\"MOESM7\" ref-type=\"media\">5</xref>). This observation likely reflects cell-to-cell transmission of Ca<sup>2+</sup> via gap junctions. Induction of Ca<sup>2+</sup> was also repeatable with successive laser exposures and was reproduced in at least four different views (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4a,b</xref> and Supplementary Movie <xref rid=\"MOESM8\" ref-type=\"media\">6</xref>). Taken together, these findings clearly demonstrate that laser-induced TRPV2–PCNH remotely controls Ca<sup>2+</sup> influx into irradiated cells and their neighbours by selectively activating TRPV2 channels.</p></sec><sec id=\"Sec5\"><title>Photoinduced apoptosis by CNHs in TRPV2-enriched cells</title><p id=\"Par10\">Calcium ions are involved in nearly every aspect of cellular processes. To investigate the wider effects of our technology on cell fate determination, we assessed cell viability at 0 and 48 h after laser stimulation of TRPV2–PCNH nanoparticles. As shown in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a, b</xref>, irradiation time-dependent cytotoxicity was observed. At 0 h post irradiation, U2OS control and TRPV2-transfected cells responded to the treatment in a similar manner (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a</xref>). However, with additional 48 h incubation, 90-s irradiation pulses caused marked reductions in viability in TRPV2 overexpressing cells, whereas U2OS control cells remained unchanged (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>). This delayed response is a common consequence of cell regulatory mechanisms requiring time to determine the cell destinies in the presence of cytotoxic stimuli. Moreover, the higher sensitivity of U2OS–TRPV2 cells to 90-s pulses of irradiation reflects selectivity of TRPV2–PCNH nanoparticles for cells that express TRPV2 (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>). To confirm selectivity of TRPV2–PCNH nanoparticles, we performed further experiments using normal TIG3 fibroblast cells. NIR irradiation for 90 s following uptake of 50 µg ml<sup>−1</sup> of nanoparticles was significantly more cytotoxic to TRPV2 overexpressing cancer cells than normal cells after 48 h (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3c</xref>). Experiments also indicated that TRPV2–PCNH nanoparticles have no intrinsic toxicity (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5</xref>). Therefore, we investigated the molecular mechanisms behind these differential effects in TRPV2 control and transfected cells.<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Laser-induced TRPV2–PCNH triggers apoptosis in cancer cells overexpressing TRPV2.</title><p><bold>a</bold> Cell viability assays of control and TRPV2-transfected cells were performed immediately after irradiation and show similar responses to increasing irradiation times. Data are represented as means ± standard errors of the mean (s.e.m.); <italic>n</italic> = 3 biologically independent tests. <bold>b</bold> Tests performed at 48 h post irradiation, however, show that U2OS–TRPV2 cells are more sensitive to 90-s pulses of NIR irradiation than control cells. Data are represented as means ± standard errors of the mean (s.e.m.); <italic>n</italic> = 3 biologically independent tests; <italic>P</italic> values were calculated by Student’s two-sided <italic>t</italic> test (comparisons with the 90 s time point in U2OS). <bold>c</bold> Viability of 90-s pulses of laser, TRPV2–PCNH and cotreated cells at 48 h post irradiation; stronger decreases were observed in TRPV2-transfected cells receiving the combination treatment. Data are presented as means ± s.e.m.; <italic>n</italic> = 5 biologically independent tests; <italic>P</italic> values were calculated by Student’s two-sided <italic>t</italic> test. <bold>d</bold> Flow cytometry analysis showing increased apoptotic cell populations in TRPV2-transfected cells after stimulation with TRPV2–PCNH and laser irradiation for 90 s; measurements were performed at 24 h post irradiation. <bold>e</bold> Quantitation from three independent experiments is shown below. Data are expressed as means ± s.e.m. Significant differences in total apoptotic cell numbers were identified using Student’s <italic>t</italic> two-sided test comparisons with control cells; <italic>n</italic> = 3 independent experiments. <bold>f</bold> Fluorescence imaging of control and TRPV2-transfected cells showing increased expression of cytochrome c and caspase-3 in the latter in the following treatments with TRPV2–PCNH and laser irradiation for 90 s; Blue, Hoechest indicates nuclei; Red, mCherry indicates TRPV2; Green, Alexa488 indicates cytochrome c and NucView488 indicates caspase-3. Cells used for all the experiments were treated with TRPV2–PCNH (50 µg ml<sup>−1</sup>) for 24 h before exposure to 1064-nm laser irradiation at 1 W (~50 mW mm<sup>−2</sup>).</p></caption><graphic xlink:href=\"41467_2020_17768_Fig3_HTML\" id=\"d30e736\"/></fig></p><p id=\"Par11\">Ca<sup>2+</sup> overload has been shown to instigate apoptosis. Thus, we performed Annexin-V/7-AAD double staining to examine apoptosis in control and nanoparticle-treated cells after laser exposure. As shown in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d</xref> and Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">6a</xref>, photothermal treatments led to stronger extent of apoptosis in TRPV2-tranfected, as compared to non-transfected, cancer cells. Quantitative data revealed 80% and 60% of apoptotic cells after treatment in U2OS–TRPV2 and C6–TRPV2 cells, respectively (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3e</xref> and Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">6b</xref>). Laser-induced apoptosis in these cells was also confirmed by up-regulation of cytochrome c and caspase 3 (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3f</xref> and Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">6c</xref>). Nanoparticle treatments without laser irradiation again had no effects in these experiments. These results suggest that laser-irradiated TRPV2–PCNH nanoparticles selectively induce Ca<sup>2+</sup>-dependent apoptosis in cells overexpressing TRPV2.</p></sec><sec id=\"Sec6\"><title>In vitro attenuation of cancer stemness</title><p id=\"Par12\">Given that calcium signalling is implicated in cancer cell stemness, which is central to the initiation and progression of malignancies<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>, we determined whether laser-induced TRPV2–PCNH nanoparticles inhibit stem cell properties using colony-forming assays in U2OS and MCF7 cells with or without TRPV2 overexpression. Compared with their untransfected counterparts, TRPV2-overexpressing cells showed greater reductions in clonogenicity after laser irradiation (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref>), with decreases of 18.7% and 38.5%, respectively (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4b, c</xref>). To further demonstrate in vitro tumorigenic capacity, we performed mammosphere formation assays in stemness-high MCF7 and TRPV2 overexpressing cells. Representative micrographs of mammospheres that were formed after laser treatments show that TRPV2–PCNH-mediated phototherapy potently diminishes numbers and sizes of mammospheres derived from cells overexpressing TRPV2 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4d</xref>). Quantitative analyses also showed that nanoparticle and laser stimulation strongly prevented mammosphere formation (3.72% to 0.7%) in TRPV2-transfected cells, but only slightly decreased (3.8% to 3.2%) this process in parent MCF7 cells (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4e</xref>). Moreover, being different from MCF7 cells showing comparable sphere-forming frequency before and after treatments (1/22 and 1/30) (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7a</xref>), nanoparticle/laser-treated MCF7-TRPV2 cells showed a clear reduction in frequency of cells possessing self-renewal ability (1/29 to 1/105) (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7b</xref>). These data would suggest laser-driven TRPV2-PCNH could inhibit tumour regenerative potential and the self-renewal capacity.<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Laser-induced TRPV2–PCNH attenuates stemness in cancer cells overexpressing TRPV2.</title><p><bold>a</bold> Colony-forming assays of control and TRPV2-transfected cells show stronger responses of the latter to TRPV2-CNH treatments after laser irradiation. Quantitative clonogenicity assays are shown in <bold>b</bold>, <bold>c</bold>. Data are expressed as means ± s.e.m.; <italic>n</italic> = 3 independent experiments; and differences were identified using Student’s two-sided <italic>t</italic> test comparisons with control cells. <bold>d</bold> Representative mammosphere formation assays of TRPV2-CNH-treated control and TRPV2 overexpressing cells after laser stimulation. <bold>e</bold> Numbers of mammospheres of 50–100 µm and over 100 µm show stronger reductions in mammosphere forming efficiency in MCF7 transfected cells than in parental cells. Data are presented as means ± s.e.m. (<italic>n</italic> = 3 independent experiments). Comparisons with controls were made using Student’s two-sided <italic>t</italic> test; *P: mammosphere 50–100-µm; #P: mammosphere ≥100-µm. <bold>f</bold> Flow cytometry analysis show that laser irradiation decreases ALDH activities and <bold>g</bold> CD44+/CD24− subpopulations in TPRV2-PCNH treated cells overexpressing TRPV2. Cells were treated with diethylaminobenzaldehyde (DEAB) or were stained with isotype antibodies for use as negative controls. Data in <bold>h</bold>, <bold>i</bold> are presented as means ± s.e.m. (<italic>n</italic> = 3 independent experiments). <italic>P</italic> values were determined using Student’s two-sided <italic>t</italic> test. <bold>j</bold> RT-qPCR analyses are represented by a Log2-fold change heatmap. TRPV2-overexpressing cells treated with TRPV2–PCNH and laser irradiation showed stronger declines in the expression of stemness-related markers genes. All cells were treated with TRPV2–PCNH (50 µg ml<sup>−1</sup>) for 24 h, followed by 90 s of laser irradiation at 1 W (~50 mW mm<sup>−2</sup>) and measurements were performed at 24 h post irradiation.</p></caption><graphic xlink:href=\"41467_2020_17768_Fig4_HTML\" id=\"d30e861\"/></fig></p><p id=\"Par13\">In order to investigate whether this technique can enhance chemosensitivity to anticancer drugs, A549 control and TRPV2-transfected cells were pretreated with nanocomplexes combining laser irradiation, followed by 48 h exposure to paclitaxel (PTX). Validation with a range of tested doses to generate dose response curves showed that nanoparticle-mediated photo-stimulation sensitized A549-TRPV2 cells to PTX, of which IC50 value decreased to 2.23 nM (from 9.11 nM) after treatment (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8</xref>). In contrast, difference in A549 control cells was modest (5.03 nM to 4.45 nM) (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8</xref>). In further analyses of cancer cell stemness, we examined aldehyde dehydrogenase (ALDH) activity using flow cytometry at 24 h post NIR irradiation. Significant reductions in proportions of ALDH-positive cells were observed among MCF7–TRPV2 cells, but not among MCF7 cells (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4f, h</xref>). Accordingly, we observed decreased numbers of CD44<sup>high</sup>/CD24<sup>low</sup> cells among laser-irradiated MCF7–TRPV2 cells, although TRPV2 transfection somehow reduced CD44<sup>high</sup> /CD24<sup>low</sup> subpopulations irrespective of irradiation (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4g, i</xref>). Mean fluorescence intensity of CD44 in MCF7-TRPV2 cells was also decreased after laser stimulation (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">9</xref>). These data were validated by real-time-quantitative polymerase chain reaction (RT-qPCR) analyses, which confirmed stronger declines in transcriptional expression levels of stemness-related markers in TRPV2 overexpressing MCF7 cells (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4j</xref>). These results collectively demonstrated that TRPV2–PCNH mediated photoactivation of Ca<sup>2+</sup> influx represses CSC characteristics in TRPV2 overexpressing cells.</p></sec><sec id=\"Sec7\"><title>In vivo inhibition of tumour growth by laser-driven CNHs</title><p id=\"Par14\">The data presented above warranted in vivo assessment of the therapeutic efficacy of TRPV2–PCNH nanoparticles using a subcutaneous xenograft nude mouse model. To investigate biological distributions of nanoparticles and quantify TRPV2 targeting effects in vivo, indocyanine green (ICG)-encapsulated PCHN or TRPV2–PCNH nanocomplexes were intravenously injected into mice bearing two tumours on opposite flanks, which were derived from HT-29 cells and TRPV2 derivative cells, respectively. Whole-body fluorescence live imaging showed that ICG-labelled TRPV2–PCNH nanocomplexes accumulated at tumour xenografts, with peak accumulations at 24 h after injection (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">10a</xref>). Fluorescent signals were stronger in HT-29–TRPV2 tumours than in HT-29 tumours throughout 48-h observations, suggesting sustained targeting of TRPV2–PCNH nanoparticles to TRPV2 overexpressing tumours (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">10b</xref>). Furthermore, after excising tumours and other major organs at 24 h post injection (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">10c</xref>), TRPV2 antibody-functionalised nanoparticles (TRPV2–PCNH) were mainly accumulated in tumours overexpressing TRPV2 (HT-29–TRPV2), with more than two-fold higher radiant efficiency than in HT-29 control tumours and vital organs such as liver, lung, spleen, heart and kidney (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">10d</xref>). In contrast, mice injected with PCNH showed comparable accumulation in the two tumours as in other organs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">10d</xref>). These observations further confirm tumour-selective accumulation of TRPV2-targeting nanoparticles.</p><p id=\"Par15\">The pharmacokinetics profile of the Cy5-labelled CNHs was also assessed by fluorometry to determine the concentrations in blood and organs at different time intervals post-injection. Higher uptake was observed in the liver and kidney while no significant uptake was detected in other organs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">11</xref>). This is consistent with above fluorescence imaging results. With dominate accumulation in the liver at 1 h post-injection, the nanocomplex concentrations gradually decreased at later time points (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">11</xref>), suggesting the body clearance. After 14 days, the residual CNHs were decreased to 8.5 % ID g<sup>−1</sup> and 2.9 % ID g<sup>−1</sup> in the liver and kidney, respectively (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">11</xref>), indicating that vast majority was cleared out from the body. In fact, it is well known that CNH has low toxicity and fully biologically degradable, as demonstrated by various systemic biocompatibility analyses<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref>–<xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. From these results, we anticipate that CNHs would be gradually eliminated from biological body over time without any severe side effect.</p><p id=\"Par16\">To evaluate the effects of anticancer phototherapy, we established tumour xenograft mouse models using subcutaneous injections with either HT-29 cells or their TRPV2-overexpressing derivatives into the flanks of mice, as illustrated in Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>. Mice bearing separate tumours from both cell lines were randomly assigned to (1) PBS (blank control); (2) PBS + laser (laser control); (3) PCNH (non-targeted nanoparticle control); (4) PCNH + laser (non-targeted phototherapy control); (5) TRPV2–PCNH (targeted nanoparticle control) and (6) TRPV2–PCNH + laser (targeted phototherapy) groups. Mice were then intraperitoneally administered with nanoparticles at equivalent doses (5 mg kg<sup>−1</sup>) every other day. In NIR irradiation groups, tumours on the right sides were exposed to NIR light (1 W, ~50 mW mm<sup>−2</sup>) for 5 min at fixed time points (2, 6, 9, 13 and 16 days) from 24 h after injections (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>). Body surface temperatures were monitored during laser irradiation using a thermographic infrared camera (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5b</xref>). As shown in Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5c</xref>, temperatures above 52 °C (TRPV2 activation threshold) occurred only in HT-29–TRPV2 tumours of the TRPV2–PCNH + laser group.<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Laser-induced TRPV2-PCNH inhibits tumour progression in in vivo xenograft models with TRPV2 overexpression.</title><p><bold>a</bold> In vivo experimental scheme: on day -8, HT-29 control and stably transfected TRPV2 cells were injected into mice to establish tumour xenograft models. On day 0, PBS, PCHN, or TRPV2–PCNH were administered (5 mg kg<sup>−1</sup>) intraperitoneally (i.p.) every other day until day 16. NIR treatments were applied to tumours on the right sides at days 2, 6, 9, 13 and 16 using a 1064-nm laser at 1 W (~50 mW mm<sup>−2</sup>) for 5 min. <bold>b</bold> Thermographic images and <bold>c</bold> laser-induced temperature increases in mice bearing HT-29 or HT-29–TRPV2 tumours. Measurements were performed at 24 h after injection of nanocomplexes. Data are represented as means ± s.d.; <italic>n</italic> = 6 biologically independent mice. <bold>d</bold> HT-29 and <bold>e</bold> HT-29–TRPV2 tumour volumes in different groups of mice during the course of treatment (mean ± s.e.m., <italic>n</italic> = 6 biologically independent mice, two-way ANOVA test). Laser-irradiated subcutaneous xenografts of HT-29–TRPV2 cells in nude mice treated with TRPV2–PCNH showed the greatest reductions in tumour volumes over time. <bold>f</bold> Photographs of HT-29 and HT-29–TRPV2 tumour-bearing mice on day 16; black arrows indicate tumours that were irradiated by laser.</p></caption><graphic xlink:href=\"41467_2020_17768_Fig5_HTML\" id=\"d30e1004\"/></fig></p><p id=\"Par17\">To demonstrate anti-tumour efficacy, we recorded tumour volumes for about 21 days after initiating the therapeutic regimen. In both xenograft tumours, NIR irradiation and nanoparticle treatments did not affect tumour growth when administered alone. Yet in combination, PCNH or TRRV2–PCNH nanoparticle treatments with laser irradiation delayed both HT-29 and HT-29-TRPV2 tumour growth compared with that in mice treated with PBS (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5d, e</xref>). HT-29–TRPV2 tumours were more strongly suppressed under these conditions in the TRRV2–PCNH + Laser group than in the other treatment groups (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5e</xref>), perhaps indicating selective targeting of TRPV2-overexpressing cells by TRRV2–PCNH. HT-29 tumour growth was also suppressed in the TRRV2–PCNH + Laser group compared with the PBS control group, but the effect was comparable to that in the PCNH + Laser group (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5d</xref>). Tumour inhibition by laser-induced TRRV2–PCNH was clearly observed in HT-29–TRPV2 xenografts at day 16 after nanoparticle injections. Moreover, laser-irradiated HT-29–TRPV2 tumours were much smaller than non-irradiated tumours on the opposite flanks of the same mice without irradiation, whereas no irradiation-related differences were observed between HT-29 xenografts (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5f</xref>). Hence, we conclude that TRRV2–PCNH selectively impedes TRPV2 positive tumour progression following NIR laser irradiation in vivo. No significant body weight losses were identified in the mice of any treatment groups, demonstrating that TRRV2–PCNH has low systemic toxicity (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">12</xref>).</p><p id=\"Par18\">In order to further demonstrate the practicability of the TRPV2-PCNH mediated phototherapy towards future medical use, the anti-tumour efficacy was next evaluated using immunocompetent mice. TRPV2 stable transfected Colon-26 cells was established to develop tumour syngeneic models (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">13a</xref>). As expected, mice treated with nanocomplexes and laser irradiation showed best performance on tumour growth inhibition (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">13b,c</xref>). Compared with Colon-26 derived tumours, the size of TRPV2-overexpressing tumours (Colon-26-TRPV2) was reduced in a greater extent (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">13b–d</xref>). Gradually increased mice body weight in all groups over treatment period confirmed biosafety of this therapy (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">13e</xref>). Importantly, such cancer therapeutic efficacy was also observed in A549 xenografts with in situ transfection of TRPV2 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">14</xref>), indicating this technique is able to work across multiple tumour types without the limitation of endogenous TRPV2 expression.</p></sec><sec id=\"Sec8\"><title>In vivo regulation of cancer stemness by laser-driven CNHs</title><p id=\"Par19\">Immunohistochemistry staining of Ki-67 (proliferation marker) and CD133 (stem cell marker) was performed to obtain molecular evidences for the regulation of cancer stemness by laser-driven TRPV2-PCNH nanoparticles. HT-29-TRPV2 tumour tissues exhibited obviously lower expression of both after laser irradiation (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6a, b</xref>). Western blotting revealed downregulation of CD133 in nanocomplexes and laser-treated A549 tumours that were transfected with TRPV2 plasmid in situ (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">15</xref>). RT-qPCR analyses of tumour tissues from mice treated with PCNH or TRPV2–PCNH nanoparticles revealed decreases in expression levels of stemness-associated markers in the latter following laser irradiation (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6c</xref>). Considering the in vitro inhibitory effects of laser-induced TRRV2–PCNH on cancer cell stemness (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>), we suggest that TRPV2-targeted phototherapy inhibits re-initiating activities of tumours. To test this hypothesis, we resected HT-29–TRPV2 tumours after treatments, digested them into single-cell cultures and modelled early tumorigenesis by transplanting titrated number of cells into nude mice (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6d</xref>). As shown in Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6e</xref>, inoculation of 500 cells from tumours without irradiation (PBS and TRPV2–PCNH groups) resulted in aggressive tumour development with a tumour formation rate of 100%. But primary tumour cells from photoirradiated xenografts were barely transformed (20% tumour formation rate) over 50 days. Comparing to untreated counterpart (PBS group), nanoparticles/laser treatment led to a substantial decrease in fraction of cells with tumour-initiating features (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">16</xref>). This significant suppression of tumorigenicity of tumour cells could offer a strategy for clinical resolution of cancer metastasis and recurrence. In vivo experiments with A549-TRPV2 xenografts further revealed showed that mice received combination treatment (TRPV2-PCNH mediated laser irradiation followed by PTX drug administration) exhibited strongest suppression in tumour growth as compared to single-approach treatments (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">17</xref>). Such synergistic effect indicates laser-induced TRPV2-PCNH may improve cancer drug resistance by inhibiting cancer stemness. These observations of phototherapeutic efficacy of TRRV2–PCNH are highly consistent with our in vitro observations.<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Laser-induced TRPV2-PCNH inhibits tumour re-initiation in in vivo xenograft models with TRPV2 overexpression.</title><p><bold>a</bold>, <bold>b</bold> Immunohistochemical stain (IHC) analysis of primary tumour sections. <bold>a</bold> Ki-67 and <bold>b</bold> CD133 expressions were down-regulated by TRPV2-PCNH-mediated NIR treatment in tumour xenografts overexpressing TRPV2. <bold>c</bold> RT-qPCR analysis showing stronger decreases in mRNA levels of stemness-associated markers in TRPV2-overexpressing tumours from the mice treated with TRPV2–PCNH and laser irradiation. <bold>d</bold> De novo tumorigenesis experiment flow (detailed in Methods). <bold>e</bold> Observations of tumour initiation and growth revealed decreases in tumorigenicity of cells isolated from tumours in mice treated with TRPV2–PCNH and laser irradiation (mean ± s.e.m., <italic>n</italic> = 5 biologically independent mice, two-way ANOVA test). 500 cells were injected for each tumour. Tumorigenesis rates are shown in brackets following the legends for each group.</p></caption><graphic xlink:href=\"41467_2020_17768_Fig6_HTML\" id=\"d30e1103\"/></fig></p></sec><sec id=\"Sec9\"><title>Photothermogenetic suppression of Wnt/β-catenin signalling</title><p id=\"Par20\">Wnt/β-catenin signalling has been widely associated with Ca<sup>2+</sup> mediated cancer cell survival and stemness regulation<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. Herein, we present immunofluorescence experiments showing changes in β-catenin expression following phototherapy. As shown in Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7a</xref>, laser irradiation of TRPV2-overexpressing cells treated with nanoparticles resulted in decreased β-catenin expression. Stabilisation of β-catenin is critical for controlling tumorigenesis, and associations of aberrant activated Wnt/β-catenin signalling with carcinogenesis are abundantly documented<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>. Therefore, blockade of β-catenin by TRPV2–PCNH mediated phototherapy offers an attractive mechanism-based therapeutic strategy for cancer treatment.<fig id=\"Fig7\"><label>Fig. 7</label><caption><title>Laser-induced TRPV2–PCNH exerts therapeutic effects by suppressing Wnt/β-catenin signalling.</title><p><bold>a</bold> Immunostaining of β-catenin showing decreased expression levels in TRPV2-enriched cells after treatment with nanocomplexes and laser irradiation; blue, Hoechst indicates nuclei; red, mCherry indicates TRPV2; green, Alexa488 indicates β-catenin; <bold>b</bold> Western blotting analyses showed increase in PKCα expression and decreased expression of non-phosphorylated and total β-catenin in nanocomplexes-treated U2OS–TRPV2 cells after irradiation. β-actin was used as an internal control. <bold>c</bold> Luciferase reporter assay driven by β-catenin consensus binding sites (TCF/LEF) showed a decrease in TRPV2-overexpressing U2OS cells after laser-induced TRPV2-PCNH treatment (mean ± s.e.m., <italic>n</italic> = 3 independent experiments, Student’s <italic>t</italic> two-sided test). <bold>d</bold> RT-qPCR analysis of cells overexpressing TRPV2 show that transcript levels of β-catenin target genes were down-regulated after treatments with TRPV2–PCNH nanocomplexes and laser irradiation. Data are represented as means ± standard errors of the mean (s.e.m.); <italic>n</italic> = 3 independent experiments. <bold>e</bold> Transcriptional inhibitions of β-catenin target genes were also observed in tumour tissues after the same treatment. <bold>f</bold> Schematic of the proposed mechanism; in the absence of stimuli (left part), TRPV2 channels are maintained in the off-state to maintain intracellular Ca<sup>2+</sup> homoeostasis. PKCα is inactivated due to low concentrations of cytosolic Ca<sup>2+</sup>. β-catenin that is stabilised and accumulated in the cytosol translocates into the nucleus and activates its target genes, leading to cancer proliferation. In the presence of stimuli (right part), antibody-guided CNH targets TRPV2 receptors and activates TPRV2 channels through the heat generated from laser radiation. Ca<sup>2+</sup> influx via TRPV2 channels increases PKCα activity, leading to β-catenin phosphorylation. This phosphorylation promotes rapid degradation of cytosolic β-catenin by the proteasome. Thus, the expression levels of genes that are involved in cell survival and stemness are repressed, resulting in apoptosis and inhibition of cancer stemness.</p></caption><graphic xlink:href=\"41467_2020_17768_Fig7_HTML\" id=\"d30e1172\"/></fig></p><p id=\"Par21\">To better characterise the mechanisms by which TRPV2-guided photothermogentic treatment inhibits β-catenin, we examined photothermal effects on PKCα, which is a protein kinase that directly phosphorylates β-catenin in the presence of Ca<sup>2+</sup> and promotes its degradation<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. Western blotting analyses showed increased PKCα expression levels in TRPV2-transfected cells after phototherapy (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7b</xref>). Simultaneously, expression levels of non-phosphorylated (stabilised form) and total β-catenin in these cells were decreased. In contrast, these protein levels were unaffected in cells without TRPV2 transfection after same treatment (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7b</xref>). Moreover, in western blotting and IHC analyses of HT-29-TRPV2 tumour tissues, expression of both non-phospho and total β-catenin were down-regulated only in mice treated with TRPV2-targeted phototherapy (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">18</xref>). Similar results were also confirmed by IHC experiment using Colon-26 control and TRPV2 overexpressing tumours (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">19</xref>). Because translocation of β-catenin from the plasma membrane to the nuclear compartment is essential for stem cell regulation and tumorigenesis<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>, we tested if TRPV2-PCNH mediated phototherapy plays a role in β-catenin nuclear localization. Surprisingly, decreased expression level of β-catenin was found in both cytoplasm and nucleus of U2OS-TRPV2 cells after treatment (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">20</xref>). It is likely attributed to β-catenin upstream regulators that were somehow affected by this photo-treatment, resulting in a substantial decline in the total β-catenin expression (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7b</xref>). In further analyses, β-catenin-dependent luciferase reporter assays showed that laser-induced nanocomplexes caused a significant decrease in TCF/LEF activity in TRPV2 harbouring U2OS cells (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7c</xref>). Consistent with the inactivated β-catenin function, RT-qPCR analyses of downstream targets of β-catenin signalling pathway also showed that, after laser irradiation, all target genes were significantly down-regulated in TRPV2 overexpressing cells and tumours pretreated with TRPV2–PCNH nanoparticles (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7d, e</xref>). The present data demonstrate that Ca<sup>2+</sup> influx induced by TRPV2–PCNH mediated photothermogenetic therapy activates PKCα, leading to inhibition of Wnt/β-catenin signalling and its target genes.</p></sec></sec><sec id=\"Sec10\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par22\">Tumours are often heterogenous and comprise multiple tumour cell types with varying tumorigenic capacities<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. CSC theory assumes that small proportions of CSCs in tumours nourish tumour growth<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. This theory is supported by clinical observations of frequent tumour recurrence in only months or years after tumour resection and chemotherapy, generally presenting as metastases. CSCs have been identified in many common cancer types, including glioma, melanoma, leukaemia, colorectal cancer and breast cancer<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. In addition to conventional CSC inhibitors, various nanomedicines have been developed to target CSCs<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref>,<xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>. Due to the plasticity of CSCs, cancer cells are capable of dynamic phenotypic transitions between non-CSC and CSC states in response to certain stimuli<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>. Thus, the design of innovative treatment strategies that reduce tumour bulk and selectively exterminate CSCs would be most welcome. Herein, we present a photothermogenetic approach using light-driven TRPV2-PCNH, and show promising induction of apoptosis in cancer cells and repression of CSC characteristics. This approach may meet the demands of dual actions.</p><p id=\"Par23\">Ca<sup>2+</sup> is a ubiquitous cellular signalling molecule that determines cell behaviours by activating or inhibiting cellular signalling cascades and Ca<sup>2+</sup>-regulated proteins<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref>,<xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>. Because Ca<sup>2+</sup> controls pathways of proliferation and apoptosis, treatments that modulate Ca<sup>2+</sup> homoeostasis may offer efficacious therapies for cancer. Moreover, in contrast with expressed potential drug targets, Ca<sup>2+</sup> channels, such as those of the TRP family, have altered expression in cancer cells but highly restricted tissue distributions<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. TRPV2 is overexpressed in bladder cancer cells, and its physiological expression is largely restricted to bone marrow and liver tissues<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>. Hence, treatments that target such molecules with limited tissue distributions are less likely to be associated with generalised side effects. Current techniques for remote stimulation of Ca<sup>2+</sup> channels are still conceptual, and therapeutic applications remain limited<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>,<xref ref-type=\"bibr\" rid=\"CR20\">20</xref>,<xref ref-type=\"bibr\" rid=\"CR21\">21</xref>,<xref ref-type=\"bibr\" rid=\"CR50\">50</xref>–<xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup>. In addition, few studies have addressed specificity, and follow up evidence for tumour inhibition after remission is lacking. We show that TRPV2 antibody-functionalised PCNH nanoparticles caused transient changes in intracellular Ca<sup>2+</sup> concentrations by photothermal activating TRPV2 ion channels. We also confirm the specificity of the nanoparticles to TRPV2 positive cells with multiple lines of evidence in vitro and in vivo. In addition to evoking apoptosis in cancer cells, our engineered nanocomplexes suppressed cancer cell stemness characteristics after exposure to 1064-nm NIR-II laser irradiation, thus inhibiting tumour re-initiation and abrogating tumour progression. Subsequent molecular analyses revealed that cellular events following excessive Ca<sup>2+</sup> influx are in part dependent on suppression of Wnt/β-catenin signalling (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7f</xref>) which is frequently activated in human cancers<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>. Besides cancer stemness, there are also evidences to suggest that intrinsic activation of Wnt/β-catenin signalling in cancer cells is associated with T cells deficiency in the tumour microenvironment<sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref></sup>. Numerous studies demonstrated that inhibition of β-catenin activity could help to reestablish anticancer immunity<sup><xref ref-type=\"bibr\" rid=\"CR56\">56</xref>–<xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>. Indeed, we found the expression level of β-catenin was significantly down-regulated in immunocompetent xenograft models by TRPV2-PCNH-guided phototherapy (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">19</xref>), suggesting this treatment may transition the tumour microenvironment to a less resistant milieu. Continued studies would accentuate the potential of this technique in combination with immuno- or chemotherapies for achieving a better cancer treatment efficacy.</p><p id=\"Par24\">Collectively, the data presented herein demonstrates the potential of nanocarbon complexes as an optical anticancer agent that inhibit the development of cancer cell stemness. This study contributes to the development of next-generation nanomaterials based optical technologies for the effective treatment of cancers via photothermogenetic activation of ion channels.</p></sec><sec id=\"Sec11\"><title>Methods</title><sec id=\"Sec12\"><title>Synthesis of TRPV2–PCNH</title><p id=\"Par25\">CNH (average diameter, ca. 80–100 nm; purity, 95%; metal-free) was kindly supplied by NEC Corporation (Tokyo, Japan). TRPV2–PCNH complexes were prepared as follows: 10 mg of CNH and 10 mg of 3-(N-succinimidyloxyglutaryl)aminopropyl, polyethyleneglycol-carbamyl distearoylphosphatidyl-ethanolamine (DSPE–PEG–NHS; SUNBRIGHT DSPE–020GS; Yuka Sangyo, Tokyo, Japan) were dissolved in 9 ml of PBS and were subjected to pulse-type sonication (VCX-600; Sonics, Danbury, CT, USA) for 10 min. The resulting PEGylted CNH (PCNH) solutions were then mixed with 1-ml aliquots of TRPV2 antibody (200 µg in PBS) at 4 °C overnight and then purified by washing. The resulting bionanoconjugates were re-dispersed in 10-ml aliquots of PBS and centrifuged at 653 x <italic>g</italic> for 10 min at 4 °C (model no. 3740; Kubota, Tokyo, Japan). TRPV2–PCNH in supernatants with CNH concentrations of about 1 mg ml<sup>−1</sup> was collected and used for experiments. Fluorescent TRPV2-PCNH nanocomplexes were generated by conjugating a FITC-labelled TPRV2 antibody. The same protocol was used to prepare IgG–PCNH, except that normal IgG was used instead of TRPV2 antibody. ICG-incorporated TPRV2–PCNH was prepared accordingly using 5 mg of ICG (MP Biomedicals, Tokyo, Japan), 5 mg of CNH and 10 mg of DSPE–PEG–NHS for PEGylation. Cy5-PCNH was obtained in the same manner except that ICG and DSPE-PEG-NHS were replaced with DSPE-PEG2000-Cy5 (Nanosoft Polymers, Winston-Salem, NC, USA).</p></sec><sec id=\"Sec13\"><title>Optical and structural characterisations of TRPV2–PCNH</title><p id=\"Par26\">Absorption spectra of TRPV2–PCNH solutions were recorded at room temperature using a UV–Vis–NIR spectrophotometer (V-730 BIO; Jasco, Tokyo, Japan). Hydrodynamic diameters of the PCNH and TRPV2–PCNH complexes were determined using DLS (Photal FPAR-1000; Otsuka Electronics, Osaka, Japan). Negative staining was used to observe morphology and structure of TRPV2–PCNH molecules with a high-resolution TEM (EM-002B; Topcon, Tokyo, Japan) at an acceleration voltage of 80 kV.</p></sec><sec id=\"Sec14\"><title>SDS–PAGE analysis of TRPV2–PCNH</title><p id=\"Par27\">IgG–PCNH nanocomplexes and their individual constituents were subjected to standard SDS–PAGE using a Laemmli buffer system. Briefly, 30 µl of IgG–PCNH (1 mg ml<sup>−1</sup>) containing ~10 µg of normal IgG antibody and equivalent constituents were mixed with Laemmli sample buffer (Bio-Rad, Tokyo, Japan), were denatured by heating at 95 °C for 5 min and then loaded onto 12% polyacrylamide gels. Following electrophoresis, gels were stained with colloidal Coomassie brilliant blue (CBB G250; Bio-Rad) for 1 h at room temperature and were then washed with de-staining solution several times. Images were captured using a Gel Doc XR + imaging system (Bio-Rad).</p></sec><sec id=\"Sec15\"><title>Photothermal conversion tests</title><p id=\"Par28\">PCNH dispersions and PBS solutions were irradiated with a 1064-nm NIR laser at 1 W (~50 mW mm<sup>−2</sup>; spot diameter, about 5 mm; LSR4064H-3W; Civil Laser, Hangzhou, Zhejiang, China) under the indicated conditions. Temperatures of solutions were measured in real time using a temperature sensor (AD-5601A; A&D, Tokyo, Japan). Thermographic images were recorded using infrared (IR) thermography (i7; FLIR, Nashua, NH, USA).</p></sec><sec id=\"Sec16\"><title>Construction of TRPV2-mCherry plasmid</title><p id=\"Par29\">To construct N-terminal mCherry fusion TRPV2 plasmid, the mCherry sequence was amplified using a 5ʼ primer with an EcoRI site (ACTAGAATTCATGGTGAGCAAGGGCGAGGAGGAT) and a 3ʼ primer with a BglII site (GAATAGATCTCTTGTACAGCTCGTCCATGCCGCC). Product fragments were digested with corresponding enzymes and were introduced into a pCMV6-Entry vector containing the full length human TRPV2 coding region (RC202821, OriGene Technologies, Inc., Rockville, MD, USA).</p></sec><sec id=\"Sec17\"><title>Cell culture, transfection and treatment</title><p id=\"Par30\">Human bone osteosarcoma (U2OS), lung carcinoma (A549) and normal diploid fibroblasts (TIG3) were obtained from the Japanese Collection of Research Bioresources Cell Bank (Tokyo, Japan). Breast adenocarcinoma (MCF7) and colorectal adenocarcinoma (HT-29) cells were purchased from DS Pharma Biomedical (Tokyo, Japan). Murine colon carcinoma (Colon-26) and rat brain glioma (C6) cells were obtained from the Cell Resource Centre for Biomedical Research, Tohoku University (Sendai, Miyagi, Japan). All cell lines were cultured in Dulbecco’s Modified Eagle’s Medium or Roswell Park Memorial Institute 1640 Medium (Gibco, Grand Island, NY, USA) containing 10% foetal bovine serum, 2-mM l-glutamine, 1-mM sodium pyruvate, gentamycin, penicillin-streptomycin (100 IU ml<sup>−1</sup>) and Hank’s balanced salt solution (Life Technologies, Carlsbad, CA, USA). Cells were maintained at 37 °C in a humidified chamber containing 5% CO<sub>2</sub> and were cryopreserved in multiple vials in liquid nitrogen. Cell stocks were regularly revived to avoid the genetic instabilities associated with high passage numbers. Transfections were performed with either Lipofectamine 3000 (Life Technologies, NY, USA) or Fugene HD (Roche Applied Sciences, Basel, Switzerland) according to the manufacturers’ instructions. Transfected cells were selected and maintained in medium supplemented with G418. Unless mentioned elsewhere, cells were incubated with TRPV2–PCNH nanocomplexes (50 µg ml<sup>−1</sup>) for 24 h and were then laser irradiated for 90 s. Subsequent analyses were performed 24 h later.</p></sec><sec id=\"Sec18\"><title>Cell viability assays</title><p id=\"Par31\">Cell viability was assessed using Cell Counting Kit (CCK)-8 (Dojindo Laboratories, Kumamoto, Japan) according to the manufacturer’s instructions. Briefly, cells (5 × 10<sup>3</sup> cells well<sup>−1</sup>) were seeded in 96-well plates and were allowed to adhere overnight. Cells were then exposed to nanocomplexes and laser irradiation as indicated. After washing with fresh medium, cells were incubated with CCK-8 solution for 2 h at 37 °C. Absorbance at 450/690 nm was then determined using a microplate reader (Infinite M200 PRO; Tecan, Männedorf, Switzerland).</p></sec><sec id=\"Sec19\"><title>Colony formation assays</title><p id=\"Par32\">Cells were treated and irradiated in 96-well plates and were then re-seeded into 6-well plates at a density of 500 cells well<sup>−1</sup>. After attachment, cells were cultured in fresh medium for around 2 weeks. Forming colonies were then washed in cold PBS and were fixed with a pre-chilled methanol/acetone (v/v, 1:1) mixture for 10 min. Fixed cells were stained overnight with 0.1% crystal violet solution (Wako, Osaka, Japan). Numbers of colonies were counted and mean numbers of colonies were calculated from three independent experiments.</p></sec><sec id=\"Sec20\"><title>Mammosphere formation assays</title><p id=\"Par33\">MCF-7 and MCF7–TRPV2 cells were initially exposed to laser-induced TRPV2–CNH and were then allowed to recover overnight. Afterwards, control and treated cells were plated at 1 × 10<sup>5</sup> cells well<sup>−1</sup> in 6-well ultralow attachment plates (Corning, NY, USA) and were incubated without disturbing in serum-free culture medium (MammoCult™ Human Medium Kit; STEMCELL Technologies, Cambridge, MA, USA) at 37 °C in a humidified atmosphere containing 5% CO<sub>2</sub>. Numbers of spheres with diameters of 50–100 μm and ≥100 μm were evaluated in each well under a microscope on day 7. Mammosphere forming efficiency was calculated as the number of spheres divided by the original number of seeded cells. Experiments were performed three times independently and data are expressed as percentage means ± standard deviations (SD).</p></sec><sec id=\"Sec21\"><title>Extreme limiting dilution sphere formation assay</title><p id=\"Par34\">Single-cell suspensions of MCF7 or MCF7-TRPV2 cells were obtained by passing cells through a 40 μm filter. Cells were then seeded at low densities (1, 5, 10, 50, 100 and 500 cells well<sup>−1</sup>) in a 96-well ultralow attached plate with stem cell medium (MammoCult™ Human Medium Kit) at a final volume of 200 μl per well. Each condition was replicated at least in 24 wells. After 14 days, the formation of sphere and their diameter was assessed. Only spheroid bigger than 50 μm in size were included in the analysis. The frequency of initiation capacity was then calculated using Extreme Limiting Dilution Analysis (ELDA).</p></sec><sec id=\"Sec22\"><title>Fluorescence microscopy imaging</title><p id=\"Par35\">Cells (1 × 10<sup>5</sup> cells well<sup>−1</sup>) were plated on glass coverslips in 12-well culture dishes. FITC-labelled TRPV2–PCNH (100 µg ml<sup>−1</sup>) was added when cells had attached to the substratum. After 24-h incubation, cells were washed with PBS and fixed with a methanol/acetone (v/v, 1:1) mixture for 10 min, and nuclei were then stained with Hoechst 33342 (1 µg ml<sup>−1</sup>; Thermo Fisher Scientific, Waltham, MA, USA) for 10 min. After serially washing with PBS, coverslips were mounted for microscope imaging (Axiovert 200 M; Carl Zeiss, Tokyo, Japan).</p><p id=\"Par36\">For immunofluorescence staining experiments, cells were fixed with 4% formaldehyde in PBS for 10 min, were permeabilised in 0.5% Triton X-100 in PBS for 10 min, were blocked in 2% bovine serum albumin in PBS for 1 h and were then incubated with specific primary antibodies (detailed in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>) overnight at 4 °C. After washing with 0.2% Triton X-100 in PBS (PBS-T), cells were incubated with Alexa Fluor conjugated secondary antibodies for 1 h at room temperature. Counterstaining was performed using Hoechst 33342 following extensive washing with PBS-T. Coverslips with stained cells were finally mounted and stored at 4 °C in the dark until imaging.</p><p id=\"Par37\">Caspase-3 activities were detected using NucView® 488 Caspase-3 Assay Kits (Biotium, Hayward, CA, USA) according to the manufacturer’s instructions. Briefly, live cells were incubated in medium containing caspase-3 substrate (5 µM) for 2 h, and nuclear staining was then performed with Hoechst 33342 for 10 min. After washing with PBS, cells that remained in RPMI 1640 Phenol Red-free medium (Thermo Fisher Scientific) were subjected to live cell imaging using a fluorescence microscope (IX73; Olympus, Tokyo, Japan).</p></sec><sec id=\"Sec23\"><title>Measurements of intracellular calcium</title><p id=\"Par38\">Intracellular calcium imaging was performed using non-wash calcium assay Fluo8 kits (AAT Bioquest, Sunnyvale, CA, USA). After 24-h treatments with TRPV2-PCNH (100 µg ml<sup>−1</sup>), cells were washed with PBS, stained with calcium dye and irradiated with a 1064-nm NIR laser (LM164-5W; OptoSigma, Tokyo, Japan) that was incorporated into the fluorescence microscopy system (IX73). Green fluorescence of Fluo-8 from laser-targeted cells was monitored in real-time using an EM-CCD camera (DP80; Olympus). Fluorescence intensities were analysed using ImageJ software (National Institute of Health, Bethesda, MD, USA).</p></sec><sec id=\"Sec24\"><title>Flow cytometric analyses</title><p id=\"Par39\">Apoptotic cells were detected using Annexin-V and 7-aminoactinomycin (7-AAD) double staining<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>. After treatments, attached and suspended cells were harvested and numbers of apoptotic cells were determined using a Guava PCA flow cytometer (Millipore) with Guava Nexin Reagent (Millipore) as described by the manufacturer.</p><p id=\"Par40\">AldeRed ALDH detection assays (Millipore) were used to identify populations of cells with high aldehyde dehydrogenase (ALDH) activity. Briefly, control and treated cells were collected and stained with AldeRed-588 substrate at 37 °C for 30 min. Non-treated cells were instead incubated with 50-mM diethylaminobenzaldehyde (DEAB) as a negative control. Labelled cells were then washed and analysed using a Cytomics FC500 flow cytometer (Beckman Coulter, Tokyo, Japan),</p><p id=\"Par41\">CD44 and CD24 expression was detected after washing detached cells with PBS and resuspending in staining buffer (eBioscience™, Thermo Fisher Scientific). Combinations of fluorochrome-conjugated antibodies against human CD44 (PerCP-Cy5.5) and CD24 (PE) or their respective isotype controls (detailed in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>) were added to cell suspensions at recommended concentrations and were incubated at 4 °C in the dark for 30 min. Analyses were performed on a Cytomics FC500 flow cytometer (Beckman Coulter).</p><p id=\"Par42\">Fractions of apoptotic, ALDH-positive and CD44+/24− cells and mean fluorescence intensity were determined using FlowJo v10 software (Tree Star, Ashland, OR, USA).</p></sec><sec id=\"Sec25\"><title>RT-qPCR</title><p id=\"Par43\">Total RNA was isolated from cells and tumour tissues using RNeasy mini kits (Qiagen, Standford Valencia, CA, USA). Concentrations and purities of RNA samples were determined by ultraviolet spectrophotometry using a NanoDrop ND-1000 (Nanodrop Technologies, Wilmington, DE, USA) instrument. Equal amounts of RNA were used for reverse transcription according to the protocol of the QuantiTect Reverse Transcription Kit (Qiagen). Real-time RT-qPCR was then performed using SYBR Select Master Mix (Applied Biosystems, Thermo Fisher Scientific) in triplicate on an Eco™ real-time system (Illumina, San Diego, CA USA). Relative mRNA expression was normalised against that of 18S using the ΔC<sub>t</sub> method. Primer sets are listed in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>. Experiments were performed at least thrice.</p></sec><sec id=\"Sec26\"><title>Dual luciferase reporter assay</title><p id=\"Par44\">TCF/LEF reporter kit (Cignal report; Qiagen) was employed to evaluate Wnt/β-catenin signalling activity according to the manufacturer’s instructions. Briefly, cells were transiently transfected in triplicate with TCF/LEF luciferase reporters by use of Fugene HD (Roche). Luciferase activity was measured at 48 h after transfection with the Dual-luciferase reporter assay system (Promega, WI, USA). The firefly luciferase activity was normalized against the Renilla luciferase activity. Experiments were performed at least three times.</p></sec><sec id=\"Sec27\"><title>Western blotting analyses</title><p id=\"Par45\">Cells and tissues were lysed in radio-immune precipitation assay buffer (Thermo Fisher Scientific) containing complete protease inhibitor cocktail (Roche Applied Science). Nuclear and cytoplasmic fractions were prepared using the NE-PER® Nuclear and Cytoplasmic Extraction reagents (Thermo Fisher Scientific) as per manufacturer’s instruction. Protein concentrations were determined using Pierce Bicinchoninic Acid Protein Assay kits (Thermo Fisher Scientific). Protein lysates (20 μg) were resolved in SDS-polyacrylamide gels, were transferred to polyvinylidene difluoride (PVDF) membranes and were then probed with primary antibodies. PVDF membranes were then incubated with respective secondary antibodies and immunolabeled proteins were imaged using a Gel Doc XR + system (Bio-Rad). Pixels in western blot bands were counted and normalised to those in actin bands (internal loading control) using ImageJ software (National Institute of Health). Antibody details and their dilutions are listed in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>.</p></sec><sec id=\"Sec28\"><title>Animal and tumour models</title><p id=\"Par46\">All animal experimental procedures were approved by the Institutional Animal Care and Use Committee of JAIST and AIST. Female BALB/cSlc (4 weeks old; average weight, 16 g) and BALB/cSlc-nu/nu mice (4 weeks old; average weight, 18 g) were purchased from Japan SLC (Hamamatsu, Japan) and were housed in specific pathogen-free facilities with a 12-h light/12-h dark cycle and free access to food and water.</p><p id=\"Par47\">To generate tumour models, nude mice were subcutaneously inoculated in the flanks with equivalent number of cells (5 × 10<sup>5</sup> cells for HT-29; 1 × 10<sup>6</sup> cells for A549 and Colon-26) suspended in 100-μl aliquots of culture medium/Matrigel (Corning) mixture (v:v = 1:1). For in vivo tumour phototherapy experiments, mice were bilaterally implanted with either control or TRPV2 overexpressing derivative cells into flanks. For in vivo biodistribution analyses, both cell types were injected into opposite flanks of the same mouse. For in vivo drug and phototherapy combination experiments, only TRPV2-transfected A549 cells were injected into the right flank of mice. Tumour sizes were monitored using vernier calipers and tumour volumes were calculated as V = L × W<sup>2</sup>/2, where L and W denote lengths and widths of tumours, respectively. Tumour models were randomly divided and used in experiments when tumour volumes reached about 100 mm<sup>3</sup>.</p></sec><sec id=\"Sec29\"><title>In vivo biodistribution analyses</title><p id=\"Par48\">To assess biodistributions of nanocomplexes, the NIR fluorescence dye ICG was incorporated into TPRV2-PCNH as described above. HT-29 and HT-29-TRPV2 tumour-bearing mice (<italic>n</italic> = 5) were then injected with 200-μl aliquots of ICG-TPRV2-PCNH or ICG-PCNH (equivalent to a total of 5-mg kg<sup>−1</sup> CNH) via the tail vein. Optical imaging analyses of whole mice and major organs were performed at the indicated time points using an IVIS Imaging Spectrum System (PerkinElmer, MA, USA) with emission at 800 nm and excitation at 740 nm. Fluorescence intensities were quantified using IVIS Living Imaging 3.0 software (PerkinElmer).</p></sec><sec id=\"Sec30\"><title>Quantitative pharmacokinetic analyses</title><p id=\"Par49\">Female Jcl:ICR mice (6 weeks old; average weight, 26 g) (Japan SLC) (<italic>n</italic> = 5) were intravenously injected with Cy5-labelled CNH (5 mg kg<sup>−1</sup>) and were killed at 1 h, 24 h, 7 days and 14 days post injection. Blood and organs were weighed and solubilized by RIPA buffer (Thermo Fisher Scientific) using a TissueLyser (Qiagen). The clear homogeneous tissue lysates were diluted 50 times and subjected to Tecan microplate reader for fluorescence intensity measurement. The blood and organs from control mice without injection of nanocomplexes were collected and used as controls to subtract the auto-fluorescence background. A series of dilutions of Cy5-PCNH standard samples were performed to obtain a standard calibration curve for quantitative determination of PCNH. The concentration of the PCNH in the various organs of the mice was calculated and presented as percentage of injected dose per gram of tissue (%ID).</p></sec><sec id=\"Sec31\"><title>In vivo transfection</title><p id=\"Par50\">Female BALB/c nude mice bearing A549 tumours randomly divided into groups of five mice each when tumours reached the size of ∼100 mm<sup>3</sup>. 5 μg of TRPV2 DNA plasmid was delivered in vivo by an intratumoral injection with in vivo-jetPEI reagent (Polyplus Transfection, NY, USA), according to the manufacturer’s instructions. Injections were repeated twice a week for next 3–4 weeks during the treatment of PCNH nanocomplexes and laser irradiation.</p></sec><sec id=\"Sec32\"><title>In vivo tumour phototherapy</title><p id=\"Par51\">Mice bearing control or TRPV2-overexpressing cell-derived tumours (<italic>n</italic> = 6) were intraperitoneally injected with 200-μl aliquots of PBS, 200-μl aliquots of PBS dispersions containing PCNH (5 mg kg<sup>−1</sup>) or 200-μl aliquots of PBS dispersions containing TRPV2-PCNH (5 mg kg<sup>−1</sup>). Injections were performed every other day. On days as indicated, tumours on the right-side of backs were irradiated with a 1064-nm laser at 1 W (~50 mW mm<sup>−2</sup>) for 5 min. When tumours were larger than the laser spot size (diameter ~5 mm), two or three locations were irradiated. Surface temperatures of irradiated tumours were monitored using IR thermography (i7; FLIR, Nashua, NH, USA). Before mice were sacrificed, tumours were isolated for further analysis as needed. Tumour volumes and overall health (body weight) were recorded every other day for the duration of experiments.</p><p id=\"Par52\">For laser and drug combination therapy, A549-TRPV2 tumour-bearing mice were randomly separated into four groups (5 mice per group). Intraperitoneal injections of nanocomplexes containing paclitaxel (50 mg kg<sup>−1</sup>, dispersed in 10% Cremophor EL (Sigma-Aldrich)), TRPV2-PCNH (50 mg kg<sup>−1</sup>) or their combination were started when small tumour buds were formed (ca. 100 mm<sup>3</sup>). Equivalent volume of PBS was used as negative control. The injections were performed every alternate day. Tumours were irradiated for 5 min using the 1064 nm laser at 1 W (ca. 50 mW mm<sup>−2</sup>) on Day 3 and Day 6 since injection began. The administration of TRPV2-PCNH was also stopped after Day 6. Tumour volumes were recorded until the experiment is completed.</p></sec><sec id=\"Sec33\"><title>Immunohistochemistry (IHC) analyses</title><p id=\"Par53\">IHC analysis was performed by New Histo. Science Laboratory (Tokyo, Japan) with standard protocols. Briefly, primary tumours were surgically removed, fixed in 10% formalin, processed for paraffin embedding, and then cut into 3~4-μm-thick sections. After incubation with primary antibodies (listed in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>) and HRP-labelled polymer, the sections were stained with hematoxylin and then examined by light microscopy (Axiovert 200 M; Carl Zeiss).</p></sec><sec id=\"Sec34\"><title>Primary cell isolation and tumour-initiating assays</title><p id=\"Par54\">HT-29–TRPV2 tumours that had been NIR irradiated were resected, washed with PBS and then cut into pieces on ice. Shredded tumours were then next immersed in 2% collagenase A (Roche) and were digested at 37 °C with gentle shaking for 1 h. Dissociated cells were then filtered through 100-µm mesh and were kept in culture medium at 4 °C. To assess tumour-initiating capacity, cells were counted and isolated from each tumour and were subcutaneously re-transplanted into the mice (Female, BALB/cAJc-nu/nu, 6 weeks old; <italic>n</italic> = 5, two tumours per mouse) in diluting concentrations (5 × 10<sup>2</sup>, 5 × 10<sup>3</sup> and 5 × 10<sup>4</sup> cells injection<sup>−1</sup>). Tumour volumes were monitored once every 3 days as described above. The frequency of stem cell initiation was analysed using ELDA.</p></sec><sec id=\"Sec35\"><title>Statistics and reproducibility</title><p id=\"Par55\">All experiments were performed in triplicate and repeated three or more times. Quantitative values are expressed as the means ± standard error of the mean (SEM), or means ± SD, of at least three independent experiments. Pairwise differences were identified using Student’s <italic>t</italic> test and comparisons of multiple groups were performed using two-way analysis of variance (ANOVA) followed by Tukey test. A <italic>p</italic> value less than 0.05 is considered to be statistically significant.</p></sec><sec id=\"Sec36\"><title>Reporting summary</title><p id=\"Par56\">Further information on research design is available in the <xref rid=\"MOESM9\" ref-type=\"media\">Nature Research Reporting Summary</xref> linked to this article.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec37\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41467_2020_17768_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41467_2020_17768_MOESM2_ESM.docx\"><caption><p>Description of Additional Supplementary Files</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"41467_2020_17768_MOESM3_ESM.mp4\"><caption><p>Supplementary Movie 1</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM4\"><media xlink:href=\"41467_2020_17768_MOESM4_ESM.mp4\"><caption><p>Supplementary Movie 2</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM5\"><media xlink:href=\"41467_2020_17768_MOESM5_ESM.mp4\"><caption><p>Supplementary Movie 3</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM6\"><media xlink:href=\"41467_2020_17768_MOESM6_ESM.mp4\"><caption><p>Supplementary Movie 4</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM7\"><media xlink:href=\"41467_2020_17768_MOESM7_ESM.mp4\"><caption><p>Supplementary Movie 5</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM8\"><media xlink:href=\"41467_2020_17768_MOESM8_ESM.mp4\"><caption><p>Supplementary Movie 6</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM9\"><media xlink:href=\"41467_2020_17768_MOESM9_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media></supplementary-material></sec></sec></body><back><fn-group><fn><p><bold>Peer review information</bold>\n<italic>Nature Communications</italic> thanks Liang Cheng and the other, anonymous, reviewer for their contribution to the peer review of this work.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> is available for this paper at 10.1038/s41467-020-17768-3.</p></sec><ack><title>Acknowledgements</title><p>This work was supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI Grant-in-Aid for Scientific Research (A) [Grant number 19H00857]; JSPS KAKENHI Grant-in-Aid for Scientific Research (B) [Grant number 16H03834]; and JSPS KAKENHI Fund for the Promotion of Joint International Research (Fostering Joint International Research) [Grant number 16KK0117]. Authors also thank Dr. Motomichi Doi (AIST) for plasmid construction, Ms. Jia Wang (AIST) for experiment assistance and DBT (Govt. of INDIA) for DAICENTER project grant.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>E.M. conceived the idea. E.M. and Y.Y. designed the experiments. E.M. and Y.Y. prepared the manuscript; Y.Y., X.Y., and S.R. performed the experiments; Y.Y. analysed the data. All the authors discussed the results, contributed in writing and reviewing the manuscript.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>All data needed to evaluate the conclusions in the paper are presented in the paper and/or the <xref rid=\"MOESM1\" ref-type=\"media\">Supplementary Materials</xref>. Additional data related to this paper are available from the corresponding author on reasonable request. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Nat Commun</journal-id><journal-id journal-id-type=\"iso-abbrev\">Nat Commun</journal-id><journal-title-group><journal-title>Nature Communications</journal-title></journal-title-group><issn pub-type=\"epub\">2041-1723</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807789</article-id><article-id pub-id-type=\"pmc\">PMC7431861</article-id><article-id pub-id-type=\"publisher-id\">17823</article-id><article-id pub-id-type=\"doi\">10.1038/s41467-020-17823-z</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Identifying proteins bound to native mitotic ESC chromosomes reveals chromatin repressors are important for compaction</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-7914-9777</contrib-id><name><surname>Djeghloul</surname><given-names>Dounia</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Patel</surname><given-names>Bhavik</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-6400-0945</contrib-id><name><surname>Kramer</surname><given-names>Holger</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-2996-2479</contrib-id><name><surname>Dimond</surname><given-names>Andrew</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Whilding</surname><given-names>Chad</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Brown</surname><given-names>Karen</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kohler</surname><given-names>Anne-Céline</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Feytout</surname><given-names>Amelie</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-3096-7867</contrib-id><name><surname>Veland</surname><given-names>Nicolas</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Elliott</surname><given-names>James</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-0168-0277</contrib-id><name><surname>Bharat</surname><given-names>Tanmay A. M.</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tarafder</surname><given-names>Abul K.</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-5218-6615</contrib-id><name><surname>Löwe</surname><given-names>Jan</given-names></name><xref ref-type=\"aff\" rid=\"Aff6\">6</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-8338-4390</contrib-id><name><surname>Ng</surname><given-names>Bee L.</given-names></name><xref ref-type=\"aff\" rid=\"Aff7\">7</xref></contrib><contrib contrib-type=\"author\"><name><surname>Guo</surname><given-names>Ya</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-8440-5667</contrib-id><name><surname>Guy</surname><given-names>Jacky</given-names></name><xref ref-type=\"aff\" rid=\"Aff8\">8</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-0258-2994</contrib-id><name><surname>Huseyin</surname><given-names>Miles K.</given-names></name><xref ref-type=\"aff\" rid=\"Aff9\">9</xref></contrib><contrib contrib-type=\"author\"><name><surname>Klose</surname><given-names>Robert J.</given-names></name><xref ref-type=\"aff\" rid=\"Aff9\">9</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0003-2889-3288</contrib-id><name><surname>Merkenschlager</surname><given-names>Matthias</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0003-3010-3644</contrib-id><name><surname>Fisher</surname><given-names>Amanda G.</given-names></name><address><email>amanda.fisher@lms.mrc.ac.uk</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.413629.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0705 4923</institution-id><institution>Lymphocyte Development Group, MRC London Institute of Medical Sciences, Imperial College London, </institution><institution>Hammersmith Hospital Campus, </institution></institution-wrap>Du Cane Road, London, W12 0NN UK </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.413629.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0705 4923</institution-id><institution>Flow Cytometry Facility, MRC London Institute of Medical Sciences, Imperial College London, </institution><institution>Hammersmith Hospital Campus, </institution></institution-wrap>Du Cane Road, London, W12 0NN UK </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.413629.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0705 4923</institution-id><institution>Biological Mass Spectrometry and Proteomics Facility, MRC London Institute of Medical Sciences, Imperial College London, </institution><institution>Hammersmith Hospital Campus, </institution></institution-wrap>Du Cane Road, London, W12 0NN UK </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.413629.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0705 4923</institution-id><institution>Microscopy Facility, MRC London Institute of Medical Sciences, Imperial College London, </institution><institution>Hammersmith Hospital Campus, </institution></institution-wrap>Du Cane Road, London, W12 0NN UK </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.4991.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 8948</institution-id><institution>Sir William Dunn School of Pathology, </institution><institution>University of Oxford, </institution></institution-wrap>OX1 3RE Oxford, UK </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.42475.30</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0605 769X</institution-id><institution>MRC Laboratory of Molecular Biology, </institution></institution-wrap>Cambridge, CB2 0QH UK </aff><aff id=\"Aff7\"><label>7</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.10306.34</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0606 5382</institution-id><institution>Wellcome Sanger Institute, </institution><institution>Wellcome Genome Campus, </institution></institution-wrap>Hinxton, Cambridge, CB10 1SA UK </aff><aff id=\"Aff8\"><label>8</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.4305.2</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 7988</institution-id><institution>The Wellcome Centre for Cell Biology, </institution><institution>University of Edinburgh, </institution></institution-wrap>Edinburgh, EH9 3BH UK </aff><aff id=\"Aff9\"><label>9</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.4991.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 8948</institution-id><institution>Department of Biochemistry, </institution><institution>University of Oxford, </institution></institution-wrap>OX1 3QU Oxford, UK </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>4118</elocation-id><history><date date-type=\"received\"><day>20</day><month>8</month><year>2019</year></date><date date-type=\"accepted\"><day>15</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Epigenetic information is transmitted from mother to daughter cells through mitosis. Here, to identify factors that might play a role in conveying epigenetic memory through cell division, we report on the isolation of unfixed, native chromosomes from metaphase-arrested cells using flow cytometry and perform LC-MS/MS to identify chromosome-bound proteins. A quantitative proteomic comparison between metaphase-arrested cell lysates and chromosome-sorted samples reveals a cohort of proteins that were significantly enriched on mitotic ESC chromosomes. These include pluripotency-associated transcription factors, repressive chromatin-modifiers such as PRC2 and DNA methyl-transferases, and proteins governing chromosome architecture. Deletion of PRC2, Dnmt1/3a/3b or Mecp2 in ESCs leads to an increase in the size of individual mitotic chromosomes, consistent with de-condensation. Similar results were obtained by the experimental cleavage of cohesin. Thus, we identify chromosome-bound factors in pluripotent stem cells during mitosis and reveal that PRC2, DNA methylation and Mecp2 are required to maintain chromosome compaction.</p></abstract><abstract id=\"Abs2\" abstract-type=\"web-summary\"><p id=\"Par2\">Epigenetic information is transmitted from mother to daughter cells through mitosis. Here, the authors isolate native chromosomes from metaphase-arrested cells and perform LC-MS/MS to identify chromosome-bound proteins in pluripotent stem cells during mitosis and reveal that PRC2, DNA methylation and Mecp2 are required to maintain chromosome compaction.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Chromosome condensation</kwd><kwd>Chromatin</kwd><kwd>Chromosomes</kwd><kwd>Proteomics</kwd><kwd>Embryonic stem cells</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par3\">Cell division requires genetic and epigenetic information to be accurately conveyed to daughter cells. This relies upon DNA synthesis during the S-phase of cell cycle and the subsequent segregation of this information during mitosis (M-phase). In recent years, huge progress has been made in understanding not only how chromosomal DNA is copied and segregated, but also how epigenetic information is transmitted through the cell cycle. For example, in S-phase we know that DNA methylation is normally reinstated on newly replicated DNA strands through the activity of Dnmt1, an enzyme that re-establishes methylation at hemi-methylated CpG sites<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. We also know that even though histones are displaced during S-phase, histone chaperones, such as minichromosome maintenance complex (Mcm)2, ensure that parental histones are evenly allocated to the leading and lagging strands<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>–<xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. In addition, the epigenetic modifier polycomb repressor complex 2 (PRC2) can both methylate histone H3 at lysine 27 (through Ezh2) and recognise this mark (through Eed)<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>, potentially ensuring that histone H3K27me3 is appropriately copied at newly synthesised DNA<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>.</p><p id=\"Par4\">Understanding how epigenetic information is transmitted through mitosis, as newly replicated genomes condense and segregate, remains only partly understood. Progressive activation of CyclinB1-Cdk1 promotes chromosome condensation<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup> so that visibly discrete individual mitotic chromosomes appear at the mitotic spindle ahead of breakdown of the nuclear envelope<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. Although it was originally thought that most DNA sequence-specific transcription factors were actively displaced from chromosomes during mitosis<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>, subsequent studies of the <italic>Hsp70</italic> gene promoter<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>, or examining the dynamic distribution of Gata1, FoxaI and Esrrb proteins in cycling cells have revealed that many factors remain bound to mitotic chromosomes, and may occupy a subset of the genomic sites bound during interphase<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref>–<xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>. Several studies have described the dynamic changes in the repertoire of chromatin- and DNA-binding proteins, as cells transit the cell cycle<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>–<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. Since the transmission of gene expression features from mother to daughter cells has been linked to DNA sequence-specific transcription factor binding through cell division<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>, much attention has been focused on defining chromatin-bound mitotic factors that could activate gene expression in daughter cells following division. Some of these factors are proposed to ‘bookmark’ the mitotic genome, effectively marking out genes for subsequent activity<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref>–<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR18\">18</xref>,<xref ref-type=\"bibr\" rid=\"CR21\">21</xref>–<xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. In comparison, the significance of repressive chromatin modifiers that have been detected in mitotic samples<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup> remains much less clear. Furthermore, although some DNA-binding factors may be retained on mitotic chromosomes through binding to their cognate motifs, other interactions may be sustained through the emergent properties of condensed mitotic chromatin<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref>–<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>.</p><p id=\"Par5\">To comprehensively evaluate the proteins that remain bound to mitotic chromosomes, we sought a high-throughput approach. As previous reports had shown that fixatives, that were intended to stabilise or cross-link mitotic chromosome preparations, can artificially displace factors from mitotic chromosomes<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref>–<xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>, it was important to use unfixed chromosome samples. Prior studies indicated that native (unfixed) chromosomes could be isolated from different cell types and species directly by staining with the DNA dyes Hoechst 33258 and chromomycin A3, and sorting chromosomes by flow cytometry<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref>–<xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. This purification step has the additional advantage over conventional approaches, as it enables a rigorous exclusion of interphase and cytoplasmic contaminants.</p><p id=\"Par6\">In this study metaphase-arrested mouse ESCs are stained with Hoechst 33258 and chromomycin A3, and flow cytometry is used to enumerate and sort specific chromosomes on the basis of AT/GC content and forward scatter (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of conventional metaphase-arrested ESCs, and highly enriched (flow-sorted) chromosomes, enables a catalogue of the factors present in mitotic ESCs to be compiled, where chromosome-bound factors are discriminated as being significantly enriched in chromosome-sorted fractions. Among 5888 proteins in mitotic ESC samples, ~10% (615) are significantly enriched on purified mitotic chromosomes. These include transcription factors, such as Esrrb, Sox2 and Sall4; members of the structural maintenance of chromosomes (Smc) family of proteins; heterochromatin-associated proteins and the chromatin repressors Dnmt1, Dnmt3a, Dnmt3b, Mecp2, PRC1 and PRC2. Interestingly, in ESCs that lack PRC2 activity, DNA methylation or Mecp2, mitotic chromosomes are de-condensed relative to equivalents in wild-type (WT) ESCs, consistent with these components being important for maintaining chromosome compaction. Our study describes an alternative approach for studying the properties of native mitotic chromosomes and offers a comprehensive catalogue of chromosome-bound proteins in pluripotent mouse ESCs during mitotic division.<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Proteins bound to ESC-derived metaphase chromosomes.</title><p><bold>a</bold> Scheme of experimental strategy used to isolate native metaphase chromosomes from ESCs and identify proteins bound to mitotic chromatin. Hoechst 33258 and chromomycin A3 bivariate karyotype was assessed by flow cytometry, and the gates used to sort all chromosomes, chromosome 19 or the X chromosome are indicated. Proteomic analysis was performed using LC-MS/MS on total mitotic cell lysate pellet, or on flow-purified chromosomes, to identify proteins bound to native metaphase chromosomes. <bold>b</bold> Diagram showing the number of proteins identified by proteomic analysis in mitotic lysate pellet and chromosome-sorted samples. <bold>c</bold> Volcano plot of proteins detected as being significantly enriched or depleted on sorted chromosomes relative to mitotic lysate pellet (unpaired two tailed Student’s <italic>t</italic>-test, permutation-based FDR < 0.01, <italic>n</italic> = 3 independent experiments each measured in duplicate, see ‘Methods’ section for details). Proteins were plotted as Log2 fold change (LFQ intensity of sorted chromosome pellet /LFQ intensity of mitotic lysate pellet) and significance (−Log10 <italic>p</italic>) using Perseus software. <bold>d</bold> Chromatin accessibility profile across chromosome 19 for asynchronous and mitotic cells, and flow-sorted chromosomes, shown as Log2 enrichment of ATAC-seq signal. <bold>e</bold>–<bold>j</bold> Volcano plots as in <bold>c</bold>, highlighting <bold>e</bold> histones, <bold>f</bold> components of the proteasome, <bold>g</bold> Smc-associated proteins, <bold>h</bold> DNA replication machinery, <bold>i</bold> pluripotency-associated transcription factors or <bold>j</bold> chromatin repressors that are enriched (red), depleted (blue) or not significantly enriched (ns, black) on ESC mitotic chromosomes versus mitotic lysate pellet. <bold>k</bold> Localisation of Esrrb, Pcl2 and Suz12 fusion proteins (green, left panels) to mitotic chromatin in live ESCs cultured with SiR-DNA (grey, right panels). Arrows show Esrrb, Pcl2 and Suz12 localisation to mitotic chromatin. Scale bars = 14 μm. Images are representative of three independent experiments.</p></caption><graphic xlink:href=\"41467_2020_17823_Fig1_HTML\" id=\"d30e685\"/></fig></p></sec><sec id=\"Sec2\" sec-type=\"results\"><title>Results</title><sec id=\"Sec3\"><title>Isolation of native metaphase chromosomes from mouse ESCs</title><p id=\"Par7\">We adapted a protocol used previously to isolate unfixed mitotic chromosomes<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. Briefly, rapidly dividing cultures of mouse ESCs are arrested in metaphase using demecolcine to achieve samples where most (85–90%) cells are in M-phase as judged by propidium iodide (PI) labelling (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>, Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1a</xref>). Condensed chromosomes are released using polyamine buffer, stained with Hoechst 33258 and chromomycin A3 as described previously<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>, and examined by flow cytometry using a Becton Dickinson Influx equipped with specialised air-cooled lasers (see ‘Methods’ section). This approach allows individual chromosomes to be discerned and either sorted en masse (upper plot, Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>), or gated on individual chromosomes, such as chromosome X or chromosome 19 (highlighted separately in lower plot, Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>, Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1b</xref>). After sorting, the integrity of sorted chromosomes was examined and verified by optical imaging using antibody to Cenpa or using Trf1-YFP to confirm centromere, and telomere number and location (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1c</xref>).</p></sec><sec id=\"Sec4\"><title>Analysis of proteins bound to metaphase ESC chromosomes</title><p id=\"Par8\">To determine the proteins bound to native (unfixed) metaphase ESC chromosomes, we performed a proteomic analysis using LC-MS/MS and analysed the data using the label-free quantification (LFQ) algorithm within the MaxQuant software platform (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a–c</xref>). In these experiments, we compare samples containing equivalent numbers (10<sup>7</sup>) of ESC metaphase chromosomes before (mitotic lysate pellet) and after chromosome sorting, in three biological replicates (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1d</xref> shows pairwise comparisons between replicates). We identify 5888 proteins in mitotic lysates, of which 5436 are identified with two or more razor or unique peptides per protein at a 1% false discovery rate (FDR). Our rationale is that chromosome-bound factors should be enriched in sorted samples, while factors that are not chromosome-associated would be depleted. In chromosome-sorted fractions, we detect 3749 proteins that are either significantly enriched as compared to pelleted mitotic lysates (615, red), are depleted (1548, blue), or showed no statistical difference between the two (1354, black; Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b, c</xref>). Hierarchical clustering of these proteomics datasets is shown in Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1e</xref>, and GO term annotation of enriched and depleted candidates is shown in Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1f</xref>. We note that the number and identity of proteins detected is broadly similar to those identified in previous studies of mitotic human and chicken cells<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup> (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1g</xref>), suggesting that our method of isolating native mitotic chromosomes does not incur any large-scale loss of chromatin-bound proteins. Furthermore, chromatin accessibility is known to be largely maintained in mitosis<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR33\">33</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>, and ATAC-seq analysis demonstrates that global chromatin accessibility patterns are broadly preserved in flow-sorted mitotic chromosomes (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>).</p><p id=\"Par9\">Proteomic comparisons between sorted- and conventional metaphase-arrested samples (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e–j</xref>) reveal a statistical enrichment in histones, Smc-associated proteins and many sequence-specific DNA-binding factors in chromosome-sorted samples (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e, g, i</xref>, respectively), consistent with these factors remaining intimately associated with chromosomes in mitosis. In contrast, as expected, components of the proteasome (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f</xref>) and members of the Mcm (known to dissociate after S-phase, Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1h</xref>), are depleted from sorted chromosome fractions, as are cytoplasmic, organelle- and membrane-associated proteins (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1f</xref>). Several transcription factors known to regulate ESC pluripotency and differentiation show differential associations with mitotic chromosomes (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1i</xref>). For example, Utf1, a transcription factor required for the proper differentiation of embryonic carcinoma cells and ESCs<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>, is enriched after chromosome sorting, while Nanog, Oct4 (Pou5f1) and Klf4, although detected, show no significant enrichment in sorted chromosome samples. Esrrb and Sox2, two previously characterised bookmarking factors<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR22\">22</xref>,<xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>, are both significantly enriched in chromosome-sorted samples, while Dppa5 and Klf5 are depleted. Consistent with Esrrb genomic bookmarking being preserved on native flow-sorted mitotic chromosomes, ATAC-seq analysis of binding sites reported to be preserved or lost in mitosis<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup> shows a characteristic retention or loss of accessibility, consistent with prior studies (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1h</xref>). Interestingly, proteins associated with gene repression and heterochromatin formation, including the histone methyl-transferases Suv39h1 and Suv39h2, PRC1 and PRC2 components Suz12, Eed, Ezh2, Jarid2 and Pcl2, are significantly enriched in sorted chromosome samples (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1j</xref>, Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1f</xref>, Supplementary Data File <xref rid=\"MOESM4\" ref-type=\"media\">1</xref>). The DNA methyl-transferases Dnmt1, Dnmt3a and Dnmt3b are also co-enriched after chromosome sorting, suggesting that most of the chromatin machinery required to sustain H3K9me3, H3K27me3 and 5mC marking of the genome remain bound to ESC chromosomes during mitosis. Likewise, the methyl-CpG-binding protein Mecp2 and the SWI/SNF-related protein Smarca5 also show significant enrichment in sorted chromosome samples (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1j</xref>). Smarca5 is an important chromatin remodelling factor that is involved in establishing regularly spaced nucleosomes, critical for DNA replication and repair, and associated with both positive and negative transcriptional outcomes. Smarca5 has also been shown to interact with Rad21 (ref. <sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>), and co-enrichment of Rad21, Smc1, Smc2 and other Smc-associated proteins is also evident in chromosome-sorted samples (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g</xref>). Antibody labelling was performed for selected factors, confirming their relative enrichment (Sox2 and Rad21) or lack of enrichment (Nanog and Oct4) on flow-sorted chromosomes (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1i</xref>). To validate the mitotic binding of candidates identified by proteomic analysis of native isolated chromosomes, we performed live cell imaging in ESCs engineered to express tdTomato or Halo-tag fusion proteins. As shown in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1k</xref>, and the accompanying video (Supplementary Movie <xref rid=\"MOESM5\" ref-type=\"media\">1</xref>), PRC2 components Suz12 and Pcl2 (green) show a dispersed nuclear distribution in interphase cells, but colocalise with chromosomal DNA (labelled with SiR, greyscale) during mitosis (arrows). This dynamic labelling profile is similar to that observed for Esrrb, a previously reported bookmarking factor in ESCs<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>.</p></sec><sec id=\"Sec5\"><title>DNMTs and PRC2 activity keep mitotic chromosomes compact</title><p id=\"Par10\">To evaluate the functional relevance of PRC2 and DNMTs on mitotic chromosomes, we examined native chromosomes isolated form ESCs that lacked either DNA methylation<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup> or PRC2 activity<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. While the overall distribution of mitotic chromosomes isolated from WT and mutant (<italic>Dnmt1,3a,3b</italic><sup>−/−</sup> or <italic>Eed</italic><sup>−/−</sup>) ESCs appears similar (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>), closer inspection reveals differences in their size and shape. To accurately measure this, we separately purified two representative mitotic chromosomes (19 and X) from WT and mutant ESCs, using Hoechst 33258 and chromomycin A3 staining and flow sorting, as described previously (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>). DNA FISH with mouse chromosome 19- or X-specific paints (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2a</xref>) confirms 99–100% sample purity. Individual chromosomes were measured by microscopy using standard imaging software to determine the total chromosome area and estimate the size of DAPI-bright pericentric domains (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2b</xref>). These analyses indicate that mitotic 19 and X chromosomes isolated from <italic>Dnmt1,3a,3b</italic><sup>−/−</sup> (23.4 ± 4.1, 43.6 ± 6.9 µm<sup>2</sup> respectively) or <italic>Eed</italic><sup>−/−</sup> (24.3 ± 4.5, 42.2 ± 4.7 µm<sup>2</sup>, respectively) ESCs are significantly larger than equivalent chromosomes isolated from WT ESCs (18.4 ± 3, 35.8 ± 5.3 µm<sup>2</sup>, respectively), while those from a Sox2-deficent ESC line<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup> are of comparable size and shape (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b, c</xref>). Centromere size is also increased in mitotic samples that lack DNA methylation or PRC2 activity (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b, c</xref>). Although <italic>Dnmt1,3a,3b</italic><sup>−/−</sup> ESCs lack 5mC and have elevated H3K27me3 at pericentric domains in interphase as compared to WT<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup> (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2c, d</xref>), we do not observe similar increases in H3K27me3 on these mitotic chromosomes (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2e</xref>, red). Instead these chromosomes have reduced H3K9me3 levels, particularly at the centromeres (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2e</xref>, middle panel, green). These data suggest that a lack of either H3K27me3 or H3K9me3 can impair chromosome compaction.<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Increased size of ESC metaphase chromosomes that lack DNA methylation or PRC2 activity.</title><p><bold>a</bold> Flow karyotype of mitotic chromosomes isolated from WT ESCs or mutant ESCs that lack Dnmt1/3a/3b, Eed or Sox2. Gates used to isolate chromosomes 19 or X are indicated. Images are representative of three independent experiments. <bold>b</bold>, <bold>c</bold> Representative images of mitotic chromosomes 19 (<bold>b</bold>) and X (<bold>c</bold>) from different ESCs are shown, where DAPI stain (grey) and Cenpa label (green) indicate the chromosome body and centromere, respectively. Scale bars = 5 μm. Chromosome and centromere sizes were calculated for each ESC line by measuring individual chromosomes (chromosome 19: <italic>n</italic> = 217, 182, 100 and 101; chromosome X: <italic>n</italic> = 189, 201, 99 and 98) and centromeres (chromosome 19: <italic>n</italic> = 100, 82, 90 and 100; chromosome X: <italic>n</italic> = 76, 71, 114 and 98) over three independent experiments, mean ± SD are shown. <italic>P</italic>-values of statistically significant increases, measured by unpaired two tailed Student’s <italic>t</italic>-tests, are indicated. <bold>d</bold> Representative image of ESC metaphase spread stained with chromosome 19 painting probe (green), gamma satellite probe (red) and DAPI (blue). Scale bars = 4 μm and 2 μm for the metaphase spread and zoom-in images, respectively. Chromosome and centromere sizes of chromosome 19 were calculated by measuring metaphase spreads of WT (<italic>n</italic> = 29), <italic>Dnmt1,3a,3b</italic><sup>−/−</sup> (<italic>n</italic> = 20), <italic>Eed</italic><sup>−/−</sup> (<italic>n</italic> = 31) and <italic>Sox2</italic><sup>−/−</sup> (<italic>n</italic> = 28) ESCs over three independent experiments, mean ± SD are shown. <italic>P</italic>-values of statistically significant increases, measured by unpaired two tailed Student’s <italic>t</italic>-tests, are indicated. <bold>e</bold> Flow karyotype and mitotic chromosome sizes of <italic>Eed</italic><sup>−/−</sup> ESCs before and after restoring <italic>Eed</italic> expression (<italic>Eed BAC</italic>). Chromosome and centromere sizes were measured for each ESC line by measuring individual chromosomes (chromosome 19: <italic>n</italic> = 137, 86 and 80; chromosome X: <italic>n</italic> = 130, 91 and 81) and centromeres (chromosome 19: <italic>n</italic> = 100, 90 and 102; chromosome X: <italic>n</italic> = 84, 113 and 100) over three independent experiments, mean ± SD values are shown. <italic>P</italic>-values of statistically significant decreases, measured by unpaired two tailed Student’s <italic>t</italic>-tests, are indicated. <bold>b</bold>–<bold>e</bold> Source data are provided as a <xref rid=\"MOESM10\" ref-type=\"media\">Source data</xref> file.</p></caption><graphic xlink:href=\"41467_2020_17823_Fig2_HTML\" id=\"d30e1058\"/></fig></p><p id=\"Par11\">To confirm that DNA methylation and PRC2 are important for maintaining chromosome compaction, we examined conventional metaphase spreads from WT and mutant ESCs, using DNA FISH to separately identify and measure mouse chromosomes 19 and pericentric γ-satellite repeats (illustrated in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2d</xref>, left). We observe an increase in the overall size of chromosome 19 in metaphase spreads from <italic>Dnmt1,3a,3b</italic><sup>−/−</sup> and <italic>Eed</italic><sup>−/−</sup> ESCs as compared to WT samples and, consistent with previous results, see no appreciable differences in <italic>Sox2</italic><sup>−/−</sup> mutants. In <italic>Dnmt1,3a,3b</italic><sup>−/−</sup> samples we also note increased chromosome 19 centromere size, although this is not significant for <italic>Eed</italic><sup>−/−</sup> samples. Together these experiments suggest that DNA methylation and PRC2 activity have roles in ensuring efficient chromosome compaction in mitosis. To exclude that the chromosome decompaction seen in PRC2-deficient ESCs is due to any inadvertent secondary effects in mutant <italic>Eed</italic><sup>−/−</sup> cells, we examined mitotic chromosomes from <italic>Eed</italic><sup>−/−</sup> ESCs, in which PRC2 activity and H3K27me3 had been restored by transfection with a BAC clone that contained <italic>Eed</italic> (clone B1.3 BAC)<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. As shown in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2e</xref>, metaphase chromosomes 19 and X isolated from these Eed-rescued ESCs (<italic>Eed BAC</italic>) are similar in size and shape to equivalent chromosomes from WT ESCs. These results confirm that the decompaction of mitotic ESC chromosomes seen in the absence of Eed is fully reversed by restoring PRC2 activity.</p></sec><sec id=\"Sec6\"><title>Mecp2 contributes to mitotic compaction of autosomes in ESCs</title><p id=\"Par12\">The methyl-binding protein Mecp2 has been implicated in regulating chromatin architecture at a range of different levels, from the juxtaposition of nucleosome arrays, to condensing pericentric heterochromatin<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup>. Mecp2 binds methylated CpG di-nucleotides, but has also been reported to interact with other partners independent of its methy-CpG-binding activity<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>. Proteomic data shown in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1j</xref> indicate that Mecp2 is bound to mitotic chromosomes. To verify this, we examined Mecp2 distribution in live cells, using a previously generated Mecp2-eGFP ESC line<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. As illustrated in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a</xref> and the associated video (Supplementary Movie <xref rid=\"MOESM6\" ref-type=\"media\">2</xref>), Mecp2 (green) co-localises with condensed chromosomes (labelled with SiR-DNA, greyscale) throughout mitosis. To test whether the mitotic chromosome decompaction observed in <italic>Dnmt1,3a,3b</italic><sup>−/−</sup> ESCs could be at least partially due to abrogated interactions with methyl-binding proteins, such as Mecp2 (rather than reduced methylation per se), we examined mitotic chromosomes from ESCs, in which Mecp2 had been deleted<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>. Comparing flow-sorted metaphase chromosomes 19, 3 and X isolated from parental male Mecp2-expressing (<italic>Mecp2</italic><sup>lox/y</sup>) and Mecp2-deficient (<italic>Mecp2</italic><sup>−/y</sup>) ESCs (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>), we observe significant increases in the sizes of both autosomes in the absence of Mecp2, but no change in the size of the active X chromosome (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3c</xref>). Although CpG methylation is found on active and inactive X chromosomes, analysis of published ChIP-seq data<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref>,<xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup> indicates that Mecp2 is less abundant on the X chromosome than on autosomes (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3a, b</xref>). Mecp2 depletion also results in chromosome 19 and chromosome 3 centromere decompaction (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3c</xref>). This is associated with less pericentric H3K9me3 and a reduction in H3K27me3 labelling on mitotic chromosomes (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3c</xref>). To confirm the importance of Mecp2 for autosome compaction during mitosis, we analysed conventional metaphase spreads prepared from WT and Mecp2-deficient ESCs, using DNA FISH to identify chromosome 19 and pericentric γ-satellite repeats. These analyses, shown in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d</xref>, confirm that Mecp2 depletion results in an increase in chromosome 19 size.<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Increased size of mitotic chromosomes in ESCs lacking Mecp2.</title><p><bold>a</bold> Mecp2 association with chromatin throughout mitosis using live cell imaging of Mecp2-eGFP fusion protein in ESCs. Selected time frames from the same dividing cell are shown. Scale bar = 14 μm. Images are representative of three independent experiments. <bold>b</bold> Flow karyotype of mitotic chromosomes isolated from <italic>Mecp2</italic><sup>lox/y</sup> or <italic>Mecp</italic><sup>−/y</sup> ESCs. Gates used to isolate chromosomes 19, 3 or X are indicated. Images are representative of three independent experiments. <bold>c</bold> Representative images of mitotic chromosomes 19, 3 and X from Mecp2<sup>lox/y</sup> and <italic>Mecp2</italic><sup>−/y</sup> ESCs are shown, where DAPI stain (grey) and Cenpa label (green) indicate the chromosome body and centromere, respectively, scale bars = 5 μm. Chromosome and centromere sizes were calculated for each ESC line by measuring individual chromosomes (chromosome 19: <italic>n</italic> = 100 and 100; chromosome 3: <italic>n</italic> = 80 and 100; chromosome X: <italic>n</italic> = 100 and 100) and centromeres (chromosome 19: <italic>n</italic> = 55 and 60; chromosome 3: <italic>n</italic> = 60 and 60; chromosome X: <italic>n</italic> = 80 and 70) over three independent experiments, mean ± SD are shown. <bold>d</bold> Representative image of <italic>Mecp2</italic><sup><italic>l</italic>ox/y</sup> ESC metaphase spread stained with chromosome 19 painting probe (green), gamma satellite probe (γsat, pink) and DAPI (blue), scale bar = 4 μm. Chromosome and centromere sizes of chromosome 19 were calculated by measuring metaphase spreads of <italic>Mecp2</italic><sup><italic>l</italic>ox/y</sup> (<italic>n</italic> = 8) or <italic>Mecp2</italic><sup>−/y</sup> (<italic>n</italic> = 14) ESCs, mean ± SD are shown. <bold>c</bold>, <bold>d</bold>\n<italic>P</italic>-values of statistically significant increases, measured by unpaired two tailed Student’s <italic>t</italic>-tests, are indicated. Source data are provided as a <xref rid=\"MOESM10\" ref-type=\"media\">Source data</xref> file.</p></caption><graphic xlink:href=\"41467_2020_17823_Fig3_HTML\" id=\"d30e1288\"/></fig></p></sec><sec id=\"Sec7\"><title>Mitotic chromosome size depends upon differentiation state</title><p id=\"Par13\">PRC2 activity and DNA methylation are required for the successful differentiation of ESCs, but not for ESC self-renewal or pluripotency (reviewed in refs. <sup><xref ref-type=\"bibr\" rid=\"CR52\">52</xref>,<xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup>). We therefore asked whether mitotic chromosomes isolated from differentiated cells and ESCs are similar. To examine this, we isolated individual chromosomes from metaphase-arrested mouse ESCs (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref>), pre-B cells (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4b</xref>), mouse cardiomyocyte HL-1 cells (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c</xref>) and primary embryonic fibroblasts (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4d</xref>). Although the success of metaphase arrest varies between the different cell types (45–90%), by applying our flow cytometry-based approach we are able to isolate and purify mitotic chromosomes, irrespective of differences arising from cell cycle synchronisation or karyotype complexity. Using chromosomes 19 and X as representatives, we find that native mitotic chromosomes isolated from differentiated pre-B cells, cardiomyocytes and fibroblasts are smaller than equivalents isolated from ESCs (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4e, f</xref>). This is confirmed by FISH analysis of conventionally prepared metaphase spreads (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4g</xref>). Interestingly, although the sizes of mitotic chromosomes 19 and X are reproducibly larger in ESCs than in each of the differentiated cell types examined, no differences in the sizes of centromeres between each of the cell types is observed (right hand panels of Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4e–g</xref>). This is consistent with constitutive heterochromatin domains being relatively well conserved across different cell types and differentiation stages<sup><xref ref-type=\"bibr\" rid=\"CR52\">52</xref></sup>. As differences in mitotic chromosome size could reflect constraints imposed earlier in the cell cycle, for example, by the size of nuclei, we measured the diameter of G1- and G2-phase nuclei in each of the different cell types (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4a</xref>). Although nuclear size varies between different cell types and mutant ESC lines (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4b, c</xref>, respectively), no correspondence between chromosome size and nuclear dimensions is evident. This observation effectively rules out a simple passive correlation between mitotic chromosome size and nuclear size, at least for the examples studied here. As a caveat, it is conceivable that differences in the arrest times required to obtain metaphase samples of different cell types could confound estimates of chromosome size in these samples.<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Native mitotic ESC chromosomes are larger than equivalents isolated from differentiated cells.</title><p><bold>a</bold>–<bold>d</bold> Cell cycle profiles of mouse ESCs (<bold>a</bold>), pre-B cells (<bold>b</bold>), cardiomyocytes (<bold>c</bold>) and embryonic fibroblasts (<bold>d</bold>) were determined by staining with propidium iodide. Left panel shows asynchronized (async) cells, right panel shows samples 6–12 h after treatment with demecolcine (m-arrested), where values indicate the percentage cells in G2/M stage. Lower panel shows flow karyotype of demecolcine-treated cells and the gates used to isolate chromosomes 19 and X. Images are representative of three independent experiments. <bold>e</bold>, <bold>f</bold> Representative images of native mitotic chromosomes 19 (<bold>e</bold>) and X (<bold>f</bold>) isolated from mouse ESCs, pre-B cells, cardiomyocytes (HL-1) and embryonic fibroblasts. DAPI stain (light grey) and Cenpa (green) labelling are shown, scale bars = 5 μm. Chromosome and centromere sizes were determined for each cell type by measuring individual chromosomes (chromosome 19: <italic>n</italic> = 137, 149, 88 and 106; chromosome X: <italic>n</italic> = 129, 132, 134 and 103) and centromeres (chromosome 19: <italic>n</italic> = 119, 100, 72 and 83; chromosome X: <italic>n</italic> = 81, 81, 70 and 60) over three independent experiments, mean ± SD are indicated. <bold>g</bold> Chromosome and centromere sizes of chromosome 19 (left panel) and chromosome X (right panel) were calculated by measuring metaphase spreads of ESCs (<italic>n</italic> = 23), pre-B cells (<italic>n</italic> = 22) and embryonic fibroblasts (<italic>n</italic> = 36), mean ± SD are indicated. <bold>e</bold>–<bold>g</bold>\n<italic>P</italic>-values of statistically significant decreases, measured by unpaired two tailed Student’s <italic>t</italic>-tests, are indicated. Source data are provided as a <xref rid=\"MOESM10\" ref-type=\"media\">Source data</xref> file.</p></caption><graphic xlink:href=\"41467_2020_17823_Fig4_HTML\" id=\"d30e1415\"/></fig></p></sec><sec id=\"Sec8\"><title>Isolated chromosomes are sensitive to in situ cohesin cleavage</title><p id=\"Par14\">A major potential advantage of purifying native chromosomes from cells in mitosis is that these unfixed chromosomes could be used as a substrate to examine the impacts of experimental perturbations applied in situ. As an example to test this, we asked whether experimental cleavage of cohesin complexes significantly alters the structure of isolated metaphase chromosomes. Cohesin complexes are composed of Smc1, Smc3, the kleisin Scc1, and one of three auxillary subunits<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup> and are required to keep sister chromatids together from DNA replication until mitosis, as well having roles in interphase genome organisation, gene transcription and DNA repair<sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref>–<xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>. Cohesin complexes are dynamically regulated during the cell cycle. While the majority of cohesin is thought to dissociate from chromosome arms during prophase, recent studies have suggested that residual cohesin is critical for retaining elongating RNA polymerase II at centromere domains<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>. Some cohesin remains bound to centromeres until anaphase when separase cleaves the kleisin subunit<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref>,<xref ref-type=\"bibr\" rid=\"CR61\">61</xref>,<xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>. Consistent with this, cohesin components are detected within the metaphase proteome, both by ourselves (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g</xref>), and others<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. To investigate the impact of cohesin cleavage on sorted native metaphase chromosomes, we used a model pre-B cell line that expresses a cleavable form of Rad21 (Rad21-TEV-myc)<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref>,<xref ref-type=\"bibr\" rid=\"CR64\">64</xref></sup>, as illustrated in Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>. In these pre-B cells, Rad21 binding is detected on metaphase samples (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5a</xref>, Rad21 and Myc labelling, green), as well as around the centromeres of sorted native mitotic chromosomes (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5b</xref>). We performed Hi-C analysis of flow-sorted mitotic chromosomes from pre-B cells to confirm that the 3D chromosome contacts present during interphase (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5c</xref>, upper panels) are lost from mitotic chromosome samples (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5c</xref>, lower panels), consistent with previous reports<sup><xref ref-type=\"bibr\" rid=\"CR65\">65</xref>,<xref ref-type=\"bibr\" rid=\"CR66\">66</xref></sup>. We then isolated native mitotic chromosomes from WT and <italic>Rad21</italic><sup>Tev/Tev</sup> pre-B cell lines (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5d</xref>), and examined the impact of cohesin cleavage induced by TEV protease (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>), using advanced optical microscopy or cryo-electron tomography (cryo-ET; Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5b</xref> shows the experimental design). TEV treatment results in efficient cleavage of Rad21-Tev, as verified by Myc immunofluorescence labelling (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5c</xref>) and western blotting (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5e</xref>). TEV-induced cohesin cleavage results in a significant increase in the size of mitotic chromosome 19, compared with either untreated (−TEV) or TEV-treated chromosomes derived from WT pre-B cells (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5d</xref>). Increased mitotic chromosome 19 size is accompanied by a de-condensation at DAPI-bright pericentric domains (arrowed). Further cryo-ET analysis of two independent experiments (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5e</xref>) confirms that TEV-induced cohesin cleavage results in an increase in the size of mitotic (<italic>Rad21</italic><sup>Tev/Tev</sup>) chromosome 19, and a widespread de-condensation is evident in representative 3D images (Supplementary Movies <xref rid=\"MOESM7\" ref-type=\"media\">3</xref> and <xref rid=\"MOESM8\" ref-type=\"media\">4</xref>).<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Experimentally induced cleavage of cohesin alters flow-sorted mitotic chromosome size.</title><p><bold>a</bold> Experimental strategy used to cleave cohesin using TEV protease; illustrated is a cohesin ring containing Smc1, 3 and Rad21-Tev-Myc. <bold>b</bold> Scheme used to isolate and image mitotic chromosomes from WT and Rad21-Tev-myc (<italic>Rad21</italic><sup>Tev/Tev</sup>) pre-B cells. Mitotic chromosomes from WT pre-B cells or from <italic>Rad21</italic><sup>Tev/Tev</sup> pre-B cells were purified by flow cytometry, and incubated with (+) or without (−) TEV protease. <bold>c</bold> Myc labelling (green) of <italic>Rad21</italic><sup>Tev/Tev</sup> purified chromosome 19 shows reduced Myc levels after treatment with TEV protease (images left, and quantified by intensity, right). Scale bar = 5 μm, <italic>n</italic> = 38 chromosomes for −TEV and <italic>n</italic> = 40 chromosomes for +TEV, mean intensity values ± SD are shown. <bold>d</bold> Representative super-resolution SIM images of purified mitotic chromosome 19 isolated from WT or <italic>Rad21</italic><sup>Tev/Tev</sup> pre-B cells, treated with TEV in situ (+TEV) or with buffer alone (−TEV). Scale bars = 2.86 μm. Chromosome and centromere sizes were determined for each condition by measuring individual chromosomes (<italic>n</italic> = 101, 101 and 101) and centromeres (<italic>n</italic> = 65, 64 and 61), values indicate mean ± SD. <bold>e</bold> Representative slices through cryo-electron tomograms (Cryo-ET) of chromosome 19 isolated from <italic>Rad21</italic><sup>Tev/Tev</sup> pre-B cells and treated with TEV in situ (+TEV) or with buffer alone (−TEV) (top panel) and Cryo-ET image explanation (middle panel). Graphs show chromosome size, calculated as area measurements from 2D electron microscopy images, mean ± SD are indicated. Values from two independent experiments are shown, <italic>n</italic> = 20 and 28 chromosomes for experiment 1 and <italic>n</italic> = 14 and 21 chromosomes for experiment 2, for −TEV and +TEV respectively. <bold>c</bold>–<bold>e</bold>\n<italic>P</italic>-values were calculated using an unpaired two tailed Student’s <italic>t</italic>-test. Source data are provided as a <xref rid=\"MOESM10\" ref-type=\"media\">Source data</xref> file.</p></caption><graphic xlink:href=\"41467_2020_17823_Fig5_HTML\" id=\"d30e1611\"/></fig></p></sec></sec><sec id=\"Sec9\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par15\">The isolation and purification of metaphase chromosomes by flow cytometry offers a different approach for studying chromosome structure and function in mitosis. By combining this approach with quantitative LC-MS/MS analysis, we have been able to characterise a large repertoire of proteins that remain bound to unfixed mitotic chromosomes in pluripotent ESCs. These included proteins with established roles in chromatin organisation, DNA and nucleosome packaging, cell cycle and chromosome architecture and function. Three subsets of transcription factors relevant for pluripotency were discerned that showed either a significant enrichment on sorted metaphase chromosomes versus lysates (such as Esrrb, Sox2, Utrf1, Dppa4, Dppa2 and Sall4), were depleted (Dppa5 and Klf5) or were similarly represented in both (Oct4, Nanog and Klf4). The first group includes Esrrb and Sox2, transcription factors that have previously been shown to bind to chromosomes throughout mitosis and implicated in mitotic bookmarking in ESCs<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>, while the latter two groups comprise candidates that may either be evicted from condensing chromosomes, or be in dynamic flux, appearing to be similarly distributed in mitotic lysates and chromosome samples. Importantly, we have shown that metaphase chromosomes isolated by flow cytometry retain the genome-wide chromatin accessibility features that characterise mitosis in ESCs<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>, but lack interphase and cytoplasmic contaminants, that may have confounded similar past studies.</p><p id=\"Par16\">Proteomic analyses revealed that many of the core component of PRC1 (Rnf2, Pcgf6, Cbx2 and Phc1) and PRC2 (Eed, Ezh2, Suz12, Pcl2 and Jarid2), that are responsible for catalysing histone H2AK119 mono-ubiquitination and histone H3K27 tri-methylation, respectively<sup><xref ref-type=\"bibr\" rid=\"CR67\">67</xref></sup>, were enriched on metaphase chromosomes in ESCs. Our results were obtained using unfixed but highly purified metaphase ESCs and our data resemble those published in a recent study of chromatin-bound changes through the cell cycle of human glioblastoma T98G cells<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>, which showed chromatin repressor complexes remaining bound throughout mitosis. Prior studies in <italic>Drosophila</italic> embryos, as well as in human primary cells, suggested that polycomb group proteins, such as PC, PH, PSC and BMI1 dissociate from condensing chromosomes in prophase to metaphase<sup><xref ref-type=\"bibr\" rid=\"CR68\">68</xref>,<xref ref-type=\"bibr\" rid=\"CR69\">69</xref></sup>. However, studies of living rather than fixed samples have suggested that although GFP-tagged PC fusion proteins were depleted in mitosis relative to interphase, some PC complexes remain bound through cell division<sup><xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup>. Here, using similar live cell imaging approaches, we have confirmed that PRC2 complexes remain bound to chromosomes throughout mitosis in ESCs. Our proteomic data also showed that Dnmt1, Dnmt3a, Dnmt3b and methyl-CpG-binding protein Mecp2, were enriched on metaphase ESC chromosomes, a result that was confirmed in live cell imaging experiments using eGFP-tagged Mecp2 fusion proteins. The observation that PRCs and DNA methylation machinery remain chromosome-associated throughout mitosis is intriguing, and is consistent with the possibility that mitotic memory is conveyed both by repressive chromatin states, as well as factors that bookmark the genome to activate and enhance gene expression<sup><xref ref-type=\"bibr\" rid=\"CR71\">71</xref></sup>.</p><p id=\"Par17\">To assess the functional impacts of chromatin repressors on chromosome structure in mitosis, we examined chromosomes from ESCs that lacked PRC2 activity, DNA methylation or Mecp2, compared with WT ESCs or cells that lacked the transcription factor Sox2. We showed that genome-wide loss of DNA methylation resulted in chromosomes and centromeres that were larger and less compact than equivalent mitotic chromosomes in WT or <italic>Sox2</italic><sup>−/−</sup> ESCs. At first sight, this is surprising since in interphase reduced DNA methylation is reported to affect nuclear organisation, histone modifications and linker histone binding, but does not directly alter chromatin compaction<sup><xref ref-type=\"bibr\" rid=\"CR72\">72</xref></sup>. Furthermore, at the nucleosome level, in vitro studies have indicated that DNA methylation alone does not induce chromatin compaction<sup><xref ref-type=\"bibr\" rid=\"CR73\">73</xref>,<xref ref-type=\"bibr\" rid=\"CR74\">74</xref></sup>. This suggests that the decompaction of metaphase chromosomes seen in <italic>Dnmt1,3a,3b</italic><sup>−/−</sup> ESCs most likely stems from secondary changes that serve to relax the higher order structure of chromatin<sup><xref ref-type=\"bibr\" rid=\"CR75\">75</xref></sup>, perhaps through altered histone H1 binding or an impaired recruitment of Mecp2. Mecp2 is a multifunctional protein that can bind both methylated and un-methylated DNA, can compete with histone H1, and has been shown to directly mediate nucleosome oligomerisation and compaction in vitro<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup>. Consistent with the reduced chromosome compaction seen in <italic>Dnmt1,3a,3b</italic><sup>−/−</sup> mutants being attributable to a failure to recruit DNA-binding proteins, rather than a lack of 5mC per se, we saw impaired mitotic chromosome compaction in <italic>Mecp2</italic> mutants, where DNA methylation is preserved. Mitotic chromosomes from ESCs that lacked PRC2 activity and H3K27me3 were also much less compact than equivalents from WT or Sox2 mutants. Importantly, mitotic chromosome compaction could be rescued in mutants by restoring PRC2 activity. These results, taken together, highlight a previously unrecognised role for chromatin repressors in maintaining mitotic chromosome structure.</p><p id=\"Par18\">Our results also indicate that the size of individual metaphase chromosomes differs between different cell types. Native mitotic chromosomes purified from ESCs were much less condensed than equivalents isolated from either lymphocytes, cardiomyocytes or fibroblasts. The idea that chromosomes of pluripotent ESCs might be more ‘loosely packed’ than somatic equivalents is consistent with previous studies in interphase, in which electron spectroscopic imaging (ESI) revealed that ESCs and cells of the mouse early epiblast (E3.5) lack the compact chromatin domains that characterise differentiated lymphocytes, liver and kidney cells<sup><xref ref-type=\"bibr\" rid=\"CR76\">76</xref></sup>. ESI studies have also shown that at later stages of development (E5.5) epiblast cells lose the dispersed 10 nm chromatin fibres that are so-called architectural hallmarks of pluripotency<sup><xref ref-type=\"bibr\" rid=\"CR77\">77</xref></sup>. Previous studies in embryos from <italic>Caenorhabditis elegans</italic> and in different <italic>Xenopus</italic> species have suggested that mitotic chromosome size scales with nuclear size, or RanGTP levels regulated by the chromatin-associated factor RCC1 (guanine nucleotide exchange factor)<sup><xref ref-type=\"bibr\" rid=\"CR78\">78</xref>–<xref ref-type=\"bibr\" rid=\"CR80\">80</xref></sup>. In the studies shown here, a correlation between nuclear size and mitotic chromosome size across different cell types was not evident. While future studies will be required to determine why mitotic chromosomes in ESCs are less condensed than those of their differentiated counterparts, we note that several candidates implicated in regulating mitotic chromosome compaction, including Cenpa, the DNA-decatenating enzyme topoisomerase II, cohesin and condensin, core and linker histones<sup><xref ref-type=\"bibr\" rid=\"CR81\">81</xref></sup>, were detected within the proteome of sorted mitotic ESC chromosomes.</p><p id=\"Par19\">Our ability to purify and compare individual native mitotic chromosomes isolated from different cell types, combined with appropriate genetic and biochemical tools that enable inducible protein cleavage or degradation, means that it is possible to examine the impact of specific proteins on mitotic chromosome structure. In this regard, we have demonstrated that dynamic cleavage of cohesin complexes on metaphase chromosomes in situ prompts a widespread chromosomal de-condensation that includes, but extends beyond, centromeric domains. This result indicates that mitotic chromosomes isolated in their native state remain sensitive to regulators of chromosome architecture, a finding that should enable the mechanisms of chromosome condensation and de-condensation to be more closely observed and interrogated at the molecular and structural level.</p></sec><sec id=\"Sec10\"><title>Methods</title><sec id=\"Sec11\"><title>Cells</title><p id=\"Par20\">Mouse ESCs used in this study were WT E14Tg2a<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>, <italic>Sox2</italic><sup>−/−</sup> (clone 2O5)<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>, <italic>Dnmt1,3a,3b</italic>\n<sup>−/−42</sup><italic>, Eed</italic><sup>−/−</sup> (clone B1.3), rescued <italic>Eed</italic> null (<italic>Eed BAC</italic>)<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>, floxed <italic>Mecp2</italic> ESC clones (<italic>Mecp2</italic><sup>lox/y</sup> and <italic>Mecp2</italic><sup>−/y</sup>) and Mecp2-eGFP (gift from Jacky Guy), Esrrb-tdTomato (gift from Nicola Festuccia), and Pcl2-halo and Suz12-halo-tagged ESC lines (gift from Robert J. Klose). ESCs were cultured on 0.1% gelatin-coated plates. Cells were grown in KO-DMEM medium supplemented with 15% FCS, non-essential amino acids, L-glutamine, 2-mercaptoethanol, antibiotics and 1000 U ml<sup>−1</sup> of leukaemia inhibitory factor. Abelson-transformed pre-B cell lines (WT and <italic>Rad21</italic><sup>Tev/Tev</sup> pre-B cells) were cultured in IMDM medium supplemented with 10% FCS, 2 mM L-glutamine, antibiotics, and 50 µM 2-mercaptoethanol and non-essential amino acids. These cells were previously derived in our lab from transgenic Rad21-Tev-Myc mice<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. The <italic>Rad21</italic><sup>Tev/Tev</sup> pre-B cell line expresses a modified and functional Rad21 protein containing three Tev cleavage sites within the flexible polypeptide connecting the N-terminal and C-terminal domains. The presence of a Myc tag allows us to follow the cleavage and the chromosomal localisation of Rad21 with an anti-Myc antibody. Mouse cardiomyocyte cells (HL-1; Gift from Stuart Cook) were cultured in Claycomb medium (Sigma-Aldrich) supplemented with 10% FBS (F2442, Sigma-Aldrich), 10 µg ml<sup>−1</sup> penicillin and streptomycin, 2 mM L-glutamine and 0.1 mM norepinephrine. Mouse embryonic fibroblasts were cultured in DMEM supplemented with 10% FCS, 10 µg ml<sup>−1</sup> penicillin and streptomycin, and 2 mM L-glutamine.</p></sec><sec id=\"Sec12\"><title>Metaphase arrest and propidium iodide staining</title><p id=\"Par21\">Twenty four hours after passaging the cells, demecolcine (D1925, Sigma-Aldrich) was added to the culture medium, to a final concentration of 0.1 μg ml<sup>−1</sup>. ESCs and pre-B cells were incubated with demecolcine for 6 h at 37 °C. Fibroblast and cardiomyocyte cells were incubated for 12 h at 37 °C. Cells were collected before and after metaphase arrest (after mitotic shake off). Next, 10<sup>6</sup> cells were fixed with ice-cold 70% ethanol overnight at −20 °C. Prior to staining, cells were washed twice with PBS and resuspended in staining buffer containing 0.05 mg ml<sup>−1</sup> of PI, 1 mg ml<sup>−1</sup> RNase A and 0.05% NP40. Samples were incubated for 10 min at room temperature (RT) and 20 min on ice. PI signal was analysed in a linear mode using a BD LSRII flow cytometer and BD DIVA software (version 8.0.1).</p></sec><sec id=\"Sec13\"><title>Chromosome preparation and flow sorting</title><p id=\"Par22\">Chromosomes were prepared using a polyamine-based method<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref>,<xref ref-type=\"bibr\" rid=\"CR82\">82</xref></sup>. Mitotic cells were collected by mitotic shake off. The cells were centrifuged at 289 × <italic>g</italic> for 5 min at RT. The cell pellets were gently resuspended in 5–10 ml of hypotonic solution (75 mM KCl, 10 mM MgSO<sub>4</sub>, 0.5 mM spermidine and 0.2 mM spermine; pH = 8) for 15 min at RT. After swelling, cells were then centrifuged at 300 × <italic>g</italic> for 5 min at RT and resuspended in 1–3 ml of freshly prepared ice-cold polyamine isolation buffer (15 mM Tris-HCl, 2 mM EDTA, 0.5 mM EGTA, 80 mM KCl, 3 mM DTT, 0.25% Triton X-100, 0.2 mM spermine and 0.5 mM spermidine; pH = 7.5). After 15 min of incubation on ice, the chromosomes were released by vortexing vigorously for 20–30 s. To increase chromosome recovery, suspensions were passed through a 22-gauge needle using a 1 ml syringe. Chromosome suspensions were centrifuged for 2 min at 200 × <italic>g</italic> at RT. The supernatant containing mitotic chromosomes was filtered using a 20 μm mesh filter into a 15 ml falcon tube. Chromosomes were stained at 4 °C overnight with 5 μg ml<sup>−1</sup> Hoechst 33258, 50 μg ml<sup>−1</sup> chromomycin A3 and 10 mM MgSO<sub>4</sub>. At least 1 h prior chromosome sorting, sodium citrate and sodium sulfite were added to chromosome suspensions to a final concentration of 10 mM and 25 mM, respectively. Chromosomes were examined by flow cytometry using a Becton Dickinson Influx (BD FACS software version 1.2.0.142), equipped with spatially separated air-cooled lasers. Hoechst 33258 was excited using a (Spectra Physics Vanguard) 355 nm laser with a power output of 350 mW. Hoechst 33258 fluorescence was collected using a 400 nm long pass filter in combination with a 500 nm short pass filter. Chromomycin A3 was excited using a (Melles Griot) 457 nm laser with a power output of 300 mW. Chromomycin A3 fluorescence was collected using a 500 nm long pass filter in combination with a 600 nm short pass filter. Forward scatter was measured using a (Coherent Sapphire) 488 nm laser with a power output of 200 mW, and this was used as the trigger signal for data collection. Chromosomes were sorted at an event rate of 15,000 per second. A 70 µm nozzle tip was used along with a drop drive frequency set to ~96 kHz and the sheath pressure was set to 65 PSI. Isolated chromosomes were collected in DNA low-binding tubes containing an excess of polyamine buffer.</p></sec><sec id=\"Sec14\"><title>Proteomics</title><p id=\"Par23\">Flow-sorted mitotic chromosomes were pelleted by centrifugation (18,000 × <italic>g</italic>, 10 min, 5 °C). Supernatant was removed and the obtained pellet was processed by the in-Stage Tip digestion protocol<sup><xref ref-type=\"bibr\" rid=\"CR83\">83</xref></sup> using commercially available iST tips (Preomics, Martinsried, Germany) according to the manufacturer’s recommendations. Briefly, pellets were suspended in lysis buffer, and heat denatured, reduced and alkylated on a heated shaking incubator (1,000 r.p.m., 10 min, 95 °C). DNA was fragmented by sonication in an ultrasonic water bath (10 min) and samples were digested with trypsin (500 r.p.m., 1 h, 37 °C). Sample clean-up and desalting was carried out in the iST device using the recommended wash buffers. Peptides were eluted with elution buffer (2 × 100 µl), concentrated in a centrifugal evaporator and resuspended in LC loading buffer (20 µl).</p><p id=\"Par24\">LC-MS/MS analysis was performed as follows. Resuspended protein digests were transferred to auto sampler vials for LC-MS analysis. Peptides were separated using an ultimate 3000 RSLC nano liquid chromatography system (Thermo Scientific) coupled to a Q-Exactive HF-X tandem mass spectrometer (Thermo Scientific) via an EASY-Spray source. Sample volumes were loaded onto a trap column (Acclaim PepMap 100C18, 100 µm × 2 cm) at 8 µl min<sup>−1</sup> in 2% acetonitrile and 0.1% TFA. Peptides were eluted on-line to an analytical column (EASY-Spray PepMap C18, 75 µm × 75 cm). Peptides were separated at 200 nl min<sup>−1</sup> using a ramped 120 min gradient from 1–42% buffer B in buffer A (buffer A: 5% DMSO and 0.1% formic acid; buffer B: 75% acetonitrile, 0.1% formic acid and 5% DMSO). Eluted peptides were analysed operating in positive polarity using a data-dependent acquisition mode. Ions for fragmentation were determined from an initial MS1 survey scan at 120,000 resolution (at <italic>m</italic>/<italic>z</italic> 200) in the orbitrap followed by HCD (higher-energy collisional dissociation) of the top 30 most abundant. MS1 and MS2 scan AGC targets set to 3e6 and 5e4 for a maximum injection time of 25 ms and 50 ms, respectively. A survey scan covering the range of 350–1750 <italic>m</italic>/<italic>z</italic> was used, with HCD parameters of isolation width 1.6 <italic>m</italic>/<italic>z</italic> and a normalised collision energy of 27%. Data obtained from biological triplicate experiments (each loaded in duplicate) were analysed using the LFQ algorithm in the MaxQuant software platform (version 1.6.2.3)<sup><xref ref-type=\"bibr\" rid=\"CR84\">84</xref></sup>, with database searches carried out by the in-built Andromeda search engine against the Swissprot <italic>Mus musculus</italic> database (16,950 entries, v.20180104). A reverse decoy database was created and results displayed at a 1% FDR for peptide spectrum matches and protein identifications. Search parameters included: trypsin, two missed cleavages, fixed modification of cysteine carbamidomethylation and variable modifications of methionine oxidation, asparagine deamidation and protein N-terminal acetylation. LFQ was enabled with an LFQ minimum ratio count of 2. ‘Match between runs’ function was used with match and alignment time limits of 0.7 and 20 min, respectively. Statistical analysis as well as data visualisation were performed using the Perseus software platform<sup><xref ref-type=\"bibr\" rid=\"CR85\">85</xref></sup>.</p><p id=\"Par25\">Following data processing in MaxQuant, the proteinGroups.txt file was analysed in Perseus (version 1.6.2.2) by uploading the data matrix with the respective LFQ intensities as main columns. The data matrix was reduced by filtering based on categorical columns to remove reverse decoy hits, potential contaminants and protein groups which were ‘only identified by site’. Gene Ontology (GO) annotations for taxonomy <italic>M. musculus</italic> (mainAnnot.mus_musculus.txt) were downloaded from <ext-link ext-link-type=\"uri\" xlink:href=\"http://annotations.perseus-framework.org\">http://annotations.perseus-framework.org</ext-link>. GO annotations for molecular function and cellular compartment were imported by annotating columns based on majority protein IDs. Groups of technical replicate injections and biological replicates of the two conditions (‘lysate pellet’ and ‘flow sorted’) were defined in categorical annotation rows. Missing values were replaced with ‘NaN’ (Quality->Convert to NaN), the technical duplicates were averaged (annot. rows->average groups->mean value; min. one valid value) and data were log transformed (Basic->Transform->log2(x)). Data were then visualised as LFQ intensity histograms (per biological replicate), and LFQ intensity Multi scatter plots and Numeric Venn Diagram numbers were generated in Analysis. Volcano plots were generated based on LFQ intensities with the following settings: test: <italic>t</italic>-test; side: both; number of randomisations: 250; preserve grouping in randomisations: <none>; FDR: 0.01; S0: 0.1. Hierarchical clustering analysis (HCA) was carried out after filtering rows based on a minimum of two valid values in at least one group, <italic>Z</italic>-scoring of values in rows and a two-sample <italic>t</italic>-test of the conditions (‘lysate pellet’ and ‘flow sorted’) using the following settings: Student’s <italic>t</italic>-test; S0: 0; side: both; FDR: 0.05. After filtering rows retaining <italic>t</italic>-test significant hits only, the HCA was generated with the following settings for both rows tree and columns tree: distance: euclidean; linkage: average; constraint: none; preprocess with <italic>k</italic>-means selected (number of clusters: 300; maximal number of iterations: 10; number of restarts: 1).</p></sec><sec id=\"Sec15\"><title>Immunofluorescence on flow-sorted chromosomes</title><p id=\"Par26\">Flow-sorted chromosomes (chromosomes 19 and X) were spun onto poly-L-lysine-coated slides (VWR) by cytocentrifugation (Cytospin3, Shandon) at 1300 r.p.m. for 10 min at RT. Chromosome samples were blocked with 6% normal goat serum for 1 h at RT and incubated overnight at 4 °C in a humid chamber with primary antibodies to Cenpa (2040 S, Cell Signaling, 1/200), Rad21 (Ab154769, Abcam, 1/100), Myc (SC40, Santa Cruz, 1/200), Sox2 (Ab97959, Abcam, 1/100), Nanog (REC-RCAB0002P-F, 2bScientific, 1/100), Oct4 (sc-5279, Santa Cruz, 1/100), H3K9me3 (07-523, Millipore, 1/200) or H3K27me3 (ab6002, Abcam, 1/200). Chromosomes were washed (buffer containing 10 mM HEPES, 2 mM MgCl<sub>2</sub>, 100 mM KCl and 5 mM EGTA) and incubated with appropriate secondary antibodies (anti-mouse-Alexa488 (A11001, Invitrogen, 1/200), anti-rabbit-Alexa488 (A11008, Invitrogen, 1/400) or anti-mouse-A566 (A11031, Invitrogen, 1/200) for 1 h at RT. Immuno-stained chromosomes were mounted in Vectorshield mounting medium containing DAPI. Wide-field epi-fluorescence microscopy was performed on an Olympus IX70 inverted microscope using a UPlanApo 100×/1.35 oil objective lens. Super-resolution structured illumination (SIM) microscopy was performed on a Zeiss Elyra microscope using a Plan-Apochromat 63×/1.4 oil objective lens. Fluorescent excitation was performed with 405 nm and 488 nm lasers, and fluorescent emission was collected using bandpass 420–480 nm, bandpass 495–550 nm and long pass 650 nm filters.</p></sec><sec id=\"Sec16\"><title>Telomere labelling</title><p id=\"Par27\">ESCs were transfected with 12 µg of TRF1-YFP plasmid<sup><xref ref-type=\"bibr\" rid=\"CR86\">86</xref></sup>. TRF1-YFP-positive cells were sorted using a BD AriaIII flow sorter and grown in normal ESC medium. Metaphase chromosomes 19 and X from TRF1-YFP-positive cells were sorted, and analysed by optical imaging.</p></sec><sec id=\"Sec17\"><title>ATAC-seq</title><p id=\"Par28\">ATAC-seq was performed in duplicate on asynchronous cells, mitotic cells and purified mitotic chromosomes. For asynchronous cells, the Omni-ATAC-seq protocol was used to obtain nuclei<sup><xref ref-type=\"bibr\" rid=\"CR87\">87</xref></sup>. Briefly, 5 × 10<sup>4</sup> ESCs were lysed on ice for 3 min in 50 μl of ATAC-resuspension buffer (10 mM Tris-HCl, pH 7.4; 10 mM NaCl; 3 mM MgCl<sub>2</sub>) containing 0.1% Igepal CA-630, 0.1% Tween-20 and 0.01% Digitonin. After adding 1 ml of ATAC-resuspension buffer containing 0.1% Tween-20, nuclei were pelleted at 500 × g (10 min at 4 °C). ATAC-seq was subsequently performed largely according to the original protocol<sup><xref ref-type=\"bibr\" rid=\"CR88\">88</xref></sup>. Briefly, nuclei from asynchronous cells, mitotic cells (5 × 10<sup>4</sup>) or purified chromosomes (2 × 10<sup>6</sup>) were resuspended in 50 μl transposase mixture (25 μl Illumina TD buffer, 22.5 μl H<sub>2</sub>O and 2.5 μl Illumina TDE1 transposase) and incubated at 37 °C for 30 min with shaking at 1000 r.p.m. After transposition, DNA was purified with the Qiagen MinElute kit and amplified with seven cycles of PCR using the NEBNext High Fidelity master mix and the primers shown in Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref> (ref. <sup><xref ref-type=\"bibr\" rid=\"CR88\">88</xref></sup>). Libraries were subjected to two rounds of Ampure XP bead (Beckman Coulter) purification, including a size selection step using 0.5× beads to remove large fragments. Libraries were assessed by Qubit, Bioanalyzer and with the KAPA Library Quantification Kit (Roche) before sequencing on the Illumina NextSeq system (75 bp, paired end). ATAC-seq data were initially processed using the nfcore/atacseq pipeline version 1.1.0 (ref. <sup><xref ref-type=\"bibr\" rid=\"CR89\">89</xref></sup>; 10.5281/zenodo.2634132), aligning to the mm10 genome to give >45 million mapped read pairs for each library. Mapped reads were imported into Seqmonk (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.bioinformatics.babraham.ac.uk/projects/seqmonk\">www.bioinformatics.babraham.ac.uk/projects/seqmonk</ext-link>, version 1.46.0) for downstream analysis. Transposase insertion centres were determined by extracting the 5′ ends of all reads (two per read-pair) and offsetting these sites by +4 bp/−5 bp (ref. <sup><xref ref-type=\"bibr\" rid=\"CR88\">88</xref></sup>). For chromosome-wide accessibility profiles, ATAC-seq enrichment was calculated as the number of insertion sites in 25 kb windows, relative to the genome-wide average. For accessibility trend plots, insertion sites were extended ±25 bp to smooth signal and plotted as the average relative distribution across 2 kb windows centred on Esrrb peak summits. Esrrb peak locations and bookmarking status were taken from ref. <sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>, and coordinates were converted to mm10 using the UCSC (genome.ucsc.edu) LiftOver tool.</p></sec><sec id=\"Sec18\"><title>Live cell imaging</title><p id=\"Par29\">Twenty four hours prior to imaging, Esrrb-tdTomato, Mecp2-eGFP, Pcl2-halo and Suz12-halo-tagged ESCs were grown in ESC medium in Ibidi μ-Sildes 8 Well (Ibidi, 80826), pre-coated with gelatin. Halo-tagged Pcl2 and Suz12 fusion proteins were labelled with 200 nM of Janelia Fluor 549 HaloTag ligand (Promega) for 15 min at 37 °C in 5% CO<sub>2</sub>. Cells were washed twice with PBS, then washed once with ESC medium for 15 min. A total of 30 min prior to live cell imaging, cells were switched to medium without phenol red (Gibco, 31053-028) and were incubated with 1 µM of SiR-DNA (SC007, SpiroChrome). Time-lapse images were acquired on a Leica TCS SP8 confocal microscope using a 63×/1.40 NA oil objective and Ludin environmental chamber kept at 37 °C with a 5% CO<sub>2</sub> supply. <italic>Z</italic>-stacks were collected every 90 s with a step size of 4 µm. To clearly visualise all stages of mitosis, individual focal planes were extracted from the <italic>z</italic>-stacks and a Gaussian Blur (sigma = 1) was applied to the time series in Fiji (version 1.52p) to reduce noise.</p></sec><sec id=\"Sec19\"><title>Flow-sorted chromosome size measurements</title><p id=\"Par30\">After flow sorting, chromosome 19 and chromosome X (10<sup>5</sup>) were spun onto poly-L-lysine-coated slides by cytocentrifugation (Cytospin3, Shandon) at 1300 r.p.m. for 10 min at RT. Chromosomes were stained with anti-Cenpa (2040S, Cell Signalling, 1/200), washed three times, then incubated with secondary antibody (A11001, Invitrogen). Immuno-stained samples were mounted in Vectorshield mounting medium containing DAPI before optical imaging. Chromosome images were acquired using wide-field epi-fluorescence microscopy performed on an Olympus IX70 inverted microscope (Micro-Manager version 1.4.22) using a UPlanApo 100×/1.35 Oil Objective lens. SIM microscopy was performed on a Zeiss Elyra microscope (ZEN 2012 SP4, version 13.0.2.518) using a Plan-Apochromat 63×/1.4 oil objective lens. Flourescent excitation was performed with 405 nm and 488 nm lasers, and fluorescent emission was collected using bandpass 420–480 nm, bandpass 495–550 nm, and long pass 650 nm filters. Images were analysed using Fiji/ImageJ (version 1.52p) software<sup><xref ref-type=\"bibr\" rid=\"CR90\">90</xref></sup>. Chromosome and centromere size measurements of SIM imaging data were assessed using a custom macro in Fiji to estimate chromosome (total DAPI) and centromere (DAPI high) areas.</p></sec><sec id=\"Sec20\"><title>Metaphase spreads</title><p id=\"Par31\">Exponentially growing cells (60–80% confluent if adherent) were incubated with 0.1 μg ml<sup>−1</sup> demecolcine solution to arrest cells at metaphase. Adherent cells were then trypsinized and pelleted; non-adherent cells were pelleted directly (5 min at 289 × <italic>g</italic>). Pelleted cells were resuspended in hypotonic solution (40 mM KCl, 0.5 mM EDTA, 20 mM HEPES, pH to 7.4 using NaOH, pre-warmed to 37 °C) for 25 min at 37 °C. Nuclei were pelleted (8 min at 500 × <italic>g</italic>) and supernatant removed (apart from a small drop to re-suspend the pellet) prior to addition of 3:1 MeOH:glacial acetic acid (both Fisher Chemical) fixative (made fresh and pre-cooled to −20 °C) to the top of the tube. The tubes were stored at −20 °C overnight. The next day nuclei were pelleted (8 min at 500 × <italic>g</italic>) and washed in fresh 3:1 MeOH:glacial acetic acid three times before preparation of metaphase spreads. To prepare chromosome spreads nuclei were pelleted and resuspended in a small volume of fixative (to a pale grey solution). A 20 μl drop of 45% acetic acid in water was pipetted onto a glass Twinfrost microscope slide and 23 μl of spread mixture dropped onto it, tilting the slide to spread the nuclei. The slides were air-dried and stored dry at RT. XCyting Mouse Chromosome Painting Probes (Metasystems Probes) to chromosomes X and 19 were used alone or together with mouse gamma satellite probes (DNA a gift from Niall Dillon) directly labelled with FluoroRed (Amersham Life Science RPN2122) by nick translation, to detect chromosomes X or 19 with pericentromeric DNA. Metaphase chromosome painting was performed according to the protocol supplied by Metasystems Probes and mounted in Vectashield containing DAPI. Leica SPII confocal microscope was used for imaging.</p></sec><sec id=\"Sec21\"><title>Chromosome painting on flow-sorted chromosomes</title><p id=\"Par32\">Flow-sorted chromosomes 19 and X (10<sup>5</sup>) were spun onto poly-L-lysine-coated slides by cytocentrifugation (Cytospin3, Shandon) at 1300 r.p.m. for 10 min at RT. Samples were hybridised with mouse chromosome 19 (D-1419-050-FI, Metasystems Probes) or X paints (D-1420-050-OR, Metasystems Probes) according to the manufacturer’s instructions.</p></sec><sec id=\"Sec22\"><title>Immunofluorescence on cells</title><p id=\"Par33\">ESCs were cultured on gelatin-coated glass coverslips, pre-B cells were spun onto poly-L-lysine-coated slides by cytocentrifugation (Cytospin3, Shandon) at 1300 r.p.m. for 10 min. Cells were fixed with 3.7% paraformaldehyde and then permeabilized with 0.1% Triton X-100. After blocking with 1% bovine serum albumin and 10% donkey serum (Sigma), cells were incubated with primary antibodies (listed above) at 4 °C overnight. Finally, cells were labelled with Alexa Fluor-conjugated secondary antibodies and nuclear stained with DAPI. For 5mC immunofluorescence, the cells were incubated with 2 N HCl at RT for 40 min and then neutralised with 0.1 M sodium borate (pH 9.0) for 15 min before the blocking step, and anti-5mC (MABE146, Millipore) was used at 1:2000 dilution. Images were taken with an Olympus IX70 inverted fluorescence microscope at 40× magnification.</p></sec><sec id=\"Sec23\"><title>Cellular and nuclear size measurements</title><p id=\"Par34\">Cells were labelled with PI as described above. Data acquisition was performed on an Amnis image stream flow cytometer and analysed using Amnis IDEAS software (version 6.2). Cells in G1 or in G2/M were discriminated on the basis of DNA content using PI intensity. Brightfield measurements were used to estimate cell size, and refined PI measurements were used to delineate and determine nuclear size.</p></sec><sec id=\"Sec24\"><title>TEV protease cleavage</title><p id=\"Par35\">Total chromosomes (10<sup>7</sup>) or chromosome 19 (2 × 10<sup>5</sup>) were incubated with or without the AcTEV protease (Invitrogen, 10 units) for 4 h at RT with gentle rotation, according to the manufacturer’s instructions. Samples were then collected for western blot, or prepared for optical imaging or Cryo-ET.</p></sec><sec id=\"Sec25\"><title>Cryo-electron tomography</title><p id=\"Par36\">Samples for cryo-ET were prepared by mixing 10 µl of flow-sorted chromosome 19 with 1 µl of protein-A conjugated to 10 nm colloidal gold (CMC, Utrecht). A total of 2.5 µl of the mixture was pipetted onto freshly glow-discharged Quantifoil Cu/Rh R3.5/1 200 mesh grids and plunge frozen into liquid ethane after removal of excess liquid using a Vitrobot Mark IV (FEI). Frozen grids were transferred to and stored in liquid nitrogen until imaging. Tilt series data were collected on a FEI Titan Krios operating at 300 keV, equipped with a Quantum energy filter and a K2 direct electron detector (Gatan) operating in counting mode, using SerialEM software (version 3.6)<sup><xref ref-type=\"bibr\" rid=\"CR91\">91</xref></sup>. Tilt series were collected in two directions starting from 0°, at an unbinned calibrated pixel size of 8.4 Å between ±60° with a 1° increment at 9 µm underfocus. A combined dose of 100 e Å<sup>−2</sup> was applied over the entire series. Tilt series data were aligned and visualised using IMOD (version 4.7)<sup><xref ref-type=\"bibr\" rid=\"CR92\">92</xref></sup>.</p></sec><sec id=\"Sec26\"><title>Hi-C on isolated chromosomes</title><p id=\"Par37\">Hi-C was adapted from ref. <sup><xref ref-type=\"bibr\" rid=\"CR93\">93</xref></sup>. Mitotic cell lysate pellets or sorted chromosomes were cross-linked in 1% formaldehyde for 10 min at RT. Chromatin was digested with 600 units of HindIII overnight at 37 °C with rotation. Digested sticky ends were filled in by DNA polymerase I, Large (Klenow) fragment in the presence of 50 nM biotin-14-dATP, 50 nM dTTP, 50 nM dGTP and 50 nM dCTP for 90 min at 37 °C with rotation. Chromatin fragment ends were ligated with 4000 units of T4 DNA ligase by incubating at RT for 6 h with slow rotation. Proteins were digested with proteinase K and chromatin was reverse cross-linked overnight. RNase A was used to remove RNA after decrosslinking. After isolation with phenol/chloroform/isoamyl alcohol (25:24:1 mixture), DNA was precipitated by sodium acetate/ethanol precipitation. DNA was sheared for 9 min with the Bioruptor sonicator (30 s on and 30 s off per minute, using high power setting). DNA fragments in the range of 300–500 bp were selected with AMPure XP beads. After biotinylated DNA was captured on Dynabeads MyOne Streptavidin T1, DNA ends were repaired in a mixture of T4 polynucleotide kinase, T4 DNA polymerase I and DNA polymerase I, large (Klenow) fragment. dATP was then added to the repaired ends using Klenow Fragment (3′→5′ exo<sup>−</sup>). NEBNext adaptors for Illumina were ligated to the dA-tailed ends. After USER enzyme digestion, NEBNext oligos for Illumina were used for library preparation. A PCR titration was performed to determine the minimal number of PCR cycles (eight cycles in this work). Hi-C libraries were sequenced on an Illumina HiSeq 2500 sequencer to generate 2 × 100 bp paired-end reads for downstream analysis. Hi-C data were mapped and processed using bowtie 2 (version 2.3.2) and HiC-Pro (version 2.7.8) with default settings<sup><xref ref-type=\"bibr\" rid=\"CR94\">94</xref>,<xref ref-type=\"bibr\" rid=\"CR95\">95</xref></sup>. Raw sequencing data were mapped to the <italic>M. musculus</italic> genome (UCSC assembly mm9, NCBI build 37). PCR duplicates and read pairs that aligned on the same restriction fragment were removed. After converting using the HiC-Pro hicpro2juicebox.sh, valid chromatin contacts were normalised by the KR matrix balancing algorithm and hic files were created using the Juicer Pre (Juicer tools version 0.7.5)<sup><xref ref-type=\"bibr\" rid=\"CR96\">96</xref></sup>. Heatmaps of chromatin interaction matrices were generated and visualised using Juicebox (version 1.6.2)<sup><xref ref-type=\"bibr\" rid=\"CR97\">97</xref></sup>.</p></sec><sec id=\"Sec27\"><title>Western blot</title><p id=\"Par38\">A total of 10<sup>7</sup> flow-purified chromosomes were incubated with ten units of recombinant TEV protease (Invitrogen) for 4 h at RT. After centrifugation, chromosome pellet was resuspended in 30 µl of cold RIPA buffer (50 mM Tris-HCl (pH 8.8), 150 mM NaCl, 1% Triton X-100, 0.5% sodium deoxycholate, 0.1% SDS, 1 mM EDTA, 3 mM MgCl<sub>2</sub> and 1× protease inhibitor cocktail (cOmplete EDTA-free, Roche 11873580001)) supplemented with 1.25 U µl<sup>−1</sup> Benzonase (Sigma, E1014). Samples were incubated for 20 min at RT, mixed with 30 µl of 2× Laemmli sample buffer (65.8 mM Tris-HCl (pH 6.8), 2.2% SDS, 22.2% glycerol, 0.01% bromophenol blue and 710 mM 2-mercaptoethanol), and denatured at 95 °C for 10 min. Western blots were performed according to standard procedures using Immobilon Block-FL (Millipore WBAVDFL01) as fluorescent blocker, and near infra-red detection was carried out using the LI-COR detection system. The following antibodies and dilutions were used: anti-c-Myc (Santa Cruz Biotechnology sc-40, 1:1000) and anti-Histone H3 (Active Motif 61476, 1:5000).</p></sec><sec id=\"Sec28\"><title>Reporting summary</title><p id=\"Par39\">Further information on research design is available in the <xref rid=\"MOESM9\" ref-type=\"media\">Nature Research Reporting Summary</xref> linked to this article.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec29\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41467_2020_17823_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41467_2020_17823_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"41467_2020_17823_MOESM3_ESM.pdf\"><caption><p>Description of Additional Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM4\"><media xlink:href=\"41467_2020_17823_MOESM4_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM5\"><media xlink:href=\"41467_2020_17823_MOESM5_ESM.avi\"><caption><p>Supplementary Movie 1</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM6\"><media xlink:href=\"41467_2020_17823_MOESM6_ESM.avi\"><caption><p>Supplementary Movie 2</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM7\"><media xlink:href=\"41467_2020_17823_MOESM7_ESM.avi\"><caption><p>Supplementary Movie 3</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM8\"><media xlink:href=\"41467_2020_17823_MOESM8_ESM.avi\"><caption><p>Supplementary Movie 4</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM9\"><media xlink:href=\"41467_2020_17823_MOESM9_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media></supplementary-material></sec></sec></body><back><app-group><app id=\"App1\"><sec id=\"Sec30\"><title>Source data</title><p id=\"Par42\"><media position=\"anchor\" xlink:href=\"41467_2020_17823_MOESM10_ESM.xlsx\" id=\"MOESM10\"><caption><p>Source Data</p></caption></media></p></sec></app></app-group><fn-group><fn><p><bold>Peer review information</bold>\n<italic>Nature Communications</italic> thanks Vytautas Iesmantavicius, Sheila Teves and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> is available for this paper at 10.1038/s41467-020-17823-z.</p></sec><ack><title>Acknowledgements</title><p>We would like to thank Richard Henderson for his encouragement to begin these studies, C. Tyler-Smith for enabling chromosome sorting, T. Adejumo, R. Maggio, F. Pereira, P. Chana, H. Pallikonda and N. Festuccia for expertise and providing reagents, A. Lisini for reading the manuscript and advice. We thank the LMS/NIHR Imperial Biomedical Research Centre Flow Cytometry Facility, as well as the LMS Genomics and LMS Bioinformatics facilities for support. This work was funded by core support from the Medical Research Council UK to the London Institute of Medical Sciences. R.J.K. was supported by the Wellcome Trust (209400/Z/17/Z) and the European Research Council (681440), and M.K.H. was supported by the Wellcome Trust (109102/Z/15/Z).</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>D.D. and A.G.F. conceived and designed the study. D.D. performed most of the experiments, designed the figures and contributed to writing the manuscript. A.D. and K.B. conducted experiments and contributed to writing the manuscript. B.P., H.K., A.-C.K., C.W., A.F., N.V., J.E. and Y.G. conducted experiments. T.A.M.B. and A.K.T. performed EM imaging. J.L., B.L.N., M.K.H., R.J.K. and J.G. provided scientific advice and support. M.M. contributed to study design and writing manuscript. A.G.F. wrote the manuscript and supervised the experiments.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ebi.ac.uk/pride/archive/projects/PXD015251\">PXD015251</ext-link>. Hi-C data are available from GEO with accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE136681\">GSE136681</ext-link>. ATAC-seq data are available from GEO with accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147552\">GSE147552</ext-link>. Previously published Hi-C data were downloaded from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE82144\">GSE82144</ext-link>, biotin-tagged Mecp2 ChIP-seq data were downloaded from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39610\">GSE39610</ext-link> and genome-wide DNA methylation data were downloaded from GSE30202. All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. The Source data underlying Figs. <xref rid=\"Fig2\" ref-type=\"fig\">2b–e</xref>, <xref rid=\"Fig3\" ref-type=\"fig\">3c, d</xref>, <xref rid=\"Fig4\" ref-type=\"fig\">4e–g</xref> and <xref rid=\"Fig5\" ref-type=\"fig\">5c–e</xref>, and Supplementary Figs. <xref rid=\"MOESM10\" ref-type=\"media\">1g</xref>, <xref rid=\"MOESM10\" ref-type=\"media\">3c</xref>, <xref rid=\"MOESM10\" ref-type=\"media\">4b, c</xref> and <xref rid=\"MOESM10\" ref-type=\"media\">5e</xref> are provided as a <xref rid=\"MOESM10\" ref-type=\"media\">Source data</xref> file. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Nat Commun</journal-id><journal-id journal-id-type=\"iso-abbrev\">Nat Commun</journal-id><journal-title-group><journal-title>Nature Communications</journal-title></journal-title-group><issn pub-type=\"epub\">2041-1723</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807777</article-id><article-id pub-id-type=\"pmc\">PMC7431862</article-id><article-id pub-id-type=\"publisher-id\">17955</article-id><article-id pub-id-type=\"doi\">10.1038/s41467-020-17955-2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>CTCF-mediated chromatin looping in EGR2 regulation and SUZ12 recruitment critical for peripheral myelination and repair</article-title></title-group><contrib-group><contrib contrib-type=\"author\" equal-contrib=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-3676-0779</contrib-id><name><surname>Wang</surname><given-names>Jincheng</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Wang</surname><given-names>Jiajia</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Yang</surname><given-names>Lijun</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhao</surname><given-names>Chuntao</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Wu</surname><given-names>Laiman Natalie</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Xu</surname><given-names>Lingli</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Feng</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0001-8026-0109</contrib-id><name><surname>Weng</surname><given-names>Qinjie</given-names></name><address><email>wengqinjie@zju.edu.cn</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-4586-3294</contrib-id><name><surname>Wegner</surname><given-names>Michael</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0001-6846-9014</contrib-id><name><surname>Lu</surname><given-names>Q. Richard</given-names></name><address><email>richard.lu@cchmc.org</email></address><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.13402.34</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1759 700X</institution-id><institution>Center for Drug Safety Evaluation and Research, College of Pharmaceutical Sciences, </institution><institution>Zhejiang University, </institution></institution-wrap>Hangzhou, 310058 China </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.239573.9</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9025 8099</institution-id><institution>Department of Pediatrics, Brain Tumor Center, Division of Experimental Hematology and Cancer Biology, </institution><institution>Cincinnati Children’s Hospital Medical Center, </institution></institution-wrap>Cincinnati, OH 45229 USA </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.5330.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2107 3311</institution-id><institution>Institut für Biochemie, Emil-Fischer-Zentrum, </institution><institution>Friedrich-Alexander-Universität Erlangen-Nürnberg, </institution></institution-wrap>Erlangen, Germany </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>4133</elocation-id><history><date date-type=\"received\"><day>16</day><month>9</month><year>2019</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Chromatin organization is critical for cell growth, differentiation, and disease development, however, its functions in peripheral myelination and myelin repair remain elusive. In this report, we demonstrate that the CCCTC-binding factor (CTCF), a crucial chromatin organizer, is essential for Schwann cell myelination and myelin regeneration after nerve injury. Inhibition of CTCF or its deletion blocks Schwann cell differentiation at the pro-myelinating stage, whereas overexpression of CTCF promotes the myelination program. We find that CTCF establishes chromatin interaction loops between enhancer and promoter regulatory elements and promotes expression of a key pro-myelinogenic factor EGR2. In addition, CTCF interacts with SUZ12, a component of polycomb-repressive-complex 2 (PRC2), to repress the transcriptional program associated with negative regulation of Schwann cell maturation. Together, our findings reveal a dual role of CTCF-dependent chromatin organization in promoting myelinogenic programs and recruiting chromatin-repressive complexes to block Schwann cell differentiation inhibitors to control peripheral myelination and repair.</p></abstract><abstract id=\"Abs2\" abstract-type=\"web-summary\"><p id=\"Par2\">Myelination by Schwann cells (SC) in the peripheral nervous system is essential for motor function, and dysregulation of SC myelination can lead to various neuropathies. Here the authors describe a critical role of CCCTC-binding factor (CTCF)-dependent chromatin reorganization in peripheral myelination and myelin regeneration after injury.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Developmental neurogenesis</kwd><kwd>Chromatin structure</kwd><kwd>Development of the nervous system</kwd><kwd>Epigenetics in the nervous system</kwd><kwd>Glial biology</kwd></kwd-group><funding-group><award-group><funding-source><institution>Cincinnati Children’s Hospital innovation fund</institution></funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par3\">High-order chromatin organization and remodeling are critical for fundamental biological processes<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. Local chromatin environments that modulate recruitment of transcriptional complexes to regulatory elements are highly dynamic and depend on stage- or cell-type-specific nucleosome positions or chromatin looping<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. The chromatin reorganization process enables long-range interactions such as those between promoters and enhancers that activate gene transcription. In addition, insulator-mediated contacts can organize the genome into functionally distinct domains<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. Defects in chromatin structural organization or looping can cause aberrant transcriptional regulation, leading to various diseases including intellectual disabilities, cancer, and aging<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>–<xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>.</p><p id=\"Par4\">Schwann cells (SCs) are myelinating glia in the peripheral nervous system (PNS) that form myelin sheaths around axons to optimize the saltatory nerve conduction. Defects in SCs lead to various peripheral neuropathies including motor and sensory disabilities<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. SC-lineage development includes the specification of neural crest cells to SC precursors that give rise to immature SCs, which further differentiate into mature myelinating SCs<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. The process of SC development is regulated by various intrinsic and extrinsic cues. Among intrinsic factors, transcriptional regulators such as SOX10, OCT6 (a.k.a. POU3F1), and EGR2 (a.k.a. KROX20) are required for sequential progression from immature to promyelinating SCs, and eventually into myelinating SCs<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref>–<xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. SC development is coordinated by a hierarchy of transcriptional regulators with a main axis that SOX10 activates OCT6, and then cooperates with OCT6 to induce EGR2 expression for SC maturation<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>–<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. EGR2 takes a center stage for myelinogenesis by activating myelin genes, such as <italic>Mpz</italic>, <italic>Pmp22</italic>, and <italic>Mbp</italic><sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref>,<xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. The negative regulatory cues that inhibit SC myelination include NOTCH, WNT, and SOX2 pathways<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. How chromatin reorganizes to promote expression of promyelin cues while preventing the differentiation-inhibitory events during SC myelination has not been determined.</p><p id=\"Par5\">CCCTC-binding factor (CTCF) is one of the most critical organizers for the high-order chromatin structure that enables long-range chromatin interactions<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR18\">18</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. Accumulating evidence suggests that CTCF mediates extensive crosstalk between promoters and distant regulatory elements and regulates local balance between active and repressive chromatin marks, therefore ensuring proper transcription levels during various biological processes<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR18\">18</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. CTCF can not only mediate long-range chromatin looping and modulate three-dimensional genomic architecture to regulate cell-type-specific transcriptional programs<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref>–<xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>, but also define boundaries between chromosomal topological associating domains (TADs)<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref>,<xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. To date, the function of CTCF-dependent long-range chromatin interactions and looping in peripheral myelination and regeneration has not been defined. In addition, given its ubiquitous role in gene regulation by CTCF, whether CTCF has a temporally specific role during SC myelination remains elusive.</p><p id=\"Par6\">The specific local genomic architecture also depends on histone modifications, DNA modification patterns, and nucleosome positioning or accessibility to maintain the proper conformation for transcription<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. Histone modifying enzymes such as the deacetylases HDAC1/2 and polycomb-repressive complex 2 (PRC2) modulate chromatin states to regulate the transcriptional program necessary for myelination and remyelination in the PNS<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref>–<xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. At present, however, it is unknown how chromatin dynamics coordinate histone modifications to control SC myelination programs.</p><p id=\"Par7\">Here, we demonstrate a critical role of CTCF-dependent chromatin reorganization during SC differentiation from their immature precursors and in remyelination after peripheral nerve injury. We show that CTCF interacts with and recruits SUZ12 to suppress SC differentiation-inhibitory pathways. Furthermore, we find that CTCF regulates promyelination transcriptional programs at least in part by establishing an interaction between promoter and enhancer elements of the locus of <italic>Egr2</italic>, a key regulatory gene for SC myelination<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. Thus, our data demonstrate a temporally specific function of the chromatin organizer CTCF for SC differentiation by modulating chromatin organization and epigenetic programs to control peripheral myelination.</p></sec><sec id=\"Sec2\" sec-type=\"results\"><title>Results</title><sec id=\"Sec3\"><title>Upregulation of CTCF expression during SC differentiation</title><p id=\"Par8\">To investigate the expression pattern of CTCF in proliferative and differentiated SCs, we treated rat SCs with cAMP to promote their differentiation in vitro. Expression of mature SC markers such as EGR2, MBP, and MPZ increased during differentiation. Strikingly, CTCF protein and mRNA expression levels were also elevated during SC differentiation (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a, b</xref>).<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>CTCF expression changes during SC-lineage progression.</title><p><bold>a</bold> Western blots for CTCF, MBP, MPZ, and EGR2 in proliferating and differentiated rat SC cultures. GAPDH served as a loading control. <italic>n</italic> = 2 independent experiments. <bold>b</bold> Relative qPCR expression of <italic>Ctcf</italic>, <italic>Mbp</italic>, <italic>Mpz</italic>, and <italic>Egr2</italic> in proliferating and differentiated rat SC cultures. Data are presented as means ± SEM., ***<italic>P</italic> < 0.001, <italic>n</italic> = 3 independent experiments; two-tailed unpaired Student’s <italic>t</italic>-test, <italic>P</italic><sub>(<italic>Ctcf)</italic></sub> = 0.00021, <italic>P</italic><sub>(<italic>Mbp)</italic></sub> = 2.8E-05, <italic>P</italic><sub>(<italic>Mpz)</italic></sub> = 1.7E-06, <italic>P</italic><sub>(<italic>Egr2)</italic></sub> = 3.9E-05. <bold>c</bold> Colocalization of CTCF with SOX10 in SC nuclei from mice at P7, P14, and P62 evaluated by immunofluorescence labeling. Representative images are shown. <italic>n</italic> = 3 nerve tissues at each time point. Arrows indicate SOX10<sup>+</sup>/CTCF<sup>+</sup> SCs; arrowheads indicate SOX10<sup>+</sup>/CTCF<sup>−</sup> SCs. Scale bars: 50 μm. <bold>d</bold> The percentage of CTCF<sup>+</sup> nuclei in SCs (SOX10<sup>+</sup>) in sciatic nerves from P7, P14, and P62 mice. <italic>n</italic> = 3 control tissues at each time point. Data are presented as means ± SEM., *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01; <italic>n</italic> = 3 nerve tissues at each time point; one-way ANOVA with multiple comparisons test. <italic>P</italic><sub>(P14)</sub> = 0.0392, <italic>P</italic><sub>(P62)</sub> = 0.0052. <bold>e</bold> Relative qPCR expression of <italic>Ctcf</italic> in mouse sciatic nerves at various developmental stages. Data are presented as means ± SEM., **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001; <italic>n</italic> = 3 nerve tissues at each time point; one-way ANOVA with multiple comparisons test, <italic>P</italic><sub>(P7)</sub> = 0.0067, <italic>P</italic><sub>(P10)</sub> = 0.0004, <italic>P</italic><sub>(P21)</sub> = 0.1503, <italic>P</italic><sub>(P60)</sub> = 0.0077. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig1_HTML\" id=\"d30e627\"/></fig></p><p id=\"Par9\">To characterize CTCF expression in vivo, we co-immunostained CTCF with SC-lineage marker SOX10 in sciatic nerves (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c</xref>). At postnatal day (P) 7, the majority of the SCs marked by SOX10 were also CTCF-positive. CTCF expression persisted in SCs in late developmental stages, but decreased in adulthood (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c, d</xref>). To confirm these changes, we performed qRT-PCR analysis of mouse sciatic nerves at different stages and found that <italic>Ctcf</italic> transcripts were detected at the neonatal stage P0.5, an immature SC stage. <italic>Ctcf</italic> levels peaked at the perinatal stage P10, when the majority of SCs undergo differentiation and then were gradually reduced in adulthood (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e</xref>).</p></sec><sec id=\"Sec4\"><title>CTCF is critical for SC differentiation</title><p id=\"Par10\">To investigate the functions of CTCF in SC differentiation, we transfected cultured rat SCs with a siRNA designed to target CTCF. Levels of pro-SC differentiation genes, <italic>Sox10, Egr2</italic>, and a SC myelin gene <italic>Mpz</italic>, were significantly reduced in differentiated SCs deficient in CTCF (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>). Similarly, expression of the promyelination factor EGR2 was diminished in SCs treated with si<italic>Ctcf</italic> (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b–d</xref>). In contrast, expression of OCT6, which marks promyelinating SCs, was not significantly altered (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c, d</xref>). These results suggest that CTCF deficiency blocks SC differentiation beyond the promyelinating stage.<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>CTCF is critical for rat SC differentiation in vitro.</title><p><bold>a</bold> qRT-PCR analysis of <italic>Ctcf</italic>, <italic>Sox10</italic>, <italic>Egr2</italic>, and <italic>Mpz</italic> expression in rat SCs transfected with control nontargeting siRNA and si<italic>Ctcf</italic> for 24 h and induced to differentiate for 9 h. <italic>n</italic> = 3 independent experiments, <italic>P</italic><sub>(<italic>Ctcf</italic>)</sub> = 3.03E-05, <italic>P</italic><sub>(<italic>Sox10</italic>)</sub> = 0.0433, <italic>P</italic><sub>(<italic>Egr2</italic>)</sub> = 0.000107, <italic>P</italic><sub>(<italic>Mpz</italic>)</sub> = 0.000293. <bold>b–d</bold> Rat SCs were transfected with control siRNA or si<italic>Ctcf</italic> for 24 h and induced to differentiate for 9 h and CTCF- (<bold>b</bold>), EGR2- and OCT6-positive (<bold>c</bold>) cells were visualized by immunofluorescence microscopy and <bold>d</bold> quantified; <italic>n</italic> = 3 independent experiments. Arrows indicate CTCF<sup>+</sup> or EGR2<sup>+</sup>/OCT6<sup>+</sup> SCs. Scale bars: 50 µm. <italic>n</italic> = 3 independent experiments, <italic>P</italic><sub>(EGR2)</sub> = 0.00069, <italic>P</italic><sub>(OCT6)</sub> = 0.99. <bold>e</bold> Western blots for CTCF and EGR2 in co-cultures of rat DRGs and SCs treated with control siRNA or si<italic>Ctcf</italic>. GAPDH served as a loading control. <italic>n</italic> = 4 independent experiments. <bold>f</bold> Rat SCs treated with control siRNA or si<italic>Ctcf</italic> were seeded onto rat DRGs. After 10 days, co-cultures were immunostained for MBP and neurofilament-M. Images are representative of <italic>n</italic> = 4 independent experiments. Scale bars: 100 μm. <bold>g</bold> Quantification of the number of MBP<sup>+</sup> segments per mm<sup>2</sup> of area in myelinating co-cultures of DRGs and SCs treated with control siRNA or si<italic>Ctcf</italic>. <italic>n</italic> = 4 independent experiments, <italic>P</italic> = 0.0068. <bold>h</bold> Western blots for CTCF in rat Schwann cells induced to differentiate following transfection with control or CTCF expression vectors. <italic>n</italic> = 2 independent experiments. <bold>i</bold> qRT-PCR quantification of differentiation regulators and negative regulators in rat SCs induced to differentiate following transfection with control or CTCF expression vectors. <italic>n</italic> = 3 independent experiments, <italic>P</italic><sub>(<italic>Egr2</italic>)</sub> = 0.0012, <italic>P</italic><sub>(<italic>Cnp</italic>)</sub> = 0.00068, <italic>P</italic><sub>(<italic>Mbp</italic>)</sub> = 0.011, <italic>P</italic><sub>(<italic>Mpz</italic>)</sub> = 2.9E-05, <italic>P</italic><sub>(<italic>Pmp22</italic>)</sub> = 6.7E-05, <italic>P</italic><sub>(<italic>Sox2</italic>)</sub> = 0.00026, <italic>P</italic><sub>(<italic>Hes1</italic>)</sub> = 0.028, <italic>P</italic><sub>(<italic>Mki67</italic>)</sub> = 0.00024. Data are presented as means ± SEM., *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001, two-tailed unpaired Student’s <italic>t</italic>-test. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig2_HTML\" id=\"d30e904\"/></fig></p><p id=\"Par11\">To further determine the function of CTCF in myelination, we silenced <italic>Ctcf</italic> in ex vivo rat SC-dorsal root ganglion co-cultures. We observed a strong reduction of MBP<sup>+</sup> myelin formation along the axons (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2e–g</xref>). In contrast, <italic>Ctcf</italic> overexpression elevated <italic>Egr2</italic> and myelination-associated genes (e.g., <italic>Mbp</italic>, <italic>Mpz</italic>, and <italic>Pmp22</italic>) and repressed expression of immature SC and proliferation-associated genes, such as <italic>Sox2</italic>, <italic>Hes1</italic>, and <italic>Mki67</italic> (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2h, i</xref>), consistent with that CTCF overexpression blocks cell-cycle progression in a variety of cell lines<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. Thus, our data suggests that CTCF is necessary to promote the SC differentiation program.</p></sec><sec id=\"Sec5\"><title>Mice lacking CTCF display severe peripheral hypomyelination</title><p id=\"Par12\">To investigate the SC-specific function of CTCF, we bred the <italic>Ctcf</italic> floxed mice (<italic>Ctcf</italic><sup><italic>loxP</italic>/<italic>loxP</italic></sup>) with SC-lineage expressing <italic>Desert hedgehog</italic> (<italic>Dhh</italic>)-<italic>Cre</italic> line<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup> to ablate CTCF (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a–c</xref>). <italic>Ctcf</italic><sup><italic>loxP/loxP</italic></sup><italic>;Dhh-Cre</italic> (referred as <italic>Ctcf</italic> cKO) mice appeared normal compared with littermate controls during the first postnatal week. Starting in the second week, however, all <italic>Ctcf</italic> cKO mice developed an unsteady gait and hindlimb paralysis, and the majority died by P80 (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d</xref>). At P13, <italic>Ctcf</italic> cKO sciatic nerves had a thin and translucent appearance, in contrast to the thick, opaque nerves of control mice, suggesting a severe deficit in myelination (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3e</xref>).<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>CTCF is required for peripheral nerve ensheathment.</title><p><bold>a</bold> Excised exon 8 of the floxed <italic>Ctcf</italic> allele by <italic>Dhh-Cre</italic>. <bold>b</bold> Co-labeling of CTCF with SOX10 in control and mutant sciatic nerves at P7 (<italic>n</italic> = 3 animals/genotype). Arrows indicate SOX10<sup>+</sup>/CTCF<sup>+</sup> SCs. Scale bars: 50 μm. <bold>c</bold> The percentage of CTCF<sup>+</sup> nuclei in SCs (SOX10<sup>+</sup>) from control and <italic>Ctcf</italic> cKO sciatic nerves at P7. <italic>n</italic> = 3 animals/genotype, <italic>P</italic> = 1.73E-05. <bold>d</bold> Survival curves of control and <italic>Ctcf</italic> cKO mice. <italic>n</italic> = 25 for control and <italic>n</italic> = 23 for <italic>Ctcf</italic> cKO mice, ***<italic>P</italic> < 0.001. <bold>e</bold> Representative photographs of sciatic nerves from P13 control and <italic>Ctcf</italic> cKO mice. <italic>n</italic> = 3 animals/genotype. <bold>f</bold> Immunofluorescence labeling of MBP (red) in P7 control and <italic>Ctcf</italic> cKO sciatic nerves. <italic>n</italic> = 3 animals/genotype. Scale bars: 50 μm. <bold>g</bold> The mRNA levels of myelin-related genes in P7 control and <italic>Ctcf</italic> cKO sciatic nerves. <italic>n</italic> = 6 animals/genotype. <italic>P</italic><sub>(<italic>Prx</italic>)</sub> = 1.9E-08, <italic>P</italic><sub>(<italic>Mbp</italic>)</sub> = 2.0E-08, <italic>P</italic><sub>(<italic>Mpz</italic>)</sub> = 8.5E-09. <bold>h, i</bold> Ultrastructure of control and <italic>Ctcf</italic> cKO sciatic nerves at (<bold>h</bold>) P1 and P7 and at (<bold>i</bold>) 8 weeks. <italic>n</italic> = 3 animals/genotype. Arrows and arrowheads indicate immature SCs and unsorted axons, respectively. Scale bars: 4 μm. <bold>j</bold> A diagram showing the tamoxifen (TAM) administration scheme. <bold>k</bold> Immunofluorescent labeling of CTCF (green) nuclei in control and <italic>Ctcf</italic> iKO sciatic nerves at P14. Scale bars: 50 μm. <italic>n</italic> = 3 animals/genotype. <bold>l</bold> EM images of P14 sciatic nerves from control and <italic>Ctcf</italic> iKO mice. <italic>n</italic> = 4 animals/genotype. Arrow indicates myelin membrane. Scale bars: 4 μm, and 1 μm in the inset on the right panel. <bold>m</bold> Myelinated axon numbers 10<sup>−4</sup> μm<sup>−2</sup> sections of P14 sciatic nerves from control and <italic>Ctcf</italic> iKO mice. <italic>n</italic> = 4 animals/genotype, <italic>P</italic> = 0.0006. Data are presented as means ± SEM., ***<italic>P</italic> < 0.001; Statistical analyses performed using two-tailed unpaired Student’s <italic>t</italic>-test; Log-rank test used for survival curve. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig3_HTML\" id=\"d30e1192\"/></fig></p><p id=\"Par13\">To assess myelinogenesis in <italic>Ctcf</italic> cKO mice, we immunostained for MBP in P7 peripheral nerves. Compared with the robust expression in control mice, sparse expression of MBP in sciatic nerves of mutants suggested severe hypomyelination (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3f</xref>). Accordingly, expression of the myelin-associated genes, <italic>Prx</italic>, <italic>Mbp</italic>, and <italic>Mpz</italic>, was substantially decreased in mutant nerves (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3g</xref>). Electron microscopy (EM) revealed that in control mice at P1 and P7, SCs were present in a 1:1 relationship with large-caliber axons and the myelin membrane had begun wrapping around axons (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3h</xref>). In contrast, in <italic>Ctcf</italic> cKO mice, the majority of SCs lacked peripheral myelination and showed abnormal axon bundles, and most SCs failed to establish 1:1 relationships with axons (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3h</xref>). There was a partial radial sorting defect at neonatal stages (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3h</xref>, arrowheads), which persists into adulthood. Among the few mutant mice that survived to adulthood (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d</xref>), the gross structure of <italic>Ctcf</italic> cKO peripheral nerves at P56 revealed no apparent sign of myelination by SCs (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3i</xref>). These observations suggest that <italic>Ctcf</italic> loss results in both a radial sorting defect and a failure of promyelinating SCs to proceed to myelination in peripheral nerves.</p></sec><sec id=\"Sec6\"><title>CTCF deletion in postnatal immature SCs blocks myelination</title><p id=\"Par14\">Since Dhh-Cre inactivated <italic>Ctcf</italic> in early immature SCs, we then evaluated the impact of CTCF loss in SC myelination during postnatal development by deleting <italic>Ctcf</italic> in promyelinating SCs in neonatal peripheral nerves by using a tamoxifen-inducible <italic>Plp-CreERT</italic> driver<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. Tamoxifen administration from P0 to P4 led to efficient downregulation of CTCF in the sciatic nerves of <italic>Ctcf</italic><sup><italic>loxP/loxP</italic></sup><italic>;Plp-creERT2</italic> mice (<italic>Ctcf</italic> iKO; Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3j, k</xref>). EM analysis indicated that ~36% of large axons remained unmyelinated in mutant nerves (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3l, m</xref>), suggesting that CTCF is required for proper initiation of SC myelinogenesis. We detected a very thin layer of myelin sheath in some nonmyelinated large diameter axons (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3l</xref>, arrow), suggesting that a thin layer of myelin membrane could be formed before myelination arrest in <italic>Ctcf</italic> iKO mice.</p><p id=\"Par15\">The reduced expression of CTCF in mature nerves (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f–h</xref>) suggested a nonessential function in myelin maintenance. Consistent with this hypothesis, neither the myelin sheath thickness nor its integrity was affected in peripheral nerves of adult mice in which tamoxifen-induced deletion of <italic>Ctcf</italic> was carried out at 4–6 weeks of age (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). Thus, our data indicate a central role of CTCF in initiating SC myelination but not in maintaining myelin sheath assembly.</p></sec><sec id=\"Sec7\"><title>CTCF is required for SC myelinogenic programs</title><p id=\"Par16\">The SC myelination process is regulated by coordinated actions of positive and negative transcription factors<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. In <italic>Ctcf</italic> cKO sciatic nerves, we detected markedly reduced levels of myelination-promoting gene <italic>Egr2</italic>, at P7 and P21 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a, b</xref>). At P7 <italic>Sox10</italic> and <italic>Oct6</italic> levels were reduced compared with controls (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref>). At P21, <italic>Sox10</italic> expression was comparable to the control, but <italic>Oct6</italic> expression was increased (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4b</xref>). The drastic reduction of <italic>Egr2</italic> concomitant with upregulation of the early differentiating factor <italic>Oct6</italic> suggests that the SC differentiation process is stagnated at the promyelinating stage in the absence of CTCF.<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>CTCF deletion in SCs inhibits SC differentiation and myelination.</title><p><bold>a</bold>, <bold>b</bold> qRT-PCR analysis of promyelinating transcriptional regulators in control and <italic>Ctcf</italic> cKO mice sciatic nerves at <bold>a</bold> P7 and <bold>b</bold> P21. <italic>n</italic> = 6 animals/genotype for P7 and <italic>n</italic> = 3 animals/genotype for P21, <bold>a</bold>\n<italic>P</italic><sub>(<italic>Sox10</italic>)</sub> = 3.03E-07, <italic>P</italic><sub>(<italic>Egr2</italic>)</sub> = 6.92E-09, <italic>P</italic><sub>(<italic>Oct6</italic>)</sub> = 0.0105; <bold>b</bold>\n<italic>P</italic><sub>(<italic>Sox10</italic>)</sub> = 0.171, <italic>P</italic><sub>(<italic>Egr2</italic>)</sub> = 4.97E-06, <italic>P</italic><sub>(<italic>Oct6</italic>)</sub> = 0.0152. <bold>c</bold> Immunolabeling of SOX10, EGR2, and OCT6 in P7 control and <italic>Ctcf</italic> cKO sciatic nerves (<italic>n</italic> = 3 animals/genotype). Scale bars: 50 μm. <bold>d–f</bold> Quantification of <bold>d</bold> EGR2<sup>+</sup>/SOX10<sup>+</sup> cells, <bold>e</bold> SOX10<sup>+</sup> cells, and <bold>f</bold> OCT6<sup>+</sup>/SOX10<sup>+</sup> cells at different stages. <italic>n</italic> = 4 animals/genotype for SOX10 at P7, <italic>n</italic> = 3 animals/genotype for others, <bold>d</bold>\n<italic>P</italic><sub>(P2)</sub> = 7.27E-05, <italic>P</italic><sub>(P4)</sub> = 0.00331, <italic>P</italic><sub>(P7)</sub> = 0.000294, <italic>P</italic><sub>(P14)</sub> = 0.00023, <italic>P</italic><sub>(P28)</sub> = 0.00126; <bold>e</bold>\n<italic>P</italic><sub>(P2)</sub> = 0.056, <italic>P</italic><sub>(P7)</sub> = 0.20; <bold>f</bold>\n<italic>P</italic><sub>(P2)</sub> = 0.011, <italic>P</italic><sub>(P4)</sub> = 0.99, <italic>P</italic><sub>(P7)</sub> = 0.18, <italic>P</italic><sub>(P14)</sub> = 0.27, <italic>P</italic><sub>(P28)</sub> = 0.026. <bold>g</bold> Immunolabeling and <bold>h</bold> analysis of BrdU and SOX10 in P7 control and <italic>Ctcf</italic> cKO sciatic nerves. Arrows indicate SOX10<sup>+</sup>/BrdU<sup>+</sup> SCs. Scale bars: 50 μm. <italic>n</italic> = 4 animals/genotype, <italic>P</italic> = 0.014. <bold>i</bold> Immunolabeling and <bold>j</bold> quantification of Ki67 in P7 control and <italic>Ctcf</italic> cKO sciatic nerves. Arrows indicate Ki67<sup>+</sup> cells. Scale bars: 50 μm. <italic>n</italic> = 3 animals/genotype, <italic>P</italic> = 0.03. <bold>k</bold> Immunolabeling and <bold>l</bold> quantification of cleaved-caspase 3 in P7 control and <italic>Ctcf</italic> cKO sciatic nerves. Arrows indicate Cleaved-Caspase 3<sup>+</sup> SCs. Scale bars: 100 μm. <italic>n</italic> = 3 animals/genotype, <italic>P</italic> = 0.12. <bold>m</bold> qPCR analysis of <italic>Sox2</italic> in P7 control and <italic>Ctcf</italic> cKO sciatic nerves. <italic>n</italic> = 3 animals/genotype, <italic>P</italic> = 0.00032. <bold>n</bold>, <bold>o</bold> Immunolabeling (<bold>n</bold>) and quantification of SOX2 and SOX10 (<bold>o</bold>) in P7 and P14 control and <italic>Ctcf</italic> cKO sciatic nerves. Scale bars: 50 μm. <italic>n</italic> = 3 animals/genotype, <italic>P</italic><sub>(P7)</sub> = 0.0004, <italic>P</italic><sub>(P14)</sub> = 0.0013. Data are presented as means ± SEM., *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001, two-tailed unpaired Student’s <italic>t</italic>-test. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig4_HTML\" id=\"d30e1661\"/></fig></p><p id=\"Par17\">Consistent with transcript levels, EGR2 expression in <italic>Ctcf</italic> cKO mice was abolished throughout postnatal 4 weeks (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c, d</xref>). The number of SOX10<sup>+</sup> SCs in mutant nerves was comparable to that in control nerves at P2 and slightly lower at P7 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c, e</xref>), which is likely due to the overall reduction in differentiated SCs. However, the proportion of OCT6<sup>+</sup> SCs was higher in <italic>Ctcf</italic> cKO nerves at P14 and P28 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c, f</xref>), suggesting that they were stalled at the promyelinating stage in <italic>Ctcf</italic> cKO nerves. This is consistent with a higher SC proliferation rate in <italic>Ctcf</italic> cKO nerves revealed by Ki67 and 5-bromo-2’-deoxyuridine (BrdU) incorporation (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4g–j</xref>).</p><p id=\"Par18\">We did not detect significant alteration in SC apoptosis in <italic>Ctcf</italic> mutants as determined by cleaved-caspase 3 in sciatic nerves at P7 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4k, l</xref>). In addition, SOX2 expression was upregulated in sciatic nerves of <italic>Ctcf</italic> cKO mice at P7 and P14 (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4m–o</xref>). This is consistent with the role of SOX2 as a negative regulator of myelination<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. Our findings indicate that <italic>Ctcf</italic> deletion arrests SCs at their promyelinating stage by blocking their transition into EGR2<sup>+</sup> differentiated SCs.</p></sec><sec id=\"Sec8\"><title>CTCF ablation in SCs blocks myelin regeneration after injury</title><p id=\"Par19\">Although CTCF was maintained at minimal levels in adult sciatic nerves, it was highly upregulated in regenerating SCs after injury (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>). Inactivation of <italic>Ctcf</italic> in SCs of adult mice using <italic>Plp-creERT2</italic> did not alter sciatic nerve morphology or myelin sheath thickness, however (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). To determine whether CTCF is required for SC remyelination, we performed peripheral nerve transection in adult control and <italic>Ctcf</italic> iKO mice with or without tamoxifen treatment (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5b</xref>). After transection, SCs undergo dedifferentiation and proliferate, reattach the distal stumps, and establish a tissue bridge that guides regenerating axons to the distal stump, followed by SC redifferentiation and remyelination<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. Tamoxifen administration prior and postinjury ablated CTCF expression in nearly all SCs from transected nerves in the <italic>Ctcf</italic> iKO mice (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5c, d</xref>).<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>CTCF is required for SC differentiation during nerve repair.</title><p><bold>a</bold> Immunolabeling for CTCF and SOX10 in uninjured (proximal) and regenerating region of sciatic nerves of control mice at 14 dpi (<italic>n</italic> = 3 animals/genotype). Arrows indicate SOX10<sup>+</sup>/CTCF<sup>+</sup> SCs. Scale bars: 50 μm. <bold>b</bold> A diagram showing the nerve transection scheme. Mice were treated with TAM via i.p. for 10 days, after 10 days, nerves were cut, and mice were then given TAM for 8 days, and nerves were analyzed at dpi 14 and 56. <bold>c</bold> Immunolabeling for CTCF and SOX10 in regenerating regions of control and <italic>Ctcf</italic> iKO sciatic nerves 14 dpi (<italic>n</italic> = 3 animals/genotype). Scale bars: 50 μm. <bold>d</bold> Proportion of CTCF<sup>+</sup> SCs in the regenerating regions of 14 dpi control and <italic>Ctcf</italic> iKO sciatic nerves (<italic>n</italic> = 3 animals/genotype). <italic>P</italic> = 0.0083. <bold>e</bold> Immunolabeling for Ki67 and SOX10 in the regenerating regions of 28 dpi control and <italic>Ctcf</italic> iKO sciatic nerves (<italic>n</italic> = 2 animals/genotype). Arrows indicate representative SOX10<sup>+</sup>/Ki67<sup>+</sup> SCs. Scale bars: 50 μm. <bold>f</bold> Proportion of Ki67<sup>+</sup> SCs in the regenerating regions of 14 dpi control and <italic>Ctcf</italic> iKO sciatic nerves (<italic>n</italic> = 3 animals/genotype). <italic>P</italic> = 0.58. <bold>g</bold> Immunolabeling of SOX10 and EGR2 in the regenerating regions of 14 dpi control and <italic>Ctcf</italic> iKO sciatic nerves (<italic>n</italic> = 3 animals/genotype). Arrows indicate representative SOX10<sup>+</sup>/EGR2<sup>+</sup> SCs. Scale bars: 50 μm. <bold>h</bold> Proportion of (left) EGR2<sup>+</sup> over SOX10<sup>+</sup> cells and (right) SOX10<sup>+</sup> cells in the regenerating regions of 14 dpi control and <italic>Ctcf</italic> iKO sciatic nerves (<italic>n</italic> = 3 animals/genotype). <italic>P</italic><sub>(left)</sub> = 0.005, <italic>P</italic><sub>(right)</sub> = 0.19. <bold>i</bold> EM images of transverse sections of control and <italic>Ctcf</italic> iKO 8 weeks after transection (<italic>n</italic> = 3 animals/genotype). Scale bar: 6 μm. <bold>j</bold> Proportion of myelinated axons from EM images of control vs. <italic>Ctcf</italic> iKO 8 weeks after injury (<italic>n</italic> = 3 animals/genotype). <italic>P</italic> = 4.26E-05. Data are presented as means ± SEM., **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001, two-tailed unpaired Student’s <italic>t</italic>-test. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig5_HTML\" id=\"d30e1912\"/></fig></p><p id=\"Par20\">For histological analyses, we harvested sciatic nerves 14 days (dpi 14) after axotomy to assess SC differentiation. In the regenerating region of sciatic nerves, the percentage of proliferative SCs (Ki67<sup>+</sup>SOX10<sup>+</sup>) was indistinguishable between <italic>Ctcf</italic> iKO and control mice (<italic>Ctcf</italic><sup><italic>loxP/loxP</italic></sup> or <italic>Ctcf</italic><sup><italic>loxP/+</italic></sup><italic>;Plp1-creERT</italic>) (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5e, f</xref>). In contrast, SC redifferentiation was severely impaired in <italic>Ctcf</italic> iKO as indicated by a substantial decrease in EGR2<sup>+</sup> differentiated SCs (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5g, h</xref>). EM analysis shows that a majority of axons were remyelinated at the regenerating site in control nerves 8 weeks postinjury, whereas <italic>Ctcf</italic> iKO mice exhibited significantly fewer remyelinated axons and thinner myelin sheath thickness (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5i, j</xref>). EGR2<sup>+</sup> differentiated SCs were substantially diminished in <italic>Ctcf</italic> iKO mice despite the presence of SC-lineage cells (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5h</xref>), suggesting that <italic>Ctcf</italic>-mutant SCs were obstructed at the promyelinating stage. Taken together, our data show that CTCF is required for SC remyelination during peripheral nerve regeneration.</p></sec><sec id=\"Sec9\"><title>CTCF regulates the transcriptional program in SCs</title><p id=\"Par21\">To investigate the potential mechanisms of CTCF regulation of SC myelination, we performed RNA-seq of control and <italic>Ctcf</italic> cKO sciatic nerves at P7. Deletion of <italic>Ctcf</italic> in sciatic nerves significantly upregulated or downregulated a number of genes (>1.5-fold change, <italic>P</italic>-value < 0.05; Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6a</xref>). Genes associated with SC myelination, laminin receptors, and lipid metabolism pathways such as <italic>Mpz</italic>, <italic>Pmp22</italic>, <italic>Egr2</italic>, <italic>Itgb8</italic>, <italic>Hmgcr</italic>, and <italic>Lss</italic> were downregulated in <italic>Ctcf</italic> cKO sciatic nerves, consistent with the dysmyelination phenotype of <italic>Ctcf</italic> cKO mice. Genes that were upregulated are associated with negative regulators of myelination and cell-cycle progression including <italic>Sox2</italic>, <italic>Notch1</italic>, and <italic>Ccnd1</italic> (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6b</xref>). Gene ontology analysis revealed that the functions of the genes most significantly downregulated were particularly enriched for lipid metabolic process, myelin sheath, and gliogenesis (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6c</xref>), whereas those most significantly upregulated genes were categorized into cell-cycle control, DNA replication, and extracellular matrix (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6d</xref>). qRT-PCR showed that the genes involved in lipid metabolic processes, myelin sheath formation, and SC differentiation were expressed at lower levels in the <italic>Ctcf</italic> cKO sciatic nerve, whereas differentiation inhibitors, such as <italic>Notch1</italic>, <italic>Id2</italic>, and <italic>Hes5</italic>, were expressed at higher levels (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6e</xref>). Gene Set Enrichment Analysis (GSEA) also confirmed that genes categorized as involved in myelinogenesis were downregulated and that cell-cycle genes were upregulated (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6f, g</xref>).<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>CTCF regulates the transcriptional program of SC differentiation.</title><p><bold>a</bold> Volcano plot of transcriptome profiles of control and <italic>Ctcf</italic> cKO sciatic nerves (<italic>n</italic> = 2 animals/genotype). Red and blue dots represent significantly downregulated and upregulated genes in <italic>Ctcf</italic> cKO nerves compared to the control, respectively (<italic>P</italic> < 0.05, fold-change > 1.5). <bold>b</bold> Heatmap of representative genes and their categories differentially expressed in control and <italic>Ctcf</italic> cKO sciatic nerves (<italic>n</italic> = 2 animals/genotype). <bold>c</bold>, <bold>d</bold> Bar plots of gene ontology analysis of genes <bold>c</bold> downregulated and <bold>d</bold> upregulated genes in <italic>Ctcf</italic> cKO sciatic nerves compared with control nerves. Each dot (connected by lines) represents the gene count of the corresponding biological function categories. <italic>n</italic> = 2 independent tissues/genotype. <bold>e</bold> qPCR analysis of genes related to SC development that are decreased (left) and increased (right) in <italic>Ctcf</italic> cKO sciatic nerves relative to control. <bold>f</bold> GSEA enrichment scores for myelin sheath (left) and lipid biosynthetic process (right) gene sets in control and <italic>Ctcf</italic> cKO sciatic nerves. <bold>g</bold> GSEA enrichment scores for cell-cycle gene sets in control and <italic>Ctcf</italic> cKO sciatic nerves. Data are presented as means ± SEM., ***<italic>P</italic> < 0.001, **<italic>P</italic> < 0.01, *<italic>P</italic> < 0.05, <italic>n</italic> = 3 animals/genotype; two-tailed unpaired Student’s <italic>t</italic>-test, <italic>P</italic><sub><italic>(Prx)</italic></sub> = 2.6e-05, <italic>P</italic><sub><italic>(Mbp)</italic></sub> = 4.9E-05, <italic>P</italic><sub><italic>(Mpz)</italic></sub> = 5.3E-06, <italic>P</italic><sub><italic>(Hmgcr)</italic></sub> = 0.0014, <italic>P</italic><sub><italic>(Egr2)</italic></sub> = 8.6E-05, <italic>P</italic><sub><italic>(Itgb1)</italic></sub> = 0.008, <italic>P</italic><sub><italic>(Itgb3bp)</italic></sub> = 0.00022, <italic>P</italic><sub><italic>(Itgb5)</italic></sub> = 0.0021, <italic>P</italic><sub><italic>(Itgb8)</italic></sub> = 0.00017, <italic>P</italic><sub><italic>(Ccnd1)</italic></sub> = 7.4E-05, <italic>P</italic><sub><italic>(Ccng1)</italic></sub> = 5.1E-05, <italic>P</italic><sub><italic>(Ccno)</italic></sub> = 0.0004, <italic>P</italic><sub><italic>(Cdc7)</italic></sub> = 6.4E-05, <italic>P</italic><sub><italic>(Cdk5r2)</italic></sub> = 1.6E-05, <italic>P</italic><sub><italic>(Ccnb1)</italic></sub> = 3.2E-05, <italic>P</italic><sub><italic>(Notch1)</italic></sub> = 0.00102, <italic>P</italic><sub><italic>(Hes5)</italic></sub> = 0.028, <italic>P</italic><sub><italic>(Id2)</italic></sub> = 0.23, <italic>P</italic><sub><italic>(Id4)</italic></sub> = 3.3E-05. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig6_HTML\" id=\"d30e2257\"/></fig></p><p id=\"Par22\">To determine the impact of <italic>Ctcf</italic> depletion on chromatin accessibility, we performed an assay for transposase-accessible chromatin (ATAC-seq) assay<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup> in rat SCs grown under differentiation conditions treated with control siRNA or with si<italic>Ctcf</italic>. We intersected the genes located in open chromatin in rat SCs treated with the control siRNA with those genes differentially expressed in <italic>Ctcf</italic> cKO sciatic nerves and identified ~1700 genes (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7a</xref>). Among them, ~200 genes were accompanied with differential accessibility upon <italic>Ctcf</italic> siRNA (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7b</xref> and Supplementary Data <xref rid=\"MOESM3\" ref-type=\"media\">1</xref>). Compared with control SCs, chromatin accessibility in the promoters or enhancers of myelination-associated genes <italic>Egr2</italic>, <italic>Pllp</italic>, <italic>Itga4</italic>, and <italic>Mtor</italic> decreased in <italic>siCtcf</italic>-treated SCs (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7c, e</xref>) whereas it increased in the promoters of SC negative or proliferation-regulatory genes, such as <italic>Sox5</italic>, <italic>Bmp5</italic>, <italic>Cenpw</italic>, and <italic>Tgfb2</italic><sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref>,<xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup> (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">7d, e</xref>). In addition, GSEA analysis of the altered genes in <italic>Egr2-</italic>deficient mice<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup> compared with <italic>Ctcf</italic> cKO mice shows that the gene expression of <italic>Egr2</italic> hypomorphic (<italic>Egr2</italic><sup>Lo/Lo</sup>) mice<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup> were corelated with those in <italic>Ctcf</italic> cKO mice (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7f</xref>), suggesting that the phenotype of CTCF knockout is likely driven by the loss of EGR2 functions.<fig id=\"Fig7\"><label>Fig. 7</label><caption><title>CTCF regulates chromatin accessibility during SC differentiation.</title><p><bold>a</bold>, <bold>b</bold> Venn diagram showing the overlap between the genes located in open chromatin sites in rat SCs (<bold>a</bold>) or the genes with differential chromatin accessibility (<bold>b</bold>) by ATAC-seq with genes differentially expressed genes between control and <italic>Ctcf</italic> cKO sciatic nerves. <bold>c</bold>, <bold>d</bold> Representative ATAC-seq signals around the <italic>Egr2</italic> MSE and myelination-related gene loci (<bold>c</bold>), as well as SC negative- or proliferation-related genes (<bold>d</bold>) in control or <italic>siCtcf</italic>-treated rat SCs, <italic>n</italic> = 2 biological replicates for control and si<italic>Ctcf</italic> SCs. <bold>e</bold> Relative fold-change of ATAC-seq peaks in panel <bold>c</bold> and <bold>d</bold>. <italic>n</italic> = 2 biological replicates for control and si<italic>Ctcf</italic> SCs. <bold>f</bold> GSEA enrichment scores for genes downregulated (left) or upregulated (right) genes in <italic>Egr2</italic>\n<sup>Lo/Lo</sup> nerves from published data<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. NES, normalized enrichment score. Data are presented as means ± SEM., *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001; one-tailed unpaired Student’s <italic>t</italic>-test, <italic>P</italic><sub><italic>(Egr2)</italic></sub> = 0.0033, <italic>P</italic><sub><italic>(Pllp)</italic></sub> = 0.0014, <italic>P</italic><sub><italic>(Itga4)</italic></sub> = 0.0021, <italic>P</italic><sub><italic>(Mtor)</italic></sub> = 0.0053, <italic>P</italic><sub><italic>(Sox5)</italic></sub> = 0.024, <italic>P</italic><sub><italic>(Bmp)</italic></sub> = 4E-07, <italic>P</italic><sub><italic>(Cenpw)</italic></sub> = 0.0017, <italic>P</italic><sub><italic>(Tgfb2)</italic></sub> = 0.039. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig7_HTML\" id=\"d30e2499\"/></fig></p><p id=\"Par23\">We next examined expression of the transcriptional factors such as NFATc3/4, YAP1, and TEADs that are known to regulate <italic>Egr2</italic><sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. Expression of <italic>Nfatc3</italic>, <italic>Nfatc4</italic>, <italic>Yap1</italic>, and <italic>Tead1</italic> was not altered substantially in <italic>Ctcf-</italic>deficient nerves, while expression of individual Tead2-4 mRNAs exhibited a differential response to <italic>Ctcf-</italic>deficiency, suggesting that CTCF may have a distinct function in the regulation of TEAD/YAP family members (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>). Together, these results suggest that CTCF modulates chromatin accessibility of genes associated with the SC differentiation and myelination program.</p></sec><sec id=\"Sec10\"><title>CTCF interacts with PRC2 to repress SC differentiation</title><p id=\"Par24\">GSEA revealed that genes related to myelin and lipid biosynthesis were downregulated, while genes repressed by H3K27me3 and PRC2 were upregulated in CTCF-deficient SCs (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8a–c</xref>). Consistent with the previous study<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>, PRC2/EED targets, which are upregulated in <italic>Eed</italic> cKO nerves, were enriched in si<italic>Ctcf</italic> SCs (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8d</xref>). This suggests that CTCF depletion leads to a downregulation of PRC2 activity, which acts as a transcriptional repressor by catalyzing methylation of histone H3 on Lys27 (H3K27me3) on gene regulatory elements<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>.<fig id=\"Fig8\"><label>Fig. 8</label><caption><title>CTCF cooperates with PRC2 complex to regulate SC differentiation.</title><p><bold>a</bold> GSEA enrichment scores for sets of genes differentially regulated in rat SCs treated with si<italic>Ctcf</italic> or control siRNA. <italic>n</italic> = 3 independent experiments. <bold>b</bold> GSEA enrichment scores for genes involved in lipid biosynthetic process (left) and myelin sheath (right) in SCs treated with control or si<italic>Ctcf</italic>. <bold>c</bold> GSEA enrichment scores for genes modified with H3K27me3 and for genes targeted by SUZ12, EZH2, or EED in SCs treated with control or si<italic>Ctcf</italic>. <bold>d</bold> GSEA enrichment scores for genes upregulated genes in <italic>Eed</italic> cKO nerves from published data<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>. NES, normalized enrichment score. <bold>e–g</bold> Immunoblotting for <bold>e</bold> PRC2 complex and CTCF proteins and for <bold>f</bold>, <bold>g</bold> histones with indicated modifications in SCs treated with control or si<italic>Ctcf</italic>. <bold>e</bold>, <bold>f</bold>\n<italic>n</italic> = 2 independent experiments; <bold>g</bold>\n<italic>n</italic> = 3 independent experiments. <bold>h–j</bold> Co-immunoprecipitation of <bold>h</bold>, <bold>i</bold> HA-SUZ12 with Flag-CTCF from extracts of transiently transfected HEK293T cells or of <bold>j</bold> endogenous SUZ12 with CTCF from rat SCs. <italic>n</italic> = 2 independent experiments. <bold>k</bold> qRT-PCR analysis showing <italic>Suz12</italic> and differentiation-related and myelin-related gene expression in rat SCs transfected with control siRNA or with <italic>Suz12</italic>-targeted siRNA (<italic>n</italic> = 3 independent experiments) and induced to differentiate for 9 h. Data are presented as means ± SEM., ***<italic>P</italic> < 0.001, **<italic>P</italic> < 0.01, *<italic>P</italic> < 0.05; two-tailed unpaired Student’s <italic>t</italic>-test, <italic>P</italic><sub>(<italic>Suz12</italic>)</sub> = 3.8E-05, <italic>P</italic><sub>(<italic>Egr2</italic>)</sub> = 3.3E-05, <italic>P</italic><sub>(<italic>Msn</italic>)</sub> = 0.0045, <italic>P</italic><sub>(<italic>Lss</italic>)</sub> = 0.0031, <italic>P</italic>\n<sub>(<italic>Prx</italic>)</sub> = 1.5E-06, <italic>P</italic><sub>(<italic>Fasn</italic>)</sub> = 0.0019, <italic>P</italic><sub>(<italic>Hdac1</italic>)</sub> = 0.0019, <italic>P</italic><sub>(<italic>Sdc4</italic>)</sub> = 0.0015, <italic>P</italic><sub>(<italic>Srebf2</italic>)</sub> = 0.0026, <italic>P</italic><sub>(<italic>Mal</italic>)</sub> = 0.008, <italic>P</italic><sub>(<italic>Pllp</italic>)</sub> = 0.018, <italic>P</italic><sub>(<italic>Mag</italic>)</sub> = 0.017, <italic>P</italic><sub>(<italic>Notch3</italic>)</sub> = 0.012, <italic>P</italic><sub>(<italic>Hes1</italic>)</sub> = 0.02. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig8_HTML\" id=\"d30e2793\"/></fig></p><p id=\"Par25\">Although the levels of PRC2 complex components SUZ12, EZH2, and EED were only modestly downregulated in si<italic>Ctcf</italic>-treated SCs (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8e</xref>), the levels of H3K27me3 and H3K27me2/3 were substantially reduced in si<italic>Ctcf</italic>-treated cells compared to the control (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8f</xref>), whereas other epigenetic modification marks, such as H3K36me3, H3K4me1, and H3K27ac, were comparable between control and si<italic>Ctcf</italic>-treated SCs (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8f, g</xref>). This suggests that downregulation of CTCF selectively diminishes PRC2 complex activity in SCs.</p><p id=\"Par26\">SUZ12 acts as scaffold for PRC2 complex integrity and functions<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref>–<xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. PRC2 components may provide distinct molecular cues in different contexts<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. To test whether CTCF and SUZ12 form a complex, we transiently transfected HEK293T cells with vectors expressing flag-tagged CTCF and HA-tagged SUZ12; in a co-immunoprecipitation assay we detected CTCF in a complex with SUZ12 (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8h, i</xref>). Furthermore, endogenous SUZ12 was co-immunoprecipitated with CTCF in differentiated SCs (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8j</xref>), consistent with previous study in other cell types<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. These results raise the possibility that CTCF coordinates with PRC2 to mediate gene silencing during SC differentiation.</p><p id=\"Par27\">To evaluate the functional role of the SUZ12-PRC2 complex, we knocked down <italic>Suz12</italic> expression by siRNA in SCs. Similar to the CTCF-deficient SCs, <italic>Suz12</italic> silencing downregulated SC differentiation-associated genes such as <italic>Egr2, Prx, Fasn, Srebf2, Mal</italic>, <italic>Pllp</italic>, and <italic>Mag</italic>, while upregulating differentiation-inhibitory genes, such as <italic>Notch3</italic> and <italic>Hes1</italic> (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8k</xref>). This suggests that SUZ12-mediated PRC2 activity is required for proper SC differentiation.</p></sec><sec id=\"Sec11\"><title>CTCF recruits SUZ12 to regulate SC differentiation</title><p id=\"Par28\">To identify genes directly targeted by CTCF, we performed chromatin immunoprecipitation sequencing (ChIP-Seq) in proliferating and differentiating SCs. The signals of CTCF peaks within ±2 kb elements proximal to transcriptional start sites appeared to have an increase in differentiating SCs compared to proliferative SCs (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9a, b</xref>), accompanied by increased levels of H3K27me3 in differentiating SCs (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9c</xref>). To identify consensus sequence motifs associated with CTCF targeted sites using the HOMER program, we observed the binding motifs for CTCF, SCRT2, BREU, ZBTB3, and MYCN (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9d</xref>). In ~5.8% of CTCF-occupied regions, H3K27me3 peaks were observed (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9e, f</xref>, and Supplementary Data <xref rid=\"MOESM4\" ref-type=\"media\">2</xref>). In addition, ~16.8% of CTCF peaks overlapped with those of the activating histone mark H3K27ac<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup> in SCs (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9g</xref>; Supplementary Data <xref rid=\"MOESM4\" ref-type=\"media\">2</xref>). These data indicate that CTCF binding sites can be associated with either repressive or activating regulatory elements. H3K27me3 was essentially excluded from H3K27ac-enriched enhancers and promoters in SCs (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9h</xref>), it suggests that CTCF may control the expression of distinct differentiation factors to regulate SC differentiation programs.<fig id=\"Fig9\"><label>Fig. 9</label><caption><title>CTCF and SUZ12 regulate transcriptomic dynamics during SC differentiation.</title><p><bold>a</bold> Heatmap of CTCF ChIP-seq peaks from proliferating and differentiated SCs. <italic>n</italic> = 1 in each condition. <bold>b</bold> ChIP-seq enrichment around TSS regions in proliferative and differentiated SCs. <bold>c</bold> Immunoblotting for H3K27me3 in proliferating and differentiating SCs. <italic>n</italic> = 2 independent experiments. <bold>d</bold> Enriched motifs of transcription factors (TF) in the CTCF-bound regions. <bold>e–g</bold> ChIP-seq enrichment around CTCF binding regions for <bold>e</bold> CTCF, and <bold>g</bold> H3K27ac in rat SCs, <bold>f</bold> H3K27me3 in rat sciatic nerves. <bold>h</bold> Heatmap for H3K27me3 and H3K27ac. The sites are ranked in ascending order of H3K27ac intensity. <italic>n</italic> = 1 in each condition. <bold>i</bold> GSEA enrichment for genes with H3K27me3 peaks associated with TSSs in siControl or si<italic>Ctcf</italic> rat SCs. NES, normalized enrichment score. <bold>j</bold> Overlap between CTCF-bound and upregulated genes differentially expressed in rat SCs treated with control siRNA or si<italic>Ctcf</italic>. <bold>k</bold> Heatmap of CTCF-targeted upregulated genes in rat SCs treated with control siRNA or si<italic>Ctcf</italic>. <italic>n</italic> = 3 in each condition. <bold>l</bold> Tracks for the indicated genes with ChIP-seq of CTCF from rat SCs and of H3K27me3 from rat sciatic nerves. <italic>n</italic> = 1 in each condition with 20 million cells. <bold>m</bold> Expression of the indicated genes in RNA-seq dataset from rat SCs treated with control siRNA or si<italic>Ctcf</italic>. <italic>n</italic> = 3 independent experiments, <italic>P</italic><sub>(<italic>Hes1</italic>)</sub> = 0.011, <italic>P</italic><sub>(<italic>Ccnd2</italic>)</sub> = 0.01007, <italic>P</italic><sub>(<italic>Rspo2</italic>)</sub> = 0.0105, <italic>P</italic><sub>(<italic>Shh</italic>)</sub> = 0.019, <italic>P</italic><sub>(<italic>Calca</italic>)</sub> = 0.041. <bold>n</bold> qRT-PCR for the indicated genes in rat SCs treated with control siRNA or si<italic>Suz12</italic>. <italic>n</italic> = 3 independent experiments, <italic>P</italic><sub>(<italic>Rspo2</italic>)</sub> = 0.006, <italic>P</italic><sub>(<italic>Shh</italic>)</sub> = 0.012, <italic>P</italic><sub>(<italic>Calca</italic>)</sub> = 0.02. <bold>o</bold> ChIP-qPCR for H3K27me3 at the promoters of the indicated genes in rat SCs treated with control siRNA or si<italic>Ctcf</italic>. IgG were normalized to 1; <italic>n</italic> = 3 independent experiments, <italic>P</italic><sub>(<italic>Rspo2</italic>)</sub> = 0.039, <italic>P</italic><sub>(<italic>Shh</italic>)</sub> = 0.011, <italic>P</italic><sub>(<italic>Calca</italic>)</sub> = 0.039, <italic>P</italic><sub>(<italic>Sox2</italic>)</sub> = 0.0094. Data are presented as means ± SEM, *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001, two-tailed unpaired Student’s <italic>t</italic>-test. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig9_HTML\" id=\"d30e3114\"/></fig></p><p id=\"Par29\">The H3K27me3-targeted genes from ChIP-seq assays were significantly more enriched in si<italic>Ctcf</italic> SCs than SCs treated with control siRNAs (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9i</xref>), suggesting that <italic>Ctcf</italic> loss leads to a derepression of H3K27me3-targeted genes. Intersection of the CTCF-occupied genes with those differentially expressed in SCs treated with control vs. <italic>Ctcf</italic>-targeted siRNA revealed approximately 1292 upregulated genes that are likely to be CTCF-regulated targets (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9j</xref>). The most significantly upregulated genes from GO analysis are associated with immature SCs and include <italic>Hes1</italic>, encoding an effector of Notch signaling that inhibits SC myelination<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>, <italic>Rspo2</italic>, which encodes a protein that confers stemness-associated traits<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref>,<xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>, <italic>Shh</italic>, reflecting a reversion from mature to proliferating immature SCs<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref>,<xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>, and <italic>Calca</italic>, encoding calcitonin-related polypeptide that is critical for SC proliferation<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref>,<xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup> (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9k, l</xref>, and Supplementary Data <xref rid=\"MOESM5\" ref-type=\"media\">3</xref>). Transcriptomic and qRT-PCR analyses validated that expression of these genes was significantly upregulated in both <italic>Ctcf</italic>- and <italic>Suz12</italic>-silenced SCs (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9m, n</xref>).</p><p id=\"Par30\">To further determine whether H3K27me3 deposition is altered over promoters after CTCF-depletion, we performed ChIP followed by qPCR (ChIP-qPCR) for H3K27me3 in control and si<italic>Ctcf</italic>-treated SCs. H3K27me3 occupancy was markedly reduced at the promoters of <italic>Rspo2, Shh, Calca</italic>, and <italic>Sox2</italic> in the absence of CTCF (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9o</xref>), suggesting that CTCF cooperates with PRC2 to repress the expression of genes that prevent SC differentiation.</p></sec><sec id=\"Sec12\"><title>CTCF-mediated chromatin looping required for EGR2 expression</title><p id=\"Par31\">To identify candidate CTCF-regulated targets, we intersected the CTCF-occupied genes with those genes differentially downregulated expressed in si<italic>Ctcf</italic> SCs compared to controls (Fig. <xref rid=\"Fig10\" ref-type=\"fig\">10a</xref>). The downregulated genes that are occupied by CTCF were associated with SC differentiation and lipid biogenesis including <italic>Egr2</italic>, <italic>Pmp22</italic>, and <italic>Lss</italic> (Fig. <xref rid=\"Fig10\" ref-type=\"fig\">10b, c</xref>).<fig id=\"Fig10\"><label>Fig. 10</label><caption><title>CTCF-mediated chromatin regulatory looping is necessary for EGR2 expression.</title><p><bold>a</bold> Venn diagrams depicting overlap between CTCF-bound genes and downregulated genes differentially expressed in rat SCs treated with control siRNA or si<italic>Ctcf</italic>. <bold>b</bold> Heatmap of CTCF-targeted genes differentially downregulated in SCs treated with si<italic>Ctcf</italic> compared to controls. <italic>n</italic> = 3 in each condition. <bold>c</bold> Genome browser tracks over the loci of selected myelin-related genes with ChIP-seq density mapping of CTCF, H3K27ac, and P300 from rat SCs. <italic>n</italic> = 1 in each condition with 20 million cells. <bold>d</bold> Quantitation of relative interaction frequencies between the indicated anchor site and neighboring <italic>Egr2</italic> genomic restriction fragments. <italic>n</italic> = 3 independent 3C experiments. <bold>e</bold> Quantitation of relative interaction frequencies between the indicated anchor site and neighboring <italic>Sox10</italic> genomic restriction fragments. <italic>n</italic> = 3 independent 3C experiments. <bold>f</bold> A model depicting a dual mode of action by CTCF in promotion of SC differentiation: CTCF stabilizes chromatin loops involving promotor-enhancer interactions to activate expression of promyelinating genes such as <italic>Egr2</italic> (left panel) and forms a transcriptional co-repressor complex with SUZ12-PRC2 (right panel) to inhibit expression of genes such as <italic>Sox2</italic> that encode factors that inhibit differentiation and cell-cycle/proliferation regulators. Data are presented as means ± SEM. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17955_Fig10_HTML\" id=\"d30e3287\"/></fig></p><p id=\"Par32\">The loss of CTCF caused a drastic reduction in expression of the critical SC myelination gene <italic>Egr2</italic>. Since CTCF is a crucial mediator of enhancer-promoter interactions and long-range genomic landscaping<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref>,<xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup>, we tested whether CTCF regulates promoter-enhancer looping on the <italic>Egr2</italic> locus by performing quantitative chromosome conformation capture (3C) in control and si<italic>Ctcf</italic>-treated SCs across ~270 kilobases of the <italic>Egr2</italic> genomic locus on rat chromosome 20<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup>. 3C assays allow capture of long-range interactions between <italic>cis</italic>-acting elements<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref>,<xref ref-type=\"bibr\" rid=\"CR52\">52</xref></sup>. We assessed interactions between the anchor site within the promoter region with upstream (primers p1-p4) and downstream (primers p5–p8) regulatory or enhancer regions marked by the activating histone mark H3K27ac of the <italic>Egr2</italic> locus in a 3C assay in SCs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3a</xref>). We found that the <italic>Egr2</italic> promoter interacted at a high frequency with the upstream p2 and downstream at p7 regulatory elements, while minimal interactions between the anchor site and other <italic>Egr2</italic> elements were observed (Fig. <xref rid=\"Fig10\" ref-type=\"fig\">10d</xref>). In the CTCF-deficient SCs, interactions between <italic>Egr2</italic> promoter and its enhancers at p1, p2, and p7 sites, were markedly reduced compared to the interaction in control cells (Fig. <xref rid=\"Fig10\" ref-type=\"fig\">10d</xref>), suggesting that CTCF is required for the enhancer-promotor interactions with the <italic>Egr2</italic> locus. The interaction between <italic>Egr2</italic> promoter and the myelin-specific element (MSE)<sup><xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup> at the p6 site (around +38 kb) does not appear to be CTCF regulated, however, the chromatin accessibility in the <italic>Egr2</italic> MSE is reduced in <italic>Ctcf</italic>-knockdown SCs (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7c, e</xref>). In contrast, CTCF downregulation does not impair the chromatin looping around the <italic>Sox10</italic> locus (Fig. <xref rid=\"Fig10\" ref-type=\"fig\">10e</xref>). Given the downregulation of EGR2, but not SOX10, in CTCF-deficient SCs, this suggests that the CTCF-mediated enhancer-promotor looping is required for expression of <italic>Egr2</italic>, but not <italic>Sox10</italic>.</p><p id=\"Par33\">Although depletion of CTCF drastically decreased expression of <italic>Egr2</italic>, the genes neighboring <italic>Egr2</italic> such as <italic>Nrbf2</italic>, and <italic>Jmjd1c</italic>, which are located outside of the genomic topologically associating domain during mouse neural development<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>, were not substantially affected by CTCF depletion (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3b-d</xref>), suggesting that the role of CTCF-mediated chromatin organization is specific to the <italic>Egr2</italic> gene during SC differentiation.</p></sec></sec><sec id=\"Sec13\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par34\">Although CTCF is a regulator of chromatin organization and enhancer function<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR18\">18</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>, little had been known regarding its function in SC development and myelin regeneration. In this study, we found that CTCF is upregulated during SC differentiation and restricts chromatin accessibility in the gene loci associated with immature SC programs while silencing differentiation-inhibitory cues through recruiting PRC2/SUZ12 repressive complexes for promyelination gene expression. <italic>Ctcf</italic> deletion in SCs resulted in severe hypomyelination with a complete absence of myelinating axons in peripheral nerves. The loss of <italic>Ctcf</italic> blocked SC differentiation and myelination at the promyelinating state but did not appear to affect immature SC survival or myelin maintenance, indicating a stage-specific function of CTCF during SC-lineage progression. Recently, a short isoform of CTCF (CTCF-s) was identified to have a noncanonical role to antagonize full-length CTCF<sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref></sup>. It remains to be defined for the function of CTCF-s in SC development.</p><p id=\"Par35\">In <italic>Ctcf</italic>-mutant sciatic nerves, we observed drastic downregulation of the critical SC differentiation regulators such as EGR2; however, expression of early stage SC regulators such as SOX2 and OCT6 was maintained in neonatal stages and even increased at later postnatal stages. Furthermore, CTCF deficiency led to an upregulation of differentiation-inhibitory cues, such as SOX2, NOTCH, SHH, and WNT pathways. These results suggested that CTCF functions as a dual-level switch to control of SC maturation through inactivation of differentiation-inhibitory signaling while promoting activation of promyelinating factors such as EGR2 during SC development.</p><p id=\"Par36\">The chromatin organizer CTCF can function as a repressor, activator, or insulator of gene expression depending on the genomic context<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>. We showed that CTCF overexpression promoted SC myelination program and repressed differentiation-inhibitory pathways, whereas the depletion of CTCF led to an increase in chromatin accessibility and derepression of genes associated with SC differentiation inhibition. This indicates that CTCF limits the expression of genes specifically expressed in immature or proliferating SCs. Thus, CTCF is required for the precise balance of proliferation versus differentiation during SC development.</p><p id=\"Par37\">We found that CTCF is critical for establishing the chromatin conformation necessary for the promotor-enhancer interaction that activates expression of the key SC promyelination factor EGR2. The activities of H3K27ac-marked enhancers around the promoters or enhancers of <italic>Egr2</italic> were substantially increased during SC differentiation (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4a, b</xref>). Our ChIP-seq analysis showed that CTCF targets the enhancers of SC myelination genes during SC differentiation. suggesting that CTCF may modulate the enhancer landscape for activating expression of myelination-promoting genes such as <italic>Egr2</italic> during SC differentiation. Intriguingly, silencing of <italic>Ctcf</italic> appeared to elevate the promoter or enhancer activity around SC differentiation-inhibitory genes (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4c</xref>). Since CTCF binding sites are interwoven with enhancer elements throughout the genome, our data indicate that CTCF plays a dual role during SC differentiation, inhibiting enhancer activity of immature SC-associated genes while activating the enhancer activity of myelin-related genes.</p><p id=\"Par38\">Our 3C assessment indicated that the enhancer region of <italic>Egr2</italic> forms a complex looped architecture that likely brings regulatory components into proximity. This hub involving the promoter and enhancer elements may ensure high levels of <italic>Egr2</italic> transcription during SC differentiation. Depletion of CTCF drastically decreased expression from <italic>Egr2</italic> but not neighboring genes outsides of the topologically associating domain (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3</xref>), suggesting that CTCF specifically acts to upregulate <italic>Egr2</italic> during SC differentiation through establishing enhancer-promoter interaction associated with its expression. These findings are consistent with the notion that TADs are evolutionarily and developmentally stable regions and preferentially interact each other within TAD<sup><xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>. It is remained to be determined whether enhancer-promoter interactions of the <italic>Egr2</italic> neighboring genes such as <italic>Nrbf2</italic> and <italic>Jmjd1c</italic> outside of the TAD domain of <italic>Egr2</italic> are altered in the absence of CTCF. Whether and how CTCF regulates organizational architectures that shape the three-dimensional regulatory elements of other SC differentiation-promoting or inhibitory genes remain to be defined. Nonetheless, our results suggest a mechanism by which CTCF-mediated long-range interactions achieve cell-type and target-specific gene expression (Fig. <xref rid=\"Fig10\" ref-type=\"fig\">10f</xref>).</p><p id=\"Par39\">The PRC2 repressive complex is the sole histone methyltransferase that catalyzes the H3K27me3 mark<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref>,<xref ref-type=\"bibr\" rid=\"CR57\">57</xref></sup>. Our data suggest that CTCF binding is critical for SUZ12-PRC2 recruitment onto the regulatory elements of genes that inhibit SC differentiation. PRC2 functions mainly to maintain rather than establish patterns of gene repression<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>, thus we reason that PRC2 functions to safeguard the SC differentiation program. In support of this hypothesis, the PRC2 component EED has been shown to be crucial for SC proliferation and differentiation and prevent alternative lineages in response to nerve injury by silencing differentiation-inhibitory and cell-cycle regulators<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref>,<xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>. EED does not, however, influence early development of myelin<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref>,<xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>. SUZ12 acts as structural scaffold for core PRC2 complex integrity and chromatin binding, but also its interactions with accessory cofactors that define distinct functions of PRC2 subcomplexes<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref>,<xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. The distinct functions of individual PRC2 subunits have been reported in other contexts<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref>,<xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>. We showed that depletion of SUZ12 reduced expression of genes that induce SC differentiation, suggesting that SUZ12 might have activities distinct from those of EED in regulation of the SC myelination program. Of note, nonsense and inactivating mutations in PRC2 subunits like EED or SUZ12 are observed in malignant peripheral nerve sheath tumors<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>, suggesting that the loss of PRC2 function contributes to the proliferation of immature SCs during malignancy.</p><p id=\"Par40\">Genome-wide ChIP-seq revealed that H3K27me3 binding sites and CTCF binding sites overlap in genes encoding a subset of factors that inhibit differentiation, suggesting that CTCF may recruit the SUZ12/PRC2 complex to repress expression of certain genes. CTCF-mediated SUZ12/PRC2 recruitment has been shown to silence gene transcription<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref>,<xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup>. Transcriptome profiling of SCs depleted of CTCF revealed a derepression of H3K27me3 target genes, suggesting that CTCF is required for the repressive activity of the PRC2 complex on these genes. The recruitment of SUZ12/PRC2 may stabilize CTCF-dependent genomic loops in genes marked with H3K27me3 to maintain their repressive states. Although CTCF may recruit other transcriptional repressors such as HDAC/SIN3a<sup><xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>, our data suggest that CTCF can promote SC differentiation by modulating the recruitment of the SUZ12/PRC2 repressive complex to silence the differentiation-inhibitory network (Fig. <xref rid=\"Fig10\" ref-type=\"fig\">10f</xref>). It is possible that the interaction between CTCF and PRC2 complexes such as SUZ12 could be indirect through other interacting mediators. Together, our studies demonstrate a critical function of CTCF-dependent chromatin architecture and local chromatin environments in control of gene expression programs necessary for SC myelination and remyelination. Although CTCF regulation is expected to be ubiquitous, our data demonstrate a temporal specificity of CTCF that is transiently required to establish a differentiated state during SC myelination. Our study revealed that expression of CTCF is upregulated during remyelinating phases after nerve injury, and is crucial for SC remyelination after nerve transection. The proliferation of SCs was not affected in the regenerating region of CTCF-deficient nerves, suggesting that CTCF may not be required for the generation of repair cells<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>, despite its essential role in normal remyelination after injury. This phenotype is similar to that of the transcriptional repressor ZEB2 in nerve repair<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref>,<xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. However, CTCF does not appear to regulate ZEB2 expression (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5</xref>), suggesting that CTCF may selectively control only certain promyelin regulators such as EGR2 to regulate myelin repair. Thus, manipulating CTCF function and activity in SCs may provide a therapeutic means of reversing adverse neuropathies.</p></sec><sec id=\"Sec14\"><title>Methods</title><sec id=\"Sec15\"><title>Animals</title><p id=\"Par41\"><italic>Ctcf</italic><sup><italic>loxP/loxP</italic></sup> mice, generated from <italic>Ctcf</italic><sup>tm1a(EUCOMM)Wtsi</sup> mice breeding with ACT-FLPe mice (Jackson Laboratory, USA), were crossed with mice carrying Dhh-cre<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup> to obtain <italic>Ctcf</italic><sup><italic>loxP</italic>/+;</sup>Dhh-cre<sup>+/−</sup> mice, which were then bred with <italic>Ctcf</italic><sup><italic>loxP/loxP</italic></sup> mice to generate control (<italic>Ctcf</italic><sup><italic>loxP/+;</italic></sup>Dhh-cre<sup>+/−</sup>) and <italic>Ctcf</italic> cKO offspring (<italic>Ctcf</italic><sup><italic>loxP/loxP</italic></sup>; Dhh-cre<sup>+/−</sup>). Littermates <italic>Ctcf</italic><sup><italic>loxP/loxP</italic></sup> or <italic>Ctcf</italic><sup><italic>loxP</italic>/+;</sup>Dhh-cre<sup>+/−</sup> mice were used as controls. Recombination, perinatally or after sciatic nerve transection, was achieved by crossing <italic>Ctcf</italic><sup><italic>loxP/loxP</italic></sup> mice with the inducible Cre recombinase CreERT2 under the control of the Plp1 promoter (Plp1-creERT) followed by tamoxifen injection<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. Animals of either sex were used in the study and littermates were used as controls. The mouse strains used in this study were generated and maintained on a mixed C57Bl/6;129 Sv background and housed in a vivarium with a 12-h light/dark cycle. Ambient temperature (22 °C) and 30–70% humidity was maintained. No more than four adult mice were housed in the same cage. The animal experiments were conducted with both genders. All animal use and studies complied with all relevant ethical regulations and were approved by the IACUC (Institutional Animal Care and Use Committee) at the Cincinnati Children’s Hospital Medical Center, USA.</p></sec><sec id=\"Sec16\"><title>Isolation, growth, and differentiation of primary rat SCs</title><p id=\"Par42\">Rat SCs were isolated from sciatic nerves of newborn rats (1–2 d old). Sciatic nerves were harvest and dissociated by Trypsin-Collagenase (Gibco, 25200-056), then cells were treated with SC medium supplemented with Ara-C (Sigma, C6645) for 3 days and washed out fibroblasts. After treating with SC medium supplemented with anti-Thy-1.1 antibody (Serotec, MCA04G), cells were wash with HBSS (Gibco, 14170-112) to remove the remaining contaminating fibroblasts. Isolated SCs were grown routinely in DMEM/10% FBS (Life Technologies), supplemented with 10 ng ml<sup>−1</sup> Neuregulin 1 (Nrg1; R&D Systems, 396-HB) and 5 μM forskolin (Sigma, F6886) plus L-glutamine and penicillin/streptomycin, hereafter termed SC proliferation medium. Cells between passages 2 and 6 were used in all experiments. >95% SC purity was achieved, assessed by SOX10 and S100β staining. To initiate differentiation, SCs were washed three times with DMEM and then cultured in differentiation medium containing DMEM/0.5% FBS and 1 mM dibutyryl cyclic AMP (Sigma, D0627) with L-glutamine and penicillin/streptomycin, for the length of time indicated in the text, depending on the assays used. All tissue culture containers and coverslips were coated with 50 μg ml<sup>−1</sup> poly-L-lysine (Sigma, P-7890) in PBS for at least 30 min at room temperature and then rinsed in distilled water for three times. Purified rat SCs seeded on coverslips were fixed in 4% paraformaldehyde (PFA) for 15 min and washed in 1× PBS four times before immunofluorescence staining.</p></sec><sec id=\"Sec17\"><title>Immunostaining and electron microscopy</title><p id=\"Par43\">The sciatic nerves of mice at defined ages were dissected and fixed for 30 min in 4% PFA, embedded in OCT, cryoprotected in 25% sucrose and sectioned at 9 μm as longitudinal sections by using cryostat. For BrdU pulse labeling, animals at P7 were injected subcutaneously with 100 mg BrdU kg-1 body weight 2 h before sciatic nerve collection. For immunostaining, we used antibodies to CTCF (rabbit, Cell Signaling, #3418, 1:600), SOX10 (goat, Santa Cruz Biotechnology, sc-17342, 1:500; rabbit, Abcam; ab155279, 1:500), Oct6 (goat, Santa Cruz Biotechnology, sc-11661, 1:200), EGR2 (rabbit, Santa Cruz Biotechnology, sc-20690, 1:200), MBP (goat, Santa Cruz Biotechnology, sc-13914, 1:500), SOX2 (goat, Santa Cruz Biotechnology, sc-17320, 1:200), Ki67 (rabbit, Thermo Scientific, RM-9106, 1:500), BrdU (rat, Abcam, ab6326, 1:200), cleaved-caspase 3 (rabbit, Cell Signaling, #9661, 1:200), NF-M (rabbit, Millipore, AB1987, 1:200). Secondary antibodies conjugated to Cy2, Cy3, or Cy5 were from Jackson ImmunoResearch Laboratories catalog numbers 705-165-147, 705-225-147, 711-225-152, 711-165-152, 711-175-152, 715-165-150, and 712-165-150 (1:500). All images were acquired using Nikon C2 confocal microscope. The threshold for the quantifications was based on the strong visual appearance of immunostaining signal of quantified cells compared with the background in a blinded manner.</p><p id=\"Par44\">For electron microscopy, mice were perfused with 4% PFA, 2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer, pH 7.2. Sciatic nerves were dissected and fixed in the same fixative solution overnight. Nerves were rinsed in PBS, postfixed in 1% OsO4 in PBS for 1 h, dehydrated in graded ethanol, infiltrated with propylene oxide and embedded in Epon. Semi-thin sections were stained with toluidine blue, and thin sections were stained with lead citrate. The morphometric measurements of axonal sorting defects were performed using electron micrographs of ultrathin sections and analyzed using NIH Image J version 1.47 software (<ext-link ext-link-type=\"uri\" xlink:href=\"http://rsb.info.nih.gov/ij/\">http://rsb.info.nih.gov/ij/</ext-link>). An entire sciatic nerve cross-section per animal was reconstructed by merging several high magnification photographs taken by bright field microscopy at ×100 magnification. The number of myelinated axons per nerve were analyzed in ultrathin sections using a JEOL 1200 EXII electron microscope.</p></sec><sec id=\"Sec18\"><title>Transient transfections in rat SCs</title><p id=\"Par45\">For plasmid transfections, rat SCs were transfected with expression vectors using Lipofectamine 3000 (Life Technologies, L3000008) per the manufacturer’s protocol and assayed for immunocytochemistry or qPCR analysis. For siRNA knockdown in SCs, we used Lipofectamine RNAiMAX (Life Technologies, 13778075) per the manufacturer’s instructions. SCs were induced to differentiate after 24 h of transfection for 9 h before harvesting for immunocytochemistry or qPCR analysis. For siRNA knockdown, target sequence for CTCF siRNA is as follows: SASI_Rn01_00100578, SASI_Rn01_00100579, SASI_Rn01_00100580, SASI_Rn01_00100581. control siRNA: MISSION siRNA Universal Negative Control #1 (SIC001).</p></sec><sec id=\"Sec19\"><title>Western blotting and co-immunoprecipitation</title><p id=\"Par46\">For western blotting, cells were lysed in modified RIPA buffer (50 mM Na-Tris, pH 7.4, 150 mM NaCl, 1% (v/v) NP-40, 0.25% sodium deoxycholate, 1 mM dithiothreitol, 10 mM NaF, 1 mM active sodium vanadate, 1 mM PMSF and 1× a cocktail of cOmplete protease inhibitors (Roche Applied Science) and centrifuged at 15,000 × <italic>g</italic>. for 15 min at 4 °C. After the determination of protein concentration (Bio-Rad), the lysates were separated by 4–12% SDS-PAGE. We used antibodies against CTCF (rabbit, Cell Signaling Technology, 3417 S, 1:1000), MBP (goat, Santa Cruz Biotechnology, sc-13914, 1:500), MPZ (rabbit, Abcam, ab31851, 1:1000), KROX20/EGR2 (rabbit, Santa Cruz Biotechnology, sc-20690, 1:200 or Guinea pig, provided by Michael Wegner, 1:1000), SUZ12 (rabbit, Cell Signaling Technology, 3737 S, 1:1000), EED (rabbit, Millipore, 17-10034, 1:1000), EZH2 (rabbit, Cell Signaling Technology, 5246 P, 1:1000), H3 (rabbit, Cell Signaling Technology, 4499 S, 1:1000), H3K27me3 (rabbit, Cell Signaling Technology, 9733 S, 1:1000), H3K27me2/3 (mouse, Active motif, 39536, 1:1000), H3K36me3 (rabbit, Abcam, ab9050, 1:1000), H3K4me1 (mouse, Active motif, 39635, 1:1000), H3K27ac (rabbit, Cell Signaling Technology, 4353 S, 1:1000), and GAPDH (mouse, Millipore, MAB374, 1:5000). Bands were visualized with secondary antibodies conjugated to horseradish peroxidase (Jackson ImmunoResearch Laboratories, 111-035-144, 705-035-147, and 115-035-062) and ECL western blotting detection reagents (Pierce) per the manufacturer’s instructions.</p><p id=\"Par47\">For immunoprecipitation, HEK293T cells cultured in 10% FBS/DMEM were transfected with expression vectors using PolyJet (Signagen) for 48 h. HEK293T or differentiating rat Schwann cells were lysed in NP-40 buffer (170 mM NaCl, 10 mM EDTA, 50 mM Tris pH 7.4, 50 mM NaF and 0.5% NP-40) supplemented with protease inhibitor cocktail and phosphatase inhibitors (Roche Diagnostics Inc.). A total of 300 μg of cell lysate proteins were incubated with 2 μg IgG or appropriate antibodies and immunoprecipitated using Protein A/G beads (Santa Cruz Biotechnology, sc-2003). Western blotting was performed using chemiluminescence with the ECL kit (Pierce). The antibodies used were CTCF (rabbit, Cell Signaling, 3417 S, 1:100), SUZ12 (rabbit, Cell Signaling Technology, 3737 S, 1:1000 for Western), anti-Flag (rabbit, 14793 S or mouse, 8146 S, Cell Signaling Technology, 1:200), and HA-tag (mouse, Cell Signaling, 2367 S, 1:200). Secondary antibodies conjugated to HRP were from Jackson ImmunoResearch Laboratories (111-035-144, and 115-035-062, 1:5000).</p></sec><sec id=\"Sec20\"><title>Tamoxifen injection for <italic>Ctcf</italic> inducible knockout</title><p id=\"Par48\">We used Tamoxifen (Sigma, T5648) dissolved to a stock concentration of 20 mg ml<sup>−1</sup> in a vehicle of ethanol and sunflower seed oil (1:9 v/v). For perinatal tamoxifen injections, tamoxifen stock was injected i.p. at 20 mg ml<sup>−1</sup> for 5 consecutive days to lactating mothers, thus administering tamoxifen to pups, beginning at P0. Sciatic nerves of pups were analyzed at P14 for either immunostaining or electron microscopy. For adult tamoxifen treatment, 100 μl (20 mg ml<sup>−1</sup>) was administered by i.p. injection once daily for 5 consecutive days. Mice were treated again for 5 days after a 2-day rest period. Control mice were treated identically.</p></sec><sec id=\"Sec21\"><title>Sciatic nerve transection injury</title><p id=\"Par49\">Mice at 6–8 weeks were under general anesthesia with injection of a mixture of ketamine (90 mg kg<sup>−1</sup> body weight) and xylazine (10 mg kg<sup>−1</sup> body weight). Right sciatic nerves were exposed and transected at midthigh level. Exposed left sciatic nerves were used as uncut controls. Mice were treated with tamoxifen to delete floxed <italic>Ctcf</italic> alleles following injury. Nerves were collected at day 14 or 56 following surgery and processed for immunohistochemistry or EM.</p></sec><sec id=\"Sec22\"><title>RNA isolation and real-time RT-PCR analysis</title><p id=\"Par50\">RNAs were isolated with the RNeasy Plus Mini kit (Qiagen, 74104) from cultured cells or snap-frozen sciatic nerves. cDNA was synthesized from 1 μg RNA using iScript Reverse Transcription Supermix (BioRad) according to the manufacturer’s instructions. qRT-PCR was performed using the StepOnePlus Real-Time PCR System (Applied Biosystems inc). qRT-PCR was performed using quantitative SYBR green PCR mix (BioRad). PCR primer sequences are available upon request. qRT-PCR analysis is based on the ΔΔCT method with normalization of the raw data to GAPDH genes according to the previous method<sup><xref ref-type=\"bibr\" rid=\"CR64\">64</xref></sup>. For each gene, ΔCT was calculated by subtracting CT<sub>GAPDH</sub> from CT<sub>GENE</sub> in either the control or experimental group. We set the average ΔCT of the control as a calibrator, then the 2<sup>–ΔΔCT</sup> method was used to calculate each relative expression in both control or experimental groups. The values in the control were normalized to 1 by dividing each data point with the averaged control value.</p></sec><sec id=\"Sec23\"><title>DRG explant cultures</title><p id=\"Par51\">DRG neurons were isolated from embryonic day (E) 16.5 rat spinal cords and plated as explants on collagen-coated coverslips. Cultures were maintained in serum-free neurobasal medium (NB medium; 2% B27 supplement, 2 mM L-glutamine, 0.4% glucose, and 50 ng/ml 2.5 S nerve growth factor (NGF) (Harlan, 005017). Non-neuronal cells were removed via feeding the cultures with NB medium containing 5-fluorodeoxyuridine and uridine. SCs were isolated from sciatic nerves taken at P2 and expanded in SC proliferation medium. SC–DRG co-cultures were established through seeding purified DRG neuron cultures with 100,000 SCs in culture medium (MEM, 10% FBS, 2 mM L-glutamine, 0.4% glucose, and 50 ng/ml 2.5 S NGF). 3 days after SC plating, SC medium was supplemented with 50 μg ml<sup>−1</sup> ascorbic acid (Sigma, A0278) to initiate myelination. SC–DRG co-cultures were allowed to myelinate for 10 days, with fresh media provided every 2 days. To determine the extent of myelination in SC–DRG co-cultures, the total number of MBP<sup>+</sup> segments was counted in micrographs from 10–12 random fields per coverslip.</p></sec><sec id=\"Sec24\"><title>RNA-seq and data analysis</title><p id=\"Par52\">RNA from siControl and si<italic>Ctcf</italic> SCs were extracted using TRIZOL (Life Technologies) followed by purification using a RNeasy Mini Kit (Qiagen). RNA-seq libraries were prepared using the Illumina TruSeq RNA Library Prep Kit v2 and sequenced by a HiSeq 2500 sequencer. RNA-seq reads were aligned to mm10 using TopHat 2.1.1 with default settings (<ext-link ext-link-type=\"uri\" xlink:href=\"http://ccb.jhu.edu/software/tophat/index.shtml\">http://ccb.jhu.edu/software/tophat/index.shtml</ext-link>). We used Cuffdiff2 2.2.1 to (1) estimate fragments per kilobase of transcript per million mapped reads (FPKM) values for known transcripts and to (2) analyze differentially expressed transcripts. In all differential expression tests, a difference was considered significant if the <italic>p</italic>-value was less than 0.05 and fold-change more 1.5 (Cuff-diff default). DEseq were used for RNA-seq of control and <italic>Ctcf</italic> cKO sciatic nerves from P7. Statistical analyses were performed to identify differentially expressed genes for each comparison using the negative-binomial model of read counts as implemented in the Bioconductor DESeq 1.39.0 package (<ext-link ext-link-type=\"uri\" xlink:href=\"https://bioconductor.org/packages/release/bioc/html/DESeq.html\">https://bioconductor.org/packages/release/bioc/html/DESeq.html</ext-link>). Gene ontology (GO) analysis was performed using ToppGene Suite (<ext-link ext-link-type=\"uri\" xlink:href=\"https://toppgene.cchmc.org/\">https://toppgene.cchmc.org/</ext-link>) and Gene Set Enrichment Analysis (GSEA 4.0.1; <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.broadinstitute.org/gsea/index.jsp\">http://www.broadinstitute.org/gsea/index.jsp</ext-link>).</p></sec><sec id=\"Sec25\"><title>Chromatin immunoprecipitation sequencing (ChIP-seq) assays</title><p id=\"Par53\">ChIP assays were performed with minor modifications<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. Purified rat SCs were fixed for 15 min at room temperature with 1% formaldehyde-containing medium. Nuclei were isolated and sonicated in sonication buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA, 0.5 mM EGTA and protease inhibitor cocktail). Sonicated chromatin (~300 µg) was used for immunoprecipitation by incubation with appropriate antibodies (4 μg) overnight at 4 °C. Prerinsed magnetic protein A/G beads (50 μL, Thermo Fisher Scientific, 26162) were added to each ChIP reaction and reactions were incubated for 1 h at 4 °C. The beads were then incubated in 200 ml elution buffer at 65 °C for 20 min to elute immunoprecipitated materials. The ChIP-seq libraries were prepared using NEBNext ChIP-seq Library Prep Master Mix Set for Illumina (NEB catalogue number E6240L) and then run on the Illumina sequencer HiSeq 2500. We used antibodies CTCF (rabbit, Cell Signaling, #3418), H3K27Ac (rabbit, Active motif, 39135), or H3K27me3 (rabbit, Cell Signaling, 9733 s) for ChIP. The crosslinked and sonicated chromatins without immunoprecipitation were used as input controls. For ChIP, real-time PCR was performed using quantitative SYBR green PCR mix (Bio-Rad, 1725121). The values of IgG were normalized to 1.</p></sec><sec id=\"Sec26\"><title>ChIP-seq peak-calling and data analysis</title><p id=\"Par54\">Reads of ChIP-seq data were aligned to Rn5 using Bowtie2 v2.3.5.1with the following options: -p 8, -m 1 (<ext-link ext-link-type=\"uri\" xlink:href=\"http://bowtie-bio.sourceforge.net\">http://bowtie-bio.sourceforge.net</ext-link>). Peak calling was performed using MACS version 1.4.2 (Model-based Analysis of ChIP-seq) (<ext-link ext-link-type=\"uri\" xlink:href=\"http://liulab.dfci.harvard.edu/MACS\">http://liulab.dfci.harvard.edu/MACS</ext-link>) with a <italic>p</italic>-value cutoff of 10<sup>−9</sup> and compared the peak sets using the ENCODE (v90) overlap rules. Motifs were predicted using the HOMER v4.11 program (<ext-link ext-link-type=\"uri\" xlink:href=\"http://homer.salk.edu/homer\">http://homer.salk.edu/homer</ext-link>). The heatmaps were drawn using the Heatmap tools provided by Cistrome (<ext-link ext-link-type=\"uri\" xlink:href=\"http://cistrome.org/ap\">http://cistrome.org/ap</ext-link>). Tracks were shown in Mochiview version 1.46. We used the FastQC 0.11.9 pipeline (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc/\">http://www.bioinformatics.babraham.ac.uk/projects/fastqc/</ext-link>), a quality control tool for high-throughput sequence data, to assure the quality of raw sequence data from ChIP-seq and ATAC-seq. Mapping tags, parameters of alignments, peak enrichment information, and FastQC results for the ChIP-seq and ATAC-seq dataset were included in Supplementary Data <xref rid=\"MOESM6\" ref-type=\"media\">4</xref>.</p></sec><sec id=\"Sec27\"><title>Assay for transposase-accessible chromatin using sequencing (ATAC-Seq)</title><p id=\"Par55\">We isolated nuclei of ~50,000 cells from a pool of control or <italic>Ctcf</italic>-knockdown SCs from two independent experiments in a cold lysis buffer (10 mM Tris-HCl, pH 7.4, 10 mM NaCl, 3 mM MgCl<sub>2</sub>, 0.1% IGEPAL CA-630). After spinning down at 500 × <italic>g</italic> for 10 min at 4 C, nuclei were resuspended in transposition mix containing 2 × reaction buffer (TD, Illumina, 20034197), Nextera Tn5 Transposase (TDE1, Illumina, 15027865) at 37 °C for 30 min. Immediately following transposition, DNA were purified using a Qiagen MinElute PCR Purification Kit. Transposed DNA fragments were subsequently amplified and the amplified library was purified using Qiagen MinElute PCR Purification Kit. Libraries were generated using the Ad1_noMX and barcoded primers and were amplified for 11 total cycles. Libraries were purified with AMPure beads (Beckman Coulter, A63880) to remove contaminating primer dimers. All libraries were sequenced on the Illumina HiSeq 2500 with 75 bp single-end reads.</p><p id=\"Par56\">Reads of ATAC-seq data were aligned to rn5 genome using Bowtie2 v2.3.5.1 with the following options: –best–chunkmbs 200 (<ext-link ext-link-type=\"uri\" xlink:href=\"http://bowtie-bio.sourceforge.net\">http://bowtie-bio.sourceforge.net</ext-link>). Peak calling and identification were performed using Model-based analysis of MACS version 1.4.2 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/taoliu/MACS\">https://github.com/taoliu/MACS</ext-link>) with specific parameters without the prebuilt model: –shift 75 –extsize 150 –nomodel –call-summits –nolambda –keep-dup all −p 0.01, to call peaks, which extend and shift the fragments to get the region cut by the Tn5 sites. The read density profiles were normalized by the library size. Tracks were shown in Mochiview version 1.46. We calculated the peak_RPKM, then used GSEA (4.0.1) to analyze the enrichment of signature gene sets in siControl and si<italic>Ctcf</italic> cells<sup><xref ref-type=\"bibr\" rid=\"CR65\">65</xref></sup>. Mapping tags, parameters of alignments, peak enrichment information, and FastQC 0.11.9 quality control results for the ATAC-seq dataset was included in Supplementary Data <xref rid=\"MOESM6\" ref-type=\"media\">4</xref>.</p></sec><sec id=\"Sec28\"><title>Chromosome conformation capture (3C) assays</title><p id=\"Par57\">siControl or si<italic>Ctcf</italic> rat SCs (2 × 10<sup>6</sup>) were differentiated for 9 h and 1 × 10<sup>7</sup> cells were crosslinked with 2% formaldehyde 10% FCS/PBS at room temperature for 10 min, followed by 1 M glycine quenching, pellet was lysed in a cold lysis buffer [10 mM Tris-HCl, pH 7.5; 10 mM NaCl; 5 mM MgCl<sub>2</sub>; 0.1 mM EGTA; 1× complete protease inhibitor (Roche, 11836145001)]. Then nuclei were digested with HindIII (NEB R3104s) overnight at 37 °C, and then T4 ligation (NEB M0202S) at 16 °C for 4 h. Then we use phenol-chloroform to purify the DNA pellet. 3C ligation products were quantified in triplicates by quantitative SYBR Green real-time PCR<sup><xref ref-type=\"bibr\" rid=\"CR66\">66</xref>,<xref ref-type=\"bibr\" rid=\"CR67\">67</xref></sup>. The relative interaction 3C signals were calculated according to a locus-specific BAC standard curve and to 3C signals and normalized to the level of the <italic>Gapdh</italic> locus. We chose the probe primer sequences for 3C experiments according to the potential enhancer elements marked by the activating histone mark H3K27ac in SCs.</p></sec><sec id=\"Sec29\"><title>Statistical analysis</title><p id=\"Par58\">All analyses were done using Microsoft Excel version 16.16.23 or GraphPad Prism 6.00 (San Diego California, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.graphpad.com\">http://www.graphpad.com</ext-link>). Data are shown in dot plots or bar graphs as mean ± SEM; <italic>P</italic> < 0.05 is deemed statistically significant. Data distribution was assumed to be normal, but this was not formally tested. Count data were assumed to be nonparametric, and appropriate statistical tests were used. Statistical analysis was performed using two-tailed or one-tailed unpaired Student’s <italic>t</italic>-tests between two samples and one-way ANOVA with Tukey’s post-hoc analysis for multiple comparisons, log-rank test for survivals or as indicated. Permutation test was carried out based on the paired <italic>t</italic>-test statistic and 100,000 permutations. Quantifications were performed from at least three experimental groups in a blinded fashion. The n value was defined as the number of experiments that were repeated independently with similar results. No statistical methods were used to predetermine sample sizes, but our sample sizes are similar to those generally employed in the field. Samples from the genotyped animals were randomly assigned for experimental analysis and data collection, and data were quantified with blinding.</p></sec><sec id=\"Sec30\"><title>Reporting summary</title><p id=\"Par59\">Further information on research design is available in the <xref rid=\"MOESM7\" ref-type=\"media\">Nature Research Life Sciences Reporting Summary</xref> linked to this article.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec31\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41467_2020_17955_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41467_2020_17955_MOESM2_ESM.pdf\"><caption><p>Description of Additional Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"41467_2020_17955_MOESM3_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM4\"><media xlink:href=\"41467_2020_17955_MOESM4_ESM.xlsx\"><caption><p>Supplementary Data 2</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM5\"><media xlink:href=\"41467_2020_17955_MOESM5_ESM.xlsx\"><caption><p>Supplementary Data 3</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM6\"><media xlink:href=\"41467_2020_17955_MOESM6_ESM.xlsx\"><caption><p>Supplementary Data 4</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM7\"><media xlink:href=\"41467_2020_17955_MOESM7_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media></supplementary-material></sec></sec></body><back><app-group><app id=\"App1\"><sec id=\"Sec32\"><title>Source data</title><p id=\"Par62\"><media position=\"anchor\" xlink:href=\"41467_2020_17955_MOESM8_ESM.xlsx\" id=\"MOESM8\"><caption><p>Source Data</p></caption></media></p></sec></app></app-group><fn-group><fn><p><bold>Peer review information</bold>\n<italic>Nature Communications</italic> thanks Gonçalo Castelo-Branco, Hongjie Yao and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Jincheng Wang, Jiajia Wang, Lijun Yang.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> is available for this paper at 10.1038/s41467-020-17955-2.</p></sec><ack><title>Acknowledgements</title><p>We thank Dr. Jianrong Lu for providing the <italic>Ctcf</italic> floxed mice, Drs. Liguo Zhang, Danyang He, and Xuelian He for suggestions, and Ed Hurlock for comments. This study was funded by the Cincinnati Children’s Hospital innovation fund to Q.R.L.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>J.C.W., Q.W., and Q.R.L. designed the experiments, analyzed the data, and wrote the manuscript with input from all authors. J.C.W., J.J.W., L.Y., C.Z., L.N.W., L.X., and F.Z. performed the in vitro, in vivo, gene profiling, ChIP-seq, and ATAC-seq analyses. M.W. provided resources and inputs. Q.R.L. supervised the project.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>All high-throughput data generated in the paper are deposited in the NCBI Gene Expression Omnibus (GEO). The accession numbers are <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138117\">GSE138117</ext-link>. ChIP-seq datasets for H3K27me3 and p300 are extracted from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84265\">GSE84265</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93161\">GSE93161</ext-link>, respectively. Egr2<sup>Lo</sup> decreased and increased genes were obtained from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.pnas.org/content/suppl/2005/02/03/0407836102.DC1#F5\">https://www.pnas.org/content/suppl/2005/02/03/0407836102.DC1#F5</ext-link> (Supporting Table <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>). The list of differentially regulated genes between the <italic>Eed</italic> cKO and WT nerves were obtained from <ext-link ext-link-type=\"uri\" xlink:href=\"https://onlinelibrary.wiley.com/doi/full/10.1002/glia.23500\">https://onlinelibrary.wiley.com/doi/full/10.1002/glia.23500</ext-link> (Supporting Information Table <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>). The data supporting this study are available in the Article, Supplementary Information, Source Data or available from the corresponding authors upon reasonable requests. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.3 20210610//EN\" \"JATS-archivearticle1-3-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:ali=\"http://www.niso.org/schemas/ali/1.0/\" article-type=\"research-article\" dtd-version=\"1.3\"><?properties open_access?><processing-meta base-tagset=\"archiving\" mathml-version=\"3.0\" table-model=\"xhtml\" tagset-family=\"jats\"><restricted-by>pmc</restricted-by></processing-meta><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807831</article-id><article-id pub-id-type=\"pmc\">PMC7431863</article-id><article-id pub-id-type=\"publisher-id\">70924</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70924-z</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>ROR1 is upregulated in endometrial cancer and represents a novel therapeutic target</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Dongli</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Gunther</surname><given-names>Kate</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Enriquez</surname><given-names>Luis A.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Daniels</surname><given-names>Benjamin</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>O’Mara</surname><given-names>Tracy A.</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Tang</surname><given-names>Katrina</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Spurdle</surname><given-names>Amanda B.</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Ford</surname><given-names>Caroline E.</given-names></name><address><email>caroline.ford@unsw.edu.au</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.1005.4</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 4902 0432</institution-id><institution>Gynaecological Cancer Research Group, Lowy Cancer Research Centre, School of Women’s and Children’s Health, Faculty of Medicine, </institution><institution>University of New South Wales, </institution></institution-wrap>Sydney, NSW 2052 Australia </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.1005.4</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 4902 0432</institution-id><institution>Medicines Policy Research Unit, Centre for Big Data Research in Health, Faculty of Medicine, </institution><institution>University of New South Wales, </institution></institution-wrap>Sydney, Australia </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.1049.c</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2294 1395</institution-id><institution>QIMR Berghofer Medical Research Institute, </institution></institution-wrap>Brisbane, Australia </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.415193.b</institution-id><institution>South Eastern Area Laboratory Services Pathology, </institution><institution>Prince of Wales Hospital, </institution></institution-wrap>Sydney, Australia </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13906</elocation-id><history><date date-type=\"received\"><day>28</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>6</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020, corrected publication 2022</copyright-statement><license><ali:license_ref specific-use=\"textmining\" content-type=\"ccbylicense\">https://creativecommons.org/licenses/by/4.0/</ali:license_ref><license-p><bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">ROR1 and ROR2 are receptor tyrosine kinases with altered expression in a range of cancers. Silencing ROR1 or ROR2 in different tumour types has been shown to inhibit proliferation and decrease metastatic potential. The aim of this study was to investigate the role of ROR1 and ROR2 in endometrial cancer via immunohistochemistry (IHC) in a large endometrial cancer patient cohort (n = 499) and through in vitro analysis in endometrial cancer cell lines. Correlation was assessed between ROR1/2 expression and clinicopathological parameters. Kaplan Meier curves were produced for 5-year progression free survival (PFS) and overall survival (OS) with low/moderate versus high ROR1/2 intensity. Cox multivariate regression was applied to analyse the effect of selected covariates on the PFS and OS. The effect of ROR1 and/or ROR2 modulation on cell proliferation, adhesion, migration and invasion was analysed in two endometrial cancer cell lines (KLE and MFE-296). We observed a significant decrease in OS and PFS in patients with high ROR1 expression. ROR1 silencing and ROR2 overexpression significantly inhibited proliferation of KLE endometrial cancer cells and decreased migration. This study supports the oncogenic role of ROR1 in endometrial cancer, and warrants investigation of future application of ROR1-targeting therapies in endometrial cancer patients.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Cancer</kwd><kwd>Cell biology</kwd><kwd>Oncology</kwd></kwd-group><funding-group><award-group><funding-source><institution>TCRN</institution></funding-source></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">http://dx.doi.org/10.13039/501100000925</institution-id><institution>National Health and Medical Research Council</institution></institution-wrap></funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Endometrial cancer (EC) is the most prevalent gynaecological cancer and the sixth most common malignancy worldwide<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. Incidence has increased significantly over the last decade, particularly in developed countries<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. This escalating worldwide burden and poor survival outcomes from advanced stage and aggressive subtypes warrants further research into novel targets and new therapies.</p><p id=\"Par3\">The pathogenesis for EC is multifactorial, with risk factors including genetic variants<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>, high BMI<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>,<xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>, high number of cumulative menstrual cycles<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>, and infertility<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. In 1983, Bokhman<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup> proposed the classic dualistic model which divided EC into estrogen driven endometrioid subtype (Type I) and the more aggressive non-endometrioid subtype (Type II). Based on the histopathological features, EC is also commonly classified into endometrioid adenocarcinoma, serous carcinoma, mucinous carcinoma, clear cell carcinoma mixed carcinoma etc.<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. There are certain overlaps between the two classification systems: Type I is generally endometrioid subtype and Type II is mostly serous. These traditional classification systems based on endocrine or histopathological features failed to take into account the heterogeneity of EC and were limited due to technical difficulties and controversies in histopathological assessment<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref>,<xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. In 2013, the Cancer Genome Atlas (TCGA) defined four genomic subgroups: Polymerase epsilon (<italic>POLE)</italic>-mutant tumours (ultrahypermutated), MSI (hypermutated), copy-number low (endometrioid) and copy-number high tumours (serous-like) through integration of multi-omics data<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. Although this system is not yet in widespread clinical use, the identification of molecular targets correlate to disease progression and development of treatment could hold translational importance.</p><p id=\"Par4\">The Wnt signalling pathway is generally divided into two arms—the canonical pathway (β-catenin dependent) and non-canonical pathway (β-catenin independent), which both have been implicated in a range of human cancers<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>. β-catenin somatic mutations are common in the endometrioid subtype of EC<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref>–<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup> but this pathway has not yet been successfully targeted therapeutically in EC. One potential avenue to target Wnt signalling may be via the recently identified Wnt receptors, ROR1 and ROR2.</p><p id=\"Par5\">ROR1 and ROR2 are tyrosine kinase-like orphan receptors that play critical roles in embryogenesis. Aberrant expression of ROR1 has been observed in a range of cancers<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref>–<xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup> compared to its limited expression in healthy adult tissue, which made it a candidate target for treating these cancers. ROR1 has been demonstrated to play an oncogenic role in many tumour types and has been broadly linked with cell proliferation, stemness<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>, the epithelial-mesenchymal transition (EMT)<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup> and other metastatic abilities<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>. In contrast, the role of ROR2 in carcinogenesis remains controversial as it acts as either a tumour suppressor or tumour promoter in different cancers<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref>,<xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. ROR2 can also function as an inhibitor of the canonical Wnt pathway<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. The interaction between the two receptors in Wnt signalling remains unclear. Wnt5a has been shown to induce the ROR1/ROR2 heterooligomers to activate signalling in chronic lymphocytic leukaemia (CLL), and neither ROR1 nor ROR2 alone was efficient in triggering the optimal downstream cascade<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. Currently it is unclear if this heterodimer is formed for all cancer types.</p><p id=\"Par6\">In ovarian cancer, we have demonstrated that both ROR1 and ROR2 are overexpressed in large cohorts of tumour tissue<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>, and that silencing ROR1 and ROR2 inhibits metastatic potential<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>, which supported the oncogenic role of the two receptors. In contrast, when we conducted a similar study in EC of limited sample size (n = 87), we identified potential distinct roles for ROR1 and ROR2<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. The aim of this study was to investigate the role of ROR1 and ROR2 in EC in a larger Australian population-based EC cohort, encompassing all major subtypes of the disease, and to perform a series of <italic>in-vitro</italic> experiments to clarify the role of each receptor.</p></sec><sec id=\"Sec2\"><title>Results</title><p id=\"Par7\">Overall the clinical cohort showed a broad range of expression levels for both ROR1 and ROR2 (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>, Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S1</xref>). Compared to the tumour tissue, normal samples showed lower expression of ROR1 or ROR2 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S1</xref>). None of the normal tissue was scored as high (i.e. 3) for either ROR1 or ROR2. Over 90% of the normal tissue had ROR1 or ROR2 stained less than 2 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S1</xref>A,B). For the matched normal and tumour tissues (n = 19), the expression level of ROR1 or ROR2 was significantly different between tumour and adjacent normal tissues (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S1</xref>C,D).<fig id=\"Fig1\"><label>Figure 1</label><caption><p>ROR1 and ROR2 protein expression as measured by immunohistochemistry. Representative images of score 0 (absence), 1 (weak), 2 (moderate), 3 (intense) for both ROR1 and ROR2.</p></caption><graphic xlink:href=\"41598_2020_70924_Fig1_HTML\" id=\"MO1\"/></fig></p><sec id=\"Sec3\"><title>ROR1 correlates with clinicopathological parameters</title><p id=\"Par8\">Among the clinical cohort (n = 360), ROR1 expression level was significantly associated with tumour grade (<italic>p</italic> = 0.013) and International Federation of Gynecology and Obstetrics (FIGO) stage (<italic>p</italic> = 0.030) (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>A,C). No significance was observed between ROR1 expression and histologic subtype (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>E) or ROR2 with any of the three parameters (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>B,D,F).<fig id=\"Fig2\"><label>Figure 2</label><caption><p>ROR1 expression was significantly correlated with tumour grade and International Federation of Gynecology and Obstetrics (FIGO) stage in endometrial cancer. (<bold>A</bold>) Expression of ROR1 in endometrial cancer stratified by tumour grade. The values in the table below showed the number of score 0, 1, 2, 3 in each grade. <italic>P</italic> values resulted from Chi-square or Fisher’s exact test indicated the significant level of the correlation. (<bold>B</bold>) Expression of ROR2 in endometrial cancer stratified by tumour grade. (<bold>C</bold>) Expression of ROR1 in endometrial cancer stratified by FIGO stage. (<bold>D)</bold> Expression of ROR2 in endometrial cancer stratified by FIGO stage. (<bold>E</bold>) Expression of ROR1 in endometrial cancer histologic subtypes including endometrioid, serous, mucinous, clear cell, mixed and malignant mixed mesodermal tumour (MMMT); expressed as a percentage of total. F: Expression of ROR2 in endometrial cancer subtypes. *Significant at <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"41598_2020_70924_Fig2_HTML\" id=\"MO2\"/></fig></p><p id=\"Par9\">In the endometrioid EC patients, the expression level of ROR1 was significantly correlated with tumour grade (<italic>p</italic> = 0.019, Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S2</xref>).</p></sec><sec id=\"Sec4\"><title>ROR1 correlates with shorter OS and PFS</title><p id=\"Par10\">A significant decrease in endometrial cancer specific OS and PFS was observed in patients with high ROR1 expression (<italic>p</italic> = 0.049 and <italic>p</italic> = 0.021, respectively, in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>) in the clinical cohort. No significant correlation was observed for ROR2 expression on OS or PFS, however patients with high ROR2 showed a trend towards better PFS.<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Kaplan–Meier analysis for ROR1 and ROR2 stratified by low/moderate (score 0, 1, 2) and high (score 3) in the complete cohort (n = 330). (<bold>A</bold>) Overall survival (OS) according to ROR1 expression. (<bold>B</bold>) Progression free survival (PFS) according to ROR1 expression. (<bold>C</bold>) OS according to ROR2 expression. (<bold>D</bold>) PFS according to ROR2 expression. *Significant at <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"41598_2020_70924_Fig3_HTML\" id=\"MO3\"/></fig></p><p id=\"Par11\">Compared to the low ROR1 expressed patients, moderate and high ROR1 was not significantly correlated with OS or PFS (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S3</xref>A,C). No significant correlation was observed for low ROR2 expression with OS or PFS (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S3</xref>B,D).</p><p id=\"Par12\">In terms of the multivariate parameters associated with OS and PFS for the analytical cohort (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>), the FIGO stage and tumour grade was significantly associated with both OS and PFS significantly. ROR1 level was significantly associated with OS and PFS while ROR2 was not significant. Compared to the low or moderate level of ROR1 expression, high ROR1 had a significantly increased risk of EC related death and relapse (hazard ratio = 2.48 and 2.45 respectively). <table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Multivariate analyses of parameters associated with overall survival (OS) and progression free survival (PFS).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Parameter</th><th align=\"left\">Value</th><th align=\"left\">HR<sup>a</sup></th><th align=\"left\">HR lower 95CI</th><th align=\"left\">HR upper 95CI</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"6\"><bold>OS</bold></td></tr><tr><td align=\"left\">Age</td><td align=\"left\">≤ 50 versus > 50</td><td char=\".\" align=\"char\">0.34</td><td char=\".\" align=\"char\">0.04</td><td char=\".\" align=\"char\">2.68</td><td char=\".\" align=\"char\">0.31</td></tr><tr><td align=\"left\">BMI</td><td align=\"left\">> 30 versus ≤ 30</td><td char=\".\" align=\"char\">1.37</td><td char=\".\" align=\"char\">0.72</td><td char=\".\" align=\"char\">2.59</td><td char=\".\" align=\"char\">0.33</td></tr><tr><td align=\"left\">FIGO stage</td><td align=\"left\">II versus I</td><td char=\".\" align=\"char\">0.55</td><td char=\".\" align=\"char\">0.13</td><td char=\".\" align=\"char\">2.37</td><td char=\".\" align=\"char\">0.42</td></tr><tr><td align=\"left\">FIGO stage</td><td align=\"left\">III, IV versus I</td><td char=\".\" align=\"char\">3.71</td><td char=\".\" align=\"char\">1.91</td><td char=\".\" align=\"char\">7.21</td><td char=\".\" align=\"char\">< .0001*</td></tr><tr><td align=\"left\">Tumour grade</td><td align=\"left\">2 versus 1</td><td char=\".\" align=\"char\">4.14</td><td char=\".\" align=\"char\">1.13</td><td char=\".\" align=\"char\">15.14</td><td char=\".\" align=\"char\">0.03*</td></tr><tr><td align=\"left\">Tumour grade</td><td align=\"left\">3 versus 1</td><td char=\".\" align=\"char\">17.5</td><td char=\".\" align=\"char\">4.91</td><td char=\".\" align=\"char\">62.43</td><td char=\".\" align=\"char\">< .0001*</td></tr><tr><td align=\"left\">Subtype</td><td align=\"left\">Nonendo versus Endo</td><td char=\".\" align=\"char\">0.65</td><td char=\".\" align=\"char\">0.3</td><td char=\".\" align=\"char\">1.45</td><td char=\".\" align=\"char\">0.30</td></tr><tr><td align=\"left\">ROR1</td><td align=\"left\">High versus low/moderate</td><td char=\".\" align=\"char\">2.48</td><td char=\".\" align=\"char\">0.99</td><td char=\".\" align=\"char\">6.18</td><td char=\".\" align=\"char\">0.05*</td></tr><tr><td align=\"left\">ROR2</td><td align=\"left\">High versus low/moderate</td><td char=\".\" align=\"char\">0.77</td><td char=\".\" align=\"char\">0.34</td><td char=\".\" align=\"char\">1.72</td><td char=\".\" align=\"char\">0.52</td></tr><tr><td align=\"left\" colspan=\"6\"><bold>PFS</bold></td></tr><tr><td align=\"left\">Age</td><td align=\"left\">≤ 50 versus > 50</td><td char=\".\" align=\"char\">0.42</td><td char=\".\" align=\"char\">0.1</td><td char=\".\" align=\"char\">1.77</td><td char=\".\" align=\"char\">0.24</td></tr><tr><td align=\"left\">BMI</td><td align=\"left\">> 30 versus ≤ 30</td><td char=\".\" align=\"char\">0.80</td><td char=\".\" align=\"char\">0.48</td><td char=\".\" align=\"char\">1.33</td><td char=\".\" align=\"char\">0.39</td></tr><tr><td align=\"left\">FIGO stage</td><td align=\"left\">II versus I</td><td char=\".\" align=\"char\">0.91</td><td char=\".\" align=\"char\">0.35</td><td char=\".\" align=\"char\">2.35</td><td char=\".\" align=\"char\">0.85</td></tr><tr><td align=\"left\">FIGO stage</td><td align=\"left\">III, IV versus I</td><td char=\".\" align=\"char\">4.25</td><td char=\".\" align=\"char\">2.43</td><td char=\".\" align=\"char\">7.43</td><td char=\".\" align=\"char\">< .0001*</td></tr><tr><td align=\"left\">Tumour Grade</td><td align=\"left\">2 versus 1</td><td char=\".\" align=\"char\">1.50</td><td char=\".\" align=\"char\">0.75</td><td char=\".\" align=\"char\">3.02</td><td char=\".\" align=\"char\">0.25</td></tr><tr><td align=\"left\">Tumour Grade</td><td align=\"left\">3 versus 1</td><td char=\".\" align=\"char\">5.81</td><td char=\".\" align=\"char\">2.83</td><td char=\".\" align=\"char\">11.92</td><td char=\".\" align=\"char\">< .0001*</td></tr><tr><td align=\"left\">ROR1</td><td align=\"left\">High versus low/moderate</td><td char=\".\" align=\"char\">2.45</td><td char=\".\" align=\"char\">1.21</td><td char=\".\" align=\"char\">4.97</td><td char=\".\" align=\"char\">0.01*</td></tr><tr><td align=\"left\">ROR2</td><td align=\"left\">High versus low/moderate</td><td char=\".\" align=\"char\">0.92</td><td char=\".\" align=\"char\">0.51</td><td char=\".\" align=\"char\">1.67</td><td char=\".\" align=\"char\">0.78</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>Hazard ratio.</p><p>*Significant at <italic>p</italic> < 0.05 level.</p></table-wrap-foot></table-wrap></p></sec><sec id=\"Sec5\"><title>ROR1 silencing and ROR2 overexpression inhibit tumour progression in KLE EC cells</title><p id=\"Par13\">The high ROR1, low ROR2 expressing KLE cell line was chosen as a model for serous EC. After 48 h, the transfection was shown to be effective at both transcription and translation levels (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>A,B). ROR1 knockdown decreased proliferation after 72 h but was not statistically significant (<italic>p</italic> = 0.071). The combination of ROR1 knockdown and ROR2 overexpression further reduced the cell proliferation significantly after 48 h and 72 h (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>C, <italic>p</italic> = 0.043 and 0.004 respectively). ROR2 overexpression reduced migration moderately (<italic>p</italic> = 0.059), and this reduction was enhanced (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>E, <italic>p</italic> = 0.037) when combining with ROR1 knockdown. No significant change was observed in adhesion or invasion assays (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>D,F).<fig id=\"Fig4\"><label>Figure 4</label><caption><p>ROR1 knockdown and ROR2 overexpression significantly decreased proliferation and migration of KLE. (<bold>A</bold>) ROR1 mRNA expression level was reduced significantly without changing ROR2 following single ROR1 siRNA transfection. ROR2 mRNA expression level was elevated significantly with no changes in ROR1 mRNA level following single ROR2 plasmid transfection. Cotransfecting ROR1 siRNA and ROR2 plasmid significantly reduced ROR1 while increased ROR2 at mRNA level. (<bold>B</bold>) Representative western blot membranes showed effective delivery of ROR1 siRNA and/or ROR2 plasmid in KLE. (<bold>C</bold>) ROR1 knockdown and ROR2 overexpression significantly reduced the cell proliferation after 48 h and 72 h (<italic>p</italic> = 0.043 and 0.004 respectively). (<bold>D</bold>): ROR1 knockdown and/or ROR2 overexpression had no effect on adhesion to collagen or fibronectin. (<bold>E</bold>): ROR1 knockdown and ROR2 overexpression decreased KLE migration ability significantly (<italic>p</italic> = 0.037). (<bold>F</bold>) No significant change was observed for invasion following ROR1 knockdown and/or ROR2 overexpression. For all panels n = 3, error bars represent standard deviation of the mean, *<italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"41598_2020_70924_Fig4_HTML\" id=\"MO4\"/></fig></p></sec><sec id=\"Sec6\"><title>ROR2 silencing and ROR1 overexpression play distinct roles in MFE-296 EC cells</title><p id=\"Par14\">The high ROR2, low ROR1 expressing MFE-296 cell line was chosen as a model for endometrioid EC. The results from qRTPCR and Western blot indicated ROR2 was suppressed after ROR2 siRNA transfection, ROR1 was elevated following ROR1 plasmids transfection (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>A,B). ROR1 overexpression or ROR2 silencing showed opposite effects on cell proliferation and migration (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>C,E). ROR1 overexpression seemed to increase cell proliferation while ROR2 knockdown tended to decrease cell proliferation. The combination of the two showed average lower proliferation ability compared to the control. Similarly, ROR1 overexpression tended to increase cell migration while ROR2 knockdown showed an opposite trend. ROR1 overexpression showed a higher average invaded cell number compared to control (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>F). However, none of these observations were significant at 0.05 level. No significant change was observed in adhesion after ROR1 overexpression or/and ROR2 knockdown (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>D).<fig id=\"Fig5\"><label>Figure 5</label><caption><p>ROR1 overexpression and ROR2 knockdown play different roles in MFE-296. (<bold>A</bold>) ROR2 mRNA level was reduced significantly without changing ROR1 following single ROR2 siRNA transfection. ROR1 mRNA level was increased significantly with no change in ROR2 following single ROR1 plasmid transfection. Cotransfecting ROR2 siRNA and ROR1 plasmid significantly reduced ROR2 while increased ROR1 at mRNA level. (<bold>B</bold>) Representative western blot membranes showed effective delivery of ROR2 siRNA and/or ROR1 plasmid in MFE-296. (<bold>C</bold>) No significant change of proliferation was observed after 48 h or 72 h following ROR1 overexpression and/or ROR2 knockdown. (<bold>D</bold>) ROR2 knockdown and/or ROR1 overexpression had no effect on adhesion to collagen or fibronectin. (<bold>E</bold>) ROR1 knockdown and/or ROR2 overexpression did not change MFE-296 cell migration significantly. (<bold>F</bold>) No significant change was observed for invasion following ROR2 knockdown and/or ROR1 overexpression. For all panels n = 3, error bars represent standard deviation of the mean, *Significant at <italic>p</italic> < 0.05.</p></caption><graphic xlink:href=\"41598_2020_70924_Fig5_HTML\" id=\"MO5\"/></fig></p></sec></sec><sec id=\"Sec7\"><title>Discussion</title><p id=\"Par15\">This study confirms ROR1 as a potential therapeutic target in EC. Despite the different ROR1 primary antibody used in the IHC compared to our previous smaller cohort<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>, we have shown the same effect of ROR1 on OS and PFS in our clinical cohort. Patients with high ROR1 expression have a significant lower OS and PFS compared to those with low or moderate level of ROR1 expression. The new anti-ROR1 antibody we have used in this study is a monoclonal antibody validated in various clinical cohorts<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>, as opposed to the polyclonal antibody used in our previous publications<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. We identified the same association of ROR1 with higher grade in EC, as reported previously in ovarian cancer and pancreatic cancer<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. The effect of ROR1 on OS or PFS was not significant when stratified as low and moderate/high.</p><p id=\"Par16\">In contrast, ROR2 appears to play a less important role in EC. Previously we reported a moderately negative correlation with OS (<italic>p</italic> = 0.06) in a small patient cohort of 87 EC patients. However, this trend was not observed when we expanded the sample size to 341. ROR2 seems to play a less important role in terms of survival or progression. However, we found a moderate correlation between ROR2 expression and tumour grade in our cohort (<italic>p</italic> = 0.079, Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>B). The Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmation/Discovery cohort (n = 131)<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup> showed a significant reduction in expression of ROR2 as the tumour grade increased in EC (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S4</xref>). ROR2 can trigger the non-canonical pathway upon binding with Wnt5a<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>, or the canonical pathway by binding with Wnt3a<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref>,<xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>. ROR2 can also inhibit canonical Wnt signalling through interacting with Wnt5a<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. Previous studies have reported an oncogenic role for ROR2 in osteosarcoma<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>, renal cell carcinoma<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>, and breast cancer<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup> while it presented a tumour suppressor role in colon cancer<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup> and hepatocellular carcinoma<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. It was hypothesised that the role ROR2 played depend on which arm of Wnt signalling played dominant role in the cancer or specific subtype<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>. In cancers where canonical Wnt signalling is a key driver of the disease (eg through mutations in β-catenin or APC), such as colon cancer, ROR2 may play a major role in inhibiting the canonical pathway through binding to Wnt5a. In contrast, ROR2 may play a more direct role in triggering the noncanonical Wnt signalling pathway in noncanonical signalling driven cancers. EC is a complex case that requires further investigation into the overlapping roles of the two key Wnt pathways. While it is clear that canonical Wnt signalling is a key driver in the endometrioid subtype of ovarian cancer (due to the high % of β-catenin mutations), its role in other subtypes needs to be clarified.</p><p id=\"Par17\">ROR1 and ROR2 share the same ligand–Wnt5a<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>, and play essential roles in Wnt signalling associated metastasis. However, the relationship between the two receptors is not well established. Simultaneous knock-down of the two receptors showed a stronger effect than silencing either individually on reducing cell metastasis potential in ovarian cancer<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref>,<xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. Unlike the ovarian cancer cell lines, most of the EC cell lines appear to express either ROR1 or ROR2 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2</xref> from<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>). A previous study reported that ROR1 overexpression in MEC1 (a CLL cell line with high ROR2 no ROR1) induced the formation of ROR1/2 heterooligomers in the context of Wnt5a and enhanced subsequent non-canonical Wnt signalling cascade<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>. However, we observed no significant change in ROR2 expression in MFE-296 after increasing ROR1 levels in this study (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>A). In fact, no change of ROR1/2 was observed after modulating the other receptor in KLE or MFE-296. It could be hypothesised that no heterooligomer was formed for non-canonical Wnt signalling.</p><p id=\"Par18\">Single ROR1 silencing and ROR2 overexpression in KLE showed a similar trend in altering cell proliferation and migration. The combination of the two treatments further strengthened the effect. In low-ROR1 expressing MFE-296, ROR1 overexpression tended to increase proliferation and migration. But ROR2 silencing did not show the same trend, therefore neutralised the effect of ROR1 overexpression in the combination treatment. The epithelial-mesenchymal-transition (EMT) through which epithelial cells gain migratory and invasive properties and become mesenchymal status, serves as a critical step in regulating tumour metastasis<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref>,<xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>. Previous studies in ovarian cancer found ROR1 played a role in the EMT procedure<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup>. Thus, it could be hypothesised that ROR1 also regulated the EMT in endometrial cancer and modulation in ROR1 could alter cell migration and invasion ability. In general, ROR1 promoted tumour growth and progression in EC cell lines in vitro. The role of ROR2 seemed to be different between endometrioid and non-endometrioid subtypes. It will be important to conduct further research using diverse EC cell lines derived from various subtypes to uncover the role of ROR2. In addition, general extracellular matrix component precoated plates or transwell membranes could not represent the real inner environment for the tumour cells to attach or invade through. Further research into 3D culture or animal models is needed to validate the influence of ROR1/2 modulation on cell adhesion and invasion.</p><p id=\"Par19\">Not only was ROR1 functionally relevant to EC tumorigenesis and progression, ROR1 expression was found to be significantly increased in EC tumour tissue compared to normal tissue (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S1</xref>). Combined with the survival data this suggests that ROR1 is a promising therapeutic target in EC. There are a number of ROR1 targeting therapies currently in development or in early phase trials. Cirmtuzumab is a monoclonal antibody that targets and inhibits ROR1. It was developed by the Kipps lab, UCSD, originally focused on Chronic Lymphocytic Leukemia (CLL). It has proven to be effective in inhibiting ROR1 signalling in preclinical trials for ovarian cancer<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup> and shown safe in a phase I trial to treat CLL<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>. It is currently being tested in phase Ib trial in triple negative breast cancer (NCT02776917). Another ROR1-targeting therapy which has been tested in clinical trials is the immunotherapy called ROR1 chimeric antigen receptor (CAR)-T cell therapy<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. A phase I trial (NCT02706392) is currently recruiting ROR1 positive cancers such as CLL and triple negative breast carcinoma. These new treatments may also benefit EC patients, especially those with high ROR1 expression.</p><p id=\"Par20\">The synergistic effect noted here in vitro of ROR1 inhibition and ROR2 overexpression may suggest a more effective combination treatment for EC patients. However, there is no current treatment that specifically targets and promotes ROR2 expression. Recently, ROR2 was found to be epigenetically inactivated in colorectal cancer and demethylation treatment with 5-aza-2-deoxycytidine could restore the expression level of ROR2 in vitro<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>. The combination of ROR1 inhibition and demethylation might make the intervention more effective to some specific subgroups of EC patients.</p><p id=\"Par21\">This study confirms the role of ROR1 plays in endometrial cancer and warrants the future application of ROR1-targeting therapies in endometrial cancer patients. With endometrial cancer rates increasing rapidly worldwide there is a clear need for more treatment options for this patient group.</p></sec><sec id=\"Sec8\"><title>Methods</title><p id=\"Par22\">All experimental protocols were approved by University of New South Wales (UNSW), Australia. Ethics approval was obtained from the UNSW Human Research Ethics Advisory Panel (#HC15771). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all patients for the clinical cohort.</p><sec id=\"Sec9\"><title>Clinical cohort</title><p id=\"Par23\">The Australian National Endometrial Cancer Study (ANECS) is an Australia-wide population-based study that recruited women with histologically confirmed EC between 2005 and 2007<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>. Tumour tissue microarray (TMA) slides from the ANECS cohort were obtained from the QIMR Berghofer Medical Research Institute. The TMA cores included 578 cancer tissues, 36 adjacent normal tissues and 32 recurrent tumours from 499 individual patients. Among the 499 patients, 93 were excluded from analysis due to missing or insufficient tissue (< 40%), 39 were excluded for no epithelium observed in the TMA core, 7 were excluded for missing all clinicopathological data, which resulted in a clinical cohort of 360 individual cases for this study. The accompanying clinicopathological data including age (grouped into ≤ 50 and > 50), body mass index (BMI, grouped into ≤ 30 kg/m<sup>2</sup> and > 30 kg/m<sup>2</sup>), FIGO stage (2009), histological subtype, tumour grade, menopause status, recurrence status and vital status etc. were provided by the ANECS and are summarised in Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>. <table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Demographic and clinicopathological characteristics of the tumour samples in the clinical cohort.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Number of cases</th><th align=\"left\">Percentage (%)</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\"><bold>Age at diagnosis (y)</bold></td></tr><tr><td align=\"left\">≤ 50</td><td align=\"left\">23</td><td char=\".\" align=\"char\">6.7</td></tr><tr><td align=\"left\">> 50</td><td align=\"left\">318</td><td char=\".\" align=\"char\">93.3</td></tr><tr><td align=\"left\">N/A<sup>a</sup></td><td align=\"left\">19</td><td char=\".\" align=\"char\">-</td></tr><tr><td align=\"left\" colspan=\"3\"><bold>BMI (kg/m</bold><sup><bold>2</bold></sup><bold>)</bold></td></tr><tr><td align=\"left\">≤ 30</td><td align=\"left\">179</td><td char=\".\" align=\"char\">53.0</td></tr><tr><td align=\"left\">> 30</td><td align=\"left\">159</td><td char=\".\" align=\"char\">47.0</td></tr><tr><td align=\"left\">N/A</td><td align=\"left\">22</td><td char=\".\" align=\"char\">–</td></tr><tr><td align=\"left\" colspan=\"3\"><bold>Tumour grade</bold></td></tr><tr><td align=\"left\">1</td><td align=\"left\">151</td><td char=\".\" align=\"char\">41.9</td></tr><tr><td align=\"left\">2</td><td align=\"left\">100</td><td char=\".\" align=\"char\">27.8</td></tr><tr><td align=\"left\">3</td><td align=\"left\">109</td><td char=\".\" align=\"char\">30.3</td></tr><tr><td align=\"left\" colspan=\"3\"><bold>FIGO stage (2009)</bold></td></tr><tr><td align=\"left\">I</td><td align=\"left\">267</td><td char=\".\" align=\"char\">76.1</td></tr><tr><td align=\"left\">II</td><td align=\"left\">26</td><td char=\".\" align=\"char\">7.4</td></tr><tr><td align=\"left\">III</td><td align=\"left\">43</td><td char=\".\" align=\"char\">12.3</td></tr><tr><td align=\"left\">IV</td><td align=\"left\">15</td><td char=\".\" align=\"char\">4.3</td></tr><tr><td align=\"left\">N/A</td><td align=\"left\">9</td><td char=\".\" align=\"char\">–</td></tr><tr><td align=\"left\" colspan=\"3\"><bold>Histological subtype</bold></td></tr><tr><td align=\"left\">Endometrioid</td><td align=\"left\">283</td><td char=\".\" align=\"char\">79.1</td></tr><tr><td align=\"left\">Serous</td><td align=\"left\">33</td><td char=\".\" align=\"char\">9.2</td></tr><tr><td align=\"left\">Clear cell</td><td align=\"left\">14</td><td char=\".\" align=\"char\">3.9</td></tr><tr><td align=\"left\">Mucinous</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.3</td></tr><tr><td align=\"left\">MMMT<sup>b</sup></td><td align=\"left\">20</td><td char=\".\" align=\"char\">5.6</td></tr><tr><td align=\"left\">Mixed</td><td align=\"left\">7</td><td char=\".\" align=\"char\">2.0</td></tr><tr><td align=\"left\">N/A</td><td align=\"left\">2</td><td char=\".\" align=\"char\">–</td></tr><tr><td align=\"left\" colspan=\"3\"><bold>Menopause status</bold></td></tr><tr><td align=\"left\">Peri</td><td align=\"left\">14</td><td char=\".\" align=\"char\">4.1</td></tr><tr><td align=\"left\">Pre</td><td align=\"left\">32</td><td char=\".\" align=\"char\">9.4</td></tr><tr><td align=\"left\">Post</td><td align=\"left\">295</td><td char=\".\" align=\"char\">86.5</td></tr><tr><td align=\"left\">N/A</td><td align=\"left\">19</td><td char=\".\" align=\"char\">–</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>Data not available, <sup>b</sup>Malignant mixed Müllerian tumour.</p></table-wrap-foot></table-wrap></p></sec><sec id=\"Sec10\"><title>Immunohistochemistry</title><p id=\"Par24\">Immunohistochemistry (IHC) for ROR1 (1:50, #564464, BD Biosciences, USA) and ROR2 (1:100, #34045, QED Bioscience, USA) were performed using the Leica Bond RX system (Leica Microsystems, USA) at the Garvan Institute of Medical Research, Sydney Australia.</p><p id=\"Par25\">The intensity of ROR1/2 staining was graded as 0 (absence), 1 (weak), 2 (moderate) and 3 (intense) as previously described<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. Representative images are shown in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>. The TMAs were scored independently and blinded by three researchers (DL, KG and LE) from the UNSW Gynaecological Cancer Research Group (GCRG) and a pathologist from the Prince of Wales Hospital (KT). The concordant scores were achieved by discussion with a fourth author (CF).</p></sec><sec id=\"Sec11\"><title>Statistical analysis of the clinical cohort</title><p id=\"Par26\">Paired t-test (2-tails) was used to evaluate the difference of ROR1/2 expression between the matched normal and tumour tissue. Chi-square or Fisher’s exact test was used to analyse the association between ROR1 and ROR2 staining intensity and clinicopathological parameters including FIGO stage, grade, and subtypes. Spearman rank correlation coefficients (Spearman's rho) were calculated to show the direction of the relationship between two measures.</p><p id=\"Par27\">There were 30 patients from the clinical cohort (n = 360) who had specified non-EC related death or missing time-to-event data, which resulted in 330 cases (complete cohort) in the following survival analysis. The filtering process of the sample size is shown in Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S5</xref>.</p><p id=\"Par28\">Kaplan Meier curves were produced for 5-year progression free survival (PFS) and overall survival (OS) with ROR1/2 intensity. The expression level of ROR1 and ROR2 were aggregated to low/moderate (score 0, 1, 2) and high (score 3), or low (score 0, 1) and moderate/high (score 2, 3). PFS was defined as the time (days) from diagnosis to recurrence or death. OS was defined from the diagnostic date to death.</p><p id=\"Par29\">Cox multivariate regression was also applied to analyse the impact of selected covariates (age, BMI, FIGO stage, tumour grade and histologic subtypes) on the PFS and OS. FIGO stage III and IV were aggregated together in the analysis. All subtypes were grouped into endometrioid and non-endometrioid groups. The log-rank test was used to evaluate the association between the covariates and PFS or OS.</p><p id=\"Par30\">All the analyses were performed by a trained biostatistician (BD) using SAS software, Version 9.4 of the SAS System for Unix. Copyright © 2016 SAS Institute Inc. Figures were provided in R (v3.6)<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup>.</p></sec><sec id=\"Sec12\"><title>Cell culture</title><p id=\"Par31\">EC cell lines MFE-296 (endometrioid) and KLE (serous) were a gift from Dr Frances Byrne and Associate Professor Kyle Hoehn (UNSW, Australia). KLE was maintained in DMEM/F12 medium and MFE-296 was cultured in MEM medium, both containing 10% foetal bovine serum (FBS), 1% GlutaMAX and 1% penicillin/streptomycin. Cells were grown in 5% CO<sub>2</sub> at 37 °C and underwent mycoplasma testing once a month. All cells were shown to be free of contamination and were confirmed via cell line identification service at the Garvan Institute.</p></sec><sec id=\"Sec13\"><title>Transfection treatment</title><p id=\"Par32\">For both KLE and MFE-296, four types of co-transfection were conducted using Lipofectamine2000 (Invitrogen, USA) according to the manufacturer’s protocol. For KLE (high ROR1, low ROR2), ROR1 silencing, ROR2 overexpression, ROR1 silencing in conjunction with ROR2 overexpression and negative control were performed. In contrast, ROR2 silencing, ROR1 overexpression, ROR2 silencing in conjunction with ROR1 overexpression and negative control were prepared for MFE-296 (high ROR2, low ROR1). We plated 5 × 10<sup>5</sup> KLE or MFE-296 cells on 6-well plates and serum starved overnight before each treatment. ROR1 or ROR2 silencing was achieved via co-transfection with 90 pmol ROR1 siRNA (#s9755, Ambion, USA) or ROR2 siRNA (#s9759, Ambion, USA) as well as empty plasmid. ROR1 pCMV3 plasmid (#HG13968-NH, Sino Biological, China) or ROR2 pFLAG plasmid (previously used in<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>) and non-targeting siRNA (#4390844, Ambion, USA) were co-transfected for ROR1 or ROR2 overexpression. All the aforementioned conditions were compared to the negative control which was prepared by transfecting both non-targeting siRNA and empty plasmid.</p></sec><sec id=\"Sec14\"><title>qRT-PCR</title><p id=\"Par33\">Total RNA was extracted and real-time RTPCR was performed as previously described<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. The expression level of <italic>ROR1</italic>, <italic>ROR2</italic> was analysed. For each gene, non-reverse transcribed RNA samples were included as a negative control. The relative expression level of each gene was calculated using 2<sup>–∆∆<bold>Ct</bold></sup> method and normalised against the mean of three house-keeping genes (<italic>HSPCB</italic>, <italic>SDHA</italic>, <italic>RPL13A</italic>)<sup><xref ref-type=\"bibr\" rid=\"CR52\">52</xref></sup>. Primer sequences were provided in<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>.</p></sec><sec id=\"Sec15\"><title>Western blot</title><p id=\"Par34\">Total protein was extracted from the cells using cell lysis buffer (Cell Signalling Technology, USA) with protease inhibitor (Sigma-Aldrich, USA). 20 µg protein samples were separated on 4–20% Mini-PROTEAN TGX precast gels (Bio-rad, Australia) and transferred onto nitrocellulose membranes. 3% non-fat milk (Coles, Australia) in 0.1% Tween in Tris buffered saline (TBST) was used as blocking buffer and antibody diluent. The membranes were blocked for 1 h at room temperature before the overnight incubation with primary antibody at 4 °C. The primary antibodies used were monoclonal rabbit anti-ROR1 (#AF2000, R&D Systems, USA), monoclonal mouse anti-ROR2 (#34045, QED Bioscience, USA) and monoclonal mouse anti-α-Tubulin (#3873, Cell Signalling, USA). After washing with TBST, the membranes were incubated with either polyclonal rabbit anti-mouse immunoglobulins/HRP (#P0260, Dako, Denmark) or polyclonal rabbit anti-goat immunoglobulins/HRP (#P0449, Dako, Denmark) at 1:5,000 dilution for 1 h at room temperature. After another set of washes, the membranes were incubated with enhanced chemiluminescence (ECL) reagent and imaged on the ImageQuant LAS4000 system (GE Healthcare Life Sciences, USA). Full-length blots with multiple exposures were provided for ROR1 in Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">S6</xref>. Replicate blots for ROR2 were also provided instead of full-length as the blots were cropped to perform reference (α-Tubulin).</p></sec><sec id=\"Sec16\"><title>Proliferation assay</title><p id=\"Par35\">Six hours following the transfection, the cells were plated in a 96-well plate at 4,000 cells per well and analysed with the Cell Counting Kit-8 (CCK-8, Sigma-Aldrich, USA) as per manufacturer protocol at 24 h, 48 h and 72 h after transfection.</p></sec><sec id=\"Sec17\"><title>Adhesion assay</title><p id=\"Par36\">The adhesion assay was performed as previously described<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. Briefly, cells adhering to 10 μg/ml type I collagen (Sigma-Aldrich, USA), 5 μg/ml fibronectin (Millipore, USA) or 3% bovine serum albumin (BSA) in PBS after 2 h were stained with 0.1% Crystal violet (Sigma-Aldrich, USA) and lysed with 50% acetic acid. The amount of cells attached was assessed using absorbance at 595 nm.</p></sec><sec id=\"Sec18\"><title>Migration assay</title><p id=\"Par37\">The migration analysis was performed using the Corning transwell insert system according to manufacturer’s protocol (Corning Life Sciences, USA). Six hours after the transfection, the cells were trypsinized and plated in the inserts in triplicates (5 × 10<sup>4</sup> cells per insert for KLE or MFE-296). After 48 h incubation, the migrated cells attached to the membranes were fixed with methanol, stained with 1% Crystal violet and imaged as previously described<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>.</p></sec><sec id=\"Sec19\"><title>Invasion assay</title><p id=\"Par38\">Corning Matrigel pre-coated transwell inserts were used for invasion assays as per manufacturer’s protocol (Corning Life Sciences, USA). Six hours after the transfection, KLE and MFE-296 (1 × 10<sup>5</sup> cells) were seeded in the inserts. The subsequent steps were the same as the migration assay.</p></sec><sec id=\"Sec20\"><title>Statistical analysis of cell assays</title><p id=\"Par39\">All assays were repeated three times. The results were shown as mean ± standard deviation. Significance cut-off was set at <italic>p</italic> = 0.050.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec21\"><p>\n<supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41598_2020_70924_MOESM1_ESM.docx\"><caption><p>Supplementary Figures.</p></caption></media></supplementary-material></p></sec></sec></body><back><fn-group><fn><p>The original online version of this Article was revised: The original version of this Article contained an error in Figure 4 where the α-Tubulin loading control was duplicated in panel B. The original Figure 4 and accompanying legend appear below.</p></fn><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p><bold>Change history</bold></p><p>6/28/2022</p><p>A Correction to this paper has been published: 10.1038/s41598-022-15043-7</p></fn></fn-group><sec><title>Supplementary information</title><p>is available for this paper at 10.1038/s41598-020-70924-z.</p></sec><ack><title>Acknowledgements</title><p>This study was supported by the Ross Trust and the Translational Cancer Research Network (TCRN). Biospecimens and data used in this research were obtained from Australian National Endometrial Cancer Study (ANECS) Group. We thank the many women who participated in the ANECS, and the support from institutes that contributed to this study. The following institutions cooperated in the study: NSW: John Hunter Hospital, Liverpool Hospital, Mater Misericordiae Hospital (Sydney), Mater Misericordiae Hospital (Newcastle), Newcastle Private Hospital, North Shore Private Hospital, Royal Hospital for Women, Royal Prince Alfred Hospital, Royal North Shore Hospital, Royal Prince Alfred Hospital, St George Hospital; Westmead Hospital, Westmead Private Hospital; QLD: Brisbane Private Hospital, Greenslopes Hospital, Mater Misericordiae Hospitals, Royal Brisbane and Women’s Hospital, Wesley Hospital, Queensland Cancer Registry; SA: Adelaide Pathology Partners, Burnside Hospital, Calvary Hospital, Flinders Medical Centre, Queen Elizabeth Hospital, Royal Adelaide Hospital, South Australian Cancer Registry; TAS: Launceston Hospital, North West Regional Hospitals, Royal Hobart Hospital; VIC: Freemasons Hospital, Melbourne Pathology Services, Mercy Hospital for Women, Royal Women’s Hospital, Victorian Cancer Registry; WA: King Edward Memorial Hospital, St John of God Hospitals Subiaco and Murdoch, Western Australian Cancer Registry. We thank the clinicians from The ANECS Group who contributed to this study (see website: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.anecs.org.au\">www.anecs.org.au</ext-link> for the full list). We also acknowledge staff at the Australian Institute of Health and Welfare for conducting the linkage to the Australian National Death Index. Recruitment and data collection for the ANECS was supported by project grants from the National Health and Medical Research Council (NHMRC) of Australia (APP339435); The Cancer Council Queensland (#4196615); Cancer Council Tasmania (#403031 and #457636); the Cancer Australia Priority-driven Collaborative Cancer Research Scheme (#552468), Cancer Australia (#1010859). Construction of tissue microarrays used for this project was supported by a collaborative clinician grant funded jointly by the Royal Brisbane and Women's Hospital Foundation and the then Queensland Institute of Medical Research. Tracy O’Mara is supported by a National Health and Medical Research Council (NHMRC) Early Career Fellowship (APP1111246). Amanda Spurdle is supported by an NHMRC Senior Research Fellowship (APP1061779). We would like to acknowledge Associate Professor Kyle Hoehn and Dr Frances Byrne from University of New South Wales for their generosity with cell lines, and the Kinghorn Cancer Centre Histopathology facility. We thank Professor Penny Webb from QIMR Berghofer Medical Research Institute for her extensive contribution to the establishment of the ANECS resource, including clinical data included in this analysis.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>D.L.: conceptualization, methodology, investigation K.G.: investigation L.A.E.: investigation B.D.: formal analysis T.A.O.: resources, writing—review and editing K.T.: investigation A.B.S.: resources, writing—review and editing C.E.F.: conceptualization, methodology, supervision, funding acquisition, writing—review and editing.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par40\">The authors declare no competing interests.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Ferlay</surname><given-names>J</given-names></name><etal/></person-group><article-title>Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012</article-title><source>Int. 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institution-id-type=\"GRID\">grid.420530.0</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0580 0138</institution-id><institution>Cell Signaling Technology Inc., </institution></institution-wrap>Danvers, MA 01923 USA </aff><aff id=\"Aff4\"><label>4</label>Department of Pediatrics, Faculty of Medicine, University of Ottawa and Children’s Hospital of Eastern Ontario Inflammatory Bowel Disease Centre and Research Institute, Ottawa, ON K1H 8L1 Canada </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>4120</elocation-id><history><date date-type=\"received\"><day>18</day><month>9</month><year>2019</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Lysine acetylation (Kac), an abundant post-translational modification (PTM) in prokaryotes, regulates various microbial metabolic pathways. However, no studies have examined protein Kac at the microbiome level, and it remains unknown whether Kac level is altered in patient microbiomes. Herein, we use a peptide immuno-affinity enrichment strategy coupled with mass spectrometry to characterize protein Kac in the microbiome, which successfully identifies 35,200 Kac peptides from microbial or human proteins in gut microbiome samples. We demonstrate that Kac is widely distributed in gut microbial metabolic pathways, including anaerobic fermentation to generate short-chain fatty acids. Applying to the analyses of microbiomes of patients with Crohn’s disease identifies 52 host and 136 microbial protein Kac sites that are differentially abundant in disease versus controls. This microbiome-wide acetylomic approach aids in advancing functional microbiome research.</p></abstract><abstract id=\"Abs2\" abstract-type=\"web-summary\"><p id=\"Par2\">Intestinal microbiota is increasingly reported to influence human health, but little is known on how its functions are regulated. Here the authors characterize microbiome protein acetylation and demonstrate its potential roles in shaping gut microbial functions and the onset of Crohn’s disease.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Mass spectrometry</kwd><kwd>Proteomic analysis</kwd><kwd>Microbiome</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/100008762</institution-id><institution>Genome Canada (Génome Canada)</institution></institution-wrap></funding-source><award-id>OGI-114 & OGI-149</award-id><principal-award-recipient><name><surname>Figeys</surname><given-names>Daniel</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/501100002790</institution-id><institution>Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC Canadian Network for Research and Innovation in Machining Technology)</institution></institution-wrap></funding-source><award-id>Daniel Figeys</award-id><principal-award-recipient><name><surname>Figeys</surname><given-names>Daniel</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/501100007202</institution-id><institution>Gouvernement du Canada | Instituts de Recherche en Santé du Canada | CIHR Skin Research Training Centre (Skin Research Training Centre)</institution></institution-wrap></funding-source><award-id>GPH-129340 and MOP-114872</award-id><principal-award-recipient><name><surname>Figeys</surname><given-names>Daniel</given-names></name></principal-award-recipient></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par3\">The intestinal microbiome is emerging as an important organ within the human body that actively interacts with its host to influence human health<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. Dysbiosis of the intestinal microbiota has been reported to be associated with a myriad of diseases, including obesity, diabetes, Crohn’s disease (CD), cancer, and cardiovascular diseases<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. In the past few years, meta-omic approaches, including metagenomics, metatranscriptomics, and metaproteomics, have been applied to study the alterations of the microbiome composition and functions in patients with these diseases<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>–<xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. However, very little is known of the regulatory processes in the microbiome, such as post-translational modifications (PTMs) that are known to regulate the activity of proteins. In fact, there are currently neither published studies on the global and deep characterization of PTMs in the human microbiome nor published techniques for efficient PTM profiling at the metaproteome level.</p><p id=\"Par4\">Acetylation is an important PTM in both Eukaryotes and Prokaryotes<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. In particular, lysine (N<sub>ε</sub>) acetylation (Kac) has been shown to be involved in the regulation of various biological processes, including transcription and metabolism<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref>,<xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. Compared with other PTMs that are commonly implicated in regulation of metabolic processes, such as phosphorylation, acetylation demonstrated higher levels in microorganisms<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. In bacteria, up to 40% of proteins can be acetylated<sup><xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>, due to the presence of both enzymatic and nonenzymatic acetylation mechanisms<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>–<xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. Protein Kac has been characterized in several single bacterial species, including <italic>Escherichia coli</italic><sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>–<xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>, <italic>Bacillus subtilis</italic><sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>, <italic>Salmonella enterica</italic><sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>, and <italic>Mycobacterium tuberculosis</italic><sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>, and widely implicated in various microbial processes, including chemotaxis<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>, nutrient metabolism<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>, stress response<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>, and virulence<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>. In <italic>E. coli</italic>, the enzymatic activities in acetate metabolism were regulated by acetylation<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. On the other hand, metabolic intermediates of acetate metabolism, such as acetylphosphate and acetyl-CoA, can non-enzymatically acetylate metabolic enzymes or provide acetyl donor for enzymatic lysine acetylation. Therefore, microorganisms may evolve elegant mechanisms in regulating cellular metabolism through acetylation<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>.</p><p id=\"Par5\">One of the most important metabolic functions of the gut microbiome is fermentation of indigestible dietary fibers to generate short-chain fatty acids (SCFAs)<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>. SCFAs can nourish the intestinal cells, maintain the acidic intestinal environment, and thereby protecting the intestinal barrier function<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. Accumulating evidence suggests that intestinal SCFAs and SCFA-producing bacteria at least partially mediate the complex host–microbiome interactions that underlie the development of many diseases, such as CD<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. Given the potential role of Kac in regulating SCFA metabolism<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref>,<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>, the study of Kac in human gut microbiome may aid in better understanding the role of gut microbiome in CD.</p><p id=\"Par6\">In this study, we first establish experimental and bioinformatic workflows for characterization of microbiome Kac. Briefly, an immuno-affinity-based approach is used for the enrichment of Kac peptides from the microbiome protein digests; the eluted peptides are analyzed with Orbitrap-based mass spectrometer (MS); and the MS data are then processed using an integrated metaproteomics/lysine acetylomics bioinformatic workflow that is developed in this study. In total, 35,200 Kac peptides corresponding to 31,821 Kac sites are identified from either human or microbial proteins. This study is a global characterization of Kac proteins in human microbiomes and achieves the highest number of site identifications in lysine acetylomic studies. We further apply the approach to study alterations of Kac in intestinal microbiomes of children with new-onset CD, which demonstrates the upregulation of Kac in host proteins, such as immune-related proteins, and downregulation of Kac in microbial proteins from the Firmicutes species that are known SCFA producers. This study provides an efficient workflow for studying lysine acetylome in the microbiomes, and our results provide additional information on the intestinal dysbiosis in pediatric CD.</p></sec><sec id=\"Sec2\" sec-type=\"results\"><title>Results</title><sec id=\"Sec3\"><title>Integrated gut metaproteomic/lysine acetylomic workflow</title><p id=\"Par7\">In this study, the proteolytic peptides generated from each microbiome sample were aliquoted for both metaproteomics and lysine acetylomics analysis. Kac peptides from the first aliquot were enriched using a seven-plex anti-Kac peptide antibody cocktail<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>; the second aliquot was directly analyzed for metaproteome profiling (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>). An integrative metaproteomics/lysine acetylomics data-processing workflow was then developed based on our previously established MetaPro-IQ workflow<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup> and MetaLab software tool<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup> (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>). We, and others, have previously shown that the Integrated Gene Catalog (IGC) database<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup> performed similarly to the matched metagenome database for metaproteomic identification<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref>,<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Therefore, in this study, we used the IGC database for the identification of both metaproteomic and lysine acetylomic data sets. Briefly, each of the raw files was first searched against the IGC protein database using MetaLab<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>; the parameters were as default, except that lysine acetylation (<italic>m/z</italic> 42.010565, H[2]C[2]O) was added as an additional variable modification. The sample-specific databases for both aliquots of all samples were then combined and concatenated with a human protein database for peptide/protein identification and quantification of both data sets.<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Experimental and bioinformatic workflows.</title><p><bold>a</bold> Experimental workflow. <bold>b</bold> Integrated metaproteomics/acetylomics data-processing workflow. <bold>c</bold> Total number of identified Kac and non-Kac peptides in metaproteomic and lysine acetylomic aliquots, respectively. <bold>d</bold> Identified protein groups with non-Kac peptide and Kac peptide sequences in the whole data set (both lysine acetylomic and metaproteomic aliquots). <bold>e</bold> Venn diagram shows the overlap of identified human and microbial protein Kac sites. <bold>f</bold> pLogo of all identified microbiome Kac sites. The n(fg) and n(bg) values indicate the number of foreground and background sequences, respectively. The red horizontal bars on the pLogo correspond to a threshold of <italic>P</italic> < 0.05. Statistical significance of motif residues at given positions was assessed using binomial probability test. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17916_Fig1_HTML\" id=\"d30e577\"/></fig></p><p id=\"Par8\">In total, this study identified 46,927 non-Kac peptides and 117 Kac peptides (171 Kac sites) from the metaproteomic aliquot; in contrast, 35,200 Kac peptides (31,821 Kac sites) and 7387 non-Kac peptides were identified from the lysine acetylomic aliquot (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c</xref>; Supplementary Data <xref rid=\"MOESM4\" ref-type=\"media\">1</xref>). This result indicates a high efficient enrichment of Kac peptides with the anti-Kac antibody cocktail, namely 83% of the identified peptides were Kac peptides. Among the 31,821 Kac sites identified, 1662 sites were quantified in all six samples and 6206 in at least four samples (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). Among the 117 Kac peptides identified in the metaproteomic aliquot, only 6 were also identified in lysine acetylomic aliquot. Evaluating the overlap of identified Kac proteins with proteins identified in unenriched samples, this study identified 25,144 protein groups, and 3814 (15%) were only inferred from Kac modified peptides (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>), suggesting an efficient enrichment of low abundant Kac proteins/peptides using the current enrichment approach.</p></sec><sec id=\"Sec4\"><title>Characterization of the gut microbial Kac motifs</title><p id=\"Par9\">Among the 31,821 Kac sites identified in lysine acetylomic aliquot, 31,307 were from microbes and 497 were of human origin (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e</xref>). We first characterized the amino acid distribution surrounding the acetylated lysine, for both human and microbial Kac sites (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2a</xref>), which suggests that both human and microbial Kac sites showed high frequency of glutamic acid (E) at −1 and leucine (L) and +1 positions. The microbiome protein Kac sites were frequently flanked by repeats of the small, hydrophobic amino acid, alanine (A); this was less common for the human protein Kac sites (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2a</xref>). We further analyzed and visualized Kac protein motifs using pLogo<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup> in a sequence window of 13 amino acids. For human Kac sites, significant over-representation was only observed for tyrosine (Y) at positions −4 and +1 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2b</xref> and Supplementary Data <xref rid=\"MOESM5\" ref-type=\"media\">2</xref>). In contrast, 68 significantly over-represented events were observed for microbiome protein Kac sites, with E and aspartic acid (D) at position −1 and phenylalanine (F) at position +1 being the most significantly overrepresented (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f</xref>; Supplementary Data <xref rid=\"MOESM5\" ref-type=\"media\">2</xref>). Interestingly, small amino acid, alanine, was observed as significantly overrepresented at all the 12 positions assessed, and its occupancy frequency was >9% (10.3 ± 0.7%; mean ± SD) for all positions (Supplementary Data <xref rid=\"MOESM5\" ref-type=\"media\">2</xref>). In contrast, the median occupation frequency of all other significantly overrepresented events was 5.8% (5.8 ± 3.6%; mean ± SD). These observations further confirm the high frequency of alanine as well as acidic amino acids (E and D) near the Kac sites in microbiome proteins.</p><p id=\"Par10\">We then examined whether different bacteria showed different protein Kac motifs in microbiome. Among the five bacterial phyla (Firmicutes, Bacteroidetes, Actibobacteria, Proteobacteria, and Fusobacteria) with >100 Kac sites identified in this data set, high similarity between different phyla was observed (all showed high frequency of E at position −1 and F at position +1; Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3a–e</xref>). Firmicutes and Bacteroidetes are the two most abundant bacterial phyla in human gut microbiota and obtained the highest number of Kac sites identified. Motif analysis using pLogo identified 57 significantly overrepresented events (position—amino acid pairs) for Firmicutes and 41 significantly overrepresented events for Bacteroidetes. Interestingly, 35 out of the 41 significantly overrepresented events in Bacteroidetes were also significantly overrepresented events in Firmicutes (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3f</xref>), indicating high conservation of Kac motif among different human gut microbial species.</p></sec><sec id=\"Sec5\"><title>Phylogenetic variations of protein Kac level in microbiome</title><p id=\"Par11\">Biodiversity analysis of the identified Kac peptides using Unipept<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup> revealed that 28,321 peptides (80%) were assigned to the kingdom Bacteria and 24,785 peptides could be classified at the phylum level (15,170 from Firmicutes, 7876 from Bacteroidetes, and 1739 from other phyla; Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>; Supplementary Data <xref rid=\"MOESM6\" ref-type=\"media\">3</xref>). A high proportion of the Kac peptides was from bacteria belonging to four genera: <italic>Prevotella</italic>, <italic>Faecalibacterium</italic>, <italic>Bacteroides</italic>, and <italic>Eubacterium</italic> (Supplementary Data <xref rid=\"MOESM6\" ref-type=\"media\">3</xref>). To explore whether Kac levels differed by taxa, the ratio of relative abundance in lysine acetylome to that in metaproteome was calculated for each taxon (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). The genus <italic>Fusicatenibacter</italic> (mainly species <italic>F. saccharivorans</italic>) had the highest acetylome-to-metaproteome ratio (a median of 14.24), while <italic>Homo sapiens</italic> (<italic>human</italic>) had the lowest ratio (a median of 0.07) (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). This indicates that the protein acetylation level is much higher in Prokaryotes than human proteins in the microbiome samples. Firmicutes was the only phylum that showed significantly higher percentage in lysine acetylomic aliquot than that in metaproteomic aliquot (<italic>P</italic> = 0.03, paired Wilcoxon signed-rank test), while Actinobacteria and Proteobacteria showed significantly lower percentages in acetylomic aliquot (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). No significant difference was observed for Bacteroidetes, despite its lower acetylome-to-metaproteome ratio (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2b</xref>). We calculated the Firmicutes-to-Bacteroidetes (F/B) ratios based on the intensities of their distinctive peptides yielding an average of 6.35 in lysine acetylome, which was significantly higher than that of metaproteome (an average of 4.90; <italic>P</italic> = 0.04, paired Wilcoxon signed-rank test), further indicating higher protein acetylation levels in Firmicutes.<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Taxon-specific lysine acetylation in human gut microbiome.</title><p><bold>a</bold> Sunburst plot of microbial taxa that were assigned using all identified Kac peptides. Sunburst plot is generated using Unipept (<ext-link ext-link-type=\"uri\" xlink:href=\"https://unipept.ugent.be/\">https://unipept.ugent.be/</ext-link>). <bold>b</bold> Lysine acetylome-to-metaproteome ratios of quantified phyla and genera in human gut microbiome. The ratios were log2-transformed for plotting. High indicates higher lysine acetylation levels, and low indicates lower lysine acetylation levels. Red star indicates statistically significance (<italic>P</italic> < 0.05, paired, two-sided Wilcoxon signed-rank test) when comparing the percentage in lysine acetylomic aliquot with that in metaproteomic aliquot. <bold>c</bold>, <bold>d</bold> Correlations of overall Kac peptide abundances with the relative abundances of protein phosphotransacetylase (<bold>c</bold>) and acetate kinase (<bold>d</bold>) in metaproteome. Mean and 95% confidence interval of the correlation coefficient are shown as line and error band, respectively. Spearman’s correlation <italic>R</italic> and <italic>P</italic> values are indicated. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17916_Fig2_HTML\" id=\"d30e739\"/></fig></p><p id=\"Par12\">Acetylphosphate, a metabolic intermediate of SCFA production, has been shown to be a critical contributor for protein lysine acetylation in prokaryotes<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. To study the association of acetylphosphate with global Kac levels in the microbiome, we correlated the abundances of acetylphosphate-producing enzymes, namely acetate kinase (ACK) and phosphate acetyltransferase (PTA), in the metaproteomic aliquots with the total abundance of all Kac peptides identified in acetylomic aliquots. Significant correlations were obtained for both PTA (<italic>R</italic> = 0.94; <italic>P</italic> = 0.02; Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2c</xref>) and ACK (<italic>R</italic> = 0.77; <italic>P</italic> = 0.10; Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2d</xref>). No or negative correlation was observed when correlating acetyl-CoA synthase (ACS) and acetyl-CoA acetyltransferase (ACAT) with total abundance of Kac peptides (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4</xref>). Taxonomic assignment of the 87 proteins annotated as ACK and PTA identified in this study showed that 62 proteins belonged to Firmicutes, and 25 belonged to other phyla including Bacteroidetes. <italic>Bacteroides</italic> (14 proteins), <italic>Blautia</italic> (13 proteins), <italic>Faecalibacterium</italic> (11 proteins), <italic>Lachnospira</italic> (8 proteins), <italic>[Eubacterium] rectale</italic> (6 proteins), butyrate-producing bacterium SS3/4 (5 proteins), and <italic>Prevotella</italic> (5 proteins) were the genera/species with the highest number of ACK or PTA proteins identified in the metaproteomic aliquots (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). These results are in agreement with the taxonomic distribution of Kac peptides identified in lysine acetylomic aliquots (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref>), and again suggest that Firmicutes had higher protein acetylation levels in microbiome.</p></sec><sec id=\"Sec6\"><title>Widespread protein Kac in gut microbial metabolic pathways</title><p id=\"Par13\">To study the functional distribution of identified microbial Kac proteins, we performed gene ontology (GO) term annotation of all identified Kac peptides, which showed that the top GO biological processes were translation (2642 peptides) and carbohydrate metabolism (1660 peptides) (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>). Enzyme Commission (EC) number annotation yielded a total of 1025 enzymes from five enzyme classes (Supplementary Data <xref rid=\"MOESM7\" ref-type=\"media\">4</xref>). The most Kac modified enzymes identified in this study belonged to class EC 2 (transferases; mainly EC 2.7: transferring phosphorus-containing groups) and EC 1 (oxidoreductases). Functional enrichment analysis using the Clusters of Orthologous Groups (COG) database revealed that microbial Kac proteins were significantly enriched in energy production and conversion, transport, or metabolism of amino acid, nucleotide, lipid, and coenzyme, cell wall biogenesis, replication as well as secondary metabolite metabolism (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a</xref>).<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Functional characterization of identified Kac proteins.</title><p><bold>a</bold> COG category distribution of microbial Kac proteins. Significantly enriched categories are highlighted in orange. Significance was determined with a hypergeometric test using the unmodified microbial proteins identified in the metaproteomic samples as background. Source data are provided as a Source Data file. <bold>b</bold> SCFA-producing metabolic pathways constructed using the identified Kac proteins. Identified Kac enzymes and metabolites are indicated using abbreviations as follows: GA3P glyceraldehyde-3-phosphate, BPG 1,3-bisphospho-glycerate, G3P glycerate 3-phosphate, G2P glycerate 2-phosphate, GAPDH glyceraldehyde-3-phosphate dehydrogenase, PGK phosphoglycerate kinase, PGM phosphoglycerate mutase, ENO enolase, MDH malate dehydrogenase, FUM fumarate hydratase, FRD fumarate reductase, SUC succinyl-CoA synthetase, MUT methylmalonyl-CoA mutase, MCEE methylmalonyl-CoA epimerase, PCC propionyl-CoA carboxylase, PCT propionate CoA transferase, PK pyruvate kinase, PFL pyruvate formate-lyase, KOR 2-oxoglutarate/2-oxoacid ferredoxin oxidoreductase, HADH 3-hydroxyacyl-CoA dehydrogenase, ECH enoyl-CoA hydratase, ENR enoyl-[acyl-carrier protein] reductase, PTB phosphate butyryltransferase, BUK butyrate kinase, BCoAT butyryl CoA:acetate CoA transferase. <bold>c</bold> Sequence alignment of identified acetylated PCK (MH0173_GL0113524, Kac peptide GFTAKacLAGTER) with known PCKs in PDB database. Taxonomic origin and starting amino acid position are indicated in the left side. The consensus sequence is colored in blue gradient according to the percentage identity. <italic>A. succinogenes</italic>\n<italic>Actinobacillus succinogenes</italic>, <italic>E. coli</italic>\n<italic>Escherichia coli,</italic>\n<italic>T. thermophiles</italic>\n<italic>Thermus thermophiles</italic>, <italic>T. cruzi</italic>\n<italic>Trypanosoma cruzi</italic>. <bold>d</bold> GTP-dependent and ATP-dependent PCKs share the same catalytic structural elements. The structure of the catalytic pocket of the GTP-dependant rat PCK (colored in gray, PBD 3DT4) is superposed with <italic>A. succiniciproducens</italic> ATP-dependant PCK (colored in Cyan, PBD 1YTM). Three catalytic elements: R loop, P loop, and Ω-loop are highlighted with light blue, light red, and light yellow, respectively, in rat PCK, and with bright blue, bright red, and bright yellow, respectively, in <italic>A. succiniciproducens</italic> PCK. The oxalate and ATP are indicated as sticks and colored by atom type. The Mg and Mn metals are indicated as green spheres. <bold>e</bold> Interaction among K384, E389, R60, and oxalate in <italic>A. succiniciproducens</italic> PCK. Protein structure was generated with PyMOL (<ext-link ext-link-type=\"uri\" xlink:href=\"https://pymol.org/\">https://pymol.org/</ext-link>).</p></caption><graphic xlink:href=\"41467_2020_17916_Fig3_HTML\" id=\"d30e871\"/></fig></p><p id=\"Par14\">Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation showed that 11,536 out of the 15,053 Kac proteins (76.6%) were mapped to 1354 KEGG orthologies (KOs) and 224 pathways. Among the 1354 KOs, 994 were enzymes and 626 were mapped to metabolic pathways. Fifty-one complete metabolic modules were constructed using Kac proteins, including glycolysis, citrate cycle, gluconeogenesis, pyruvate oxidation, and dissimilatory sulfate reduction (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">3</xref>). Intestinal microbiota is known to process complex carbohydrates, such as indigestible dietary fiber, to generate SCFAs that maintain the homeostasis of the intestinal microenvironment<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>. We found that carbohydrate metabolism was the most widely acetylated metabolic pathway in microbiome, in particular pyruvate metabolism (49 KOs), amino sugar and nucleotide sugar metabolism (46 KOs), glycolysis/gluconeogenesis (41 KOs), fructose and mannose metabolism (39 KOs), butanoate metabolism (36 KOs), propanoate metabolism (35 KOs), and starch and sucrose metabolism (35 KOs) (Supplementary Data <xref rid=\"MOESM8\" ref-type=\"media\">5</xref>). In addition, we established complete anaerobic fermentation pathways that produce SCFAs using the identified Kac enzymes in this study (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>), indicating a widespread protein acetylation of the enzymes involved in these important gut microbial metabolic pathways.</p><p id=\"Par15\">Examining the top ten most abundant Kac peptides identified in this study, we found that nine were from bacteria and one from human chymotrypsin-like elastase family member 3A (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">4</xref>). Moreover, all the nine most abundant microbial Kac peptides were from enzymes involved in the SCFA production, including glyceraldehyde-3-phosphate dehydrogenase (GAPDH, three peptides), 3-phosphoglycerate kinase (PGK, two peptides), pyruvate: ferredoxin oxidoreductase (PFOR, two peptides), phosphoenolpyruvate carboxykinase (ATP-dependent) (PCK, one peptide), and 3-hydroxyacyl-CoA dehydrogenase (HADH, one peptide) (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>). Six of the nine microbial Kac peptides had the lowest-common ancestor (LCA) of bacteria or root (shared by all organisms), while the other three were unique to species belonging to <italic>Clostridiales</italic> (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">4</xref>), the major SCFA producers in microbiota.</p><p id=\"Par16\">We then took PCK as an example to examine whether the identified Kac site is important for regulating enzymatic activity. PCK catalyzes the reversible conversion of phosphoenolpyruvate (PEP) into oxaloacetate (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>). A blastp search against NCBI-nr database revealed high sequence similarity of PCKs across different bacterial species (>89% identity and >98% coverage for top 100 hits; >99% hits were from Firmicutes). Alignment of the identified PCK protein sequence in this study to known PCK proteins in Protein Data Bank (PDB) identified <italic>Anaerobiospirillum succiniciproducens</italic> PCK as the most similar one (full-length protein sequence identity of 82% and <italic>E</italic>-value of 1E-81) (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3c</xref>). There are three essential catalytic structural elements in PCK, namely P loop, R loop, and Ω loop (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d</xref>). P loop and the R loop are directly involved in catalysis and substrate binding, while Ω-loop act as a lid gate by switching from a closed-active conformation to an open-inactive conformation<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. <italic>E. coli</italic> PCK with the truncated or shortened Ω-loop has been reported to loss enzyme activity<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>. In the crystal structure of <italic>A. succiniciproducens</italic> PCK with a closed-active conformation, the mapped site K384 at Ω-loop forms a salt bridge with glutamine 389 (E389) which interacts with key catalytic residue arginine 60 (R60) located at R loop (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3e</xref>). Therefore, the acetylation of K384 could interrupt these interactions and de-stabilize the active conformation of PCK. Further biochemical validation is still required, however, this finding suggests that the exploration of the current data set helps the study of how protein acetylation may regulate gut microbial activity.</p></sec><sec id=\"Sec7\"><title>Alterations of gut microbial lysine acetylome in CD patients</title><p id=\"Par17\">To demonstrate the applicability of the integrated lysine acetylomic/metaproteomic approach, we analyzed intestinal MLI aspirate samples collected from pediatric CD patients. We first analyzed the lysine acetylomes of time-series MLI aspirate samples collected from three pediatric CD patients who were undergoing disease alleviation following treatment (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">5</xref>). In total, 10,085 protein groups and 9225 Kac sites were identified for metaproteomic and lysine acetylomic aliquots, respectively. PCA analysis showed that samples collected from the same patients clustered together for both metaproteome and lysine acetylome, as well as their ratios, albeit collected up to 46 months apart, and with disease alleviation (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5</xref>). This result suggests that both the metaproteome and lysine acetylome of an individual’s microbiome are relatively stable over time. This is in agreement with previous metagenomic studies showing long-term stability of both functional- and strain-level compositions of gut microbiota<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>. We then analyzed 18 intestinal aspirate samples collected from treatment-naive patients, including 10 pediatric CD patients (male/female, 6/4; age, 13.1 ± 2.1) and 8 control subjects (male/female, 2/6; age, 13.0 ± 4.5) (Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">6</xref>). In total, we accurately quantified 4623 Kac sites in lysine acetylomic aliquot and 17,684 protein groups in metaproteomic aliquot. Principal component analysis (PCA) showed a trend to separate both the metaproteome and the lysine acetylome for CD versus control (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref>, b). Partial least square discriminant analysis (PLS-DA) was then used for supervised group classification and identifying key variables in the metaproteome (<italic>Q</italic><sup>2</sup> = 0.59), which identified 208 increased and 284 decreased protein groups in CD compared with control subjects (Supplementary Data <xref rid=\"MOESM9\" ref-type=\"media\">6</xref>). PLS-DA of the lysine acetylome (<italic>Q</italic><sup>2</sup> = 0.33) identified 51 increased and 29 decreased Kac sites in CD compared with control subjects (Supplementary Data <xref rid=\"MOESM9\" ref-type=\"media\">6</xref>). In addition to those PLS-DA-identified differentially abundant proteins or Kac sites, the proteins and Kac sites that were detected in ≥75% of samples in the one group and ≤25% of samples in the other group were also considered as differentially abundant. Altogether, the current study identified 82 Kac sites that were increased, and 68 Kac sites that were decreased, in CD compared with control subjects (Supplementary Data <xref rid=\"MOESM9\" ref-type=\"media\">6</xref>).<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Lysine acetylome alterations of the intestinal microbiome in pediatric CD.</title><p><bold>a</bold> PCA score plot of the metaproteome of the intestinal microbiome. <bold>b</bold> PCA score plot of the lysine acetylome of the intestinal microbiome. <bold>c</bold> Differentially abundant microbial Kac sites. The COG category and taxonomy (phylum and genus) for the differentially abundant Kac sites are shown in the Sankey plot. The numbers after the colons indicate the numbers of differentially abundant Kac sites. The phylum-genus links and genus-function (COG category) links are shown. Each letter corresponds to a COG category as shown in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>. The Sankey plot was generated using SankeyMATIC (<ext-link ext-link-type=\"uri\" xlink:href=\"http://sankeymatic.com/\">http://sankeymatic.com/</ext-link>). Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17916_Fig4_HTML\" id=\"d30e1010\"/></fig></p><p id=\"Par18\">Among the 82 Kac sites that were increased in CD compared with control subjects, 46 were from human proteins and 36 were from microbiome proteins. However, only six Kac sites that were decreased in CD were from human proteins, and the remaining 62 downregulated Kac sites were from microbiome proteins. Interestingly, 11 and 21 upregulated microbial Kac sites, while 35 and 9 downregulated microbial Kac sites were derived from Firmicutes and Bacteroidetes, respectively (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c</xref>). The majority of the 35 downregulated Firmicutes-derived Kac sites were from known SCFA-producing bacteria, including <italic>Faecalibacterium</italic> (nine sites), <italic>Ruminococcus</italic> (four sites), <italic>Roseburia</italic> (four sites), <italic>Eubacterium</italic> (three sites), <italic>Subdoligranulum</italic> (three sites), <italic>Clostridium</italic> (three sites), and <italic>Blautia</italic> (three sites). Taxon-specific functional analysis showed that the microbial Kac sites that showed increased abundances in CD were mainly from translation-related proteins of <italic>Bacteroides</italic>; and the downregulated microbial Kac sites in CD were mainly from proteins that are involved in translation and carbohydrate metabolism of known SCFA producers as mentioned above (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c</xref>).</p><p id=\"Par19\">We also performed comparative taxonomic analysis using the quantified Kac microbial peptides as well as non-Kac peptides in metaproteomic aliquots with linear discriminant analysis effect size (LEfSe) analysis. The results showed that the acetylome-based abundances of species <italic>Roseburia inulinivorans</italic>, <italic>Eubacterium eligens</italic>, and <italic>Megamonas funiformis</italic> were significantly decreased in CD compared with that of controls, and the abundance of <italic>Bacilli</italic> was significantly increased (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>). Metaproteome-based taxonomic analysis identified 12 taxa that were decreased and 10 taxa that were increased in CD compared with control subjects (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5b</xref>). Interestingly, the metaproteome-based abundance of <italic>Bacilli</italic> was decreased in CD, which is opposite to the observations in lysine acetylome (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5c</xref>). Accordingly, the ratios of acetylome-based abundance to metaproteome-based abundance of <italic>Bacilli</italic> were significantly increased in CD compared with controls (<italic>P</italic> < 0.0001, Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5d</xref>), highlighting the additional information provided by lysine acetylome in this study. LEfSe analysis using the acetylome-to-metaproteome ratios of all 103 quantified taxa identified six taxa that exhibited significantly decreased ratios in CD compared to control, and two taxa (<italic>Bacilli</italic> and <italic>Ruminococcus</italic>) that exhibited increased ratios (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5e</xref>). Evaluation of the six abundant bacterial phyla revealed that, compared to control subjects, CD patients displayed higher acetylome-to-metaproteome ratios for Actinobacteria, Bacteroidetes and Proteobacteria, while lower ratios for Firmicutes (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">6</xref>). Interestingly, we found that the acetylome-to-metaproteome ratios of these abundant phyla were all partially reverted during disease alleviation, in particular for the first post-treatment time point when all three patients were in remission (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7</xref>). <italic>Bacilli</italic> and <italic>Ruminococcus</italic> also showed a trend of decreased acetylome-to-metaproteome ratios when the patients were in remission in the first post-treatment time point (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7</xref>).<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Taxonomic alterations of protein acetylation in the pediatric CD microbiome.</title><p><bold>a</bold> LEfSe analysis of lysine acetylome-based taxonomic compositions. <bold>b</bold> LEfSe analysis of metaproteome-based taxonomic compositions. <bold>c</bold> Percentage of <italic>Bacilli</italic> in metaproteome and lysine acetylome data sets. Control, <italic>n</italic> = 8 biologically independent samples; CD, <italic>n</italic> = 10 biologically independent samples. Statistical significance of the difference between groups was evaluated using two-sided Mann–Whitney <italic>U</italic> test. <bold>d</bold> Acetylome-to-metaproteome ratios of <italic>Bacilli</italic> in pediatric CD and control subjects. Control, <italic>n</italic> = 8 biologically independent samples; CD, <italic>n</italic> = 10 biologically independent samples. Statistical significance of the difference between groups was evaluated using two-sided Mann–Whitney <italic>U</italic> test. <bold>e</bold> LEfSe analysis of the acetylome-to-metaproteome ratios of all quantified taxa in the lysine acetylome data set. For scatter dot plot, mean (long line) and standard deviation (short line) are indicated. Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17916_Fig5_HTML\" id=\"d30e1156\"/></fig></p></sec><sec id=\"Sec8\"><title>Altered microbiome-associated human protein Kac levels in CD</title><p id=\"Par20\">As mentioned above, we identified 46 human protein Kac sites that showed increased abundances and six human protein Kac sites that showed decreased abundances in CD compared to control (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6</xref>). The increased human protein Kac sites were mainly from the proteins lactotransferrin (LTF; ten sites) and calprotectin (protein S100A8 and S100A9; seven sites). In addition, there were 11 elevated protein Kac sites from immunoglobulins (Ig), including Ig heavy-constant alpha (IGHA1 and IGHA2), Ig heavy-constant mu (IGHM), Ig lambda constant 3 (IGLC3), Ig kappa constant (IGKC), and Ig lambda-like polypeptide 5 (IGLL5). Calculating the ratios between each of the 52 differentially abundant Kac sites and their corresponding protein abundances in the metaproteomic aliquot revealed that 27 out of the 52 Kac sites exhibited significantly different site-to-protein ratios between control and CD patients (Mann–Whitney test, <italic>P</italic> < 0.05) or frequencies of detection.<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Abundance alterations of human protein Kac sites in CD microbiome samples.</title><p>A heatmap of differentially abundant human protein Kac sites is shown on the left, and a Kac site-to-protein ratio heatmap is shown on the right. Each row of the heatmap is a protein Kac site (indicated in between the two panels). The UniProt protein entry name and protein name for each Kac site are indicated on the left side and right side, respectively. The Kac sites highlighted in red stars retained the differences in their site-to-protein ratios. Protein names highlighted in blue indicate the proteins with no significant difference between CD and control in unenriched samples. The heatmap was generated using iMetaLab (<ext-link ext-link-type=\"uri\" xlink:href=\"http://imetalab.ca/\">http://imetalab.ca/</ext-link>). Source data are provided as a Source Data file.</p></caption><graphic xlink:href=\"41467_2020_17916_Fig6_HTML\" id=\"d30e1183\"/></fig></p><p id=\"Par21\">In this study, we found that the total protein levels of both S100A8 and S100A9, two monomers of a known CD biomarker calprotectin<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref>,<xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>, were significantly increased in CD compared with controls (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8a</xref>). In addition, we identified five Kac sites for protein S100A8 and seven Kac sites for protein S100A9 in the lysine acetylome data set, among which two for S100A8 and five for S100A9 were detected in <20% of control samples and >80% of CD samples (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8b, c</xref>). Among all the identified calprotectin Kac sites, only one site for S100A8 (K18) and one site for S100A9 (K38) were quantified in ≥3 control samples, and both showed significantly lower abundances in control than CD. Although there is no significant difference on the site-to-protein ratios of individual Kac site between CD and control samples, the ratios between the sum intensities of all Kac sites on S100A8, S100A9, and their corresponding proteins were significantly higher in CD compared with control samples (S100A8, <italic>P</italic> = 0.02; S100A9, <italic>P</italic> = 0.02) (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8d</xref>). Evaluating the changes of Kac sites of S100A8 and S100A9 following disease alleviation in the treatment cohort, we found that the overall Kac levels of both S100A8 and S100A9 showed decreasing trend after treatment (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">9</xref>). Among the Kac sites identified in this therapeutic cohort, a general decreasing trend was observed, in particular for those that have been identified as being significantly increased in CD compared to control, such as S100A8 K18, K35, S100A9 K4, and K38 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">9</xref>). These findings further validate the alterations of lysine acetylome identified when comparing CD with controls.</p></sec></sec><sec id=\"Sec9\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par22\">Lysine acetylation is an important PTM event regulating various biological processes and cellular functions in all kingdoms of organisms<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref>,<xref ref-type=\"bibr\" rid=\"CR42\">42</xref>,<xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. The global profiling of Kac has been performed in many organisms, including bacterial species such as <italic>Escherichia coli</italic><sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>–<xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. However, as mentioned above, the global characterization of Kac sites in the microbiome has not yet been studied. This is mainly due to the extremely high complexity of microbiome samples, which requires an enrichment approach with better coverage, and due to the bioinformatic challenges in efficiently identifying and quantifying the microbiome Kac peptides. In this study, we utilized the seven anti-Kac monoclonal antibody cocktail, which was developed by Svinkina et al.<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>, for the enrichment of Kac peptides from tryptic digest of microbiome proteins. In addition, we developed an integrated metaproteomics/lysine acetylomics data-processing workflow based on our previously developed MetaPro-IQ approach<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup> and MetaLab software tool<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. Altogether, the experimental and bioinformatic workflow enabled a successful Kac peptide enrichment, identification, and quantification for microbiome samples. In total, over 35,000 Kac peptides were identified in enriched samples, which is far higher than that in unenriched samples (117 Kac peptides). It is worth noting that only 6 out of the 117 Kac peptides that were identified in unenriched samples overlapped with those identified in the enriched samples. Given that only 0.2% of the peptides identified in unenriched aliquot were Kac peptides (less than the FDR threshold of 1% for target-decoy database search) and their lower peptide-spectrum match (PSM) scores (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">10</xref>), the Kac peptides identified from the unenriched aliquot were potentially false identifications. This finding further suggests that an enrichment step during sample preparation is necessary to deeply and reliably identify protein lysine acetylation in the microbiome.</p><p id=\"Par23\">In bacteria, nonenzymatic acetylation by acetylphosphate has been considered as a major contributor for protein acetylation<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref>,<xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>. In enzymatic acetylation mechanism, a catalytic glutamate (E) residue in the enzyme is required to deprotonate the epsilon–amino group of the target lysine<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. Similarly, an internal acidic amino acid, such as E or D, near the target lysine is required to deprotonate the epsilon–amino group in a nonenzymatic mechanism<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. Accordingly, we found that the −1 position of microbiome Kac site was significantly enriched by E and D (top 1 and 2, respectively; Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f</xref>), suggesting that nonenzymatic acetylation mechanism is predominantly present in the gut microbiome. In addition, the relative abundance of enzymes for the generation of acetylphosphate from acetyl-CoA significantly correlated with the overall Kac levels in microbiome samples, while ACAT (converts acetyl-CoA to acetoacetyl-CoA for the production of butyrate) negatively correlated with the overall Kac levels. These findings provide evidence for a nonenzymatic protein acetylation mechanism in prokaryotes at the microbiome level.</p><p id=\"Par24\">Acetylphosphate is a key metabolic intermediate in acetate metabolism, abundantly present in SCFA producers in gut microbiota. Firmicutes is one of the most abundant bacterial phyla and major SCFA-producing bacteria, which plays important roles in human health at least in part through generating SCFAs and harvesting energy from indigestible dietary fibers<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref>,<xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup>. Accordingly, nearly half of the identified Kac peptides in both adult stool and pediatric MLI aspirate samples were derived from Firmicutes. This is also in agreement with the observations that most acetylphosphate-generating enzymes identified in this study are derived from Firmicutes, and the latter had higher lysine acetylome-to-metaproteome ratios than other bacterial phyla. Interestingly, we found that Kac is a common PTM event for almost all the important enzymes in SCFA metabolism in gut microbiome, which may be due to non-enzymatically acetylation by the excessive acetylphosphate within the cellular compartment. Castano-Cerezo et al. previously reported that many proteins involved in acetate metabolism, including ACS which converts acetate to acetyl-CoA, are acetylated proteins and their activities are also regulated by lysine acetylation<sup><xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. In <italic>Salmonella</italic>, Wang et al. demonstrated that enzymes in central metabolic pathways were extensively acetylated and protein acetylation regulated the direction of carbohydrate metabolic flux in response to environmental changes<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. In this study, the structural analysis of PCK, one of the most abundant Kac enzymes, also suggested that acetylation might be involved in regulating the direction of SCFA metabolism. We found that the catalytically essential structure loop of PCK, which regulates enzyme conformation<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>, was among the most abundantly acetylated proteins in the microbiome. The acetylation of K473 in rat PCK loop, which shares highly similar secondary structure to that of bacterial PCK (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d</xref>), has been shown to significantly increase the efficiency of conversion from phosphoenolpyruvate (PEP) into oxaloacetate, while decrease the efficiency of gluconeogenic reaction (oxaloacetate to PEP)<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>. This suggests that the identified Kac site on gut microbial PCK might be involved in accelerating the metabolic flow of PEP to oxaloacetate and thereby succinate/propionate (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3b</xref>). Taken together, these findings suggest that gut microbial Kac might be an important mechanism regulating the SCFA metabolism and influences the complex host–microbiome interactions in diseases.</p><p id=\"Par25\">Intestinal dysbiosis, in particular the depletion of SCFA-producing bacteria, is commonly associated with the development of both adult and pediatric CD<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref>,<xref ref-type=\"bibr\" rid=\"CR48\">48</xref>,<xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>. In this study, we demonstrated that the intestinal dysbiosis observed in pediatric CD patients includes alterations in the lysine acetylation of microbiome proteins, in particular the decreased Kac levels of proteins of butyrate/acetate-producers. As mentioned above, lysine acetylation may act as a regulating factor for SCFA production in gut microbiome. Although further studies are still needed to understand whether the decrease of protein Kac levels may contribute to the depletion of SCFA producers or not in CD patients, the findings in this study indicate that lysine acetylation might be a potential target for manipulating the growth and functional activity of SCFA producers, and thereby for the treatment of diseases such as CD. Accordingly, previous studies have shown that lysine deacetylase (KDAC) inhibitors, such as butyrate, suberyolanilide hydroxamic acid (SAHA), valproic acid (VPA), and statin hydroxamate, effectively treat colitis in animal models<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref>–<xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup>. In particular, Wei et al. reported that statin hydroxamate alleviated colitis and reduced the blood endotoxin (lipopolysaccharide) level<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup>, an indicator of dysbiosis of gut microbiota<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>. Although the mechanism has been attributed to their beneficial effects on the intestinal epithelium cells, our findings indicate that the KDAC inhibitors may in part directly interact with the microbiome for the alleviation of intestinal colitis. Further studies examining the microbiota composition and microbial lysine acetylome of animals or patients after KDAC inhibition treatment would be worthwhile.</p><p id=\"Par26\">To conclude, we demonstrated that a recently developed, commercially available anti-Kac monoclonal antibody cocktail can be used to successfully enrich Kac peptides from the human microbiome. Based on this, we established an experimental and bioinformatic data-analysis workflow for microbiome-wide characterization of protein lysine acetylation. We revealed widespread protein lysine acetylation in important metabolic pathways of human gut microbiota, in particular those for producing SCFAs in Firmicutes. Analyzing the microbiome samples collected from the intestinal mucosal surface of pediatric CD and control subjects revealed that the majority of downregulated Kac sites in CD patients belonged to the SCFA-producing bacteria in Firmicutes. In addition to the microbiome Kac sites, we also identified Kac sites on human proteins that were associated with the microbiomes and demonstrated the alterations of Kac levels on immune response proteins, such as immunoglobulins and calprotectin. This study was limited by the number of CD patients, however, the findings provide valuable information for designing further studies to understand the functionality of the microbiome in CD.</p></sec><sec id=\"Sec10\"><title>Methods</title><sec id=\"Sec11\"><title>Subjects and sample collections</title><p id=\"Par27\">Fresh fecal samples were collected from six healthy adult volunteers at the University of Ottawa with protocol (Protocol # 20160585-01H) approval by the Ottawa Health Science Network Research Ethics Board at the Ottawa Hospital. Informed consent was obtained from all adult subjects. Descending colon MLI aspirate samples were collected from pediatric patients that were undergoing initial diagnostic evaluation for possible CD and then subsequent colonoscopy required for their medical care. The study was approved by the Research Ethics Board of the Children’s Hospital of Eastern Ontario (CHEO), Ottawa, ON, Canada. Written informed consent form was obtained from their parents. The study complies with all relevant ethical regulations for research with human participants.</p><p id=\"Par28\">At diagnosis, all participants (<18 years old) were treatment-naive. CD was diagnosed through clinical, endoscopic, histologic, and radiological evaluations according to standard criteria<sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref></sup>. Disease severity was determined using the Pediatric Crohn’s Disease Activity Index (PCDAI)<sup><xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>. The Simplified-Endoscopy Score-Crohn’s disease (SES-CD) was used as segmental description of colonoscopic mucosal characteristics (i.e., presence and size of ulcers, extent of ulcerated surfaces, extent of affected surfaces, and presence and severity of luminal narrowing) with each characteristic being scored from 0 to 3<sup><xref ref-type=\"bibr\" rid=\"CR57\">57</xref></sup>. Control patients had visually normal mucosa, histologically normal mucosal biopsies and normal imaging. The following exclusion criteria were implemented to further refine the cohort enrolled in this study: (1) presence of diabetes mellitus; (2) presence of infectious gastroenteritis within the past 2 months; (3) use of any antibiotics or probiotics within the past 4 weeks, or (4) irritable bowel syndrome. Patient clinical data were collected and managed using REDCap (Research Electronic Data Capture) hosted at the CHEO Research Institute. REDCap is a secure, web-based application designed to support data capture for research studies<sup><xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>. The MLI aspirate samples were collected, as previously described<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. Briefly, any existing fluid and debris were first aspirated and discarded during colonoscopy. Sterile water was then flushed onto the mucosal surface to dislodge the mucus layer from the epithelial cells; the resulting fluid was then aspirated into a container. The latter was immediately put on ice and transferred to the laboratory for further processing.</p></sec><sec id=\"Sec12\"><title>Sample processing, protein extraction, and tryptic digestion</title><p id=\"Par29\">The fresh stool sample was immediately put on ice and subjected to differential centrifugation, and washed according to the procedures described previously<sup><xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>. The resulting microbial pellets were then subjected to protein extraction using lysis buffer containing 4% (w/v) SDS, 50 mM Tris-HCl (pH 8.0), and protease inhibitor (cOmplete<sup>TM</sup>, mini protease inhibitor cocktail; Roche Diagnostics GmbH). Protein lysates were then precipitated by adding five times the volume of lysis buffer of ice-cold acidified acetone/ethanol buffer overnight at −20 °C. Precipitated proteins were then collected with centrifugation at 16,000 <italic>g</italic> for 25 min at 4 °C, and washed three times by ice-cold acetone before re-suspending in 6 M urea, 100 mM ammonium bicarbonate buffer<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. Protein concentration was determined using a DC protein assay (Bio-Rad Laboratories, Inc) according to the manufacturer’s instruction. Approximately 10–15 mg of proteins for each sample were then used for in-solution proteolytic digestion. Briefly, proteins were first reduced with 10 mM dithiothreitol (DTT) for 1 h and 20 mM iodoacetamide (IAA) for 40 min at room temperature; then the samples were diluted by tenfold with 100 mM ammonium bicarbonate buffer followed by digestion using lysyl endopeptidase (Lys-C; Wako Pure Chemical Corp., Osaka, Japan) for 4 h and trypsin (Worthington Biochemical Corp., Lakewood, NJ, USA) for overnight at room temperature. The resulting digests were then subjected to desalting using Waters Sep-Pak® Vac 3cc (200 mg) tC18 cartridges and eluted using 80% acetonitrile/0.1% formic acid. A small portion of the proteolytic peptides from each sample was used for metaproteomic analysis (directly load for MS analysis), and the remainder was used for Kac peptide enrichment.</p><p id=\"Par30\">For MLI aspirate samples, upon arriving at the laboratory, the samples were immediately mixed with protease inhibitor (cOmplete<sup>TM</sup>, mini protease inhibitor cocktail; Roche Diagnostics GmbH). The aspirate samples were first centrifuged at 700 <italic>g</italic> for 5 min at 4 °C, and the supernatant collected for another centrifugation at 14,000 <italic>g</italic> for 20 min at 4 °C. The pellet fraction was harvested for protein extraction using lysis buffer consisting of 4% (w/v) SDS, 8 M urea, 50 mM Tris-HCl (pH 8.0), and cOmplete<sup>TM</sup> mini protease inhibitor cocktail. Protein lysates were then precipitated and washed using ice-cold acetone as described above. An equal amount (2.5 mg) of proteins for each sample was then used for in-solution trypsin digestion and desalting using Waters Sep-Pak® Vac 3cc (200 mg) tC18 cartridges as described above. A small portion of the tryptic peptides (equivalent to 40 µg proteins) from each sample was used for metaproteomic analysis (directly load for MS analysis), and the remainder was used for Kac peptide enrichment.</p></sec><sec id=\"Sec13\"><title>Kac peptide enrichment</title><p id=\"Par31\">Kac peptides were enriched using PTMScan® Motif antibody kits (Cell Signaling technology, Inc.) according to the manufacturer’s instruction. Briefly, tryptic peptides were first re-suspended in PTMScan® IAP Buffer and centrifuged at 10,000 <italic>g</italic> for 5 min at 4 °C to remove any insoluble pellets. The supernatant was then added directly to the tube containing Kac motif antibody beads and mixed immediately by slowly pipetting up and down. The mixture was then incubated at 4 °C for 2 h on a rotator. After incubation, the beads were collected by centrifugation at 2000 <italic>g</italic> for 30 s. The beads were then washed twice with cold IAP buffer and three times with H<sub>2</sub>O. The peptides were eluted by adding 55 µl of 0.15% (v/v) trifluoroacetic acid (TFA) to the beads and incubating for 10 min while mixing gently. The supernatant was collected through centrifuging at 2000 <italic>g</italic> for 30 s, and the remaining beads were mixed with another 50 µl of 0.15% (v/v) TFA for another round of elution. Both eluents were then combined for desalting using 10-µm C18 columns (5 mg per column). After two washes using 0.1% formic acid, peptides were eluted using 80% acetonitrile/0.1% formic acid and evaporated with a Speed-Vac concentrator for mass spectrometry analysis<sup><xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>.</p></sec><sec id=\"Sec14\"><title>Tandem mass spectrometry analysis</title><p id=\"Par32\">The peptides generated from metaproteomic and lysine acetylomic aliquotes of fecal microbiome samples were analyzed using Q Exactive HF-X mass spectrometer (ThermoFisher Scientific Inc.). For unenriched samples, peptides equivalent to 250 ng proteins were loaded for MS analysis; for enriched samples, all peptides were re-suspended in 20 μl 0.1% (v/v) formic acid, and 4 μl was used for MS analysis. Peptides were separated on an analytical column (75 μm × 15 cm) packed with reverse-phase beads (1.9 μm; 120-Å pore size; Dr. Maisch GmbH, Ammerbuch, Germany) with 2 h gradient from 5 to 35% (v/v) acetonitrile at a flow rate of 300 nl/min. The instrument method consisted of one full MS scan from 350 to 1400 <italic>m/z</italic> followed by data-dependent MS/MS scan of the 16 most intense ions and a dynamic exclusion duration of 20 s. Mass spectrometry analysis of MLI aspirate samples, including both enriched and unenriched samples, was performed on a Q Exactive mass spectrometer (ThermoFisher Scientific Inc.). For unenriched samples, peptides equivalent to 1 μg proteins were loaded for MS analysis; for enriched samples, all peptides were re-suspended in 20 μl 0.1% (v/v) formic acid, and 4 μl was used for MS analysis. Briefly, peptides were separated on an analytical column (75 μm × 15 cm) packed with reverse-phase beads (1.9 μm; 120-Å pore size; Dr. Maisch GmbH, Ammerbuch, Germany) with 4 h gradient from 5 to 35% (v/v) acetonitrile at a flow rate of 300 nl/min. The instrument method consisted of one full MS scan from 300 to 1800 <italic>m/z</italic> followed by data-dependent MS/MS scan of the 12 most intense ions, a dynamic exclusion repeat count of 2, and repeat exclusion duration of 30 s. The MS data were recorded with the Thermo Xcalibur<sup>TM</sup> software (version 3.1) and exported in RAW format for further bioinformatic data processing.</p></sec><sec id=\"Sec15\"><title>Identification of Kac and non-Kac peptides and proteins</title><p id=\"Par33\">Protein identification and quantification for both metaproteomic and lysine acetylomic data sets were performed using MetaLab<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup> with a modified MetaPro-IQ workflow<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup> as detailed in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>. In this study, we used the human fecal microbial Integrated Gene Catalog database (IGC; downloaded from China National GeneBank <ext-link ext-link-type=\"uri\" xlink:href=\"https://db.cngb.org/microbiome/genecatalog/genecatalog_human/\">https://db.cngb.org/microbiome/genecatalog/genecatalog_human/</ext-link>) for adult stool samples and the protein database that was generated in our previous metaproteomic studies of pediatric MLI aspirate samples<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>, which we termed the IGC + database in this study, for MLI aspirate samples. Briefly, the IGC + database consists of protein sequences from IGC database<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>, NCBI viral proteins, predicted protein sequences from shotgun metagenomic sequencing of MLI aspirate samples, and representative fungal species (details in ref. <sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>). The same database search parameters were used for both metaproteomic and lysine acetylomic data sets as follows: (1) up to four missed cleavages are allowed, (2) fixed modification includes cysteine carbamidomethylation, (3) potential modifications include methionine oxidation, lysine acetylation, and protein N-terminal acetylation, and (4) a parent ion tolerance of 10 ppm and a fragment ion tolerance of 20 ppm. The peptide and protein identification were performed with a false discovery rate (FDR) threshold of 0.01.</p><p id=\"Par34\">The identified peptides and protein groups in unenriched samples were obtained from the modificationSpecificPeptides.txt and proteinGroups.txt files, respectively. The identified Kac peptides and sites in enriched samples were obtained from the modificationSpecificPeptides.txt and Acetyl (K) Sites files, respectively. Only Kac sites with a localization probability of >0.75 were used for further analysis.</p></sec><sec id=\"Sec16\"><title>Kac-motif analysis</title><p id=\"Par35\">The sequences of amino acids surrounding Kac sites were analyzed and visualized using WebLogo (<ext-link ext-link-type=\"uri\" xlink:href=\"https://weblogo.berkeley.edu/\">https://weblogo.berkeley.edu/</ext-link>)<sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>. Sequence windows of 11 amino acids surrounding Kac site were created. pLogo (<ext-link ext-link-type=\"uri\" xlink:href=\"https://plogo.uconn.edu/\">https://plogo.uconn.edu/</ext-link>)<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup> was used to identify statistically overrepresented Kac motifs for the identified Kac sites. Briefly, sequence windows with six upstream and downstream amino acids surrounding the Kac site were extracted from the database and submitted to Motif-X for Kac-motif extraction. The total identified microbial proteins from one randomly selected unenriched sample were used as background for microbial protein Kac-motif extraction.</p></sec><sec id=\"Sec17\"><title>Taxonomy and functional analysis</title><p id=\"Par36\">Taxonomic annotation of both unmodified and Kac peptides was performed using Unipept 4.0 with the Equal I and L and Advanced misscleavage handling options allowed<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. Gene ontology (GO) term and Enzyme Commission (EC) number annotations were directly exported from Unipept analysis (<ext-link ext-link-type=\"uri\" xlink:href=\"https://unipept.ugent.be/\">https://unipept.ugent.be/</ext-link>). For enriched samples, only Kac peptides or proteins with Kac peptides were used. KEGG annotation and metabolic module construction were performed using GhostKOLA (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.kegg.jp/ghostkoala/\">https://www.kegg.jp/ghostkoala/</ext-link>).</p><p id=\"Par37\">Linear discriminant analysis (LDA) effect size (LEfSe) analysis<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup> was used to identify differentially abundant microbial taxa between control and CD at all taxonomic levels. Relative abundance of taxon was calculated at each taxonomic rank level, namely kingdom, phylum, class, order, family, genus, and species. For LEfSe analysis, all values were multiplied by 1,000,000 according to the user instructions and the taxa with a logarithmic LDA score >2.0 were considered to be significantly different between groups.</p></sec><sec id=\"Sec18\"><title>Multivariate and statistical analysis</title><p id=\"Par38\">PCA was used to demonstrate the inter-sample distance/clustering in a non-supervised manner. PLS-DA, a supervised multivariate statistical method, was used for modeling the group classification and identifying variables that drive such discriminations. PCA and PLS-DA were performed on quantified protein groups for unenriched samples and Kac sites for enriched samples. Briefly, the protein groups or Kac sites that were quantified in >50% of the samples were used and missing values were imputed using <italic>K</italic>-nearest neighbor (KNN) method (<italic>K</italic> = 5). PCA and KNN imputation were performed in MATLAB (The MathWorks Inc.). PLS-DA was performed using MetaboAnalyst 4.0<sup><xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>, and the proteins or Kac sites that had a Variable Importance in Projection (VIP) value ≥1 and <italic>P</italic> < 0.05 (Mann–Whitney <italic>U</italic> test) were considered to be significantly different between control and CD groups.</p><p id=\"Par39\">Statistical significance of the difference between groups was evaluated using Mann–Whitney <italic>U</italic> test (nonparametric test of the null hypothesis which is suitable for data that does not pass normality test), unless otherwise indicated. Functional enrichment analysis of identified microbial Kac proteins was performed with hypergeometric probability analysis using the microbial proteins identified in unenriched samples as background. Briefly, each of the proteins identified in unenriched or enriched samples was annotated with a COG database (<ext-link ext-link-type=\"uri\" xlink:href=\"ftp://ftp.ncbi.nih.gov/pub/COG/COG2014/data\">ftp://ftp.ncbi.nih.gov/pub/COG/COG2014/data</ext-link>) using DIAMOND<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup> (default parameters, <italic>E</italic>-value = 0.001) and the best hit for each query was selected for COG category assignment. The numbers of proteins assigned to each COG category from Kac proteins and background were used for calculating the significance <italic>P</italic> values of enrichment using <italic>hygecdf</italic> function and Benjamini–Hochberg-adjusted FDR values using <italic>mafdr</italic> function in MATLAB (The MathWorks Inc.).</p></sec><sec id=\"Sec19\"><title>Reporting summary</title><p id=\"Par40\">Further information on research design is available in the <xref rid=\"MOESM10\" ref-type=\"media\">Nature Research Reporting Summary</xref> linked to this article.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec20\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41467_2020_17916_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41467_2020_17916_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"41467_2020_17916_MOESM3_ESM.docx\"><caption><p>Description of Additional Supplementary Files</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM4\"><media xlink:href=\"41467_2020_17916_MOESM4_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM5\"><media xlink:href=\"41467_2020_17916_MOESM5_ESM.xlsx\"><caption><p>Supplementary Data 2</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM6\"><media xlink:href=\"41467_2020_17916_MOESM6_ESM.xlsx\"><caption><p>Supplementary Data 3</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM7\"><media xlink:href=\"41467_2020_17916_MOESM7_ESM.xlsx\"><caption><p>Supplementary Data 4</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM8\"><media xlink:href=\"41467_2020_17916_MOESM8_ESM.xlsx\"><caption><p>Supplementary Data 5</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM9\"><media xlink:href=\"41467_2020_17916_MOESM9_ESM.xlsx\"><caption><p>Supplementary Data 6</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM10\"><media xlink:href=\"41467_2020_17916_MOESM10_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media></supplementary-material></sec></sec></body><back><app-group><app id=\"App1\"><sec id=\"Sec21\"><title>Source data</title><p id=\"Par43\"><media position=\"anchor\" xlink:href=\"41467_2020_17916_MOESM11_ESM.xlsx\" id=\"MOESM11\"><caption><p>Source Data</p></caption></media></p></sec></app></app-group><fn-group><fn><p><bold>Peer review information</bold>\n<italic>Nature Communications</italic> thanks Jonathan Braun, Pedro Beltrao and the other anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> is available for this paper at 10.1038/s41467-020-17916-9.</p></sec><ack><title>Acknowledgements</title><p>This work was supported by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC), the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-114 & OGI-149), CIHR grant number GPH-129340 and MOP-114872. D.F. acknowledges a Distinguished Research Chair from the University of Ottawa. We acknowledge Ruth Singleton and Christine Figeys at the CHEO, Ottawa ON, for their help in collecting intestinal aspirate samples. We also thank Dr. Kendra Hodgkinson at the University of Ottawa for her help in editing the paper.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>D.F., A.S., D.M., and X.Z. designed the study. D.M. collected patient samples and clinical data. S.A.D. and K.W. pre-processed the samples. X.Z. performed the experiments. X.Z., Z.N., Y.Y., and J.C. performed data analysis. M.S. and C.F. provided materials and involved in discussion of the study design. X.Z., D.F., A.S., D.M., and J.M. wrote the paper. All authors participated in the data interpretation, discussion and edits of the paper.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>All MS proteomics data that support the findings of this study have been deposited to the ProteomeXchange Consortium (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.proteomexchange.org\">http://www.proteomexchange.org</ext-link>) with the data set identifier <ext-link ext-link-type=\"uri\" xlink:href=\"http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD015482\">PXD015482</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD013427\">PXD013427</ext-link>. Source data are provided with this paper.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par41\">D.F., A.S., and D.M. have co-founded MedBiome, a clinical microbiomics company. C.F. and M.S. are employees of Cell Signaling Technology. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807898</article-id><article-id pub-id-type=\"pmc\">PMC7431865</article-id><article-id pub-id-type=\"publisher-id\">70705</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70705-8</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Cellists’ sound quality is shaped by their primary postural behavior</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Rozé</surname><given-names>Jocelyn</given-names></name><address><email>roze@prism.cnrs.fr</email></address><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Aramaki</surname><given-names>Mitsuko</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Kronland-Martinet</surname><given-names>Richard</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ystad</surname><given-names>Sølvi</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\"/></contrib><aff id=\"Aff1\">Aix Marseille Univ., CNRS, PRISM (Perception, Representations, Image, Sound, Music), 31 Chemin J. Aiguier, CS 70071, 13402 Marseille Cedex 09, France </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13882</elocation-id><history><date date-type=\"received\"><day>10</day><month>12</month><year>2019</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">During the last 20 years, the role of musicians’ body movements has emerged as a central question in instrument practice: Why do musicians make so many postural movements, for instance, with their torsos and heads, while playing musical instruments? The musical significance of such ancillary gestures is still an enigma and therefore remains a major pedagogical challenge, since one does not know if these movements should be considered essential embodied skills that improve musical expressivity. Although previous studies established clear connections between musicians’ body movements and musical structures (particularly for clarinet, piano or violin performances), no evidence of direct relationships between body movements and the quality of the produced timbre has ever been found. In this study, focusing on the area of bowed-string instruments, we address the problem by showing that cellists use a set of primary postural directions to develop fluid kinematic bow features (velocity, acceleration) that prevent the production of poor quality (i.e., harsh, shrill, whistling) sounds. By comparing the body-related angles between normal and posturally constrained playing situations, our results reveal that the chest rotation and vertical inclination made by cellists act as coordinative support for the kinematics of the bowing gesture. These findings support the experimental works of Alexander, especially those that showed the role of head movements with respect to the upper torso (the so-called primary control) in ensuring the smooth transmission of fine motor control in musicians all the way to the produced sound. More generally, our research highlights the importance of focusing on this fundamental postural sense to improve the quality of human activities across different domains (music, dance, sports, rehabilitation, working positions, etc.).</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Skeleton</kwd><kwd>Acoustics</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Playing a musical instrument is an activity that involves complex auditory-motor interactions. Whether creating a short sound or developing a whole phrase, musicians must continuously establish a clear relationship between the actions <italic>afforded</italic> by their instrument and the auditory feedback resulting from their actions<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. Research in neuroscience has demonstrated that such an active process intricately interweaves the auditory and motor regions of the brain as a neural substrate of cognitive representation<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>,<xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. In the case of the cello, for example, longitudinal studies conducted with non-musician participants and an MRI-compatible (Magnetic Resonance Imaging) instrument revealed that “brain plasticity” emerged as an integrative function of the neural network in auditory-motor information processing<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. Both musical actions and percepts would thus depend on a single underlying mental representation governing both auditory encoding and motor control along the same goal-directed action. From these perspectives of embodied music cognition, we should consider the musical expressivity produced by instrumentalists as a link between sonic and corporeal movements and analyze their musical intentionality through the prism of a repertoire of learned gestural primitives<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Research in human biomechanics has highlighted that such a repertoire is composed of synergies, i.e., muscular cooperation patterns aiming to attain a given action<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. A strong consequence of the synergetic mechanisms is that each voluntary action, such as moving a bow on a string, should be accompanied by anticipatory postural adjustments called APAs<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref>–<xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. Anticipation is crucial in musical practice because of the coupling between coordination and postural balance, which implies that the fulfillment of a single goal-directed action may be encoded beforehand as a selective activation of the musicians’ joint degrees of freedom (DOFs)<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref>–<xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. In dance practice, conversely, the mirror neuron system may decode the perceived expressiveness into fine movement structures through the same kind of grounded synergetic processes<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>–<xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. In the domain of rehabilitation, rhythmic auditory stimuli were efficient in reducing movement disorders and improving walking abilities in Parkinson’s disease and stroke patients<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref>–<xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>.</p><p id=\"Par3\">Due to the weight of teaching habits, ignorance or misunderstandings, the role of embodiment among musicians has been largely underestimated, despite evidence of its importance for the development of proficiency in many domains<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. This underestimation is a recurrent problem in higher music education institutions that traditionally encourage the rapid acquisition of technical skills without sufficiently considering the development of musicians’ postural relations with their instruments<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. Such pedagogical methods have always been the subject of heated debates and remain controversial today because of the high rates of dropouts due to psychological frustrations and musculoskeletal disorders among musicians<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>. Many students actually need to stop this <italic>end-gaining</italic> process and adopt alternative methods drawn from experimental psychology<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref>,<xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>, particularly the Alexander technique, which states that a well-directed <italic>primary postural control</italic>, i.e., a dynamic orientation of the head, neck, and upper back, has many benefits for coordination and musical expressivity<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. Although very efficient in practice, this assumption has never been scientifically examined due to the difficulty of making accurate measurements of a musician’s primary postural control and of assessing its acoustical influence in an undisturbed way. In the area of bowed-string instruments, pioneer musical research analyzed sound features with bowing machines by focusing on physical control variables such as bow force and bow velocity but without considering the musician’s body<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref>–<xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>. Other studies assessed the instrumentalists’ auditory-motor mappings by means of motion and sound synthesis techniques with an electric violin<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref>–<xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. More recently, psycholinguistic studies explored violinists’ cognitive processes by correlating perceptual adjectives of violin sounds (<italic>round, harsh, light, mellow, dark, etc.</italic>) to physical features of the acoustic signal and haptic feedback of the instrument<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref>–<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup>. Over the past two decades, we thus observed an increasing interest among the scientific community in better understanding the significance of musicians’ corporeal movements related to their expressive sound features. The results revealed the importance of such “ancillary” gestures in supporting or accompanying the instrumentalists’ “effective gestures” that are directly responsible for sound production<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref>–<xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup>. In particular, investigations of clarinetists’ movements have shown that their sense of musical phrasing may be affected during ancillary impairment, i.e., when asked to move as little as possible while keeping their natural expressive intention or when the bell of their instrument was immobilized<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup>. Such <italic>disembodied</italic> experimental conditions enable us to infer stable and reproducible patterns between musicians’ nonobvious movements and their audible components.</p><p id=\"Par4\">In this study, we examined the key influence of musicians’ primary postural directions on their sound quality. This study is based on an experimental protocol<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup> that enabled us to compare the auditory-motor interactions of highly skilled cellists between two postural conditions: a natural condition and a posturally constrained condition in which the chest and the head were blocked by a safety race harness and a neck collar, respectively (cf “<xref rid=\"Sec7\" ref-type=\"sec\">Methods</xref>” section). In the context of postural immobilization, the cellists’ timbre quality was consistently degraded on some key notes of the more demanding passages (cf Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>). We supposed that this loss of expressiveness may correspond to specific deficiencies in the motor coordination of the right arm and impact the fluency, i.e., the level of precision, of the kinematic variations of the bow velocity. This assumption was inferred from the specialized literature on cellists’ physiology: the term bow “speed” can be used to describe the <italic>degree of motor coordination</italic> between the cellist’s body segments<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>; bow/string adherence, which shapes the timbre of the sound, would be more related to bow displacement than to bow pressure because no sound can be produced by only pressing the bow on a string without any movement<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>. We also built our experimental design on the assumptions provided by motor theories of perception<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref>–<xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup>, that predict complementary relationships between nonverbal “gesticulations” in the case of speech and ancillary gestures in the case of music<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref>,<xref ref-type=\"bibr\" rid=\"CR55\">55</xref></sup>. A psycholinguistic protocol actually revealed that inhibitions of nonverbal gestures caused speech to become much more laborious and tense, altering both intonation and expressiveness of the message<sup><xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>. This kind of connection was hypothesized in the music area through the existence of sonic-gestural objects, i.e., mental constructs in which auditory and motion elements co-occur both in the minds of the performer and the listener<sup><xref ref-type=\"bibr\" rid=\"CR57\">57</xref></sup>. Such motor imagery of the musical experience would contain dyadic properties likely to activate linkages between the structure of the written score and esthetic concepts of the perceived sound<sup><xref ref-type=\"bibr\" rid=\"CR58\">58</xref>,<xref ref-type=\"bibr\" rid=\"CR59\">59</xref></sup>. According to this model, features that characterize the produced sounds may reveal the morphology of moving sonic shapes related to the kinematic displacements of the cellists.</p><p id=\"Par5\">Here, we chose to analyze cello sounds, commonly judged as poor or “harsh” in classical music, in terms of incorrect <italic>moving sonic forms</italic><sup><xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>. In practice, this means that the acoustic signal variations analyzed within a harsh cello note may be expected to correlate with unsuitable bow velocity patterns, potentially induced from erroneous chest or head directions. Such sonic movements can be highlighted by crossing advanced methodological aspects of functional anatomy and acoustic processing (cf “<xref rid=\"Sec7\" ref-type=\"sec\">Methods</xref>” section). Actually, movement scientists consider movement coordination the result of an organized motor activity, which can be divided into several elementary actions, also called <italic>functional units</italic><sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup>. Similarly, psychoacousticians represent instrumental timbre within a perceptual space of several dimensions that are often related to temporal and spectral sound facets<sup><xref ref-type=\"bibr\" rid=\"CR62\">62</xref>,<xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. As cellists continuously modulate their gestures while playing, we may thus suppose that they use specific functional motion units to shape particular features of their sound production. This assumption guided us to design a statistical framework and to perform functional comparisons of the cellists’ kinematic and acoustic features between the normal and constrained conditions (cf Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>b). The conception of this approach was inspired by research in the medical and biological engineering fields that provides efficient methods for comparing human motion patterns over time and for quantitatively emphasizing pathological deviations from a reference control group<sup><xref ref-type=\"bibr\" rid=\"CR64\">64</xref>–<xref ref-type=\"bibr\" rid=\"CR67\">67</xref></sup>. The results of those studies demonstrate that functional data analysis (FDA)<sup><xref ref-type=\"bibr\" rid=\"CR68\">68</xref></sup> and especially functional principal component analysis (FPCA)<sup><xref ref-type=\"bibr\" rid=\"CR69\">69</xref>,<xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup> have better discriminatory power than the classical PCA multivariate approach<sup><xref ref-type=\"bibr\" rid=\"CR71\">71</xref></sup>. FPCA is an emerging modern technique that extracts the principal modes (PCs) of a set of continuous waveforms and quantifies their differences across subjects as temporal deviations from the mean curve<sup><xref ref-type=\"bibr\" rid=\"CR72\">72</xref></sup>. The technique has proven valuable for modeling simple motor behaviors<sup><xref ref-type=\"bibr\" rid=\"CR73\">73</xref>–<xref ref-type=\"bibr\" rid=\"CR75\">75</xref></sup> or biomechanics of complex sport movements<sup><xref ref-type=\"bibr\" rid=\"CR76\">76</xref>,<xref ref-type=\"bibr\" rid=\"CR77\">77</xref></sup>, and in analyzing coarticulation patterns of musicians<sup><xref ref-type=\"bibr\" rid=\"CR78\">78</xref>–<xref ref-type=\"bibr\" rid=\"CR81\">81</xref></sup> or spontaneous movement responses to music<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref>,<xref ref-type=\"bibr\" rid=\"CR82\">82</xref></sup>.</p><p id=\"Par6\">In this study, we carried out functional PCA to determine the dominant components of the cellists’ audiomotor functional units and to assess their degradation on both the motion and the acoustic sides. The cellists’ bow velocity variations were defined as the main goal-directed actions, and the functional units set up to reach this goal were defined as the linear combinations of joint-related angular time series (cf Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>). The acoustic variations were modeled by means of the descriptors highlighted in our previous work<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup> for characterizing the perceived harsh phenomenon (cf Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>).<fig id=\"Fig1\"><label>Figure 1</label><caption><p>The musical passage and note investigated for this study. Spectrograms correspond to examples of the acoustic signal of an E4 note (the first one of this score sequence) played by the same cellist with good timbre quality (round) in the normal situation [N] and poor timbre quality (harsh) in the posturally-constrained situation [SCH] (Static Chest and Head).</p></caption><graphic xlink:href=\"41598_2020_70705_Fig1_HTML\" id=\"MO1\"/></fig><fig id=\"Fig2\"><label>Figure 2</label><caption><p>(<bold>a</bold>) Kinematic model of the cellists’ trunk and right arm bowing presented at rest (frontal view). This inertial system is composed of six key joints modeled as three single axes rotational joints in the Cardan/Euler angle representation {roll (<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{\\text {n}}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:msub><mml:mi>ψ</mml:mi><mml:mtext>n</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq1.gif\"/></alternatives></inline-formula>), pitch (<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _{\\text {n}}$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:msub><mml:mi>θ</mml:mi><mml:mtext>n</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq2.gif\"/></alternatives></inline-formula>), yaw (<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _{\\text {n}}$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mtext>n</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq3.gif\"/></alternatives></inline-formula>)} where <inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n\\in [1\\ldots 6]$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mrow><mml:mi>n</mml:mi><mml:mo>∈</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>1</mml:mn><mml:mo>…</mml:mo><mml:mn>6</mml:mn><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq4.gif\"/></alternatives></inline-formula> is the key joint number. (<bold>b</bold>) Statistical framework illustrated for a given anatomic variable of the kinematic model. This framework is based on functional principal component analyses (cf “<xref rid=\"Sec7\" ref-type=\"sec\">Methods</xref>” section) and extracts two principal modes of variation of the cellists’ behavior, which are referred to as major mode and minor mode in the text. The effects of each mode are highlighted as functional deviations of the average time series between the normal (curves of blue circles) and the constrained situation (curves of red crosses).</p></caption><graphic xlink:href=\"41598_2020_70705_Fig2_HTML\" id=\"MO2\"/></fig><table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Anatomic variables described as joint-related Euler angles {<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi ,\\theta ,\\phi$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:mrow><mml:mi>ψ</mml:mi><mml:mo>,</mml:mo><mml:mi>θ</mml:mi><mml:mo>,</mml:mo><mml:mi>ϕ</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq5.gif\"/></alternatives></inline-formula>} of the segmental kinematics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Euler angle</th><th align=\"left\" colspan=\"2\">Relation to segmental kinematics</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"3\"><bold>Postural angles</bold></td></tr><tr><td align=\"left\" colspan=\"3\">root (1)</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _1$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq6.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Abdomen torsion</td><td align=\"left\"><italic>To the left</italic> [<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq7.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq8.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>To the right</italic> [<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq9.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq10.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _1$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq11.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Abdomen vertical inclination</td><td align=\"left\"><italic>Forward</italic> [<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq12.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq13.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Backward</italic> [<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq14.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq15.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _1$$\\end{document}</tex-math><mml:math id=\"M32\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq16.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Abdomen lateral swing</td><td align=\"left\"><italic>To the left</italic> [<inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq17.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M36\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq18.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>To the right</italic> [<inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M38\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq19.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq20\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M40\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq20.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" colspan=\"3\">midtorso (2)</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _2$$\\end{document}</tex-math><mml:math id=\"M42\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq21.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Chest torsion</td><td align=\"left\"><italic>To the left</italic> [<inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M44\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq22.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq23\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M46\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq23.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>To the right</italic> [<inline-formula id=\"IEq24\"><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M48\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq24.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq25\"><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M50\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq25.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq26\"><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _2$$\\end{document}</tex-math><mml:math id=\"M52\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq26.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Chest vertical inclination</td><td align=\"left\"><italic>Forward</italic> [<inline-formula id=\"IEq27\"><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M54\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq27.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq28\"><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M56\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq28.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Backward</italic> [<inline-formula id=\"IEq29\"><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M58\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq29.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq30\"><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M60\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq30.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq31\"><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _2$$\\end{document}</tex-math><mml:math id=\"M62\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq31.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Chest lateral swing</td><td align=\"left\"><italic>To the left</italic> [<inline-formula id=\"IEq32\"><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M64\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq32.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq33\"><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M66\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq33.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>To the right</italic> [<inline-formula id=\"IEq34\"><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M68\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq34.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq35\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M70\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq35.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" colspan=\"3\">neck (3)</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq36\"><alternatives><tex-math id=\"M71\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _3$$\\end{document}</tex-math><mml:math id=\"M72\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq36.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Head torsion</td><td align=\"left\"><italic>To the left</italic> [<inline-formula id=\"IEq37\"><alternatives><tex-math id=\"M73\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M74\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq37.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq38\"><alternatives><tex-math id=\"M75\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M76\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq38.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>To the right</italic> [<inline-formula id=\"IEq39\"><alternatives><tex-math id=\"M77\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M78\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq39.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq40\"><alternatives><tex-math id=\"M79\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M80\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq40.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq41\"><alternatives><tex-math id=\"M81\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _3$$\\end{document}</tex-math><mml:math id=\"M82\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq41.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Head vertical inclination</td><td align=\"left\"><italic>Forward</italic> [<inline-formula id=\"IEq42\"><alternatives><tex-math id=\"M83\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M84\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq42.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq43\"><alternatives><tex-math id=\"M85\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M86\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq43.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Backward</italic> [<inline-formula id=\"IEq44\"><alternatives><tex-math id=\"M87\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M88\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq44.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq45\"><alternatives><tex-math id=\"M89\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M90\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq45.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq46\"><alternatives><tex-math id=\"M91\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _3$$\\end{document}</tex-math><mml:math id=\"M92\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq46.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Head lateral swing</td><td align=\"left\"><italic>To the left</italic> [<inline-formula id=\"IEq47\"><alternatives><tex-math id=\"M93\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M94\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq47.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq48\"><alternatives><tex-math id=\"M95\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M96\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq48.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>To the right</italic> [<inline-formula id=\"IEq49\"><alternatives><tex-math id=\"M97\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M98\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq49.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq50\"><alternatives><tex-math id=\"M99\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M100\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq50.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"><inline-formula id=\"IEq51\"><alternatives><tex-math id=\"M101\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{12} = \\psi _1+\\psi _2$$\\end{document}</tex-math><mml:math id=\"M102\"><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq51.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Torso rotation</td><td align=\"left\"><italic>To the left</italic> [<inline-formula id=\"IEq52\"><alternatives><tex-math id=\"M103\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M104\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq52.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq53\"><alternatives><tex-math id=\"M105\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M106\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq53.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>To the right</italic> [<inline-formula id=\"IEq54\"><alternatives><tex-math id=\"M107\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M108\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq54.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq55\"><alternatives><tex-math id=\"M109\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M110\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq55.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" colspan=\"3\"><bold>Instrumental angles</bold></td></tr><tr><td align=\"left\" colspan=\"3\">rshoulder (4)</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq56\"><alternatives><tex-math id=\"M111\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _4$$\\end{document}</tex-math><mml:math id=\"M112\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq56.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Upper arm rotation</td><td align=\"left\"><italic>External</italic> [<inline-formula id=\"IEq57\"><alternatives><tex-math id=\"M113\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M114\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq57.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq58\"><alternatives><tex-math id=\"M115\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M116\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq58.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Internal</italic> [<inline-formula id=\"IEq59\"><alternatives><tex-math id=\"M117\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M118\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq59.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq60\"><alternatives><tex-math id=\"M119\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M120\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq60.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq61\"><alternatives><tex-math id=\"M121\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _4$$\\end{document}</tex-math><mml:math id=\"M122\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq61.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Upper arm abduction</td><td align=\"left\"><italic>Abduction</italic> [<inline-formula id=\"IEq62\"><alternatives><tex-math id=\"M123\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M124\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq62.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq63\"><alternatives><tex-math id=\"M125\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M126\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq63.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Adduction</italic> [<inline-formula id=\"IEq64\"><alternatives><tex-math id=\"M127\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M128\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq64.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq65\"><alternatives><tex-math id=\"M129\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M130\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq65.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq66\"><alternatives><tex-math id=\"M131\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _4$$\\end{document}</tex-math><mml:math id=\"M132\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq66.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Upper arm anteversion</td><td align=\"left\"><italic>Antepulsion</italic> [<inline-formula id=\"IEq67\"><alternatives><tex-math id=\"M133\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M134\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq67.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq68\"><alternatives><tex-math id=\"M135\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M136\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq68.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Retropulsion</italic> [<inline-formula id=\"IEq69\"><alternatives><tex-math id=\"M137\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M138\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq69.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq70\"><alternatives><tex-math id=\"M139\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M140\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq70.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" colspan=\"3\">relbow (5)</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq71\"><alternatives><tex-math id=\"M141\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _5$$\\end{document}</tex-math><mml:math id=\"M142\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq71.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Forearm rotation</td><td align=\"left\"><italic>Supination</italic> [<inline-formula id=\"IEq72\"><alternatives><tex-math id=\"M143\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M144\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq72.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq73\"><alternatives><tex-math id=\"M145\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M146\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq73.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Pronation</italic> [<inline-formula id=\"IEq74\"><alternatives><tex-math id=\"M147\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M148\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq74.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq75\"><alternatives><tex-math id=\"M149\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M150\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq75.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq76\"><alternatives><tex-math id=\"M151\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _5$$\\end{document}</tex-math><mml:math id=\"M152\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq76.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Forearm extension</td><td align=\"left\"><italic>Full flexion</italic> [<inline-formula id=\"IEq77\"><alternatives><tex-math id=\"M153\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M154\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq77.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Full extension</italic> [<inline-formula id=\"IEq78\"><alternatives><tex-math id=\"M155\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+180^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M156\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>180</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq78.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" colspan=\"3\">rwrist (6)</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq79\"><alternatives><tex-math id=\"M157\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _6$$\\end{document}</tex-math><mml:math id=\"M158\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq79.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Hand rotation</td><td align=\"left\"><italic>Supination</italic> [+]</td></tr><tr><td align=\"left\"><italic>Pronation</italic> [−]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq80\"><alternatives><tex-math id=\"M159\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _6$$\\end{document}</tex-math><mml:math id=\"M160\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq80.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Hand abduction</td><td align=\"left\"><italic>Ulnar abduction</italic> [<inline-formula id=\"IEq81\"><alternatives><tex-math id=\"M161\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M162\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq81.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq82\"><alternatives><tex-math id=\"M163\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M164\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq82.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Radial abduction</italic> [<inline-formula id=\"IEq83\"><alternatives><tex-math id=\"M165\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M166\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq83.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq84\"><alternatives><tex-math id=\"M167\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M168\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq84.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"> <inline-formula id=\"IEq85\"><alternatives><tex-math id=\"M169\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _6$$\\end{document}</tex-math><mml:math id=\"M170\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq85.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Hand flexion</td><td align=\"left\"><italic>Palmar flexion</italic> [<inline-formula id=\"IEq86\"><alternatives><tex-math id=\"M171\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M172\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq86.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq87\"><alternatives><tex-math id=\"M173\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M174\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq87.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Dorsal extension</italic> [<inline-formula id=\"IEq88\"><alternatives><tex-math id=\"M175\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M176\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq88.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq89\"><alternatives><tex-math id=\"M177\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M178\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq89.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\" rowspan=\"2\"><inline-formula id=\"IEq90\"><alternatives><tex-math id=\"M179\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{56} = \\psi _5+\\psi _6$$\\end{document}</tex-math><mml:math id=\"M180\"><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>56</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq90.gif\"/></alternatives></inline-formula></td><td align=\"left\" rowspan=\"2\">Forearm rotation</td><td align=\"left\"><italic>Supination</italic> [<inline-formula id=\"IEq91\"><alternatives><tex-math id=\"M181\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M182\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq91.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq92\"><alternatives><tex-math id=\"M183\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M184\"><mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq92.gif\"/></alternatives></inline-formula>]</td></tr><tr><td align=\"left\"><italic>Pronation</italic> [<inline-formula id=\"IEq93\"><alternatives><tex-math id=\"M185\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M186\"><mml:msup><mml:mn>0</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq93.gif\"/></alternatives></inline-formula>...<inline-formula id=\"IEq94\"><alternatives><tex-math id=\"M187\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$-90^{\\circ }$$\\end{document}</tex-math><mml:math id=\"M188\"><mml:mrow><mml:mo>-</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mo>∘</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq94.gif\"/></alternatives></inline-formula>]</td></tr></tbody></table><table-wrap-foot><p>The sign of each angle depends on its rotational direction that can be established from the resting kinematic model (cf Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a) by following the right-hand rule.</p></table-wrap-foot></table-wrap><table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Acoustic descriptors used in the study and their correlation to the perceived harshness phenomenon.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Name</th><th align=\"left\">Description</th><th align=\"left\">Correlation to harshness</th></tr></thead><tbody><tr><td align=\"left\">HSV</td><td align=\"left\">Harmonic Spectral Variation<sup><xref ref-type=\"bibr\" rid=\"CR83\">83</xref></sup></td><td align=\"left\">Increase of harmonic asynchrony</td></tr><tr><td align=\"left\">ATS</td><td align=\"left\">Attack Time Slope<sup><xref ref-type=\"bibr\" rid=\"CR84\">84</xref></sup></td><td align=\"left\">Slower attack slope of the temporal envelope</td></tr><tr><td align=\"left\">MFCCratio</td><td align=\"left\">Ratio between MFCC coefficients c2 and c1<sup><xref ref-type=\"bibr\" rid=\"CR85\">85</xref></sup></td><td align=\"left\">Emergence of formantic area</td></tr><tr><td align=\"left\">SC</td><td align=\"left\">Harmonic Spectral Centroid<sup><xref ref-type=\"bibr\" rid=\"CR86\">86</xref></sup></td><td align=\"left\">Increase of spectral centroid</td></tr><tr><td align=\"left\">TRIratio</td><td align=\"left\">Ratio between tristimulus tr3 and tr1 + tr2<sup><xref ref-type=\"bibr\" rid=\"CR87\">87</xref></sup></td><td align=\"left\">Spectral energy transfer towards high-frequency components</td></tr></tbody></table></table-wrap></p></sec><sec id=\"Sec2\"><title>Results</title><p id=\"Par7\">By applying the steps of our analysis framework, which are thoroughly described in “<xref rid=\"Sec7\" ref-type=\"sec\">Methods</xref>” section, we could infer two main functional auditory-motor linkages responsible for the perceived quality of cello sounds. In this paper, these two principal modes of variation are referred to as the major mode and minor mode. Each functional mode can be considered the coupling between an <italic>eigenposture</italic><sup><xref ref-type=\"bibr\" rid=\"CR65\">65</xref></sup> and an <italic>eigensonicform</italic><sup><xref ref-type=\"bibr\" rid=\"CR88\">88</xref></sup>: an <italic>eigenposture</italic> describes a specific aggregate of postural and instrumental joint motions, and an <italic>eigensonicform</italic> describes a specific interaction of bow kinematics and acoustic features. FPCA analyses (cf Eq. <xref rid=\"Equ3\" ref-type=\"\">3</xref>) revealed that the major and minor eigenpostures captured approximately 95% of the total data variance, i.e., 70% for FPC1 and 25% for FPC2. Similarly, the major and minor eigensonicforms captured approximately 75% of the total data variance after smoothing, i.e., 60% for FPC1 and 15% for FPC2. Such percentages of the largest explained variance were sufficient to reveal the two most prominent timbre features and establish correlations with the kinematic behavior variations. Here, we present this <italic>eigenfunction</italic> structure with two figures describing the major mode (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>) and the minor mode (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). For the sake of clarity, these figures only highlight the functional variables that presented significantly different behaviors between the normal and constrained situations.<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Major mode of the cellists’ functional variations. This mode explained 70% of the variance contained in the kinematic data—(<bold>a</bold>) postural, (<bold>b</bold>) instrumental, (<bold>c</bold>) physical—and 60% of the variance contained in the (<bold>d</bold>) acoustical data. At each stage of this functional unit, the effect of the major mode is visualized as functional deviations of the average time series between the normal situation (curves of blue circles) and the constrained situation (curves of red crosses). The attached boxplots present the distribution of FPC1 scores, i.e. the way each individual curve contributed to the major mode, for each variable that significantly discriminated the postural conditions. Normal and constrained functional components were added or subtracted to or from the mean curve, according to the mean sign of the FPC1 scores in each postural condition. The bottom right panel (<bold>e</bold>) shows the graph obtained by linear regression of the major scores (FPC1) of the bow velocity with respect to those of anatomic angles, which were significantly different between the postural conditions (<inline-formula id=\"IEq95\"><alternatives><tex-math id=\"M189\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R^{2}=0.90^{**}$$\\end{document}</tex-math><mml:math id=\"M190\"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq95.gif\"/></alternatives></inline-formula>, <inline-formula id=\"IEq96\"><alternatives><tex-math id=\"M191\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R^{2}_{adjusted}=0.77^{**}$$\\end{document}</tex-math><mml:math id=\"M192\"><mml:mrow><mml:msubsup><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">adjusted</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>77</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq96.gif\"/></alternatives></inline-formula>).</p></caption><graphic xlink:href=\"41598_2020_70705_Fig3_HTML\" id=\"MO3\"/></fig><fig id=\"Fig4\"><label>Figure 4</label><caption><p>Minor mode of the cellists’ functional variations. This mode explained 25% of the variance contained in the kinematic data—(<bold>a</bold>) postural, (<bold>b</bold>) instrumental, (<bold>c</bold>) physical—and 15% of the variance contained in the (<bold>d</bold>) acoustic data. At each stage of this functional unit, the effect of the minor mode is visualized as functional deviations of the average time series between the normal situation (curves of blue circles) and the constrained situation (curves of red crosses). The attached boxplots present the distribution of FPC2 scores, i.e. the way each individual curve contributed to the minor mode, for each variable that significantly discriminated the postural conditions. Normal and constrained functional components were added or subtracted to or from the mean curve, according to the mean sign of the FPC2 scores in each postural condition. The bottom right panel (<bold>e</bold>) shows the graph obtained by linear regression of the minor scores (FPC2) of the bow velocity with respect to those of anatomic angles, which were significantly different between the postural conditions (<inline-formula id=\"IEq127\"><alternatives><tex-math id=\"M193\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R^{2}=0.91^{*}$$\\end{document}</tex-math><mml:math id=\"M194\"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>91</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq127.gif\"/></alternatives></inline-formula>, <inline-formula id=\"IEq128\"><alternatives><tex-math id=\"M195\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R^{2}_{adjusted}=0.73^{*}$$\\end{document}</tex-math><mml:math id=\"M196\"><mml:mrow><mml:msubsup><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">adjusted</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>73</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq128.gif\"/></alternatives></inline-formula>).</p></caption><graphic xlink:href=\"41598_2020_70705_Fig4_HTML\" id=\"MO5\"/></fig></p><sec id=\"Sec3\"><title>Major mode of variations</title><p id=\"Par9\">As observed in Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>, the first (or major) functional unit corresponds to global amplitude variations at all the different stages of the sound-gesture chain. This was particularly salient at the physical stage, which reflects the “effective” sound-producing gesture (cf Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>c), where the bow velocity globally decreased in the constrained condition (<italic>Bowvel</italic>: <inline-formula id=\"IEq97\"><alternatives><tex-math id=\"M197\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=2.07$$\\end{document}</tex-math><mml:math id=\"M198\"><mml:mrow><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mn>2.07</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq97.gif\"/></alternatives></inline-formula>).</p><p id=\"Par10\">At the postural stage of the trunk motor chain (cf Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>a), which reflects the ancillary gestures, this bowing alteration effect appeared to be associated with marked amplitude reductions of the natural chest torsion (<inline-formula id=\"IEq98\"><alternatives><tex-math id=\"M199\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{12}$$\\end{document}</tex-math><mml:math id=\"M200\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq98.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq99\"><alternatives><tex-math id=\"M201\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=8.37^{***}$$\\end{document}</tex-math><mml:math id=\"M202\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>8</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>37</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq99.gif\"/></alternatives></inline-formula>) and head torsion (<inline-formula id=\"IEq100\"><alternatives><tex-math id=\"M203\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _3$$\\end{document}</tex-math><mml:math id=\"M204\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq100.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq101\"><alternatives><tex-math id=\"M205\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-3.10^{*}$$\\end{document}</tex-math><mml:math id=\"M206\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>10</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq101.gif\"/></alternatives></inline-formula>). The analyses of the normal condition in the graph actually revealed surprising symmetrical evolutions towards zero for these two movements, with the chest torsion moving from the left and the head torsion from the right, while these tendencies were lost in the constrained condition. In accordance with Mantel<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup>, we suggest that such a grounded tendency characterizes the need for a strong helicoidal energy transfer along the spine during the bow pulling movements to ensure optimal bow velocity amplitudes. The constrained condition also clearly affected the other degrees of freedom of the head, i.e., head elevation (<inline-formula id=\"IEq102\"><alternatives><tex-math id=\"M207\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _3$$\\end{document}</tex-math><mml:math id=\"M208\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq102.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq103\"><alternatives><tex-math id=\"M209\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-3.42^{*}$$\\end{document}</tex-math><mml:math id=\"M210\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>42</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq103.gif\"/></alternatives></inline-formula>) and head lateral swing (<inline-formula id=\"IEq104\"><alternatives><tex-math id=\"M211\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _3$$\\end{document}</tex-math><mml:math id=\"M212\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq104.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq105\"><alternatives><tex-math id=\"M213\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=2.38^{*}$$\\end{document}</tex-math><mml:math id=\"M214\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>38</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq105.gif\"/></alternatives></inline-formula>), for which the amplitude variations were considerably smaller than their natural counterparts. Interestingly, these two analyses of the head under natural conditions in the graph revealed that the bouncing trend during the bow pulling movement, <italic>up-and-down</italic> and <italic>right-and-left</italic> was absorbed by the constraint.</p><p id=\"Par11\">At the instrumental stage (cf Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>b), which reflects the interaction between effective and ancillary gestures, the major effect of postural impairments resulted in consistent amplitude alterations of the shoulder articulation, i.e., a loss of upper arm abduction (<inline-formula id=\"IEq106\"><alternatives><tex-math id=\"M215\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _4$$\\end{document}</tex-math><mml:math id=\"M216\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq106.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq107\"><alternatives><tex-math id=\"M217\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=4.40^{**}$$\\end{document}</tex-math><mml:math id=\"M218\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>4</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>40</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq107.gif\"/></alternatives></inline-formula>) and external rotation (<inline-formula id=\"IEq108\"><alternatives><tex-math id=\"M219\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _4$$\\end{document}</tex-math><mml:math id=\"M220\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq108.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq109\"><alternatives><tex-math id=\"M221\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=3.40^{*}$$\\end{document}</tex-math><mml:math id=\"M222\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>40</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq109.gif\"/></alternatives></inline-formula>). This insufficient upper arm external rotation also appeared to be symmetrically coupled to a loss of forearm pronation (<inline-formula id=\"IEq110\"><alternatives><tex-math id=\"M223\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{56}$$\\end{document}</tex-math><mml:math id=\"M224\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>56</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq110.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq111\"><alternatives><tex-math id=\"M225\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-2.32$$\\end{document}</tex-math><mml:math id=\"M226\"><mml:mrow><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>2.32</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq111.gif\"/></alternatives></inline-formula>). Thus, in the constrained condition, the major mode reflects a systematic locking position of the whole right arm through unsuitably combined tendencies of upper arm internal rotations and forearm supinations that affected the bow velocity.</p><p id=\"Par12\">The results of multivariate regression on the major FPC scores of these anatomical angles was significant (<inline-formula id=\"IEq112\"><alternatives><tex-math id=\"M227\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R^{2}=0.90^{**}$$\\end{document}</tex-math><mml:math id=\"M228\"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq112.gif\"/></alternatives></inline-formula>, <inline-formula id=\"IEq113\"><alternatives><tex-math id=\"M229\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R^{2}_{adjusted}=0.77^{**}$$\\end{document}</tex-math><mml:math id=\"M230\"><mml:mrow><mml:msubsup><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">adjusted</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>77</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq113.gif\"/></alternatives></inline-formula>, cf regression graph of Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>e). It was therefore possible to infer a linear relationship predicting the global bow velocity amplitudes based on the set of anatomic angles selected by the first functional unit. More importantly, an additional stepwise regression extracted a combination of two angular degrees of freedom that explained the global variations of the bow velocity:<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M231\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} {\\mathbf{Bowvel}} = 0.75 \\times \\psi _{2} - 0.52 \\times \\psi _{4} \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M232\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi mathvariant=\"bold\">Bowvel</mml:mi><mml:mo>=</mml:mo><mml:mn>0.75</mml:mn><mml:mo>×</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>-</mml:mo><mml:mn>0.52</mml:mn><mml:mo>×</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70705_Article_Equ1.gif\" position=\"anchor\"/></alternatives></disp-formula>This simple predictive relation highlights a major mechanism of the cellist’s coordination, in which the coupling between the chest torsion (ancillary gesture) and the external rotation of the right arm (instrumental gesture) guaranteed suitable bow velocity amplitudes. More details on this major coordination mode (or <italic>eigenposture</italic>) could be obtained by computing correlations between the FPC scores. Interestingly, these results revealed that chest torsion was the coordinative support for bow velocity amplitudes (<inline-formula id=\"IEq114\"><alternatives><tex-math id=\"M233\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\psi _{12}}^{Bowvel}=0.55^{*}$$\\end{document}</tex-math><mml:math id=\"M234\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">Bowvel</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>55</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq114.gif\"/></alternatives></inline-formula>). Further cross-correlations of the major angle scores revealed a chain of three coupling systems, which characterizes the coordination transfer within the major mode: (1) the system {<inline-formula id=\"IEq115\"><alternatives><tex-math id=\"M235\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{12}|\\psi _{3}|\\theta _{3}$$\\end{document}</tex-math><mml:math id=\"M236\"><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq115.gif\"/></alternatives></inline-formula>} showed the abovementioned symmetry of the chest/head torsions (<inline-formula id=\"IEq116\"><alternatives><tex-math id=\"M237\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\psi _{12}}^{\\psi _{3}}=-0.61^{*}$$\\end{document}</tex-math><mml:math id=\"M238\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>61</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq116.gif\"/></alternatives></inline-formula>); (2) the system {<inline-formula id=\"IEq117\"><alternatives><tex-math id=\"M239\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{3}|\\theta _{3}|\\phi _{3}|\\psi _{4}$$\\end{document}</tex-math><mml:math id=\"M240\"><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq117.gif\"/></alternatives></inline-formula>} showed the importance of all the degrees of freedom of the head, especially of the head torsion, for activating the external rotation of the arm (<inline-formula id=\"IEq118\"><alternatives><tex-math id=\"M241\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\psi _{3}}^{\\psi _{4}}=-0.71^{**}$$\\end{document}</tex-math><mml:math id=\"M242\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>71</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq118.gif\"/></alternatives></inline-formula>); and (3) the system {<inline-formula id=\"IEq119\"><alternatives><tex-math id=\"M243\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _{3}|\\theta _{4}$$\\end{document}</tex-math><mml:math id=\"M244\"><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq119.gif\"/></alternatives></inline-formula>} showed that up-and-down head bouncing contributed to the amplitude of shoulder abduction (<inline-formula id=\"IEq120\"><alternatives><tex-math id=\"M245\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\theta _{3}}^{\\theta _{4}}=-0.53^{*}$$\\end{document}</tex-math><mml:math id=\"M246\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>53</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq120.gif\"/></alternatives></inline-formula>). No significant cross-correlations were obtained with the angle of prono-supination (<inline-formula id=\"IEq121\"><alternatives><tex-math id=\"M247\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{56}$$\\end{document}</tex-math><mml:math id=\"M248\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>56</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq121.gif\"/></alternatives></inline-formula>), which confirms that global bow velocity amplitudes were not controlled by the forearm but by the upper arm at the shoulder level through the helicoidal work of the trunk.</p><p id=\"Par13\">From the acoustical point of view (cf Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>d), the major mode revealed that three of the five sound signal descriptors characterized the time-dependent perceptual differences between round and harsh cello sounds in terms of global amplitude variations. The graph analyses between normal and constrained situations revealed energy decreases within the temporal envelope (<italic>Rms</italic>: <inline-formula id=\"IEq122\"><alternatives><tex-math id=\"M249\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=3.04^{*}$$\\end{document}</tex-math><mml:math id=\"M250\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>04</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq122.gif\"/></alternatives></inline-formula>), energy increases on the upper partials of the spectral envelope (<italic>Triratio</italic>: <inline-formula id=\"IEq123\"><alternatives><tex-math id=\"M251\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-3.53^{**}$$\\end{document}</tex-math><mml:math id=\"M252\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>53</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq123.gif\"/></alternatives></inline-formula>), and more harmonic asynchrony, especially during the birth phase of the sound (<italic>Hsv</italic>: <inline-formula id=\"IEq124\"><alternatives><tex-math id=\"M253\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-2.08$$\\end{document}</tex-math><mml:math id=\"M254\"><mml:mrow><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>2.08</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq124.gif\"/></alternatives></inline-formula>). More details about this major acoustic mode (or <italic>eigensonicform</italic>) could be obtained by computing correlations between the FPC scores. Surprisingly, these results revealed that the temporal energy level was the main descriptor impacted by global changes in bow velocity (<inline-formula id=\"IEq125\"><alternatives><tex-math id=\"M255\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{Rms}^{Bowvel}=0.52^{*}$$\\end{document}</tex-math><mml:math id=\"M256\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">Rms</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">Bowvel</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>52</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq125.gif\"/></alternatives></inline-formula>). More trivially, the cross-correlations of major acoustic scores revealed a strong collinearity between the amounts of harmonic asynchronicity and high-frequency spectral energy (<inline-formula id=\"IEq126\"><alternatives><tex-math id=\"M257\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{Hsv}^{Triratio}=0.75^{***}$$\\end{document}</tex-math><mml:math id=\"M258\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">Hsv</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">Triratio</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>75</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq126.gif\"/></alternatives></inline-formula>). No significant cross-correlations were obtained with the amount of temporal energy (Rms). These results suggest that the major functional coordination unit essentially captured the temporal variations of the sound shape responsible for harshness perception, independent of its purely spectral aspects.</p></sec><sec id=\"Sec3422\"><title>Minor mode of variations</title><p id=\"Par15\">As observed in Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>, the second (or minor) functional unit corresponds to local variations of data amplitudes at the different stages of the sound-gesture chain. At the physical stage, which reflects the “effective” sound-producing gesture (cf Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>c), the bow velocity decreased faster in the constrained condition than in the normal postural condition (<italic>Bowvel</italic>: <inline-formula id=\"IEq129\"><alternatives><tex-math id=\"M259\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=4.37^{**}$$\\end{document}</tex-math><mml:math id=\"M260\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>4</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>37</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq129.gif\"/></alternatives></inline-formula>).</p><p id=\"Par16\">At the postural stage (cf Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>a), which reflects the ancillary gestures, this bowing deceleration appeared to be associated with a loss of natural bouncing between the chest torsion (<inline-formula id=\"IEq130\"><alternatives><tex-math id=\"M261\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{12}$$\\end{document}</tex-math><mml:math id=\"M262\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq130.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq131\"><alternatives><tex-math id=\"M263\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=8.80^{***}$$\\end{document}</tex-math><mml:math id=\"M264\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>8</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>80</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq131.gif\"/></alternatives></inline-formula>) and the head torsion (<inline-formula id=\"IEq132\"><alternatives><tex-math id=\"M265\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _3$$\\end{document}</tex-math><mml:math id=\"M266\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq132.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq133\"><alternatives><tex-math id=\"M267\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-2.35$$\\end{document}</tex-math><mml:math id=\"M268\"><mml:mrow><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>2.35</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq133.gif\"/></alternatives></inline-formula>). The analyses of the normal condition in the graph actually revealed surprising symmetrical delays, chest torsion bouncing to the left and head torsion to the right, while these tendencies were lost in the constrained condition. In accordance with Hoppenot<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>, we suggest that such a grounded tendency characterized the need for a phase of active postural resistance to the bow pulling expansions to ensure optimal bow accelerations. This effect could also be observed in the lateral swings of the head (<inline-formula id=\"IEq134\"><alternatives><tex-math id=\"M269\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _3$$\\end{document}</tex-math><mml:math id=\"M270\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq134.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq135\"><alternatives><tex-math id=\"M271\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-2.36$$\\end{document}</tex-math><mml:math id=\"M272\"><mml:mrow><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>2.36</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq135.gif\"/></alternatives></inline-formula>) whose natural <italic>right-and-left</italic> bouncing disappeared in the constrained condition. Another interesting minor effect concerned the decrease in amplitude of the naturally vertical <italic>down-to-up</italic> inclinations of the chest (<inline-formula id=\"IEq136\"><alternatives><tex-math id=\"M273\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _2$$\\end{document}</tex-math><mml:math id=\"M274\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq136.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq137\"><alternatives><tex-math id=\"M275\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-3.09^{*}$$\\end{document}</tex-math><mml:math id=\"M276\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>09</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq137.gif\"/></alternatives></inline-formula>) along the bow-pulling movements.</p><p id=\"Par17\">At the instrumental stage (cf Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>b), which reflects the interaction between effective and ancillary gestures, the lack of an active resistance phase to the bow expansion was evidenced by the behavior of shoulder articulation through the loss of upper arm abduction (<inline-formula id=\"IEq138\"><alternatives><tex-math id=\"M277\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _4$$\\end{document}</tex-math><mml:math id=\"M278\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq138.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq139\"><alternatives><tex-math id=\"M279\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=3.39^{*}$$\\end{document}</tex-math><mml:math id=\"M280\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>39</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq139.gif\"/></alternatives></inline-formula>) during the beginning of the movement. The difference in external rotation was also very interesting (<inline-formula id=\"IEq140\"><alternatives><tex-math id=\"M281\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _4$$\\end{document}</tex-math><mml:math id=\"M282\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq140.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq141\"><alternatives><tex-math id=\"M283\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-2.83^{*}$$\\end{document}</tex-math><mml:math id=\"M284\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>83</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq141.gif\"/></alternatives></inline-formula>) because it highlights the role of the shoulder in providing natural support that ensured the projection of the whole right arm. Actually, the amount of external rotation remained quite constant along a natural bow-pulling movement, whereas it drastically decreased in the constrained condition. It could also be observed that this naturally sustained external rotation guaranteed a reinforcement of the forearm pronation along the movement (<inline-formula id=\"IEq142\"><alternatives><tex-math id=\"M285\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{56}$$\\end{document}</tex-math><mml:math id=\"M286\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>56</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq142.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq143\"><alternatives><tex-math id=\"M287\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-2.05$$\\end{document}</tex-math><mml:math id=\"M288\"><mml:mrow><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>2.05</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq143.gif\"/></alternatives></inline-formula>), whereas in the constrained condition, a forearm supination appeared as soon as the upper arm switched in internal rotation. Importantly, the minor mode of variations also revealed a strong difference in elbow flexion/extension between the two conditions (<inline-formula id=\"IEq144\"><alternatives><tex-math id=\"M289\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _5$$\\end{document}</tex-math><mml:math id=\"M290\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq144.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq145\"><alternatives><tex-math id=\"M291\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=3.54^{**}$$\\end{document}</tex-math><mml:math id=\"M292\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>54</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq145.gif\"/></alternatives></inline-formula>). In the normal condition, the elbow remained slightly bent during the phase of active resistance before it considerably stretched out during the phase of bow expansion. By contrast, in the constrained condition, the elbow increasingly flexed and locked the whole arm movement. This elbow-locking effect was also reflected by two losses of mobility at the wrist level: the <italic>flexion-to-extension</italic> progression (<inline-formula id=\"IEq146\"><alternatives><tex-math id=\"M293\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _6$$\\end{document}</tex-math><mml:math id=\"M294\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq146.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq147\"><alternatives><tex-math id=\"M295\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=3.48^{*}$$\\end{document}</tex-math><mml:math id=\"M296\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>48</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq147.gif\"/></alternatives></inline-formula>) and the <italic>ulnar-to-radial</italic> inclination (<inline-formula id=\"IEq148\"><alternatives><tex-math id=\"M297\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _6$$\\end{document}</tex-math><mml:math id=\"M298\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq148.gif\"/></alternatives></inline-formula>: <inline-formula id=\"IEq149\"><alternatives><tex-math id=\"M299\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=2.38^{*}$$\\end{document}</tex-math><mml:math id=\"M300\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>38</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq149.gif\"/></alternatives></inline-formula>).</p><p id=\"Par18\">As for the major mode, the results from multivariate regression on the minor FPC scores of these anatomic angles were significant (<inline-formula id=\"IEq150\"><alternatives><tex-math id=\"M301\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R^{2}=0.91^{*}$$\\end{document}</tex-math><mml:math id=\"M302\"><mml:mrow><mml:msup><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>91</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq150.gif\"/></alternatives></inline-formula>, <inline-formula id=\"IEq151\"><alternatives><tex-math id=\"M303\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R^{2}_{adjusted}=0.73^{*}$$\\end{document}</tex-math><mml:math id=\"M304\"><mml:mrow><mml:msubsup><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">adjusted</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>73</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq151.gif\"/></alternatives></inline-formula>, cf regression graph of Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>e). It was therefore possible to infer a linear relationship predicting the local bow velocity amplitudes, or bow accelerations, based on the set of anatomic angles selected by the second functional unit. More importantly, an additional stepwise regression extracted a combination of two angular degrees of freedom that explained the local variations of bow velocity:<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M305\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} {\\mathbf{Bowvel}} = 0.41 \\times \\theta _{2} + 0.64 \\times \\phi _{6} \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M306\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi mathvariant=\"bold\">Bowvel</mml:mi><mml:mo>=</mml:mo><mml:mn>0.41</mml:mn><mml:mo>×</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mn>0.64</mml:mn><mml:mo>×</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70705_Article_Equ2.gif\" position=\"anchor\"/></alternatives></disp-formula>This simple predictive relation highlights a minor mechanism of the cellist’s coordination, in which the coupling between the vertical inclination of the chest (ancillary gesture) and the extension of the right wrist (instrumental gesture) ensured suitable bow accelerations. More details concerning this minor coordination mode (or <italic>eigenposture</italic>) could be obtained by computing correlations between the FPC scores. Interestingly, the results confirmed the importance of the vertical inclination of the chest (<inline-formula id=\"IEq152\"><alternatives><tex-math id=\"M307\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\theta _{2}}^{Bowvel}=-0.58^{*}$$\\end{document}</tex-math><mml:math id=\"M308\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">Bowvel</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>58</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq152.gif\"/></alternatives></inline-formula>) and of the extension of the wrist (<inline-formula id=\"IEq153\"><alternatives><tex-math id=\"M309\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\phi _{6}}^{Bowvel}=0.73^{**}$$\\end{document}</tex-math><mml:math id=\"M310\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">Bowvel</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>73</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq153.gif\"/></alternatives></inline-formula>) during bow accelerations. The scores of elbow extension were also marginally correlated to those of the bow accelerations (<inline-formula id=\"IEq154\"><alternatives><tex-math id=\"M311\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\phi _{5}}^{Bowvel}=0.47$$\\end{document}</tex-math><mml:math id=\"M312\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">Bowvel</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0.47</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq154.gif\"/></alternatives></inline-formula>,<inline-formula id=\"IEq155\"><alternatives><tex-math id=\"M313\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=0.063$$\\end{document}</tex-math><mml:math id=\"M314\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>0.063</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq155.gif\"/></alternatives></inline-formula>). Further cross-correlations of minor angle scores revealed a chain of four coupling systems, which characterized the coordination transfer within the minor mode: (1) system {<inline-formula id=\"IEq156\"><alternatives><tex-math id=\"M315\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _{2}|\\psi _{12}|\\psi _{3}|\\phi _{3}$$\\end{document}</tex-math><mml:math id=\"M316\"><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq156.gif\"/></alternatives></inline-formula>} showed the postural coupling among the chest torsion and vertical inclination (<inline-formula id=\"IEq157\"><alternatives><tex-math id=\"M317\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\theta _{2}}^{\\psi _{12}}=-0.51^{*}$$\\end{document}</tex-math><mml:math id=\"M318\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>51</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq157.gif\"/></alternatives></inline-formula>), the bouncing symmetry of chest/head torsions (<inline-formula id=\"IEq158\"><alternatives><tex-math id=\"M319\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\psi _{12}}^{\\psi _{3}}=-0.56^{*}$$\\end{document}</tex-math><mml:math id=\"M320\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>56</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq158.gif\"/></alternatives></inline-formula>), and the strong dependence between head torsions and lateral swings (<inline-formula id=\"IEq159\"><alternatives><tex-math id=\"M321\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\psi _{3}}^{\\phi _{3}}=0.90^{***}$$\\end{document}</tex-math><mml:math id=\"M322\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>90</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq159.gif\"/></alternatives></inline-formula>); (2) system {<inline-formula id=\"IEq160\"><alternatives><tex-math id=\"M323\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{3}|\\phi _{3}|\\theta _{4}|\\psi _{4}|\\psi _{56}$$\\end{document}</tex-math><mml:math id=\"M324\"><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>56</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq160.gif\"/></alternatives></inline-formula>} showed the importance of the degrees of freedom of the head, especially of the head torsion, to activate the external rotation of the arm (<inline-formula id=\"IEq161\"><alternatives><tex-math id=\"M325\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\psi _{3}}^{\\psi _{4}}=-0.71^{**}$$\\end{document}</tex-math><mml:math id=\"M326\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>71</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq161.gif\"/></alternatives></inline-formula>) and that of the coupling between this external rotation and the forearm pronation (<inline-formula id=\"IEq162\"><alternatives><tex-math id=\"M327\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\psi _{4}}^{\\psi _{56}}=0.52^{*}$$\\end{document}</tex-math><mml:math id=\"M328\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>56</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>52</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq162.gif\"/></alternatives></inline-formula>); (3) system {<inline-formula id=\"IEq163\"><alternatives><tex-math id=\"M329\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{12}|\\psi _{3}|\\phi _{3}|\\theta _{6}$$\\end{document}</tex-math><mml:math id=\"M330\"><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq163.gif\"/></alternatives></inline-formula>} showed the indirect influence of many postural angles, especially the angles linked to head torsion and lateral swing on the wrist inclination (<inline-formula id=\"IEq164\"><alternatives><tex-math id=\"M331\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\psi _{3}}^{\\theta _{6}}=-0.64^{**}$$\\end{document}</tex-math><mml:math id=\"M332\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>64</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq164.gif\"/></alternatives></inline-formula> and <inline-formula id=\"IEq165\"><alternatives><tex-math id=\"M333\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\phi _{3}}^{\\theta _{6}}=-0.66^{**}$$\\end{document}</tex-math><mml:math id=\"M334\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>θ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>66</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq165.gif\"/></alternatives></inline-formula> respectively); and (4) system {<inline-formula id=\"IEq166\"><alternatives><tex-math id=\"M335\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _{5}|\\phi _{6}$$\\end{document}</tex-math><mml:math id=\"M336\"><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq166.gif\"/></alternatives></inline-formula>} showed that the wrist extension was conditioned by the elbow extension (<inline-formula id=\"IEq167\"><alternatives><tex-math id=\"M337\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{\\phi _{5}}^{\\phi _{6}}=0.71^{**}$$\\end{document}</tex-math><mml:math id=\"M338\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:mrow><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>71</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq167.gif\"/></alternatives></inline-formula>). These results confirmed the importance of the double phase of postural resistance/expansion along the movement for ensuring optimal bow pulling accelerations.</p><p id=\"Par19\">From the acoustical point of view (cf Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>d), the minor mode revealed that the same acoustic descriptors as in the major mode with an additional fourth descriptor, the Mfccratio, were significantly affected by the constrained condition. The analyses in the graph revealed an inability to maintain the acoustic signal energy during the entire movement in the constrained condition. This effect was noticeable both in the temporal domain and in spectral domains (<italic>Rms</italic>: <inline-formula id=\"IEq168\"><alternatives><tex-math id=\"M339\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=2.08$$\\end{document}</tex-math><mml:math id=\"M340\"><mml:mrow><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mn>2.08</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq168.gif\"/></alternatives></inline-formula>, <italic>Triratio</italic>: <inline-formula id=\"IEq169\"><alternatives><tex-math id=\"M341\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-2.45^{*}$$\\end{document}</tex-math><mml:math id=\"M342\"><mml:mrow><mml:mi>t</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>45</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq169.gif\"/></alternatives></inline-formula>, respectively). In particular, the Mfccratio revealed an excessive amount of high-frequency spectral energy at the beginning of the sound that corresponded to the emergence of a formantic area (<italic>Mfccratio</italic>: <inline-formula id=\"IEq170\"><alternatives><tex-math id=\"M343\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t(7)=-2.04$$\\end{document}</tex-math><mml:math id=\"M344\"><mml:mrow><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>7</mml:mn><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>2.04</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq170.gif\"/></alternatives></inline-formula>). More details concerning the minor acoustic mode (or <italic>eigensonicform</italic>) could be obtained by computing correlations between the PC scores. Surprisingly, the results revealed that the amount of high-frequency spectral energy was the main descriptor impacted by local changes in bow velocity (<inline-formula id=\"IEq171\"><alternatives><tex-math id=\"M345\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{Triratio}^{Bowvel}=-0.58^{*}$$\\end{document}</tex-math><mml:math id=\"M346\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">Triratio</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">Bowvel</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>58</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq171.gif\"/></alternatives></inline-formula>). More trivially, the cross-correlations of minor acoustic scores revealed a strong collinearity between the amounts of spectral energy (<inline-formula id=\"IEq172\"><alternatives><tex-math id=\"M347\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{Triratio}^{Hsc}=0.88^{***}$$\\end{document}</tex-math><mml:math id=\"M348\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">Triratio</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">Hsc</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>88</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq172.gif\"/></alternatives></inline-formula>) and formantic energy (<inline-formula id=\"IEq173\"><alternatives><tex-math id=\"M349\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$r_{Triratio}^{Mfccratio}=0.60^{*}$$\\end{document}</tex-math><mml:math id=\"M350\"><mml:mrow><mml:msubsup><mml:mi>r</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">Triratio</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">Mfccratio</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>60</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq173.gif\"/></alternatives></inline-formula>). No significant cross-correlations were obtained with the amount of temporal energy (Rms). Complementary to the major mode, these results suggested that the minor functional coordination unit had essentially captured the variations in the spectral shape of the sound responsible for harshness perception (independent of its temporal aspects).</p></sec></sec><sec id=\"Sec5\"><title>Discussion</title><p id=\"Par20\">In summary, our functional analyses revealed that two primary postural directions are involved in the sound quality produced by highly skilled cellists: first, a major mechanism controlling bowing velocity (cf Eq. <xref rid=\"Equ1\" ref-type=\"\">1</xref>) linked to the evolution of the temporal shape of the sound and, second, a minor mechanism controlling bowing acceleration (cf Eq. <xref rid=\"Equ2\" ref-type=\"\">2</xref>) linked to the evolution of the spectral content of the sound. These results are consistent with the physics of the instrument and the pioneering acoustic studies based on bowing machines. First, the bow velocity should be correlated to the amount of transmitted vibrations to the surrounding air by the body of the cello and thus determine the energy level or intensity of the acoustic signal. Actually, harsh sounds correspond to global decreases in bow velocity and weaker temporal profiles of acoustic energy (cf major mode of Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>c,d). Second, the bow acceleration should be correlated to the amount of high-frequency energy and thus determine the quenching rates of upper partials in the spectrum<sup><xref ref-type=\"bibr\" rid=\"CR89\">89</xref></sup>. Harsh sounds actually correspond to global bow decelerations and higher quenching rates of spectral energy for upper partials (cf minor mode of Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>c,d). Among the set of acoustic descriptors that characterize perceived harshness, harmonic asynchrony remains only poorly explained by kinematic bowing analyses. This indicator of spectral fluctuations might be influenced more strongly by the strict bow/string adherence finely tuned by the bow force parameter<sup><xref ref-type=\"bibr\" rid=\"CR90\">90</xref></sup>. As a perspective, it may thus be interesting to reiterate the same kind of functional analyses with dynamic features, i.e., the prediction of variations in bow force from the muscular efforts estimated for each cellist’s body segment. Nevertheless, the bow/string adherence quality involved in the perceived sound <italic>density</italic> depends on the bow velocity<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>, which was insufficient in this study when the right arm remained locked in a position of excessive supination and internal rotation.</p><p id=\"Par21\">From our coordination study, such a tighter instrumental bowing gesture would be caused by inadequate combinations of postural variables, particularly the loss of a symmetric combination between chest and head torsion movements. The freedom of the head movement was particularly important to balance the chest torsion with the external rotation of the right arm involved in both kinetic functionalities of the bowing (velocity and acceleration). These results are consistent with previous studies on cellists’ right arm behaviors, especially the role of shoulder mobility during musical playing on the A string<sup><xref ref-type=\"bibr\" rid=\"CR91\">91</xref>,<xref ref-type=\"bibr\" rid=\"CR92\">92</xref></sup>. Furthermore, our findings emerged from large bow pulling gestures on one note, for which the impaired cellists could not compensate as simply as elsewhere in the score. As the chest and head constraints affected the cellists’ sound quality on other notes to a lesser extent, the execution of this particular note would stand for a limit in terms of postural adaptation, which clearly depends on the score structure and not only on the ergonomics of the instrument. By generalizing to the whole score, we suggest that this salient local effect of recurrent sound degradation highlights a more generic deficiency of cellists’ postural control, also called <italic>posturo-kinetic capacity</italic><sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup> in movement science. Even though its variations may remain subtle, such a capacity would guarantee body stability during any goal-directed action, such as bowing on one or several notes. Actually, the postural deviations of our highly skilled cellists were no more than 5 degrees from the mean value in the major mode (cf Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>a), but this was enough to globally influence the quality of their auditory-motor interactions. This postural capacity is also highlighted through a double phase of postural resistance/mobility to bow expansion in the minor mode (cf Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>a), which resulted in spectral alterations in the sound when the musicians were posturally impaired. The constraints thus revealed the cellists’ primary postural directions by <italic>disembodiment</italic><sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>, which supports the idea that the musicians’ structural and expressive concepts are grounded in their sensorimotor networks.</p><p id=\"Par22\">The correlations established between the cellists’ movements and their sound quality features also provide knowledge on their theoretical physiological principles<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref>,<xref ref-type=\"bibr\" rid=\"CR93\">93</xref></sup>. Actually, our results suggest that the cellists’ bowing actions would be more effective if organized in terms of “distal events”<sup><xref ref-type=\"bibr\" rid=\"CR94\">94</xref>,<xref ref-type=\"bibr\" rid=\"CR95\">95</xref></sup>, i.e., when their attention is not centered on the movement itself but more on its potential influence on the sound quality. Here, we suppose that the postural impairment considerably disrupted the musicians’ natural sensations, i.e., the external focus of attention needed to correctly perform an expressive musical task (professional cellists often talk about “playing without thinking”). As such, the context of this experiment may be considered a relevant “constrained action hypothesis”<sup><xref ref-type=\"bibr\" rid=\"CR96\">96</xref>,<xref ref-type=\"bibr\" rid=\"CR97\">97</xref></sup> for reinforcing the concept of <italic>supra-postural activity</italic><sup><xref ref-type=\"bibr\" rid=\"CR98\">98</xref>,<xref ref-type=\"bibr\" rid=\"CR99\">99</xref></sup>: the quality and efficiency of a task would depend on this supra-postural control, i.e., the way individual body movements are subsumed into a unified Gestalt for achieving the given goal. Interestingly, two of the seven cellists in our experiment stated that they became more aware of their belly respiration in situations of postural impairment. In our opinion, these remarks indicate that before being impaired, both respiratory and postural control were naturally piloted by an external focus, i.e., by supra-postural commands of their attention. The constraint forced the cellists to adopt an internal focus and to compensate by more conscious control of their movements. We hereby consider that these scientific deductions give strong support to the concept of <italic>primary postural control</italic>, which was postulated as part of the Alexander technique<sup><xref ref-type=\"bibr\" rid=\"CR100\">100</xref></sup>, not only in the context of instrumentalists but also for any goal-directed actions requiring a strong supra-postural activity. By encouraging performers to focus on the results of the actions rather than on the actions themselves, the motor system could be trained in a more embodied and self-organized way for natural and efficient performances.</p><p id=\"Par23\">These findings clearly suggest important applications for improving and optimizing practice habits among musicians. This subject is a hot topic in research areas that assess the risk of musculoskeletal disorders among musicians and search for strategies to promote health or reduce injury<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref>,<xref ref-type=\"bibr\" rid=\"CR101\">101</xref>,<xref ref-type=\"bibr\" rid=\"CR102\">102</xref></sup>. Feedback analyses of students in higher music education institutions especially revealed the upper limb, upper trunk, and neck as the main body parts affected by muscle pain syndromes<sup><xref ref-type=\"bibr\" rid=\"CR102\">102</xref>–<xref ref-type=\"bibr\" rid=\"CR104\">104</xref></sup>. The population of bowed-string players would also be more affected by these postural disorders because of the asymmetric arm positions related to the trunk<sup><xref ref-type=\"bibr\" rid=\"CR105\">105</xref>,<xref ref-type=\"bibr\" rid=\"CR106\">106</xref></sup>. Such results are clearly compliant with those of our study and reinforce the importance of integrating musicians’ primary postural control within individual rehabilitation programs. The magnitude of the cellists’ spinal curvatures that we highlighted in relation to their sound quality may particularly help in developing strengthening-flexibility exercises targeting the trunk muscles of bowed-string players. As a whole, we think that the constrained condition of our experiment altered the natural musicians’ action/perception cycle in a way that could be referred to as a “phenomenological experience on non-sense”<sup><xref ref-type=\"bibr\" rid=\"CR107\">107</xref></sup>. If cognition is our way of dealing with non-sense experiences, then the tools established to reeducate the musicians’ proprioceptive feedback should authorize such an experience on nondoing or nonactivity consciousness, also known as <italic>inhibition</italic> in the Alexander technique<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. We hereby support the idea that the quality disruptions occurring in a musical discourse find their origin in a <italic>faulty postural awareness</italic><sup><xref ref-type=\"bibr\" rid=\"CR100\">100</xref></sup>, and may be solved by refining the musicians’ global perception of somatosensory processing.</p><p id=\"Par24\">The findings presented in this paper may also have a strong impact in other areas related to expert performance, especially due to the statistical framework that we established. Sports biomechanics is one example of a domain where body posture, dynamic somatic practice, and motor control need to remain inherently and strongly connected to ensure the efficiency of a given action<sup><xref ref-type=\"bibr\" rid=\"CR66\">66</xref>,<xref ref-type=\"bibr\" rid=\"CR76\">76</xref>,<xref ref-type=\"bibr\" rid=\"CR77\">77</xref>,<xref ref-type=\"bibr\" rid=\"CR108\">108</xref></sup>. For example, research on human-material interfaces demonstrated that tennis players or runners need to finely tune the shock vibrations induced by the racket or the ground surface<sup><xref ref-type=\"bibr\" rid=\"CR109\">109</xref>–<xref ref-type=\"bibr\" rid=\"CR111\">111</xref></sup>. In that context, functional data analyses may provide an opportunity to infer continuous patterns of adaptation between the effector limb (hand or foot) and the entire body of these athletes. By extension, such analyses could also highlight a functional interdependence between the sound produced in reaction to the impact (with a racket/ground surface) and the biomechanical propagation of shock-induced vibrations. Such examples suggest that our statistical framework may be suitable for analyzing the sound-gesture relationships in a reverse way, i.e., assessing the role of auditory information on perceptual-motor processes. In recent years, many studies have highlighted the benefits of <italic>gestural sonification</italic><sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR112\">112</xref>–<xref ref-type=\"bibr\" rid=\"CR115\">115</xref></sup>, especially in the domains of sports performance and motor rehabilitation<sup><xref ref-type=\"bibr\" rid=\"CR116\">116</xref>,<xref ref-type=\"bibr\" rid=\"CR117\">117</xref></sup>. For example, sonification efficiently reduced the variability of golf swing gestures in novices<sup><xref ref-type=\"bibr\" rid=\"CR118\">118</xref>,<xref ref-type=\"bibr\" rid=\"CR119\">119</xref></sup>, or improved the pedal force effectiveness among cyclists<sup><xref ref-type=\"bibr\" rid=\"CR120\">120</xref></sup>. The beneficial effects of sonification in reeducating patients with severe gait dysfunctions, such as Parkinson’s disease patients, by rhythmic auditory cueing<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref>–<xref ref-type=\"bibr\" rid=\"CR23\">23</xref>,<xref ref-type=\"bibr\" rid=\"CR121\">121</xref></sup>, or neuromotor deficits related to the fluency of handwriting, such as dysgraphia<sup><xref ref-type=\"bibr\" rid=\"CR122\">122</xref>–<xref ref-type=\"bibr\" rid=\"CR126\">126</xref></sup>, were also well recognized. In the same way, we suppose that such continuous auditory feedback may help musicians and dancers improve or recover their body awareness, for example, through experiments of <italic>sound tracing</italic> and motor mimicry, which are already known to stimulate covert mental images associated with musical experience<sup><xref ref-type=\"bibr\" rid=\"CR58\">58</xref>,<xref ref-type=\"bibr\" rid=\"CR81\">81</xref>,<xref ref-type=\"bibr\" rid=\"CR127\">127</xref>,<xref ref-type=\"bibr\" rid=\"CR128\">128</xref></sup>.</p></sec><sec id=\"Sec6\"><title>Conclusion</title><p id=\"Par25\">In this paper, we assessed how postural impairments of highly skilled musicians affected their perceived sound quality. Through functional analyses of cellists’ kinematic and acoustic interactions, it could be demonstrated that feedforward deficiencies of the primary postural command locally altered the quality of their musical expression. Such findings suggest that musical teaching should, to a much greater extent, consider the student?s body as a global flexible and proactive structure rather than focusing on specialized cognitive patterns that break the sensorimotor processes into rigid units. This conclusion is consistent with embodied learning frameworks, especially the Alexander technique, that correlate optimal body usage to proper directions of the spinal structure and fine balance mechanisms between the head, neck, and trunk. It should therefore be possible to influence expressive perceptual processes and thus shape the musical mind by developing a kinesthetic awareness of the sensory-motor relationships, i.e., integrating the sensations of joint mobility, muscular stability, and posture as a whole. If such <italic>indirect procedures</italic> would contribute to reinforcing musculoskeletal health and the quality of the performance in the musical domain, they may also be applied in a reverse way for learning dance and sport skills or for patients in clinical rehabilitation by means of experimental manipulations of auditory feedback.</p><p id=\"Par26\">As a promising perspective of this study, we started to develop a complementary approach for assessing the effects of harsh timbre degradation on cellists’ motor behavior. By means of our statistical framework of functional analyses, we expect to close the perceptual loop that links cellists’ timbre quality to their postural control. The methodological aspects of such a work are based on the use of an electric silent cello and the setup of a multimodal platform combining a motion capture system and spatial rendering to study sound/gesture interactions. We think that augmenting the perceptual information, especially through fine sound synthesis techniques applied to gestural sonification, might provide a suitable means to strengthen the understanding of the <italic>body schema</italic> related to cognitive interpretation and physical expression of structures within music or dance performance. Such an approach has the potential to guide research on the design of skill training or rehabilitation scenarios in the context of real-world applications, and it is particularly well-suited for (but not limited to) musicians and dancers.</p></sec><sec id=\"Sec7\"><title>Methods</title><sec id=\"Sec8\"><title>Participants</title><p id=\"Par27\">Seven highly skilled cellists (males = 4; females = 3; mean age = <inline-formula id=\"IEq174\"><alternatives><tex-math id=\"M351\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$40.5\\pm 11.1$$\\end{document}</tex-math><mml:math id=\"M352\"><mml:mrow><mml:mn>40.5</mml:mn><mml:mo>±</mml:mo><mml:mn>11.1</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq174.gif\"/></alternatives></inline-formula>) were recruited on a voluntary basis from the Music Conservatory and the Opera of Marseille to participate in a 3-h experiment that, as they were told, consisted of‘ “exploring cellists’ sound/gesture relationships”. Before the experiment, each musician signed a consent form that advised them of the precise the nature of the postural conditions and in which they agreed to the publication of the information/image(s) collected during the experiment in an online open-access publication. The musicians were also given an honorarium for their participation. All the procedures of the protocol were approved by a local ethics board at the ISM-Aix-Marseille University and were carried out according to the relevant guidelines expressed in the 1964 Declaration of Helsinki.</p></sec><sec id=\"Sec9\"><title>Design and apparatus</title><p id=\"Par28\">The design of our experiment was based on four postural conditions of gradual difficulty<sup><xref ref-type=\"bibr\" rid=\"CR129\">129</xref></sup>. For each condition, the cellists were asked to play a score composed of different technical patterns as expressively as possible. The full score was executed three times by postural condition, according to two tempi [45/70 bpm] and bowing modes [<italic>detached/legato</italic>]. The postural conditions and repetitions of factor combinations were randomly presented to each participant. At the end of each postural session, we collected the participants’ impressions regarding their difficulties in terms of motion and sound production by means of a short questionnaire. In this paper, we focus on the two extreme experimental conditions (cf Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>): the natural performance (entitled [N]: <italic>Normal</italic>) and the fully constrained condition (entitled [SCH]: <italic>Static Chest and Head</italic>). This fully constrained condition consisted of impairing the cellists by two immobilization devices that reduced their primary postural control in a noninvasive way: a six-point safety race harness that restrained the torso displacements and an adjusted neck collar that limited the freedom of head movements. We installed this equipment on the musicians so that their shoulder mobility was not affected.</p><p id=\"Par29\">The cellists’ movements were recorded by an infrared motion capture system (Vicon 8, fps=125 Hz) that tracked the three-dimensional positions of the reflective markers positioned on the performer’s body and the instrument. We followed the anatomical “Plug-in-Gait” (Vicon Motion Systems. Plug-in-Gait product guide. Oxford: Vicon Motion Systems, 2010, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.c-motion.com/download/IORGaitFiles/pigmanualver1.pdf\">https://www.c-motion.com/download/IORGaitFiles/pigmanualver1.pdf</ext-link>) standard to distribute the marker locations on the instrumentalist’s body. For this study, we focused the kinematic analyses on a subset of seven key markers covering the cellists’ postural chain (torso/head) and the instrumental chain responsible for the bowing gestures produced by the right arm. Some of these markers were virtually computed from the Plug-in-Gait anatomical landmarks located on each segment, in accordance with the Dempster model convention<sup><xref ref-type=\"bibr\" rid=\"CR130\">130</xref></sup> (cf Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). The acoustic signals produced by the instrument were recorded by a DPA 4096 microphone placed under the cello bridge and connected to a MOTU interface (Ultralite MIC3, fps = 44.1 kHz). Both recording systems were synchronized by a manual clap.</p></sec><sec id=\"Sec10\"><title>Stimuli and procedure</title><p id=\"Par31\">The stimuli were extracted from the cellists’ post-experimental feedback, which identified a part of the performed score as frequently degraded in the constrained postural situation. Actually, several notes belonging to this passage sounded harsher and shriller in agreement with the cellists’ comments regarding their performance, in particular, their impression of producing “tighter and tenser sounds”, or “sounds lacking depth and natural resonance”. Such harshness phenomena (i.e., degraded, metallic sound color) occurred during the execution of quick syncopated patterns requiring excellent synchronization between the two arms and were quite consistent among cellists on the first note of the sequence (cf Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>). This dotted sixteenth of pitch E4 is a key note that provides the motion impulse to the musical phrase through a large bow-pulling gesture on the first (A) cello string. Spectrogram analyses of this note between the normal and constrained postural situations revealed salient signal differences, which were thoroughly explored and connected to the musicians’ perception in a previous work<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. We assessed the qualitative harshness phenomenon judgments according to esthetic criteria of classical music by means of perceptual tests administered to a population of 15 trained cellists, both teachers and advanced students. None of these cellists had participated in the experiment and had no knowledge of the constrained postural conditions.</p><p id=\"Par32\">For this paper, we used the same corpus as in our previous work, which was built from perceptual evaluations of harshness between the normal and constrained performances of the seven cellists. This corpus was composed of the eight most salient pairs of round/harsh (good/poor quality) sounds of the E4 note, extracted from the cellists’ performances in the normal and constrained contexts (mean note duration = <inline-formula id=\"IEq175\"><alternatives><tex-math id=\"M353\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$310\\pm 60$$\\end{document}</tex-math><mml:math id=\"M354\"><mml:mrow><mml:mn>310</mml:mn><mml:mo>±</mml:mo><mml:mn>60</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq175.gif\"/></alternatives></inline-formula> ms <inline-formula id=\"IEq176\"><alternatives><tex-math id=\"M355\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\forall$$\\end{document}</tex-math><mml:math id=\"M356\"><mml:mo>∀</mml:mo></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq176.gif\"/></alternatives></inline-formula> [N/SCH]). Each round[N]/harsh[SCH] note pair belonged to a given cellist performing in slow tempo (45 bpm) and <italic>legato</italic> bowing mode. The pairs of samples also belonged to different cellists and could thus be considered independent.</p></sec><sec id=\"Sec11\"><title>Motion analyses</title><p id=\"Par33\">Motion analyses were based on the anatomic displacements of the cellists’ joints associated with each sound of the corpus and on their bow velocity over time. To assess fine coordination features, we designed a kinematic model describing the temporal evolution of these body joints (cf Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>a). The model was composed of a linkage of six main rotary joints (cf Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>) articulating seven segments related to the body trunk and the right arm (pelvis, abdomen, chest, head, upper arm, forearm, hand). Each corporeal segment was assumed to be a rigid link, and the six articulations were approximated from the skeleton geometry as spherical joints of three-dimensional degrees of freedom (DOFs)<sup><xref ref-type=\"bibr\" rid=\"CR131\">131</xref>,<xref ref-type=\"bibr\" rid=\"CR132\">132</xref></sup>. We computed 18 DOFs (6 joints <inline-formula id=\"IEq177\"><alternatives><tex-math id=\"M357\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\times$$\\end{document}</tex-math><mml:math id=\"M358\"><mml:mo>×</mml:mo></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq177.gif\"/></alternatives></inline-formula> 3 angles) as joint-related triplets of anatomic angles {<inline-formula id=\"IEq178\"><alternatives><tex-math id=\"M359\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _n,\\theta _n,\\phi _n$$\\end{document}</tex-math><mml:math id=\"M360\"><mml:mrow><mml:msub><mml:mi>ψ</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>θ</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>ϕ</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq178.gif\"/></alternatives></inline-formula>}, <inline-formula id=\"IEq179\"><alternatives><tex-math id=\"M361\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n\\in [1\\ldots 6]$$\\end{document}</tex-math><mml:math id=\"M362\"><mml:mrow><mml:mi>n</mml:mi><mml:mo>∈</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>1</mml:mn><mml:mo>…</mml:mo><mml:mn>6</mml:mn><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq179.gif\"/></alternatives></inline-formula> (cf Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>) by performing Cardan/Euler conversions of their segment-related marker coordinates<sup><xref ref-type=\"bibr\" rid=\"CR132\">132</xref>,<xref ref-type=\"bibr\" rid=\"CR133\">133</xref></sup>. For each joint, the method consisted of computing the way the distal segment of the join was spatially rotated with respect to its proximal segment (cf Supplementary Figure <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). In geometric terms, this approach merely defined a rotation matrix between two bases {<inline-formula id=\"IEq180\"><alternatives><tex-math id=\"M363\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\vec {i}^{p},\\vec {j}^{p},\\vec {k}^{p}$$\\end{document}</tex-math><mml:math id=\"M364\"><mml:mrow><mml:msup><mml:mover accent=\"true\"><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">→</mml:mo></mml:mover><mml:mi>p</mml:mi></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mover accent=\"true\"><mml:mi>j</mml:mi><mml:mo stretchy=\"false\">→</mml:mo></mml:mover><mml:mi>p</mml:mi></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mover accent=\"true\"><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">→</mml:mo></mml:mover><mml:mi>p</mml:mi></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq180.gif\"/></alternatives></inline-formula>} and {<inline-formula id=\"IEq181\"><alternatives><tex-math id=\"M365\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\vec {i}^{d},\\vec {j}^{d},\\vec {k}^{d}$$\\end{document}</tex-math><mml:math id=\"M366\"><mml:mrow><mml:msup><mml:mover accent=\"true\"><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">→</mml:mo></mml:mover><mml:mi>d</mml:mi></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mover accent=\"true\"><mml:mi>j</mml:mi><mml:mo stretchy=\"false\">→</mml:mo></mml:mover><mml:mi>d</mml:mi></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mover accent=\"true\"><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">→</mml:mo></mml:mover><mml:mi>d</mml:mi></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq181.gif\"/></alternatives></inline-formula>} attached to the joint proximal and distal segments, respectively (cf Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">2</xref>). Such a matrix represents a succession of three rotations needed to transform a joint proximal basis into its relative distal basis: first rotation around X by an angle <inline-formula id=\"IEq182\"><alternatives><tex-math id=\"M367\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi$$\\end{document}</tex-math><mml:math id=\"M368\"><mml:mi>ψ</mml:mi></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq182.gif\"/></alternatives></inline-formula> (<italic>roll</italic>), second rotation around Y by an angle <inline-formula id=\"IEq183\"><alternatives><tex-math id=\"M369\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta$$\\end{document}</tex-math><mml:math id=\"M370\"><mml:mi>θ</mml:mi></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq183.gif\"/></alternatives></inline-formula> (<italic>pitch</italic>), and third rotation around Z by an angle <inline-formula id=\"IEq184\"><alternatives><tex-math id=\"M371\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi$$\\end{document}</tex-math><mml:math id=\"M372\"><mml:mi>ϕ</mml:mi></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq184.gif\"/></alternatives></inline-formula> (<italic>yaw</italic>). As six rotation matrices should be computed to model all the DOFs, we iterated the process along the six reference body hinges of the cellists’ motor chain. In addition to these joint single-axis rotations, we also defined two composite angles for characterizing the global torso rotation (<inline-formula id=\"IEq185\"><alternatives><tex-math id=\"M373\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{12}$$\\end{document}</tex-math><mml:math id=\"M374\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq185.gif\"/></alternatives></inline-formula>) and the global forearm pronation/supination (<inline-formula id=\"IEq186\"><alternatives><tex-math id=\"M375\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{56}$$\\end{document}</tex-math><mml:math id=\"M376\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>56</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq186.gif\"/></alternatives></inline-formula>). Note that angle <inline-formula id=\"IEq187\"><alternatives><tex-math id=\"M377\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta _{5}$$\\end{document}</tex-math><mml:math id=\"M378\"><mml:msub><mml:mi>θ</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq187.gif\"/></alternatives></inline-formula> was removed because of the redundancy with the external/internal rotation of the shoulder (<inline-formula id=\"IEq188\"><alternatives><tex-math id=\"M379\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{4}$$\\end{document}</tex-math><mml:math id=\"M380\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq188.gif\"/></alternatives></inline-formula>); most biomechanics literature actually expresses the elbow joint by only two DOFs: flexion/extension (<inline-formula id=\"IEq189\"><alternatives><tex-math id=\"M381\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\phi _{5}$$\\end{document}</tex-math><mml:math id=\"M382\"><mml:msub><mml:mi>ϕ</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq189.gif\"/></alternatives></inline-formula>) and pronation/supination (<inline-formula id=\"IEq190\"><alternatives><tex-math id=\"M383\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\psi _{5}$$\\end{document}</tex-math><mml:math id=\"M384\"><mml:msub><mml:mi>ψ</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq190.gif\"/></alternatives></inline-formula>)<sup><xref ref-type=\"bibr\" rid=\"CR134\">134</xref></sup>. At the end of this chain of anatomic angles, the bow velocity was computed as the velocity vector norms of the bow “frog” marker (cf Supplementary Table <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>) along the duration of each note composing the corpus: <inline-formula id=\"IEq191\"><alternatives><tex-math id=\"M385\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Bowvel = \\sqrt{(v_x^2+v_y^2+v_z^2)}$$\\end{document}</tex-math><mml:math id=\"M386\"><mml:mrow><mml:mi>B</mml:mi><mml:mi>o</mml:mi><mml:mi>w</mml:mi><mml:mi>v</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mo>=</mml:mo><mml:msqrt><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>v</mml:mi><mml:mi>x</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mi>v</mml:mi><mml:mi>y</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mi>v</mml:mi><mml:mi>z</mml:mi><mml:mn>2</mml:mn></mml:msubsup><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msqrt></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq191.gif\"/></alternatives></inline-formula>, where the triplet (<inline-formula id=\"IEq192\"><alternatives><tex-math id=\"M387\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$v_x,v_y,v_z$$\\end{document}</tex-math><mml:math id=\"M388\"><mml:mrow><mml:msub><mml:mi>v</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mi>y</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:mi>z</mml:mi></mml:msub></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq192.gif\"/></alternatives></inline-formula>) refers to the derivatives of the spatial coordinates of the bow frog at a given time.</p></sec><sec id=\"Sec12\"><title>Acoustic analyses</title><p id=\"Par34\">Acoustic analyses were based on the computation of five acoustic descriptors over time (cf Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>), which had been determined to be significant in our previous work<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup> for discriminating between round and harsh cello sounds. The extraction process for note E4 relied on a pitch-tracking algorithm adapted from the MIR toolbox (Music Information Retrieval)<sup><xref ref-type=\"bibr\" rid=\"CR135\">135</xref></sup> of MATLAB software. We developed a dedicated workflow in MATLAB to compute the five acoustic descriptors over time by following the MPEG-7 standards<sup><xref ref-type=\"bibr\" rid=\"CR136\">136</xref></sup>: HSV (Harmonic Spectral Variations) relates to the sound spectral flux as a time-varying spectral content of its harmonic components<sup><xref ref-type=\"bibr\" rid=\"CR83\">83</xref></sup>; it was obtained from the spectral variation of harmonic amplitudes between adjacent temporal frames. ATS (Attack Time Slope) corresponds to the attack time slope of the sound signal; it was determined from the logarithmic rise time of the signal energy during the attack phase. MFCCratio is a ratio between the two first MFCCs (Mel-Frequency Cepstral Coefficients)<sup><xref ref-type=\"bibr\" rid=\"CR85\">85</xref></sup>, which we designed to highlight specific variations of the sound spectral envelope in a perceptual way; the coefficients were classically obtained through a DCT (Discrete Cosine Transform) applied to the logarithmic spectral envelope. SC (Spectral Centroid) corresponds to an amplitude-weighted mean of the harmonic spectral peaks; it was obtained through a decomposition in subbands centered on the signal harmonics<sup><xref ref-type=\"bibr\" rid=\"CR137\">137</xref></sup>. TRIratio describes the spectral energy distribution in three frequency bands as an energy ratio between each band and the total number of harmonics. The first band contains the fundamental frequency, the second band contains the medium partials (two, three, four) and the last band contains the higher partials (five and more). The three tristimulus coordinates were obtained by spectral centroid computations for each band<sup><xref ref-type=\"bibr\" rid=\"CR87\">87</xref></sup>.</p></sec><sec id=\"Sec13\"><title>Statistical framework</title><p id=\"Par35\">Our statistical framework was designed with the aim of carrying out functional comparisons of the cellists’ sound-gesture interactions between the normal and constrained conditions. This process can be divided into five steps, which are described below by referring to the schema components of Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>b. All the calculations were performed with the help of MATLAB software and the FDA toolbox<sup><xref ref-type=\"bibr\" rid=\"CR138\">138</xref></sup>.</p><sec id=\"Sec14\"><title>Functional data analyses (FDA)</title><p id=\"Par36\">In contrast with the classic PCA approach, functional data analysis considers the entire sequence of measurements a function or a single entity rather than a series of individual data points<sup><xref ref-type=\"bibr\" rid=\"CR76\">76</xref></sup>. To represent our motion features (anatomic angles, bow velocity) and acoustic descriptors as time-varying functions, the FDA methodology consisted of decomposing each time-series of variables as a linear combination of B-spline basis functions. We chose an equally spaced 6-order B-spline basis because it was better suited for numerical calculations than polynomials that are less stable. Furthermore, B-spline functions were very useful for smoothing acoustic data of noisier natures than kinematic data while efficiently accommodating changes in local behavior. A semi-sampled spline basis was sufficient to keep a fine-grained definition of each curve. The B-spline mathematical decomposition is also required to align the <inline-formula id=\"IEq193\"><alternatives><tex-math id=\"M389\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n=16$$\\end{document}</tex-math><mml:math id=\"M390\"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>16</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq193.gif\"/></alternatives></inline-formula> time series (eight {normal/constraint} data pairs) of motion and acoustic descriptors to the duration of the longest series beforehand. This duration was normalized between 0 and 1 to be consistent with the FDA time-warping mechanism.</p></sec><sec id=\"Sec15\"><title>Functional principal component analyses (FPCA)</title><p id=\"Par37\">FPCA was carried out based on the spline-based representation of time-point data. This technique has the major advantage of producing functional principal components that can be interpreted in the same domain as the original observations (kinematic and acoustic). Actually, this technique models each descriptor time-series <inline-formula id=\"IEq194\"><alternatives><tex-math id=\"M391\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f_i$$\\end{document}</tex-math><mml:math id=\"M392\"><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq194.gif\"/></alternatives></inline-formula> as a linear combination of weighted deviations from its mean dataset <inline-formula id=\"IEq195\"><alternatives><tex-math id=\"M393\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{f_i(t)}$$\\end{document}</tex-math><mml:math id=\"M394\"><mml:mover><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq195.gif\"/></alternatives></inline-formula>:<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M395\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} f_i(t) = \\overline{f_i(t)} + \\sum _{k=1}^{K} c_{ik} \\xi _k(t) + \\epsilon _i, \\; c_{ik} = \\int \\xi _k(t) f_i(t) dt \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M396\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mover><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mo>+</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:munderover><mml:msub><mml:mi>c</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ik</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mi>ξ</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>ϵ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mspace width=\"0.277778em\"/><mml:msub><mml:mi>c</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ik</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>∫</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70705_Article_Equ3.gif\" position=\"anchor\"/></alternatives></disp-formula>where <inline-formula id=\"IEq196\"><alternatives><tex-math id=\"M397\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\xi _k(t)$$\\end{document}</tex-math><mml:math id=\"M398\"><mml:mrow><mml:msub><mml:mi>ξ</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq196.gif\"/></alternatives></inline-formula> are the functional principal components (FPCs), also called <italic>eigenfunctions</italic>, that captured the <italic>K</italic> first main hidden modes of variations. The coefficients <inline-formula id=\"IEq197\"><alternatives><tex-math id=\"M399\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$c_{ik}$$\\end{document}</tex-math><mml:math id=\"M400\"><mml:msub><mml:mi>c</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ik</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq197.gif\"/></alternatives></inline-formula> correspond to score projections as in classical PCA but assess the extent to which the shape of each individual behavior <inline-formula id=\"IEq198\"><alternatives><tex-math id=\"M401\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f_i$$\\end{document}</tex-math><mml:math id=\"M402\"><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq198.gif\"/></alternatives></inline-formula> of the dataset matches with the global mean trend <inline-formula id=\"IEq199\"><alternatives><tex-math id=\"M403\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{f_i(t)}$$\\end{document}</tex-math><mml:math id=\"M404\"><mml:mover><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq199.gif\"/></alternatives></inline-formula>. <inline-formula id=\"IEq200\"><alternatives><tex-math id=\"M405\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon _i$$\\end{document}</tex-math><mml:math id=\"M406\"><mml:msub><mml:mi>ϵ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq200.gif\"/></alternatives></inline-formula> is the prediction error between the observations <inline-formula id=\"IEq201\"><alternatives><tex-math id=\"M407\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f_i(t)$$\\end{document}</tex-math><mml:math id=\"M408\"><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq201.gif\"/></alternatives></inline-formula> and their model as a sum of projections on the <italic>K</italic> principal modes.</p><p id=\"Par38\">In this study, for each kinematic or acoustic descriptor, we performed an FPCA on the set of its spline-based time-series <inline-formula id=\"IEq202\"><alternatives><tex-math id=\"M409\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f_i(t),i \\in [1,16]$$\\end{document}</tex-math><mml:math id=\"M410\"><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>16</mml:mn><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq202.gif\"/></alternatives></inline-formula>, without considering, for the moment, a separation between the normal and constrained conditions. The acoustic descriptors were processed by adding a small amount of smoothing to the B-spline model to more easily capture the main variation trends while avoiding distortion of the data. The deviation patterns obtained by FPCA, especially those related to the acoustic descriptors, took into account this compromise between data smoothness and the largest proportion of explained variance. According to the statistics literature<sup><xref ref-type=\"bibr\" rid=\"CR68\">68</xref>,<xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup>, FPCA should be interpreted through graphs that present the ensemble mean curve of the original observations (<inline-formula id=\"IEq203\"><alternatives><tex-math id=\"M411\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{f_i(t)}$$\\end{document}</tex-math><mml:math id=\"M412\"><mml:mover><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq203.gif\"/></alternatives></inline-formula>) and the functions obtained by adding or subtracting a suitable multiple of each FPC (<inline-formula id=\"IEq204\"><alternatives><tex-math id=\"M413\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\xi _k(t)$$\\end{document}</tex-math><mml:math id=\"M414\"><mml:mrow><mml:msub><mml:mi>ξ</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq204.gif\"/></alternatives></inline-formula>) to or from this mean. Generally, this multiple corresponds to the percentage <italic>p</italic> of explained variance, which can be written in this way: <inline-formula id=\"IEq205\"><alternatives><tex-math id=\"M415\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{f_i(t)} \\pm p \\times \\xi _{k}(t)$$\\end{document}</tex-math><mml:math id=\"M416\"><mml:mrow><mml:mover><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mo>±</mml:mo><mml:mi>p</mml:mi><mml:mo>×</mml:mo><mml:msub><mml:mi>ξ</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq205.gif\"/></alternatives></inline-formula>. We followed this methodology in the paper to explain the <inline-formula id=\"IEq206\"><alternatives><tex-math id=\"M417\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$K=2$$\\end{document}</tex-math><mml:math id=\"M418\"><mml:mrow><mml:mi>K</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq206.gif\"/></alternatives></inline-formula> main modes of variation resulting from our analyses through two figures describing their detailed effects on both motion and acoustic sides (cf Figs. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref> and <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). In these graphs, the decision to add or subtract a functional component to the mean curve was made according to the mean sign of the FPC scores of each postural condition.</p></sec><sec id=\"Sec16\"><title>Statistical comparisons of the functional principal components (FPCs)</title><p id=\"Par39\">The functional principal component scores returned by the FPCA could be used to compare the behavior of each kinematic or acoustic variable between the two postural conditions. We carried out these comparisons by means of two-tailed paired Student’s t-tests on the eight normal (N) and constrained (SCH) score samples of each variable. The effects were considered significant for p-values equal to or less than .05, and the proportion of significance was indicated by a number of stars related to p-values: <inline-formula id=\"IEq207\"><alternatives><tex-math id=\"M419\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p<0.05^{*}, \\;p<0.01^{**}, \\;p<0.001^{***}$$\\end{document}</tex-math><mml:math id=\"M420\"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>05</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup><mml:mo>,</mml:mo><mml:mspace width=\"0.277778em\"/><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>01</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup><mml:mo>,</mml:mo><mml:mspace width=\"0.277778em\"/><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:msup><mml:mn>001</mml:mn><mml:mrow><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo><mml:mrow/><mml:mo>∗</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq207.gif\"/></alternatives></inline-formula>. For the first functional behavior (referred to as <italic>major mode</italic>), we retained the FPC scores that significantly and directly separated the postural conditions. For the secondary functional behavior (referred to as <italic>minor mode</italic>), we performed a Varimax rotation of the PCA structure for prior insignificant score discrimination and retained the rotated scores if their t-test comparisons highlighted significant postural differences. Varimax rotation is a procedure of variance distribution and represents a convenient way to focus on the structure of the second variation mode to facilitate interpretation. As a consequence of this process, the <italic>eigenfunctions</italic> capturing the first and second behavioral differences may not be perfectly orthogonal. In practice, however, the functional units related to each of the two motor behaviors enabled clearly distinct interpretations.</p></sec><sec id=\"Sec17\"><title>Functional principal component regressions (FPCR)</title><p id=\"Par40\">When the FPC scores of an analysis variable could be significantly discriminated between the two postural conditions, we needed to conduct further analyses to compute the functional principal components corresponding to each intergroup variation (i.e., normal and constrained). Actually, the <italic>eigenfunctions</italic> returned by FPCA did not integrate the criteria for separating the postural conditions. Such a problem could be resolved by applying an inverse methodology of functional principal component regression (FPCR). This technique also allowed us to rebuild the original set of curves from the scores computed by FPCA and finally assess the fitting accuracy of our model<sup><xref ref-type=\"bibr\" rid=\"CR68\">68</xref>,<xref ref-type=\"bibr\" rid=\"CR74\">74</xref></sup>. Starting from a design matrix <italic>Z</italic> of the significant PC scores, FPCR determines <italic>K</italic> regression functions <inline-formula id=\"IEq208\"><alternatives><tex-math id=\"M421\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta _{k}$$\\end{document}</tex-math><mml:math id=\"M422\"><mml:msub><mml:mi>β</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq208.gif\"/></alternatives></inline-formula> to fit at best the shape of the time series <inline-formula id=\"IEq209\"><alternatives><tex-math id=\"M423\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f_i(t),i \\in [1,16]$$\\end{document}</tex-math><mml:math id=\"M424\"><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>∈</mml:mo><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>16</mml:mn><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq209.gif\"/></alternatives></inline-formula>:<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M425\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} f_i(t)&= Z \\beta (t) + \\epsilon _i = \\beta _0(t) + \\sum _{k=1}^{K} z_{ik} \\beta _{k}(t) + \\epsilon _i \\nonumber \\\\&= \\beta _0(t) + \\sum _{k=1}^{K} z_{ik}^{N} \\beta _{k}^{N}(t) + \\sum _{k=1}^{K} z_{ik}^{SCH} \\beta _{k}^{SCH}(t) + \\epsilon _i \\nonumber \\\\&= \\overline{f_i(t)} + \\sum _{k=1}^{K} c_{ik}^{N} \\xi _{k}^{N}(t) + \\sum _{k=1}^{K} c_{ik}^{SCH} \\xi _{k}^{SCH}(t) + \\epsilon _i \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M426\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mo>=</mml:mo><mml:mi>Z</mml:mi><mml:mi>β</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>ϵ</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:munderover><mml:msub><mml:mi>z</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ik</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mi>β</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>ϵ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:munderover><mml:msubsup><mml:mi>z</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ik</mml:mi></mml:mrow><mml:mi>N</mml:mi></mml:msubsup><mml:msubsup><mml:mi>β</mml:mi><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mi>N</mml:mi></mml:msubsup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:munderover><mml:msubsup><mml:mi>z</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ik</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">SCH</mml:mi></mml:mrow></mml:msubsup><mml:msubsup><mml:mi>β</mml:mi><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">SCH</mml:mi></mml:mrow></mml:msubsup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>ϵ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow/></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mo>=</mml:mo><mml:mover><mml:mrow><mml:msub><mml:mi>f</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mo>+</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:munderover><mml:msubsup><mml:mi>c</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ik</mml:mi></mml:mrow><mml:mi>N</mml:mi></mml:msubsup><mml:msubsup><mml:mi>ξ</mml:mi><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mi>N</mml:mi></mml:msubsup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:munderover><mml:msubsup><mml:mi>c</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ik</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">SCH</mml:mi></mml:mrow></mml:msubsup><mml:msubsup><mml:mi>ξ</mml:mi><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">SCH</mml:mi></mml:mrow></mml:msubsup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>ϵ</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70705_Article_Equ4.gif\" position=\"anchor\"/></alternatives></disp-formula>where the function <inline-formula id=\"IEq210\"><alternatives><tex-math id=\"M427\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta _0$$\\end{document}</tex-math><mml:math id=\"M428\"><mml:msub><mml:mi>β</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq210.gif\"/></alternatives></inline-formula> corresponds to the mean curve of the time series, and <inline-formula id=\"IEq211\"><alternatives><tex-math id=\"M429\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta _{k},k \\in [1,K]$$\\end{document}</tex-math><mml:math id=\"M430\"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>∈</mml:mo><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>K</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq211.gif\"/></alternatives></inline-formula> stands for unbundled <italic>eigenfunctions</italic> (<inline-formula id=\"IEq212\"><alternatives><tex-math id=\"M431\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\xi _{k}^{N}$$\\end{document}</tex-math><mml:math id=\"M432\"><mml:msubsup><mml:mi>ξ</mml:mi><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mi>N</mml:mi></mml:msubsup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq212.gif\"/></alternatives></inline-formula> and <inline-formula id=\"IEq213\"><alternatives><tex-math id=\"M433\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\xi _{k}^{SCH}$$\\end{document}</tex-math><mml:math id=\"M434\"><mml:msubsup><mml:mi>ξ</mml:mi><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">SCH</mml:mi></mml:mrow></mml:msubsup></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq213.gif\"/></alternatives></inline-formula>), which could not be differentiated in the FPCA context (cf Eq. <xref rid=\"Equ3\" ref-type=\"\">3</xref>). The score matrix <italic>Z</italic> enabled such separation between the two postural conditions [N/SCH] by dividing each group of eight <inline-formula id=\"IEq214\"><alternatives><tex-math id=\"M435\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$z_{ik}$$\\end{document}</tex-math><mml:math id=\"M436\"><mml:msub><mml:mi>z</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ik</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq214.gif\"/></alternatives></inline-formula> scores into <inline-formula id=\"IEq215\"><alternatives><tex-math id=\"M437\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$K=2$$\\end{document}</tex-math><mml:math id=\"M438\"><mml:mrow><mml:mi>K</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href=\"41598_2020_70705_Article_IEq215.gif\"/></alternatives></inline-formula> columns.</p></sec><sec id=\"Sec18\"><title>Multiple regressions and correlations of FPC scores</title><p id=\"Par41\">Standard statistical techniques were used to highlight the main functional units shared by the cellists between the normal and constrained conditions. We determined how their motor coordination influenced the variations in bow velocity by carrying out two multivariate linear regressions, one for each functional principal component. In this design, the significant FPC scores of anatomic angles were considered predictors of the bow velocity FPC scores. This approach resulted in two models of functional motor units, which are presented in the bottom right part of Figs. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>e and <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>e. Each model is also characterized by a linear relationship (cf Eqs. <xref rid=\"Equ1\" ref-type=\"\">1</xref> and <xref rid=\"Equ2\" ref-type=\"\">2</xref>) between the most significant anatomic variables involved in the coordination chain.</p><p id=\"Par42\">Two kinds of correlation analyses were finally performed in both motion and acoustic domains. First, we extracted the important joint coupling chains of motor coordination by means of crossed correlations between the significant anatomic FPC scores. Second, we assessed functional sound-gesture interactions by computing standard Pearson correlations between the FPC scores of bow velocity and those of each acoustic descriptor. The most relevant correlations of these analyses provided a better understanding of how cellists’ motor programs influence their functional sound features in subtle ways.</p></sec></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec21\"><p>\n<supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41598_2020_70705_MOESM1_ESM.pdf\"><caption><p>Supplementary Information.</p></caption></media></supplementary-material></p></sec></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p>is available for this paper at 10.1038/s41598-020-70705-8.</p></sec><ack><title>Acknowledgements</title><p>We acknowledge the ISM (<italic>Institut des Sciences du Mouvement</italic>) of Marseille for providing the technological environment of the motion capture system and support related to the computations of anatomical Cardan/Euler angles. This work is part of the “SoniMove” project (ANR-14-CE24-0018).</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>All authors participated to the design of the experiment and reviewed the manuscript. J.R. and S.Y. conducted the experiment. With the help of ISM, J.R. conducted the functional motion analyses. M.A. contributed to statistical analyses and R.K.M. contributed to acoustical analyses.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par45\">The authors declare no competing interests.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Maes</surname><given-names>P-J</given-names></name><name><surname>Wanderley</surname><given-names>MM</given-names></name><name><surname>Palmer</surname><given-names>C</given-names></name></person-group><article-title>The role of working memory in the temporal control of discrete and continuous movements</article-title><source>Exp. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Disaster Med Public Health Prep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Disaster Med Public Health Prep</journal-id><journal-id journal-id-type=\"publisher-id\">DMP</journal-id><journal-title-group><journal-title>Disaster Medicine and Public Health Preparedness</journal-title></journal-title-group><issn pub-type=\"ppub\">1935-7893</issn><issn pub-type=\"epub\">1938-744X</issn><publisher><publisher-name>Cambridge University Press</publisher-name><publisher-loc>New York, USA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32580814</article-id><article-id pub-id-type=\"pmc\">PMC7431866</article-id><article-id pub-id-type=\"pii\">S1935789320002207</article-id><article-id pub-id-type=\"doi\">10.1017/dmp.2020.220</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Original Research</subject></subj-group></article-categories><title-group><article-title>Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data: The Case for COVID-19</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0003-4496-5149</contrib-id><name><surname>Senel</surname><given-names>Kerem</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"a1\"/><xref ref-type=\"corresp\" rid=\"cor1\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ozdinc</surname><given-names>Mesut</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"a2\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ozturkcan</surname><given-names>Selcen</given-names></name><degrees>PhD</degrees><xref ref-type=\"aff\" rid=\"a3\"/></contrib></contrib-group><aff id=\"a1\">Faculty of Health Sciences, <institution>Istanbul University - Cerrahpasa</institution>, <city>Istanbul</city>, <country>Turkey</country></aff><aff id=\"a2\">School of Economics and Business, <institution>Åbo Akademi University</institution>, <city>Turku</city>, <country>Finland</country>; Department of Statistics, <institution>Mimar Sinan FA University</institution>, <city>Istanbul</city>, <country>Turkey</country></aff><aff id=\"a3\">School of Business and Economics, <institution>Linnaeus University</institution>, <city>Kalmar</city>, <country>Sweden</country>; Sabanci Business School, <institution>Sabanci University</institution>, <institution>Istanbul</institution>, <country>Turkey</country></aff><author-notes><corresp id=\"cor1\">Correspondence and reprint requests to Kerem Senel, Faculty of Health Sciences, <institution>Istanbul University - Cerrahpasa</institution>, <city>Istanbul</city>, <country>Turkey</country> (e-mail: <email>keremsenel@istanbul.edu.tr</email>).</corresp></author-notes><pub-date publication-format=\"electronic\" date-type=\"pub\"><day>25</day><month>6</month><year>2020</year></pub-date><fpage>1</fpage><lpage>15</lpage><history><date date-type=\"received\"><day>24</day><month>5</month><year>2020</year></date><date date-type=\"rev-recd\"><day>07</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>11</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>© Society for Disaster Medicine and Public Health, Inc. 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Society for Disaster Medicine and Public Health, Inc.</copyright-holder><license license-type=\"open-access\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (<uri xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</uri>), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"S1935789320002207a.pdf\"/><abstract abstract-type=\"normal\"><sec id=\"as1\"><title>Objective:</title><p>The susceptible-infected-removed (SIR) model and its variants are widely used to predict the progress of coronavirus disease 2019 (COVID-19) worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and possibly noisy data in the initial phase of the pandemic.</p></sec><sec id=\"as2\"><title>Methods:</title><p>The K-means algorithm is used to perform a cluster analysis of the top 10 countries with the highest number of COVID-19 cases, to observe if there are any significant differences among countries in terms of robustness.</p></sec><sec id=\"as3\"><title>Results:</title><p>As a result of model variation tests, the robustness of parameter estimates is found to be particularly problematic in developing countries. The incompatibility of parameter estimates with the observed characteristics of COVID-19 is another potential problem. Hence, a series of research questions are visited.</p></sec><sec id=\"as4\"><title>Conclusions:</title><p>We propose a Single Parameter Estimation (SPE) approach to circumvent these potential problems if the basic SIR is the model of choice, and we check the robustness of this new approach by model variation and structured permutation tests. Dissemination of quality predictions is critical for policy- and decision-makers in shedding light on the next phases of the pandemic.</p></sec></abstract><kwd-group><title>Key Words:</title><kwd>coronavirus</kwd><kwd>COVID-19</kwd><kwd>epidemic models</kwd><kwd>robust estimation</kwd><kwd>SIR</kwd></kwd-group><counts><fig-count count=\"10\"/><table-count count=\"6\"/><equation-count count=\"3\"/><ref-count count=\"45\"/><page-count count=\"15\"/></counts></article-meta></front><body><p>Coronavirus disease 2019 (COVID-19) is recognized as the worst pandemic in modern times in terms of both mortality and infectiousness since the flu pandemic of the early 20th century, ie, the so-called Spanish flu. The first case being reported in the Republic of China on December 8, 2019,<sup><xref rid=\"r1\" ref-type=\"bibr\">1</xref></sup> COVID-19 spread quickly into other countries and continents, which led to its classification as “pandemic” by the World Health Organization (WHO) on March 11, 2020.<sup><xref rid=\"r2\" ref-type=\"bibr\">2</xref></sup>\n</p><p>The susceptible-infected-removed (SIR) model is widely used to predict the progress of COVID-19 in many countries,<sup><xref rid=\"r3\" ref-type=\"bibr\">3</xref>-<xref rid=\"r10\" ref-type=\"bibr\">10</xref></sup> despite its rather simplistic nature, such as its underlying assumptions regarding the homogeneity of the population. It is a basic deterministic compartmental model that simplifies the mathematical modeling of infectious diseases. Its origins date to the seminal work by Kermack and McKendrick in the early 20th century.<sup><xref rid=\"r11\" ref-type=\"bibr\">11</xref></sup> The model involves many variants, such as the SIRD model,<sup><xref rid=\"r12\" ref-type=\"bibr\">12</xref></sup> the MSIR model,<sup><xref rid=\"r13\" ref-type=\"bibr\">13</xref></sup> the SEIR model,<sup><xref rid=\"r14\" ref-type=\"bibr\">14</xref></sup> the MSEIR model,<sup><xref rid=\"r15\" ref-type=\"bibr\">15</xref></sup> and the SIR-A model.<sup><xref rid=\"r16\" ref-type=\"bibr\">16</xref></sup>\n</p><p>Although deterministic models such as the SIR are simpler than stochastic or agent-based simulation models, a deterministic model may be preferred in the case of COVID-19. This is especially the case for developing and underdeveloped countries where quality and detailed data required by more sophisticated models may be hard or even impossible to collect. Stochastic models are better suited for smaller populations, whereas agent-based simulation models require numerous parameters to be estimated, and they are also more challenging to interpret and perform sensitivity analysis on.<sup><xref rid=\"r17\" ref-type=\"bibr\">17</xref></sup>\n</p><p>On the other hand, the robust estimation of even the most basic SIR model parameters is a significant challenge, especially with limited and potentially noisy data in the initial phases of the pandemic.<sup><xref rid=\"r18\" ref-type=\"bibr\">18</xref></sup> Another problem with parameter estimation is observed on the discrepancy between parameter estimates and actual disease characteristics. These potential problems shadow the reliability of model outputs, which are most needed by decision- and policy-makers in forecasting the progress of the pandemic and taking the necessary measures accordingly.</p><p>Our study addresses 4 research questions regarding the basic SIR model: (1) Is it possible to estimate the model parameters simultaneously in a robust manner? (2) What is the impact of time on the degree of robustness? (3) Are there any significant differences between countries in terms of robustness? (4) Is it possible to obtain model parameters that are compatible with actual disease characteristics when model parameters are estimated simultaneously?</p><p>Accordingly, we have 4 testable hypotheses corresponding to these research questions: <italic>Hypothesis 1:</italic> Robust estimation of model parameters is not possible if the model parameters are estimated simultaneously. <italic>Hypothesis 2:</italic> Robustness improves with more data as time progresses. <italic>Hypothesis 3:</italic> Robustness is relatively more problematic for developing countries compared with developed countries. <italic>Hypothesis 4:</italic> Simultaneous estimation of model parameters leads to parameter estimates that are not compatible with actual disease characteristics.</p><p>This study has 2 primary objectives. We first focus on the problems in the estimation of the basic SIR model parameters and their real-life implications observed throughout the development of COVID-19. Second, we propose a Single Parameter Estimation (SPE) approach that enables us to obtain robust parameter estimates. This approach also helps to bridge the gap between parameter estimates and actual disease characteristics.</p><p>It is also imperative to point out that it is more appropriate to use more sophisticated models than the basic SIR model whenever the available data permits. Our proposed approach is not a panacea or a general modeling method for modeling COVID-19 or any other pandemic. It is just a convenient way of obtaining robust parameter estimates if the basic SIR is the model of choice.</p><sec sec-type=\"other\" id=\"s1\"><title>THE SIR MODEL</title><p>The SIR model assumes 3 homogeneous compartments that comprise the population. Hence, it may not be appropriate to use this model if the population under consideration is remarkably heterogeneous. A prime example of such heterogeneity is in the United States of America. There is a stark difference between New York and the rest of the country in terms of the impact of COVID-19. As of May 30, 2020, 11.5% of all confirmed cases in the United States are in New York City,<sup><xref rid=\"r19\" ref-type=\"bibr\">19</xref></sup> which represents a mere 2.6% of the total population.<sup><xref rid=\"r20\" ref-type=\"bibr\">20</xref></sup> This difference is mainly due to population density, which affects the transmission dynamics of the disease.</p><p>S, I, and R stand for the number of susceptible, infected, and removed individuals, respectively. Removed individuals are those who either recovered or lost their lives so that they can no longer spread the disease. The SIR model is represented by 3 differential equations (<xref ref-type=\"disp-formula\" rid=\"disp1\">1</xref>, <xref ref-type=\"disp-formula\" rid=\"disp2\">2</xref>, and <xref ref-type=\"disp-formula\" rid=\"disp3\">3</xref>) Sthat define the change in these variables with respect to time.<disp-formula id=\"disp1\"><label>(1)</label><graphic xlink:href=\"S1935789320002207_eqn1.jpg\" mime-subtype=\"png\" mimetype=\"image\" position=\"float\" orientation=\"portrait\"/></disp-formula>\n<disp-formula id=\"disp2\"><label>(2)</label><graphic xlink:href=\"S1935789320002207_eqn2.jpg\" mime-subtype=\"png\" mimetype=\"image\" position=\"float\" orientation=\"portrait\"/></disp-formula>\n<disp-formula id=\"disp3\"><label>(3)</label><graphic xlink:href=\"S1935789320002207_eqn3.jpg\" mime-subtype=\"png\" mimetype=\"image\" position=\"float\" orientation=\"portrait\"/></disp-formula>\n</p><p>In Equations (<xref ref-type=\"disp-formula\" rid=\"disp1\">1</xref>) to (<xref ref-type=\"disp-formula\" rid=\"disp3\">3</xref>), <italic>N</italic> is the population, whereas <italic>β</italic> and <italic>γ</italic> are the infection and recovery rates, respectively. In most studies, <italic>N</italic> is assumed to be constant, which is also a reasonable assumption for the case of COVID-19. Hence, <italic>β</italic> and <italic>γ</italic> are the parameters to be estimated.</p></sec><sec sec-type=\"other\" id=\"s2\"><title>ROBUSTNESS OF PARAMETER ESTIMATES</title><p>Problems arise when these parameters are estimated simultaneously, particularly with limited and potentially noisy data at the initial phase of the pandemic. We first observed these problems with our own code in R when we estimated the model parameters for successive dates.<sup><xref rid=\"r8\" ref-type=\"bibr\">8</xref></sup> The model parameter estimates were not robust from 1 d to the next, and the estimated parameters were not compatible with actual disease dynamics. We observed the same problems in another study that reported the SIR model parameter estimates for successive dates.<sup><xref rid=\"r10\" ref-type=\"bibr\">10</xref></sup> Realizing that these problems arise from the lack of sufficient number of data points, we adopted an approach to take <italic>γ</italic> from the literature and estimate <italic>β</italic> only.<sup><xref rid=\"r8\" ref-type=\"bibr\">8</xref></sup>\n</p><p>For this study, we decided to use the code authored by Batista in MATLAB<sup><xref rid=\"r10\" ref-type=\"bibr\">10</xref></sup> instead of our own code in R.<sup><xref rid=\"r8\" ref-type=\"bibr\">8</xref></sup> The reason behind this choice is 2-fold. First, the code written by Batista is open to the public, and it has been downloaded 1123 times, with an average 5-star rating of a total of 43 ratings as of May 31, 2020.<sup><xref rid=\"r21\" ref-type=\"bibr\">21</xref></sup> Therefore, the code is subject to public and expert scrutiny and more reliable from the viewpoint of an outsider compared with our own code in R. Second, the code was used in a very popular study by the Singapore University of Technology and Design (SUTD) that tried to estimate the ending dates of the COVID-19 for different countries.<sup><xref rid=\"r22\" ref-type=\"bibr\">22</xref></sup> The predictions of this study proved to be inaccurate, and we think that this is closely related to the problems associated with the estimation of SIR model parameters. Using the same code by Batista may provide further insight into why these predictions have gone awry. Other than these motivations, there is nothing special behind our choice of code. There is also nothing faulty about the code authored by Batista apart from the universal problems of estimation, which mainly stem from the lack of sufficient and quality data.</p><p>Batista authored a function in MATLAB, “fitVirusCV19”, to implement the SIR model,<sup><xref rid=\"r10\" ref-type=\"bibr\">10</xref></sup> for which we selected the top 10 countries with the highest number of COVID-19 cases as of May 20, 2020<sup><xref rid=\"r23\" ref-type=\"bibr\">23</xref></sup> to apply the SIR model by means of fitVirusCV19. As a model variation test, the estimates of <italic>β</italic> and <italic>γ</italic> and the absolute value of the percent daily changes in parameter estimates are presented in <xref rid=\"tbl1\" ref-type=\"table\">Table 1</xref> for April 21 and 22, 2020.</p><p>\n<table-wrap id=\"tbl1\" orientation=\"portrait\" position=\"float\"><label>TABLE 1</label><caption><p><italic>β</italic> and <italic>γ</italic> Estimates With % Daily Change Between April 21 and 22, 2020</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th colspan=\"1\" rowspan=\"1\">Country</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 04/22/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δβ</italic>)</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 04/22/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δγ</italic>)</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Brazil</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.927</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.797</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">14.0%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.793</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.663</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">16.4%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">France</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.327</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.320</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.1%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.163</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.157</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">3.7%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Germany</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.336</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.330</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.8%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.160</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.156</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.5%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Iran</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.036</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.528</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">25.0%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.930</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.422</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">26.3%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Italy</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.294</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.297</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.0%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.157</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.163</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">3.8%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Russia</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.742</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.433</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">41.6%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.579</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.268</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">53.7%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Spain</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.339</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.332</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.1%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.161</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.157</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.5%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Turkey</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.331</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.907</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">174.0%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.743</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">312.8%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United Kingdom</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.349</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.347</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.6%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.191</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.191</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United States</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.360</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.350</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.8%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.188</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.183</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.7%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Mean</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.604</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.564</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">26.5%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.450</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.410</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">42.4%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Median</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.344</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.349</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.5%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.184</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.187</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">3.8%</td></tr></tbody></table><graphic xlink:href=\"S1935789320002207_tab1\"/></alternatives></table-wrap>\n</p><p>The results support <italic>Hypotheses 1</italic> and <italic>3</italic>. Parameter estimates change significantly from 1 d to the next, and the daily changes are particularly pronounced for developing countries.</p><p>The countries can be broadly categorized into 3 groups in terms of the robustness of parameter estimates. For France, Germany, Italy, Spain, the United Kingdom, and the United States, the absolute value of the percent daily change in parameter estimates ranges between 0.6% and 2.8% for <italic>β</italic> and 0.0% and 3.8% for <italic>γ</italic>. For Brazil, Iran, and Russia, the absolute value of the percent daily change in parameter estimates ranges between 14.0% and 41.6% for <italic>β</italic> and 16.4% and 53.7% for <italic>γ</italic>. Turkey stands out as an outlier with very high percent daily changes in both parameter estimates.</p><p>\n<xref ref-type=\"fig\" rid=\"f1\">Figure 1</xref> shows a graphical representation of the distance matrix of countries calculated from abs(%<italic>Δβ</italic>) and abs(%<italic>Δγ</italic>) for April 21 and 22, 2020. If the color of a box is green (smaller distance), it means that the corresponding 2 countries are similar in terms of robustness. A red box, on the other hand, is an indication of greater distance and dissimilarity.</p><p>\n<fig id=\"f1\" orientation=\"portrait\" position=\"float\"><label>FIGURE 1</label><caption><p>Distance Matrix Calculated From abs(%<italic>Δβ</italic>) and abs(%<italic>Δγ</italic>) for April 21 and 22, 2020.</p></caption><graphic xlink:href=\"S1935789320002207_fig1\"/></fig>\n</p><p>To perform a formal cluster analysis, we used the k-means algorithm. K-means is one of the most popular unsupervised machine learning algorithms to group similar data points into clusters and discover underlying patterns.<sup><xref rid=\"r24\" ref-type=\"bibr\">24</xref></sup> The algorithm identifies k number of centroids, ie, the imaginary or real locations representing the centers of the clusters, and then allocates every data point to the nearest cluster. The most common distance metric is the usual Euclidean distance, but it is possible to use other metrics, such as the Manhattan distance, Chebyshev distance, or the Minkowski distance.</p><p>To determine the optimal number of clusters, there are various methods, such as the elbow method and the average silhouette method. We prefer to use the average silhouette method, because it provides an objective estimate for the optimal number of clusters. <xref ref-type=\"fig\" rid=\"f2\">Figure 2</xref> shows the results of the average silhouette method for k-means clustering of the countries in terms of abs(%<italic>Δβ</italic>) and abs(%<italic>Δγ</italic>) for April 21 and 22, 2020.</p><p>\n<fig id=\"f2\" orientation=\"portrait\" position=\"float\"><label>FIGURE 2</label><caption><p>Average Silhouette Width for April 21 and 22, 2020.</p></caption><graphic xlink:href=\"S1935789320002207_fig2\"/></fig>\n</p><p>The results show that 2 clusters maximize the average silhouette width, whereas using 3 clusters is the second optimal choice. Using 2 clusters seems to be a trivial option considering that Turkey stands out as a significant outlier, and the k-means algorithm will be forced to include Turkey in 1 cluster and all the other 9 countries in the other cluster. Therefore, we decided to use 3 clusters, which is also in line with our initial rough guess.</p><p>\n<xref ref-type=\"fig\" rid=\"f3\">Figure 3</xref> shows the results of our cluster analysis. We used 2 graphs, 1 with only country names and 1 with only data points, to provide a better visual representation.</p><p>\n<fig id=\"f3\" orientation=\"portrait\" position=\"float\"><label>FIGURE 3</label><caption><p>K-Means Cluster Analysis for April 21 and 22, 2020.</p></caption><graphic xlink:href=\"S1935789320002207_fig3\"/></fig>\n</p><p>The only difference between these results and our initial guess concerns Brazil. It turns out that Brazil is clustered with 6 developed countries, ie, France, Germany, Italy, Spain, the United Kingdom, and the United States. Yet, after carefully examining the second graph in <xref ref-type=\"fig\" rid=\"f3\">Figure 3</xref>, it is evident that these developed countries stand closely grouped. In contrast, Brazil stands close to the border with the cluster of Iran and Russia.</p><p>These results clearly showed that obtaining robust parameter estimates is a bigger challenge in developing countries compared with developed countries. The higher gap between daily forecasts in developing countries can be attributed to potentially noisier data.</p><p>To explore the impact of time and more data on robustness, the model variation test is replicated with the same countries for May 19 and 20, 2020, and the results are presented in <xref rid=\"tbl2\" ref-type=\"table\">Table 2</xref>.</p><p>\n<table-wrap id=\"tbl2\" orientation=\"portrait\" position=\"float\"><label>TABLE 2</label><caption><p><italic>β</italic> and <italic>γ</italic> Estimates With % Daily Change Between May 19 and 20, 2020</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th colspan=\"1\" rowspan=\"1\">Country</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 05/19/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 05/20/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δβ</italic>)</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 05/19/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 05/20/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δγ</italic>)</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Brazil</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.499</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.122</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">75.6%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.420</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.054</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">87.1%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">France</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.237</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.235</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.8%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.097</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.096</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Germany</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.244</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.242</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.8%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.099</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.098</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Iran</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.181</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.176</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.8%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.094</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.092</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">2.1%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Italy</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.181</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.6%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.081</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.081</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Russia</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.438</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.419</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">4.3%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.325</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.307</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.5%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Spain</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.255</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.251</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.6%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.105</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.099</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.7%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Turkey</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.217</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.215</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.9%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.092</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.092</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United Kingdom</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.210</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.208</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.0%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.108</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.108</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United States</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.203</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.5%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.102</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.101</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Mean</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.267</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.225</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">9.0%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.152</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.113</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.4%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Median</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.227</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.212</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.2%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.101</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.097</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.0%</td></tr></tbody></table><graphic xlink:href=\"S1935789320002207_tab2\"/></alternatives></table-wrap>\n</p><p>The results support <italic>Hypothesis 2</italic>. The parameter estimates become more robust as time progresses, particularly for developing countries. The apparent divergence between developing and developed countries in terms of robustness seems to have vanished with more data, except for Brazil. For countries other than Brazil, the absolute value of the percent daily change in parameter estimates ranges between 0.6% and 4.3% for <italic>β</italic> and 0.0% and 5.7% for <italic>γ</italic>. This time, Brazil stands out as an outlier with very high percent daily changes in both parameter estimates.</p><p>\n<xref ref-type=\"fig\" rid=\"f4\">Figure 4</xref> shows a graphical representation of the distance matrix of countries calculated from abs(%<italic>Δβ</italic>) and abs(%<italic>Δγ</italic>) for May 19 and 20, 2020.</p><p>\n<fig id=\"f4\" orientation=\"portrait\" position=\"float\"><label>FIGURE 4</label><caption><p>Distance Matrix Calculated From abs(%<italic>Δβ</italic>) and abs(%<italic>Δγ</italic>) for May 19 and 20, 2020.</p></caption><graphic xlink:href=\"S1935789320002207_fig4\"/></fig>\n</p><p>Again, we used the k-means algorithm to perform a formal cluster analysis. <xref ref-type=\"fig\" rid=\"f5\">Figure 5</xref> shows the results of the average silhouette method for determining the optimal number of clusters.</p><p>\n<fig id=\"f5\" orientation=\"portrait\" position=\"float\"><label>FIGURE 5</label><caption><p>Average Silhouette Width for May 19 and 20, 2020.</p></caption><graphic xlink:href=\"S1935789320002207_fig5\"/></fig>\n</p><p>Similar to our previous analysis for April 21 and 22, 2020, using 2 clusters seems to be the optimal choice, whereas the use of 3 clusters was the second-best option. However, this time, using 2 clusters can indeed be reasonable considering our observation that the results for all countries other than Brazil converge.</p><p>\n<xref ref-type=\"fig\" rid=\"f6\">Figure 6</xref> shows the results of our cluster analysis. As before, we used 2 graphs, 1 with only country names and 1 with only data points, to provide a better visual representation.</p><p>\n<fig id=\"f6\" orientation=\"portrait\" position=\"float\"><label>FIGURE 6</label><caption><p>K-Means Cluster Analysis for May 19 and 20, 2020.</p></caption><graphic xlink:href=\"S1935789320002207_fig6\"/></fig>\n</p><p>An examination of the second graph provides a visual proof that using 2 clusters was indeed the optimal choice. Because the marginal impact of each new data point on parameter estimates becomes smaller as time passes, the results were in line with our expectations. It is essential to point out that the impact of time on robustness was more significant for developing countries.</p></sec><sec sec-type=\"other\" id=\"s3\"><title>INCOMPATIBILITY OF PARAMETER ESTIMATES WITH OBSERVED CHARACTERISTICS OF COVID-19</title><p>The recovery rate, <italic>γ</italic>, can be estimated as the reciprocal of the average number of days for the transition from I to R. For instance, a <italic>γ</italic> of 0.2 corresponds to 5 d for the infectious period. To this date, there is still no consensus in the medical community on the length of the contagious period for COVID-19.<sup><xref rid=\"r25\" ref-type=\"bibr\">25</xref>,<xref rid=\"r26\" ref-type=\"bibr\">26</xref></sup>\n</p><p>In this study, the median gamma estimate for COVID-19 was 0.187 on April 22, 2020, and 0.097 on May 20, 2020. These figures correspond to 5.3 d and 10.3 d for the infectious period, respectively. A recent study used 5 d for the infectious period of COVID-19.<sup><xref rid=\"r27\" ref-type=\"bibr\">27</xref></sup> Another study argued that the infectious period seems longer for COVID-19 based on the few available clinical virological studies, perhaps lasting for 10 d or more after the incubation period.<sup><xref rid=\"r25\" ref-type=\"bibr\">25</xref></sup> Hence, the median <italic>γ</italic> estimates can be deemed to be plausible.</p><p>On the other hand, <italic>γ</italic> estimates for Brazil, Turkey, and Iran on April 22, 2020, were 0.663, 0.743, and 1.422, respectively. These estimates correspond to a range of 0.7 to 1.5 d for the infectious period. Although the contagious period for COVID-19 is still deemed uncertain, this parameter range was unrealistic. These findings support <italic>Hypothesis 4</italic>. The model parameter estimates for some countries were not compatible with the actual disease dynamics. Hence, the models obtained at the end of this estimation procedure were unreliable.</p><p>Even with more data on May 20, 2020, the <italic>γ</italic> estimates for Brazil and Russia significantly diverged from the <italic>γ</italic> projections for other countries, which converge to a range of 0.08 to 0.11.</p><p>As a salient example, the SUTD did some research for the timing of the end of COVID-19 in different countries,<sup><xref rid=\"r22\" ref-type=\"bibr\">22</xref></sup> using the same code from Batista,<sup><xref rid=\"r10\" ref-type=\"bibr\">10</xref></sup> ie, the fitVirusCV19 function in MATLAB. The study achieved wide-spread instant popularity through news outlets all around the world, probably due to its optimistic predictions regarding the timing of the end of COVID-19.</p><p>For instance, for Turkey, the study predicted the date to reach 97% of the total expected cases as of May 16, 2020.<sup><xref rid=\"r28\" ref-type=\"bibr\">28</xref></sup> Despite the favorable impact of preventive measures, the daily number of new cases in Turkey was still around 1000 (972 on May 20, 2020), while the pandemic was far from over. Considering the problems in parameter estimation, as mentioned earlier, particularly for developing countries such as Turkey, it was not surprising that the predictions turned out to be inaccurate and potentially misleading, both for the public and, more importantly, for policy- and decision-makers.</p><p>Furthermore, as Faranda et al. indicated, early estimates of COVID-19 show enormous fluctuations, despite the importance of having robust estimates of the time-asymptotic total number of infections.<sup><xref rid=\"r18\" ref-type=\"bibr\">18</xref></sup> They showed that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached.</p><p>SUTD, now, acknowledged that “model and data are inaccurate to the complex, evolving, and heterogeneous realities of different countries over time, and earlier predictions are no longer valid because the real-world scenarios have changed rapidly.” Thus, they removed the predictions from their website. They indicated that “the project is internalized,” and they referred visitors to other live public COVID-19 forecasting efforts around the world.<sup><xref rid=\"r29\" ref-type=\"bibr\">29</xref></sup>\n</p></sec><sec sec-type=\"other\" id=\"s4\"><title>ROBUST ESTIMATION OF SIR MODEL</title><p>The curse of dimensionality states that the number of data points needed to estimate an arbitrary function with a given level of accuracy grows exponentially with the number of input variables (ie, dimensionality) of the function.<sup><xref rid=\"r30\" ref-type=\"bibr\">30</xref></sup>\n</p><p>For instance, an n-th order polynomial will achieve a perfect fit for n+1 data points. However, such a model will seriously lack the ability to generalize, and it will not be able to generate accurate predictions. Instead, a simple linear regression will be much superior in terms of predictive performance and the ability to generalize over unseen data.</p><p>The presence of noise exacerbates the problem, and the real-world data are inherently noisy. The data for COVID-19 are imperfect and incomplete. This finding is even more so for developing and underdeveloped countries. Most developing countries suffer from an acute lack of COVID-19 testing capacity, and they either collect low-quality data or do not record deaths at all.<sup><xref rid=\"r31\" ref-type=\"bibr\">31</xref></sup>\n</p><p>\n<xref ref-type=\"fig\" rid=\"f7\">Figure 7</xref> depicts the number of tests per 100,000 for the top 25 most populous countries as of May 30, 2020.<sup><xref rid=\"r23\" ref-type=\"bibr\">23</xref></sup>\n</p><p>\n<fig id=\"f7\" orientation=\"portrait\" position=\"float\"><label>FIGURE 7</label><caption><p>Tests per 100,000 for the Top 25 Most Populous Countries as of May 30, 2020.</p></caption><graphic xlink:href=\"S1935789320002207_fig7\"/></fig>\n</p><p>As can be seen from the figure, the number of tests per 100,000 for Ethiopia, Egypt, Indonesia, Nigeria, Mainland China, Democratic Republic of Congo, and United Republic of Tanzania is below 100, which suggests a serious lack of COVID-19 testing capacity for some of the most populous countries in the world.</p><p>In addition, death tolls are sporadically revised in many countries, which casts further doubt on the reported figures.<sup><xref rid=\"r32\" ref-type=\"bibr\">32</xref>-<xref rid=\"r34\" ref-type=\"bibr\">34</xref></sup> This inevitably makes the COVID-19 data highly noisy, especially for developing countries. Even for developed countries, such as the United States and Italy, there is new research that shows that coronavirus deaths could be up to double the official counts.<sup><xref rid=\"r35\" ref-type=\"bibr\">35</xref></sup> More complex models tend to learn the noise as well as signal, which is not intended.</p><p>This phenomenon is closely related to the principle of “Occam’s razor”,<sup><xref rid=\"r36\" ref-type=\"bibr\">36</xref></sup> ie, “<italic>pluralitas non est ponenda sine necessitate”</italic> or “plurality should not be posited without necessity.” In other words, “of two competing theories, the simpler explanation of an entity is to be preferred.”</p><p>Therefore, especially in the initial phase of the pandemic with insufficient data, we propose to estimate only <italic>β</italic> instead of trying to estimate <italic>β</italic> and <italic>γ</italic>, simultaneously. The infection rate, <italic>β</italic>, is dependent on many factors, such as population density,<sup><xref rid=\"r37\" ref-type=\"bibr\">37</xref></sup> demographics,<sup><xref rid=\"r38\" ref-type=\"bibr\">38</xref></sup> and social distancing measures.<sup><xref rid=\"r39\" ref-type=\"bibr\">39</xref></sup> On the other hand, the removal rate, <italic>γ</italic>, is the reciprocal of the infectious period, which is expected to be more stable compared with <italic>β</italic>. Hence, we prefer to take <italic>γ</italic> from the literature and estimate <italic>β</italic> only. As demonstrated below, this effectively overcomes the problem of estimating robust parameters for the basic SIR model, particularly for noisier data from developing countries. It also eliminates the problem of incompatibility between parameter estimates and actual disease characteristics.</p><p>Because the code provided by Batista<sup><xref rid=\"r10\" ref-type=\"bibr\">10</xref></sup> estimates <italic>β</italic> and <italic>γ</italic>, simultaneously, we modified the code to allow for single parameter estimation.</p><p>First, we repeat the model variation test for April 21 and 22, 2020, with <italic>γ</italic> set equal to 0.2 by using the modified code to estimate the remaining parameter, <italic>β</italic>. A <italic>γ</italic> of 0.2 corresponds to 5 d for the infectious period of COVID-19,<sup><xref rid=\"r27\" ref-type=\"bibr\">27</xref></sup> The estimate of <italic>β</italic> and the absolute value of the percent daily changes in parameter estimates are presented in <xref rid=\"tbl3\" ref-type=\"table\">Table 3</xref> for April 21 and 22, 2020.</p><p>\n<table-wrap id=\"tbl3\" orientation=\"portrait\" position=\"float\"><label>TABLE 3</label><caption><p><italic>β</italic> Estimates With % Daily Change for Fixed <italic>γ</italic> Between April 21 and 22, 2020</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th colspan=\"1\" rowspan=\"1\">Country</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 04/22/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δβ</italic>)</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 04/22/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δγ</italic>)</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Brazil</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.337</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.336</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.3%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">France</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.364</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.350</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">3.9%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Germany</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.357</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.355</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.4%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Iran</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.313</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.319</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.8%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Italy</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.316</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.314</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.5%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Russia</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.382</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.378</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.9%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Spain</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.366</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.379</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">3.4%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Turkey</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.374</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.370</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.1%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United Kingdom</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.359</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.357</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.6%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United States</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.370</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.368</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.6%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Mean</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.354</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.353</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.4%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Median</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.362</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.356</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.7%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td></tr></tbody></table><graphic xlink:href=\"S1935789320002207_tab3\"/></alternatives></table-wrap>\n</p><p>Compared with the results in <xref rid=\"tbl1\" ref-type=\"table\">Table 1</xref>, the new results obtained by estimating <italic>β</italic> only were evidently more robust. The absolute value of the percent daily change in <italic>β</italic> estimate ranges between 0.3% and 3.9%, with a mean of 1.4%. On the other hand, the same measure in the previous version, where both parameters were estimated simultaneously, ranged between 0.6% and 174.0%, with a mean of 26.5%.</p><p>Next, we perform a structured permutation test by means of perturbing <italic>γ</italic> by ±10% for April 21, 2020. The results are presented in <xref rid=\"tbl4\" ref-type=\"table\">Tables 4</xref> and <xref rid=\"tbl5\" ref-type=\"table\">5</xref>.</p><p>\n<table-wrap id=\"tbl4\" orientation=\"portrait\" position=\"float\"><label>TABLE 4</label><caption><p><italic>β</italic> Estimates for <italic>γ</italic> = 0.20 and <italic>γ</italic> = 0.22 on April 21, 2020</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th colspan=\"1\" rowspan=\"1\">Country</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δβ</italic>)</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δγ</italic>)</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Brazil</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.337</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.357</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.7%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">France</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.364</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.384</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.5%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Germany</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.357</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.397</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">11.3%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Iran</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.313</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.333</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">6.3%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Italy</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.316</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.335</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">6.2%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Russia</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.382</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.402</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.2%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Spain</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.366</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.402</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">9.8%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Turkey</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.374</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.394</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.3%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United Kingdom</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.359</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.379</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.6%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United States</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.370</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.392</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">6.2%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Mean</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.354</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.377</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">6.7%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Median</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.362</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.388</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.9%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.220</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr></tbody></table><graphic xlink:href=\"S1935789320002207_tab4\"/></alternatives></table-wrap>\n</p><p>\n<table-wrap id=\"tbl5\" orientation=\"portrait\" position=\"float\"><label>TABLE 5</label><caption><p><italic>β</italic> Estimates for <italic>γ</italic> = 0.20 and <italic>γ</italic> = 0.18 on April 21, 2020</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th colspan=\"1\" rowspan=\"1\">Country</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>β</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δβ</italic>)</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\"><italic>γ</italic> 04/21/2020</th><th colspan=\"1\" rowspan=\"1\">abs(%<italic>Δγ</italic>)</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Brazil</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.337</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.318</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.9%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">France</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.364</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.344</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.5%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Germany</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.357</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.357</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.1%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Iran</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.313</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.293</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">6.3%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Italy</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.316</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.316</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.0%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Russia</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.382</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.344</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Spain</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.366</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.362</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.2%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Turkey</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.374</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.355</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.3%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United Kingdom</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.359</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.339</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.6%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">United States</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.370</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.352</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">4.7%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Mean</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.354</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.338</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">4.5%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">Median</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.362</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.344</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">5.4%</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.200</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.180</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">10.0%</td></tr></tbody></table><graphic xlink:href=\"S1935789320002207_tab5\"/></alternatives></table-wrap>\n</p><p>When <italic>γ</italic> increases by 10%, the absolute value of the percent change in <italic>β</italic> estimate ranges between 5.2% and 11.3%, with a mean of 6.7%. On the other hand, when <italic>γ</italic> decreases by 10%, the absolute value of the percent change in <italic>β</italic> estimate ranges between 0.0% and 10.0%, with a mean of 4.5%. Hence, the results of the structured permutation test also validate the robustness of the SPE approach.</p><p>In addition, the incompatibility of parameter estimates with actual disease characteristics is also resolved by this new approach. As <italic>γ</italic> is set equal to a figure taken from the literature, <italic>β</italic> remains as the only potential source of incompatibility. Yet, the resulting <italic>β</italic> estimates range in a relatively tight and plausible interval of 0.313 and 0.382, with a mean of 0.354 for <italic>γ</italic> = 0.2.</p></sec><sec sec-type=\"other\" id=\"s5\"><title>AN ILLUSTRATIVE EXAMPLE FROM NORWAY AND NORWEGIAN COUNTIES</title><p>Norway was one of the countries that implemented tough restrictions to follow the containment strategy toward the COVID-19 pandemic. Following WHO’s declaration of the pandemic, the announced measures involved emergency shutdowns of many public and private institutions, including schools and kindergartens. The country managed to bring down the effective reproduction number, R<italic><sub>e</sub></italic>, to 0.7 by early April.<sup><xref rid=\"r40\" ref-type=\"bibr\">40</xref></sup> It was also among the countries that provided open access data at the county-level.</p><p>We used Norwegian data to test our proposed SPE approach both at the country and county levels. <italic>γ</italic> is set equal to 0.2, corresponding to 5 d for the infectious period, which is taken from a report published by the Norwegian Institute of Public Health.<sup><xref rid=\"r27\" ref-type=\"bibr\">27</xref></sup> We obtained a time-series of the infection rate, <italic>β</italic>, the basic reproduction number, R<sub>0</sub>, and the effective reproduction number, R<italic><sub>e</sub></italic>, for the 11 counties and the whole country. The time series covered a 1-mo period, which was between the day 35 and 64 of the pandemic. <xref ref-type=\"fig\" rid=\"f8\">Figures 8</xref>, <xref ref-type=\"fig\" rid=\"f9\">9</xref>, and <xref ref-type=\"fig\" rid=\"f10\">10</xref> depict these time series, whereas the time series for R<italic><sub>e</sub></italic> is also tabulated in <xref rid=\"tbl6\" ref-type=\"table\">Table 6</xref>.</p><p>\n<fig id=\"f8\" orientation=\"portrait\" position=\"float\"><label>FIGURE 8</label><caption><p><italic>β</italic> (Infection Rate) for Norway and Counties in Norway.</p></caption><graphic xlink:href=\"S1935789320002207_fig8\"/></fig>\n</p><p>\n<fig id=\"f9\" orientation=\"portrait\" position=\"float\"><label>FIGURE 9</label><caption><p><italic>R</italic><sub>0</sub> (Basic Reproduction Number) for Norway and Counties in Norway.</p></caption><graphic xlink:href=\"S1935789320002207_fig9\"/></fig>\n</p><p>\n<fig id=\"f10\" orientation=\"portrait\" position=\"float\"><label>FIGURE 10</label><caption><p><italic>R<sub>e</sub></italic> (Effective Reproduction Number) for Norway and Counties in Norway.</p></caption><graphic xlink:href=\"S1935789320002207_fig10\"/></fig>\n</p><p>\n<table-wrap id=\"tbl6\" orientation=\"portrait\" position=\"float\"><label>TABLE 6</label><caption><p><italic>R<sub>e</sub></italic> (Effective Reproduction Number) for Norway and Counties in Norway</p></caption><alternatives><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/><col span=\"1\"/></colgroup><thead><tr><th colspan=\"1\" rowspan=\"1\">Days</th><th colspan=\"1\" rowspan=\"1\">Agder</th><th colspan=\"1\" rowspan=\"1\">Innlandet</th><th colspan=\"1\" rowspan=\"1\">More_og_Romsdal</th><th colspan=\"1\" rowspan=\"1\">Nordland</th><th colspan=\"1\" rowspan=\"1\">Oslo</th><th colspan=\"1\" rowspan=\"1\">Rogaland</th><th colspan=\"1\" rowspan=\"1\">Troms_og_Finnmark</th><th colspan=\"1\" rowspan=\"1\">Trondelag</th><th colspan=\"1\" rowspan=\"1\">Vestfold_og_Telemark</th><th colspan=\"1\" rowspan=\"1\">Vestland</th><th colspan=\"1\" rowspan=\"1\">Viken</th><th colspan=\"1\" rowspan=\"1\">NorwayAll</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>35</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.92</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.68</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.46</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.78</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.94</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.13</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.53</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.81</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.69</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.72</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.96</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>36</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.93</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.65</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.45</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.67</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.90</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">1.09</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.79</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.67</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.71</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.92</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>37</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.89</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.67</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.87</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.49</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.51</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.77</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.65</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.70</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.89</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>38</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.89</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.61</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.71</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.84</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.48</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.54</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.49</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.74</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.68</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.84</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>39</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.85</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.59</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.40</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.64</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.80</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.45</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.89</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.48</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.72</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.61</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.67</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.82</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>40</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.84</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.69</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.76</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.44</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.40</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.74</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.64</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.80</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>41</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.82</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.38</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.72</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.44</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.38</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.46</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.68</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.77</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>42</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.80</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.54</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.40</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.72</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.71</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.45</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.67</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.65</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.75</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>43</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.80</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.53</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.37</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.69</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.70</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.31</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.44</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.65</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.59</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>44</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.76</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.36</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.64</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.67</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.45</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.30</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.44</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.64</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.59</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.59</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.70</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>45</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.75</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.51</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.39</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.65</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.42</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.26</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.68</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>46</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.78</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.50</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.36</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.66</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.44</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.26</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.70</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>47</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.74</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.58</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.25</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.64</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>48</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.49</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.61</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.24</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.42</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.61</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.61</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.55</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>49</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.49</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.23</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.42</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.71</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.55</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.61</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>50</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.48</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.22</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.42</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.59</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.71</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.54</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>51</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.48</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.59</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.21</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.42</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.59</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.59</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>52</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.48</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.55</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.36</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.42</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.58</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.53</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.58</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>53</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.54</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.58</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.40</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.22</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.58</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.53</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>54</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.53</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.40</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.20</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.53</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>55</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.73</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.21</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>56</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.72</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.21</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>57</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.72</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.21</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.55</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>58</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.71</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.41</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.21</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.49</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.63</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.55</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>59</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.71</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.49</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.51</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.42</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.20</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.55</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>60</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.74</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.51</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.20</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.44</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.61</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.58</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>61</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.70</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.47</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.35</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.51</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.20</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.51</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.56</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.54</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>62</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.70</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.48</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.37</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.50</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.20</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.60</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.54</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>63</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.70</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.48</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.37</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.50</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.57</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.20</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.55</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.62</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.54</td></tr><tr><td rowspan=\"1\" colspan=\"1\" align=\"center\">\n<bold>64</bold>\n</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.70</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.48</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.36</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.50</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.58</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.20</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.43</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.55</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.61</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.52</td><td rowspan=\"1\" colspan=\"1\" align=\"center\">0.54</td></tr></tbody></table><graphic xlink:href=\"S1935789320002207_tab6\"/></alternatives></table-wrap>\n</p><p>An examination of <xref ref-type=\"fig\" rid=\"f8\">Figures 8</xref> and <xref ref-type=\"fig\" rid=\"f9\">9</xref> provides a visual proof that robust estimates of <italic>β</italic> and R<sub>0</sub> are obtained for all the counties in Norway, with the possible exception of Troms og Finnmark. This is probably due to data collection problems in that particular county because the data for all the other counties and the whole country generated robust parameter estimates.</p><p>If R<italic><sub>e</sub></italic> is above 1.0, then the number of infected people grows exponentially. Hence, the threshold level for R<italic><sub>e</sub></italic> that can be deemed safe should be less than or equal to 1.0. Countries, such as Germany and Czechia, have declared this threshold level to be 1.0 to start easing restrictions and preventive measures.<sup><xref rid=\"r41\" ref-type=\"bibr\">41</xref>,<xref rid=\"r42\" ref-type=\"bibr\">42</xref></sup>\n</p><p>Norway, on the other hand, waited until R<italic><sub>e</sub></italic> came down to 0.7, to even consider easing. Bent Hoeie, the Norwegian Minister of Health and Care Services, announced that R<italic><sub>e</sub></italic> was equal to 0.7 as of April 6, 2020.<sup><xref rid=\"r40\" ref-type=\"bibr\">40</xref></sup> This date corresponded to day 46 of the pandemic. An examination of <xref rid=\"tbl6\" ref-type=\"table\">Table 6</xref> shows that our R<italic><sub>e</sub></italic> estimate for day 46 is indeed 0.70, which is congruent with the estimate made by the Norwegian health authorities.</p><p>\n<xref ref-type=\"fig\" rid=\"f10\">Figure 10</xref> and <xref rid=\"tbl6\" ref-type=\"table\">Table 6</xref> show that almost half of the counties in Norway were already in the safe zone in terms of R<italic><sub>e</sub></italic> at the beginning of the 1-mo period, ie, day 35 of the pandemic. Agder, Nordland, Oslo, Troms og Finnmark, Vestfold og Telemark, and Viken had R<italic><sub>e</sub></italic> values higher than 0.7. R<italic><sub>e</sub></italic> values for all these counties quickly came down to 0.7 in a few days with the exception of Agder, which reached the threshold level on day 61 of the pandemic.</p><p>Norway did not start easing the restrictions until April 13, 2020, ie, day 53 of the pandemic.<sup><xref rid=\"r43\" ref-type=\"bibr\">43</xref></sup> The easing has been slow and gradual.</p></sec><sec sec-type=\"other\" id=\"s6\"><title>CONCLUDING REMARKS</title><p>Predicting the progress of COVID-19 is a crucial problem for policy- and decision-makers. However, the models used for this purpose are prone to significant estimation errors. Therefore, the results obtained from these models should be viewed with extreme caution.</p><p>We do not claim that our proposed SPE approach makes the basic SIR model an optimal tool for predicting the progress of COVID-19 or any other pandemic. If data permit, more sophisticated models should be preferred. The SPE can be a useful approach to obtain robust parameter estimates if the basic SIR is the model of choice. In fact, the SPE approach is nothing more than an application of one of the most critical tenets of data science, ie, the predilection for simpler models if data are limited and noisy. Hence, the same principle can be applied to any model with any number of parameters. For instance, if a model requires the estimation of 3 parameters, fixing 1 parameter and estimating the other 2 is going to be more robust than the simultaneous estimation of all 3 parameters.</p><p>From a policy perspective, monitoring the current state of the pandemic is at least as important as predicting its progress. A fundamental policy question persists regarding the timing for easing and eventually lifting limitations such as lockdowns. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"methods-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849212</article-id><article-id pub-id-type=\"pmc\">PMC7431867</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00750</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Study Protocol</subject></subj-group></subj-group></article-categories><title-group><article-title>Clinical and Neurochemical Effects of Transcranial Magnetic Stimulation (TMS) in Multiple Sclerosis: A Study Protocol for a Randomized Clinical Trial</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Agüera</surname><given-names>Eduardo</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>‡</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/895841/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Caballero-Villarraso</surname><given-names>Javier</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"author-notes\" rid=\"fn003\"><sup>‡</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/516098/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Feijóo</surname><given-names>Montserrat</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Escribano</surname><given-names>Begoña M.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Conde</surname><given-names>Cristina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Bahamonde</surname><given-names>María C.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Giraldo</surname><given-names>Ana I.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Paz-Rojas</surname><given-names>Elier</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Túnez</surname><given-names>Isaac</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/195838/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC)</institution>, <addr-line>Córdoba</addr-line>, <country>Spain</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Unidad de Gestión Clínica de Neurología, Hospital Universitario Reina Sofía</institution>, <addr-line>Córdoba</addr-line>, <country>Spain</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Departmento de Bioquímica y Biología Molecular, Facultad de Medicina y Enfermería, Universidad de Córdoba</institution>, <addr-line>Córdoba</addr-line>, <country>Spain</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Unidad de Gestión Clínica de Análisis Clínicos, Hospital Universitario Reina Sofía</institution>, <addr-line>Córdoba</addr-line>, <country>Spain</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Departamento de Biología Celular, Fisiología e Inmunología, Universidad de Córdoba</institution>, <addr-line>Córdoba</addr-line>, <country>Spain</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Canvax Biotech S.L.</institution>, <addr-line>Córdoba</addr-line>, <country>Spain</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Letizia Leocani, San Raffaele Hospital (IRCCS), Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Angel Perez Sempere, Hospital General Universitario de Alicante, Spain; Javier J. Gonzalez-Rosa, University of Cádiz, Spain</p></fn><corresp id=\"c001\">*Correspondence: Isaac Túnez <email>fm2tufii@uco.es</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Multiple Sclerosis and Neuroimmunology, a section of the journal Frontiers in Neurology</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†ORCID: Isaac Túnez <ext-link ext-link-type=\"uri\" xlink:href=\"http://orcid.org/0000-002-4493-708\">orcid.org/0000-002-4493-708</ext-link></p></fn><fn fn-type=\"other\" id=\"fn003\"><p>‡These authors have contributed equally to this work and share first authorship</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>750</elocation-id><history><date date-type=\"received\"><day>29</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>17</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Agüera, Caballero-Villarraso, Feijóo, Escribano, Conde, Bahamonde, Giraldo, Paz-Rojas and Túnez.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Agüera, Caballero-Villarraso, Feijóo, Escribano, Conde, Bahamonde, Giraldo, Paz-Rojas and Túnez</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Background:</bold> Transcranial Magnetic Stimulation (TMS) is a technique based on the principles of electromagnetic induction. It applies pulses of magnetic radiation that penetrate the brain tissue, and it is a non-invasive, painless, and practically innocuous procedure. Previous studies advocate the therapeutic capacity of TMS in several neurodegenerative and psychiatric processes, both in animal models and in human studies. Its uses in Parkinson's disease, Alzheimer's disease and in Huntington's chorea have shown improvement in the symptomatology and in the molecular profile, and even in the cellular density of the brain. Consequently, the extrapolation of these TMS results in the aforementioned neurodegenerative disease to other entities with etiopathogenic and clinical analogy would raise the relevance and feasibility of its use in multiple sclerosis (MS). The overall objective will be to demonstrate the effectiveness of the TMS in terms of safety and clinical improvement, as well as to observe the molecular changes in relation to the treatment.</p><p><bold>Methods and Design:</bold> Phase II clinical trial, unicentric, controlled, randomized, single blind. A total of 90 patients diagnosed with relapsing-remitting multiple sclerosis (RRMS) who meet all the inclusion criteria and do not present any of the exclusion criteria that are established and from which clinically evaluable results can be obtained. The patients included will be assigned under the 1:1:1 randomization formula, constituting three groups for the present study: 30 patients treated with natalizumab + white (placebo) + 30 patients treated with natalizumab + TMS (1 Hz) + 30 patients treated with natalizumab + TMS (5 Hz).</p><p><bold>Discussion:</bold> Results of this study will inform on the efficiency of the TMS for the treatment of MS. The expected results are that TMS is a useful therapeutic resource to improve clinical status (main parameters) and neurochemical profile (surrogate parameters); both types of parameters will be checked.</p><p><bold>Ethics and Dissemination:</bold> The study is approved by the Local Ethics Committee and registered in <ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov\">https://clinicaltrials.gov</ext-link> (NCT04062331). Dissemination will include submission to a peer-reviewed journal, patients, associations of sick people and family members, healthcare magazines and congress presentations.</p><p><bold>Trial Registration:</bold>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://ClinicalTrials.gov\">ClinicalTrials.gov</ext-link> ID: NCT04062331 (registration date: 19<sup>th</sup>/ August/2019).</p><p><bold>Version Identifier:</bold> EMTr-EMRR, ver-3, 21/11/2017.</p></abstract><kwd-group><kwd>clinical trial</kwd><kwd>multiple sclerosis</kwd><kwd>neurodegenerative diseases</kwd><kwd>transcranial magnetic stimulation</kwd><kwd>neuroplasticity</kwd><kwd>neurochemistry</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"52\"/><page-count count=\"10\"/><word-count count=\"8217\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Transcranial magnetic stimulation (TMS) is based on the principles of Maxwell's electromagnetism, by which an electric field is capable of generating a magnetic field perpendicular to it and vice versa. This peculiar therapeutic strategy with more than two decades of application was authorized in 2008 for the treatment of refractory major depression by the Food and Drug Administration (FDA) of the United States and later by the European Medicines Agency (EMEA). At present, its approval for diseases such as schizophrenia (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>), neuropathic pain (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>), effects of ischemic stroke (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>) and amyotrophic lateral sclerosis (ALS) (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>) and multiple sclerosis (MS) (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B6\" ref-type=\"bibr\">6</xref>), among others, is under study.</p><p>In recent years, the evidences have revealed the therapeutic potential of TMS in the treatment of Alzheimer's disease, improving psychomotor skills and the memory of affected patients (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>–<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Similarly, it has been observed that the application of TMS in Parkinson's patients produces a symptomatic improvement at different levels, such as the decrease of resting tremor, spastic rigidity and bradykinesia (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>–<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). Furthermore, some studies have shown a certain degree of improvement when using TMS to reduce neuropathic pain (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>) or the severity of spasticity subsequent to an ischemic stroke (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>–<xref rid=\"B22\" ref-type=\"bibr\">22</xref>).</p><p>Experimental models confirm the usefulness of this therapeutic resource at different levels, showing even what could be the intimate mechanisms (at cellular and molecular level) involved in the therapeutic effect that the induction of the electromagnetic current that TMS produces. As an example of this we can find studies in murine models of Parkinson's disease (induced by 6-hydroxydopamine) and Alzheimer's disease that show a behavioral improvement after the administration of TMS. Such clinical improvement would be related to a decrease in circulating levels of cyclooxygenase-2 and TNF-alpha, as well as an increase in subventricular neurogenesis (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>–<xref rid=\"B26\" ref-type=\"bibr\">26</xref>).</p><p>In this regard, data published by our group show how the application of TMS on an animal model of Huntington's chorea (induced by 3-nitropropionic acid) (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>), show not only an improvement in the clinical aspects, but also a quantitative cellular increase, corroborated by neurohistological studies, as well as a decrease in oxidative and cellular damage (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>–<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). Our group has also recently participated in studies that relate the administration of TMS to an improvement in parameters that characteristically deteriorate with age, such as sleep quality, learning ability and memory; such findings have been observed in an experimental model of an elderly subject developed in rodents of the aged Octodon degus type (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). Finally, in line with the above, we could add the results obtained by our group in an experimental model of Wistar rat major depression (induced by olfactory bulbectomy), in which after treatment with TMS there is an increase in the levels of serotonin and in the cellularity of the brain, which at a symptomatic level correlates with an improvement in the depression and anxiety scales of the animal (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>).</p><p>The aforementioned entities have analogies at the symptomatological and semiological level with other neurodegenerative diseases, such as multiple sclerosis (MS). In some of these entities, it could even be said that they also present some factors common to MS at the etiological or pathogenic level, with molecular or cellular processes of some similarity being involved. Multiple sclerosis (MS) is a demyelinating disease of neuroinflammatory type and autoimmune nature. It is the main cause of non-traumatic invalidating neurological disease in young adults, constituting the first cause of disability acquired between 20 and 40 years of age in Spain. It leads to a significant reduction in the quality of life, a high health cost and a reduction in the productive working life of patients (due to possible absenteeism and reduction of the patient's active professional life). It presents a higher frequency in women than in men (3:2) and according to recent data, it is estimated that in recent years its prevalence has increased to 120 cases per 100,000 inhabitants. It denotes a geographical distribution, concentrating more casuistry in western countries, increasing its frequency as its latitude moves away from the equator. Its clinical manifestations can be very varied, both in its debut and in possible subsequent outbreaks, often manifesting itself with asthenia, muscle weakness, ataxia, spasticity, dysarthria and dysphagia, among other symptoms and signs. Its etiology is still unknown, although several studies have linked the origin of MS with certain infections, vitamin D deficiency, dietary factors and genetic susceptibility, among other possible factors. Similarly, in its pathogenesis the presence of inflammatory processes and oxidative stress has been identified, among other elements. In this regard, recent studies of our group have found an important role of oxidative damage in the origin and evolution of this entity, as well as of inadequate (decreased) levels of melatonin; This is an endogenous neurohormone regulator of sleep (which is usually altered in these patients) and of high antioxidant power. Due to the heterogeneity of forms and manifestations of MS, the treatment must be individualized. However, it should be noted that currently there is no curative treatment for this condition. Corticosteroids are used to treat acute attacks, while maintenance treatment is based mainly on the use of immunomodulators, among which interferons and glatiramer acetate are prevalent. Fifteen years ago, the FDA authorized the use of Natalizumab (a monoclonal antibody), and Alemtuzumab 5 years ago, mainly for forms that occur in the form of recurrent outbreaks that do not respond to other treatments. Other immunomodulatory strategies even try to stop the evolution by promoting remyelination processes; such as in the case of anti-LINGO, which is currently being used in phase II-III clinical trials (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>).</p><p>Consequently, the absence of a definitive or curative treatment for this disease encourages us to seek the identification and validation of alternative therapeutic targets, as well as the evaluation of new treatment options for MS patients. From this perspective, the use of TMS in patients with MS is considered. TMS has proven to be an effective therapeutic resource in certain neurodegenerative and neuropsychiatric pathologies without defined therapeutic options or in such situations in which with available treatments the disease is refractory to treatment, as it can occur in cases of major depression. Along with these considerations, TMS has proven to be a relatively innocuous procedure in terms of adverse effects. To this we should add that it is a non-invasive technique that is easy to apply as well as being safe. The suggested side effects are extremely infrequent, given that only certain cases of headache and (even more rarely) some seizure episodes, are described (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>).</p><p>In the decade of the 90s, Sandyk showed in different studies how electromagnetic stimulation manages to improve the visual and cognitive deficit associated with MS, as well as reducing the symptomatic exacerbation (of the general case) associated with the premenstrual state (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Professor Centonze's group, since the beginning of the XXI century has been studying the effect of the TMS on the spasticity typical of the patients affected by MS. This has shown how the application of TMS at 5 Hz in the areas of the motor cortex for 2 weeks, significantly improved spasticity of the lower limbs. This effect was maintained until 7 days after the last session (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Additionally, they described the relationship of this pattern with improvement in sphincter control, cerebellar symptoms, and manual dexterity (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). In a 2010 article, the same group showed how another pattern; in this case intermittent theta burst stimulation (iTBS), also produced lower limb spasticity attenuation when applying TMS in patients with MS, while describing its ease of application, safety and tolerance (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Elzamarany et al. presented the results of the application of TMS in the sagittal mid axis of the motor cortex at 5 Hz and 900 pulses of application in patients with relapsing-remitting MS (RRMS) and secondarily progressive MS (PMSS). After that the patients showed a clear improvement, more evident in the forms of RRMS (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>).</p><p>Data derived from the experimental model of demyelination of the corpus callosum by inoculation of lysophosphatidylcholine, show how the application of electromagnetism (with the same experimental protocol as that used by our group in an experimental model of Huntington's disease), induces improvement in the symptoms of the animal, which is associated with neurogenesis and a reduction in inflammatory phenomena. In this line, published data from our group show how the application of TMS in the model similar to RRMS in rats with experimental autoimmune encephalomyelitis (EAE) show a symptomatic improvement in the mobility scale, as well as in oxidative and cellular damage, decreasing the degree of cerebral astrocytosis (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>–<xref rid=\"B46\" ref-type=\"bibr\">46</xref>).</p><p>However, despite the vast information available in the scientific literature about the potential therapeutic use of TMS, pointing out its effectiveness and safety, the following four premises must be borne in mind (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>): (i) The mechanisms of action that achieve the therapeutic effects described are not well-known at the molecular level. (ii) There is a wide variety of administration protocols, which reveals absence of consensus and standardization of guidelines. (iii) It is not known exactly how long the effects of TMS will last after the administration of a cycle of sessions. (iv) Any possible change in any of its variables (frequency, intensity, number of pulses, etc.) could imply the creation of a new protocol and, therefore, a new therapeutic strategy in itself.</p><p>All these arguments reveal the relevance of conducting clinical trials that result in understanding the effectiveness of TMS in the treatment of MS. In this way, the presumed potentialities mentioned above could be verified, as well as the elaboration of different specific protocols whenever possible formulas of optimisation of administration guidelines are known (in terms of intensity, number, and chronology of sessions). For these purposes, the present study proposes the comparative application of TMS of low frequency (1 Hz) and high frequency (5 Hz) in patients affected by relapsing-remitting MS, compared to a treatment in use. It is based on the hypothesis that the administration of TMS in MS aim to achieve neuromodulation for a therapeutic purpose in patients with relapsing-remitting multiple sclerosis (RRMS), and this implies a neuroprotective effect against the progression of the disease, resulting in a clinical improvement (attenuation of symptoms and signs, as direct measures of the therapeutic effect) and a biochemistry improvement (decrease of serum oxidative stress molecules and acute phase reactants, as indirect measures).</p></sec><sec id=\"s2\"><title>Methods and Analysis</title><sec><title>Study Design</title><p>Phase II clinical trial, unicentric, controlled, randomized, single blind. A Consolidated Standards of Reporting Trials (CONSORT) flow diagram for enrollment and randomization in the GOAL study is showed in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>. The patients included will be assigned under the 1:1:1 randomization formula, constituting three groups for the present study: 30 patients treated with natalizumab + white (placebo) + 30 patients treated with natalizumab + TMS (1 Hz) + 30 patients treated with natalizumab + TMS (5 Hz).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>CONSORT flow diagram. Consolidated Standards of Reporting Trials (CONSORT) flow diagram for enrollment and randomization GOAL study.</p></caption><graphic xlink:href=\"fneur-11-00750-g0001\"/></fig></sec><sec><title>Eligibility Criteria/Participants</title><p>Participants will be recruited from the Reina Sofía University Hospital (Córdoba, Spain). Ninety patients diagnosed with RRMS, who meet all the inclusion criteria and do not present any of the exclusion criteria that are established below and from which clinically evaluable results can be obtained. In <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>, we present the study's inclusion and exclusion criteria. Patients in the three RRMS groups are treated with natalizumab. It could have been decided to recruit those treated with another pharmacological therapy (such as alemtuzumab, being a more modern monoclonal antibody); however, natalizumab offers us four advantages: (i) Its idiosyncrasy of administration (intravenous) leads to the patient going to the hospital and undergoing a blood analysis, which facilitates obtaining the sample needed in the present study. (ii) It is the drug with which a greater number of patients with RRMS is currently being treated in our hospital, which makes it possible to maximize the possibilities of recruitment. (iii) Regarding the previous point, considering patients under the same treatment (pharmacological) allows us to homogenize the characteristics of the three groups of the sample recruited, which increases the internal and external validity of results. (iv) To the above we can add that natalizumab is the drug with which the research group has the most experience. (v) In our healthcare area, it is the drug that allows us greater recruitment capacity and therefore greater viability to carry out the study (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>–<xref rid=\"B50\" ref-type=\"bibr\">50</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Inclusion and exclusion criteria for the study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Inclusion criteria</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Exclusion criteria</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• Patients diagnosed with RRMS in their inflammatory forms who have completed a 14-dose treatment with natalizumab.<break/>• Normal analytical parameters, defined by: Leukocytes>3,000/mcl, Neutrophils>1,500/mcl, Platelets>100,000/mcl, AST/ALT <2.5 IU/L, Creatinine <2.5 mg/dl.<break/>• Patients of both sexes aged between 18 and 60 years.<break/>• EDSS: between 3.0 and 6.5 points.<break/>• Patients who give their informed consent for participation in the clinical trial.<break/>• Women of childbearing potential must obtain negative results in a pregnancy test performed at the time of inclusion in the study and commit to using a medically approved method of contraception for the duration of the study.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• Any active or chronic infection, including HIV infection, or hepatitis B or C.<break/>• History of neoplasia (basal cell carcinoma of the skin and <italic>in situ</italic> carcinoma in remission are excluded for more than 1 year).<break/>• Life expectancy severely limited by other co-morbidities.<break/>• Endocrine disease such as diabetes, hyper, or hypothyroidism.<break/>• Chronic inflammatory or autoimmune disease such as ulcerative colitis, Crohn's disease, systemic lupus erythematosus and any other form of connective tissue disease or chronic arthropathy.<break/>• Chronic obstructive pulmonary disease.<break/>• Severe psychiatric illnesses.<break/>• Hepatic, or renal, or cardiac dysfunction (including coronary heart disease and heart failure).<break/>• Chronic anemia.<break/>• Pregnancy or risk of pregnancy (including refusal to use contraception).<break/>• Women in breastfeeding period.<break/>• Inability to undergo MRI scans.<break/>• Inability to grant written informed consent.<break/>• Taking lipid-lowering drugs and vitamin supplements.<break/>• Treatment with steroids and/or non-steroidal anti-inflammatories, or alcohol intake 40 h before the blood extraction and/or development of the different tests.<break/>• Chronic enolism and/or abuse of drugs of abuse (sporadic or chronic).<break/>• Metallic implants in the head.<break/>• Cardiac pacemaker device.</td></tr></tbody></table><table-wrap-foot><p><italic>RRMS, Relapsing-Remitting Multiple Sclerosis; EDSS, Expanded Disability Status Scale</italic>.</p></table-wrap-foot></table-wrap></sec><sec><title>Identification of the Participants</title><p>Each patient will be identified with an alphanumeric code that will be assigned according to his/her correlative order of inclusion (when the patient signs the informed consent). In case of one patient voluntarily withdraws from the study, or in the event that after being included in the trial (upon signing the consent form) the selection criteria are reviewed and it is considered as unfit to continue the study, his/her identification code it cannot be reused for another patient. Therefore, the identification code of each patient will be unique in any way.</p><p>Members of research group will only be able to identify the subjects by the code assigned to them, their date of birth and their gender. The principal investigator must keep a confidential record with the patients' names and their assigned identification codes.</p></sec><sec><title>Evaluation of Patients</title><p>The main goal is to demonstrate the therapeutic effect of TMS in patients with MS by means of measurement of clinical changes according to the Expanded Disability Status Scale (EDSS). Consequently, the specific objectives are:</p><list list-type=\"bullet\"><list-item><p>To determine the consequences of the administration of TMS (1 Hz/5 Hz) in patients with MS, paying special attention to its clinical impact according to the MSFC scale (Multiple Sclerosis Functional Composite).</p></list-item><list-item><p>To assess the effect of the application of TMS (1 Hz/5 Hz) on fatigue in people with MS, according to the FIS scale (Fatigue Impact Scale).</p></list-item><list-item><p>To observe the effect of the application of TMS (1 Hz/5 Hz) on the degree of depression, according to the Beck scale.</p></list-item><list-item><p>To study the impact of TMS (1 Hz/5 Hz) on cognitive changes in patients with MS, in relation to the BRB scale (Brief Repeatable Battery of Neuropsychological Test).</p></list-item><list-item><p>To identify the changes induced by the application of TMS (1 Hz/5 Hz) on neurochemical biomarkers, oxidative damage, acute phase reactants, and in differential expression proteomic profiles, in patients affected by MS.</p></list-item><list-item><p>To establish the possible associations between the parameters studied and the likely changes that may be observed in them after TMS therapy.</p></list-item></list><p>For these purposes, participants will be assessed through an extensive evaluation including blood tests, image analysis, and cognitive functioning, fatigue and depression degree using several scales (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). As it is exposed in the evaluations schedule (in the timeline of every evaluation and variables to examine) (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>), the period of follow-up per patient is 56 weeks (around 13 months). Given that the project is designed for 36 months, the idea is that the inclusion of patients is staggered during the first 18 months, at a rate of 5 monthly patients, so that at that time (18 months) the first patients have concluded their period of treatment and clinical assessment and at 24–30 months the field work concerning clinical examinations of patients is concluded. The laboratory studies (determination of routine biochemical magnitudes, laboratory parameters, and proteomics studies) will be carried out in parallel with the recruitment of patients, while the biological samples are obtained. In this way, the work of the entire research team will be continuous from the beginning to the final assessment of the last patient recruited. After that (months 24–30), that is, around 40 months, the statistical study would be carried out, until the conclusion of the proposed study at 36 months.</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Variables to study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" style=\"background-color:#bbbdc0\" rowspan=\"1\" colspan=\"1\"><bold>EVALUATION BATTERY DURING THE PROPOSED TRIAL</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Clinical parameters</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• Activity degree of the disease: Expanded Disability Status Scale (EDSS).<break/>• Comprehensive clinical assessment of the disease: Multiple Sclerosis Functional Composite (MSFC).<break/>• Cognitive function: Brief Repeatable Battery of Neuropsychological Test (BRB).<break/>• Assessment of fatigue: Fatigue Impact Scale (FIS).<break/>• Depression assessment: Beck depression scale.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Image analysis</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">•Radiological study: Nuclear Magnetic Resonance (NMR) with and without contrast.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Laboratory study</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">•Hematimetry and routine biochemistry analysis (glucose, lipid profile, total proteins, albumin, transaminases, CK and LDH).<break/>• Biomarkers of oxidative damage: lipoperoxidation products and plasma carbonylated proteins.<break/>• Redox state of glutathione (total glutathione, GSH, GSSG and GSH/GSSG ratio).<break/>• Levels of neurotrophic factors (BDNF and NGF).<break/>• Cytokines: TNF-alpha.</td></tr></tbody></table></table-wrap><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Schedule of visits and parameters to check.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Visit number</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>1</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>2</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>3</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>4</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>5</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>6</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>7</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>8</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>9</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>10</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>11</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>12</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>13</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>14</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>15</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>16</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>17</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Week number</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">(<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>*</sup></xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Informed consent</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Clinical record</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">General examination</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vital signs (BP, HR, BR)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Neurological examination</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Absence outbreaks</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Neuropsychological study</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">EDSS scale</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MSFC scale</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Routine laboratory tests</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Quality of life scale</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inclusion criteria</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Exclusion criteria</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Adverse effects</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Other previous therapies</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Therapy in trial (TMS/Placebo)</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Motor threshold</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">X</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><fn id=\"TN1\"><label>*</label><p><italic>Start time (recruitment moment); BP, Blood Pressure; HR, Heart Rhythm; BR, Breathing Rate; EDSS, Expanded Disability Status Scale; MSFC, Multiple Sclerosis Functional Composite; TMS, Transcranial magnetic stimulation</italic>.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Sample Size</title><p>Lacking precedents in the scientific literature on the use of TMS in MS, we have based our work on three premises to estimate the sample size: (1st) Taking as a reference the dimensions of the groups of patients referred in clinical trials in which TMS has been applied to other neurodegenerative diseases. (2nd) The main objective of the trial is to achieve clinical improvement of the patient. In the absence of outcome variables of a clinical nature, surrogate variables (indirect outcome indicators) could be considered, such as some of the parameters of oxidative stress. In this case, if we take the redox ratio (GSH/GSSG) as a dependent variable, its standard deviation is 0.26 and the minimum expected difference of 10. With an error α = 0.05, an error ß = 0.20 and estimating a 20% loss of follow-up, at least 10 patients are required for each group (x 3 groups = 30 subjects in total). Note: There is a higher prevalence of MS in females, so it is assumed that according to the sampling technique, in the three groups considered there will be a similar gender ratio. It is also assumed that the response to TMS will not be conditioned by the patient's gender, which would work to improve the external validity of the results. (3rd) Being a phase II clinical trial, the initial piloting could justify the establishment of a number of subjects according to the investigator's criteria, according to their experience and the “state of the art” of the technique, allowing for a reduced number of patients.</p></sec><sec><title>Statistical Analyses</title><p>The feasibility analysis will be done by intention to treat and will include all patients for whom we have some feasibility data. The qualitative variables will be expressed by absolute and percentage frequencies, while the quantitative variables will be presented through the mean, median, standard deviation, maximum, minimum and number of observations. All <italic>p</italic>-values and confidence intervals will be calculated and evaluated using a 95% confidence level. The main effectiveness variable will be analyzed in the following way: the comparison of means of the values obtained in the quantitative variables studied during the successive visits, will be carried out through the ANOVA test of repeated measures if the data allow to apply a parametric statistics (as follows a normal distribution) and/or by the Friedman test (as a non-parametric equivalent). The evaluation of the qualitative parameters will be carried out through the Chi-square test.</p></sec><sec><title>Possible Loss of Patients: Withdrawal Criteria and Analysis of Anticipated Loss and Abandonments</title><p>A withdrawal is defined as the situation in which a subject included in the Clinical Trial ends his/her participation in it before completing the protocol in its entirety, independently of the circumstances that motivate the termination. Patients will interrupt their participation and will be removed from the clinical trial in any of these situations: (i) Presence of a serious adverse event since the patient's recruitment. (ii) Clinical conditions of the patient that prevent his/her continuity. (iii) Other reasons: protocol violation, lack of cooperation, revocation of informed consent, loss of follow-up.</p><p>The date and reason why a subject interrupts his/her participation in the Clinical Trial must be recorded in the Data Collection Notebook. The circumstance of the interruption should be notified immediately to the monitor and if this has been a Serious Adverse Event. The patient has the full right to leave the study at any time and any patient can be removed from the study for any reason beneficial to his/her well-being. According to the standards of Good Clinical Practice (GCP), all patients who leave the study before the foreseen time will be recommended the best alternative treatment.</p></sec><sec><title>Contingency Plan</title><p>This has been planned from a preventive point of view. Therefore, before starting it was assumed up to 20% loss of patients' follow-up in the calculation of the sample size.</p></sec><sec><title>Interruption of the Clinical Trial</title><p>The trial will be interrupted if any of the following circumstances are met: (i) Serious treatment-related toxicity, as the appearance of seizures following the application of TMS. In this case, when it happens to 50% of the patients enrolled in the study. (ii) Lack of adherence to treatment, as the abandonment of more than 60% of the recruited population. (iii) If one patient dies for reasons directly related to TMS.</p></sec><sec><title>Intervention/Experimental Setup</title><p>By means of the magnetic stimulator with the coil located in the primary motor cortex we induce a cerebral electric current that is able to obtain a motor potential in the first dorsal interosseous bone (PID) of the left hand. We will measure muscle stimulation by placing conventional surface electrodes connected to a device of evoked potentials. It is a Compound Muscle Action Potential (PAMC) that represents the sum of the action potentials of all the individual muscle fibers underlying the electrodes. For this, the electromyograph is programmed with the following parameters: (A) Sensitivity: 50 uV; (B) Frequency filter: between 2,000 Khz and 1 Hz; (C) Scan speed 10 ms/div; (D) Digitized preamplification signs, and (v) Surface electrodes are placed: active in the eminence of the dorsal interosseous muscle and reference in the dorsal bony prominence of the second finger. The procedure will depend on the group to which the patient has been assigned. In groups 2 and 3, we will place the probe from 8 to 3 centimeters in front of the vertex (Cz) medially and perpendicular to the craniocaudal axis.</p><p>The intervention procedure consists of two steps: First step: Obtaining the Motor Evoked Threshold at Rest: Regardless of to the group to which they belong, each patient will have his/her threshold evoked motor calculated at rest, by stimulation of the motor cortex in both hemispheres, evoking electromyographic responses (EMG) in the contralateral muscles, called motor evoked potentials (MEP). The threshold of motor excitability at rest (TM) is defined as the minimum intensity (expressed as the percentage of the maximum output power of the stimulator) capable of producing a reproducible MEP in a resting muscle in 50% of 10 shots. Second step: Administration of the Transcranial Magnetic Stimulation: A Rapid 2 Magstim device (Magstim Co.®, Whitland, Carmarthenshire, Wales) equipped connected to a figure-of-eight coil of 70 mm will be used. This equipment is used to calculate the TM and the percentage of it to which the treatment will have to be applied. The selection of the specific point of stimulation in the Supplementary Motor Area (SMA) will be sufficiently anterior to prevent the propagation of the impulse from triggering the muscular contraction of the shoulders, trunk and lower limbs. The position of the coil will be marked on the scalp to ensure consistent placement of the coil throughout the experiments, the patient will be fitted with a lycra cap on which to indicate and mark also the exact stimulation point, as well as on which to place and hold the coil during the therapeutic session. The coil will be oriented toward the posterior area in order to trigger a postero-anterior current. The TM will be calculated in relation to the evoked potential and according to international standards; based on it, TM is defined as the lowest stimulus intensity that elicited a minimum MEP amplitude of 50 mV in the completely relaxed FDI muscle in at least 5 out of 10 consecutive trials (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>, <xref rid=\"B52\" ref-type=\"bibr\">52</xref>). TMS will be applied through a non-ferromagnetic figure-ofeight coil (70-mm outer wing diameter) connected to a Magstim Rapid stimulator (The Magstim Co.®, Wales, UK) which generates biphasic electrical pulses of ~250 ms duration, at 90% of each individual's motor threshold at a frequency of 1 Hz or 5 Hz depending on the group, applying a total of 900 pulses, distributed in three sessions of 300 pulses with a 10 min pause between each session and a total duration of 45 min. Its location will be 3 cm ahead of the midpoint (Cz) on the cranio-caudal axis to simultaneously stimulate the SMA of both hemispheres, bearing in mind that the RRMS is characterized by diffuse lesions in both hemispheres. The treatment will be administered for 5 consecutive days, with 3 weeks of rest, between each stimulation. To complete a treatment period of 14 months (based on previous studies of the group in RRMS patients treated with natalizumab). In the case of the placebo group (patients with RRMS treated with natalizumab and placebo coil) patients will be stimulated with an inactive probe, the perception being indistinguishable. The stimulation with TMS (or administration of placebo) will be carried out every day in the same time slot for 5 consecutive days every 4 weeks, during a period of 14 months.</p></sec></sec><sec sec-type=\"discussion\" id=\"s3\"><title>Discussion</title><p>After carrying out this study presented, it would possibly have a high scientific and social relevance according to it aims to make a positive, relevant and innovative difference to: (i) its contribution to scientific knowledge and advancement in neurosciences field; (ii) its contribution to generate new tools (especially in therapies for neurodegenerative diseases), models or analysis systems that could enable improvement, boosting or creation of scientific research fields; (iii) its contribution to improve the health and well-being of citizens, due to its focus on a high prevalent disabling disease (as multiple sclerosis is); in addition, its incidence is increasing.</p><p>In the case of obtaining the expected results, these would involve the design of a new therapeutic strategy for patients with MS. It would be a new treatment that would improve the quality of life and the patient's activity, which would suggest the inclusion of TMS in the therapeutic approach algorithms for MS. Consequently, on the one hand it can lead to the design of a patentable application protocol (when MS is established as a new use), and on the other hand it can contribute to the design of new models of magnetic stimulators in relation to the variants of their physical foundations. Similarly, biomarkers for clinical use (diagnosis and/or prognosis) and therapeutic targets that are identified in the study of molecular profiles related to the clinical variables of these patients would be subsidiary to being patented. Therefore, the possibilities of patentability are high, especially considering the environment of industrial protection and the intellectual protection of the results subsequent to biomedical research. Suffice it to recall that, together with the field of biomaterials, electromechanical devices, and devices (both for diagnostic and therapeutic use) represent the largest source of patents in this research sector. In addition to this (and of special interest in the project proposed here) it would be patentable to establish the use of a device or equipment (already patented) for a new indication. Therefore, although various models of equipment for administration of TMS have already been patented, as of today there are no patents for use in MS. Currently, this technique is approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMEA) for other indications, as stated in the project application report; however, its application in MS would be the first.</p><p>If we rely on the results obtained in other neurodegenerative diseases and in experimental models of MS, we could foresee a clinical improvement of the patients, as well as their molecular profile. In relation to this improvement, such benefits could have an impact at these three levels: (i) Impact at the level of routine clinical care practice: It would involve the design of new therapeutic strategies for, at least, patients with multiple sclerosis. It would be a new treatment that would improve the patient's state and with it the quality of life and the activity of the patient, which would make it a subsidiary to be included in the algorithms of therapeutic approach of the patient with MS; (ii) Impact at the organizational and health resources management level: We are developing a new therapeutic application of a system (TMS) already used in the usual practice of the health system in neurophysiology for the study of potentials and the neurological communication channels. It is also currently approved by both the FDA and the European Drug Agency for the treatment of psychiatric disorders such as depression resistant to conventional treatment; iii) Possible inclusion of the expected results in consensus documents, clinical practice guidelines or care protocols: As previously mentioned, application protocols could be generated, potentially involving their potential inclusion in clinical practice guidelines, as has happened in the administration of TMS in other neurodegenerative and psychiatric processes.</p></sec><sec id=\"s4\"><title>Ethics and Dissemination</title><p>The proposed clinical trial will be conducted in accordance with the protocol following the standard procedures established at the participating hospital. Said trial will be carried out according to the recommendations for Clinical Trials and product evaluation in human research phase, which appear in the Declaration of Helsinki, reviewed in the successive world assemblies (WMA, 2008), and the current Spanish Legislation in the field of Clinical Trials (RD 1090/2015). The ICH-GCP standards (CPMP/ICH/135/95) will be followed. The Clinical Research Ethics Committee (CEIC) of Córdoba has already reviewed and approved the protocol and informed consent in December 2017, as well as the completion of the present clinical trial itself. Before carrying out any of the procedures specified in the protocol, the participating subject must sign and date the informed consent document approved by the CEIC. In order to guarantee the confidentiality of the trial data, the original data will be kept in the hospital and will only be accessed by the researcher and his/her team of collaborators, the trial monitor and the CEIC of Córdoba, which is the body that would protect the present essay. The researcher will allow the audits and inspections of the Spanish or European Health Authorities. The content of the data collection notebooks and the confidentiality of the data of each patient will be respected at all times. Appropriate procedures will be followed to ensure compliance with the provisions of Organic Law 15/99 of December 13 on the Protection of Personal Data. The documents generated during the study, will be protected from uses not allowed by people outside the investigation and, therefore, will be considered strictly confidential and will not be disclosed to other people.</p><p>The plan of initial dissemination of the results would include two important and complementary aspects at the same time: (i) Dissemination in biomedical scientific forums: in the first place, the results of this research project will be presented in the National and International Congresses of the Scientific Societies; secondly, the results will be published through peer-reviewed publications. (ii) Social and health dissemination: our research group participates continuously in forums, meetings, conferences and dissemination meetings on neurosciences, neurodegenerative diseases and advances in medicine, and interacts with the associations of patients and relatives of patients with MS.</p></sec><sec id=\"s5\"><title>Ethics Statement</title><p>The Clinical Research Ethics Committee (CEIC) of Córdoba has already reviewed and approved the protocol and informed consent in December 2017, as well as the completion of the present clinical trial itself. Before carrying out any of the procedures specified in the protocol, the participating subject must sign and date the informed consent document approved by the CEIC.</p></sec><sec id=\"s6\"><title>Author Contributions</title><p>EA, JC-V, and IT: conceptualization. EA and MB: methodology. CC: neuropsicological analysis. EA and MB: neurological analysis. JC-V and AG: laboratory analysis. BE and MF: data analysis. EA and JC-V: writing—original draft preparation. IT: supervision. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Physiol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Physiol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Physiol.</journal-id><journal-title-group><journal-title>Frontiers in Physiology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-042X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848826</article-id><article-id pub-id-type=\"pmc\">PMC7431868</article-id><article-id pub-id-type=\"doi\">10.3389/fphys.2020.00846</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Physiology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>PRDM16 Upregulation Induced by MicroRNA-448 Inhibition Alleviates Atherosclerosis via the TGF-β Signaling Pathway Inactivation</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Dongxing</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Song</surname><given-names>Jiantao</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Ji</surname><given-names>Xianfei</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Zunqi</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Tao</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Hu</surname><given-names>Bo</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/919954/overview\"/></contrib></contrib-group><aff><institution>Department of Emergency, Shandong Provincial Hospital Affiliated to Shandong First Medical University</institution>, <addr-line>Jinan</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: John D. Imig, Medical College of Wisconsin, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Ningjun Li, Virginia Commonwealth University, United States; Savneet Kaur, Institute of Liver and Biliary Sciences, India</p></fn><corresp id=\"c001\">*Correspondence: Bo Hu, <email>hubo200015@sina.com</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Vascular Physiology, a section of the journal Frontiers in Physiology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>846</elocation-id><history><date date-type=\"received\"><day>28</day><month>2</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Liu, Song, Ji, Liu, Li and Hu.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Liu, Song, Ji, Liu, Li and Hu</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The dysregulated expression of microRNAs (miRs) has been associated with pathological and physiological processes of atherosclerosis (AS). In addition, PR domain-containing 16 (PRDM16), a transcriptional mediator of brown fat cell identity and smooth muscle cell activities, may be involved in the hypercholesterolemia during development of AS. The bioinformatic analysis identified a regulatory miR-448 of PRDM16. Hence, the current study aimed to explore whether miR-448 influenced the activities of aortic smooth muscle cell (ASMCs) in AS. We validated that miR-448 was highly expressed in peripheral blood of patients with AS and aortic smooth muscle of AS model mice. Whereas, PRDM16 was downregulated in the aortic smooth muscle of AS model mice. PRDM16 overexpression was observed to inhibit oxidative stress injury and cell proliferation, and promote apoptosis of ASMCs. Mechanistic studies revealed that miR-448 targeted PRDM16 and negatively regulated the PRDM16 expression, while PRDM16 blocked the TGF-β signaling pathway. Furthermore, Downregulated miR-448 alleviated oxidative stress injury, and attenuated ASMC cell proliferation, migration and enhanced cell apoptosis through upregulation of PRDM16. Taken together, silencing of miR-448 upregulates PRDM16 and inactivates the TGF-β signaling pathway, thereby impeding development of AS by repressing the proliferation, migration and invasion of ASMCs.</p></abstract><kwd-group><kwd>microRNA-448</kwd><kwd>PR domain-containing 16</kwd><kwd>atherosclerosis</kwd><kwd>proliferation</kwd><kwd>apoptosis</kwd><kwd>migration</kwd><kwd>oxidative stress</kwd><kwd>TGF-β signaling pathway</kwd></kwd-group><counts><fig-count count=\"5\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"43\"/><page-count count=\"14\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Atherosclerosis (AS) is a chronic inflammatory disease characterized by the formation of atherosclerotic plaque in the intima of arteries (<xref rid=\"B33\" ref-type=\"bibr\">Takx et al., 2016</xref>). Existing literature has emphasized the distinct effect of smooth muscle cells (SMCs) on the initiation and progression of AS (<xref rid=\"B8\" ref-type=\"bibr\">Doran et al., 2008</xref>). The phenotypical transformation of SMCs is a key process in the development of AS (<xref rid=\"B12\" ref-type=\"bibr\">Gomez and Owens, 2012</xref>). AS is a primary instigator of coronary artery disease and manifests itself in other conditions including cerebrovascular disease as well as cardiovascular events such as myocardial infarctions, all of which are accompanied by high mortality and morbidity worldwide (<xref rid=\"B25\" ref-type=\"bibr\">Mach et al., 2008</xref>; <xref rid=\"B28\" ref-type=\"bibr\">Rader and Daugherty, 2008</xref>). Although several therapeutic approaches can be employed for the treatment of AS, there are subgroups of high risk patients who fail to benefit from existing treatment modalities (<xref rid=\"B31\" ref-type=\"bibr\">Ren et al., 2017</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Tian et al., 2017</xref>).</p><p>PR domain containing 16 (PRDM16) is a member of the PR-domain gene family and has been shown to play a role in the pathogenesis of various diseases (<xref rid=\"B15\" ref-type=\"bibr\">Horn et al., 2011</xref>). Furthermore, the involvement of PRDM16 has been reported in the regulation of SMC proliferation and differentiation (<xref rid=\"B7\" ref-type=\"bibr\">Davis et al., 2006</xref>), suggesting that PRDM16 may participate in the pathogenesis of AS. However, the potential effects of PRDM16 on AS remain largely unknown at present. MicroRNAs (miRs) have been widely documented to regulate various cellular mechanisms, including cytothesis and lipid metabolism, inflammation, as well as the progressive development of AS (<xref rid=\"B30\" ref-type=\"bibr\">Rayner et al., 2012</xref>). Mature miRs have been shown to bind to the 3’-untranslated region (3’-UTR) of their target messenger RNAs (mRNAs), inducing the degradation of target mRNAs or inhibiting its translation (<xref rid=\"B16\" ref-type=\"bibr\">Iwakawa and Tomari, 2015</xref>). High levels of miR-448 have been underlined in vascular smooth muscle cells (VSMCs) from coronary atherosclerotic plaques, while inhibition of miR-448 has been demonstrated to inhibit the proliferation and migration of VSMCs by targeting MEF2C (<xref rid=\"B39\" ref-type=\"bibr\">Zhang et al., 2017</xref>). Moreover, PRDM16 involves in orofacial structures by regulating the transforming growth factor β (TGF-β) signaling pathway (<xref rid=\"B36\" ref-type=\"bibr\">Warner et al., 2007</xref>). The inactivation of TGF-β protein expression has also been previously proposed to attenuate reactive oxygen species (ROS)/oxidative stress in AS (<xref rid=\"B5\" ref-type=\"bibr\">Cheng et al., 2019</xref>). TGF-β signaling has been reported to suppress AS by inhibiting T cell activation (<xref rid=\"B32\" ref-type=\"bibr\">Robertson et al., 2003</xref>). Additionally, the TGF-β signaling pathway plays a regulatory role in SMC activities whereby it helps to inhibit the formation of abdominal aortic aneurysms (<xref rid=\"B10\" ref-type=\"bibr\">Gao et al., 2014</xref>), suggesting that the TGF-β signaling may participate in regulating SMCs in AS. Thus, the present work explored potential effects of miR-448 targeting PRDM16 in AS, which may be associated with the TGF-β signaling pathway.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Ethics Statement</title><p>Written informed consent was obtained from all participants prior to the study. Study protocols were approved by the Ethics Committees of Shandong Provincial Hospital Affiliated to Shandong University, based on the ethical principles for medical research involving human subjects of the Helsinki Declaration. Animal experiments were conducted in strict accordance with the Guide to the Management and Use of Laboratory Animals issued by the National Institutes of Health. All procedures related to animal care were conducted under approval of the Institutional Animal Ethics Committee (IAEC No. 39/03/2014).</p></sec><sec id=\"S2.SS2\"><title>miR Prediction</title><p>The upstream regulatory miRs of PRDM16 were predicted using the microRNA database<sup><xref ref-type=\"fn\" rid=\"footnote1\">1</xref></sup>, miRSearch database<sup><xref ref-type=\"fn\" rid=\"footnote2\">2</xref></sup> and TargetScan database<sup><xref ref-type=\"fn\" rid=\"footnote3\">3</xref></sup>. The binding sites between miRs and genes were acquired from the TargetScan database.</p></sec><sec id=\"S2.SS3\"><title>Study Subjects</title><p>A total of 158 patients (81 males and 77 females, mean age: 54.66 ± 7.85 years) with moderate-severe AS diagnosed by coronary computed tomographic angiography and coronary angiography in the Department of Cardiology from Shandong Provincial Hospital Affiliated to Shandong University were enrolled. Subjects fasted for 12 h prior to blood collection. Venous blood samples (5 mL) were subsequently obtained on the next morning and then centrifuged at 3000 g for 15 min at room temperature. The upper serum was collected, sub-packed and stored at −80°C for subsequent experiment. Ninety-three serum samples (52 males and 41 females, mean age: 55.71 ± 7.15 years) were collected under controlled conditions from healthy subjects who underwent physical examination serving as normal controls. Clinical data of enrolled patients and healthy subjects are shown in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary Table S1</xref>. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was employed to detect the expression of miR-448 and miR-133b from the respective serum samples.</p></sec><sec id=\"S2.SS4\"><title>Establishment and Identification of AS Mouse Model</title><p>A total of 153 male ApoE<sup>–/–</sup> mice aged 7–8 weeks, weighing 20–25 g were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). The animals were placed on a controlled standard pelleted diet and provided free access to drinking water. The animals were housed at comfortable controlled room temperature conditions (22 ± 1°C) that were adequately humidified (55 ± 5%). The atherogenic diet was comprised of 10% butterfat, 20% sucrose, 2% cholesterol, and 0.5% cholic acid. Thirty mice were divided into the normal group (<italic>n</italic> = 15) fed with standard food and the AS group (<italic>n</italic> = 15) treated with a 10-week high-fat diet (15% lard, 20% sugar, and 1.2% cholesterol). After 12 weeks, blood samples were collected via retrograde orbital bleeding with the serum subsequently separated. The levels of total cholesterol (TC), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) in the serum were measured using a fully automated biochemical analyzer (Beckman Coulter Inc., Fullerton, CA, United States). The mice were euthanized by administering sodium pentobarbital (50 mg/kg), with the aortic tissues of mice dissected and their respective histopathological changes observed and analyzed using hematoxylin-eosin (HE) staining and Oil Red O (ORO) staining methods.</p></sec><sec id=\"S2.SS5\"><title>Production of Lentiviral Particles</title><p>A total of 12.5 μg nucleic acid was first diluted into 1 μg/μL with endotoxin-free pure water, and then added with 12.5 μL of water and 25 μL of 10% glucose solution to reach a final volume of 50 μL. Next, 25 μL of Entranster<sup>TM</sup> (18668-11-1, Engreen, Beijing, China) was diluted with an identical volume of 10% glucose solution culminating with a final volume of 50 μL, followed by the addition of diluted nucleic acid solution which was allowed to rest at room temperature for 15 min. At the fourth week of a high-fat diet, the mice were treated with 50 μL/g (virus titer: 1.0 × 10<sup>13</sup> viral genome/mL) via intraperitoneal injection of adenovirus-packaged nucleic acid or dimethyl sulfoxide (DMSO) (<xref rid=\"B37\" ref-type=\"bibr\">Ye et al., 2019</xref>). After 12 weeks, the mice were euthanized by sodium pentobarbital overdose after which their respective aortas were dissected. The TGF-β signaling pathway inhibitor, LY-364947, was dissolved in 5 mg/mL DMSO solution, diluted with phosphate buffered saline (PBS) and intraperitoneally injected at 1 mg/kg (3 times a week for 3 weeks) (<xref rid=\"B26\" ref-type=\"bibr\">Oka et al., 2008</xref>) (<xref ref-type=\"supplementary-material\" rid=\"FS1\">Supplementary Figure S1</xref>). The mice were classified into the following groups: the overexpressed (oe-) PRDM16 negative control (NC) group (mice infected with lentiviral vector containing oe-PRDM16 NC), the oe-PRDM16 group (mice infected with lentiviral vector containing oe-PRDM16), the miR-448 inhibitor NC group (mice treated with miR-448 inhibitor NC), the miR-448 inhibitor group (mice treated with miR-448 inhibitor), the miR-448 inhibitor + small interfering RNA targeting PRDM16 (si-PRDM16) NC group (mice treated with miR-448 inhibitor and lentiviral vector containing si-PRDM16 NC), the miR-448 inhibitor + si-PRDM16 group (mice treated with miR-448 inhibitor and lentiviral vector containing si-PRDM16), the DMSO group (mice injected with DMSO), and the LY-364947 group (mice injected with TGF-β pathway inhibitor LY-364947).</p></sec><sec id=\"S2.SS6\"><title>Extraction of Aortic Smooth Muscle Cells (ASMCs)</title><p>Three mice were selected randomly and placed into the supine position on a small polystyrene foam plate under a dissecting microscope. The chest was cut open to expose the heart and lungs. The aorta was then removed, placed in a 100 mm petri dish, and added with one or two drops of Fungizone solution. Two pairs of forceps were employed to remove the adventitia and obtain a smooth aortic canal. The aorta was then placed into a fresh new 100 mm Petri dish containing one to two drops of the medium. The aorta was cut into square pieces with a side length of approximately 1–2 mm. The aortic mass was placed in a small tissue culture tube containing 100 μL of enzyme solution and incubated in 5% CO<sub>2</sub> at 37°C for 4–6 h. After gradient suspension and centrifugation, the cells were transferred to a single well of a 48-well plate, placed in 5% CO<sub>2</sub> at 37°C and permitted to stand for a period of 5 days (<xref rid=\"B29\" ref-type=\"bibr\">Ray et al., 2001</xref>).</p></sec><sec id=\"S2.SS7\"><title>HE Staining</title><p>The aorta of the mice was fixed, embedded in paraffin, sectioned at a 4-μm thickness, dewaxed with xylene (I) for 5 min, toluene (II) for 5 min, dehydrated using gradient ethanol and subsequently washed with distilled water for 2 min. The sections were then stained using hematoxylin for 5 min, differentiated with hydrochloric acid-ethanol for 30 s, and placed in eosin solution for 2 min. Conventional dehydration, transparency, and blockade with neutral resin were then conducted respectively. Finally, the sections were observed and analyzed under an inverted microscope (XSP-8CA, Shanghai Optical Instrument Factory, Shanghai, China).</p></sec><sec id=\"S2.SS8\"><title>ORO Staining</title><p>After the adjacent fat had been removed, the upper part of the thoracic aorta was placed flat on a glass slide, differentiated in 60% isopropanol for 10 min, stained using ORO working solution (ORO:deionized water = 3:2) for 3 h, differentiated in 60% isopropanol for 6–7 times until the background color was observed to have turned white. At last, the image was observed and analyzed using an Olympus digital camera (Olympus Optical Co., Ltd., Tokyo, Japan).</p></sec><sec id=\"S2.SS9\"><title>Immunohistochemistry (IHC)</title><p>The paraffin-embedded aortic tissues of the mice were sliced into sections, which were then dewaxed in xylene and hydrated using ethanol of gradient concentrations. Antigen was extracted in citric acid buffer solution. The tissue sections were probed with primary antibodies to intercellular cell adhesion molecule-1 (ICAM-1) (#4915, 1:50, Cell Signaling Technology, Danvers, MA, United States) and vascular cell adhesion molecule-1 (VCAM-1) (ab134047, 1:250, Abcam, Cambridge, United Kingdom) at 4°C overnight. On the next day, sections were re-probed with secondary antibody of goat anti-rabbit antibody to immunoglobulin G (IgG) at 37°C for 20 min and incubated with horseradish peroxidase (HRP)-labeled streptavidin working solution (0343-10000U, Imunbio Co., Ltd., Beijing, China) at 37°C for 20 min. Following development by 3,3’-diaminobenzidine (ST033, Whiga Co., Ltd., Guangzhou, Guangdong, China), sections were counterstained with hematoxylin (PT001, Bogoo Co., Ltd., Shanghai, China) for 1 min. After dehydration by ethanol and clearing by xylene, sections were mounted by neutral resin. Under microscopic observation, five high-power fields were selected from each section on a random basis with 100 cells counted in each field. The percentage of positive cells was calculated.</p></sec><sec id=\"S2.SS10\"><title>Enzyme-Linked Immunosorbent Assay (ELISA)</title><p>The levels of malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH) and glutathione peroxidase (GSH-PX) in the cell supernatants were detected using commercially available kits (RAPIDBIO, West Hills, CA, United States). The optical density (OD) value of each well was determined at 450 nm using a Spectramax M5 microplate reader (Molecular Devices, Sunnyvale, CA, United States).</p></sec><sec id=\"S2.SS11\"><title>Detection of ROS Level</title><p>The minced aortic tissues of the mice were lysed in order to prepare a single cell suspension, after which the intracellular ROS levels were measured using a peroxide-sensitive fluorescent probe 2’7’-dichlorofluorescein diacetate (DCFH-DA) (Beyotime Biotechnology Inc., Nantong, Jiangsu, China). The cells were exposed to serum-free medium containing 10 μM DCFH-DA for 30 min. Fluorescence was measured using FACSCalibur flow cytometer (Becton-Dickinson, Sunnyvale, CA, United States).</p></sec><sec id=\"S2.SS12\"><title>Dual-Luciferase Reporter Gene Assay</title><p>The artificially synthetized PRDM16-3’UTR gene fragments were inserted into pMIR-reporter (Beijing Huayueyang Biotechnology Co., Ltd., Beijing, China) through endonuclease sites SpeI and Hind III. The complementary sequence mutation sites of the seed sequence were designed on the wild-type (WT) PRDM16. The target fragment was subsequently inserted into the pMIR-reporter plasmids by restriction endonuclease digestion using T4 DNA ligase. The correctly sequenced WT and mutant (MUT) plasmids were also co-delivered with miR-448 mimic and mimic NC (GenePhama Co., Ltd., Shanghai, China) into HEK-293T cells (Center for Cell Resources, Shanghai Institute of Life Sciences, Chinese Academy of Sciences, Shanghai, China). Luciferase activity was detected using GloMax 20/20 luminometer (Promega, Madison, WI, United States). The sequences of WT and MUT PRDM16 were as follows: UUAUACAUGAGAUUGAUAUGCAA (WT); TTATACAUTGAGATTGCAGGTACG (MUT).</p></sec><sec id=\"S2.SS13\"><title>5-Ethynyl-2’-Deoxyuridine (EdU) Assay</title><p>The cells (1.6 × 10<sup>5</sup> cells/well) were incubated with 50 mM EdU (Cell-LightTM EdU Apollo<sup>®</sup> 488 In Vitro Imaging Kit, Guangzhou Ribobio Biotechnology Co., Ltd., Guangzhou, Guangdong, China) at 37°C for 4 h. The cells were subsequently fixed with 4% formaldehyde for 15 min and treated with 0.5% Triton X-100 for 20 min for permeabilization. The cells were then incubated with 100 mL Apollo<sup>®</sup> mixture in each well for 30 min, stained with 100 mL Hoechst 33342 dye for 30 min and imaged under a fluorescence microscope (Olympus Corporation, Tokyo, Japan). The number of EdU-positive cells (erythrocytes) was calculated using Image-Pro Plus (IPP) 6.0 software (Media Cybernetics, Bethesda, MD, United States).</p></sec><sec id=\"S2.SS14\"><title>Flow Cytometry</title><p>On the next day after transduction, the cells were detached with 0.25% trypsin. The trypsinization process was terminated following the addition of Roswell Park Memorial Institute (RPMI)-1640 medium containing 10% fetal bovine serum. The supernatant was removed through centrifugation at 400 g for 5 min. The cells were fixed with 70% pre-cold ethanol (4°C). The cell density was adjusted to 1 × 10<sup>6</sup> cells/mL, followed by staining with 10 mL Annexin-V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) (556547, Shanghai Solja Technology Co., Ltd., Shanghai, China) at 4°C for 15–30 min. The cell apoptosis was then detected using a flow cytometer (XL type, conlter, United States). The 525 and 620 nm band-pass filters were excited at 488 nm allowing for FITC and PI fluorescence detection, respectively.</p></sec><sec id=\"S2.SS15\"><title>Immunofluorescence Assay</title><p>The cells were fixed with ice-cold acetone and then blocked with 5% (w/v) bovine serum albumin, and incubated with primary antibodies including CD31 (rabbit anti-human, ab28364, 1:20), α-smooth muscle aorta (α-SMA) (rabbit anti-human, ab32575, 1:1000), and vimentin (rabbit anti-human, ab92547, 1:250) overnight at 4°C, followed by incubation with fluorescence-labeled IgG secondary antibody (goat anti-rabbit, 1:200). All antibodies mentioned above were obtained from Abcam. After incubation for 1 h, cells were stained with PI (eBioscience, San Diego, CA, United States), mounted, and visualized using fluorescence microscopy (Olympus IX51, Tokyo, Japan).</p></sec><sec id=\"S2.SS16\"><title>Scratch Assay</title><p>Horizontal lines were drawn on the back of the 6-well plate. The cells were trypsinized with 0.25% trypsin and triturated into a single cell suspension. The cells were then counted and inoculated into a 6-well plate at 1 × 10<sup>6</sup> cells/well. Following a 24-h period of incubation, the cells were cultured in RPMI 1640 medium. An artificial scratch was then made with the sterile 10 μL micropipette tip. The cells detached on the tip of the pipette were then removed. The remaining cells were then cultured in serum-free medium at 37°C with 5% CO<sub>2</sub>. Photographs were taken at 0 h and at 48 h under a microscope (Olympus Corporation, Tokyo, Japan).</p></sec><sec id=\"S2.SS17\"><title>RT-qPCR</title><p>Total RNA was extracted and reversely transcribed into cDNA. The primers were designed and submitted to Sangon Biotech (Shanghai, China) for design and synthesis (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). The RT-qPCR assay was performed by EasyScript First-Strand cDNA Synthesis SuperMix (Catalog No. AE301-02, TransGen Biotech, Beijing, China). Fluorescent qPCR was conducted using SYBR<sup>®</sup> Premix Ex Taq<sup>TM</sup> II Kit (TaKaRa, Dalian, Liaoning, China). RT-qPCR assays were performed using the ABI’s 7500-type real-time PCR. Gene expression was detected using 2<sup>–ΔΔCt</sup>.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Primer sequences for RT-qPCR.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Targeted genes</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Primer sequences</bold></td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-448</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F: 5′-TTATTGCGATGTGTTCCTTATG-3′</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R: 5′-ATGCATGCCACGGGCATATACACT-3′</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miR-133b</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F: 5′-GCGCTTTGGTCCCCTTC-3′</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R: 5′-CAGTGCAGGGTCCGAGGT-3′</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PRDM16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F: 5′-CCACCAGCGAGGACTTCAC-3′</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R: 5′-GGAGGACTCTCGTAGCTCGAA-3′</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TGF-β</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F: 5′-CCACCTGCAAGACCATCGAC-3′</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R: 5′-CTGGCGAGCCTTAGTTTGGAC-3′</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Smad2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F: 5′-AAGCCATCACCACTCAGAATTG-3′</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R: 5′-CACTGATCTACCGTATTTGCTGT-3′</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Smad3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F: 5′-AGGGGCTCCCTCACGTTATC-3′</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R: 5′-CATGGCCCGTAATTCATGGTG-3′</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">U6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F: 5′-CTCGCTTCGGCAGCACA-3′</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R: 5′-AACGCTTCACGAATTTGCGT-3′</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GAPDH</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">F: 5′-GGAGCGAGATCCCTCCAAAAT-3′</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">R: 5′-GGCTGTTGTCATACTTCTCATGG-3′</td></tr></tbody></table><table-wrap-foot><attrib><italic>F, forward; R, reverse; miR-448, microRNA-448; miR-133b, microRNA-133b; PRDM16, PR domain containing 16; TGF-β, transforming growth factor β; Smad2, SMAD family member 2; Smad3, SMAD family member 3; RT-qPCR, reverse transcription quantitative polymerase chain reaction; GAPDH, glyceraldehyde-3-phosphate dehydrogenase.</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S2.SS18\"><title>Western Blot Analysis</title><p>The total tissues and cells were lysed using radio-immunoprecipitation assay (RIPA) lysis buffer (P0013B, Beyotime Biotechnology Co., Ltd., Shanghai, China), and added with phenylmethylsulfonyl fluoride (PMSF) and phosphatase inhibitor to collect total protein, which was separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred onto the nitrocellulose membrane. The membrane was then incubated with the primary antibodies at 4°C overnight: PRDM16 (rabbit, ab106410, 1:1000), TGF-β (rabbit, ab92486, 1:2000), Smad2 (rabbit, ab33875, 1:1000), phosphorylated (p)-SMAD family member 2 (Smad2) (rabbit, ab53100, 1:1000), SMAD family member 3 (Smad3) (rabbit, ab40854, 1:1000), p-Smad3 (rabbit, a52903, 1:2000), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (rabbit anti-human, ab9485, 1:2500). All antibodies and dilutions were purchased from Abcam. After blocking, the membrane was probed using diluted HRP-labeled secondary antibody IgG (goat anti-rabbit, ab205718, 1:5000), and then incubated for 2 h. The membrane was visualized by enhanced chemiluminescence using the SmartView Pro 2000 (UVCI-2100, Major Science, United States). The gray value ratio of protein was analyzed using Quantity One software.</p></sec><sec id=\"S2.SS19\"><title>Statistical Analysis</title><p>Statistical analyses were performed using SPSS 21.0 statistical software (IBM Corp. Armonk, NY, United States). All measurement data were expressed as mean ± standard deviation. The data of two groups with normal distribution and equal variance were compared by unpaired <italic>t</italic>-test. A <italic>p</italic> < 0.05 value was regarded as being of statistical significance.</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>miR-448 Was Highly Expressed While PRDM16 Was Poorly Expressed in AS Patients and Mice</title><p>In order to elucidate the regulatory mechanism by which the PRDM16 gene influences AS, the upstream regulatory miRs of PRDM16 were predicted by such databases as microRNA, miRSearch and TargetScan, and the intersection of the predicted results was obtained. Among the database predicted results, two miRs (miR-448 and miR-133b) were considered to target PRDM16 (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). Quantitative analyses revealed that miR-448 was highly expressed in the serum of patients with AS (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>). Further prediction of the targeted binding sites between miR-448 and PRDM16 in mice and humans illustrated that miR-448 might target PRDM16, while the sequence of miR-448 and its binding site to the gene were almost identical in both human and mice forms (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>), suggesting that miR-448 may participate in AS in mice by targeting PRDM16.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>miR-448 was upregulated while PRDM16 was downregulated in AS patients and mice. <bold>(A)</bold> The intersection of PRDM16 upstream miRs predicted by MicroRNA and other databases. The three circles in the figure represent the prediction results of three databases for PRDM16, while the overlapped part represents the intersection of three databases. <bold>(B)</bold> RT-qPCR results of miR-448 and miR-133b in serum from 158 AS samples and 93 normal samples. <bold>(C)</bold> Targeted binding site prediction of PRDM16 and miR-448 in human and mice. <bold>(D)</bold> The quantitative analysis of TC, TG, and HDL-C in peripheral blood of mice. <bold>(E)</bold> The content of oxidative stress-related indicators (MDA, SOD, GSH, and GSH-PX) as detected by ELISA. <bold>(F)</bold> ROS levels in mice serum. <bold>(G)</bold> The number of aortic thrombosis in AS mice as detected by ORO staining (×400, scale bar: 25 μm). <bold>(H)</bold> The aortic plaque changes in AS mice as detected by HE staining (×200, scale bar: 50 μm). <bold>(I)</bold> The relative expression of miR-448 and PRDM16 in mice aorta as detected by RT-qPCR. <bold>(J)</bold> PRDM16 protein expression in mice aorta as detected by Western blot analysis. <bold>(K)</bold> Gray value quantitation of panel <bold>(J)</bold>. * <italic>p</italic> < 0.05 compared with normal samples. The measurement data were expressed as mean ± standard deviation, and the data were analyzed by independent-sample <italic>t</italic>-test, <italic>n</italic> = 15.</p></caption><graphic xlink:href=\"fphys-11-00846-g001\"/></fig><p>Following the successful AS mouse model establishment, biochemical indicators (<xref ref-type=\"fig\" rid=\"F1\">Figure 1D</xref>) and ELISA for oxidative stress-related indicators (<xref ref-type=\"fig\" rid=\"F1\">Figures 1E,F</xref>) demonstrated that in comparison with the normal mice, the levels of TC, TG, MDA and ROS were upregulated, while the HDL-C, SOD, GSH, and GSH-PX levels were downregulated in mice with AS (<italic>p</italic> < 0.05). Both ORO staining (red staining area: the thrombus block) and HE staining (the achromatic block-like area: AS plaque) revealed that the numbers of thrombi and AS plaque were dramatically elevated in the aortic tissues of mice with AS when compared with the normal tissues (<italic>p</italic> < 0.05) (<xref ref-type=\"fig\" rid=\"F1\">Figures 1G,H</xref>). RT-qPCR and Western blot analysis results demonstrated that the expression of miR-448 was upregulated while that of PRDM16 was downregulated in the aortic smooth muscle of mice with AS when compared with the normal mice (<italic>p</italic> < 0.05) (<xref ref-type=\"fig\" rid=\"F1\">Figures 1I–K</xref>). Based on the aforementioned analysis results, it was concluded that miR-448 was highly expressed while PRDM16 was poorly expressed in AS, indicating that miR-448 and PRDM16 may participate in the regulation of AS.</p></sec><sec id=\"S3.SS2\"><title>Overexpressed PRDM16 Inhibited Oxidative Stress Injury and ASMC Proliferation While Promoting Cell Apoptosis</title><p>Next, we set out to further investigate the biological function of PRDM16 on ASMCs in AS mice and normal mice. The ELISA detection results regarding the oxidative stress-related indicators (<xref ref-type=\"supplementary-material\" rid=\"FS2\">Supplementary Figures S2A,B</xref>), EdU proliferation assay (<xref ref-type=\"fig\" rid=\"F2\">Figures 2A,B</xref>), and flow cytometry apoptosis assay (<xref ref-type=\"fig\" rid=\"F2\">Figures 2C,D</xref>) demonstrated that there had been a distinct downregulation of MDA and ROS levels, remarkable upregulation of SOD, GSH, and GSH-PX levels, notably diminished cell proliferation, as well as an obvious elevation in cell apoptosis in the supernatant of ASMCs of the oe-PRDM16 group compared with its NC (<italic>p</italic> < 0.05). The immunofluorescence detection results (<xref ref-type=\"fig\" rid=\"F2\">Figures 2E,F</xref>) and scratch assay results (<xref ref-type=\"fig\" rid=\"F2\">Figures 2G,H</xref>) indicated that in comparison with corresponding NC, fluorescence intensity of CD31 in response to oe-PRDM16 was increased, while α-SMA, vimentin and cell migration were all reduced (all <italic>p</italic> < 0.05). As shown in <xref ref-type=\"fig\" rid=\"F2\">Figures 2I–K</xref>, compared with oe-PRDM16 NC, the expression of PRDM16 was increased in cells transduced with oe-PRDM16 (<italic>p <</italic> 0.05). In conclusion, PRDM16 overexpression can inhibit oxidative stress injury and cell proliferation, and promote apoptosis of ASMCs.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Overexpression of PRDM16 suppressed proliferation of ASMCs while enhancing apoptosis in AS mice. <bold>(A)</bold> EdU proliferation staining of cells in each group (×200, scale bar: 50 μm). <bold>(B)</bold> The relative fold of EdU positive cell in each group. <bold>(C)</bold> The apoptosis of cells in each group as detected by flow cytometry. <bold>(D)</bold> The apoptosis rate in each group as detected by flow cytometry. <bold>(E)</bold> Immunofluorescence of cells in each group (×200, scale bar: 50 μm), green staining was regarded as positive results. <bold>(F)</bold> The fluorescence intensity of CD31, α-SMA, and vimentin of cells in each group. <bold>(G)</bold> The cell migration in each group as detected by scratch assay. <bold>(H)</bold> The relative migration in each group. <bold>(I)</bold> The relative expression of miR-448 and PRDM16 as detected by RT-qPCR. <bold>(J,K)</bold> The expression of PRDM16 protein normalized to GAPDH as detected by Western blot analysis. *<italic>p</italic> < 0.05 compared with ASMCs transduced with oe-PRDM16 NC. The measurement data were expressed as mean ± standard deviation, and analyzed by independent-sample <italic>t</italic>-test, and the experiment was repeated three times independently.</p></caption><graphic xlink:href=\"fphys-11-00846-g002\"/></fig></sec><sec id=\"S3.SS3\"><title>Silencing of miR-448 Upregulates PRDM16 to Inhibit Oxidative Stress Injury, Cell Proliferation, Migration, and Invasion While Promoting Apoptosis of ASMCs</title><p>As illustrated in <xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>, dual luciferase reporter gene assay revealed that luciferase activity of PRDM16-WT was decreased in cells transduced with miR-448 mimic when compared with its NC (<italic>p</italic> < 0.05), with no significant change detected in PRDM16-MUT (<italic>p</italic> > 0.05), indicating that miR-448 could target PRDM16.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>Silencing of miR-448 inhibited the cell proliferation, migration, and invasion while promoting the apoptosis of ASMCs by upregulating the expression of PRDM16. <bold>(A)</bold> The relative luciferase activity of miR-448 and PRDM16, *<italic>p</italic> < 0.05 compared with the NC group. <bold>(B)</bold> ROS levels of cells in each group. <bold>(C)</bold> Representative images of EdU proliferation staining of cells in each group (×200, scale bar: 50 μm). <bold>(D)</bold> The relative fold of EdU positive cell in each group. <bold>(E)</bold> The apoptosis of cells in each group as detected by flow cytometry. <bold>(F)</bold> The apoptosis rate in each group as detected by flow cytometry. <bold>(G)</bold> Immunofluorescence of cells in each group (×200, scale bar: 50 μm). <bold>(H)</bold> The fluorescence intensity of CD31, α-SMA, and vimentin of cells in each group. <bold>(I)</bold> The cell migration in each group as detected by scratch assay. <bold>(J)</bold> The relative migration in each group. <bold>(K)</bold> The relative expression of miR-448 and PRDM16 as detected by RT-qPCR. <bold>(L,M)</bold> The expression of PRDM16 protein normalized to GAPDH as detected by Western blot analysis. *<italic>p</italic> < 0.05 compared with ASMCs transduced with miR-448 inhibitor NC, <sup>#</sup><italic>p</italic> < 0.05 compared with ASMCs transduced with miR-448 inhibitor + si-PRDM16 NC. The measurement data were expressed as mean ± standard deviation, and the data of two groups were analyzed by independent-sample <italic>t</italic>-test. The experiment was repeated three times independently.</p></caption><graphic xlink:href=\"fphys-11-00846-g003\"/></fig><p>Next, to further verify the effects of miR-448 and PRDM16 on the biological functions of ASMCs, ELISA (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref> and <xref ref-type=\"supplementary-material\" rid=\"FS3\">Supplementary Figure S3</xref>), EdU proliferation assay (<xref ref-type=\"fig\" rid=\"F3\">Figures 3C,D</xref>), flow cytometry assay (<xref ref-type=\"fig\" rid=\"F3\">Figures 3E,F</xref>), immunofluorescence assay (<xref ref-type=\"fig\" rid=\"F3\">Figures 3G,H</xref>), and scratch assay (<xref ref-type=\"fig\" rid=\"F3\">Figures 3I,J</xref>) were performed, respectively. The results demonstrated that compared with cells transduced with miR-448 inhibitor NC, the MDA and ROS levels were downregulated while SOD, GSH, and GSH-PX levels were upregulated in cells transduced with miR-448 inhibitor, also, cell proliferation and migration were diminished while apoptosis was increased, the levels of CD31 expression were increased, while α-SMA and vimentin were decreased (<italic>p</italic> < 0.05). There was no significant difference detected regarding the levels of MDA, SOD, GSH, GSH-PX, ROS, cell proliferation, migration and apoptosis, CD31, α-SMA, and vimentin in cells transduced with miR-448 inhibitor + si-PRDM16 (<italic>p</italic> > 0.05). Relative to cells transduced with miR-448 inhibitor + si-PRDM16 NC, MDA, and ROS levels in cells transduced with miR-448 inhibitor + si-PRDM16 were upregulated while SOD, GSH, and GSH-PX levels were downregulated; cell proliferation and migration were increased while cell apoptosis was decreased; CD31 expression was decreased while α-SMA and vimentin expression was increased (<italic>p</italic> < 0.05).</p><p>As shown in <xref ref-type=\"fig\" rid=\"F3\">Figures 3K–M</xref>, the expression of miR-448 was decreased while PRDM16 expression was increased in cells transduced with miR-448 inhibitor when compared with cells transduced with miR-448 inhibitor NC (all <italic>p</italic> < 0.05). The expression of miR-448 in cells transduced with miR-448 inhibitor + si-PRDM16 was decreased (<italic>p</italic> < 0.05). Relative to cells transduced with miR-448 inhibitor + si-PRDM16 NC, PRDM16 expression was decreased (<italic>p</italic> < 0.05), while the expression of miR-448 remained unchanged in cells transduced with miR-448 inhibitor + si-PRDM16. Therefore, miR-448 inhibition can upregulate the expression of PRDM16, thus inhibiting oxidative stress injury, cell proliferation, migration and invasion, but promoting apoptosis of ASMCs.</p></sec><sec id=\"S3.SS4\"><title>PRDM16 Inactivated the TGF-β Signaling Pathway to Inhibit Oxidative Stress Injury, Proliferation, Migration, and Invasion of ASMCs While Promoting Apoptosis</title><p>The regulatory role of PRDM16 in TGF-β2 signaling pathway was previously explored from a hematopoiesis perspective (<xref rid=\"B1\" ref-type=\"bibr\">Avagyan et al., 2011</xref>). The inactivation of the protein expression of TGF-β was also proposed as an approach to restrict ROS in AS (<xref rid=\"B17\" ref-type=\"bibr\">Jones et al., 2009</xref>). Thus, we speculated that PRDM16 could be involved in the development of AS and act to regulate the TGF-β signaling pathway. The ELISA regarding oxidative stress-related indicators (<xref ref-type=\"supplementary-material\" rid=\"FS4\">Supplementary Figures S4A,B</xref>), EdU proliferation assay (<xref ref-type=\"fig\" rid=\"F4\">Figures 4A,B</xref>), flow cytometry assay (<xref ref-type=\"fig\" rid=\"F4\">Figures 4C,D</xref>), immunofluorescence assay (<xref ref-type=\"fig\" rid=\"F4\">Figures 4E,F</xref>), and scratch assay (<xref ref-type=\"fig\" rid=\"F4\">Figures 4G,H</xref>) demonstrated that, when compared with mice treated with DMSO, mice treated with LY-364947 exhibited downregulated MDA and ROS levels, upregulated levels of SOD, GSH, and GSH-PX, restricted ASMC proliferation and migration and accelerated apoptosis, along with elevated CD31 level and diminished levels of α-SMA and vimentin (<italic>p</italic> < 0.05). Relative to mice treated with oe-PRDM16 NC, MDA, and ROS levels were downregulated, while SOD, GSH, and GSH-PX levels were upregulated, cell proliferation, and migration were diminished while apoptosis was increased, CD31 expression was enhanced, while α-SMA and vimentin were suppressed in mice treated with oe-PRDM16 (<italic>p</italic> < 0.05).</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Overexpressed PRDM16 inhibited the cell proliferation, migration, and invasion while promoting the apoptosis of ASMCs by inactivation of the TGF-β signaling pathway. <bold>(A)</bold> Representative images of EdU proliferation staining of cells in each group (×200, scale bar: 50 μm). <bold>(B)</bold> The relative fold of EdU positive cell in each group. <bold>(C)</bold> The apoptosis of cells in each group as detected by flow cytometry. <bold>(D)</bold> The apoptosis rate in each group as detected by flow cytometry. <bold>(E)</bold> Immunofluorescence of cells in each group (×200, scale bar: 50 μm). <bold>(F)</bold> The fluorescence intensity of CD31, α-SMA, and vimentin of cells in each group. <bold>(G)</bold> The cell migration in each group as detected by scratch assay. <bold>(H)</bold> The relative migration in each group. <bold>(I)</bold> The relative expression of PRDM16, TGF-β, p-Smad2, and p-Smad3 as detected by RT-qPCR. <bold>(J,K)</bold> The expression of PRDM16 protein normalized to GAPDH as detected by Western blot analysis. *<italic>p</italic> < 0.05 compared with ASMCs treated with DMSO, <sup>#</sup><italic>p</italic> < 0.05 compared with ASMCs treated with oe-PRDM16 NC. The measurement data were expressed as mean ± standard deviation, and analyzed by independent-sample <italic>t</italic>-test. The experiment was repeated three times independently.</p></caption><graphic xlink:href=\"fphys-11-00846-g004\"/></fig><p>As illustrated in <xref ref-type=\"fig\" rid=\"F4\">Figures 4I–K</xref>, when compared with mice treated with DMSO, the extent of Smad2 and Smad3 phosphorylation in the LY-364947-treated mice was decreased (<italic>p</italic> < 0.05). Relative to mice treated with oe-PRDM16 NC, the expression of PRDM16 in mice treated with oe-PRDM16 was elevated while the expression of TGF-β, and extent of Smad2 and Smad3 phosphorylation were decreased (<italic>p</italic> < 0.05). In a word, PRDM16 overexpression can block the TGF-β signaling pathway, thereby inhibiting oxidative stress injury, cell proliferation, migration and invasion while promoting apoptosis of ASMCs.</p></sec><sec id=\"S3.SS5\"><title>PRDM16 Impeded AS by Inhibiting the Activation of the TGF-β Signaling Pathway <italic>in vivo</italic></title><p>In order to clarify the effects of PRDM16 and the downstream TGF-β signaling pathway on the progression of AS, the AS mice were transduced with LY-364947 or oe-PRDM16. The results of biochemical indicators (<xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref>), HE staining (<xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref>), ORO staining (<xref ref-type=\"fig\" rid=\"F5\">Figure 5C</xref>), and IHC (<xref ref-type=\"fig\" rid=\"F5\">Figure 5D</xref>) illustrated that, vs. mice treated with DMSO, TC and TG levels were downregulated and positive rate of AS markers (ICAM-1 and VCAM-1) reduced in mice treated with LY-364947, which also displayed upregulated HDL-C levels, and reduced number of AS plaques and thrombi in the aortic tissues (all <italic>p</italic> < 0.05). Relative to mice treated with oe-PRDM16 NC, TC, and TG levels were downregulated and positive rate of AS markers (ICAM-1 and VCAM-1) reduced in mice treated with oe-PRDM16, which also exhibited upregulated HDL-C levels, and diminished number of AS plaques and thrombi in the aortic tissues (<italic>p</italic> < 0.05). The aforementioned results demonstrated that PRDM16 suppresses activation of the TGF-β signaling pathway to halt the progression of AS in mice.</p><fig id=\"F5\" position=\"float\"><label>FIGURE 5</label><caption><p>PRDM16 inhibited AS through inactivation of the TGF-β signaling pathway. <bold>(A)</bold> The content of TC, TG, and HDL-C in peripheral blood in mice in each group as detected by biochemical indicators. <bold>(B)</bold> The changes of aortic plaques in AS mice as detected by HE staining (×200, scale bar: 50 μm). <bold>(C)</bold> The number of aortic thrombosis in AS mice as detected by ORO staining (×400, scale bar: 25 μm). <bold>(D)</bold> Positive rates of AS markers (ICAM-1 and VCAM-1) in the aorta of AS mice as detected by IHC (×400). *<italic>p</italic> < 0.05 compared with mice treated with DMSO, <sup>#</sup><italic>p</italic> < 0.05 compared with mice treated with oe-PRDM16 NC. The measurement data were expressed as mean ± standard deviation, and analyzed by independent-sample <italic>t</italic>-test, <italic>n</italic> = 15.</p></caption><graphic xlink:href=\"fphys-11-00846-g005\"/></fig></sec></sec><sec id=\"S4\"><title>Discussion</title><p>AS is a complicated multi-factor disease associated with various risk factors, including arterial hypertension, dyslipidemia, diabetes mellitus, and smoke (<xref rid=\"B9\" ref-type=\"bibr\">Fava and Montagnana, 2018</xref>). Thus, the identification of novel therapeutic targets is of urgent need in order to facilitate the diagnosis and treatment of patients with AS. In the present study, we investigated that inhibition of miR-448 could potentially prevent AS development <italic>via</italic> inactivation of the TGF-β signaling pathway by targeting PRDM16.</p><p>PRDM16 exhibited downregulated levels in the serum samples of both AS patients as well as the aortic smooth muscle of AS mouse models. Overexpressed PRDM16 was found to restrict ASMC oxidative stress injury, proliferation and migration, while acting to enhance apoptosis. The dysregulation of PRDM16 has been implicated in the progression of various diseases (<xref rid=\"B19\" ref-type=\"bibr\">Kinameri et al., 2008</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Zhu et al., 2017</xref>). The ectopic expression of PRDM16 has been shown to promote the trans-differentiation of VSMCs into beige adipocytes, indicating that PRDM16 may regulate development of AS through VSMCs (<xref rid=\"B4\" ref-type=\"bibr\">Brown et al., 2014</xref>). Evidence exists reporting that overexpression of PRDM16 decreases the levels of ROS (<xref rid=\"B6\" ref-type=\"bibr\">Chuikov et al., 2010</xref>). Decreased levels of SOD, GSH and GSH-Px and increased MDA levels were indicative of aggravated oxidative stress damage (<xref rid=\"B40\" ref-type=\"bibr\">Zhao et al., 2018</xref>). Therefore, PRDM16 overexpression could curb oxidative stress. Interestingly, PRDM16 is a transcriptional mediator of brown fat cell identity (<xref rid=\"B18\" ref-type=\"bibr\">Kajimura et al., 2010</xref>) and brown fat like gene expression is correlated with HDL-C and TC levels in coronary artery disease (<xref rid=\"B11\" ref-type=\"bibr\">Gautron, 2015</xref>). Further, brown fat activation attenuates hypercholesterolaemia and prevents development of AS (<xref rid=\"B3\" ref-type=\"bibr\">Berbee et al., 2015</xref>). Moreover, decreased contents of TC, and TG, along with increased contents of HDL-C can be indicative of the attenuated AS (<xref rid=\"B35\" ref-type=\"bibr\">Wang et al., 2018</xref>). Thus, PRDM16 overexpression could impede lipid accumulation in AS, and PRDM16 downregulation-induced by miR-448 accelerated development of AS. CD31, also known as platelet/endothelial cell adhesion molecule-1, has been elucidated to be expressed at a high level at endothelial cell-cell junctions to maintain the integrity and accelerate the recovery of the vascular permeability barrier in response to inflammatory or thrombotic challenge (<xref rid=\"B22\" ref-type=\"bibr\">Liu and Shi, 2012</xref>; <xref rid=\"B20\" ref-type=\"bibr\">Lertkiatmongkol et al., 2016</xref>). Additionally, a functional study has deciphered that α-SMA-positive VSMCs appreciably elevated in the progression of AS (<xref rid=\"B42\" ref-type=\"bibr\">Zhu et al., 2019</xref>). Also, Vimentin has been recognized as an indicator of cell migration, and loss of vimentin triggers enhanced oxidative stress and promoted vascular inflammation in macrophages, alleviating AS in a mouse model (<xref rid=\"B2\" ref-type=\"bibr\">Battaglia et al., 2018</xref>; <xref rid=\"B14\" ref-type=\"bibr\">Haversen et al., 2018</xref>). Accordingly, ectopic PRDM16 expression could attenuate oxidative stress injury and lipid accumulation, as evidenced by the elevated levels of CD31, HDL-C, SOD, GSH and GSH-Px and decreased levels of α-SMA, vimentin, TC, TG, MDA. Besides, miR-448-mediated PRDM16 inhibition accelerated development of AS.</p><p>Notably, PRDM16 was targeted and negatively regulated by miR-448. miR-448 was upregulated in serum samples of AS patients and AS mice, and its silencing inhibited ASMC oxidative stress injury, proliferation, and migration. Previous studies have demonstrated that miR-448 is upregulated in VSMCs of coronary atherosclerotic plaques, while the inhibition of miR-448 suppresses oxidative stress injury, proliferation, and migration of VSMCs by targeting MEF2C (<xref rid=\"B39\" ref-type=\"bibr\">Zhang et al., 2017</xref>). There is an accumulating amount of evidence suggesting that multiple miRs may regulate PRDM16 in various diseases. For instance, inhibition of miR-133 has been shown to trigger upregulation of thermogenesis and energy expenditure by targeting PRDM16 (<xref rid=\"B24\" ref-type=\"bibr\">Liu and Kuang, 2013</xref>). Besides, miR-133 has been reported to inhibit the brown adipose determination by targeting PRDM16 (<xref rid=\"B38\" ref-type=\"bibr\">Yin et al., 2013</xref>). Moreover, PRDM16 inhibited AS by blocking the TGF-β signaling pathway. The TGF-β signaling pathway shares a strong association with migration and proliferation of SMCs, as well as AS progression (<xref rid=\"B41\" ref-type=\"bibr\">Zhao et al., 2019</xref>). Prior evidence also revealed that PRDM16 could exert its regulation through the TGF signaling pathway (<xref rid=\"B1\" ref-type=\"bibr\">Avagyan et al., 2011</xref>). Besides, the activation of the TGF-β signaling pathway has been reported to elevate oxidative stress as well as the levels of ROS in AS (<xref rid=\"B5\" ref-type=\"bibr\">Cheng et al., 2019</xref>). Thus, downregulation of PRDM16 induced by miR-448 activated the TGF-β signaling pathway, which ultimately influenced the development of AS. Of note, multiple miRs have been suggested to harbor clinical application value for AS (<xref rid=\"B27\" ref-type=\"bibr\">Qin et al., 2018</xref>; <xref rid=\"B13\" ref-type=\"bibr\">Han et al., 2019</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Li et al., 2019</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Liu et al., 2019</xref>), highly suggestive of the therapeutic implication of miR-448 as a physiologically validated approach for the prevention and treatment of AS.</p></sec><sec id=\"S5\"><title>Conclusion</title><p>Taken together, inhibition of miR-448 upregulated the expression of PRDM16 thereby inactivating the TGF-β signaling pathway, which suppressed the oxidative stress injury, proliferation and migration of ASMCs. The present study provides evidence highlighting the involvement of the miR-448/PRDM16/TGF-β axis in the pathophysiological process of AS. However, further investigations are required into the modulation of miR-448 and its downstream targets, which may be a novel therapeutic and diagnostic strategy for AS.</p></sec><sec sec-type=\"data-availability\" id=\"S6\"><title>Data Availability Statement</title><p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.</p></sec><sec id=\"S7\"><title>Ethics Statement</title><p>Written informed consent was obtained from all participants prior to the study. Study protocols were approved by the Ethics Committees of the Shandong Provincial Hospital Affiliated to Shandong University and based on the ethical principles for medical research involving human subjects of the Helsinki Declaration. Animal experiments were conducted in strict accordance with the Guide to the Management and Use of Laboratory Animals issued by the National Institutes of Health. All procedures related to animal care were conducted under approval of the Institutional Animal Ethics Committee (IAEC No. 39/03/2014).</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>DL designed the study. DL and JS collated the data. DL, XJ, and ZL analyzed and produced the initial draft of the manuscript. TL and BH contributed to the drafting of the manuscript. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We acknowledge and appreciate our colleagues for their valuable suggestions and technical assistance for this study.</p></ack><fn-group><fn id=\"footnote1\"><label>1</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.microrna.org/microrna/home.do?tdsourcetag=s_pcqq_aiomsg\">http://www.microrna.org/microrna/home.do?tdsourcetag=s_pcqq_aiomsg</ext-link></p></fn><fn id=\"footnote2\"><label>2</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.exiqon.com/miRSearch\">https://www.exiqon.com/miRSearch</ext-link></p></fn><fn id=\"footnote3\"><label>3</label><p><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.targetscan.org/vert_71/\">http://www.targetscan.org/vert_71/</ext-link></p></fn></fn-group><sec id=\"S10\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fphys.2020.00846/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fphys.2020.00846/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"FS1\"><label>FIGURE S1</label><caption><p>The flow diagram depicting the development of AS model in mice. <bold>(A)</bold> Normal mice on standard diet. <bold>(B)</bold> ApoE<sup>–/–</sup> mice on atherogenic diet for 10 weeks to induce AS. <bold>(C)</bold> Adenovirus or TGF-β inhibitor (LY-364947) was intraperitoneally injected into ApoE<sup>–/–</sup> mice on atherogenic diet from the 4th week, 3 times a week for 3 weeks.</p></caption><media xlink:href=\"Image_1.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS2\"><label>FIGURE S2</label><caption><p>Overexpression of PRDM16 suppressed oxidative stress injury of ASMCs in AS mice. <bold>(A)</bold> The content of oxidative stress-related indicators (MDA, SOD, GSH, and GSH-PX) in each group of cell supernatant as detected by ELISA. <bold>(B)</bold> ROS levels in cells in each group. <sup>∗</sup><italic>p</italic> < 0.05 compared with ASMCs transduced with oe-PRDM16 NC. The measurement data were expressed as mean ± standard deviation, and analyzed by independent-sample <italic>t</italic>-test, and the experiment was repeated three times independently.</p></caption><media xlink:href=\"Image_2.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS3\"><label>FIGURE S3</label><caption><p>Silencing of miR-448 inhibited the oxidative stress injury of ASMCs by up-regulating the expression of PRDM16. The content of oxidative stress-related indicators (MDA, SOD, GSH, and GSH-PX) in the cell supernatant was measured by ELISA. <sup>∗</sup><italic>p</italic> < 0.05 compared with ASMCs transduced with miR-448 inhibitor NC, <sup>#</sup><italic>p</italic> < 0.05 compared with ASMCs transduced with miR-448 inhibitor + si-PRDM16 NC. The measurement data were expressed as mean ± standard deviation, and analyzed by independent-sample <italic>t</italic>-test between two groups. The experiment was repeated three times independently.</p></caption><media xlink:href=\"Image_3.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"FS4\"><label>FIGURE S4</label><caption><p>Overexpressed PRDM16 inhibited the oxidative stress injury of ASMCs by inactivation of the TGF-β signaling pathway. <bold>(A)</bold> The content of oxidative stress-related indicators (MDA, SOD, GSH, and GSH-PX) as detected by ELISA. <bold>(B)</bold> ROS levels of cells in each group. <sup>∗</sup><italic>p</italic> < 0.05 compared with ASMCs treated with DMSO, <sup>#</sup><italic>p</italic> < 0.05 compared with ASMCs treated with oe-PRDM16 NC. The measurement data were expressed as mean ± standard deviation, and analyzed by independent-sample <italic>t</italic>-test. The experiment was repeated three times independently.</p></caption><media xlink:href=\"Image_4.JPEG\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"TS1\"><media xlink:href=\"Table_1.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Avagyan</surname><given-names>S.</given-names></name><name><surname>Aguilo</surname><given-names>F.</given-names></name><name><surname>Kamezaki</surname><given-names>K.</given-names></name><name><surname>Snoeck</surname><given-names>H. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Med (Lausanne)</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Med (Lausanne)</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Med.</journal-id><journal-title-group><journal-title>Frontiers in Medicine</journal-title></journal-title-group><issn pub-type=\"epub\">2296-858X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850927</article-id><article-id pub-id-type=\"pmc\">PMC7431869</article-id><article-id pub-id-type=\"doi\">10.3389/fmed.2020.00515</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Medicine</subject><subj-group><subject>Mini Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Coronavirus Disease-2019 Conundrum: RAS Blockade and Geriatric-Associated Neuropsychiatric Disorders</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>de Miranda</surname><given-names>Aline Silva</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/246227/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Teixeira</surname><given-names>Antonio Lucio</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/172739/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Laboratório Interdisciplinar de Investigação Médica, Faculdade de Medicina, Universidade Federal de Minas Gerais</institution>, <addr-line>Belo Horizonte</addr-line>, <country>Brazil</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Laboratório de Neurobiologia, Departamento de Morfologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais</institution>, <addr-line>Belo Horizonte</addr-line>, <country>Brazil</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Instituto de Ensino e Pesquisa Santa Casa BH</institution>, <addr-line>Belo Horizonte</addr-line>, <country>Brazil</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Neuropsychiatry Program, Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston</institution>, <addr-line>Houston, TX</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Tzvi Dwolatzky, Technion Israel Institute of Technology, Israel</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Marta Riera, Mar Institute of Medical Research (IMIM), Spain; Dulce Elena Casarini, Federal University of São Paul, Brazil</p></fn><corresp id=\"c001\">*Correspondence: Aline Silva de Miranda <email>mirandas.aline@gmail.com</email>; <email>mirandaas@icb.ufmg.br</email></corresp><corresp id=\"c002\">Antonio Lucio Teixeira <email>altexr@gmail.com</email>; <email>antonio.l.teixeira@uth.tmc.edu</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Geriatric Medicine, a section of the journal Frontiers in Medicine</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>11</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>7</volume><elocation-id>515</elocation-id><history><date date-type=\"received\"><day>04</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 de Miranda and Teixeira.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>de Miranda and Teixeira</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Coronavirus Disease 2019 (COVID-19) is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which primarily targets the human respiratory system and may lead to severe pneumonia and ultimately death. Mortality rate is particurlarly high among people beyond the sixth decade of life with cardiovascular and metabolic diseases. The discovery that the SARS-CoV-2 uses the renin-angiotensin system (RAS) component ACE2 as a receptor to invade host epithelial cells and cause organs damage resulted in a debate regarding the role of ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) therapies during COVID-19 pandemic. Some authors proposed the discontinuation of ACEIs and ARBs for cardiovascular, kidney, and metabolic diseases, while expert opinions have discouraged that due to limited empirical evidence of their negative effect on COVID-19 outcomes, and that withdrawing treatment may contribute to clinical decompensation in high-risk patients. Moreover, as cardiovascular and metabolic diseases are associated with neurodegenerative and psychiatric disorders, especially among older adults, a critical appraisal of the potential positive effects of ACEIs and ARBs is highly needed. Herein, we aim to discuss the conundrum of ACEIs and ARBs use in high-risk patients for COVID-19, and their potential protective role on the development and/or progression of geriatric neuropsychiatric disorders.</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd>SARS-CoV-2</kwd><kwd>RAS</kwd><kwd>ACE2</kwd><kwd>ACEIs</kwd><kwd>ARBs</kwd><kwd>geriatrics</kwd><kwd>neuropsychiatric disorders</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"120\"/><page-count count=\"9\"/><word-count count=\"8215\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Coronavirus Disease 2019 (COVID-19, named by WHO on Feb 11, 2020) outbreak was officially reported in December 2019 in Wuhan, Hubei Province, China, and rapidly reached a pandemic status (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>–<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). The COVID-19 is caused by a novel positive-sense single-stranded RNA virus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Similar to other coronavirus like the SARS-CoV-1, the novel SARS-CoV-2 primarily targets the human respiratory system and may cause severe pneumonia and ultimately death. The mortality rate ranges from 2 to 4% of the cases, being particurlarly high among those beyond the sixth decade of life with cardiovascular (CVD) and metabolic diseases like diabetes (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Less severe clinical manifestations include fever, fatigue, chills, dry cough, rhinorrhoea, sneezing, and sore throat (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B9\" ref-type=\"bibr\">9</xref>).</p><p>Apart from the respiratory and systemic symptoms, there is growing evidence that the 2019-nCoV may also affect the central nervous system (CNS). Approximately, 36.4% (78/214) of patients diagnosed with COVID-19 experienced neurological symptoms like dizziness, headache, impaired arousal, ataxia, and seizure. It is worthing noticing that these symptoms were mainly related to other severe symptoms of the disease (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Anosmia and dysgeusia have also been reported, and may proceed the typical respiratory symptoms (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). The first case of viral meningoencephalitis caused by the 2019-nCoV was reported in a 24-years-old man admitted to a hospital with seizures accompanied by impaired arousal, with the virus genome being identified in the cerebrospinal fluid (CSF) (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Further evidence provided by systematic reviews and meta-analysis has supported the occurrence of neurological manifestations in patients with COVID-19 (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><p>Like the SARS-CoV-1, the SARS-CoV-2 seems to exploit the angiotensin-converting enzyme 2 (ACE2) receptor to entry the host cells (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). The evidence mainly from pre-clinical studies suggesting that ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs), drugs often prescribed for CVD, kidney, and metabolic diseases, might up-regulate circulating and tissue expression of ACE2 (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>–<xref rid=\"B20\" ref-type=\"bibr\">20</xref>), raised the question whether those therapies increase SARS-CoV-2 infectivity and COVID-19 severity. Accordingly, some researches proposed the discontinuation of ACEIs and ARBs, both prophylactically and in the context of suspected Covid-19 (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>–<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). In a period when all information, especially with alarmist content, spreads fast in social media, this suggestion increased the anxiety among people using those medications. However, expert opinions have discouraged treatment discontinuation due to limited evidence on the potential effects of this strategy in COVID-19 outcomes, and that withdrawal may contribute to clinical decompensation of high-risk patients (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>).</p><p>In this rapidly evolving scenario, herein, we aim to discuss the use of ACEIs and ARBs in high-risk patients for COVID-19. We propose that beyond the risk of clinical complications with the discontinuation of ACEIs and ARBs, these drugs might exert a potentially protective role against the emergence and/or progression of geriatric neuropsychiatric disorders. To support our proposal, we first address the dilemma of discontinuation of RAS blockers during the COVID-19 pandemic. Second, we review the role of renin-angiotensin system (RAS) components in neurodegenerative and neuropsychiatry disorders. Finally, we discuss the potential protective role of ACEIs and ARBs on the development and/or progression of geriatric neuropsychiatric disorders in high-risk patients for COVID-19.</p></sec><sec id=\"s2\"><title>ACEIs and ARBs Use During COVID-19: Foe or Friend?</title><p>Coexisting conditions such as older age, CVD and diabetes seem to be key prognostic determinants in response to the infection with 2019-nCoV. Severe symptoms of COVID-19 and high mortality have been associated with these conditions (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>).</p><p>ACEIs and ARBs are frequently prescribed for older adults with CVD, kidney and metabolic diseases. Among multiple biological effects, ACEIs, and ARBs seem to increase the expression of ACE2 (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>). The increase in ACE2 expression in response to ACEIs and ARBs treatments has been shown mostly in pre-clinical studies (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>–<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). There are only few human studies specifically addressing this issue, with conflicting results. The ARB olmesartan increased urinary levels of ACE2 in hypertensive patients (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>) and patients with diabetic nephropathy (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Conversely, no effect in ACE2 urinary levels was found with the ACEI enalapril or other ARBs (losartan, candesartan, valsartan, and telmisartan) (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Although ACE2 shows a 40% structural homology with ACE, they present a different conformational structure of the catalytic site, which may explain why ACEI in clinical use do not directly affect ACE2 activity or expression (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). The failure of most ARBs in changing ACE2 urinary levels revealed that such effects might not be uniform across RAS blockers even considering the same drug class (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Moreover, no changes in ACE2 activity was found in the plasma of patients with heart failure, atrial fibrillation, aortic stenosis, and coronary artery disease under ACEIs or ARBs therapy compared with untreated patients (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>–<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). Importantly, there is no available experimental or clinical evidence regarding the effects of ACEIs or ARBs on the expression of ACE2 in the lung, the primary tissue target by SARS-CoV-2 infection (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>).</p><p>The recent discovery that the 2019-nCoV uses the renin–angiotensin system (RAS) component ACE2 as a receptor to invade host epithelial cells and cause organs damage, prompted the debate regarding ACEIs and ARBs use during COVID-19 pandemic. Based on the debatable claim that ACEIs and ARBs would increase ACE2 expression in humans, some authors proposed the discontinuation of ACEIs and ARBs for CVD, kidney, and metabolic diseases (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>–<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). However, in the absence of clinical evidence, professional societies have advocated their continued use (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Supporting this position, a clinical study conducted at the Central Hospital of Wuhan, China, with 362 hypertension patients hospitalized with COVID-19, demonstrated that ACEIs and ARBs therapies were not associated with increase in COVID-19 severity or mortality. The comparison among patients under ACEI/ARBs combined or monotherapy (115, 31.8%), patients taking other hypertensive drugs, especially calcium-channel blockers (168, 46.4%), or not receiving any drug treatment (65, 18%) showed no significant differences in laboratory results, including blood counts, inflammatory markers, renal, and liver function tests, and cardiac biomarkers, or clinical outcomes (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Later other studies conducted in selected health care systems in North America (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>) and Europe (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>) supported the concept that ACEIs and ARBs are not associated with worst clinical outcomes. Taken together, these studies provide convincing evidence against the discontinuation of ACEIs and ARBs use in patients with or at risk for COVID-19.</p><p>It is also important to consider the multiple roles of ACE2 as a component of RAS, a cascade of vasoactive peptides that regulates key physiological functions, including blood pressure and hydroelectrolyte balance (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Apart from acting as a circulating hormonal system, RAS components are locally expressed in several organs and tissues, including kidney, brain, and lung, exerting physiological actions through tissue-specific mechanisms (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>). In the RAS pathway, angiotensinogen mainly produced by the liver is cleaved by renin, synthesized by the kidneys, in Ang I (pro-angiotensin). The angiotensin converting enzyme (ACE) cleaves the deca-peptide Ang I to the 8-amino acid peptide Ang II, which exerts its effects mainly through the Ang II type 1 (AT1) receptor. Ang II is also a substrate for ACE2, a cell membrane protein with a 17-amino acids N-terminal signal peptide and a C-terminal membrane anchor that acts as monocarboxypeptidase with a catalytically active ectodomain located at the extracellular side of the cell (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>). Importantly, the C-terminal domain of ACE2 shares significant homology with Collectrin, a type I membrane protein highly expressed on renal proximal tubules. Collectrin is involved in the process of vesicle transport and membrane fusion, properties that ACE2 also owns, which may facilitate the use of ACE2 as a receptor for SARS-CoV-2 gain entry in the host cells resulting in COVID-19 (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). As a RAS component, ACE2 directly converts Ang II in the seven-amino-acid heptapeptide Ang-(1-7), which activates G protein-coupled MAS receptor. This type 1 transmembrane glycoprotein also cleaves the C-terminal amino acid of Ang I to the non-peptide Ang-(1-9), which in turn is converted to Ang-(1-7) by ACE and Neprilysin, an enzyme also known as neutral endopeptidase. The catalytic efficiency of ACE2 is 400 times higher on Ang II than on Ang I, favoring the direct production of Ang-(1-7) (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>).</p><p>The RAS is composed traditionally categorized into two arms: the classical one, including ACE, Angiotensin (Ang) II, Ang type 1 (AT1) receptor (ACE/AngII/AT1), and the “alternative” one, comprising ACE2, Ang-(1-7), Mas receptor (ACE2/Ang1-7/Mas). The classical arm mediates pro-inflammatory, pro-thrombotic, and pro-fibrotic processes, mainly through the activation of AT1 receptors (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). On the other hand, the alternative arm seems to play protective roles by frequently opposing Ang II actions through Mas receptors activation (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B48\" ref-type=\"bibr\">48</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>). For instance, pre-clinical and clinical evidence revealed that up-regulation of ACE2 expression protects acute lung injury at least in part by decreasing AT1 receptors activation (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Accordingly, therapeutic strategies have been designed to inhibit ACE/Ang II/AT1 axis and to stimulate ACE2/Ang-(1-7)/Mas receptor activities (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>).</p><p>SARS-CoV-2 binds to ACE2 in order to gain initial entry in host lung epithelial cells. Theoretically, this process promotes down-regulation of ACE2 expression on epithelial cell surface, which in turn contributes to up-regulation of Ang II inflammatory signaling, enhancing the acute lung injury (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>, <xref rid=\"B53\" ref-type=\"bibr\">53</xref>). These findings were observed in a murine model of SARS-CoV-1 induced by administration of Spike (S318-510)-Fc. In this model, acute severe lung injury was associated with decreased tissue expression of ACE2 and enhanced levels of Ang II. Importantly, the ARBs losartan (15 mg/kg) rescued mice from SARS-CoV-1 Spike–mediated lung failure, potentially by restoring ACE2 levels in the lung and favoring the conversion of Ang II in Ang-(1-7) (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). Supporting these findings, administration of the ACE2 agonist diminazene aceturate to mice submitted to hyperoxic lung injury increased lung ACE2 expression/activity and decreased Ang II/Ang-(1-7) ratio, which in turn reduced inflammation and severity of lung failure (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). While no study has replicated these results in experimental models of SARS-CoV-2, elevated plasma levels of Ang II were positively correlated with viral load and lung injury scores in patients diagnosed with COVID-19 (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). Together these results suggest that an imbalance between ACE/AngII/AT1 and ACE2/Ang1-7/Mas axes toward the activation of the former might play a pathophysiological role in COVID-19.</p><p>It is worth mentioning that ACE2 levels decline with age, which may predispose to a pro-inflammatory profile as the result of RAS classical arm activation (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref>). A pro-inflammatory profile also underlies hypertension and diabetes pathophysiology, conditions highly prevalent with aging (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). A possible decrease in ACE2 induced by the SARS-CoV-2 infection in older people, especially those with CVD and diabetes, may exacerbate the pro-inflammatory background, leading to greater COVID-19 severity and mortality (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). Therefore, beyond the risk of clinical decompensation, discontinuation of ACEIs and ARBs is potentially harmful because the subsequent enhanced ACE/Ang II/AT1 receptor activity can worsen inflammatory lung injury and other organs damage. In fact, experimental suppression of ACE2 through genetic deletion or inhibitors was associated with myocardial damage and severe acute lung injury (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>–<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Conversely, strategies focused in increasing ACE2 levels or activity such as administration of recombinant human ACE2 (rhACE2), have shown protective effects in CVD and pulmonary diseases (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>, <xref rid=\"B60\" ref-type=\"bibr\">60</xref>, <xref rid=\"B62\" ref-type=\"bibr\">62</xref>) and have been suggested as a potential biological therapy against SARS-CoV-2 infection (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>, <xref rid=\"B64\" ref-type=\"bibr\">64</xref>).</p></sec><sec id=\"s3\"><title>ACE2-Angiotensin (1-7)-MAS Receptors Axis Role in Geriatric-Related Neuropsychiatric Disorders</title><p>Over the past decades, accumulating evidence has pointed out the role for RAS components in neuropsychiatric disorders [for review see (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>, <xref rid=\"B65\" ref-type=\"bibr\">65</xref>, <xref rid=\"B66\" ref-type=\"bibr\">66</xref>)]. Our research group has extensively investigated the profile of RAS molecules in the blood and/or cerebrospinal fluid (CSF) of patients with different neurodegenerative and psychiatric conditions, including Parkinson's disease (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>), Alzheimer's disease (AD) (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>), and schizophrenia (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>). For example, patients with Parkinson's disease presented decreased circulating levels of Ang II and Ang-(1-7) along with increased severity of depressive symptoms (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>). Lower CSF levels of ACE were found in patients with AD compared with healthy controls. A significant positive correlation between ACE and Aβ42 levels among patients was also observed, reinforcing the hypothesis that ACE is associated with amyloid-β pathology in AD (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>).</p><p>The protective effects exerted by ACEIs and ARBs treatments in pre-clinical and clinical settings have supported the involvement of RAS in neuropsychiatric conditions as well (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>, <xref rid=\"B70\" ref-type=\"bibr\">70</xref>, <xref rid=\"B71\" ref-type=\"bibr\">71</xref>). In animal models of Parkinson's disease induced by MPTP or 6-hydroxydopamine, administration of AT1 receptor antagonists like losartan and ACEIs such as perindopril prevented motor dysfunction and resulted in increased dopamine striatal levels and neuronal survival (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>–<xref rid=\"B76\" ref-type=\"bibr\">76</xref>). In a mouse model of AD induced by intracerebroventricular injection of amyloid-β, administration of the ARB telmisartan improved cognitive decline, increased cerebral blood flow, and attenuated brain inflammation and oxidative stress (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>). Similar findings were reported following intranasal administration of the ARB losartan in the transgenic APP/PS1 model of AD (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>). Epidemiological studies also revealed that ACEIs and ARBs significantly reduced the risk of AD and aging-associated cognitive decline (<xref rid=\"B79\" ref-type=\"bibr\">79</xref>). A 6-months treatment with the ARB telmisartan in hypertensive patients with AD resulted in more positive effects in cognition and cerebral blood flow than other anti-hypertensive drugs such as amlodipine (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>).</p><p>Based on the role of RAS components in regulating hemodynamic functions, a wide range of studies have also supported the involvement of RAS in cerebrovascular diseases, especially stroke (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Experimental studies with animal models of cerebral ischemia-reperfusion injury demonstrated that central or systemic infusions of Ang II decrease blood flow in the penumbra and increase cerebral inflammation, oxidative stress and edema, which in turn increase stroke-associated mortality (<xref rid=\"B81\" ref-type=\"bibr\">81</xref>–<xref rid=\"B84\" ref-type=\"bibr\">84</xref>). Administration of the ACEI captopril in rats following hemorrhagic stroke attenuated cerebral herniation and hematoma expansion, prevented new hemorrhage formation, and restored cerebral blood flow regulation (<xref rid=\"B85\" ref-type=\"bibr\">85</xref>). Additionally, clinical trials conducted with ARBs including losartan and eprosartan revealed decrease of ~25% in stroke incidence compared with other anti-hypertensive drugs like atenolol and nitrendipine. The effectiveness of ARBs in stroke prevention could not be explained only by blood pressure reduction, indicating that other mechanisms like anti-inflammatory and antioxidant effects may underly their neuroprotection (<xref rid=\"B86\" ref-type=\"bibr\">86</xref>, <xref rid=\"B87\" ref-type=\"bibr\">87</xref>).</p><p>The expression of RAS components seems to be influenced by ACEIs and ARBs. For instance, patients with hypertension and chronic kidney diseases taking ACEI or ARB presented enhanced circulating levels of Ang-(1-7) (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). As Ang-(1-7) exerts beneficial effects by opposing Ang II actions (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>), it is tempting to hypothezise that ACEIs and ARBs neuroprotection may involve in part the activation of the ACE2/ Ang-(1-7) / Mas receptor arm. Supporting this hypothesis, mice overexpressing ACE2 had lower infarct volume and increased cerebral blood flow and neurological function compared to wild type mice. The neuroprotective effects were associated with increased Ang (1-7)/Ang II ratio, angiogenic factors, and attenuated oxidative stress in the brain (<xref rid=\"B88\" ref-type=\"bibr\">88</xref>). Moreover, several studies employed pharmacological and/or genetic strategies in order to increase ACE2/Ang-(1-7)/Mas axis activity and revealed protective effects of those RAS components in neuropsychiatric and cerebrovascular conditions [for review see (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>, <xref rid=\"B89\" ref-type=\"bibr\">89</xref>)]. For instance, intracerebroventricular infusion of Ang-(1-7) for 4 weeks significantly improved cognitive function and cerebrovascular reactivity in 5XFAD mice, a model of AD (<xref rid=\"B90\" ref-type=\"bibr\">90</xref>). Intracerebroventricular infusion of Ang-(1-7) for 2 weeks also prevented cognitive decline and decrease the expression of hippocampal phospho-tau, amyloid-ß oligomer, and both soluble (Aβ 1-42) and insoluble (Aβ 1-40) ß- amyloid peptide in an AD-like rat model resulting from streptozotocin-induced diabetes. Importantly, the beneficial effects of Ang-(1-7) infusion were hampered by the coadministration of A-779, an antagonist of Mas receptors, suggesting that Ang-(1-7) protective activity was mediated by the activation of Mas receptors (<xref rid=\"B91\" ref-type=\"bibr\">91</xref>). A more recent study provided evidence that ACE2 activation also exerts protective effects in a transgenic mouse model of AD. Chronic intraperitoneal administration of DIZE (15 mg/kg/day), an established activator of ACE2, restored cognitive decline in Tg25676 mice, which was associated with reduced hippocampal levels of soluble Aβ 1-42 and of pro-inflammatory mediator IL-1ß alongside increased expression of Mas receptor. DIZE also reinstated the balance of hippocampal RAS activity, by increasing the ACE2/ACE activity ratio. Moreover, DIZE-mediated protection was abolished when co-administered with C16, an ACE2 inhibitor, indicating that neuroprotective effects resulted specifically from the enhancement of ACE2 activity (<xref rid=\"B92\" ref-type=\"bibr\">92</xref>).</p><p>Finally, a post-mortem study revealed that low activity of ACE2 in mid-frontal cortex of patients with AD negatively correlated with Aβ expression and phosphorylated tau pathology. The ratio of Ang II to Ang (1-7) was also reduced in the brain of patients compared with age-matched non-demented controls (<xref rid=\"B93\" ref-type=\"bibr\">93</xref>), while low circulating levels of Ang-(1-7) correlated with cognitive decline severity in patients with AD (<xref rid=\"B94\" ref-type=\"bibr\">94</xref>). The inverse correlation between concentrations of Ang-(1-7) and tau hyperphosphorylation was also reported in the cerebral cortex and hippocampus of the senescence-accelerated mouse prone 8 (SAMP8) mice, a model of sporadic AD and of the P301S mice, an animal model of tauopathy (<xref rid=\"B95\" ref-type=\"bibr\">95</xref>). Taken together these studies corroborate the view that potentiating the systemic or local expression of ACE2 and/or Ang-(1-7) is potentially beneficial under several pathological conditions.</p><p>Apart from ACE2, Neprilysin or neutral endopeptidase, a type II membrane protein that belongs to the family of zinc dependent metalloproteases and is expressed in several tissues such as kidney, brain, heart, and lungs, also potentiates the alternative arm of RAS (ACE2/Ang1-7/Mas receptors) by converting Ang-(1-9) in Ang-(1-7) (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). The combination of anti-hypertensive drugs like ARBs with neprilysin inhibitors seems to be protective in hypertension, a risk factor for AD (<xref rid=\"B96\" ref-type=\"bibr\">96</xref>). Based on the fact that Neprilysin plays a pivotal role as an amyloid β peptide (Aβ)- degrading enzyme (<xref rid=\"B97\" ref-type=\"bibr\">97</xref>), the effect of Neprilysin in AD goes in opposite direction. Therefore, an anti-AD therapeutic strategy should rely on potentiating Neprilysin actions (<xref rid=\"B96\" ref-type=\"bibr\">96</xref>). It is worth mentioning that to the best of our knowledge, no strategy combining Neprilysin activation with RAS blockers has been investigated as a potential treatment for AD.</p></sec><sec id=\"s4\"><title>Discussion: Challenges and Opportunities</title><p>ACEIs and ARBs are commonly prescribed for hypertension, myocardial infarction, heart failure, and diabetic nephropathy (<xref rid=\"B98\" ref-type=\"bibr\">98</xref>–<xref rid=\"B100\" ref-type=\"bibr\">100</xref>). The discovery of the expression of RAS components in the brain stimulated the investigation of the potential effects of ACE inhibition and AT1 receptor antagonism on the physiopathology of neuropsychiatric disorders (<xref rid=\"B71\" ref-type=\"bibr\">71</xref>).</p><p>A low-grade pro-inflammatory profile has been associated with aging, a process that has been called “inflammaging” (<xref rid=\"B101\" ref-type=\"bibr\">101</xref>). This pro-inflammatory profile has been associated with late-life depression and neurocognitive disorders, and increased risk for the development of neurodegenerative diseases (<xref rid=\"B101\" ref-type=\"bibr\">101</xref>–<xref rid=\"B103\" ref-type=\"bibr\">103</xref>). For instance, patients with mild neurocognitive disorder who progressed to major neurocognitive disorder had significantly higher baseline levels of inflammatory mediators compared to those who retained the diagnosis of mild neurocognitive disorder on follow-up (<xref rid=\"B104\" ref-type=\"bibr\">104</xref>).</p><p>Recent studies have shown that SARS-CoV-2 can induce a severe systemic inflammatory response, which has been associated with multiple organ failure and, as consequence, a large number of fatalities (<xref rid=\"B105\" ref-type=\"bibr\">105</xref>–<xref rid=\"B107\" ref-type=\"bibr\">107</xref>). Increased circulating levels of interleukin (IL)-6 were positively correlated with pneumonia severity in patients diagnosed with COVID-19 (<xref rid=\"B107\" ref-type=\"bibr\">107</xref>). It is worth mentioning that cytokines like IL-6 are important mediators of the continuous cross-talk between the periphery and the brain (<xref rid=\"B108\" ref-type=\"bibr\">108</xref>). Increased levels of systemic cytokines can lead to cognitive and behavioral changes in response to viral infections like influenza (<xref rid=\"B109\" ref-type=\"bibr\">109</xref>–<xref rid=\"B112\" ref-type=\"bibr\">112</xref>). Mice infected intranasally with live influenza A/PR8/34 (H1N1) exhibited loss of body weight, decreased locomotor activity, and hippocampal-dependent memory impairment. Behavioral and cognitive symptoms were associated with enhanced mRNA expression of inflammatory cytokines (IL-1β, IL-6, IFN-α, and TNF-α) alongside increased microglia reactivity and alterations in neuronal architecture in the hippocampus (<xref rid=\"B111\" ref-type=\"bibr\">111</xref>, <xref rid=\"B112\" ref-type=\"bibr\">112</xref>). Patients with influenza-associated acute encephalopathy/encephalitis exhibited neurological symptoms like seizure, altered arousal, and abnormal behaviors, which were associated with increased concentrations of IL-1β, IL-6, and TNF-α in serum and CSF (<xref rid=\"B110\" ref-type=\"bibr\">110</xref>). High serum levels of IL-6 in patients with influenza virus-associated encephalopathy were associated with poor clinical prognosis including neurological sequelae and death (<xref rid=\"B109\" ref-type=\"bibr\">109</xref>). To date these effects have not been systematically reported and/or studied in the COVID-19 as all efforts have been dedicated to battle the epidemic and minimize its death toll. Therefore, this unchartered area must be explored.</p><p>It is highly possible that, after the epidemic is controlled or over, post-COVID-19 neuropsychiatric conditions, notably neurocognitive disorders, will be unveiled. As older adults with CVD and/or diabetes display a more intense pro-inflammatory profile than older adults without these comorbidities (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref>), and seem to be more vulnerable to the systemic inflammation induced by SARS-CoV-2 (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>), they are at higher risk of CNS dysfunction and neurodegeneration as well. Besides these indirect effects, SARS-CoV-2 may also invade the CNS through the olfactory trait, but it remains to be established whether the virus can directly damage neurons and glial cells (<xref rid=\"B113\" ref-type=\"bibr\">113</xref>, <xref rid=\"B114\" ref-type=\"bibr\">114</xref>).</p><p>The neuroprotective effects of ACEIs and ARBs seem to rely on the anti-inflammatory response exerted by the activation of ACE2/Ang-(1-7) /Mas axis and the decrease in Ang-II inflammatory signaling (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>, <xref rid=\"B89\" ref-type=\"bibr\">89</xref>). Therefore, the anti-inflammatory effects induced by ACEIs and ARBs may constitute a protective mechanism not only for the lung but also for other organs, including the brain, especially at high-risk subjects as older adults with comorbities (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Potential mechanisms by which ACE inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may protect the development and/or progression of neuropsychiatric diseases in older adults with cardiovascular (CVD) and metabolic diseases during COVID-19 pandemic. By increasing the expression of angiotensin-converting enzyme 2 (ACE2), ACEIs, and ARBs decrease angiotensin II (Ang II) inflammatory signaling and vascular damage induced by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which in turn may protect the central nervous system damage.</p></caption><graphic xlink:href=\"fmed-07-00515-g0001\"/></fig><p>Respiratory-related infections like influenza seem to be an independent risk factor for stroke (<xref rid=\"B115\" ref-type=\"bibr\">115</xref>–<xref rid=\"B117\" ref-type=\"bibr\">117</xref>). Patients with severe symptoms of COVID-19 presented elevated levels of D-dimer and significant platelet reduction, which also pedispose these patients to acute cerebrovascular events (<xref rid=\"B118\" ref-type=\"bibr\">118</xref>). As ACE2 is a cardio-cerebral vascular protection molecule (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>), the dysregulation of ACE2 induced by SARS-CoV-2 infection may lead to abnormally elevated blood pressure and increase the risk of cerebral hemorrhage. Moreover, by binding to ACE2 receptors expressed on the capillary endothelium, the SARS-CoV-2 may disrupt the blood-brain barrier and get access into the CNS (<xref rid=\"B113\" ref-type=\"bibr\">113</xref>). ACEIs and ARBs may prevent or attenuate the deleterious vascular events induced by the SARS-CoV-2, also minimizing its potential damage to CNS in older adults.</p><p>Another aspect that must be considered during the COVID-19 pandemic is the development of post-traumatic stress disorder (PTSD). Besides emotional aspects including fear to be ill or die as well as the impact of isolation may significantly account for the development of PTSD, neurobiological factors must be taken into account. PTSD has been associated with CVD and changes in the RAS (<xref rid=\"B119\" ref-type=\"bibr\">119</xref>, <xref rid=\"B120\" ref-type=\"bibr\">120</xref>). For instance, a cross-sectional study revealed that patients diagnosed with PTSD undergoing ACEIs or ARBs treatment for hypertension presented less PTSD symptoms including hyperarousal symptoms, avoidance, and intrusive thoughts when compared with PTSD patients not on ACEIs or ARBs treatments. Of note, other anti-hypertensive drugs, including beta-blockers, calcium channel blockers, and diuretics, were not significantly associated with reduced PTSD symptoms (<xref rid=\"B119\" ref-type=\"bibr\">119</xref>). This is particularly relevant for high-risk patients taking ACEIs and/or ARBs, further supporting the potential harmful effects of discontinuation of these drugs during the treatment of COVID-19. It remains to be answered whether there is any benefit of prescribing ACEIs or ARBs for older adults not taking these medications in order to minimize the complications related to the COVID-19, including the neuropsychiatric ones.</p></sec><sec id=\"s5\"><title>Concluding Remarks</title><p>The discovery that SARS-CoV-2, the virus responsible for the COVID-19, enters the host cells by binding ACE2 receptors, generated a debate regarding the discontinuation or not of ACEIs and ARBs in patients with CVD and diabetes. These comorbities are prevalent among older adults, also being associated with COVID-19 severity and mortality. At first, some authors proposed the discontinuation of ACEIs and ARBs based on the evidence that those drugs can enhance ACE2 levels, supposedly facilitating virus infection. However, this generated a significant backlash with expert opinions recommending against the discontinuation due to the lack of empirical evidence to support the proposal and the potential risk of cardiovascular complications. Moreover, from a theoretical perspective, ACEIs and ARBs may stimulate the anti-inflammatory properties of ACE2/Ang-(1-7)/Mas axis and, therefore, improve COVID-19 associated severity and mortality.</p><p>Besides potentially inducing severe systemic inflammatory response, SARS-CoV-2 also seems to present neurotropism, although the exact extension and mechanisms by which the virus affect the CNS is unclear. In this scenario of enhanced systemic inflammation and potential neuroinflammation, older adults, especially those with CVD and diabetes, are more likely to develop cognitive and behavioral changes alongside neurodegenerative diseases in response to SARS-CoV-2 infection. Given the vascular and anti-inflammatory properties of ACE2 and Ang (1-7), beneficial not harmful effects are expected from ACEIs and ARBs, so these medications should not be withdrawn in older adults.</p></sec><sec id=\"s6\"><title>Author Contributions</title><p>AM drafted the initial version of the manuscript that was revised and modified by AT. All authors have read and approved the final version of the manuscript.</p></sec><sec id=\"s7\"><title>Conflict of Interest</title><p>AT is a CNPq fellowship recipient while AM is a 2019 “For Women in Science” Grant Awardee from the L'Oreal Brazil-UNESCO- Brazilian Academy of Science (ABC).</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> The authors have received financial support from the Brazilian government funding agencies: FAPEMIG (Fundação de Amparo à Pesquisa do Estado de Minas Gerais, Brazil), CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil), and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior). 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Palliat Med</journal-id><journal-id journal-id-type=\"iso-abbrev\">Palliat Med</journal-id><journal-id journal-id-type=\"publisher-id\">PMJ</journal-id><journal-id journal-id-type=\"hwp\">sppmj</journal-id><journal-title-group><journal-title>Palliative Medicine</journal-title></journal-title-group><issn pub-type=\"ppub\">0269-2163</issn><issn pub-type=\"epub\">1477-030X</issn><publisher><publisher-name>SAGE Publications</publisher-name><publisher-loc>Sage UK: London, England</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32799739</article-id><article-id pub-id-type=\"pmc\">PMC7431876</article-id><article-id pub-id-type=\"doi\">10.1177/0269216320950089</article-id><article-id pub-id-type=\"publisher-id\">10.1177_0269216320950089</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Original Articles</subject></subj-group></article-categories><title-group><article-title>The pervasive relevance of COVID-19 within routine paediatric palliative care consultations during the pandemic: A conversation analytic study</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-8237-1459</contrib-id><name><surname>Ekberg</surname><given-names>Katie</given-names></name><xref ref-type=\"aff\" rid=\"aff1-0269216320950089\">1</xref><xref ref-type=\"corresp\" rid=\"corresp1-0269216320950089\"/></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-8618-3527</contrib-id><name><surname>Weinglass</surname><given-names>Lara</given-names></name><xref ref-type=\"aff\" rid=\"aff1-0269216320950089\">1</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0001-8837-7440</contrib-id><name><surname>Ekberg</surname><given-names>Stuart</given-names></name><xref ref-type=\"aff\" rid=\"aff2-0269216320950089\">2</xref><xref ref-type=\"aff\" rid=\"aff3-0269216320950089\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Danby</surname><given-names>Susan</given-names></name><xref ref-type=\"aff\" rid=\"aff1-0269216320950089\">1</xref><xref ref-type=\"aff\" rid=\"aff4-0269216320950089\">4</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">https://orcid.org/0000-0002-9777-1105</contrib-id><name><surname>Herbert</surname><given-names>Anthony</given-names></name><xref ref-type=\"aff\" rid=\"aff5-0269216320950089\">5</xref><xref ref-type=\"aff\" rid=\"aff6-0269216320950089\">6</xref><xref ref-type=\"aff\" rid=\"aff7-0269216320950089\">7</xref></contrib></contrib-group><aff id=\"aff1-0269216320950089\"><label>1</label>School of Early Childhood and Inclusive Education, Queensland University of Technology, Australia</aff><aff id=\"aff2-0269216320950089\"><label>2</label>School of Psychology & Counselling, Queensland University of Technology, Australia</aff><aff id=\"aff3-0269216320950089\"><label>3</label>Centre for Healthcare Transformation, Queensland University of Technology, Australia</aff><aff id=\"aff4-0269216320950089\"><label>4</label>Australian Research Council Centre of Excellence for the Digital Child, Queensland University of Technology, Australia</aff><aff id=\"aff5-0269216320950089\"><label>5</label>Children’s Health Queensland Hospital and Health Service, Australia</aff><aff id=\"aff6-0269216320950089\"><label>6</label>Centre for Children’s Health Research, Australia</aff><aff id=\"aff7-0269216320950089\"><label>7</label>School of Nursing, Queensland University of Technology, Australia</aff><author-notes><corresp id=\"corresp1-0269216320950089\">Katie Ekberg, School of Early Childhood and Inclusive Education, Faculty of Education, Queensland University of Technology, Kelvin Grove, Brisbane, Queensland 4059, Australia. Email: <email>katie.ekberg@qut.edu.au</email></corresp></author-notes><pub-date pub-type=\"epub\"><day>16</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>10</month><year>2020</year></pub-date><volume>34</volume><issue>9</issue><fpage>1202</fpage><lpage>1219</lpage><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder content-type=\"sage\">SAGE Publications</copyright-holder><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p>This article is distributed under the terms of the Creative Commons Attribution 4.0 License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">https://creativecommons.org/licenses/by/4.0/</ext-link>) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (<ext-link ext-link-type=\"uri\" xlink:href=\"https://us.sagepub.com/en-us/nam/open-access-at-sage\">https://us.sagepub.com/en-us/nam/open-access-at-sage</ext-link>).</license-p></license></permissions><abstract><sec id=\"section1-0269216320950089\"><title>Background:</title><p>The importance of caring for children with complex and serious conditions means that paediatric palliative care must continue during pandemics. The recent pandemic of Coronavirus Disease 2019 (COVID-19) provides a natural experiment to study health communication during pandemic times. However, it is unknown how communication within consultations might change during pandemics.</p></sec><sec id=\"section2-0269216320950089\"><title>Aim:</title><p>This study, a sub-study of a larger project, aimed to examine real-world instances of communication in paediatric palliative care consultations prior to and during the COVID-19 pandemic to understand how clinicians and families talk about the pandemic.</p></sec><sec id=\"section3-0269216320950089\"><title>Design:</title><p>Paediatric palliative care consultations prior to, during, and immediately following the initial peak of COVID-19 cases in Australia were video recorded and analysed using Conversation Analysis methods.</p></sec><sec id=\"section4-0269216320950089\"><title>Setting/participants:</title><p>Twenty-five paediatric palliative care consultations (including face-to-face outpatient, telehealth outpatient and inpatient consultations) were video recorded within a public children’s hospital in Australia. Participants included 14 health professionals, 15 child patients, 23 adult family members and 5 child siblings.</p></sec><sec id=\"section5-0269216320950089\"><title>Results:</title><p>There was a pervasive relevance of both serious and non-serious talk about COVID-19 within the consultations recorded during the pandemic. Topics typical of a standard paediatric palliative care consultation often led to discussion of the pandemic. Clinicians (55%) and parents (45%) initiated talk about the pandemic.</p></sec><sec id=\"section6-0269216320950089\"><title>Conclusions:</title><p>Clinicians should not be surprised by the pervasiveness of COVID-19 or other pandemic talk within standard paediatric palliative care consultations. This awareness will enable clinicians to flexibly address family needs and concerns about pandemic-related matters that may impact health and wellbeing.</p></sec></abstract><kwd-group><kwd>Palliative care</kwd><kwd>COVID-19</kwd><kwd>pandemics</kwd><kwd>communication</kwd><kwd>child</kwd></kwd-group><funding-group><award-group id=\"award1-0269216320950089\"><funding-source id=\"funding1-0269216320950089\"><institution-wrap><institution>Australian Research Council</institution><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/501100000923</institution-id></institution-wrap></funding-source><award-id rid=\"funding1-0269216320950089\">DP180101941</award-id></award-group></funding-group><custom-meta-group><custom-meta><meta-name>typesetter</meta-name><meta-value>ts1</meta-value></custom-meta></custom-meta-group></article-meta></front><body><boxed-text id=\"boxed-text1-0269216320950089\" position=\"float\" orientation=\"portrait\"><p>\n<bold>What is already known about the topic?</bold>\n</p><list list-type=\"bullet\" id=\"list1-0269216320950089\"><list-item><p>The urgency of caring for children with complex and serious conditions ensures that care must continue during the Coronavirus Disease 2019 (COVID-19) pandemic.</p></list-item><list-item><p>As yet, guidelines for communication with families about the COVID-19 pandemic are not based on direct observational evidence of actual communication practices within palliative care during the pandemic.</p></list-item></list><p>\n<bold>What this paper adds?</bold>\n</p><list list-type=\"bullet\" id=\"list2-0269216320950089\"><list-item><p>The current study provides evidence of the pervasive relevance of communication about the COVID-19 pandemic during clinician-family paediatric palliative care consultations.</p></list-item><list-item><p>There was a pervasive relevance of serious and non-serious talk about the pandemic.</p></list-item><list-item><p>Topics typical of standard paediatric palliative care consultations often led to discussion of the pandemic, including medical discussions and psychosocial and lifestyle discussions.</p></list-item><list-item><p>Clinicians (55%) and parents (45%) initiated talk about the pandemic.</p></list-item></list><p>\n<bold>Implications for practice, theory, or policy</bold>\n</p><list list-type=\"bullet\" id=\"list3-0269216320950089\"><list-item><p>Clinicians should expect and be prepared for the pervasiveness of talk about the COVID-19 pandemic within standard paediatric palliative care consultations, so that they can be flexible in how they respond to families.</p></list-item><list-item><p>Future guidelines should consider the pervasive and varied ways that conversations about a pandemic are raised within and across routine consultations.</p></list-item></list></boxed-text><sec sec-type=\"intro\" id=\"section7-0269216320950089\"><title>Introduction</title><p>The Coronavirus Disease 2019 (COVID-19) has had a significant impact on the global human population since its development into a pandemic in early 2020. This paper examines care of children with life-limiting conditions during the COVID-19 pandemic. Although children, overall, appear to be less affected than adults,<sup><xref rid=\"bibr1-0269216320950089\" ref-type=\"bibr\">1</xref><xref rid=\"bibr2-0269216320950089\" ref-type=\"bibr\"/><xref rid=\"bibr3-0269216320950089\" ref-type=\"bibr\"/>–<xref rid=\"bibr4-0269216320950089\" ref-type=\"bibr\">4</xref></sup> the implications of COVID-19 for children with life-limiting conditions is less clear. The lower incidence rate of the disease amongst children, and an apparently low rate of severe cases, means little was known during the early months of the pandemic about the potential impact of COVID-19 on children.<sup><xref rid=\"bibr5-0269216320950089\" ref-type=\"bibr\">5</xref><xref rid=\"bibr6-0269216320950089\" ref-type=\"bibr\"/><xref rid=\"bibr7-0269216320950089\" ref-type=\"bibr\"/>–<xref rid=\"bibr8-0269216320950089\" ref-type=\"bibr\">8</xref></sup> There was some evidence that children with severe COVID-19 had pre-existing comorbidities,<sup><xref rid=\"bibr9-0269216320950089\" ref-type=\"bibr\">9</xref>,<xref rid=\"bibr10-0269216320950089\" ref-type=\"bibr\">10</xref></sup> although this only emerged many months into the pandemic. In addition, children were potentially vulnerable to both health and psychosocial<sup><xref rid=\"bibr11-0269216320950089\" ref-type=\"bibr\">11</xref></sup> implications of the pandemic given the unprecedented changes to their normal daily routines (e.g., school closures, changes to parents’ employment, and cancellations of social and sporting commitments).<sup><xref rid=\"bibr8-0269216320950089\" ref-type=\"bibr\">8</xref></sup></p><p>Communication is pivotal for realising the holistic focus of palliative care,<sup><xref rid=\"bibr12-0269216320950089\" ref-type=\"bibr\">12</xref><xref rid=\"bibr13-0269216320950089\" ref-type=\"bibr\"/>–<xref rid=\"bibr14-0269216320950089\" ref-type=\"bibr\">14</xref></sup> providing the means to understand the physical, mental, social, cultural, and spiritual needs of patients and their families.<sup><xref rid=\"bibr15-0269216320950089\" ref-type=\"bibr\">15</xref><xref rid=\"bibr16-0269216320950089\" ref-type=\"bibr\"/>–<xref rid=\"bibr17-0269216320950089\" ref-type=\"bibr\">17</xref></sup> The urgency of caring for children with complex and serious conditions requires that care must continue during the pandemic, albeit with adjustments to service delivery.<sup><xref rid=\"bibr18-0269216320950089\" ref-type=\"bibr\">18</xref>,<xref rid=\"bibr19-0269216320950089\" ref-type=\"bibr\">19</xref></sup> To reduce the risk of infection, a greater proportion of palliative care consultations during the pandemic were reconfigured to telehealth.<sup><xref rid=\"bibr18-0269216320950089\" ref-type=\"bibr\">18</xref><xref rid=\"bibr19-0269216320950089\" ref-type=\"bibr\"/>–<xref rid=\"bibr20-0269216320950089\" ref-type=\"bibr\">20</xref></sup> Beyond changes to the <italic>medium</italic> for consultations, it is still relatively unknown how the <italic>content</italic> of consultations within paediatric palliative care also might have changed during the pandemic. Guidelines and scripts were developed to guide clinicians confronted with the rapid changes of the pandemic, both within healthcare systems and across societies more generally.<sup><xref rid=\"bibr21-0269216320950089\" ref-type=\"bibr\">21</xref><xref rid=\"bibr22-0269216320950089\" ref-type=\"bibr\"/>–<xref rid=\"bibr23-0269216320950089\" ref-type=\"bibr\">23</xref></sup> Initial resources about communication during the pandemic focused on communicating with and about patients who had COVID-19.<sup><xref rid=\"bibr24-0269216320950089\" ref-type=\"bibr\">24</xref>,<xref rid=\"bibr25-0269216320950089\" ref-type=\"bibr\">25</xref></sup> Additional resources were developed later for communicating with patients and families receiving standard, ongoing care for other conditions during the pandemic.<sup><xref rid=\"bibr26-0269216320950089\" ref-type=\"bibr\">26</xref></sup> These guidelines provided suggestions for how clinicians might set up a specific conversation about the pandemic with patients as part of an otherwise routine clinical encounter. These guidelines were not, however, based on direct observational evidence of communication within palliative care during the COVID-19 pandemic. Moreover, most guidance focussed on adult, rather than paediatric, care. The current study addresses these gaps, by comparing video-recordings of actual, real-world instances of communication in paediatric palliative care consultations prior to and during the COVID-19 pandemic. The peak months of the pandemic became an opportunity to understand how the provision of paediatric palliative care is maintained, potentially with adaptations, during the uncertainty of a pandemic.</p></sec><sec sec-type=\"methods\" id=\"section8-0269216320950089\"><title>Method</title><sec id=\"section9-0269216320950089\"><title>Setting</title><p>This study was part of a larger project that examined communication between healthcare professionals and families within paediatric palliative care services in Australia. Analysis was based on a corpus of 25 paediatric palliative care consultations that were video recorded in a public children’s hospital in Australia. Fifteen of these consultations were recorded in the months of September 2019–February 2020, prior to COVID-19 being declared a pandemic.<sup><xref rid=\"bibr27-0269216320950089\" ref-type=\"bibr\">27</xref></sup> Ten consultations were recorded during the months of March–May 2020, which was during and immediately following the initial peak of COVID-19 cases in Australia. The epidemiological trajectory of the initial peak of COVID-19 in Australia can be found in <xref ref-type=\"fig\" rid=\"fig1-0269216320950089\">Figure 1</xref>.<sup><xref rid=\"bibr28-0269216320950089\" ref-type=\"bibr\">28</xref></sup></p><fig id=\"fig1-0269216320950089\" orientation=\"portrait\" position=\"float\"><label>Figure 1.</label><caption><p>The epidemiological trajectory of COVID-19 in Australia March-May 2020.</p></caption><graphic xlink:href=\"10.1177_0269216320950089-fig1\"/></fig></sec><sec id=\"section10-0269216320950089\"><title>Data collection</title><p>Staff and families involved in the paediatric palliative care service were informed about the project by a member of hospital staff. All participants provided informed written consent themselves (adults) or by their guardian (for all child patients). Prior to the start of each recorded consultation, a clinician set up two video cameras in the consultation room. In the telehealth consultations, one camera captured the interactions of the clinicians in the room and the other camera was directed towards the computer showing the family via videoconference. No researchers were present during the video-recorded consultations.</p></sec><sec id=\"section11-0269216320950089\"><title>Data analysis</title><p>The video-recorded data were transcribed using the standard conversation analytic transcription conventions developed by Jefferson (see <xref ref-type=\"app\" rid=\"app1-0269216320950089\">Appendix 1</xref> for transcription notations).<sup><xref rid=\"bibr29-0269216320950089\" ref-type=\"bibr\">29</xref></sup> The transcripts include details of pauses, overlapping talk, intonational contours and non-verbal communication found to be consequential for how participants manage social interactions. All names of people and places in the transcripts are pseudonyms. The data were analysed using conversation analysis (CA) by authors KE, LW, SE, and SD.<sup><xref rid=\"bibr30-0269216320950089\" ref-type=\"bibr\">30</xref></sup> Increasingly, the method of conversation analysis is used in clinical settings,<sup><xref rid=\"bibr31-0269216320950089\" ref-type=\"bibr\">31</xref>,<xref rid=\"bibr32-0269216320950089\" ref-type=\"bibr\">32</xref></sup> including in paediatric palliative care, to capture the complexity of the interactions as they unfold, moment-by-moment.<sup><xref rid=\"bibr33-0269216320950089\" ref-type=\"bibr\">33</xref>,<xref rid=\"bibr34-0269216320950089\" ref-type=\"bibr\">34</xref></sup></p><p>A CA approach uses observation to ensure analysis is based on participants’ experiences and differs from other research approaches that begin with assumptions, intuitions, or hypotheses.<sup><xref rid=\"bibr35-0269216320950089\" ref-type=\"bibr\">35</xref></sup> Analysis involves building an exhaustive collection of sequences of interaction that identify a particular communication practice, and then undertakes a turn-by-turn examination of each and all of these sequences to understand how specific conversational practices influence an ongoing interaction.<sup><xref rid=\"bibr30-0269216320950089\" ref-type=\"bibr\">30</xref></sup> In this analysis, all sequences of talk related to COVID-19 and the associated changes to lifestyle during the pandemic were extracted from the appointments recorded during the pandemic (<italic>n</italic> = 33 fragments) and were examined for how this talk was initiated and responded to within the broader sequence of conversation within the consultation. These fragments were also compared with similar sequences of interaction in the pre-pandemic data. Fragments that display clear examples of the phenomena being discussed are presented below. In this paper, we have included examples of both serious and non-serious pandemic talk to show the breadth of the types of sequences identified in the corpus.</p></sec><sec id=\"section12-0269216320950089\"><title>Ethical approval</title><p>This study was approved by the Children’s Health Services Queensland Human Research Ethics Committee (HREC/18/QRCH/86) and Queensland University of Technology Human Research Ethics Committee (1800000468), in addition to site-specific governance approvals. The study adhered to the principles of the Australian National Health and Medical Research Statement on Research Involving Human Subjects.</p></sec></sec><sec id=\"section13-0269216320950089\"><title>Analysis</title><p>Key demographic information about each appointment is found in <xref rid=\"table1-0269216320950089\" ref-type=\"table\">Table 1</xref>. The pre-pandemic consultations comprised 13 face-to-face outpatient consultations and two face-to-face inpatient consultations. The pandemic consultations comprised four telehealth outpatient consultations and six face-to-face inpatient consultations.</p><table-wrap id=\"table1-0269216320950089\" orientation=\"portrait\" position=\"float\"><label>Table 1.</label><caption><p>Appointment demographics.</p></caption><alternatives><graphic xlink:href=\"10.1177_0269216320950089-table1\"/><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"char\" char=\".\" span=\"1\"/><col align=\"char\" char=\".\" span=\"1\"/><col align=\"char\" char=\".\" span=\"1\"/><col align=\"char\" char=\".\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Appointment number</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Appointment type</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Child in appointment (Y/N)</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Family members in appointment</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Healthcare professionals in appointment</th></tr></thead><tbody><tr><td colspan=\"5\" rowspan=\"1\">\n<bold>Pre-pandemic</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F09_E01_2019-08-22</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F10_E01_2019-09-05</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctors ×2</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F11_E01_2019-09-19</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Older sister</td><td rowspan=\"1\" colspan=\"1\">Doctors ×2</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F12_E01_2019-11-19</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Father<break/>Older sister</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Training Nurse</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F12_E02_2020-02-10</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">N</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Father</td><td rowspan=\"1\" colspan=\"1\">Doctor</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F13_E01_2019-11-26</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Father</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Medical student<break/>Physiotherapist</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F14_E01_2019-11-28</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Father<break/>Younger brother</td><td rowspan=\"1\" colspan=\"1\">Doctor</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F14_E02_2020-02-20</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Father<break/>Support worker</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurse</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F15_E01_2019-12-12</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Twin younger brothers</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Medical student</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F17_E01_2019-12-17</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Father</td><td rowspan=\"1\" colspan=\"1\">Doctor</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F18_E01_2020-02-10</td><td rowspan=\"1\" colspan=\"1\">F2F inpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurse</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F18_E02_2020-03-13</td><td rowspan=\"1\" colspan=\"1\">F2F inpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F19_E01_2020-02-20</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">N</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Father</td><td rowspan=\"1\" colspan=\"1\">Doctor</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F20_E01_2020-02-24</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Support worker</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Registrar<break/>Training nurse</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F21_E01-2020-02-27</td><td rowspan=\"1\" colspan=\"1\">F2F outpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurse</td></tr><tr><td colspan=\"5\" rowspan=\"1\">\n<bold>During initial peak of pandemic</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F10_E02_2020-04-16</td><td rowspan=\"1\" colspan=\"1\">F2F inpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurse</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F10_E03_2020-04-29</td><td rowspan=\"1\" colspan=\"1\">F2F inpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurse</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F12_E03_2020-05-08</td><td rowspan=\"1\" colspan=\"1\">Telehealth</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Father</td><td rowspan=\"1\" colspan=\"1\">Doctor</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F17_E02_2020-05-07</td><td rowspan=\"1\" colspan=\"1\">Telehealth</td><td rowspan=\"1\" colspan=\"1\">N</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurse<break/>Medical student</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F23_E01_2020-04-09</td><td rowspan=\"1\" colspan=\"1\">Telehealth</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Father<break/>Twin sister</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Fellow</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F24_E01_2020-04-16</td><td rowspan=\"1\" colspan=\"1\">F2F inpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurses ×2<break/>Music Therapist</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F24_E02_2020-04-17</td><td rowspan=\"1\" colspan=\"1\">F2F inpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurse</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F24_E03_2020-04-22</td><td rowspan=\"1\" colspan=\"1\">F2F inpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurse</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F24_E04_2020-05-19</td><td rowspan=\"1\" colspan=\"1\">F2F inpatient</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother</td><td rowspan=\"1\" colspan=\"1\">Doctor<break/>Nurses ×3</td></tr><tr><td rowspan=\"1\" colspan=\"1\">S1_F25_E01_2020-04-30</td><td rowspan=\"1\" colspan=\"1\">Telehealth</td><td rowspan=\"1\" colspan=\"1\">Y</td><td rowspan=\"1\" colspan=\"1\">Mother<break/>Father<break/>Younger sister</td><td rowspan=\"1\" colspan=\"1\">Doctor</td></tr></tbody></table></alternatives><table-wrap-foot><fn id=\"table-fn1-0269216320950089\"><p>F2F: face-to-face consultation.</p></fn></table-wrap-foot></table-wrap><p>Consultations had a mean duration of 41 ½ min (SD = 23.3), with a total of 17 h and 15 min of data. Participants included 14 health professionals (including doctors, nurses, and allied health professionals), 15 child patients, 23 adult family members, and 5 child siblings present in the consultations. Six families had two or more consultations recorded. Key demographics of the child patients are presented in <xref rid=\"table2-0269216320950089\" ref-type=\"table\">Table 2</xref>. The children ranged in age from 3 to 15 years old (M = 7.73 years, SD = 4.38) and sixty percent of the patients were male. No children in the study had received a diagnosis of Coronavirus at the time of their recorded consultation(s). Child diagnoses included Neurological (<italic>n</italic> = 6), cerebral palsy (<italic>n</italic> = 4), metabolic (<italic>n</italic> = 3), genetic (<italic>n</italic> = 1), and rare (<italic>n</italic> = 1).</p><table-wrap id=\"table2-0269216320950089\" orientation=\"portrait\" position=\"float\"><label>Table 2.</label><caption><p>Child patient demographics.</p></caption><alternatives><graphic xlink:href=\"10.1177_0269216320950089-table2\"/><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"char\" char=\".\" span=\"1\"/><col align=\"char\" char=\".\" span=\"1\"/><col align=\"char\" char=\".\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Participant</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Sex</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Age</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Diagnosis</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">F09</td><td rowspan=\"1\" colspan=\"1\">F</td><td rowspan=\"1\" colspan=\"1\">6</td><td rowspan=\"1\" colspan=\"1\">Cerebral Palsy</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F10</td><td rowspan=\"1\" colspan=\"1\">M</td><td rowspan=\"1\" colspan=\"1\">5</td><td rowspan=\"1\" colspan=\"1\">Pontocerebellar Hypoplasia</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F11</td><td rowspan=\"1\" colspan=\"1\">F</td><td rowspan=\"1\" colspan=\"1\">14</td><td rowspan=\"1\" colspan=\"1\">Cerebral Palsy</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F12</td><td rowspan=\"1\" colspan=\"1\">M</td><td rowspan=\"1\" colspan=\"1\">7</td><td rowspan=\"1\" colspan=\"1\">Sanfillippo Syndrome (mucopolysaccharidosis type III)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F13</td><td rowspan=\"1\" colspan=\"1\">M</td><td rowspan=\"1\" colspan=\"1\">15</td><td rowspan=\"1\" colspan=\"1\">Sanfillippo Syndrome (mucopolysaccharidosis type III)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F14</td><td rowspan=\"1\" colspan=\"1\">F</td><td rowspan=\"1\" colspan=\"1\">9</td><td rowspan=\"1\" colspan=\"1\">Cerebral Palsy</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F15</td><td rowspan=\"1\" colspan=\"1\">M</td><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">Epileptic Encephalopathy</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F17</td><td rowspan=\"1\" colspan=\"1\">M</td><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">Phelan Mcdermid Syndrome</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F18</td><td rowspan=\"1\" colspan=\"1\">M</td><td rowspan=\"1\" colspan=\"1\">3</td><td rowspan=\"1\" colspan=\"1\">Epileptic Encephalopathy</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F19</td><td rowspan=\"1\" colspan=\"1\">F</td><td rowspan=\"1\" colspan=\"1\">13</td><td rowspan=\"1\" colspan=\"1\">Cerebral Palsy</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F20</td><td rowspan=\"1\" colspan=\"1\">F</td><td rowspan=\"1\" colspan=\"1\">14</td><td rowspan=\"1\" colspan=\"1\">Epileptic Encephalopathy</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F21</td><td rowspan=\"1\" colspan=\"1\">M</td><td rowspan=\"1\" colspan=\"1\">7</td><td rowspan=\"1\" colspan=\"1\">Lysosomal Storage Disorder</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F23</td><td rowspan=\"1\" colspan=\"1\">M</td><td rowspan=\"1\" colspan=\"1\">9</td><td rowspan=\"1\" colspan=\"1\">Lymphangiomatosis</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F24</td><td rowspan=\"1\" colspan=\"1\">F</td><td rowspan=\"1\" colspan=\"1\">4</td><td rowspan=\"1\" colspan=\"1\">Pontocerebellar Hypoplasia</td></tr><tr><td rowspan=\"1\" colspan=\"1\">F25</td><td rowspan=\"1\" colspan=\"1\">M</td><td rowspan=\"1\" colspan=\"1\">4</td><td rowspan=\"1\" colspan=\"1\">Genetic Neurodevelopmental Disorder</td></tr></tbody></table></alternatives></table-wrap><p>Analysis of routine care consultations recorded during the COVID-19 pandemic revealed the pervasive relevance of the pandemic during consultations. Topics typical of standard paediatric palliative care consultations often led to discussion of the pandemic. This was introduced between one and seven times within each of the ten consultations recorded during the pandemic (total = 33, mean = 3.3 times per consultation) and often led to extended sequences of interaction. Within these consultations, topics typical of standard paediatric palliative care consultations often led to discussion of the pandemic. These topics were diverse, spanning medical discussions (e.g. pain medication) and psychosocial and lifestyle discussions that are a key part of the holistic, supportive care provided within palliative care services (e.g. school, parents’ caring/work responsibilities). Of the 33 fragments analysed, the pandemic was raised by clinicians in 18 fragments (55%) and parents in 15 fragments (45%). The pervasive and varied nature of the talk about the pandemic is analysed in detail in the below fragments, which are exemplars of the broader corpus of consultations. First, some fragments of typical talk from consultations recorded prior to the pandemic are considered. These will be compared with similar fragments from the consultations recorded during the pandemic.</p><sec id=\"section14-0269216320950089\"><title>Consultations prior to the COVID-19 pandemic</title><p>Fragments 1-4, recorded prior to the peak of the pandemic, show examples of typical sequences of conversation within palliative care consultations before the pandemic. Each fragment involves a suggestion or enquiry from the doctor often observed within these types of consultations: making a suggestion about pain medication (Fragment 1); enquiring about the child’s school (Fragment 2); enquiring about the parent’s work responsibilities (Fragment 3); and enquiring about what the child is watching on a digital tablet (Fragment 4).</p><p>\n<disp-quote><p>\n<monospace><bold>(1)  [S1_F12_E02_2020-02-10 48:41]</bold></monospace>\n</p><p>\n<monospace>01   (12.1)</monospace>\n</p><p>\n<monospace>02 Doc: U::m (3.0) so the Panadol um he <underline>was</underline> on three seventy five</monospace>\n</p><p>\n<monospace>03   but he could probably ah go up to four <underline>fifty</underline> now.</monospace>\n</p><p>\n<monospace>04 Dad: °Mmhm°.</monospace>\n</p><p>\n<monospace>05   (2.0)</monospace>\n</p><p>\n<monospace>06 Doc: And Nurofen he could go up to 300 °milligrams°.</monospace>\n</p><p>\n<monospace>07   (21.0)</monospace>\n</p><p>\n<monospace>08 Doc: Oh so um Hannah says she can do a phone order so that’s good.</monospace>\n</p><p>\n<monospace>09  (7.0)</monospace>\n</p><p>\n<monospace><bold>(2) [S1_F11_E01_2019-09-19 23:20]</bold></monospace>\n</p><p>\n<monospace>01 Doc: Which school are you- is she at?</monospace>\n</p><p>\n<monospace>02 Mum: Magpie Park [near Flagstaff]</monospace>\n</p><p>\n<monospace>03 Doc:  [ Magpie Park ]</monospace>\n</p><p>\n<monospace>04 Doc: Okay yeah an’ is she (.) what level is she sort’ve at school</monospace>\n</p><p>\n<monospace>05   sort’ve (0.5) um I guess at the upper [levels maybe,]</monospace>\n</p><p>\n<monospace>06 Mum:      [ Oh:::::: ]</monospace>\n</p><p>\n<monospace>07 Doc: or sort’ve high school at least?</monospace>\n</p><p>\n<monospace>08 Mum: Oh [<underline>yeah</underline>. Yep]</monospace>\n</p><p>\n<monospace>09 Doc:  [Yeah yep] yep <underline>mid</underline>-high school I guess [they’d call it? ]</monospace>\n</p><p>\n<monospace>10 Mum:            [Something like that.]</monospace>\n</p><p>\n<monospace>11   I suppose.</monospace>\n</p><p>\n<monospace>12 Doc: Yeah okay.</monospace>\n</p><p>\n<monospace>13 Mum: Yeah she’s a <underline>senior</underline>.</monospace>\n</p><p>\n<monospace>14 Doc: Yeah.</monospace>\n</p><p>\n<monospace>15   (2.1)</monospace>\n</p><p>\n<monospace>16 Doc: U::m so then in terms of <underline>prac</underline>tical supports you um you’ve</monospace>\n</p><p>\n<monospace>17   mentioned the (0.3) <underline>belt</underline> particularly for plane flights. . .</monospace>\n</p><p>\n<monospace><bold>(3) [S1_F14_E01_2019-11-28 36:16]</bold></monospace>\n</p><p>\n<monospace>01 Doc: And u- u- m- it <underline>sounds</underline> like you’ve got a full-time job looking</monospace>\n</p><p>\n<monospace>02  after Bianca? Yeah,</monospace>\n</p><p>\n<monospace>03  (0.9)</monospace>\n</p><p>\n<monospace>04 Dad: Um what do you mean?=</monospace>\n</p><p>\n<monospace>05 Doc: =Like do you work as w↑ell [or yeah okay]</monospace>\n</p><p>\n<monospace>06 Dad:         [Yeah I- so I] started a business.</monospace>\n</p><p>\n<monospace>07 Doc: Okay wow yeah [what’s] your business?</monospace>\n</p><p>\n<monospace>08 Dad:      [Yeah. ]</monospace>\n</p><p>\n<monospace>09 Dad: Uh lawn mowing an’ that.</monospace>\n</p><p>\n<monospace>10 Doc: Okay.</monospace>\n</p><p>\n<monospace>11 Dad: Yep.</monospace>\n</p><p>\n<monospace><bold>(4) [S1_F17_E01_2019-12-17 06:52]</bold></monospace>\n</p><p>\n<monospace>01 Doc: heh he:h what- what’s he watching?</monospace>\n</p><p>\n<monospace>02 Dad: Oh he’s watching a car one.</monospace>\n</p><p>\n<monospace>03 Doc: Yeah.</monospace>\n</p><p>\n<monospace>04 Mum: Just Blippi it’s only [thing] he watches.</monospace>\n</p><p>\n<monospace>05 Doc:      [Okay ]</monospace>\n</p><p>\n<monospace>06 Doc: Yeah yeah what’s it called Blippi?</monospace>\n</p><p>\n<monospace>07 Dad: [Blippi]</monospace>\n</p><p>\n<monospace>08 Mum: [Blippi] it’s a- it’s an American education: guy</monospace>\n</p><p>\n<monospace>09 Doc: [Okay]</monospace>\n</p><p>\n<monospace>10 Mum: [that] goes ar[ound] <underline>sh</underline>owing them different things</monospace>\n</p><p>\n<monospace>11 Doc:  [okay]</monospace>\n</p><p>\n<monospace>12 Mum: [like <underline>cars</underline> or] [£rasp]berry farms£ or</monospace>\n</p><p>\n<monospace>13 Doc: [yeah heh heh][ yeah]</monospace>\n</p><p>\n<monospace>14 Doc: Yeah yeah</monospace>\n</p><p>\n<monospace>15 Mum: I don’t know why he likes it but he does.</monospace>\n</p><p>\n<monospace>16 Doc: Yeah.</monospace>\n</p></disp-quote>\n</p><p>These typical sequences of conversation also were observed in consultations recorded during the pandemic. As will be shown in the next section, however, the sequences shifted to a different trajectory relevant to life during the pandemic. A comparable instance to each of the above fragments is considered now, drawn from consultations recorded during the pandemic.</p></sec><sec id=\"section15-0269216320950089\"><title>Consultations during the COVID-19 pandemic</title><p>The next five fragments recorded during the COVID-19 pandemic demonstrate how similar topics of conversation were initiated, and how they soon shifted to talk about COVID-19 by either clinician or parent. In the first example below, a concern was raised by a parent about the treatment of their child during the pandemic period. In the initial conversation, a telehealth consultation between the doctor and father, the father had mentioned that ibuprofen seemed to work better than paracetamol for relieving their child’s headaches. This topic of pain relief has been raised again now, this time by the doctor (lines 1-2), who recommends giving the child <italic>Nurofen®</italic> (a popular brand of ibuprofen) for pain relief from these headaches.</p><p>\n<disp-quote><p>\n<monospace><bold>(5) [S1_F23_E01_2020-04-09 13:49]</bold></monospace>\n</p><p>\n<monospace>01 Doc: Okay yeah no >that’s really good< and maybe .hh try n’ sort’ve</monospace>\n</p><p>\n<monospace>02   give him medications: ea- like the Nurofen early maybe,</monospace>\n</p><p>\n<monospace>03  (1.0)</monospace>\n</p><p>\n<monospace>04 Dad: Yeah [yeah] but we’ve had some concerns with corona[virus]</monospace>\n</p><p>\n<monospace>05 Doc:  [yeah]           [°Mm° ]</monospace>\n</p><p>\n<monospace>06 Dad: around at the moment and (.) y’know there is some (0.4) sort’ve</monospace>\n</p><p>\n<monospace>07   murmurs out there that it’s: it’s not the best thing to use</monospace>\n</p><p>\n<monospace>08   Nurofen at the moment but um y’know a lot of the times we don’t</monospace>\n</p><p>\n<monospace>09   really have any choice.</monospace>\n</p><p>\n<monospace>10 Doc: Yeah I have a- yeah I di- I have looked into the coronavirus and</monospace>\n</p><p>\n<monospace>11   the Nurofen, (.) u::m (.) and um (.) the- (.) the- (.) there <underline>was</underline> a</monospace>\n</p><p>\n<monospace>12   suspicion that maybe (.) u::m some patients who had coronavirus</monospace>\n</p><p>\n<monospace>13  (.) uh there was an association with Nurofen and poorer outcomes</monospace>\n</p><p>\n<monospace>14  [but] first >thing< is that these were much older patients,</monospace>\n</p><p>\n<monospace>15 Dad: [Yep]</monospace>\n</p><p>\n<monospace>16 Doc: and many of them sorta [had] things like heart conditions and</monospace>\n</p><p>\n<monospace>17 Dad:       [Yep]</monospace>\n</p><p>\n<monospace>18 Doc: lung- and lung conditions and <underline>re</underline>nal issues,</monospace>\n</p><p>\n<monospace>19   (0.2)</monospace>\n</p><p>\n<monospace>20 Doc: um and it was ONLY the patients with <underline>corona</underline>virus that had this</monospace>\n</p><p>\n<monospace>21   ↑issue,</monospace>\n</p><p>\n<monospace>22   (0.4)</monospace>\n</p><p>\n<monospace>23 Doc: um so- so- and the World Health Organisation have actually come</monospace>\n</p><p>\n<monospace>24   out and said that they <underline>think</underline> (.) um that (.) um taking Nurofen is</monospace>\n</p><p>\n<monospace>25   okay with (.) coronavirus, so just to reas[sure] you,</monospace>\n</p><p>\n<monospace>26 Dad:            [Okay]</monospace>\n</p><p>\n<monospace>27 Doc: u:m but ↑if- if- >and I guess hopefully< Sam- Sammy doesn’t</monospace>\n</p><p>\n<monospace>28   sort’ve have any issues with um (0.2) getting coronavirus but um</monospace>\n</p><p>\n<monospace>29   (0.4) um</monospace>\n</p><p>\n<monospace>30 Dad: We’ve been £locked down fo(h)r a few weeks [now ]£</monospace>\n</p><p>\n<monospace>31 Doc:            [Yeah] yeah and um (.)</monospace>\n</p><p>\n<monospace>32   we seem to be doing okay in ((State)) at the moment although we</monospace>\n</p><p>\n<monospace>33   don’t want to sort of be too um confident but (.) but maybe um</monospace>\n</p><p>\n<monospace>34   it’s OKAY while he’s well to take it, but maybe if he did get sick</monospace>\n</p><p>\n<monospace>35   you might stop it then, yeah so um so yeah.</monospace>\n</p><p>\n<monospace>36   (0.5)</monospace>\n</p><p>\n<monospace>37 Doc: Does that reassure you?</monospace>\n</p><p>\n<monospace>38   (1.5)</monospace>\n</p><p>\n<monospace>39 Dad: <underline>Yeah</underline>.</monospace>\n</p><p>\n<monospace>40 Doc: Yeah</monospace>\n</p><p>\n<monospace>41   (.)</monospace>\n</p><p>\n<monospace>42 Doc: [So-]</monospace>\n</p><p>\n<monospace>43 Dad: [Yep] that’s fine.</monospace>\n</p><p>\n<monospace>44 Doc: And the World Health Organization=>they actually put out a< and I</monospace>\n</p><p>\n<monospace>45   can try to send you the link but they actually put out a sort of a</monospace>\n</p><p>\n<monospace>46   statement saying that there’s no <underline>real</underline> association between Nurofen</monospace>\n</p><p>\n<monospace>47   and- and um worse outcomes with coronavirus.</monospace>\n</p><p>\n<monospace>48   (1.0)</monospace>\n</p><p>\n<monospace>49 Dad: Okay.</monospace>\n</p><p>\n<monospace>50 Doc: Mm.</monospace>\n</p><p>\n<monospace>51 Dad: That’s good to know.</monospace>\n</p><p>\n<monospace>52 Doc: Yeah.</monospace>\n</p><p>\n<monospace>53   (0.5)</monospace>\n</p><p>\n<monospace>54 Doc: The ↑other thing that’s a little bit reassuring is- and it’s not</monospace>\n</p><p>\n<monospace>55   <underline>to</underline>tally reassuring but y’know that (0.2) w- they’re tending to</monospace>\n</p><p>\n<monospace>56   find that children are having less <underline>severe</underline> (0.3) and the length of</monospace>\n</p><p>\n<monospace>57   their illness is shorter as well and it’s really seems to be an</monospace>\n</p><p>\n<monospace>58  ↑adult illness,</monospace>\n</p><p>\n<monospace>59   (.)</monospace>\n</p><p>\n<monospace>60 Doc: U::m (.) [but] I know- I know you know patients with a complex</monospace>\n</p><p>\n<monospace>61 Dad:   [Yep]</monospace>\n</p><p>\n<monospace>62 Doc: illness such- such as Sammy y’know he: y’know you also need to (.)</monospace>\n</p><p>\n<monospace>63   u:m .tch y’know look after Sammy as well and protect him as well,</monospace>\n</p><p>\n<monospace>64   so yeah but there is a little bit of a [reas]surance but I’m also</monospace>\n</p><p>\n<monospace>65 Dad:        [Yes ]</monospace>\n</p><p>\n<monospace>66 Doc: not wanting to minimise your concerns as well yeah</monospace>\n</p><p>\n<monospace>67  (1.0)</monospace>\n</p><p>\n<monospace>68 Dad: <underline>Yeah</underline> well you know Mary’s a nurse and midwife as well so she’s</monospace>\n</p><p>\n<monospace>69   pretty ah- pretty strict on all of us for now (.) we’re uh wash</monospace>\n</p><p>\n<monospace>70   hands and obviously use the ha(h)nd sanitiser and things like that</monospace>\n</p><p>\n<monospace>71 Doc: [Yeah]</monospace>\n</p><p>\n<monospace>72 Dad: [And ] we haven’t taken our- either of the kids out u::m (.)</monospace>\n</p><p>\n<monospace>73   anywhere where they shouldn’t be we’ve- we’ve pretty much done the</monospace>\n</p><p>\n<monospace>74   right thing the whole way along so</monospace>\n</p><p>\n<monospace>75 Doc: <underline>Yeah</underline>.</monospace>\n</p><p>\n<monospace>76 Dad: ah better safe than sorry.</monospace>\n</p></disp-quote>\n</p><p>The doctor’s recommendation at lines 1-2 involves standard advice related to giving the child <italic>Nurofen®</italic>. As seen in Fragment 1 above, recommendations to give paracetamol or ibuprofen for pain relief is a common practice in paediatric palliative care consultations. They are often used as pain relief for mild to moderate pain and are typically accepted by parents without further discussion. In this instance, however, this standard recommendation leads to a different response from the father. The father provides some initial agreement (‘yeah yeah’, line 4) and then provides a ‘but’ conjunction (‘but we’ve had some concerns with coronavirus’). The conjunctive ‘but’ here acts as a pivot in the conversation: rather than just responding with the expected acceptance of the recommendation, the father raises a new topic (coronavirus) and presents a new concern.</p><p>This is the first time that coronavirus is mentioned in the consultation; there had been no lead up to the shift in conversation from the father at this point. The father’s concern is expanded in lines 6-9 when he explains that he has heard that <italic>Nurofen®</italic> is not the best medication to use ‘at the moment’, that is, during the coronavirus pandemic. Here, the father refers to media reports at the time that suggested that ibuprofen might aggravate the symptoms of coronavirus (this link was subsequently debunked).<sup><xref rid=\"bibr36-0269216320950089\" ref-type=\"bibr\">36</xref>,<xref rid=\"bibr37-0269216320950089\" ref-type=\"bibr\">37</xref></sup> Even though the child did not have coronavirus, the father expressed concern about using <italic>Nurofen®</italic> during the pandemic. From line 10, across an extended sequence, the doctor responds by providing both information and reassurance to the father. The father accepts this response from the doctor (‘Yep that’s fine’ line 43, ‘That’s good to know’, line 51), and finishes with an idiomatic expression (‘better safe than sorry’, line 76), which works to close this topic.<sup><xref rid=\"bibr38-0269216320950089\" ref-type=\"bibr\">38</xref>,<xref rid=\"bibr39-0269216320950089\" ref-type=\"bibr\">39</xref></sup> This fragment shows how, during the pandemic, a relatively simple recommendation to give a child mild pain relief led to a parental concern being raised, and a need for the doctor to provide up-to-date information about COVID-19 and reassurance to the parent in response.</p><p>A second serious concern raised by parents within consultations recorded during the pandemic was attending the hospital for care. Prior to the pandemic, families often attended the hospital for regular consultations, including travelling long distances from remote towns to see clinicians face-to-face. As the next fragment shows, however, some parents expressed caution in visiting the hospital during the pandemic, even when concerned about their child’s immediate health problems. In this next fragment, a telehealth consultation, the parents are concerned about their son’s breathing. They had told the doctor previously that they did not want to visit the hospital due to the risk of COVID-19.</p><p>\n<disp-quote><p>\n<monospace><bold>(6) [S1_F25_E01_2020-04-30 07:48]</bold></monospace>\n</p><p>\n<monospace>01 Doc:  o:kay. (1.2) .hh AND UM .hh you’re- <underline>YOU</underline>:’re- (.) <underline>YO</underline>U’re not</monospace>\n</p><p>\n<monospace>02         keen to come up to the hospital at the moment are you?</monospace>\n</p><p>\n<monospace>03        (1.3)</monospace>\n</p><p>\n<monospace>04 Dad:  n↑a:h.</monospace>\n</p><p>\n<monospace>05        (0.3)</monospace>\n</p><p>\n<monospace>06 Doc:  mhm.</monospace>\n</p><p>\n<monospace>07 Dad:  er I- I <underline>li</underline>ke to visit you to: say gid<underline>da</underline>y and all ↑that [but ah]</monospace>\n</p><p>\n<monospace>08 Doc:                                                              [mm    ]</monospace>\n</p><p>\n<monospace>09        (0.5)</monospace>\n</p><p>\n<monospace>10 Dad:  I’m very conc- <I:’m <underline>ov</underline>erly cautious> with this COVID nineteen</monospace>\n</p><p>\n<monospace>11        <underline>th</underline>ing (0.7) er if <underline>I</underline>: get it (.) <underline>Ry</underline>an gets it (.) and we’re a</monospace>\n</p><p>\n<monospace>12  <underline>go</underline>:ner.</monospace>\n</p><p>\n<monospace>13 Doc:  mhm=</monospace>\n</p><p>\n<monospace>14 Dad:  =y’know,</monospace>\n</p><p>\n<monospace>15 Doc:  mhm</monospace>\n</p><p>\n<monospace>16        (0.8)</monospace>\n</p><p>\n<monospace>17 Dad:  erm (.) and ↑I KNOW the: hospital’s got a (.) clean <underline>sl</underline>ate at</monospace>\n</p><p>\n<monospace>18        the moment >but look at that nursing home down in ↑Sydney. .hh</monospace>\n</p><p>\n<monospace>19        wh- <underline>whe</underline>re there’s casual <underline>wo</underline>rkers you’ve got a ↑risk.</monospace>\n</p><p>\n<monospace>20        (0.3)</monospace>\n</p><p>\n<monospace>21 Doc:  mhm.</monospace>\n</p><p>\n<monospace>22        (0.7)</monospace>\n</p><p>\n<monospace>23 Dad:  and you’ve got a <underline>ri</underline>sk in the hospital.</monospace>\n</p><p>\n<monospace>24        (1.0)</monospace>\n</p><p>\n<monospace>25 Doc:  o:kay.</monospace>\n</p></disp-quote>\n</p><p>In this Fragment 6, at lines 1-2, the doctor asks the parents about visiting the hospital. The question is framed as a negative declarative, displaying the doctor’s prior knowledge that the parents are ‘not keen’ to visit the hospital despite their current concerns about their son’s dystonia and breathing. Within his question, the doctor refers to the COVID-19 pandemic through to the temporal reference ‘at the moment’. The father provides a straight no-response: ‘nah’ (line 3). He then expands this response, explicitly raising COVID-19 as the justification for his unwillingness to visit the hospital. The father’s turn contains an intensifier (‘overly cautious’, line 10) and the use of the idiomatic expression ‘we’re a goner’ to make reference to the risk of the child’s death if he caught the coronavirus (lines 11-12). The father thus uses accentuated and emotive language to emphasise his perceived risk of visiting the hospital in person. This risk appears to be a greater concern to the parents than continuing to care for the child’s health concern at home. This fragment shows another example of how serious concerns about the pandemic were raised by parents during consultations while discussing routine aspects of the child’s ongoing care.</p><p>A third form of talk about the COVID-19 pandemic became relevant in interactions when talking about issues related to the families’ lifestyle, such as school (Fragment 7) and work (Fragment 8).</p><p>\n<disp-quote><p>\n<monospace><bold>(7) [S1_F23_E01_2020-04-09 16:57]</bold></monospace>\n</p><p>\n<monospace>01 Doc: ◦okay◦ (1.3) u:m okay (.) so um (.) so that’s a good point .hh</monospace>\n</p><p>\n<monospace>02   and um .hh wha- what <underline>sc</underline>hoo:l does Sammy <underline>go</underline> to?</monospace>\n</p><p>\n<monospace>03   (1.3)</monospace>\n</p><p>\n<monospace>04 Dad: tsk um [Rickshaw State]</monospace>\n</p><p>\n<monospace>05 Pat:   [Rickshaw State] School</monospace>\n</p><p>\n<monospace>06   (1.3) ((doc typing))</monospace>\n</p><p>\n<monospace>07 Doc: .hh and what <underline>gr</underline>ade are you in Sammy?</monospace>\n</p><p>\n<monospace>08   (1.1)</monospace>\n</p><p>\n<monospace>09 Pat:  fi:ve.</monospace>\n</p><p>\n<monospace>10 Doc: ◦okay◦ (0.8) AND UM (0.3) did you um- (.) >how many (0.3) when</monospace>\n</p><p>\n<monospace>11   did <underline>yo</underline>u sort of stop going to <underline>sc</underline>hoo:l ah did you:: (.) like um</monospace>\n</p><p>\n<monospace>12   (0.8) a:h (0.3) <underline>tw</underline>o weeks ago:, or a <underline>mo</underline>nth ago:,</monospace>\n</p><p>\n<monospace>13   (1.2)</monospace>\n</p><p>\n<monospace>14 Pat: tsk <underline>o:</underline>h. I: ((shaking head))</monospace>\n</p><p>\n<monospace>15   (0.4)</monospace>\n</p><p>\n<monospace>16 Dad: three weeks.</monospace>\n</p><p>\n<monospace>17 Doc: [okay yeah ]</monospace>\n</p><p>\n<monospace>18 Pat: [(yeah a month)] ago.</monospace>\n</p><p>\n<monospace>19 Doc: ◦yea:h.◦</monospace>\n</p><p>\n<monospace>20   (0.3)</monospace>\n</p><p>\n<monospace>21 Dad: <underline>bas</underline>ically u::m (.) y’know before the lockdowns happened we were</monospace>\n</p><p>\n<monospace>22   about a- a week before tha:t (.) sort of started (1.0) ((doc</monospace>\n</p><p>\n<monospace>23   typing)) we- we (.) >y’know we wanted to be proactive< once</monospace>\n</p><p>\n<monospace>24   again (.) with this</monospace>\n</p><p>\n<monospace>25   (.)</monospace>\n</p><p>\n<monospace>26 Doc: yeah</monospace>\n</p><p>\n<monospace>27 Dad: <underline>di</underline>dn’t want to take any ch<underline>an</underline>ces</monospace>\n</p></disp-quote>\n</p><p>This fragment begins with the same question from the doctor as seen in Fragment 2 above from the pre-pandemic consultations: asking what school the child attends. After receiving information from the child and father about the child’s school and grade level, the doctor asks another question that shifts the conversation to the relevance of the pandemic. At the time of this consultation, schools were providing home learning and only children of essential workers were physically attending school. In this community-wide context of most children not attending school, asking the child about his school raises the relevance of the temporary change to school routine that the child would be experiencing.</p><p>The doctor’s next question, across lines 10-12, orients to this relevance by asking how long the child has been home from school. Both the child and father reply with the number of weeks the child has been home, and the father then expands by explaining that they were ‘proactive’ and the child started staying home from school around a week before the official school closures (‘before the lockdowns happened’, line 21). The father’s response concluded with an idiomatic expression that they ‘didn’t want to take any chances’.<sup><xref rid=\"bibr38-0269216320950089\" ref-type=\"bibr\">38</xref>,<xref rid=\"bibr39-0269216320950089\" ref-type=\"bibr\">39</xref></sup> With this response, the father displays to the doctor that he took the child’s isolation from the community seriously and took precautions beyond the standard recommendations. Again, this fragment demonstrates how a routine question about the child’s school, asked during the pandemic period, leads to a shift in the conversation to pandemic-related lifestyle changes including home-schooling and the father displaying he was taking isolation of the child seriously.</p><p>In Fragment 8, below, the child’s father has already spoken about the child’s mother (Mary) working as a nurse (discussed in Fragment 5 above). At the beginning, the doctor enquires about the father’s own work responsibilities.</p><p>\n<disp-quote><p>\n<monospace><bold>(8) [S1_F23_E01_2020-04-09 27:57]</bold></monospace>\n</p><p>\n<monospace>01 Doc: um (.) .hh <underline>H</underline>enry um do you um (◦eh◦) (0.3) <underline>li</underline>ke with Ma:ry</monospace>\n</p><p>\n<monospace>02   worki:ng .hh are <underline>yo</underline>u: >sort of< <underline>wo</underline>rking as well or you’re</monospace>\n</p><p>\n<monospace>03   mainly more <underline>ho</underline>me based? or</monospace>\n</p><p>\n<monospace>04   (1.3)</monospace>\n</p><p>\n<monospace>05 Dad: I <underline>wo</underline>rk from home at the mo:ment, ye[a:h ] which is ah .hh</monospace>\n</p><p>\n<monospace>06 Doc:      [okay]</monospace>\n</p><p>\n<monospace>07 Dad: ><underline>no</underline>t doing a lot of work< I’m in the u:m (.) tsk in the <underline>ga</underline>ming</monospace>\n</p><p>\n<monospace>08   industry so:: dealing with ca<underline>si</underline>nos and <underline>cl</underline>ubs an- and <underline>pu</underline>bs and</monospace>\n</p><p>\n<monospace>09   that sort of thing so I think .hh everything’s <underline>sh</underline>ut do:wn at</monospace>\n</p><p>\n<monospace>10   the <underline>mo</underline>ment it’s very <underline>qu</underline>iet,</monospace>\n</p><p>\n<monospace>11   (0.7)</monospace>\n</p><p>\n<monospace>12 Doc: yea:h (2.5) ◦and um◦ <underline>so</underline> you s- can <underline>st</underline>ill do a little bit of</monospace>\n</p><p>\n<monospace>13   work (.) remotely hhh</monospace>\n</p><p>\n<monospace>14   (0.8)</monospace>\n</p><p>\n<monospace>15 Dad: <underline>ye</underline>ah I work from home ni- l-=</monospace>\n</p><p>\n<monospace>16 Doc: =yeah=</monospace>\n</p><p>\n<monospace>17 Dad: =<underline>ni</underline>:nety per cent of the <underline>ti</underline>:me and then I <underline>tr</underline>avel (.) a:h >for</monospace>\n</p><p>\n<monospace>18   the< [rest]</monospace>\n</p><p>\n<monospace>19 Doc:   [yeah]</monospace>\n</p><p>\n<monospace>20 Dad: so: .hh when (.) before coronavirus >I was< <underline>on</underline> the road</monospace>\n</p><p>\n<monospace>21   probably about w- (.) <underline>on</underline>e week out of every ↑four</monospace>\n</p><p>\n<monospace>22   (0.3)</monospace>\n</p><p>\n<monospace>23 Doc: yea:h.</monospace>\n</p><p>\n<monospace>24 Dad: most of my work’s over (in Asia) so:</monospace>\n</p><p>\n<monospace>25   (0.3)</monospace>\n</p><p>\n<monospace>26 Doc: yeah. okay, (1.7) and um tsk h- okay yeah >and I <underline>gu</underline>ess that’s</monospace>\n</p><p>\n<monospace>27   probably a:h has that had sort of a fi<underline>na</underline>ncial impact on you</monospace>\n</p><p>\n<monospace>28   guys: um</monospace>\n</p><p>\n<monospace>29   (2.0)</monospace>\n</p><p>\n<monospace>30 Dad: yea:h I- I’ve- I was pretty lucky (when) (0.3) I’ve take a</monospace>\n</p><p>\n<monospace>31  <underline> tw</underline>enty per cent pay cut (.) to get us through this <underline>pe</underline>riod which</monospace>\n</p><p>\n<monospace>32   is (0.4) you know not too bad it’s better than the Job Keeper</monospace>\n</p><p>\n<monospace>33   payment that’s for sure.</monospace>\n</p><p>\n<monospace>34 Doc: yeah okay yeah well that’s a relief yeah (0.3) and um .hh</monospace>\n</p><p>\n<monospace>35   <underline>Ma</underline>ry’s work I guess um (0.3) (w-/eh) <underline>fo</underline>rtunately they’ll be:</monospace>\n</p><p>\n<monospace>36  <underline> wa</underline>nting her to continue in that role won’t they so ◦yeah◦ .hh</monospace>\n</p><p>\n<monospace>37 Dad: yeah yeah [we’ve-] we’re <underline>go</underline>od from that perspective we’re-</monospace>\n</p><p>\n<monospace>38 Doc:    [yeah ]</monospace>\n</p><p>\n<monospace>39 Dad: we’re the lucky ones.</monospace>\n</p></disp-quote>\n</p><p>The doctor’s question at lines 1-3 is a routine question often observed in the paediatric palliative care consultations and can be compared to the similar question seen in Fragment 3 above. The father initially responds that he works ‘from home at the moment’ (line 5). With this initial response, the father already makes an apparent reference to the pandemic period, marking his current home-based work routine as being different to usual. In his expanded response across lines 7-10, the father explains he is in the ‘gaming industry’ and makes further reference to the pandemic in stating that ‘everything’s shut down at the moment it’s very quiet’. In a similar way to the other fragments, the father’s response to a routine question shifts the conversation to the changed routine due to COVID-19. Further in the sequence, the father explains that, ‘before coronavirus,’ he travelled one week in four for his work (lines 20-21). With travel bans in place at the time, the father’s response here suggests that his job probably has been impacted significantly during the pandemic.</p><p>What was a routine question has led now to some sensitive information-sharing by the father related to the impact of the pandemic on the family. The doctor orients to this sensitivity by making an inference: ‘I guess that’s probably ah has that had sort of a financial impact on you guys’ (lines 26-28). The father confirms the financial impact and further shares that he had to take a 20% reduction in salary during this time (lines 30-33). He follows on with a positive stance, that this was better than losing his job (as many Australians did) and needing to rely on Government subsidy payments. The doctor provides an empathic receipt (‘that’s a relief’, line 34) and adds that at least his wife’s work (as a nurse) will continue. The father aligns with the doctor, using the idiomatic expression ‘we’re the lucky ones’ to close down the sequence<sup><xref rid=\"bibr38-0269216320950089\" ref-type=\"bibr\">38</xref>,<xref rid=\"bibr39-0269216320950089\" ref-type=\"bibr\">39</xref></sup> (line 39). Again, here, a routine question from the doctor quickly shifts into talk about the pandemic; in this case leading to sensitive talk in relation to the impact of the pandemic on the parent’s work and finances, requiring an empathic response from the doctor.</p><p>The final example shows how talk about the pandemic led to non-serious talk and laughter in some consultations. The fragment comes from an inpatient consult where the child is lying in bed watching a TV show on a digital tablet.</p><p>\n<disp-quote><p>\n<monospace><bold>(9) [S1_F24_E02_2020-04-17 00:29]</bold></monospace>\n</p><p>\n<monospace>01 Doc: have u:m (0.3) >so that-< which <underline>th</underline>a:t’s which ↑one Sesame</monospace>\n</p><p>\n<monospace>02   Street. (.) or=</monospace>\n</p><p>\n<monospace>03 Mum: =Yep</monospace>\n</p><p>\n<monospace>04 Doc: [Okay ]</monospace>\n</p><p>\n<monospace>05 Mum: [It’s <underline>lit</underline>]erally the only show she watches.</monospace>\n</p><p>\n<monospace>06 Doc: Okay <underline>ye</underline>a:h.</monospace>\n</p><p>\n<monospace>07 Mum: Yep.</monospace>\n</p><p>\n<monospace>08   (0.7)</monospace>\n</p><p>\n<monospace>09 Doc: >actually I ↑see y-< I sa:w Sesame Street have done a thing on:</monospace>\n</p><p>\n<monospace>10   (.) COVID  nine↑<underline>te</underline>en so[: ]</monospace>\n</p><p>\n<monospace>11 Nur:     [◦oh hh◦]</monospace>\n</p><p>\n<monospace>12 Doc: <underline>ye</underline>a:h. so:=</monospace>\n</p><p>\n<monospace>13 Mum: =heh. (.) [£there’s a] <underline>me</underline>:me (.) that says£=</monospace>\n</p><p>\n<monospace>14 Doc:   [yea:h. ]</monospace>\n</p><p>\n<monospace>15 Doc: =yeah.</monospace>\n</p><p>\n<monospace>16   (0.2)</monospace>\n</p><p>\n<monospace>17 Mum: £Sesame Street i(h)s brou(h)ght to you by the letter (.) ↑C:</monospace>\n</p><p>\n<monospace>18   (.) and the n:umber nineteen£</monospace>\n</p><p>\n<monospace>19 Nur: Heh [heh heh] heh heh ]</monospace>\n</p><p>\n<monospace>20 Doc:  [Heh heh]</monospace>\n</p><p>\n<monospace>21 Mum:  [Hah hah hah hah hah] hah hah hah=</monospace>\n</p><p>\n<monospace>22 Nur: =hh=</monospace>\n</p><p>\n<monospace>23 Mum: =.hh hh .hh</monospace>\n</p><p>\n<monospace>24   (0.2)</monospace>\n</p><p>\n<monospace>25 Doc: ◦yeah◦</monospace>\n</p></disp-quote>\n</p><p>In a similar way to Fragment 4 (a pre-pandemic consultation), the doctor (line 2) enquires as to whether the TV show the child is currently watching is Sesame Street but leaving incomplete his turn for other possible programs. The mother confirms and adds ‘it’s literally the only show she watches’ (line 5), which is similar to the mother’s response in Fragment 4. Subsequently, the doctor makes reference to COVID-19 in relation to the show: ‘Sesame Street have done a thing on COVID-19’ (lines 8-9).<sup><xref rid=\"bibr40-0269216320950089\" ref-type=\"bibr\">40</xref></sup> The mother responds by relaying the details of a Sesame Street meme she had seen in relation to COVID-19 (lines 12, 14-15). Her turn displays the laughable nature of the meme, indicated by smile voice and interpolated laugh particles. The mother, doctor and nurse all join in laughter in response (lines 16-18) and this shared laughter closes down the sequence.<sup><xref rid=\"bibr41-0269216320950089\" ref-type=\"bibr\">41</xref>,<xref rid=\"bibr42-0269216320950089\" ref-type=\"bibr\">42</xref></sup> Similar to the other fragments, a commonly observed question from the doctor leads to talk about the COVID-19 pandemic. In this instance, the COVID-19 talk leads to appreciation of a meme with shared laughter.</p></sec></sec><sec sec-type=\"discussion\" id=\"section16-0269216320950089\"><title>Discussion</title><p>Guidelines released for inpatient and outpatient care during the COVID-19 pandemic provide recommendations for framing a <italic>specific</italic> conversation about COVID-19 with patients.<sup><xref rid=\"bibr20-0269216320950089\" ref-type=\"bibr\">20</xref><xref rid=\"bibr21-0269216320950089\" ref-type=\"bibr\"/><xref rid=\"bibr22-0269216320950089\" ref-type=\"bibr\"/>–<xref rid=\"bibr23-0269216320950089\" ref-type=\"bibr\">23</xref>,<xref rid=\"bibr26-0269216320950089\" ref-type=\"bibr\">26</xref></sup> While these guidelines are valuable, this study has shown that talk about the COVID-19 pandemic was pervasively raised by both parents (45%) and clinicians (55%) <italic>throughout</italic> actual consultations. Routine questions and recommendations from the doctor often inadvertently raised the relevance of the COVID-19 pandemic and the associated changes to care and lifestyle.</p><p>It appears helpful to have direct and specific questions to ask about COVID-19 (e.g. ‘What have you been thinking about COVID and your situation?’).<sup><xref rid=\"bibr23-0269216320950089\" ref-type=\"bibr\">23</xref></sup> At the same time, parents and patients often raised the topic themselves or in response to indirect questioning such as a check-in type question (‘How are you doing with all of this?’).<sup><xref rid=\"bibr23-0269216320950089\" ref-type=\"bibr\">23</xref></sup> The holistic and comprehensive nature of a paediatric palliative care consultation can also facilitate conversation on a variety of topics ranging from school to family functioning.<sup><xref rid=\"bibr13-0269216320950089\" ref-type=\"bibr\">13</xref>,<xref rid=\"bibr14-0269216320950089\" ref-type=\"bibr\">14</xref></sup> More serious health related conversations also occurred around pain and symptom management, and how to manage deterioration of the child if this were to occur. The importance of maintaining emotional support, empathy and compassion during such sensitive conversations is critical. As shown in the reported data (e.g., Fragment 6), families who might otherwise physically attend consultations shifted to telehealth consultations during the pandemic. Virtual and telehealth technologies present extra challenges to providing psychosocial support, but do not preclude it.<sup><xref rid=\"bibr43-0269216320950089\" ref-type=\"bibr\">43</xref><xref rid=\"bibr44-0269216320950089\" ref-type=\"bibr\"/>–<xref rid=\"bibr45-0269216320950089\" ref-type=\"bibr\">45</xref></sup></p><sec id=\"section17-0269216320950089\"><title>Strengths and limitations</title><p>This is the first study to directly analyse real-life, video-recorded paediatric palliative care consultations prior to, during, and immediately following the initial peak of the COVID-19 pandemic. It provides insight into how the communication between clinicians and families changed as a result of the pandemic in these consultations. It adds to the small, and growing, body of research examining naturally-occurring communication within paediatric palliative care.<sup><xref rid=\"bibr33-0269216320950089\" ref-type=\"bibr\">33</xref>,<xref rid=\"bibr34-0269216320950089\" ref-type=\"bibr\">34</xref></sup> A limitation of the study is that data were collected from one hospital in Australia. Another study limitation is that the diagnoses in this study are not totally representative of the patients in this service. Thirty-five percent of patients usually seen by this service have an oncologic diagnosis, not present in the current study.<sup><xref rid=\"bibr46-0269216320950089\" ref-type=\"bibr\">46</xref>,<xref rid=\"bibr47-0269216320950089\" ref-type=\"bibr\">47</xref></sup> Future research might seek to include a representative group of diagnoses, to collect data at additional sites and locations, as well as during future waves of the pandemic (e.g., one State in Australia is currently experiencing a second wave of COVID-19 in July 2020, including cases within a Children’s hospital).</p></sec><sec id=\"section18-0269216320950089\"><title>Practice implications</title><p>An awareness of the pervasiveness of both serious and non-serious talk about the COVID-19 pandemic within standard paediatric palliative care consultations during the pandemic can encourage clinicians to be prepared and flexible in how they respond to patients. In some instances, the pandemic influenced families’ decision-making about the way their child received care from the palliative care service. For example, Fragment 6 showed that a family was hesitant to come to hospital for inpatient care. In other consultations, some concerns may be raised by families that require reassurance in response from clinicians. Other topics of conversation may require a sensitive acknowledgement of the additional life stressors that families can confront during pandemics (e.g., home schooling, financial stress due to loss of reliable employment). Pandemic topics may be raised also when discussing other psychosocial or lifestyle issues, such as education (e.g., remote school learning) or culture (e.g., television programs). Some of these conversations may involve more light-hearted discussion around the changes to everyday routines. Given the holistic purpose of paediatric palliative care in providing both medical and supportive care to families,<sup><xref rid=\"bibr12-0269216320950089\" ref-type=\"bibr\">12</xref><xref rid=\"bibr13-0269216320950089\" ref-type=\"bibr\"/>–<xref rid=\"bibr14-0269216320950089\" ref-type=\"bibr\">14</xref></sup> this service is in an ideal position to listen to and meet families’ concerns to talk about pandemic-related matters that may potentially affect their child’s health outcomes and family’s everyday practices.</p><p>Guidelines based on real-life conversations, rather than on imagined scripts of conversations, are more likely to show the diverse range of talk about the pandemic within clinical consultations.<sup><xref rid=\"bibr35-0269216320950089\" ref-type=\"bibr\">35</xref></sup> It is unlikely that script writers could ever imagine the diversity of content, far broader than health concerns, that may surface within consultations during pandemics. Future guidelines might consider the pervasive and varied ways that conversations about a pandemic are raised within and across routine consultations to prepare clinicians for the flexible ways that they may need to respond to patients and families during such challenging times.</p></sec></sec></body><back><ack><p>We thank the clinical staff and families who participated in the study. We also thank Angela Delaney for her help with recruitment and data collection at the clinical site.</p></ack><fn-group><fn fn-type=\"other\"><p><bold>Authorship:</bold> All listed authors made a substantial contribution to the concept and design of the work, acquisition and analysis of data and drafting or revising the article. All authors have approved the version to be published and have participated sufficiently in the work to take public responsibility for appropriate portions of the content.</p></fn><fn fn-type=\"COI-statement\"><p><bold>Declaration of conflicting interests:</bold> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding:</bold> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded by the Australian Research Council through the Discovery Project program (Reference: DP180101941).</p></fn><fn fn-type=\"other\"><p><bold>ORCID iDs:</bold> Katie Ekberg <inline-graphic xlink:href=\"10.1177_0269216320950089-img1.jpg\"/>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://orcid.org/0000-0002-8237-1459\">https://orcid.org/0000-0002-8237-1459</ext-link></p><p>Lara Weinglass <inline-graphic xlink:href=\"10.1177_0269216320950089-img1.jpg\"/>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://orcid.org/0000-0002-8618-3527\">https://orcid.org/0000-0002-8618-3527</ext-link></p><p>Stuart Ekberg <inline-graphic xlink:href=\"10.1177_0269216320950089-img1.jpg\"/>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://orcid.org/0000-0001-8837-7440\">https://orcid.org/0000-0001-8837-7440</ext-link></p><p>Anthony Herbert <inline-graphic xlink:href=\"10.1177_0269216320950089-img1.jpg\"/>\n<ext-link ext-link-type=\"uri\" xlink:href=\"https://orcid.org/0000-0002-9777-1105\">https://orcid.org/0000-0002-9777-1105</ext-link></p></fn></fn-group><app-group><app id=\"app1-0269216320950089\"><title>Appendix 1: Jeffersonian transcription system</title><p>This list represents the most widely-used transcription symbols used in this study. For a more comprehensive list, see Jefferson.<sup><xref rid=\"bibr29-0269216320950089\" ref-type=\"bibr\">29</xref></sup></p><p>(.)   Micro-pause – less than a tenth of a second</p><p>(0.2), (2.6)   Examples of timed pauses</p><p>↑word    Onset of noticeable pitch rise</p><p>↓word   Onset of noticeable pitch fall</p><p>A: word [word   Square brackets aligned across adjacent lines denote the start of</p><p>B: [word   overlapping talk.</p><p>.   Falling vocal pitch</p><p>?   Rising vocal pitch</p><p>.hhh   n-breath</p><p>hhh   Out-breath</p><p>wo(h)rd   Within-speech aspirations</p><p>wor-   A sharp cut-off</p><p>wo:rd   Colons show that the speaker has stretched the preceding sound</p><p>(words)   A guess at what might have been said if unclear</p><p>(  )   Unclear talk</p><p>A: word=   The equals sign shows that there is no discernible pause</p><p>B: =word   between two speakers’ turns</p><p><underline>word</underline>   Vocal emphasis</p><p>WORD   Talk pronounced loudly in comparison with surrounding talk</p><p>°word°   Talk between ‘degree signs’ is quieter than surrounding talk</p><p>>word word<    Talk between inward arrows is delivered faster than surrounding talk</p><p><word word>   Talk between outward arrows is delivered slower than surrounding talk</p><p>((<italic>sniff</italic>))   Transcriber’s effort at representing something difficult, or impossible, to write phonetically</p><p>£word£   Words spoken with smiley voice</p></app></app-group><ref-list><title>References</title><ref id=\"bibr1-0269216320950089\"><label>1</label><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Mehta</surname><given-names>NS</given-names></name><name><surname>Mytton</surname><given-names>OT</given-names></name><name><surname>Mullins</surname><given-names>EWS</given-names></name></person-group>, <etal>et al</etal>\n<article-title>SARS-CoV-2 (COVID-19): what do we know about children? 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Oncol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Oncol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Oncol.</journal-id><journal-title-group><journal-title>Frontiers in Oncology</journal-title></journal-title-group><issn pub-type=\"epub\">2234-943X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850350</article-id><article-id pub-id-type=\"pmc\">PMC7431877</article-id><article-id pub-id-type=\"doi\">10.3389/fonc.2020.01203</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Oncology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Khadirnaikar</surname><given-names>Seema</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Chatterjee</surname><given-names>Annesha</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kumar</surname><given-names>Pranjal</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/857436/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Shukla</surname><given-names>Sudhanshu</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/851566/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad</institution>, <addr-line>Dharwad</addr-line>, <country>India</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Electrical Engineering, Indian Institute of Technology Dharwad</institution>, <addr-line>Dharwad</addr-line>, <country>India</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Giulia Veronesi, Humanitas Research Hospital, Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Renat Shaykhiev, Cornell University, United States; Fabrizio Bianchi, Casa Sollievo della Sofferenza (IRCCS), Italy</p></fn><corresp id=\"c001\">*Correspondence: Sudhanshu Shukla <email>sudhanshu@iitdh.ac.in</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Thoracic Oncology, a section of the journal Frontiers in Oncology</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†These authors have contributed equally to this work</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>1203</elocation-id><history><date date-type=\"received\"><day>26</day><month>11</month><year>2019</year></date><date date-type=\"accepted\"><day>12</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Khadirnaikar, Chatterjee, Kumar and Shukla.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Khadirnaikar, Chatterjee, Kumar and Shukla</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Cancer stem cells play an essential role in therapy response and aggressiveness of various cancers, including lung adenocarcinoma (LUAD). Interestingly it also shares many features of embryonic stem cells (ESCs). Recently, long non-coding RNAs (lncRNAs) have emerged as a critical regulator of cell physiology. Here, we used expression data of ESCs, LUAD, and normal lung to identify 198 long non-coding hESC-associated lncRNAs (hESC-lncRNAs). Intriguingly, K-means clustering of hESC-associated lncRNAs identified a subgroup of LUAD patients [undifferentiated LUAD (uLUAD)] with high stem cell–like characteristic, decreased differentiation genes expression, and poor survival. We also observed that the uLUAD patients had overexpression of proteins associated with cell proliferation. Interestingly, uLUAD patients were highly enriched with the stemness-related gene sets, and had higher mutation load. A notable result observed was high infiltration of T cells and a higher level of neopeptides in uLUAD patients, making these patients an optimal candidate for immunotherapy. Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs–based classification, and identified a group of patients with high stem cell–like characteristic in the 10 most common cancer types and CCLE cell lines. These results suggest the conventional role of hESC-lncRNAs in stem cell biology. In summary, we identified a novel subgroup of LUAD patients (uLUAD) using a set of hESC-lncRNAs. The uLUAD patients had high stem cell–like characteristic and reduced survival rate and may be referred for immunotherapy. Furthermore, our analysis also showed the importance of lncRNAs in cancer and cancer stem cells.</p></abstract><kwd-group><kwd>lung adenocarcinoma</kwd><kwd>lncRNA</kwd><kwd>embryonic stem cells</kwd><kwd>immune cells</kwd><kwd>greedy algorithm</kwd></kwd-group><counts><fig-count count=\"4\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"58\"/><page-count count=\"13\"/><word-count count=\"7861\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Lung adenocarcinoma (LUAD) is a primary subtype of lung cancer with an abysmal survival rate (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>–<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). The majority of LUAD patients are diagnosed at a later stage and are medicated with radiation and chemotherapy irrespective of heterogeneous disease (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>).</p><p>Recently, advancement in immunotherapy has proved to be useful for the treatment of LUAD patients (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). However, not all patients respond to immunotherapy efficiently (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Thus, it is imperative to identify the novel subgroups of LUAD patients for better treatment strategies.</p><p>Experimental results and bioinformatics analysis of existing high-throughput data have shown that cancer stem cells (CSCs) play a crucial role in the determination of aggressiveness, response to the drug, and resistance to various kinds of therapies in many cancer types, including LUAD (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). It has been hypothesized that carcinogenesis and early development of embryo share molecular similarities, and dedifferentiation leads to the pluripotent nature of cancer cells (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). Additionally, many factors associated with reprogramming in the embryonic state are implicated in cancers (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). These observations also suggest that carcinogenesis and pluripotency share activation of common signaling pathways (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>).</p><p>Recently, long non-coding RNAs (lncRNAs) have been implicated in various aspects of cancer development (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Previous findings have shown that lncRNAs play a significant role in regulating pluripotency in ESCs (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>–<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). However, a comprehensive analysis to identify the lncRNAs that regulate pluripotency and carcinogenesis must be explored.</p><p>In the current study, we have utilized the ESC RNA Sequencing (RNA-Seq) data and The Cancer Genome Atlas (TCGA) cancer patients' data to identify and catalog the lncRNAs with a potential role in cancer development and progression. Further, we applied a greedy algorithm to propose a signature for the identification of a subclass of LUAD patients with high stem cell–like characteristics and poor survival. We also validated the utility of this signature in other solid tumor types. Lastly, we concluded that cancer cell lines with high stem cell–like characteristics, as identified by the lncRNA signature, showed high resistance to various kinds of chemotherapy, suggesting that patients with high stem cell–like characteristics may require an alternative approach for more effective therapy.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>Patients, RNA-Sequencing Data, and Expression Analysis</title><p>Level 3 count and FPKM RNA-Seq data for normal and tumors were obtained from TCGA–Genomic Data Commons (GDC) website. Lung cancer Michigan RNA-Seq data from previous publication were used as a validation set for expression data analysis and test set for survival analysis (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). RNA Sequencing data corresponding to embryonic stem cells (ESCs) H7, HUES1, HUES8, and HUES9 were obtained from GEO series accession number <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"GSE102311\">GSE102311</ext-link> (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). Another set of RNA-Seq data for H9 and SC12-03 was downloaded from GEO series accession number <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"GSE107552\">GSE107552</ext-link> (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Clinical data for all the survival analysis were obtained from TCGA-GDC. The TCGA-LUAD patients also included 13 patients with large cell neuroendocrine carcinoma (LCNEC).</p><p>The stemness data for the TCGA patients were downloaded from the National Cancer Institute GDC website and the neoantigen data from the previous publications (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B22\" ref-type=\"bibr\">22</xref>). The reverse-phase protein expression data were downloaded from the Cancer Proteome Atlas (<ext-link ext-link-type=\"uri\" xlink:href=\"https://bioinformatics.mdanderson.org/public-software/tcpa/\">https://bioinformatics.mdanderson.org/public-software/tcpa/</ext-link>). For the normal human bronchial epithelial cells, the raw data (fastq files) were downloaded from NCBI SRA (SRA: SRP157114) using the SRA toolkit. fastp with the default values was used for quality control and adapter trimming (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). The reference genome and annotation files for GRCh37 were downloaded from Ensembl. STAR (2.7.3) was used for alignment and to obtain the count values. Coordinate sorted bam files obtained from STAR were used with StringTie (2.1.1) to obtain the FPKM values.</p></sec><sec><title>Expression Analysis</title><p>Raw counts were used for all the differential expression analysis. All the genes with an expression of more than five average counts across the sample group were considered as “expressed,” and genes with fewer than five average counts across the sample groups were considered “not expressed.” To identify the hESC-lncRNAs, lncRNAs expressed in LUAD and not expressed in normal were pulled out, and differential expression analysis was performed using a <italic>t</italic>-test. Long non-coding RNAs with > 5-fold higher expression in LUAD compared to normal with 5% FDR and more than five average counts in ESCs were considered as positive stemness-associated lncRNAs. Similarly, lncRNAs with <0.2-fold differential expression in LUAD compared to normal with 5% FDR were considered as negative stemness-associated lncRNAs.</p></sec><sec><title>Pathway Analysis, Network Analysis, and Gene Set Enrichment Analysis</title><p>To get a broad understanding of the function of the protein-coding genes (PcGs), pathway and network analyses were done using the Metascape tool (<ext-link ext-link-type=\"uri\" xlink:href=\"http://metascape.org\">http://metascape.org</ext-link>) (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Metascape performs the comparative analysis of datasets across multiple experiments. Gene Ontology (GO) analysis was also performed using Metascape. Gene set enrichment analysis (GSEA) software from Broad Institute (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>) was used for both preranked and default GSEA.</p></sec><sec><title>Survival Analysis</title><p>Clinical data for TCGA patients were downloaded from the TCGA-GDC server and merged with expression data. For Michigan dataset (test set), patients' data were obtained from previous publications (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>). All Kaplan–Meier (KM) analysis was done using the log–rank test in GraphPad software version 8.2.1 (San Diego, CA, USA). For Cox regression analysis, the <italic>survival</italic> package was used in the R environment. Hazard ratio (HR) with a <italic>p</italic> <0.05 was considered significant. For stemness prognostic score (SPS) calculation, the following equation was used: <bold>SPS</bold> = ∑ (<bold>β</bold><sub><italic>i</italic></sub> × expression<sub><italic>i</italic></sub>)</p><p>where β is Cox regression coefficient, and <italic>i</italic> is gene.</p></sec><sec><title>Statistical Analysis</title><p>The comparison of two groups was made using a two-sided <italic>t</italic>-test, and the resulting lncRNAs with <italic>p</italic> <0.05 were considered significant. Similarly, three or more groups' comparison was made using two-sided analysis of variance, and the resulting lncRNAs whose <italic>p</italic> <0.05 was considered significant.</p></sec><sec><title>Greedy Analysis and Random Forest Model Building</title><p>To remove the redundancy, greedy signature algorithm was used. For verification of the 17-lncRNA model, the random forest method was used.</p><p>The details of the greedy analysis and random forest model building are given in the <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Methods</xref>.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>Expression Pattern of lncRNAs and PcGs in the Normal Lung, LUAD, and ESCs</title><p>Recent efforts on cataloging the transcripts expressed in human cells have shown that lncRNAs show a restricted expression pattern; that is, expression of lncRNAs shows higher tissue and lineage specificity than the PcGs (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). To understand the expression pattern of genes in normal human lung epithelial cells (NHLEs), normal lung (NL), LUAD, lung cancer cell lines (LCCs), and ESC expression data were analyzed as elaborated in the <italic>Materials and Methods</italic> section. Our analysis showed that significantly more genes are expressed in LCCs and ESCs compared to NHLEs (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). Similarly, significantly more genes were expressed in LUAD compared to NL (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>). These observations suggest a comprehensive transcriptional dissimilarity among NHLEs, ESCs, and LCCs and between LUAD and NL.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Identification of stemness-associated lncRNA. <bold>(A)</bold> The total numbers of expressed genes (>5 average count) in NHBL, lung cancer cell lines, and ESC were counted and plotted. An analysis of variance was done to find the significance. <bold>(B)</bold> The total number of expressed genes (>5 average counts) in normal lung and LUAD were counted and plotted. A <italic>t</italic>-test was performed to find the significance. <bold>(C)</bold> Differential expression analysis was performed, and lncRNAs with 5% FDR and more than 2-fold log2 difference were considered as differentially expressed lncRNAs. Volcano plot shows the overexpressed genes in yellow and underexpressed genes in green. <bold>(D)</bold> Heatmap showing the expression of differentially expressed 198 lncRNAs in ESCs, LUAD, and normal. Yellow shows high expression, and green shows low expression. <bold>(E)</bold> Gene Ontology analysis was performed using PcGs correlating with stemness lncRNAs (198), and enriched GO terms were plotted. The color of the bar indicates a higher significance. Metascape analysis was done using PcGs correlating with stemness lncRNAs (198) to identify the interactome network. Each dot represents one GO term, and the color key is given in the bar diagram.</p></caption><graphic xlink:href=\"fonc-10-01203-g0001\"/></fig></sec><sec><title>Identification of hESC-lncRNAs Associated lncRNAs in LUAD</title><p>To identify the lncRNAs associated with high stem cell–like characteristics in LUAD, first, we performed a differential expression analysis to identify the lncRNAs differentially expressed in LUAD compared to NL (<italic>Materials and Methods</italic>). The list of differentially expressed lncRNAs was then checked for their expression status in ESCs and lncRNA overexpressed in LUAD, and at least five average count in ESCs and downregulated in LUAD and fewer than five average count in hESCs were selected. This analysis identified a total of 198 lncRNAs associated with hESC and dysregulated in LUAD compared to NL, which we named as hESC-lncRNAs (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 1</xref>). Among these lncRNAs, 169 lncRNAs were overexpressed, and 29 lncRNAs were underexpressed in LUAD to NL samples (<xref ref-type=\"fig\" rid=\"F1\">Figures 1C,D</xref>). We checked the expression of 169 lncRNAs in normal human bronchial epithelium cells and found that average expressions of these lncRNAs were lower in NHEB than in ESCs (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 1A</xref>). We also validated the expression of hESC-lncRNAs in another set of ESC (GSE107552) and LUAD samples (Michigan dataset) and found that 198 lncRNAs had similar expression patterns in another dataset as well (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figures 1B,C</xref>). Further, to understand the functioning of hESC-lncRNAs, we performed pathway analysis using the PcGs, which had a high correlation (Pearson ρ > 0.3, <italic>p</italic> <0.05) with the selected 198 lncRNAs. The analysis identified that the PcGs with high correlation with hESC-lncRNAs were involved in the regulation of cell cycle, cell proliferation, DNA replication, DNA repair, and so on (<xref ref-type=\"fig\" rid=\"F1\">Figure 1E</xref>). We also found that these pathways form a strong network in the cellular signaling (<xref ref-type=\"fig\" rid=\"F1\">Figure 1E</xref>), suggesting a common role of hESC-lncRNAs in cellular proliferation and stem cell maintenance. These results also suggest that lncRNAs associated with high stem cell–like characteristics regulate various pathways involved in cell proliferation and growth. We also performed the canonical pathway analysis to identify the specific pathways regulating the cell cycle and proliferation. This analysis identified PLK1, Aurora, ATR, FOXM1, ATM, telomerase, ILK, P53, RB1, and MYC pathway associated with the genes correlating with 198 lncRNAs (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 1D</xref>). Further, to understand the specific pathways regulated by individual lncRNA, we identified RP11-89K21.1 as one of the most LUAD-specific and prognostic lncRNAs among all the 198 hESC-lncRNAs (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figures 2A,B</xref>). We then identified the PcGs with the most similar expression correlation to RP11-89K21.1 and performed Metascape analysis. Interestingly, we found that genes correlating with RP11-89K21.1 are associated with stem cell proliferation and Wnt signaling pathway regulation hESC-lncRNAs (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 2C</xref>).</p></sec><sec><title>High Stem Cell–Like Characteristic–Related lncRNAs Are Associated With the Prognostic Subtype of LUAD</title><p>To further understand the interrelation of high stem cell–like characteristics–associated lncRNAs and LUAD subtype, we performed K-means clustering, which identified three clusters of patients, namely, clusters I, II, and III (<xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 2</xref>). To delineate the clinical difference in these three clusters, we performed KM analysis. The KM analysis showed that patients belonging to cluster III had a significant poor survival compared to the other two clusters, clusters I and II (<italic>p</italic> = 0.026) (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 3A</xref>). Thus, we combined clusters I and II patients for further analysis. As evident, we found that cluster III patients exhibited poor survival compared to cluster I + II patients (<italic>p</italic> = 0.015, HR = 1.55) (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 3B</xref>). More importantly, in most clinically relevant stage I patients, cluster III patients showed significantly much poor survival compared to cluster I + II patients (<italic>p</italic> = 0.009, HR =2.07) (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>). Interestingly, we found that genes DNAI1, NKX2-1, and SCGB1A1, associated with the differentiation of different types of lung cells, were downregulated in cluster III patients compared to cluster I + II patients (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>). Similarly, ALDH1A1, CD133, CD24, and SOX2 markers of LUAD stem cells were upregulated in cluster III patients compared to cluster I + II patients, suggesting poor differentiation of cluster III patients (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>). Further, a preranked GSEA using genes upregulated during the early and late stage of differentiation of J1 ESC showed that these genes are significantly negatively enriched in cluster III compared to cluster I + II (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 3C</xref>). These results suggest that cluster III patients' tumors are less differentiated and more aggressive compared to cluster I + II patients' tumors. Thus, we renamed the clusters as differentiated LUAD (dLUAD, cluster I + II) and undifferentiated LUAD (uLUAD, Cluster III) for further characterization (<xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>). To identify the stemness base- prognostic signature for the LUAD patients, Cox regression analysis was performed on the TCGA LUAD patients using 198 lncRNAs (<xref ref-type=\"fig\" rid=\"F2\">Figure 2D</xref>). The analysis identified two stemness-associated lncRNAs (SATB2-AS1 and ABCA9-AS1) whose expression correlated with the survival of LUAD patients. Further, we calculated an SPS for each patient by combining the regression coefficient and expression of both the lncRNAs (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 3</xref>). In a univariate Cox regression analysis, SPS significantly correlated with survival (HR = 2.23, <italic>p</italic> = 6.99 ×10<sup>−5</sup>). Interestingly, in a multivariate analysis with tumor stage, SPS was an independent predictor of prognosis in TCGA-LUAD patients (<italic>p</italic> = 5.54 ×10<sup>−5</sup>) (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 3D</xref>). Furthermore, to validate the prognostic utility of SPS, we utilized another set of 67 LUAD patients as the testing set (<xref ref-type=\"fig\" rid=\"F2\">Figure 2D</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Table 4</xref>). Interestingly, SPS was found to be an independent prognosticator of survival in the testing set as well (<italic>p</italic> = 7.45 ×10<sup>−3</sup>) (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 3E</xref>). More importantly, KM analysis showed a significant difference in survival of the patients with high and low SPS in both TCGA-LUAD (HR = 1.53, <italic>p</italic> = 5.00 ×10<sup>−3</sup>) and testing patient set (HR = 2.23, <italic>p</italic> = 1.50 ×10<sup>−2</sup>) (<xref ref-type=\"fig\" rid=\"F2\">Figures 2E,F</xref>). Gene sets associated with stemness were significantly enriched in patients with high SPS and poor survival (<xref ref-type=\"fig\" rid=\"F2\">Figure 2G</xref>). Stemness score, as identified by Malta et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>), was also significantly high for the high-SPS patients (<italic>p</italic> <0.0001) (<xref ref-type=\"fig\" rid=\"F2\">Figure 2H</xref>).</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Clustering analysis identifies a novel cluster of uLUAD patients with the poor prognosis. <bold>(A)</bold> Heatmap of 198 lncRNAs in the clusters as identified by the K-means clustering algorithm. Yellow shows high expression, and green shows low expression. <bold>(B)</bold> A Kaplan–Meier plot showing the difference in survival between stage I patients of cluster I + II and cluster III as identified by K-means clustering. The <italic>p</italic>-value and hazard ratio were obtained by log–rank analysis. <bold>(C)</bold> Boxplots showing the expression difference in differentiation and stem cell markers. A non-parametric <italic>t</italic>-test was done to obtain the <italic>p</italic>-values. Bars show the standard deviation. <bold>(D)</bold> Table showing the patients' characteristics used in Cox regression analysis to identify the stemness-associated prognostic signature. A non-parametric test was done to show that patients from TCGA and test set did not have a significant difference in age. A Fisher exact test was done to show that the proportion of male and female and pathological stage distribution was similar in TCGA and test set. <bold>(E)</bold> Kaplan–Meier plot to show the significant difference in high- and low-SPS samples in TCGA patients set. Patients were divided into high and low SPS at the median. A log–rank test was performed to obtain the <italic>p</italic>-value and hazard ratio. <bold>(F)</bold> Kaplan–Meier plot to show the significant difference in high- and low-SPS patients in test set patients. Patients were divided into high and low SPS at the median. A log–rank test was performed to obtain the <italic>p</italic>-value and hazard ratio. <bold>(G)</bold> Gene set enrichment analysis showing enrichment of stemness gene sets in high- vs. low-SPS groups. <bold>(H)</bold> A boxplot showing the stemness scores of low-SPS and high-SPS patients, as described by Malta et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Bars show the standard deviation.</p></caption><graphic xlink:href=\"fonc-10-01203-g0002\"/></fig></sec><sec><title>Characterization of Novel High Stem Cell–Like Characteristic–Associated lncRNA–Based Subtype of LUAD</title><p>The RPPA data from TCGA were downloaded and analyzed to identify the direct changes in the proteins in dLUAD and uLUAD. This analysis identified 23 overexpressed and 18 underexpressed proteins (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 4A</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Table 5</xref>). Reactome analysis was performed using the default setting for background correction to identify the function of these proteins. Interestingly, proteins overactive in uLUAD patients were associated with the cell cycle (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A</xref>). The analysis revealed that the majority of the proteins associated with uLUAD patients (c-ABL, CCNE1/2, FOXM1, TS, PCNA, NRF2, CCNB1, CDK1, FOXM1, and STMN1) were regulating cell cycle positively at all the cell cycle stages (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 4B</xref>). In contrast, the negative regulator of the cell cycle (CDK2, pRB, E2F) were downregulated (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B</xref>). Also, proteins differentially expressed in uLUAD compared to dLUAD appeared to have a close interaction in string analysis (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 4C</xref>). Further global cancer analysis showed that most of the proteins overexpressed in uLUAD were highly active in various cancer types (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 5A</xref>) compared to proteins underexpressed in uLUAD patients (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 5B</xref>). These results suggest a high proliferative activity in uLUAD tumors.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Undifferentiated LUAD patient subgroup is enriched in stemness and mutations. <bold>(A)</bold> A reactome analysis was done using the proteins differentially expressed between uLUAD and dLUAD patients. The bar plot was plotted using the significant reactome pathways. Pathways enriched in dLUAD are shown in green, and pathways enriched in uLUAD are shown in yellow. The color intensity indicates the <italic>p</italic>-value. <bold>(B)</bold> A model of the cell cycle was plotted, and proteins differentially expressed in uLUAD, and dLUAD were overlaid at the cell cycle stage where they function. The proteins indicated in red are overexpressed in uLUAD. The proteins shown in blue are significantly overexpressed at the RNA level, and proteins in green are significantly more mutated in uLUAD samples. <bold>(C)</bold> A GSEA was done using the hallmark gene set in dLUAD and uLUAD samples. The gene sets with <5% FDR were considered enriched. The significantly enriched gene sets are shown in yellow, and insignificant gene sets are shown in green. The size of the yellow bubble shows the enrichment score. <bold>(D)</bold> Gene set enrichment analysis was done using various gene sets obtained from stem cell markers. Normalized enrichment score and FDR are shown with GSEA plots. <bold>(E)</bold> A boxplot showing the stemness scores of dLUAD and uLUAD patients, as described by Malta et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Bars show the standard deviation. <bold>(F)</bold> Bar plot showing the enrichment of molecular subtypes of LUAD as identified by Chen et al. (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). <bold>(G)</bold> Boxplots showing the total mutation (left) and copy number aberrations (right) in dLUAD and uLUAD samples. The <italic>p</italic>-value was obtained using a <italic>t</italic>-test. Bars show the standard deviation. <bold>(H)</bold> Bar diagram showing the mutation pattern of commonly mutated genes in uLUAD and dLUAD patients. <bold>(I)</bold> A bar diagram showing the total neopeptide (neoantigen) in uLUAD and dLUAD samples. The green line shows the average neopeptides in dLUAD, and the yellow line shows the average neopeptide in uLUAD samples. The <italic>p</italic>-value was obtained from a <italic>t</italic>-test. Bars show the standard deviation. <bold>(J)</bold> A CIBERSORT analysis was done in dLUAD and uLUAD samples using absolute quantification settings. The <italic>p</italic>-value was obtained from a <italic>t</italic>-test. Bars show the standard deviation.</p></caption><graphic xlink:href=\"fonc-10-01203-g0003\"/></fig><p>To further illustrate the molecular differences between these novel subtypes of LUAD patients, we performed GSEA using the hallmark gene sets (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Table 6</xref>). Interestingly, we found that patients belonging to uLUAD showed enrichment of gene sets associated with oncogenic signaling (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>). More importantly, one of the significantly enriched gene sets was MYC targets. As MYC-mediated transcriptional changes are associated with pluripotency (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>), we performed GSEA using stem cell marker gene sets and found that uLUAD samples were significantly enriched with genes related to the stem cells (<xref ref-type=\"fig\" rid=\"F3\">Figure 3D</xref>). Furthermore, using the stemness score identified by Malta et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>), we showed that uLUAD patients have significantly higher average stemness score compared to dLUAD patients (<xref ref-type=\"fig\" rid=\"F3\">Figure 3E</xref>) (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Additionally, Metascape analysis using overexpressed genes in uLUAD compared to dLUAD identified the enrichment of pathways associated with cell cycle, DNA replication, and DNA damage response (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 5C</xref>). In contrast, genes overexpressed in dLUAD were associated with cell adhesion and immune-related pathways (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 5D</xref>). Recently, Chen et al. (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>) have identified the nine molecular subtypes of NSCLC patients using a cluster of cluster analysis. We checked the enrichment of these molecular subtypes in dLUAD and uLUAD patients. Interestingly, dLUAD patients showed significantly higher enrichment of AD.4, AD.5b, and AD.2, whereas uLUAD patients showed the highest enrichment of AD.1 subtype, which shows poor differentiation of uLUAD tumors (<xref ref-type=\"fig\" rid=\"F3\">Figure 3F</xref>). Notably, there was no significant difference in the enrichment of dLUAD and uLUAD patients in AJCC T, N, and M subtypes and LUAD stages (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figures 6A–D</xref>). We also showed that there was no significant difference in the enrichment of dLUAD and uLUAD samples in histopathological subtypes of LUAD (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 6E</xref>).</p><p>Recently, Tomasetti et al. (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>) have suggested that tissues with high replication rates generate more random mutations, and these mutations are the most common cause of cancer. To validate this hypothesis, we compared the total somatic mutations in dLUAD and uLUAD. As expected, the mutation burden and copy number aberration are significantly higher in uLUAD samples (<xref ref-type=\"fig\" rid=\"F3\">Figure 3G</xref>). Further analysis of specific mutations showed that Tp53 and RB1 were significantly more mutated in uLUAD compared to dLUAD (Fisher exact test <italic>p</italic> = 0.049 and 0.0004 for TP53 and RB1, respectively) (<xref ref-type=\"fig\" rid=\"F3\">Figure 3H</xref>). More importantly, uLUAD patients showed a significantly higher number of neoepitopes compared to dLUAD patients, making these patients a better candidate for immunotherapy (<xref ref-type=\"fig\" rid=\"F3\">Figure 3I</xref>). The immune response of cancer cells depends on the presence of neoepitopes and enrichment of CD8<sup>+</sup> T cells, CD4<sup>+</sup> T cells, and antigen-presenting cells (APCs) such as dendritic cells (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). CIBERSORT analysis was performed for both dLUAD and uLUAD samples to compare the enrichment of various immune cells in tumor milieu (<xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Table 7</xref>). Interestingly, uLUAD samples showed significantly higher enrichment of CD8<sup>+</sup> and CD4<sup>+</sup> T cells in uLUAD samples compared to dLUAD samples (<xref ref-type=\"fig\" rid=\"F3\">Figure 3J</xref>) (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>). However, many other types of cells, including antigen-presenting dendritic cells, mast cells, M2 macrophages, and monocytes, were significantly enriched in dLUAD samples (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 6F</xref>). The absence of APCs may be the reason for the weak immune activity of uLUAD samples despite the high neoepitopes load and presence of T cells.</p></sec><sec><title>Identification of lncRNAs for High Stem Cell–Like Characteristics Signature in LUAD Patients Using the Greedy Algorithm</title><p>In earlier results, we have identified a novel subgroup of LUAD patients (uLUAD) with highly aggressive disease, most likely due to the presence of a higher fraction of LUAD stem cells. This subgroup of patients was identified using 198 hESC-lncRNAs with high expression in ESCs and LUAD. We hypothesized that not all 198 lncRNAs might be required for the high stem cell–like characteristics determination of the patients, and there may be redundancy. Hence, to identify a strong and non-redundant lncRNA-based signature, we performed feature selection analysis. Exhaustive search using all the possible combinations of the features is not a feasible solution as it is computationally complex. Therefore, we used a greedy forward feature selection approach where the model is built successively by adding one feature in each iteration, and the chosen feature will be the optimal feature in the current iteration (<xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref>).</p><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>Stemness-associated lncRNA signature identifies high stemness patients and cell lines from various cell types. <bold>(A)</bold> A greedy algorithm was used to identify the stemness-associated lncRNA signature. This flowchart depicts the various steps involved in the analysis. <bold>(B)</bold> A precision–recall curve to show the consistent performance of 17 lncRNAs in classifying the stemness in LUAD patients compared to 198 lncRNA model. <bold>(C)</bold> Boxplots showing the difference in stemness in the top 10 cancers. The clusters were identified using K-means clustering of given cancers using 17 lncRNAs. The <italic>p</italic>-value was obtained using a <italic>t</italic>-test. Bars show the standard deviation. <bold>(D)</bold> A GSEA was done using the Muller PluriNet gene set in high and low stemness cluster of given cancer types. A ridge plot showing the running enrichment score and <italic>p</italic>-value in top tumor types. Red indicates a higher significance, and blue indicates lower significance. <bold>(E)</bold> The K-means clustering identified two clusters of cell lines based on the 17-lncRNA expression. <bold>(F)</bold> A GSEA was done in using stemness-associated gene set in high and low stem cell lines. The significantly enriched gene sets are shown in yellow, and insignificant gene sets are shown in black. The size of the bubble shows the <italic>p</italic>-value.</p></caption><graphic xlink:href=\"fonc-10-01203-g0004\"/></fig><p>This analysis identified 17 discriminant hESC-lncRNAs as the optimum number of features to classify the uLUAD and dLUAD patients (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Method</xref>). Further, to check the discriminative ability of the selected hESC-lncRNAs classification, a model was built using a random forest algorithm and smote sampling (<xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref>). To avoid overfitting, 10-fold cross-validation was repeated 10 times, and optimal hyperparameters were chosen by grid search. The precision–recall area under the curve of this model showed that 17 hESC-lncRNAs could classify the dLUAD and uLUAD patients without any significant degradation in performance as compared to 198 hESC-lncRNAs (<xref ref-type=\"fig\" rid=\"F4\">Figure 4B</xref>). To further verify the significance of these 17 hESC-lncRNAs in high stem cell–like characteristics classification, we carried out K-means consensus clustering in the 10 most common tumor types using the optimum number of clusters as two. The clustering analysis showed that the 17-lncRNAs could classify various cancer types into high and low stem cell–like characteristics categories with a significant difference in stemness score as identified by Malta et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>) (<xref ref-type=\"fig\" rid=\"F4\">Figure 4C</xref>). Moreover, a GSEA using Mueller PluriNet gene set (includes common characteristics of pluripotent cells from the different origin) found significant enrichment in six cancer types and enrichment approaching significance in the other four cancer types (<xref ref-type=\"fig\" rid=\"F4\">Figure 4D</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 7A</xref>). Differential expression analysis of matched normal vs. tumor showed a high expression of all the 17 signature lncRNAs in cancer (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 7B</xref>). Receiver operating characteristic (ROC) analysis also showed that expression of all the 17-lncRNA could discriminate between matched normal and tumor with high specificity and sensitivity. Furthermore, ROC analysis of tumor vs. normal in general showed that these 17-lncRNAs could discriminate between tumor and normal with high specificity and sensitivity (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figures 7B,C</xref>). Next, we performed K-means consensus clustering analysis using the selected 17 lncRNAs in CCLE cells to classify the cell lines based on their stemness. This analysis classified the cell lines into two clusters, namely, high stemness cell lines and low stemness cells lines (<xref ref-type=\"fig\" rid=\"F4\">Figure 4E</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 7B</xref>) The expressions of 17 classifying hESC-lncRNAs were significantly higher in high stem cell–like cell lines compared to low stem cell–like cell lines (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 7D</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Table 8</xref>). As expected, gene sets associated with stemness were significantly enriched in cell lines classified as high stem cell–like compared to low stem cell–like cell lines (<xref ref-type=\"fig\" rid=\"F4\">Figure 4F</xref>).</p></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>Recent experimental and clinical observations have shown that aggressiveness and drug resistance of cancers, including lung cancer, are sustained by CSCs (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>). Cancer stem cells share many characteristics with ESCs, which give rise to various properties including anchorage-independent growth, proliferation, metabolic requirements, inhibition of differentiation, and so on (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Also, multiple studies have shown that the dedifferentiation of normal cells is one of the initial steps in carcinogenesis, and cancer cells have a similar molecular regulatory network as ESCs (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B36\" ref-type=\"bibr\">36</xref>). Similarly, lung cancer cells also show a higher expression of ESC-associated genes (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>–<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Validating this observation, we proved that LUADs share a much higher transcriptomic (including lncRNAs expression) overlap with ESCs compared to NL cells (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). We also identified a group of 198 lncRNAs (hESC-associated lncRNA, hESC-lncRNAs) with high differential expression in ESCs and LUAD compared to NL. The PcGs with similar expression patterns to 198 lncRNAs indicated the involvement of high stem cell–like characteristics–associated lncRNAs in cell proliferation and other cancer-associated roles. We identified various pathways that are associated with PcGs with similar expression pattern to 198 lncRNAs (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 1D</xref>). PLK1 pathway is essential for the initiation and completion of mitosis and thus required for cell proliferation (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Similarly, the Aurora kinase pathway is one of the crucial pathways for successful cell division and proliferation of the cells (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Also, Aurora-A is required for the maintenance of the ESC self-renewal and undifferentiated state (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). Aurora kinase B is also needed for the maintenance of telomerase activity and stem cells (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). FOXM1 pathway is required for cell proliferation, self-renewal, and tumorigenesis (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). Similarly, RB1-E2F is necessary for cancer cell growth, migration, self-renewal differentiation, and so on (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). These results suggest the role of hESC-associated lncRNAs in cell proliferation and differentiation.</p><p>Unsupervised clustering has been used to identify the novel subgroups of many cancer types (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>–<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). Here, we utilized the K-means clustering algorithm with high stem cell–like characteristics–associated lncRNAs to identify a unique subset of LUAD patients. These patients showed poor survival and lower expression differentiation markers such as DNAI1, NKX2-1, and SCGB1A1, and higher expression of stem cell markers such as ALDH1A1 and CD133 (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>). The expressions of DNAI1, NKX2-1, and SCGB1A1 genes are required for the differentiation of many cell types, including secretory (club) cells (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B51\" ref-type=\"bibr\">51</xref>, <xref rid=\"B52\" ref-type=\"bibr\">52</xref>). This observation suggests that the novel LUAD subgroup named as uLUAD cells is less differentiated and has high ESC-like properties. The poor differentiation is associated with various cancer hallmarks such as proliferation, replicative immortality, angiogenesis, higher metastasis, and so on (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). These properties make poorly differentiated cancers more aggressive with poor outcomes, as found in uLUAD patients. A Cox regression analysis identified two stemness-associated lncRNAs with a high correlation with survival. We developed a stemness lncRNA prognostic score (SPS) and proved the prognostic ability of SPS in two independent cohorts of samples (<xref ref-type=\"fig\" rid=\"F2\">Figures 2F,G</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figures 2C,D</xref>).</p><p>Many transcription factors, including MYC, play a vital role in stem cell biology (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>, <xref rid=\"B55\" ref-type=\"bibr\">55</xref>). MYC has been shown to induce ESC-like characters in normal and cancer cells (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref>). We showed that the novel uLUAD patients had higher MYC activity. SOX4, another stem cell pluripotency factor, is also significantly more active in uLUAD samples compared to dLUAD samples. Furthermore, the direct ESC-related genes were also significantly enriched in uLUAD compared to dLUAD. These observations validated the high stem cell–like characteristics of uLUAD patients identified by stemness-associated lncRNAs. The high stem cell–like characteristics are linked with high cellular proliferation, which in turn causes more genetic instability and high mutation rate and copy number aberrations. We also found the activation of proteins involved in cellular proliferation (<xref ref-type=\"fig\" rid=\"F3\">Figures 3A,B</xref>). This observation was further supported by the high mutational load of uLUAD patients (<xref ref-type=\"fig\" rid=\"F3\">Figure 3F</xref>). The uLUAD patients also showed high neoantigens compared to dLUAD patients. Recently, Chen et al. (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>) have classified NSCLC in nine genomic subtypes, that is, SQ.1, SQ.2a, SQ.2b, AD.1, AD.2, AD.3, AD.4 AD.5a, and AD.5b. We found that all the LUAD patients used in this analysis were enriched in five of nine subtypes, SQ.1, SQ.2b, AD.1, AD.2, AD.3, AD.4, and AD.5b. Undifferentiated LUAD patients' proportion was significantly higher in the AD.1 subtype. AD.1 subtype is associated with poor differentiation, association with LCNEC, and expression of CT antigens, confirming our findings. In comparison, dLUAD patients were considerably higher in AD.4, AD.5b, and AD.2 subtypes. AD.4 subtype is associated with high immune infiltration, lower neoantigen, and lower mutation rate. AD.5b subtype is associated with lower mutation rate and high mTOR pathway activation, whereas the AD.2 subtype is associated with the high immune cell and checkpoint pathway activation. All three subtypes (AD.4, AD.5b, and AD.2) are also associated with excellent survival. These results positively confirm our finding that dLUAD patients have lower neoantigen and show good survival compared to uLUAD patients. Other subtypes are associated with high SOX2 and CT antigen expression (SQ.1), high SOX2, CT antigen expression, better OS (SQ.2A), distinct methylation patterns compared to SQ.2a (SQ.2B), high immune cell infiltration, and CT antigen expression (AD.3). Recent reports have suggested that the presence of neoantigens is essential for the checkpoint inhibitor–mediated immune response of T cells (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). Interestingly, we also found that uLUAD patients have a higher level of CD8<sup>+</sup> and CD4<sup>+</sup> T cells in the tumor milieu. These observations suggest that uLUAD patients could be a significant group of patients for immunotherapy. Interestingly, checkpoint inhibitors such as CD274 are also overexpressed in uLUAD patients, making these tumors more immunoactive. However, we believe that the absence of APCs such as dendritic cells from uLUAD cells may be a responsible weak T-cell activity (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 2E</xref>). Also, we did not find any association of stage and hESC-like characteristic, suggesting the expression of hESC-like lncRNAs is probably an early event in LUAD development.</p><p>To build a classification model that efficiently distinguishes uLUAD from dLUAD samples, there was a need to eliminate the redundant features and retain only the discriminant lncRNAs as building a model with redundant features not only increases the computational complexity but also may lead to overfitting. Using a greedy algorithm, we identified a list of 17 lncRNAs. As these lncRNAs had a very high expression in ESCs, we hypothesized that unsupervised clustering of other tumor types using these hESC-associated lncRNAs should identify subgroups of cancer with high stemness. Interestingly, the 17-hESC-associated-lncRNA signature identified the subgroups in the top 10 tumors with a significant difference in stemness. This observation suggested that these hESC-associated lncRNAs were involved in stemness determination in general. Further, the hESC-associated lncRNA signature also identified a group of cell lines with high stemness characters. These cell lines could prove to be a useful tool for stem cell research and drug discovery.</p><p>Here, we performed various <italic>in silico</italic> analyses to show the importance of lncRNA in stemness determination and prognosis. However, experimental validation of stemness-associated lncRNAs is essential to show the direct effect on stemness determination, and it is an important shortcoming of this study. Taken together, we have utilized a large set of tumor patients to identify the stemness-associated lncRNAs. We have also identified a subgroup of LUAD patients who showed a significant difference in survival and stem cell–like characteristics. We propose that the aggressiveness of these patients is due to the presence of CSCs. We also showed that these patients could be an important target for immunotherapy.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>Publicly available datasets were analyzed in this study. This data can be found in The Cancer Genome Atlas (<ext-link ext-link-type=\"uri\" xlink:href=\"https://portal.gdc.cancer.gov/\">https://portal.gdc.cancer.gov/</ext-link>) and the NCBI Gene Expression Omnibus (<ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"GSE102311\">GSE102311</ext-link>).</p></sec><sec id=\"s6\"><title>Author Contributions</title><p>SK and SS designed the research. SK, AC, PK, and SS performed the experiments. SK, AC, and SS analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s7\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>TCGA-GDC is acknowledged for the RNA-Seq and Clinical data. We also acknowledge Dr. Bharath BN, Assistant Professor, IIT Dharwad, and Dr. Naveen MB, Assistant Professor, IIT Dharwad, for their comments on feature selection methods.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> SS is supported by grants from Department of Biotechnology, Govt of India (grant id: BT/PR27478/MED/30/1954/2018) and Department of Science and Technology, Govt. of India (grant id: ECR/2018/000528).</p></fn></fn-group><sec sec-type=\"supplementary-material\" id=\"s8\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fonc.2020.01203/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fonc.2020.01203/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM2\"><media xlink:href=\"Data_Sheet_2.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Cheng</surname><given-names>TYD</given-names></name><name><surname>Cramb</surname><given-names>SM</given-names></name><name><surname>Baade</surname><given-names>PD</given-names></name><name><surname>Youlden</surname><given-names>DR</given-names></name><name><surname>Nwogu</surname><given-names>C</given-names></name><name><surname>Reid</surname><given-names>ME</given-names></name></person-group>. <article-title>The international epidemiology of lung cancer: latest trends, disparities, and tumor characteristics</article-title>. <source>J Thorac Oncol</source>. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"case-report\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849216</article-id><article-id pub-id-type=\"pmc\">PMC7431878</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00757</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Case Report</subject></subj-group></subj-group></article-categories><title-group><article-title>Increased Neurofilament Light Chain and YKL-40 CSF Levels in One Japanese IBMPFD Patient With VCP R155C Mutation: A Clinical Case Report With CSF Biomarker Analyses</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Ikeda</surname><given-names>Masaki</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/893348/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Kuwabara</surname><given-names>Takeo</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Takai</surname><given-names>Eriko</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1044141/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Kasahara</surname><given-names>Hiroo</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Furuta</surname><given-names>Minori</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Sekine</surname><given-names>Akiko</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Makioka</surname><given-names>Kouki</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Yamazaki</surname><given-names>Tsuneo</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/893633/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fujita</surname><given-names>Yukio</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1044580/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Nagashima</surname><given-names>Kazuaki</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Higuchi</surname><given-names>Tetsuya</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/957727/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Tsushima</surname><given-names>Yoshito</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/359381/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ikeda</surname><given-names>Yoshio</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/540743/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Neurology, Gunma University Graduate School of Medicine</institution>, <addr-line>Maebashi</addr-line>, <country>Japan</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Neurology, Jobu Hospital for Respiratory Diseases</institution>, <addr-line>Maebashi</addr-line>, <country>Japan</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Occupational Therapy, Gunma University Graduate School of Health Sciences</institution>, <addr-line>Maebashi</addr-line>, <country>Japan</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine</institution>, <addr-line>Maebashi</addr-line>, <country>Japan</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Rosanna Tortelli, University College London, United Kingdom</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Alberto Benussi, University of Brescia, Italy; Julien Couthouis, Stanford University, United States</p></fn><corresp id=\"c001\">*Correspondence: Masaki Ikeda <email>mikeda@gunma-u.ac.jp</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Dementia and Neurodegenerative Diseases, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>757</elocation-id><history><date date-type=\"received\"><day>22</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>19</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Ikeda, Kuwabara, Takai, Kasahara, Furuta, Sekine, Makioka, Yamazaki, Fujita, Nagashima, Higuchi, Tsushima and Ikeda.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Ikeda, Kuwabara, Takai, Kasahara, Furuta, Sekine, Makioka, Yamazaki, Fujita, Nagashima, Higuchi, Tsushima and Ikeda</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Inclusion body myopathy (IBM) with Paget's disease of bone (PDB) and frontotemporal dementia (IBMPFD) presents with multiple symptoms and an unknown etiology. Valosin-containing protein (<italic>VCP</italic>) has been identified as the main causative gene of IBMPFD. However, no studies on neurofilament light chain (NFL) as a cerebrospinal fluid (CSF) marker of axonal neurodegeneration or on YKL-40 as a CSF marker of glial neuroinflammation have been conducted in IBMPFD patients with <italic>VCP</italic> mutations. A 65-year-old man presented with progressive muscle atrophy and weakness of all limbs, non-fluent aphasia, and changes in personality and behavior. Cerebral MRI revealed bilateral frontal and temporal atrophy. <sup>99m</sup>Tc-HMDP bone scintigraphy and pelvic CT revealed remodeling changes and active osteoblastic accumulations in the right medial iliac bone. Muscle biopsy demonstrated multiple rimmed vacuoles in muscle cells with myogenic and neurogenic pathological alterations. After the patient was clinically diagnosed with IBMPFD, DNA analysis of the <italic>VCP</italic> gene revealed a cytosine (C) to thymine (T) (C→ T) mutation, resulting in an amino acid exchange of arginine to cysteine (p.R155C mutation). The CSF levels of NFL at two time points (12 years apart) were higher than those in non-dementia controls (CTR) and Alzheimer's disease (AD); lower than those in frontotemporal dementia with motor neuron disease (FTD-MND); and comparable to those in patients with behavioral variant frontotemporal dementia (bvFTD), progressive supranuclear palsy (PSP), and corticobasal syndrome (CBS). The CSF levels of YKL-40 were comparable at both time points and higher than those in CTR; lower than those in FTD-MND; and comparable to those in bvFTD, PSP, CBS, and AD. The CSF levels of phosphorylated tau 181 (P-Tau) and total tau (T-Tau) were not significantly different from those in CTR and other neurodegenerative diseases, except those in AD, which were significantly elevated. This is the first report that demonstrates increased NFL and YKL-40 CSF levels in an IBMPFD patient with a <italic>VCP</italic> mutation (p.R155C); NFL and YKL-40 levels were comparable to those in bvFTD, PSP, CBS, and AD and higher than those in CTR. Our results suggest that IBMPFD neuropathology may involve both axonal neurodegeneration and glial neuroinflammation.</p></abstract><kwd-group><kwd>IBMPFD</kwd><kwd><italic>VCP</italic></kwd><kwd>mutation</kwd><kwd>CSF</kwd><kwd>NFL</kwd><kwd>YKL-40</kwd><kwd>AD</kwd><kwd>frontotemporal dementia</kwd></kwd-group><counts><fig-count count=\"2\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"36\"/><page-count count=\"8\"/><word-count count=\"4716\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Inclusion body myopathy (IBM) with Paget's disease of bone (PDB) and frontotemporal dementia (IBMPFD) is a multi-organ disease with still unknown etiology (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>). In IBMPFD with autosomal dominant inheritance, valosin-containing protein (<italic>VCP</italic>) has been identified as the major causative gene (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Neurofilament light chain (NFL), which is indicative of axonal neurodegeneration (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>), has been validated as a CSF biomarker of behavioral variant frontotemporal dementia (bvFTD), FTD with motor neuron disease (FTD-MND), amyotrophic lateral sclerosis (ALS), progressive supranuclear palsy (PSP), corticobasal syndrome (CBS), and Alzheimer's disease (AD) (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Furthermore, YKL-40 (known as chitinase 3-like 1) has been reported as a CSF biomarker of glial neuroinflammation in neurodegenerative diseases (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). With regard to IBMPFD with <italic>VCP</italic> mutations, no CSF studies on NFL or YKL-40 have been conducted yet.</p></sec><sec id=\"s2\"><title>Case Presentation</title><p>We describe the case of a 65-year-old man who presented with muscle weakness and atrophy of all limbs. At the age of 42 years, he experienced difficulties in standing from a sitting position and raising his arms over his head. At the age of 48 years, he was affected by gait disturbances with difficulties squatting and was able to walk only at a slow pace. Further, the patient could not raise his arms over his head and experienced difficulties moving his head and neck freely. These symptoms gradually deteriorated. During the first hospitalization at the age of 52 years, the patient showed atrophy and weakness of the muscles of all limbs but most prominently of the bilateral quadriceps. The neuropsychological examination revealed decline in his cognitive function. The scores of the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) were 26/30 and 18/30, respectively, with disturbances of attention and executive functions. The score of the frontal assessment battery (FAB) was 8/18 with disturbances of “similarities,” “lexical fluency,” and “motor series.” However, no remarkable changes of character, behavior, voice, and speech were observed. The patient showed generalized hyporeflexia without pathologic reflexes. He exhibited no respiratory difficulty. The CT showed prominent atrophy of the quadriceps and other muscles, e.g., hamstrings, iliopsoas, and anterior tibial muscles (not shown). Because of gait difficulty due to weakness of the legs, the patient used a cane or a walker at the age of 52 years (after the first hospitalization), and he used a wheelchair at the age of 55 years. He had occasional cough due to dysphasia and difficulty expectorating, when he was 60 years old; at the same time, he exhibited character changes including self-centered thinking, extreme dependence on his wife, irritation, and frustration. Furthermore, the patient rejected or was indifferent to advice from others. At the age of 61 years, he frequently coughed and experienced shortness of breath due to saliva and food; subsequently, he suffered from dysphagic pneumonia due to massive saliva and was finally readmitted to our hospital.</p><p>During the second hospitalization, the muscles of the patient's four limbs revealed more pronounced weakness and atrophy than during the first hospitalization. Generalized hyporeflexia was still present; however, bilateral Babinski reflexes were observed. A neuropsychological examination was conducted, when the patient improved after the pneumonia. The MMSE score was 21/30, whereas the MoCA score was 12/30 with disturbed attention, visuospatial cognition, and executive functions. The FAB score was 6/18 with disturbances of “similarities,” “lexical fluency,” “motor series,” and “prehension behavior.” The results of the neuropsychological tests revealed a deterioration of cognitive functions including mainly language and speech disturbances due to predominantly frontal and temporal lobe dysfunctions. His speech was apparently affected by non-fluent agrammatic primary progressive aphasia (naPPA) with word-finding difficulties and mistakes of words and characters. The changes in personality presented as adhesion, irritation, dependent tendencies, and self-centered behavior with childish manners. After the pneumonia improved, the patient was moved to another hospital, and his treatment continued. The patient was alert and could speak with the help of a speech cannula after a tracheotomy; however, he could also communicate independently with blinking. He needed frequent aspiration of saliva and oxygen inhalation to support his respiration. At the present age of 65 years, a lumbar puncture was performed, after we obtained the patient's informed consent.</p><p>The patient's mother had also shown muscular weakness and bilateral atrophy of the lower limbs at the age of 60 years, eventually also involving the upper limbs, which had resulted in her becoming bed-ridden. She was diagnosed with amyotrophic lateral sclerosis (ALS) and died from pneumonia at the age of 68 years; it was not confirmed whether she had been affected by dementia. The patient's father died from pancreatic cancer, whereas his elder sister suffered from gait disturbance of unknown etiology since her childhood and died from brain tumor at the age of 40 years. His younger brother died from malignant lymphoma at the age of 36 years. The patient did not have any children. During the first hospitalization, cerebral MRI showed bilateral frontal and temporal atrophy (<xref ref-type=\"fig\" rid=\"F1\">Figures 1A–C</xref>). During the second hospitalization, CT of the extremities exhibited severe bilateral muscle atrophy of the upper arms, forearms, thighs, and lower legs (<xref ref-type=\"fig\" rid=\"F1\">Figures 1D–G</xref>). During the second hospitalization, <sup>99m</sup>Tc-HMDP bone scintigraphy showed active osteoblastic accumulation in the right medial iliac bone (<xref ref-type=\"fig\" rid=\"F1\">Figure 1H</xref>), whereas pelvic CT revealed remodeling changes in the corresponding area indicated by arrows (<xref ref-type=\"fig\" rid=\"F1\">Figure 1I</xref>). Hematoxylin and eosin staining of the muscle biopsy specimens demonstrated multiple rimmed vacuoles in muscle cells (<xref ref-type=\"fig\" rid=\"F1\">Figure 1J</xref>) and numerous small fibers and round-shaped fibers (<xref ref-type=\"fig\" rid=\"F1\">Figure 1K</xref>). Gomori trichrome staining showed rimmed vacuoles in muscle cells and small angulated fibers (<xref ref-type=\"fig\" rid=\"F1\">Figure 1L</xref>), which were compatible with the pathological findings of IBMPFD during the first hospitalization. DNA analysis revealed a cytosine (C) to thymine (T) (C→T<sup>*</sup>) mutation, resulting in an amino acid exchange of arginine to cysteine (p.R155C) (<xref ref-type=\"fig\" rid=\"F1\">Figure 1M</xref>) as previously described (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B9\" ref-type=\"bibr\">9</xref>–<xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Findings of images, pathological examinations, and DNA sequences. Cerebral MRIs of the <bold>(A)</bold> transverse view, <bold>(B)</bold> coronal view, and <bold>(C)</bold> sagittal view demonstrated frontal and temporal lobe atrophy. CT of muscles of the <bold>(D)</bold> upper arms, <bold>(E)</bold> forearms, <bold>(F)</bold> thighs, and <bold>(G)</bold> lower legs showed muscle atrophy in the four extremities. <bold>(H)</bold>\n<sup>99m</sup>Tc-HMDP bone scintigraphy of the pelvis revealed active osteoblastic accumulation in the right medial iliac bone. <bold>(I)</bold> Pelvic CT showed remodeling changes in the corresponding area designated with arrows <bold>(H)</bold>. Microscopic findings. Hematoxylin eosin staining showed multiple rimmed vacuoles in muscle cells <bold>(J)</bold> and numerous small fibers <bold>(K)</bold>. <bold>(L)</bold> Gomori trichrome staining demonstrated rimmed vacuoles and small angulated fibers. <bold>(M)</bold> Genomic DNA analysis revealed a missense mutation in the <italic>VCP</italic> gene that exchanged CGT (Arg) to T*GT (Cys).</p></caption><graphic xlink:href=\"fneur-11-00757-g0001\"/></fig><p>The neurological finding of this case revealed general muscle weakness and atrophy, especially, proximal muscles of lower extremities, progressive cognitive decline, speech disturbance, and character change. In muscle biopsy, rimmed vacuoles were pathologically confirmed and neurogenic muscle changes were also observed. <sup>99m</sup>Tc-HMDP bone scintigraphy of the patient was compatible with Paget's disease of bone (PDB).</p></sec><sec id=\"s3\"><title>Methods and Results of the CSF Analyses</title><p>The patient was examined by lumbar puncture two times (during the present hospitalization and 13 years earlier). The CSF samples obtained from the patient by the two lumbar punctures were stored separately in 1.5-ml Eppendorf tubes. The CSF samples were strictly stored in a −80°C freezer and never opened nor freeze-thawed, until they were measured using enzyme-linked immunosorbent assay (ELISA) kits. Phosphorylated Tau (P-Tau), human total tau (T-Tau), neurofilament light chain (NFL), and YKL-40 were measured. The CSF samples were analyzed in this patient, patients with neurodegenerative diseases (bvFTD: <italic>n</italic> = 7, FTD-MND: <italic>n</italic> = 5, PSP: <italic>n</italic> = 7, CBS: <italic>n</italic> = 7, and AD: <italic>n</italic> = 24), and non-dementia control subjects (CTR: <italic>n</italic> = 18). The patients with IBMPFD, bv-FTD, FTD-MND, PSP, CBS, and AD were diagnosed in accordance with the global clinical criteria [IBMPFD (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>); bv-FTD and FTD-MND (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>–<xref rid=\"B21\" ref-type=\"bibr\">21</xref>); PSP (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>); CBS (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>); and AD (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>–<xref rid=\"B27\" ref-type=\"bibr\">27</xref>)] by experienced neurologists (M.I., T.K., H.K., M.F., K.M, K.N., Y.F., and Y.I.) at the Department of Neurology, Gunma University Hospital (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). <sup>99m</sup>Tc-HMDP bone scintigraphy findings in IBMPFD patients were evaluated by senior radiologists (T.H. and Y.T.). P-Tau and T-Tau in CSF were analyzed with sandwich ELISA INNOTEST® PHOSPHO-TAU(181P) (Fujirebio Europe N.V., Gent, Belgium) (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>) and sandwich ELISA INNOTEST® T-Tau-Ag (Fujirebio Europe N.V., Gent, Belgium) (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>), respectively. NFL and YKL-40 CSF levels were measured utilizing sandwich ELISA NF-light® (IBL International, Hamburg, Germany) (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>) and MicroVue™ YKL-40 EIA kits (Quidel, San Diego, CA, USA) (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>), respectively.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Demographic characteristics of the patients with IBMPFD and neurodegenerative diseases and of non-dementia control subjects.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>IBMPFD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>bvFTD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>FTD-MND</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>PSP</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>CBS</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>AD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>CTR</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No.</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Male</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">100</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">71.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">42.86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">57.14</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45.83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">50.00</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age at onset (years old)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">52 ± 3.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60 ± 3.22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69 ± 1.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">68 ± 2.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">64 ± 1.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age at CSF analysis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#1: 52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">55 ± 2.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62 ± 2.82</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">71 ± 1.90</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">70 ± 2.53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69 ± 1.49</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">65 ± 2.34</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#2: 65</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MMSE (/30)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#1: 26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14 ± 3.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18 ± 1.59</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22 ± 2.28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 ± 3.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">20 ± 1.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29 ± 0.25</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#2: 21</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MoCA (/30)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#1: 18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 ± 2.76</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13 ± 2.40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19.5 ±4.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14 ± 4.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 ± 0.99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28.5 ± 0.36</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#2:12</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FAB (/18)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#1: 8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.5 ± 3.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.5 ± 1.31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7 ± 1.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 ± 2.65</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.5 ± 0.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17 ± 0.28</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#2: 6</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NFL (pg/ml)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#1: 5,255.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5,493.71 ± 814.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9,371.82 ± 1,134.69</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4,413.78 ± 741.49</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4,217.29 ± 936.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1,531.70 ± 167.56</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">452.93 ± 58.90</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#2: 5,394.98</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">YKL-40 (ng/ml)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#1: 125.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">146.07 ± 25.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">154.39 ± 62.41</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">99.94 ± 17.62</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">84.59 ± 17.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">107.23 ± 10.26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60.53 ± 5.43</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#2: 132.41</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">P-Tau (pg/ml)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#1: 31.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41.75 ± 6.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31.45 ± 6.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36.60 ± 3.61</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">33.93 ± 7.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">76.42 ± 7.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">25.52 ± 2.53</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#2: 34.72</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">T-Tau (pg/ml)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#1: 148.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">319.93 ± 50.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">213.99 ± 68.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">231.09 ± 63.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.61 ± 50.96</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">506.86 ± 71.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">143.56 ± 16.07</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">#2: 157.16</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table><table-wrap-foot><p><italic>Clinical information and CSF data are described for this patient; the patients with neurodegenerative diseases (bvFTD, FTD-MND, PSP, CBS, and AD) and the CTR subjects. Patient #1: the first hospitalization, #2: the present hospitalization. The data represent median ± standard error (S.E.)</italic>.</p></table-wrap-foot></table-wrap><p>The NFL CSF levels (pg/ml) in the patient were comparable at the two measurement points separated by 13 years (#1: the first puncture and #2: the second puncture). Both NFL CSF levels in the patient (#1: 5255.24 and #2: 5394.98) were higher than those in CTR individuals [452.93 ± 58.90; median ± standard error (S.E.)] and AD patients (1,531.70 ± 167.56), lower than those in FTD-MND patients (9,371.82 ± 1,134.69), and comparable to those in bvFTD (5,493.71 ± 814.18), PSP (4,413.78 ± 741.49), and CBS patients (4,217.29 ± 936.81; <xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>). The YKL-40 CSF levels (ng/ml) in the patient were comparable at the two times points (#1: the first time 125.03 and #2: the second time 132.41); furthermore, they were higher than those in CTR individuals (60.53 ± 5.43) and comparable to those in FTD-MND (154.39 ± 62.41), bvFTD (146.07 ± 25.87), PSP (99.94 ± 17.62), and AD (107.23± 10.26) (<xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>). The P-Tau CSF levels (pg/ml) in the patient (#1: 31.79 and #2: 34.72) were comparable to those in CTR individuals (25.52 ± 2.53), bvFTD (41.75 ± 6.10), FTD-MND (31.45 ± 6.87), PSP (36.60 ± 3.61), and CBS (33.93 ± 7.08), whereas the P-Tau levels (76.42 ± 7.73) in CSF of AD patients were higher than those in CTR individuals and patients with other neurodegenerative diseases (<xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>). The CSF levels of T-Tau (pg/ml) in the patient (#1: 148.60 and #2: 157.16) were comparable to those in CTR individuals (143.56 ± 16.07) and patients with other neurodegenerative diseases, whereas the CSF levels of T-Tau in AD patients (506.86 ± 71.60) were higher than those in CTR individuals (143.56 ± 16.07) and patients with other neurodegenerative diseases (<xref ref-type=\"fig\" rid=\"F2\">Figure 2D</xref>). The CSF levels of both P-Tau and T-Tau in the patient were comparable to those in bvFTD, FTD-MND, PSP, and CBS patients (<xref ref-type=\"fig\" rid=\"F2\">Figures 2C,D</xref>). These data are presented in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>CSF analyses of NFL, YKL-40, phosphorylated tau 181 (P-Tau), and total human tau (T-Tau). <bold>(A)</bold> Both CSF NFL levels in the patient (#1: the first hospitalization and #2: the present hospitalization) were higher than those in CTR and lower than those in FTD-MND; further, the CSF NFL levels in bvFTD, PSP, CBS, and AD were higher than those in CTR. <bold>(B)</bold> Both CSF YKL-40 levels in the patient (#1 and #2) were higher than those in CTR; moreover, CSF YKL-40 levels in FTD-MND, bvFTD, PSP, and AD were higher than those in CTR. <bold>(C)</bold> CSF P-Tau levels in AD were higher than those in CTR and in neurodegenerative diseases including the patient (#1 and #2). <bold>(D)</bold> CSF T-Tau levels in AD were higher than those in CTR and other neurodegenerative diseases including the patient (#1 and #2). Bars in each graph present mean data.</p></caption><graphic xlink:href=\"fneur-11-00757-g0002\"/></fig></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>IBMPFD is clinically characterized by adult-onset muscle weakness and atrophy, early-onset PDB, and frontotemporal dementia (FTD) (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B15\" ref-type=\"bibr\">15</xref>). <italic>VCP</italic> is identified as the most predominant causative gene among IBMPFD patients, and the R155C mutation has been reported including Japanese ethnic background (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B9\" ref-type=\"bibr\">9</xref>–<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). VCP-related IBMPFD represents a unique class D subtype of the neurodegenerative diseases named TDP-43 proteinopathies with numerous ubiquitin-positive neuronal intranuclear inclusions and dystrophic neurites (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>–<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). Recently, CSF NFL has been investigated as a diagnostic marker of axonal neurodegeneration, especially ALS and frontotemporal lobar degeneration (FTLD) including bvFTD, PSP, CBS, and AD (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Furthermore, YKL-40 has been identified as a CSF biomarker of glial neuroinflammation in ALS, FTLD, PSP, CBS, and AD (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>).</p><p>This is the first report of a Japanese IBMPFD patient demonstrating higher and comparable levels, over 13 years, of the CSF biomarkers NFL and YKL-40 in an IBMPFD patient with a <italic>VCP</italic> mutation than in CTR individuals. Up to date, there is no other report but this case at least within the Japanese Consortium for Amyotrophic Lateral Sclerosis Research (JaCALS). The symptoms of this patient were not compatible with the typical ALS phenotype; however, the patient showed neurogenic changes in the EMG examination (data not shown) and neurogenic pathological changes in muscle biopsy. This patient is clinically expected to have poor prognosis, because his respiratory function has gradually deteriorated due to progressive general muscle weakness and atrophy due to IBMPFD. The patient will still require frequent aspirations of saliva and oxygen inhalation to support his respiration.</p><p><italic>VCP</italic> mutations presumably lead to a dominant negative loss or alteration of VCP function culminating in impaired degradation of TDP-43 (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Whereas IBMPFD is a multisystem proteinopathy (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>), mutant VCP proteins are reportedly targets of autophagic-lysosomal degeneration, mitochondrial dysfunction, and ubiquitin–proteasome system disorders (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). A limitation of this study is the fact that only one patient of IBMPFD with a <italic>VCP</italic> mutation was included, which impeded statistical analyses for the other neurological diseases and CTR groups. NFL and YKL-40 levels were not compared in blood samples among the patient, noncarriers, and asymptomatic carriers with a <italic>VCP</italic> mutation to prove the utility of blood biomarkers for IBMPFD.</p><p>Higher NFL and YKL-40 CSF levels in the IBMPFD patient with a <italic>VCP</italic> mutation may be related to both axonal neurodegeneration and glial neuroinflammation. The implicated multifaceted pathological mechanisms should be elucidated, which may allow the discovery of new therapeutic targets for the <italic>VCP</italic> gene and/or the VCP protein in IBMPFD.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>All data generated or analyzed during this study are included in this published article.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by this study was approved by the ethics committee of the Gunma University Hospital (Masaki Ikeda) and the Jobu Hospital for Respiratory Diseases (Takeo Kuwabara). The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"s7\"><title>Consent for Publication</title><p>Written consent to publish the clinical information was obtained from the patient's family.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>MI and TK collected the clinical data and interpreted the data, and MI wrote the manuscript. ET analyzed the genomic DNA from the patient's blood samples and CSF biomarkers from the patient's CSF. YF performed the pathological examinations and evaluated the results. TH and YT evaluated the neuroimaging information. HK, MF, KM, AS, KN, and TY discussed the clinical information in terms of neurological features. MI and YI performed the clinical data analysis and evaluated their specificity and neurological significance. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We thank the patient and his family for providing clinical data and allowing the publication of this case report. We also acknowledge Professor Masashi Aoki (Tohoku University) and Dr. Naoki Atsuta (Nagoya University) from the Japanese Consortium for Amyotrophic Lateral Sclerosis Research (JaCALS). We thank Editage (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.editage.com\">www.editage.com</ext-link>) for the American English language editing.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This study received Grants-in-Aid for Scientific Research (C) (MI: 18K07491, TY: 17K09790, TH: 19K08220, YT: 18K07627, and YI: 19K07813) from the Ministry of Education, Culture, Sports, Science and Technology of Japan and an Ai-no-bokin (Donation of love) research grant for dementia research of the JOMO-SHINBUN newspaper company (Maebashi, Gunma, Japan) (MI and HK). 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Cell Dev Biol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Cell Dev Biol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Cell Dev. Biol.</journal-id><journal-title-group><journal-title>Frontiers in Cell and Developmental Biology</journal-title></journal-title-group><issn pub-type=\"epub\">2296-634X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850869</article-id><article-id pub-id-type=\"pmc\">PMC7431879</article-id><article-id pub-id-type=\"doi\">10.3389/fcell.2020.00777</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Cell and Developmental Biology</subject><subj-group><subject>Mini Review</subject></subj-group></subj-group></article-categories><title-group><article-title>The Enigmatic Role of Lipids in Cilia Signaling</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Nechipurenko</surname><given-names>Inna V.</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/866088/overview\"/></contrib></contrib-group><aff><institution>Department of Biology and Biotechnology, Worcester Polytechnic Institute</institution>, <addr-line>Worcester, MA</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Roberto Botelho, Ryerson University, Canada</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Richard A. Kahn, Emory University School of Medicine, United States; Hemant Khanna, University of Massachusetts Medical School, United States</p></fn><corresp id=\"c001\">*Correspondence: Inna V. Nechipurenko, <email>inechipurenko@wpi.edu</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Membrane Traffic, a section of the journal Frontiers in Cell and Developmental Biology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>8</volume><elocation-id>777</elocation-id><history><date date-type=\"received\"><day>02</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Nechipurenko.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Nechipurenko</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Primary cilia are specialized cellular structures that project from the surface of most cell types in metazoans and mediate transduction of major signaling pathways. The ciliary membrane is contiguous with the plasma membrane, yet it exhibits distinct protein and lipid composition, which is essential for ciliary function. Diffusion barriers at the base of a cilium are responsible for establishing unique molecular composition of this organelle. Although considerable progress has been made in identifying mechanisms of ciliary protein trafficking in and out of cilia, it remains largely unknown how the distinct lipid identity of the ciliary membrane is achieved. In this mini review, I summarize recent developments in characterizing lipid composition and organization of the ciliary membrane and discuss the emerging roles of lipids in modulating activity of ciliary signaling components including ion channels and G protein-coupled receptors.</p></abstract><kwd-group><kwd>cilia</kwd><kwd>flagella</kwd><kwd>lipids</kwd><kwd>polyphosphoinositides</kwd><kwd>cholesterol</kwd></kwd-group><counts><fig-count count=\"2\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"121\"/><page-count count=\"10\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Cilia (or flagella) are hair-like cellular projections that are highly conserved across eukaryotes (<xref rid=\"B20\" ref-type=\"bibr\">Carvalho-Santos et al., 2011</xref>). Based on their structural features, cilia are classified into motile and non-motile subtypes. Non-motile cilia, also known as primary cilia, are present on nearly all vertebrate cell types and function as signaling hubs during development and in differentiated tissues. In fact, components of all major signaling pathways including Hedgehog (Hh), Wnt, Notch, transforming growth factor β, G protein-coupled receptors (GPCRs), receptor tyrosine kinases, and extracellular matrix receptors localize to cilia and require these organelles for proper transduction (<xref rid=\"B81\" ref-type=\"bibr\">Mykytyn and Askwith, 2017</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Anvarian et al., 2019</xref>). While most cells in the human body possess a solitary primary cilium, motile cilia are also present on the surface of some specialized cells in the airway, oviduct, and brain ventricles (<xref rid=\"B19\" ref-type=\"bibr\">Brooks and Wallingford, 2014</xref>). Like their non-motile counterparts, motile cilia can detect and transmit diverse sensory cues in addition to beating and propelling fluids (<xref rid=\"B12\" ref-type=\"bibr\">Bloodgood, 2010</xref>). Due to the central role of cilia in signaling and their nearly ubiquitous distribution across human tissues, perturbations in cilia structure and/or function manifest in a spectrum of genetic disorders called ciliopathies (<xref rid=\"B95\" ref-type=\"bibr\">Reiter and Leroux, 2017</xref>). These diseases affect most human organ systems and present with pleiotropic developmental and adult phenotypes that include blindness, kidney and heart disease, obesity, and cognitive deficits (<xref rid=\"B5\" ref-type=\"bibr\">Badano et al., 2006</xref>).</p><p>Since the discovery of motile cilia in the 17th century by Antonie van Leeuwenhoek until the early 2000s, cilia research was rather scarce and focused primarily on the axoneme – the microtubule backbone of the organelle (<xref rid=\"B11\" ref-type=\"bibr\">Bloodgood, 2009</xref>). It was at the dawn of the 21st century, when the sensory functions and clinical relevance of cilia were broadly demonstrated, that an interest in cilia surged, and attention of the scientific community shifted to the ciliary membrane. Unlike other cellular organelles, cilia are not fully enclosed by membrane. Instead, the ciliary membrane is continuous with the plasma membrane, and at their base, cilia are exposed to the cytosol. Despite continuity with the plasma membrane, the ciliary membrane exhibits a unique protein and lipid composition that is maintained, at least in part, by multiple diffusion barriers at the cilia base (<xref rid=\"B109\" ref-type=\"bibr\">Verhey and Yang, 2016</xref>). During the last two decades, much progress has been made in identifying the protein constituents of the ciliary membrane and molecular mechanisms of their trafficking in and out of cilia (<xref rid=\"B83\" ref-type=\"bibr\">Nachury and Mick, 2019</xref>). In contrast, the ciliary lipidome or mechanisms controlling its establishment are only starting to come to light.</p><p>This mini-review briefly summarizes current knowledge about ciliary membrane lipid composition and the molecular mechanisms that regulate ciliary lipid content. I also discuss the emerging roles of lipids in cilia signaling and outline major outstanding questions regarding the roles of lipids in modulating cilia-based pathways and shaping ciliary membrane morphology. Addressing these questions in the future may provide insight into human pathological conditions linked to altered membrane lipid constitution.</p></sec><sec id=\"S2\"><title>Cilia Architecture</title><p>The cilium is comprised of a core microtubule-based structure called the axoneme ensheathed by a specialized membrane. Nine radially symmetric microtubule doublets (A- and B-tubules) of the axoneme extend from the basal body – a modified mother centriole, which nucleates the axoneme and anchors the cilium at the cell surface (<xref rid=\"B51\" ref-type=\"bibr\">Ishikawa and Marshall, 2011</xref>; <xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). In addition to the nine peripheral microtubule doublets, the axoneme of the motile cilium typically contains a central pair of singlet microtubules required for ciliary beating (9 + 2 arrangement), while the axoneme of the primary cilium lacks it (9 + 0 arrangement) (<xref rid=\"B98\" ref-type=\"bibr\">Satir and Christensen, 2007</xref>; <xref ref-type=\"fig\" rid=\"F1\">Figures 1B,C</xref>). Most motile cilia also have radial spokes and inner and outer dynein arms attached to the microtubule doublets of the axoneme to drive motility (<xref rid=\"B52\" ref-type=\"bibr\">Ishikawa, 2017</xref>; <xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>).</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>The structural organization of the cilium. <bold>(A)</bold> Diagram of a cilium depicting major structural components and ciliary sub-compartments. <bold>(B,C)</bold> Cross-section schematics of a typical motile <bold>(B)</bold> and primary <bold>(C)</bold> cilium. BB, basal body; TZ, transition zone.</p></caption><graphic xlink:href=\"fcell-08-00777-g001\"/></fig><p>Since there are no ribosomes inside the cilium, all ciliary proteins are imported from the cytosol. The transition zone (TZ), which constitutes the proximal 0.5–1.0 μm of the axoneme, is comprised of several macromolecular complexes that serve as a gate controlling selective entry and exit of ciliary cargoes. At the ultrastructural level, the TZ is characterized by Y-shaped fibers (Y-links) connecting the microtubule doublets of the axoneme to the ciliary membrane (<xref rid=\"B10\" ref-type=\"bibr\">Blacque and Sanders, 2014</xref>; <xref rid=\"B40\" ref-type=\"bibr\">Garcia-Gonzalo and Reiter, 2017</xref>; <xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). Together with the transition fibers, which anchor the basal body to the membrane, Y-links provide a physical barrier that separates the cilium proper from the cytoplasm, and the membrane attachment points of the transition fibers demarcate the boundary between the plasma and ciliary membranes. Cilia assembly and maintenance are mediated by a bi-directional transport system called intraflagellar transport (IFT). Microtubule motors in conjunction with three multi-subunit complexes – IFT-A, IFT-B, and the Bardet–Biedl syndrome (BBS)ome – traffic proteins along the axoneme between the ciliary base and tip (reviewed in <xref rid=\"B107\" ref-type=\"bibr\">Taschner and Lorentzen, 2016</xref>; <xref rid=\"B116\" ref-type=\"bibr\">Wingfield et al., 2018</xref>; <xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). Notably, mutations in genes encoding components of the basal body, TZ, and IFT are associated with ciliopathies including Meckel-Gruber and Joubert syndromes, nephronophthisis, polycystic kidney disease, and Bardet–Biedl syndrome, underscoring the importance of cilia in human health (<xref rid=\"B95\" ref-type=\"bibr\">Reiter and Leroux, 2017</xref>).</p></sec><sec id=\"S3\"><title>Lipid Composition of the Ciliary Membrane</title><sec id=\"S3.SS1\"><title>Polyphosphoinositide Distribution and Roles in Ciliary Protein Trafficking</title><p>Considerable progress has been made in understanding how cilia establish their unique protein content (reviewed in <xref rid=\"B40\" ref-type=\"bibr\">Garcia-Gonzalo and Reiter, 2017</xref>; <xref rid=\"B78\" ref-type=\"bibr\">Mukhopadhyay et al., 2017</xref>; <xref rid=\"B76\" ref-type=\"bibr\">Morthorst et al., 2018</xref>; <xref rid=\"B83\" ref-type=\"bibr\">Nachury and Mick, 2019</xref>). On the other hand, much remains to be discovered about how cells maintain the ciliary membrane lipid identity. Some lipid biosynthetic enzymes localize to distinct sub-ciliary compartments and locally modulate membrane lipid composition. Conversion of polyphosphoinositides (PPIs) by multiple kinases and phosphatases provides the best-known example of lipid generation at local sites in the ciliary membrane. Polyphosphoinositides are signaling lipids generated by reversible phosphorylation of phosphatidylinositol (PI) at positions 3, 4, and 5 of its inositol ring (<xref rid=\"B7\" ref-type=\"bibr\">Balla, 2013</xref>). These phosphorylation derivatives of PI populate distinct membrane domains within cells, where they regulate many aspects of cellular physiology (<xref rid=\"B26\" ref-type=\"bibr\">Di Paolo and De Camilli, 2006</xref>). The ciliary membrane in mammals and sea urchin contains high levels of phosphatidylinositol-4-phosphate [PI(4)P] relative to the adjacent plasma membrane (<xref rid=\"B22\" ref-type=\"bibr\">Chávez et al., 2015</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Garcia-Gonzalo et al., 2015</xref>). In contrast, phosphatidylinositol-4,5-bisphosphate [PI(4,5)P2] is largely depleted from the ciliary membrane in mammals, <italic>Caenorhabditis elegans</italic>, <italic>Drosophila melanogaster</italic>, and <italic>Trypanosoma brucei</italic>. Instead, PI(4,5)P2 localizes to distinct membrane domains at the cilia base creating a sharp boundary in PPI composition (<xref rid=\"B22\" ref-type=\"bibr\">Chávez et al., 2015</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Garcia-Gonzalo et al., 2015</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Jensen et al., 2015</xref>; <xref rid=\"B87\" ref-type=\"bibr\">Park et al., 2015</xref>; <xref rid=\"B28\" ref-type=\"bibr\">DiTirro et al., 2019</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Dyson et al., 2017</xref>; <xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>). In retinal pigmented epithelial cells and primary mouse embryonic fibroblasts, PI(4,5)P2 is concentrated at the TZ, which also contains phosphatidylinositol-3,4,5-trisphosphate [PI(3,4,5)P3] (<xref rid=\"B30\" ref-type=\"bibr\">Dyson et al., 2017</xref>). Conversely, in <italic>C. elegan</italic>s neurons and <italic>T. brucei</italic>, PI(4,5)P2 is enriched in an endocytic membrane domain (periciliary membrane compartment/ciliary pocket), which lies proximal to the TZ (<xref rid=\"B25\" ref-type=\"bibr\">Demmel et al., 2014</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Jensen et al., 2015</xref>; <xref rid=\"B28\" ref-type=\"bibr\">DiTirro et al., 2019</xref>). In photoreceptors, while PI(4,5)P2 is also largely excluded from the outer segment (OS), which is a modified cilium, PI(4)P localizes to the OS as well as inner segment and perinuclear regions (<xref rid=\"B85\" ref-type=\"bibr\">Nasuhoglu et al., 2002</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Finkelstein et al., 2020</xref>; <xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>). More studies are needed to systematically evaluate PPI composition across all ciliated cell types and to better understand the physiological significance of cell-specific differences in PPI composition and sub-ciliary distribution.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Lipid composition of the ciliary membrane. <bold>(A)</bold> Schematic representation of PPI distribution in the ciliary membrane of mammalian cells. BB, basal body; TZ, transition zone. <bold>(B)</bold> Distribution of the indicated PPI species in rod photoreceptors of mammalian retina. <bold>(C)</bold> Intraciliary localization of PI(4)P and PI(4,5)P2 in mammalian wild-type and <italic>Inpp5e</italic> mutant cells. Distribution of Tulp3/IFT-A trafficking complex and its GPCR cargo in control and <italic>Inpp5e</italic> mutant cells is also shown. <bold>(D)</bold> PPI species and a subset of PPI metabolizing enzymes that have been reported inside cilia. The presence of PI(3,4)P2 in the ciliary membrane is inferred based on intraciliary localization of Inpp5e and its substrate PI(3,4,5)P3 (<xref rid=\"B75\" ref-type=\"bibr\">Moore et al., 2016</xref>; <xref rid=\"B30\" ref-type=\"bibr\">Dyson et al., 2017</xref>). DAG, diacylglycerol, IP<sub>3</sub>, inositol 1,4,5-trisphosphate, PLC, phospholipase C. <bold>(E)</bold> Diagrammatic representation of changes in PPI composition at the TZ in response to Hh pathway activation. Blue and orange arrows mark direction of the observed changes in PI(4,5)P2 and PI(3,4,5)P3 levels, respectively, in the wild-type SAG-treated or untreated control cells. <bold>(F)</bold> A schematic representation of major raft-associated components (proteins and lipids) known to be enriched in the ciliary membrane. Dashed lines at cilia base represent condensed lipid microdomains detected by Laurdan microscopy in some cell types. <bold>(G)</bold> A schematic diagram of the mammalian rod photoreceptor. Inset shows an enlarged view of the disc and surrounding OS plasma membrane highlighting their distinct lipid content. PUFAs, polyunsaturated fatty acids.</p></caption><graphic xlink:href=\"fcell-08-00777-g002\"/></fig><p>How does the cilium maintain a unique PPI distribution? Inpp5e inositol polyphosphate-5-phosphatase, which converts PI(3,4,5)P3 and PI(4,5)P2 into PI(3,4)P2 and PI(4)P, respectively, localizes to mammalian cilia (<xref rid=\"B9\" ref-type=\"bibr\">Bielas et al., 2009</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Jacoby et al., 2009</xref>; <xref rid=\"B71\" ref-type=\"bibr\">Luo et al., 2012</xref>, <xref rid=\"B70\" ref-type=\"bibr\">2013</xref>). Mutations in <italic>INPP5E</italic> cause Joubert and MORM (mental retardation, truncal obesity, retinal dystrophy, and micropenis) syndromes in humans, and <italic>Inpp5e</italic> knockout mice display phenotypes consistent with ciliopathies (<xref rid=\"B53\" ref-type=\"bibr\">Jacoby et al., 2009</xref>). In the absence of <italic>Inpp5e</italic>, PI(4,5)P2 accumulates in the cilium, while ciliary PI(4)P levels drop (<xref rid=\"B22\" ref-type=\"bibr\">Chávez et al., 2015</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Garcia-Gonzalo et al., 2015</xref>; <xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>). Similarly, in fly and worm sensory neurons, loss of INPP5E orthologs leads to increased PI(4,5)P2 levels in cilia (<xref rid=\"B87\" ref-type=\"bibr\">Park et al., 2015</xref>; <xref rid=\"B28\" ref-type=\"bibr\">DiTirro et al., 2019</xref>). Together, these findings are consistent with the model that PI(4,5)P2 diffuses laterally from the plasma to the ciliary membrane, where it is converted to PI(4)P by Inpp5e. Two other inositol polyphosphate-5-phosphatases (Inpp5b and Ocrl) have been reported to localize inside cilia of mammalian cells (<xref rid=\"B9\" ref-type=\"bibr\">Bielas et al., 2009</xref>; <xref rid=\"B53\" ref-type=\"bibr\">Jacoby et al., 2009</xref>; <xref rid=\"B71\" ref-type=\"bibr\">Luo et al., 2012</xref>, <xref rid=\"B70\" ref-type=\"bibr\">2013</xref>). Mutations in human <italic>OCRL</italic> cause Lowe syndrome, a multisystemic disorder with characteristics of a ciliopathy, and cilia from Lowe syndrome patient fibroblasts contain high levels of PI(4,5)P2 and low levels of PI(4)P similarly to <italic>Inpp5e</italic> mutant cilia (<xref rid=\"B24\" ref-type=\"bibr\">Coon et al., 2012</xref>; <xref rid=\"B89\" ref-type=\"bibr\">Prosseda et al., 2017</xref>). Therefore, it is likely that several inositol polyphosphate-5-phosphatases contribute to the ciliary membrane PPI composition (<xref ref-type=\"fig\" rid=\"F2\">Figure 2D</xref>). It is tempting to speculate that differences in cell and tissue distribution of PPIs and their metabolizing enzymes might contribute to symptomatic variability observed in patients carrying mutations in inositol polyphosphate-5-phosphatases (e.g., cataracts in Lowe and MORM patients versus retinitis pigmentosa in Joubert patients) (<xref rid=\"B46\" ref-type=\"bibr\">Hampshire et al., 2006</xref>; <xref rid=\"B72\" ref-type=\"bibr\">Madhivanan et al., 2012</xref>; <xref rid=\"B114\" ref-type=\"bibr\">Wang et al., 2018</xref>).</p><p>Beside inositol polyphosphate-5-phosphatases, several other phospholipid-metabolizing enzymes have been reported in the photoreceptor OS, where lipid metabolism has been extensively studied (reviewed in <xref rid=\"B41\" ref-type=\"bibr\">Giusto et al., 2000</xref>; <xref rid=\"B92\" ref-type=\"bibr\">Rajala, 2020</xref>; <xref rid=\"B115\" ref-type=\"bibr\">Wensel, 2020</xref>). Among these enzymes are phosphatidylinositol 3-kinase, which converts PI, PI(4)P, and PI(4,5)P2 into PI(3)P, PI(3,4)P2, and PI(3,4,5)P3, respectively, and phospholipase C, which cleaves PI(4,5)P2 to generate second messengers inositol 1,4,5-trisphosphate and diacylglycerol (<xref ref-type=\"fig\" rid=\"F2\">Figure 2D</xref>). More studies are needed, however, to fully understand how ciliary phospholipid composition is modulated by these enzymes in different contexts, and how it contributes to cilia-mediated cellular functions. Since PPIs constitute <1% of total phospholipid mass in eukaryotic cells, with PI(4)P and PI(4,5)P2 being most abundant (∼0.05% each) (<xref rid=\"B36\" ref-type=\"bibr\">Fliesler and Anderson, 1983</xref>; <xref rid=\"B66\" ref-type=\"bibr\">Lemmon, 2008</xref>), PPI detectability in ciliary membranes presents a technical challenge. Development of more sensitive tools such as a recently reported ELISA-based method (<xref rid=\"B47\" ref-type=\"bibr\">He et al., 2016</xref>) is necessary to accurately measure these low-abundance lipids.</p><p>Although it remains to be determined whether diffusion barriers at the cilia base directly influence PPI distribution, the intact TZ is required for ciliary localization of Inpp5e. Mutations in TZ genes <italic>Tctn1</italic>, <italic>Tmem231</italic>, <italic>B9d1</italic>, and <italic>Mks1</italic> disrupt Inpp5e ciliary localization (<xref rid=\"B39\" ref-type=\"bibr\">Garcia-Gonzalo et al., 2015</xref>; <xref rid=\"B96\" ref-type=\"bibr\">Roberson et al., 2015</xref>; <xref rid=\"B102\" ref-type=\"bibr\">Slaats et al., 2016</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Goetz et al., 2017</xref>). The same genes are also necessary for ciliary localization of a small GTPase Arl13b, which regulates trafficking of several ciliary proteins, including Inpp5e. Thus, it is conceivable that the Tmem231/B9d1/Mks1/Tctn1 TZ complex localizes Inpp5e to the cilium via Arl13b, thereby indirectly regulating ciliary PPI distribution (<xref rid=\"B38\" ref-type=\"bibr\">Garcia-Gonzalo et al., 2011</xref>; <xref rid=\"B50\" ref-type=\"bibr\">Humbert et al., 2012</xref>).</p><p>PPIs can also directly bind to transmembrane proteins (<xref rid=\"B7\" ref-type=\"bibr\">Balla, 2013</xref>). Interestingly, the TZ levels of Mks1/Tctn1/Tmem231/B9d1 following SAG (Smoothened receptor agonist) treatment are lower in <italic>Inpp5e</italic> null compared to wild-type embryonic fibroblasts. Additionally, cilia base localization of the oligomeric GTPase Septin2 was similarly reduced under these conditions (<xref rid=\"B30\" ref-type=\"bibr\">Dyson et al., 2017</xref>). Septins interact with phospholipids including PI(4,5)P2, which in turn facilitate septin filament polymerization (<xref rid=\"B77\" ref-type=\"bibr\">Mostowy and Cossart, 2012</xref>). Like TZ proteins, septins localize to the cilia base, where they are proposed to form a diffusion barrier between the plasma and ciliary membranes and regulate localization of select TZ proteins including Tmem231 and B9d1 (<xref rid=\"B49\" ref-type=\"bibr\">Hu et al., 2010</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Chih et al., 2012</xref>). Catalytic activity of Inpp5e is required for proper localization of TZ proteins and Septin2; therefore, it is likely that Inpp5e-modulated PPI composition at the cilia base can dynamically regulate TZ assembly. Future studies are needed to determine whether PPIs regulate TZ composition via direct binding to TZ proteins, indirectly by controlling Septin2 localization, or through other mechanisms.</p><p>In addition to regulating the TZ, PPIs play a key role in ciliary import of channels and GPCRs (<xref rid=\"B6\" ref-type=\"bibr\">Badgandi et al., 2017</xref>). The tubby family proteins TUB and TULP3 bind membrane PI(4,5)P2 and IFT-A and thereby serve as adaptors for delivery of transmembrane proteins into the cilium (<xref rid=\"B79\" ref-type=\"bibr\">Mukhopadhyay et al., 2010</xref>). The current model posits that the interaction of TUB/TULP3 with PI(4,5)P2 in the plasma membrane facilitates association of TUB/TULP3 with transmembrane proteins that are subsequently transported into the cilium via the IFT-A complex. Since the TUB/TULP3 interaction with protein cargoes is PI(4,5)P2-dependent, absence of PI(4,5)P2 in the ciliary membrane causes TUB/TULP3 cargoes to be released inside the cilium after traversing the TZ (<xref rid=\"B6\" ref-type=\"bibr\">Badgandi et al., 2017</xref>). Consistent with this model, depletion of Inpp5e and subsequent intraciliary accumulation of PI(4,5)P2 results in increased levels of Tulp3/IFT-A proteins and their transmembrane cargoes such as GPCR Gpr161 – a negative regulator of Shh signaling – inside cilia (<xref rid=\"B80\" ref-type=\"bibr\">Mukhopadhyay et al., 2013</xref>; <xref rid=\"B22\" ref-type=\"bibr\">Chávez et al., 2015</xref>; <xref rid=\"B39\" ref-type=\"bibr\">Garcia-Gonzalo et al., 2015</xref>; <xref ref-type=\"fig\" rid=\"F2\">Figure 2C</xref>). Other ciliary proteins including components of the BBSome (e.g., BBS5) and the exocyst can bind PPIs <italic>in vitro</italic> suggesting a broad role for phospholipids in mediating ciliary protein trafficking (<xref rid=\"B68\" ref-type=\"bibr\">Liu et al., 2007</xref>; <xref rid=\"B82\" ref-type=\"bibr\">Nachury et al., 2007</xref>; <xref rid=\"B56\" ref-type=\"bibr\">Jin et al., 2010</xref>). Notably, recent cryo-electron microscopy structures of the native BBSome from bovine retina suggested that BBS5 may not bind PPIs <italic>in vivo</italic> or may do so via an unknown motif or after a conformational change (<xref rid=\"B101\" ref-type=\"bibr\">Singh et al., 2020</xref>).</p><p>Recent studies in mammals and <italic>C. elegans</italic> demonstrated that, similar to ciliary protein composition, PPI content of the ciliary membrane is dynamic and can change in response to signaling. For example, <italic>C. elegans</italic> mutants in <italic>odr-1</italic>, which encodes a receptor guanylyl cyclase, display elevated levels of intraciliary PI(4,5)P2 relative to wild type in a specialized sensory neuron type (<xref rid=\"B28\" ref-type=\"bibr\">DiTirro et al., 2019</xref>). In mammals, activation of Hh signaling with SAG increases PI(3,4,5)P3 while decreasing PI(4,5)P2 levels at the TZ (<xref rid=\"B30\" ref-type=\"bibr\">Dyson et al., 2017</xref>; <xref ref-type=\"fig\" rid=\"F2\">Figure 2E</xref>). The latter study also showed that TZ levels of both PPI species were higher in <italic>Inpp5e</italic> null compared to wild-type cells upon SAG treatment, suggesting that Inpp5e is responsible, at least in part, for signaling-dependent modulation of PPI composition at the TZ. In the rod OS, several studies reported activation of PI-metabolizing enzymes in response to light as well as light-dependent changes in PI(4)P and PI(4,5)P2 levels (reviewed in (<xref rid=\"B41\" ref-type=\"bibr\">Giusto et al., 2000</xref>; <xref rid=\"B115\" ref-type=\"bibr\">Wensel, 2020</xref>). However, the direction of change in PPI composition differed among studies, and the physiological significance of these effects requires further investigation. It will be interesting to examine whether levels of other ciliary lipids are also modulated by signaling across cell types.</p></sec><sec id=\"S3.SS2\"><title>Microdomains of High Lipid Order</title><p>Early studies in diverse biological systems detected high levels of sterols and sphingolipids in the ciliary membrane, suggesting the presence of ordered lipid domains (i.e., “lipid rafts”) (<xref rid=\"B74\" ref-type=\"bibr\">Montesano, 1979</xref>; <xref rid=\"B104\" ref-type=\"bibr\">Souto-Padron and De Souza, 1983</xref>; <xref rid=\"B60\" ref-type=\"bibr\">Kaneshiro et al., 1984</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Chailley and Boisvieux-Ulrich, 1985</xref>). More recently, sphingolipids including ceramide and raft-associated gangliosides GM1 and GM3 have been identified in primary cilia of Madin–Darby Canine Kidney (MDCK) epithelial cells by immunofluorescence (<xref rid=\"B54\" ref-type=\"bibr\">Janich and Corbeil, 2007</xref>; <xref rid=\"B48\" ref-type=\"bibr\">He et al., 2012</xref>; <xref ref-type=\"fig\" rid=\"F2\">Figure 2F</xref>). Sphingolipids have also been detected in pure intact flagella of <italic>T. brucei</italic> using reverse-phase liquid chromatography high resolution tandem mass spectrometry (<xref rid=\"B100\" ref-type=\"bibr\">Serricchio et al., 2015</xref>). Membrane microdomains enriched in cholesterol and sphingolipids are resistant to detergent solubilization, and detergent-resistant membranes have been used as a proxy for rafts in studies probing lipid-raft composition (<xref rid=\"B33\" ref-type=\"bibr\">Farnoud et al., 2015</xref>). Caveolin-1 – an intra-membranous protein that stabilizes cholesterol-rich raft domains – localizes to the TZ in a cholesterol-dependent manner in mammalian cells and is present in the detergent resistant membranes of the photoreceptor OS (<xref rid=\"B84\" ref-type=\"bibr\">Nair et al., 2002</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Lajoie et al., 2009</xref>; <xref rid=\"B99\" ref-type=\"bibr\">Schou et al., 2017</xref>). Similarly, another lipid raft scaffold flotillin-2 was detected at the TZ in epithelial cells (<xref rid=\"B99\" ref-type=\"bibr\">Schou et al., 2017</xref>; <xref ref-type=\"fig\" rid=\"F2\">Figure 2F</xref>). In further support of the raft-like composition of ciliary membranes, the TZ membrane in <italic>Chlamydomonas reinhardtii</italic> is exceptionally resistant to detergent extraction, and Laurdan microscopy of <italic>T. brucei</italic> and MDCK cells showed condensed lipid microdomains in the trypanosome flagella and at the base of primary cilia (<xref rid=\"B59\" ref-type=\"bibr\">Kamiya and Witman, 1984</xref>; <xref rid=\"B110\" ref-type=\"bibr\">Vieira et al., 2006</xref>; <xref rid=\"B108\" ref-type=\"bibr\">Tyler et al., 2009</xref>). Collectively, these studies suggest that the ciliary membrane has unique lipid composition with distinct membrane microdomains. More research is needed, however, to determine how distinct membrane lipid domains form and contribute to cilia function.</p><p>While select phosphoinositide-metabolizing enzymes localize to cilia and directly modify intraciliary PPI content, none of the enzymes involved in sphingolipid or cholesterol metabolism have been identified inside the cilium to date. The “picket fence model” of membrane compartmentalization may provide one possible mechanism for ciliary lipid organization. This model posits that transmembrane proteins anchored to the actin network act as a “picket fence” impeding diffusion of the adjacent lipid molecules via steric hinderance and hydrodynamic slowing effects (<xref rid=\"B64\" ref-type=\"bibr\">Kusumi et al., 2012</xref>). In fact, entire raft assemblies can be confined to distinct membrane compartments by the “picket fence” according to this model. Many ciliary proteins are transmembrane, and therefore may form “pickets” to restrict diffusion of membrane molecules. Furthermore, using cryo-electron tomography, a recent study demonstrated that actin filaments surround and are intertwined with microtubules of the axoneme inside the cilia of MDCKII cells, adding further credence to the “picket fence” model as a possible mechanism of the ciliary membrane compartmentalization (<xref rid=\"B61\" ref-type=\"bibr\">Kiesel et al., 2020</xref>). Future work will need to experimentally test this model of ciliary membrane organization and determine whether same or different mechanisms regulate compartmentalization of ciliary membranes across cell types.</p></sec></sec><sec id=\"S4\"><title>Lipids in Cilia-Based Signaling</title><sec id=\"S4.SS1\"><title>PPI-Dependent Transmembrane Signaling</title><p>PPIs are key mediators of cell signaling in eukaryotes. At the plasma membrane, phospholipase C-dependent hydrolysis of PI(4,5)P2 downstream of growth factor receptors and GPCRs generates second messengers that amplify and transmit signaling from the cell surface downstream (<xref rid=\"B32\" ref-type=\"bibr\">Falkenburger et al., 2010</xref>). Furthermore, PI(4,5)P2 and PI(3,4,5)P3 facilitate assembly of signalosomes by recruiting different classes of proteins with lipid-binding domains (reviewed in <xref rid=\"B88\" ref-type=\"bibr\">Prestwich, 2004</xref>; <xref rid=\"B91\" ref-type=\"bibr\">Rajala, 2010</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Hammond and Burke, 2020</xref>). Among PI(3,4,5)P3 interacting proteins are guanine nucleotide exchange factors and GTPase activating proteins for small GTPases as well as kinases and signaling scaffold proteins (<xref rid=\"B7\" ref-type=\"bibr\">Balla, 2013</xref>). In photoreceptor cilia, light stimulates PI(3,4,5)P3 binding and subsequent activation of the kinase Akt1 - a major signaling protein downstream of receptor tyrosine kinases (<xref rid=\"B67\" ref-type=\"bibr\">Li et al., 2008</xref>). Growth factor-dependent activation of Akt has also been reported at the cilia base in other cellular contexts (<xref rid=\"B121\" ref-type=\"bibr\">Zhu et al., 2009</xref>; <xref rid=\"B112\" ref-type=\"bibr\">Wang et al., 2015</xref>; <xref rid=\"B106\" ref-type=\"bibr\">Suizu et al., 2016</xref>; <xref rid=\"B111\" ref-type=\"bibr\">Walia et al., 2019</xref>). More studies are needed, however, to address the contribution of PPIs and their metabolites to cilia-based signaling in different cellular contexts.</p></sec><sec id=\"S4.SS2\"><title>Lipid-Dependent Regulation of Ion Channels</title><p>Membrane lipids, including phospholipids and cholesterol, can also directly modulate ion channels. For example, PI(4,5)P2 binds to and regulates the activity of voltage- and ligand-gated ion channels, inward rectifier channels, and transporters (reviewed in <xref rid=\"B105\" ref-type=\"bibr\">Suh and Hille, 2008</xref>; <xref rid=\"B29\" ref-type=\"bibr\">Duncan et al., 2020</xref>). Transient receptor potential (TRP) channels (e.g., PKD2, TRPM4, and TRPC1), voltage-gated potassium channels, cyclic nucleotide-gated channels, and epithelial sodium channels are all targets of PI(4,5)P2-dependent modulation and localize to cilia (<xref rid=\"B117\" ref-type=\"bibr\">Womack et al., 2000</xref>; <xref rid=\"B94\" ref-type=\"bibr\">Raychowdhury et al., 2005</xref>; <xref rid=\"B105\" ref-type=\"bibr\">Suh and Hille, 2008</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Enuka et al., 2012</xref>; <xref rid=\"B35\" ref-type=\"bibr\">Flannery et al., 2015</xref>; <xref rid=\"B97\" ref-type=\"bibr\">Sanchez et al., 2016</xref>). The ciliary channels TRPM4 and PKD2, the latter of which is mutated in autosomal dominant polycystic kidney disease, can also bind cholesterol, suggesting that both lipids may regulate these channels’ activity (<xref rid=\"B3\" ref-type=\"bibr\">Autzen et al., 2018</xref>; <xref rid=\"B113\" ref-type=\"bibr\">Wang et al., 2019</xref>). Another ciliary channel TRPV4 possesses cholesterol recognition motifs, and both TRPV4 and TRPC1 depend on caveolin-1 and cholesterol for proper positioning in the plasma membrane (<xref rid=\"B8\" ref-type=\"bibr\">Bergdahl et al., 2003</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Brazer et al., 2003</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Gradilone et al., 2007</xref>; <xref rid=\"B63\" ref-type=\"bibr\">Kumari et al., 2015</xref>). Since both caveolin-1 and cholesterol have been detected in the ciliary membrane, it is possible that similar mechanisms contribute to TRPV4 and TRPC1 ciliary localization. Function of olfactory cyclic nucleotide-gated channels is also altered by cholesterol depletion (<xref rid=\"B16\" ref-type=\"bibr\">Brady et al., 2004</xref>), and both olfactory and cone cyclic nucleotide-gated channels are inhibited by PI(3,4,5)P3 (<xref rid=\"B120\" ref-type=\"bibr\">Zhainazarov et al., 2004</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Brady et al., 2006</xref>; <xref rid=\"B18\" ref-type=\"bibr\">Bright et al., 2007</xref>). Taken together, these studies suggest that PPI and cholesterol compartmentalization of the ciliary membrane may be of major significance for proper function of cilia-localized ion channels. In <italic>C. elegans</italic>, polyunsaturated fatty acids also modulate function of TRPV ciliary channels, although it remains to be tested whether they do so via direct interactions (<xref rid=\"B57\" ref-type=\"bibr\">Kahn-Kirby et al., 2004</xref>). More work is needed to address the contribution of specific lipids to localization and function of different ciliary channels.</p></sec><sec id=\"S4.SS3\"><title>Lipid-Mediated Regulation of GPCRs</title><p>In addition to regulating channels, membrane lipids interact with and modulate multiple aspects of protein receptor physiology including oligomerization and signaling dynamics. For example, PI(4,5)P2 can bind and stabilize the active conformation of several class A GPCRs (<xref rid=\"B119\" ref-type=\"bibr\">Yen et al., 2018</xref>). Many class A GPCRs are present in cilia, where they may be similarly regulated by PPIs (<xref rid=\"B2\" ref-type=\"bibr\">Anvarian et al., 2019</xref>). Some ciliary GPCRs transiently pool in the “intermediate compartment” demarcated by the TZ distally and the transition fibers proximally before exiting or re-entering the cilium. This region is enriched in PI(4,5)P2 and may function as a distinct GPCR signaling domain (<xref rid=\"B118\" ref-type=\"bibr\">Ye et al., 2018</xref>).</p><p>The prototypical GPCR rhodopsin is enriched in the disc membrane of the photoreceptor OS. The rod OS contains a stack of closed membranous compartments (discs) encased by the OS plasma membrane (<xref ref-type=\"fig\" rid=\"F2\">Figure 2G</xref>). Although discs form by evagination of the plasma membrane at the base of the OS followed by apical displacement, disc and OS membrane display distinct lipid composition (<xref rid=\"B13\" ref-type=\"bibr\">Boesze-Battaglia et al., 1994</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Ding et al., 2015</xref>). For example, the disc membrane is enriched in polyunsaturated fatty acids and low in cholesterol relative to the surrounding OS membrane, suggesting an elaborate lipid sorting mechanism at the base of photoreceptor cilia (<xref rid=\"B4\" ref-type=\"bibr\">Aveldano and Bazan, 1983</xref>; <xref rid=\"B14\" ref-type=\"bibr\">Boesze-Battaglia and Schimmel, 1997</xref>; <xref rid=\"B84\" ref-type=\"bibr\">Nair et al., 2002</xref>; <xref ref-type=\"fig\" rid=\"F2\">Figure 2G</xref>). Unique lipid content of disc and OS plasma membrane is critical for photoreceptor function, as aberrant distribution of cholesterol in the OS membranes is associated with photoreceptor degeneration in rats (<xref rid=\"B13\" ref-type=\"bibr\">Boesze-Battaglia et al., 1994</xref>). Both cholesterol and polyunsaturated docosahexaenoic acid interact with rhodopsin but have opposite effects on photocycle kinetics, further highlighting the significance of the lipid environment for receptor and cell function (<xref rid=\"B1\" ref-type=\"bibr\">Albert et al., 1996</xref>; <xref rid=\"B73\" ref-type=\"bibr\">Mitchell et al., 2001</xref>; <xref rid=\"B86\" ref-type=\"bibr\">Niu et al., 2002</xref>; <xref rid=\"B103\" ref-type=\"bibr\">Soubias and Gawrisch, 2005</xref>; <xref rid=\"B44\" ref-type=\"bibr\">Grossfield et al., 2006</xref>). Membrane cholesterol can also modulate ligand affinity, G protein coupling, and receptor oligomerization in select GPCRs, and membrane docosahexaenoic acid content was suggested to alter receptor oligomerization kinetics (<xref rid=\"B90\" ref-type=\"bibr\">Pucadyil and Chattopadhyay, 2004; Gahbauer and Böckmann</xref>, <xref rid=\"B37\" ref-type=\"bibr\">2016</xref>). Cholesterol and endogenous ciliary oxysterols also bind to Smoothened and activate the Hh pathway (<xref rid=\"B69\" ref-type=\"bibr\">Luchetti et al., 2016</xref>; <xref rid=\"B93\" ref-type=\"bibr\">Raleigh et al., 2018</xref>). Cholesterol accessibility (or chemical activity) is further modulated by sphingolipids, which sequester cholesterol in complexes thereby blocking Hh transduction (<xref rid=\"B62\" ref-type=\"bibr\">Kinnebrew et al., 2019</xref>). Besides Hh, sphingolipids regulate several other cilia-based pathways including GPCRs (reviewed in <xref rid=\"B58\" ref-type=\"bibr\">Kaiser et al., 2020</xref>). More studies are needed to further evaluate the effects of lipid dynamics on ciliary signaling.</p></sec></sec><sec id=\"S5\"><title>Conclusion and Future Perspectives</title><p>Lipids have recently emerged as critical regulators of cilia function. The distinct lipid composition and compartmentalization of the ciliary membrane are essential for ciliary protein trafficking and transduction of cilia-based signaling cascades. The importance of lipids in cilia biology is further underscored by the fact that many ciliopathies display defects in the membrane lipid organization. Despite the key importance of lipids in cilia biology, our knowledge about cell-specific differences in the ciliary lipid composition, dynamics, and organization in distinct microdomains remains fragmented, as does our understanding of the roles that lipids play in cilia signaling. To bridge this gap in our understanding of cilia biology, systematic analysis of the lipid composition and lipid structure of sub-ciliary compartments in different cellular contexts <italic>in vivo</italic> is necessary. Ciliary membranes across and within organisms exhibit remarkably diverse morphologies, which are important for cell-specific cilia functions and can be modulated in response to signaling. It will be important to examine whether cell-specific differences in the ciliary lipid composition and/or dynamics also contribute to the morphological diversity of ciliary membranes. Single molecule tracking of specific lipids and mass spectrometry imaging may provide some insight into these outstanding questions and advance our understanding of the repertoire of lipid-mediated physiological functions.</p></sec><sec id=\"S6\"><title>Author Contributions</title><p>The author wrote the manuscript and generated the figures.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by Worcester Polytechnic Institute.</p></fn></fn-group><ack><p>I thank Michael P. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">J Biomed Opt</journal-id><journal-id journal-id-type=\"iso-abbrev\">J Biomed Opt</journal-id><journal-id journal-id-type=\"coden\">JBOPFO</journal-id><journal-id journal-id-type=\"publisher-id\">JBO</journal-id><journal-title-group><journal-title>Journal of Biomedical Optics</journal-title></journal-title-group><issn pub-type=\"ppub\">1083-3668</issn><issn pub-type=\"epub\">1560-2281</issn><publisher><publisher-name>Society of Photo-Optical Instrumentation Engineers</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32812412</article-id><article-id pub-id-type=\"pmc\">PMC7431880</article-id><article-id pub-id-type=\"doi\">10.1117/1.JBO.25.8.086502</article-id><article-id pub-id-type=\"publisher-manuscript\">JBO-200024R</article-id><article-id pub-id-type=\"publisher-id\">200024R</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Microscopy</subject></subj-group><subj-group subj-group-type=\"SPIE-art-type\"><subject>Paper</subject></subj-group></article-categories><title-group><article-title>Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial–mesenchymal transition</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-3550-4968</contrib-id><name><surname>Strbkova</surname><given-names>Lenka</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">a</xref><xref ref-type=\"corresp\" rid=\"cor1\">*</xref><xref ref-type=\"other\" rid=\"b1\"/><email>lenka.strbkova@ceitec.vutbr.cz</email></contrib><contrib contrib-type=\"author\"><name><surname>Carson</surname><given-names>Brittany B.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">b</xref><xref ref-type=\"other\" rid=\"b2\"/><email>carson@lunenfeld.ca</email></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0002-7248-066X</contrib-id><name><surname>Vincent</surname><given-names>Theresa</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">b</xref><xref ref-type=\"aff\" rid=\"aff3\">c</xref><xref ref-type=\"other\" rid=\"b3\"/><email>theresa.vincent@igp.uu.se</email></contrib><contrib contrib-type=\"author\"><name><surname>Vesely</surname><given-names>Pavel</given-names></name><xref ref-type=\"aff\" rid=\"aff4\">d</xref><xref ref-type=\"author-notes\" rid=\"fn1\">†</xref><xref ref-type=\"other\" rid=\"b4\"/><email>pavel.vesely@ceitec.vutbr.cz</email></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">https://orcid.org/0000-0001-5410-4794</contrib-id><name><surname>Chmelik</surname><given-names>Radim</given-names></name><xref ref-type=\"aff\" rid=\"aff4\">d</xref><xref ref-type=\"author-notes\" rid=\"fn1\">†</xref><xref ref-type=\"other\" rid=\"b5\"/><email>chmelik@fme.vutbr.cz</email></contrib><aff id=\"aff1\"><label>a</label><institution>Brno University of Technology</institution>, Central European Institute of Technology, Brno, <country>Czech Republic</country></aff><aff id=\"aff2\"><label>b</label><institution>Uppsala University, Department of Immunology, Genetics, and Pathology (IGP)</institution>, Rudbeck Laboratory, Uppsala, <country>Sweden</country></aff><aff id=\"aff3\"><label>c</label><institution>NYU School of Medicine, Department of Microbiology</institution>, New York, <country>United States</country></aff><aff id=\"aff4\"><label>d</label><institution>Brno University of Technology</institution>, Institute of Physical Engineering, Faculty of Mechanical Engineering, Brno, <country>Czech Republic</country></aff></contrib-group><author-notes><corresp id=\"cor1\"><label>*</label>Address all correspondence to Lenka Strbkova, E-mail: <email>lenka.strbkova@ceitec.vutbr.cz</email></corresp><fn id=\"fn1\"><label>†</label><p>Pavel Vesely and Radim Chmelik share senior authorship.</p></fn></author-notes><pub-date pub-type=\"epub\"><day>18</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>18</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>25</volume><issue>8</issue><elocation-id>086502</elocation-id><history><date date-type=\"received\"><day>30</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020 The Authors</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>The Authors</copyright-holder><license license-type=\"open-access\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\"><license-p>Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.</license-p></license></permissions><self-uri xlink:title=\"pdf\" xlink:href=\"JBO_25_8_086502.pdf\"/><abstract><title>Abstract.</title><p><bold>Significance:</bold> Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification.</p><p><bold>Aim:</bold> We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes.</p><p><bold>Approach:</bold> The methodology was demonstrated by studying epithelial–mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images.</p><p><bold>Results:</bold> In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes.</p><p><bold>Conclusions:</bold> Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.</p></abstract><kwd-group><title>Keywords:</title><kwd>digital holographic microscopy</kwd><kwd>quantitative phase imaging</kwd><kwd>supervised machine learning</kwd><kwd>epithelial–mesenchymal transition</kwd></kwd-group><funding-group><award-group id=\"sp1\"><funding-source>Grant Agency of the Czech Republic</funding-source><award-id>18-01396S</award-id></award-group><award-group id=\"sp2\"><funding-source>Ministry of Education Youth and Sports of the Czech Republic</funding-source><award-id>LQ1601</award-id><award-id>CEITEC 2020</award-id><award-id>LM2015062</award-id></award-group><award-group id=\"sp3\"><funding-source>Brno University of Technology</funding-source><award-id>FSI-S-17-4506</award-id></award-group><award-group id=\"sp4\"><funding-source>OP RDE</funding-source><award-id>CZ.02.1.01/0.0/0.0/16_013/0001775</award-id></award-group><award-group id=\"sp5\"><funding-source>Swedish Research Council Formas<named-content content-type=\"fundref:id\">https://doi.org/10.13039/501100001862</named-content></funding-source><award-id>VR, 2012-01604/2017-03056</award-id></award-group><award-group id=\"sp6\"><funding-source>Cancerfonden<named-content content-type=\"fundref:id\">https://doi.org/10.13039/501100002794</named-content></funding-source><award-id>180775</award-id></award-group></funding-group><counts><fig-count count=\"7\"/><table-count count=\"3\"/><ref-count count=\"37\"/><page-count count=\"16\"/></counts><custom-meta-group><custom-meta><meta-name>running-head</meta-name><meta-value>Strbkova et al.: Automated interpretation of time-lapse quantitative phase image by machine learning…</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"sec1\"><label>1</label><title>Introduction</title><p>Currently, automated image acquisition systems enable microscopic experiments that generate large image datasets. Manual observation and evaluation of the microscopic images require a trained biologist who performs an inspection for every image, which is both time-consuming and requires considerable effort and concentration by the investigator. Human analysis can be biased, varying with skill and scientific rigor. Consequently, these and other aspects impose significant constraints on the speed, reliability, and validity of such evaluation of microscopic images.</p><p>One approach to address these limitations is supervised machine learning,<xref rid=\"r1\" ref-type=\"bibr\"><sup>1</sup></xref> which is increasingly being applied to the classification of microscopic data.<xref rid=\"r2\" ref-type=\"bibr\"><sup>2</sup></xref><sup>,</sup><xref rid=\"r3\" ref-type=\"bibr\"><sup>3</sup></xref> As an objective unbiased method of scoring the content of microscopic images, this method has been argued to be more sensitive, consistent, and accurate in comparison to subjective manual interpretation.</p><p>When applying the supervised machine learning to cell classification, a computer is trained based on example images of cells belonging to predefined cell classes.<xref rid=\"r4\" ref-type=\"bibr\"><sup>4</sup></xref> Once segmented successfully, the cells are often represented by a set of unique features for the purpose of dimensionality reduction. The features are summarized into a feature vector, which serves as an input to the classifier. After the classifier is trained on the user-labeled training examples, it is then able to distinguish between a defined set of cell classes in an experimental sample.</p><p>When exploiting machine learning in light microscopy, most microscopic techniques only provide intensity images and do not detect the phase delay induced by the imaged cells. Digital holographic microscopy (DHM) enables such phase detection and hence provides quantitative phase images (QPIs) of live cells with high intrinsic contrast without labeling. The phase in the image corresponds to the dry mass density distribution within the cell and correspondingly it is quantitative in terms of cell mass. As such, DHM provides additional information, which has great potential for automated interpretation of cell behavior. Also other label-free microscopic techniques have been applied to cell imaging, including harmonic generation microscopy<xref rid=\"r5\" ref-type=\"bibr\"><sup>5</sup></xref><named-content content-type=\"online\"><xref rid=\"r6\" ref-type=\"bibr\"/></named-content><named-content content-type=\"print\"><sup>–</sup></named-content><xref rid=\"r7\" ref-type=\"bibr\"><sup>7</sup></xref> or Raman imaging.<xref rid=\"r8\" ref-type=\"bibr\"><sup>8</sup></xref> These methods, however, require intense laser light passing through the specimen that could influence the cell behavior and moreover entail scanning, hence are not widefield. Both approaches enable visualization of cell structure and function and could be considered complementary techniques to QPI.</p><p>Several studies have applied machine learning classification algorithms to QPI gained by DHM with outcomes such as morphology-based classification of red blood cells, automated detection and classification of living organisms in drinking water resources, and automated diagnosis of breast and prostate cancer from tissue biopsies.<xref rid=\"r9\" ref-type=\"bibr\"><sup>9</sup></xref><named-content content-type=\"online\"><xref rid=\"r10\" ref-type=\"bibr\"/><xref rid=\"r11\" ref-type=\"bibr\"/></named-content><named-content content-type=\"print\"><sup>–</sup></named-content><xref rid=\"r12\" ref-type=\"bibr\"><sup>12</sup></xref> We have previously reported the advantage of an automated DHM-based analysis in the classification of different cell morphologies in response to nutritional deprivation. This study demonstrated that the quantitative nature of single-time-point images acquired by coherence-controlled holographic microscope (CCHM) improves the classification of cellular morphologies as compared to other techniques.<xref rid=\"r13\" ref-type=\"bibr\"><sup>13</sup></xref><sup>,</sup><xref rid=\"r14\" ref-type=\"bibr\"><sup>14</sup></xref> However, some complex dynamic processes demand time-resolved live-cell imaging in order to gain more information and correctly interpret the cell behavior poststimuli. Efforts to analyze more complex dynamic cellular processes using single-cell kinetic states from holographic cytometry of human melanoma cells have also been reported, acquiring single-time-point images from time-lapse microscopy for analysis.<xref rid=\"r15\" ref-type=\"bibr\"><sup>15</sup></xref> Several other studies applied machine learning for classification of cells using time-lapse QPI.<xref rid=\"r16\" ref-type=\"bibr\"><sup>16</sup></xref><named-content content-type=\"online\"><xref rid=\"r17\" ref-type=\"bibr\"/></named-content><named-content content-type=\"print\"><sup>–</sup></named-content><xref rid=\"r18\" ref-type=\"bibr\"><sup>18</sup></xref></p><p>To our present knowledge, these studies have not applied machine learning for cell classification of time-lapse QPIs using the features extracted from time-lapse images, and thus a temporal context, for classification. We hypothesize that the inclusion of time data will allow better assessment and characterization of live cell behavior.</p><p>Herein we report a methodology for cell classification during cellular transitions based on time-lapse QPI using the Namru Mus musculus mammary gland (NMuMG) cell line, a well-established <inline-formula><mml:math id=\"math1\"><mml:mrow><mml:mi>TGF</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:math></inline-formula>-inducible epithelial–mesenchymal transition (EMT) model system.<xref rid=\"r19\" ref-type=\"bibr\"><sup>19</sup></xref><sup>,</sup><xref rid=\"r20\" ref-type=\"bibr\"><sup>20</sup></xref> EMT is a fundamental process occurring during development and during pathological conditions, particularly in fibrosis and wound healing. In addition, EMT is believed to be the key, initial step in cancer metastasis and has been linked to chemotherapy resistance.<xref rid=\"r21\" ref-type=\"bibr\"><sup>21</sup></xref><sup>,</sup><xref rid=\"r22\" ref-type=\"bibr\"><sup>22</sup></xref> During EMT, cell cycle is arrested, the cells lose their epithelial features and acquire a more mesenchymal, fibroblast-like phenotype visible as increased cell area and cell elongation.</p><p>Although EMT has been well characterized, a better understanding of the regulation and dynamics of this process is necessary to better predict disease progression and to develop novel therapies for metastatic disease. EMT in breast cancer cells has already been studied using QPI,<xref rid=\"r23\" ref-type=\"bibr\"><sup>23</sup></xref> but only single-time-point images were used for the monitoring. Given the gradual and time-dependent morphological changes occurring during EMT, assessment of only the epithelial and morphological end states will provide partial information about this complex transition. We therefore reasoned that analysis of EMT through time-lapse QPI may reveal novel cell phenotypic changes previously undetected. The additional benefit with using this microscopic approach is the lack of requirement for cell modifications with fluorescent reporters. The identification of novel physical characteristics of either end state, or of cells during this transition, could help provide guidance for additional studies and thus a better understanding of EMT and metastasis.</p><p>The two morphologically distinct phenotypes observed during EMT (epithelial and mesenchymal) represent the categories for cell classification in these sets of experiments. The time-lapse images of cells were obtained by CCHM<xref rid=\"r14\" ref-type=\"bibr\"><sup>14</sup></xref><sup>,</sup><xref rid=\"r24\" ref-type=\"bibr\"><sup>24</sup></xref> during the 48-h <inline-formula><mml:math id=\"math2\"><mml:mrow><mml:mi>TGF</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:math></inline-formula> treatment period. The imaging in CCHM is based on the interference of the object and the reference light beams, which enables detection of the phase delay induced by the specimen. This quantitative nature of the images enables the extraction of features representing cell behavior, which formed input for the cell classification. The cells were classified by several supervised machine learning algorithms and the results were compared with the single-time-point approach used in our previous paper.<xref rid=\"r13\" ref-type=\"bibr\"><sup>13</sup></xref> Collectively, this system allowed assessment of the contribution of time-lapse QPI to cell classification of a dynamic and time-dependent cell phenotypic switch as well as comparison of this approach with classification based purely on single-time-point images.</p></sec><sec id=\"sec2\"><label>2</label><title>Methods</title><sec id=\"sec2.1\"><label>2.1</label><title>Cell Culture Techniques</title><p>NMuMG cells (provided by Dr. Theresa Vincent) were grown in Dulbecco’s modified Eagle’s medium (Sigma-Aldrich, Czech Republic) supplemented with GlutaMAX™ (Life Technologies, Czech Republic), 10% fetal bovine serum (Sigma-Aldrich, Czech Republic), <inline-formula><mml:math id=\"math3\"><mml:mrow><mml:mn>100</mml:mn><mml:mtext>  </mml:mtext><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>ml</mml:mi></mml:mrow></mml:math></inline-formula> penicillin, and <inline-formula><mml:math id=\"math4\"><mml:mrow><mml:mn>0.1</mml:mn><mml:mtext>  </mml:mtext><mml:mi>mg</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>ml</mml:mi></mml:mrow></mml:math></inline-formula> streptomycin (Life Technologies, Czech Republic). The cells were harvested by trypsinization and transferred into 10 sterilized observation chambers <inline-formula><mml:math id=\"math5\"><mml:mrow><mml:mi>μ</mml:mi></mml:mrow></mml:math></inline-formula>-Slide I (Ibidi GmbH, Germany) at <inline-formula><mml:math id=\"math6\"><mml:mrow><mml:mn>50</mml:mn><mml:mtext>  </mml:mtext><mml:msup><mml:mrow><mml:mtext>cells</mml:mtext><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>mm</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> to ensure a low density for segmentation of individual cells. Once the cells were transferred to the observation chambers, they were kept in the incubator and imaged the day after. The 10 chambers were divided into control and <inline-formula><mml:math id=\"math7\"><mml:mrow><mml:mi>TGF</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:math></inline-formula>-treated (<inline-formula><mml:math id=\"math8\"><mml:mrow><mml:mn>10</mml:mn><mml:mtext>  </mml:mtext><mml:mi>ng</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>ml</mml:mi></mml:mrow></mml:math></inline-formula>) with imaging beginning immediately after treatment.</p></sec><sec id=\"sec2.2\"><label>2.2</label><title>CCHM</title><p>Quantitative phase imaging of cells was performed by CCHM,<xref rid=\"r14\" ref-type=\"bibr\"><sup>14</sup></xref><sup>,</sup><xref rid=\"r24\" ref-type=\"bibr\"><sup>24</sup></xref><sup>,</sup><xref rid=\"r25\" ref-type=\"bibr\"><sup>25</sup></xref> now also available as Q-Phase (TESCAN ORSAY HOLDING, a.s., Brno, Czech Republic). The optical setup of the microscope is based on Mach–Zehnder type interferometer modified for incoherent, off-axis holographic microscopy as shown in <xref ref-type=\"fig\" rid=\"f1\">Fig. 1(b)</xref>. The illumination is formed by a low-coherence source (halogen lamp) while the beam is split into two separated optical pathways—reference and object arm. Both arms contain matching condensers, objectives, and tube lenses. In the reference arm, the diffraction grating is placed in order to ensure the achromatic formation of the interference pattern (hologram) in the output plane. The hologram is recorded by the CCD camera and numerically reconstructed using a Q–Phase software (TESCAN ORSAY HOLDING, a. s., Brno, Czech Republic). The numerical reconstruction of the image is based on carrier removal in the Fourier plane.<xref rid=\"r26\" ref-type=\"bibr\"><sup>26</sup></xref> The hologram is Fourier transformed using the 2-D fast Fourier transform (FFT) algorithm. The image spectrum in extracted by a windowing operation, whereas the window is centered at the carrier frequency. The frequency origin is translated to the center of the window, and the 2-D inverse FFT is applied to obtain the complex amplitude. Amplitude and phase are derived from the complex amplitude as modulus and argument, respectively. Since the values in the raw phase image are wrapped on the interval (<inline-formula><mml:math id=\"math9\"><mml:mrow><mml:mo form=\"prefix\">−</mml:mo><mml:mi>π</mml:mi><mml:mo>,</mml:mo><mml:mi>π</mml:mi></mml:mrow></mml:math></inline-formula>), the phase unwrapping algorithm<xref rid=\"r27\" ref-type=\"bibr\"><sup>27</sup></xref> is applied. After the reconstruction, the image can still be burdened by the optical aberrations of the imaging system, imperfect adjustment of the microscope, or possibly by surrounding temperature changes. This issue is solved by the subtraction of the compensation surface described in detail in Ref. <xref rid=\"r28\" ref-type=\"bibr\">28</xref>. In this way, a final unwrapped and compensated phase image is obtained. Such reconstructed QPI is proportional to the optical path difference of the two arms according to the following equation: <disp-formula id=\"e001\"><mml:math id=\"math10\"><mml:mrow><mml:mi>φ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi>π</mml:mi></mml:mrow><mml:mi>λ</mml:mi></mml:mfrac><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mi>c</mml:mi></mml:msub><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>−</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:mo stretchy=\"false\">]</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:math><label>(1)</label></disp-formula>where <inline-formula><mml:math id=\"math11\"><mml:mrow><mml:mi>λ</mml:mi></mml:mrow></mml:math></inline-formula> is the illumination wavelength, <inline-formula><mml:math id=\"math12\"><mml:mrow><mml:mi>d</mml:mi></mml:mrow></mml:math></inline-formula> is the cell thickness, and <inline-formula><mml:math id=\"math13\"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is the mean axially integrated refractive index of the cell immersed in the culture medium of refractive index <inline-formula><mml:math id=\"math14\"><mml:mrow><mml:msub><mml:mi>n</mml:mi><mml:mi>m</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula><xref rid=\"r29\" ref-type=\"bibr\"><sup>29</sup></xref> as depicted in <xref ref-type=\"fig\" rid=\"f1\">Fig. 1(a)</xref>. According to Refs. <xref rid=\"r30\" ref-type=\"bibr\">30</xref> and <xref rid=\"r31\" ref-type=\"bibr\">31</xref>, measured phase is proportional to the dry mass density within the cell (units of pg <inline-formula><mml:math id=\"math15\"><mml:mrow><mml:mi>μ</mml:mi><mml:msup><mml:mi mathvariant=\"normal\">m</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>), which can be obtained from the measured phase as follows: <disp-formula id=\"e002\"><mml:math id=\"math16\"><mml:mrow><mml:mi>ρ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>λ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>π</mml:mi><mml:mi>γ</mml:mi></mml:mrow></mml:mfrac><mml:mi>φ</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:math><label>(2)</label></disp-formula>where <inline-formula><mml:math id=\"math17\"><mml:mrow><mml:mi>γ</mml:mi></mml:mrow></mml:math></inline-formula> is the refraction increment.<xref rid=\"r29\" ref-type=\"bibr\"><sup>29</sup></xref> Based on the refractive index model of a cell introduced by Barer,<xref rid=\"r32\" ref-type=\"bibr\"><sup>32</sup></xref> the effective cell refractive index is linearly proportional to the concentration of protein in the cell where a proportionality constant is represented by <inline-formula><mml:math id=\"math18\"><mml:mrow><mml:mi>γ</mml:mi></mml:mrow></mml:math></inline-formula>. Several unconjugated proteins were measured, and the refraction increment for proteins is and approximated as 0.18 to <inline-formula><mml:math id=\"math19\"><mml:mrow><mml:mn>0.21</mml:mn><mml:mtext>  </mml:mtext><mml:mi>ml</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mi mathvariant=\"normal\">g</mml:mi></mml:mrow></mml:math></inline-formula>.</p><fig id=\"f1\" orientation=\"portrait\" position=\"float\"><label>Fig. 1</label><caption><p>Imaging in CCHM. (a) Model of an adhered cell in the observation chamber imaged by CCHM. (b) Optical setup of CCHM: light source (S), relay lens (L), beamsplitters (BS), condensers (C), specimen (SP), reference object (RO), microobjectives (O), tube lenses (TL), diffraction grating (DG), output lenses (OL), output plane (OP), and detector (D).</p></caption><graphic xlink:href=\"JBO-025-086502-g001\"/></fig><p>The use of incoherent illumination enables strong suppression of coherent noise and parasitic interferences. Moreover, the low illumination power of the source (<inline-formula><mml:math id=\"math20\"><mml:mrow><mml:mn>0.2</mml:mn><mml:mtext>  </mml:mtext><mml:mi>μ</mml:mi><mml:mi mathvariant=\"normal\">W</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:msup><mml:mi>cm</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>) is not likely to influence the physiological functions of imaged cells, making CCHM very convenient for live cell imaging.</p></sec><sec id=\"sec2.3\"><label>2.3</label><title>Image Acquisition</title><p>The NMuMG cells were imaged by CCHM. During the experiment, the samples were illuminated with the halogen lamp through the interference filter (<inline-formula><mml:math id=\"math21\"><mml:mrow><mml:mi>λ</mml:mi><mml:mo>=</mml:mo><mml:mn>650</mml:mn><mml:mtext>  </mml:mtext><mml:mi>nm</mml:mi></mml:mrow></mml:math></inline-formula>, 10 nm FWHM). Microscope objectives (Nikon Plan Fluor <inline-formula><mml:math id=\"math22\"><mml:mrow><mml:mn>20</mml:mn><mml:mo>×</mml:mo><mml:mo stretchy=\"false\">/</mml:mo><mml:mn>0.5</mml:mn></mml:mrow></mml:math></inline-formula>) were utilized for the imaging, providing the field of view <inline-formula><mml:math id=\"math23\"><mml:mrow><mml:mn>140</mml:mn><mml:mtext>  </mml:mtext><mml:mi>μ</mml:mi><mml:mi mathvariant=\"normal\">m</mml:mi></mml:mrow></mml:math></inline-formula>. For the purpose of classification, it was essential to acquire a reasonably large number of cells undergoing EMT. Therefore, six fields of view were imaged with a 5-min interval, each <inline-formula><mml:math id=\"math24\"><mml:mrow><mml:mo form=\"prefix\">∼</mml:mo><mml:mn>1</mml:mn><mml:mtext>  </mml:mtext><mml:mi>mm</mml:mi></mml:mrow></mml:math></inline-formula> apart from each other. Each chamber was imaged for 48 h in the presence or absence of <inline-formula><mml:math id=\"math25\"><mml:mrow><mml:mi>TGF</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:math></inline-formula> to obtain the time-lapse QPI for the classification. The media were not changed during the imaging period and conditions within the microscope mimicked that of the cell incubator (temperature 37°C) to ensure the cells were not subjected to stress.</p><p>All time-lapse images of cells were gathered in the database. The database consisted of six 48-h-long records. Since none of the cells remained in the field of view for the whole imaging due to migration, 150 min (30 time-lapse images with interval 5 min) were determined as an optimal length of the time-lapse record for one cell. Hundred and eighty cells were chosen for the monitoring. Based on their morphology, the cells were labeled by the expert biologist as either epithelial (95 cells) or mesenchymal (85 cells). The cells with uncertain class membership were not considered and were excluded from the database. The two types of classified cell morphologies are shown in <xref ref-type=\"fig\" rid=\"f2\">Fig. 2</xref>.</p><fig id=\"f2\" orientation=\"portrait\" position=\"float\"><label>Fig. 2</label><caption><p>Examples of segmented QPIs of (a) epithelial and (b) mesenchymal phenotype gained by CCHM 2 and 30 h after the application of <inline-formula><mml:math id=\"math26\"><mml:mrow><mml:mi>TGF</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:math></inline-formula>, respectively. During EMT, the cell morphology changed from rounded to elongated, with the cell mass distributing relatively equally over the cell area, while the cell area increased significantly. QPIs are shown in grayscale in units of <inline-formula><mml:math id=\"math27\"><mml:mrow><mml:mi>p</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:mi>μ</mml:mi><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">m</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula> recalculated from phase (in radians) according to Davies.<xref rid=\"r31\" ref-type=\"bibr\"><sup>31</sup></xref></p></caption><graphic xlink:href=\"JBO-025-086502-g002\"/></fig></sec><sec id=\"sec2.4\"><label>2.4</label><title>Classification</title><p>Classification, as a category of supervised machine learning, aims to build a model that makes predictions based on self-learning procedure on known labeled data. In the case of cell classification, the algorithm identifies patterns in the input images and trains a model based on labels assigned to the cells by an expert biologist. Such trained model is able to classify cells in new, previously unseen images. The essential requirement for successful classification is a sufficiently large database of labeled cell images, in which the classifier is trained. The overview of the classification process based on time-lapse QPI is shown in <xref ref-type=\"fig\" rid=\"f3\">Fig. 3</xref> and is described in more detail in the following paragraphs.</p><fig id=\"f3\" orientation=\"portrait\" position=\"float\"><label>Fig. 3</label><caption><p>Overview of the proposed feature-based classification process based on time-lapse QPI. First, image preprocessing is carried out. The cells in the image are segmented from the background and identified as ROIs. Cell features are extracted for every ROI. Feature values in several time instants create a time series. Dynamic features are extracted from the time-series while creating the feature vectors representing behavior of cells. The data are split into training and testing set. Both training and testing data are labeled by the expert biologist. After the feature selection, testing data form input for the classifier. The classifier is trained on the training data and prepared to perform the classification on testing data.</p></caption><graphic xlink:href=\"JBO-025-086502-g003\"/></fig></sec><sec id=\"sec2.5\"><label>2.5</label><title>Image Preprocessing and Feature Extraction</title><p>Before the classification itself, the cells were first segmented from the background in the time-lapse images by marker-controlled watershed segmentation approach.<xref rid=\"r33\" ref-type=\"bibr\"><sup>33</sup></xref> The individual cells were tracked using the cell tracking algorithm scripted in MATLAB (MathWorks, Inc.). The algorithm performs cell tracking by linking every segmented cell in the given frame to the nearest cell in the next frame. Only cells remaining in the field of view throughout the specified time were considered for assessment. Further, highly overlapping cells where the segmentation was not clear were not included, nor were cells located on the border of the image. The included cells were identified as separate regions of interest (ROIs), where each ROI was represented by a set of cell features—a procedure referred to as feature extraction. Two types of features were extracted from each cell: morphometric and QPI features. Morphometric features mostly reflect the shape of the cell. These features involved footprint area, perimeter of the footprint area, convex area, perimeter of the convex area, solidity, roundness, indentation, eccentricity, extent, and centroid of the cell. QPI features are extracted from the phase values of the cell in QPI and therefore, contain quantitative information about the dry mass density distribution within the cell. QPI features were composed of the total phase of the cell, average phase, median, variance, standard deviation, skewness, and kurtosis of the phase values. Both types of features are described in more detail in our previous work.<xref rid=\"r13\" ref-type=\"bibr\"><sup>13</sup></xref> In the next step, all extracted features undergo normalization in order to scale the feature values to a fixed range from 0 to 1.</p><p>Each cell in a time instant therefore is represented by a feature vector composed of the cell features captured. Since every cell was recorded in time, each cell feature provides a univariate time series composed of the values of cell features over time. Accordingly, the consideration of all cell features, gives rise to a multivariate time series. There are two possible ways for the representation of time series. In the first, the values of time series itself represent the input for the classification, which will be referred to as a value-based approach. In the second, the feature-based approach, the time series is further represented by the newly defined time-lapse features, which subsequently form the time-lapse feature vector.</p><p>In order to explain the formation of the final time-lapse feature vector in the feature-based approach, the brief notation will be introduced. Let <inline-formula><mml:math id=\"math28\"><mml:mrow><mml:mi mathvariant=\"bold\">X</mml:mi><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">{</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>Q</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">}</mml:mo></mml:mrow></mml:math></inline-formula> represent a collection of <inline-formula><mml:math id=\"math29\"><mml:mrow><mml:mi>Q</mml:mi></mml:mrow></mml:math></inline-formula> multivariate time series, where <inline-formula><mml:math id=\"math30\"><mml:mrow><mml:mi>Q</mml:mi></mml:mrow></mml:math></inline-formula> is the number of cells in the experiment. Each multivariate time series <inline-formula><mml:math id=\"math31\"><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is formed by <inline-formula><mml:math id=\"math32\"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math></inline-formula> observations (<inline-formula><mml:math id=\"math33\"><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:math></inline-formula> is the number of time points) and <inline-formula><mml:math id=\"math34\"><mml:mrow><mml:mi>d</mml:mi></mml:mrow></mml:math></inline-formula>-dimensional variable (<inline-formula><mml:math id=\"math35\"><mml:mrow><mml:mi>d</mml:mi></mml:mrow></mml:math></inline-formula> is the number of cell features) as shown in <xref ref-type=\"fig\" rid=\"f4\">Fig. 4(a)</xref>. The multivariate time series <inline-formula><mml:math id=\"math36\"><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> can be written as <disp-formula id=\"e003\"><mml:math id=\"math37\"><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">{</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>ijt</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">}</mml:mo><mml:mo>,</mml:mo><mml:mspace depth=\"0.0ex\" height=\"0.0ex\" width=\"1.0em\"/><mml:mtext>for  </mml:mtext><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mo>;</mml:mo><mml:mi>t</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>n</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math><label>(3)</label></disp-formula>with the total number of observations <inline-formula><mml:math id=\"math38\"><mml:mrow><mml:mi>d</mml:mi><mml:mo>×</mml:mo><mml:mi>n</mml:mi><mml:mo>×</mml:mo><mml:mi>Q</mml:mi></mml:mrow></mml:math></inline-formula>.</p><fig id=\"f4\" orientation=\"portrait\" position=\"float\"><label>Fig. 4</label><caption><p>Overview of the time-lapse feature extraction process. (a) Each cell is represented by the multivariate time series composed of univariate time series (formed by cell feature values obtained within the time period). (b) Time-lapse feature extraction from univariate time series composing a partial time-lapse feature set. Individual segments represent the group of time-lapse features obtained by the extraction technique. The length of the segments indicates the approximate number of extracted time-lapse features for the group. (c) Construction of the final time-lapse feature vector. The final vector representing a single cell is formed by the concatenation of partial time-lapse feature sets belonging to a cell. In addition, the motion and PCA features are added.</p></caption><graphic xlink:href=\"JBO-025-086502-g004\"/></fig><p>We will consider the <inline-formula><mml:math id=\"math39\"><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:math></inline-formula>’th component of the <inline-formula><mml:math id=\"math40\"><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:math></inline-formula>’th time series <inline-formula><mml:math id=\"math41\"><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">{</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">}</mml:mo></mml:mrow></mml:math></inline-formula> to be a univariate time series. Therefore, the univariate time series will be composed of the values of one cell feature recorded in time. For each univariate time series <inline-formula><mml:math id=\"math42\"><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>, a feat time-lapse feature vector <inline-formula><mml:math id=\"math43\"><mml:mrow><mml:mi>M</mml:mi><mml:mo>=</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mi>L</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></inline-formula> is formed, where each <inline-formula><mml:math id=\"math44\"><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:math></inline-formula> is a time-lapse feature extracted from the time series and <inline-formula><mml:math id=\"math45\"><mml:mrow><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula> is the number of time-lapse features. In this way, each time series <inline-formula><mml:math id=\"math46\"><mml:mrow><mml:msub><mml:mi>X</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula> is transformed into a partial time-lapse feature vector <inline-formula><mml:math id=\"math47\"><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula><italic>.</italic> Each multivariate time series is therefore transformed into <inline-formula><mml:math id=\"math48\"><mml:mrow><mml:mi>d</mml:mi></mml:mrow></mml:math></inline-formula> time-lapse feature vectors. The vectors are then concatenated into a final time-lapse feature vector of <inline-formula><mml:math id=\"math49\"><mml:mrow><mml:mi>d</mml:mi><mml:mo>×</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:math></inline-formula> dimensions.</p><p>The idea of the transformation is extracting information which would otherwise not be obvious as well as reducing the number of features compared to the value-based approach and therefore lowering computational time. The latter is obvious when encountering longer time series. There are several possible methods used for dealing with the feature-based representation of the time series. The employed feature extraction techniques are briefly described in the following paragraph.</p><p>Statistical features carry global information about the time series. The following metrics were chosen in order to statistically represent the structure of the time series: mean value, median value, standard deviation, minimum value, maximum value, skewness, and kurtosis. Fourier transform features are formed by the most significant coefficients gained by FFT algorithm. Wavelet transformation features are formed by detail and approximation coefficients computed by discrete wavelet transform algorithm. The trend is represented by the coefficients obtained by the linear least squares fitting of the time series and characterizes a long-term change in the mean value of a cell feature. Approximate entropy quantifies the unpredictability of fluctuations in the time series. The presence of repetitive patterns of fluctuation in a time series renders it more predictable and leads to a relatively small approximate entropy. Symbolic aggregate approximation (SAX) features are gained by SAX method,<xref rid=\"r34\" ref-type=\"bibr\"><sup>34</sup></xref> which is composed of two steps: piecewise aggregate approximation (PAA)<xref rid=\"r35\" ref-type=\"bibr\"><sup>35</sup></xref> and the conversion of a PAA sequence into a string composed of letters, where the original time series is converted to a symbol string.</p><p>All of the above time-lapse features were extracted from each of the univariate time series and created a partial time-lapse feature vector as shown in <xref ref-type=\"fig\" rid=\"f4\">Fig. 4(b)</xref>. Subsequently, the partial time-lapse feature vectors obtained from each univariate time series were concatenated into a final time-lapse feature vector, whereas other extracted time-lapse features (principal components analysis and motion features) were added on the tail as shown in <xref ref-type=\"fig\" rid=\"f4\">Fig. 4(c)</xref>.</p><p>Principal components analysis (PCA) features were gained by applying PCA<xref rid=\"r36\" ref-type=\"bibr\"><sup>36</sup></xref> on the whole multivariate time series, while mapping the multivariate data into a lower dimensional space. Motion features are composed of accumulated distance (overall distance travelled by the cell between the initial and the end point during the time interval), Euclidean distance (length of the straight line between the cell’s starting and end point reached during the time of monitoring), velocity (overall distance travelled by the cell over the elapsed time), and directionality (ratio of the Euclidian and accumulated distance). The position of the cell was determined by the cell centroids for all calculated motion features. Both PCA and motion features were added into the final time-lapse feature vector.</p><p>In the value-based approach, the extraction of time-lapse features is omitted, since the final time-lapse feature vector is composed of the raw data (values in each time point) contained in the multivariate time series. The final time-lapse feature vector is created by concatenating the univariate time series behind each other.</p><p>In both approaches, the final time-lapse feature vector represents a unique behavioral pattern of a cell. Before passing the vectors to the classification algorithms, the time-lapse feature values are scaled to a fixed range from 0 to 1. The example of a set of final time-lapse feature vectors gained by feature-based approach can be seen in <xref ref-type=\"fig\" rid=\"f5\">Fig. 5</xref>, where the first 32 rows represent feature vectors extracted from epithelial cells and the other 35 rows from mesenchymal cells with the columns representing individual time-lapse feature values. The data are further split into training and testing set and are labeled by the expert biologist. Since the final time-lapse feature vectors are of substantial size, the next step is the selection of features with the highest potential to distinguish between the given classes, which would then form input for the machine learning classification algorithms.</p><fig id=\"f5\" orientation=\"portrait\" position=\"float\"><label>Fig. 5</label><caption><p>Example of the final time-lapse feature vectors concatenated into matrix. Elements of the matrix contain the (normalized) time-lapse feature values and are visualized using color: from blue (low values) to yellow (high values). First 32 rows represent time-lapse feature vectors extracted from epithelial cells and the other 35 rows from mesenchymal cells.</p></caption><graphic xlink:href=\"JBO-025-086502-g005\"/></fig></sec><sec id=\"sec2.6\"><label>2.6</label><title>Feature Selection</title><p>Since the time-lapse feature vectors are composed of a high number of features, feature selection is performed in order to reduce the dimensionality of the data, which leads to lower computation complexity and makes the training of the classification algorithms less time-consuming. Moreover, in this case, when the number of observations is limited in comparison to the large number of features, the limited observations may lead the learning algorithm to overfit to the noise. Reducing the number of features is therefore, in this case, an essential step before the classification.</p><p>We applied the filter approach for the feature selection.<xref rid=\"r37\" ref-type=\"bibr\"><sup>37</sup></xref> First, the <inline-formula><mml:math id=\"math50\"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math></inline-formula>-test was applied to each feature and the <inline-formula><mml:math id=\"math51\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math></inline-formula>-value for each feature was compared as a measure of the feature’s ability to discriminate between the two classes. To estimate the order of class separation by the features, the empirical cumulative distribution function (CDF) of the <inline-formula><mml:math id=\"math52\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math></inline-formula>-values was computed. There were <inline-formula><mml:math id=\"math53\"><mml:mrow><mml:mo form=\"prefix\">∼</mml:mo><mml:mn>15</mml:mn><mml:mo form=\"postfix\">%</mml:mo></mml:mrow></mml:math></inline-formula> of features, which have the <inline-formula><mml:math id=\"math54\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math></inline-formula>-values close to zero and 30% of features having the <inline-formula><mml:math id=\"math55\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math></inline-formula>-values smaller than 0.05. It can be concluded that there are roughly 200 features in the original time-lapse feature set, which have the potential to separate the two cell classes. In the value-based approach, there are <inline-formula><mml:math id=\"math56\"><mml:mrow><mml:mo form=\"prefix\">∼</mml:mo><mml:mn>18</mml:mn><mml:mo form=\"postfix\">%</mml:mo></mml:mrow></mml:math></inline-formula> of features, which have the <inline-formula><mml:math id=\"math57\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math></inline-formula>-values close to zero and 30% of features having the <inline-formula><mml:math id=\"math58\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math></inline-formula>-values smaller than 0.05. CDF of the <inline-formula><mml:math id=\"math59\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math></inline-formula>-values showed that there are roughly 150 features from the original time-lapse feature set having rather high discriminative power.</p><p>The features were subsequently ordered by their <inline-formula><mml:math id=\"math60\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math></inline-formula>-values. In order to define the appropriate number of features to be selected, the classification error (the number of misclassified observations divided by the number of observations) as a function of the number of features was plotted. To obtain the classification error, several classification algorithms were employed. The results of the classification error in feature-based approach is shown in <xref ref-type=\"fig\" rid=\"f6\">Fig. 6</xref>. The classification error was computed for different numbers of features between 2 and 25. The final number of selected features was determined as the mean value of the results produced by employing different classification algorithms. In the feature-based approach, the filter feature selection method obtains the smallest classification error when 10 features are engaged. Only these 10 features with the highest discriminative power are kept in the reduced time-lapse feature vectors used for the classification. In the value-based approach, 12 features were determined as optimal.</p><fig id=\"f6\" orientation=\"portrait\" position=\"float\"><label>Fig. 6</label><caption><p>Classification error as a function of the number of features in feature-based approach.</p></caption><graphic xlink:href=\"JBO-025-086502-g006\"/></fig></sec><sec id=\"sec2.7\"><label>2.7</label><title>Classification Algorithms</title><p>After the features with the highest potential to distinguish between the epithelial and mesenchymal cell classes were selected, they create the input for the classification algorithms. Since the performance of the classification is highly dependent on the selection of the classification algorithm, we employed several supervised machine learning algorithms to correctly compare the performance of the classification based on single-time-point and time-lapse QPI. The following algorithms were tested in several possible variations with differently set parameters: decision trees (complex, medium, and simple tree, with defined maximum number of splits 100, 20, and 4, respectively), discriminant analysis (linear and quadratic discriminant), support vector machines (with linear, quadratic, cubic, and Gaussian kernel), <inline-formula><mml:math id=\"math61\"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math></inline-formula>-nearest neighbor classifier (KNN), (fine KNN with <inline-formula><mml:math id=\"math62\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math></inline-formula> and Euclidean distance, medium KNN with <inline-formula><mml:math id=\"math63\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>10</mml:mn></mml:mrow></mml:math></inline-formula> and Euclidean distance, cosine KNN with <inline-formula><mml:math id=\"math64\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>10</mml:mn></mml:mrow></mml:math></inline-formula> and cosine distance, cubic KNN with <inline-formula><mml:math id=\"math65\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>10</mml:mn></mml:mrow></mml:math></inline-formula> and cubic distance, weighted KNN with <inline-formula><mml:math id=\"math66\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>10</mml:mn></mml:mrow></mml:math></inline-formula>, and weighted by the inverse square of the Euclidean distance) ensemble classifiers (bagged trees, boosted trees, subspace discriminant, and subspace KNN) and artificial neural network (feed-forward backpropagation neural network with one hidden layer containing 10 hidden neurons).</p><p>Several performance measures were calculated from the confusion matrix for each classification algorithm: accuracy, precision, recall, and <inline-formula><mml:math id=\"math67\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score. Classification accuracy of a classifier is calculated as the ratio of the sum of the principal diagonal values to the sum of all values in the confusion matrix. It expresses the ratio of correctly classified examples by the classifier. Precision is the ratio of correctly classified positive examples to the total number of positive examples, while recall is the ratio of correctly classified positive examples to the all examples in actual class. <inline-formula><mml:math id=\"math68\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score can be interpreted as a harmonic mean of precision and recall. Fivefold cross validation was used to evaluate the performance of the classification algorithms. The data were partitioned into five randomly chosen subsets of roughly equal size. One subset (testing set) was used for testing of the classifier, which had been trained on the remaining subsets (training set). This process was repeated five times, such that each subset was used for the validation. Since cross validation does not use all the data for training, it is a commonly used method to avoid overfitting. The overall performance of the classification algorithm was determined as the mean of performance measure values reached in the iterations. The whole classification procedure was performed in MATLAB.</p></sec></sec><sec id=\"sec3\"><label>3</label><title>Results and Discussion</title><p>After 48 h, the cells in the control conditions maintained their epithelial state, whereas the cells treated with <inline-formula><mml:math id=\"math69\"><mml:mrow><mml:mi>TGF</mml:mi><mml:mi>β</mml:mi></mml:mrow></mml:math></inline-formula> transitioned to the mesenchymal state as previously shown.<xref rid=\"r19\" ref-type=\"bibr\"><sup>19</sup></xref><sup>,</sup><xref rid=\"r20\" ref-type=\"bibr\"><sup>20</sup></xref> We observed changes in cell morphology to occur <inline-formula><mml:math id=\"math70\"><mml:mrow><mml:mo form=\"prefix\">∼</mml:mo><mml:mn>17</mml:mn><mml:mtext>  </mml:mtext><mml:mi mathvariant=\"normal\">h</mml:mi></mml:mrow></mml:math></inline-formula> posttreatment. The cells lost their epithelial features and acquired more mesenchymal, fibroblast-like phenotype. The cells became elongated, with the cell mass distributing relatively equally over the cell area, while the cell area increased significantly.</p><p>The classification was first performed on the reduced time-lapse feature vectors gained by value-based approach. The same procedure was then repeated for the reduced time-lapse feature vectors gained by feature-based approach. The classification was also performed on the features extracted from the single-time-point QPI to evaluate the contribution of the methodology based on time-lapse QPI.</p><p>The performance of the classification implementing the value-based approach is summarized in <xref rid=\"t001\" ref-type=\"table\">Table 1</xref>. The overall accuracy of the classification was <inline-formula><mml:math id=\"math71\"><mml:mrow><mml:mn>0.923</mml:mn><mml:mo>±</mml:mo><mml:mn>0.053</mml:mn></mml:mrow></mml:math></inline-formula>. The overall precision, recall, and <inline-formula><mml:math id=\"math72\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score were <inline-formula><mml:math id=\"math73\"><mml:mrow><mml:mn>0.907</mml:mn><mml:mo>±</mml:mo><mml:mn>0.052</mml:mn></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id=\"math74\"><mml:mrow><mml:mn>0.882</mml:mn><mml:mo>±</mml:mo><mml:mn>0.089</mml:mn></mml:mrow></mml:math></inline-formula>, and <inline-formula><mml:math id=\"math75\"><mml:mrow><mml:mn>0.893</mml:mn><mml:mo>±</mml:mo><mml:mn>0.070</mml:mn></mml:mrow></mml:math></inline-formula>, respectively. The performance of the classification using the feature-based approach is summarized in <xref rid=\"t002\" ref-type=\"table\">Table 2</xref>. Assessing the cell behavior by the time-lapse features led to higher performance of the classifier, as observed with the value-based approach, with the overall accuracy of the classification reaching <inline-formula><mml:math id=\"math76\"><mml:mrow><mml:mn>0.978</mml:mn><mml:mo>±</mml:mo><mml:mn>0.011</mml:mn></mml:mrow></mml:math></inline-formula>. Further, with the incorporation of time-lapse features, the overall precision, recall, and <inline-formula><mml:math id=\"math77\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score were <inline-formula><mml:math id=\"math78\"><mml:mrow><mml:mn>0.968</mml:mn><mml:mo>±</mml:mo><mml:mn>0.014</mml:mn></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id=\"math79\"><mml:mrow><mml:mn>0.961</mml:mn><mml:mo>±</mml:mo><mml:mn>0.013</mml:mn></mml:mrow></mml:math></inline-formula>, and <inline-formula><mml:math id=\"math80\"><mml:mrow><mml:mn>0.964</mml:mn><mml:mo>±</mml:mo><mml:mn>0.013</mml:mn></mml:mrow></mml:math></inline-formula>, respectively.</p><table-wrap id=\"t001\" orientation=\"portrait\" position=\"float\"><label>Table 1</label><caption><p>Performance of the classification by supervised machine learning algorithms using value-based approach.</p></caption><!--OASIS TABLE HERE--><table frame=\"hsides\" rules=\"groups\"><colgroup><col/><col/><col/><col/><col/></colgroup><thead><tr><th valign=\"top\"> </th><th valign=\"top\">Accuracy</th><th valign=\"top\">Precision</th><th valign=\"top\">Recall</th><th valign=\"top\"><inline-formula><mml:math id=\"math81\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score</th></tr></thead><tbody><tr><td>Decision trees (complex)</td><td>0.79</td><td>0.84</td><td>0.631</td><td>0.726</td></tr><tr><td>Decision trees (medium)</td><td>0.876</td><td>0.836</td><td>0.826</td><td>0.831</td></tr><tr><td>Decision trees (simple)</td><td>0.889</td><td>0.863</td><td>0.829</td><td>0.842</td></tr><tr><td>Linear discriminant analysis</td><td>0.974</td><td>0.952</td><td>0.951</td><td>0.954</td></tr><tr><td>Quadratic discriminant analysis</td><td>0.919</td><td>0.89</td><td>0.882</td><td>0.893</td></tr><tr><td>SVM (linear)</td><td>0.951</td><td>0.943</td><td>0.928</td><td>0.932</td></tr><tr><td>SVM (quadratic)</td><td>0.971</td><td>0.963</td><td>0.947</td><td>0.953</td></tr><tr><td>SVM (cubic)</td><td>0.982</td><td>0.979</td><td>0.971</td><td>0.972</td></tr><tr><td>SVM (Gaussian medium)</td><td>0.98</td><td>0.984</td><td>0.981</td><td>0.983</td></tr><tr><td>KNN (fine)</td><td>0.945</td><td>0.941</td><td>0.926</td><td>0.93</td></tr><tr><td>KNN (medium)</td><td>0.918</td><td>0.896</td><td>0.887</td><td>0.891</td></tr><tr><td>KNN (cosine)</td><td>0.938</td><td>0.889</td><td>0.874</td><td>0.88</td></tr><tr><td>KNN (cubic)</td><td>0.889</td><td>0.852</td><td>0.84</td><td>0.844</td></tr><tr><td>KNN (weighted)</td><td>0.884</td><td>0.83</td><td>0.829</td><td>0.829</td></tr><tr><td>Bagged trees</td><td>0.82</td><td>0.827</td><td>0.705</td><td>0.761</td></tr><tr><td>Subspace discriminant</td><td>0.954</td><td>0.941</td><td>0.945</td><td>0.937</td></tr><tr><td>Subspace KNN</td><td>0.981</td><td>0.962</td><td>0.961</td><td>0.963</td></tr><tr><td>Boosted trees</td><td>0.931</td><td>0.913</td><td>0.906</td><td>0.909</td></tr><tr><td>Neural networks</td><td>0.952</td><td>0.941</td><td>0.942</td><td>0.943</td></tr><tr><td>Mean ± SD</td><td><inline-formula><mml:math id=\"math82\"><mml:mrow><mml:mn>0.923</mml:mn><mml:mo>±</mml:mo><mml:mn>0.053</mml:mn></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math83\"><mml:mrow><mml:mn>0.907</mml:mn><mml:mo>±</mml:mo><mml:mn>0.052</mml:mn></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math84\"><mml:mrow><mml:mn>0.882</mml:mn><mml:mo>±</mml:mo><mml:mn>0.089</mml:mn></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math85\"><mml:mrow><mml:mn>0.893</mml:mn><mml:mo>±</mml:mo><mml:mn>0.070</mml:mn></mml:mrow></mml:math></inline-formula></td></tr></tbody></table></table-wrap><table-wrap id=\"t002\" orientation=\"portrait\" position=\"float\"><label>Table 2</label><caption><p>Performance of the classification by supervised machine learning algorithms using feature-based approach.</p></caption><!--OASIS TABLE HERE--><table frame=\"hsides\" rules=\"groups\"><colgroup><col/><col/><col/><col/><col/></colgroup><thead><tr><th valign=\"top\"> </th><th valign=\"top\">Accuracy</th><th valign=\"top\">Precision</th><th valign=\"top\">Recall</th><th valign=\"top\"><inline-formula><mml:math id=\"math86\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score</th></tr></thead><tbody><tr><td>Decision trees (complex)</td><td>0.981</td><td>0.969</td><td>0.962</td><td>0.965</td></tr><tr><td>Decision trees (medium)</td><td>0.986</td><td>0.981</td><td>0.975</td><td>0.976</td></tr><tr><td>Decision trees (simple)</td><td>0.991</td><td>0.983</td><td>0.981</td><td>0.981</td></tr><tr><td>Linear discriminant analysis</td><td>0.965</td><td>0.956</td><td>0.954</td><td>0.957</td></tr><tr><td>Quadratic discriminant analysis</td><td>0.981</td><td>0.969</td><td>0.959</td><td>0.961</td></tr><tr><td>SVM (linear)</td><td>0.951</td><td>0.952</td><td>0.944</td><td>0.947</td></tr><tr><td>SVM (quadratic)</td><td>0.989</td><td>0.986</td><td>0.981</td><td>0.985</td></tr><tr><td>SVM (cubic)</td><td>0.991</td><td>0.984</td><td>0.971</td><td>0.982</td></tr><tr><td>SVM (Gaussian medium)</td><td>0.991</td><td>0.989</td><td>0.98</td><td>0.981</td></tr><tr><td>KNN (fine)</td><td>0.971</td><td>0.961</td><td>0.945</td><td>0.952</td></tr><tr><td>KNN (medium)</td><td>0.988</td><td>0.981</td><td>0.962</td><td>0.97</td></tr><tr><td>KNN (cosine)</td><td>0.982</td><td>0.977</td><td>0.963</td><td>0.971</td></tr><tr><td>KNN (cubic)</td><td>0.965</td><td>0.955</td><td>0.951</td><td>0.95</td></tr><tr><td>KNN (weighted)</td><td>0.974</td><td>0.951</td><td>0.948</td><td>0.949</td></tr><tr><td>Bagged trees</td><td>0.987</td><td>0.97</td><td>0.971</td><td>0.973</td></tr><tr><td>Subspace discriminant</td><td>0.981</td><td>0.965</td><td>0.956</td><td>0.958</td></tr><tr><td>Subspace KNN</td><td>0.964</td><td>0.941</td><td>0.942</td><td>0.941</td></tr><tr><td>Boosted trees</td><td>0.969</td><td>0.954</td><td>0.949</td><td>0.952</td></tr><tr><td>Neural networks</td><td>0.973</td><td>0.962</td><td>0.956</td><td>0.958</td></tr><tr><td><inline-formula><mml:math id=\"math87\"><mml:mrow><mml:mi>Mean</mml:mi><mml:mo>±</mml:mo><mml:mi>SD</mml:mi></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math88\"><mml:mrow><mml:mn>0.978</mml:mn><mml:mo>±</mml:mo><mml:mn>0.011</mml:mn></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math89\"><mml:mrow><mml:mn>0.968</mml:mn><mml:mo>±</mml:mo><mml:mn>0.014</mml:mn></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math90\"><mml:mrow><mml:mn>0.961</mml:mn><mml:mo>±</mml:mo><mml:mn>0.013</mml:mn></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math91\"><mml:mrow><mml:mn>0.964</mml:mn><mml:mo>±</mml:mo><mml:mn>0.013</mml:mn></mml:mrow></mml:math></inline-formula></td></tr></tbody></table></table-wrap><p>In order to correctly evaluate the benefit of incorporating the temporal information over the classification based solely on the static QPI, the classification was performed also on the static QPIs of cells undergoing EMT. The static QPI images were obtained from the time-lapse data by selecting one image from each time-lapse sequence. The classification of epithelial and mesenchymal cells based on the static QPI was performed according to the methodology previously described in our paper.<xref rid=\"r13\" ref-type=\"bibr\"><sup>13</sup></xref> The performance of the classification based on single-time-point QPI is summarized in <xref rid=\"t003\" ref-type=\"table\">Table 3</xref>. The overall accuracy of the classification was <inline-formula><mml:math id=\"math92\"><mml:mrow><mml:mn>0.889</mml:mn><mml:mo>±</mml:mo><mml:mn>0.053</mml:mn></mml:mrow></mml:math></inline-formula>. The overall precision, recall, and <inline-formula><mml:math id=\"math93\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score were <inline-formula><mml:math id=\"math94\"><mml:mrow><mml:mn>0.873</mml:mn><mml:mo>±</mml:mo><mml:mn>0.053</mml:mn></mml:mrow></mml:math></inline-formula>, <inline-formula><mml:math id=\"math95\"><mml:mrow><mml:mn>0.838</mml:mn><mml:mo>±</mml:mo><mml:mn>0.198</mml:mn></mml:mrow></mml:math></inline-formula>, and <inline-formula><mml:math id=\"math96\"><mml:mrow><mml:mn>0.853</mml:mn><mml:mo>±</mml:mo><mml:mn>0.077</mml:mn></mml:mrow></mml:math></inline-formula>, respectively.</p><table-wrap id=\"t003\" orientation=\"portrait\" position=\"float\"><label>Table 3</label><caption><p>Performance of the classification by supervised machine learning algorithms using the static QPI.</p></caption><!--OASIS TABLE HERE--><table frame=\"hsides\" rules=\"groups\"><colgroup><col/><col/><col/><col/><col/></colgroup><thead><tr><th valign=\"top\"> </th><th valign=\"top\">Accuracy</th><th valign=\"top\">Precision</th><th valign=\"top\">Recall</th><th valign=\"top\"><inline-formula><mml:math id=\"math97\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score</th></tr></thead><tbody><tr><td>Decision trees (complex)</td><td>0.891</td><td>0.839</td><td>0.833</td><td>0.83</td></tr><tr><td>Decision trees (medium)</td><td>0.886</td><td>0.86</td><td>0.882</td><td>0.872</td></tr><tr><td>Decision trees (simple)</td><td>0.923</td><td>0.929</td><td>0.918</td><td>0.922</td></tr><tr><td>Linear discriminant analysis</td><td>0.962</td><td>0.946</td><td>0.945</td><td>0.945</td></tr><tr><td>Quadratic discriminant analysis</td><td>0.892</td><td>0.842</td><td>0.82</td><td>0.832</td></tr><tr><td>SVM (linear)</td><td>0.912</td><td>0.897</td><td>0.879</td><td>0.888</td></tr><tr><td>SVM (quadratic)</td><td>0.88</td><td>0.961</td><td>0.949</td><td>0.955</td></tr><tr><td>SVM (cubic)</td><td>0.944</td><td>0.934</td><td>0.927</td><td>0.93</td></tr><tr><td>SVM (Gaussian medium)</td><td>0.937</td><td>0.912</td><td>0.906</td><td>0.907</td></tr><tr><td>KNN (fine)</td><td>0.895</td><td>0.869</td><td>0.844</td><td>0.857</td></tr><tr><td>KNN (medium)</td><td>0.762</td><td>0.808</td><td>0.618</td><td>0.701</td></tr><tr><td>KNN (cosine)</td><td>0.899</td><td>0.867</td><td>0.85</td><td>0.857</td></tr><tr><td>KNN (cubic)</td><td>0.79</td><td>0.791</td><td>0.632</td><td>0.697</td></tr><tr><td>KNN (weighted)</td><td>0.791</td><td>0.763</td><td>0.661</td><td>0.71</td></tr><tr><td>Bagged trees</td><td>0.871</td><td>0.841</td><td>0.775</td><td>0.806</td></tr><tr><td>Subspace discriminant</td><td>0.89</td><td>0.849</td><td>0.837</td><td>0.842</td></tr><tr><td>Subspace KNN</td><td>0.94</td><td>0.919</td><td>0.895</td><td>0.908</td></tr><tr><td>Boosted trees</td><td>0.935</td><td>0.917</td><td>0.91</td><td>0.914</td></tr><tr><td>Neural networks</td><td>0.89</td><td>0.846</td><td>0.842</td><td>0.841</td></tr><tr><td>Mean ± SD</td><td><inline-formula><mml:math id=\"math98\"><mml:mrow><mml:mn>0.889</mml:mn><mml:mo>±</mml:mo><mml:mn>0.053</mml:mn></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math99\"><mml:mrow><mml:mn>0.873</mml:mn><mml:mo>±</mml:mo><mml:mn>0.053</mml:mn></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math100\"><mml:mrow><mml:mn>0.838</mml:mn><mml:mo>±</mml:mo><mml:mn>0.098</mml:mn></mml:mrow></mml:math></inline-formula></td><td><inline-formula><mml:math id=\"math101\"><mml:mrow><mml:mn>0.853</mml:mn><mml:mo>±</mml:mo><mml:mn>0.077</mml:mn></mml:mrow></mml:math></inline-formula></td></tr></tbody></table></table-wrap><p>The performance of the classification obtained by the mentioned classification approaches was compared by statistical hypothesis testing. The Wilcoxon signed rank test was used in order to reveal the significant differences between the three distributions, with a null hypothesis that the median difference between pairs of observations is zero and a <inline-formula><mml:math id=\"math102\"><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:math></inline-formula>-value of 0.05 to be considered statistically significant. The test revealed very significant differences between the feature-based and value-based time-lapse classification approaches (<inline-formula><mml:math id=\"math103\"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.001</mml:mn></mml:mrow></mml:math></inline-formula>) in terms of all performance parameters (accuracy, precision, recall, and <inline-formula><mml:math id=\"math104\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score). Significantly different results (<inline-formula><mml:math id=\"math105\"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.001</mml:mn></mml:mrow></mml:math></inline-formula>) were obtained also from the classification based on static QPI and the classification based on time-lapse QPI employing the feature-based approach. According to the test, the classification based on static QPI and the classification based on time-lapse QPI using the value-based approach provided different performance of the classification with a lower significance (<inline-formula><mml:math id=\"math106\"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.01</mml:mn></mml:mrow></mml:math></inline-formula> for precision and <inline-formula><mml:math id=\"math107\"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.05</mml:mn></mml:mrow></mml:math></inline-formula> for other performance parameters). The performance results of all approaches are shown in the form of box-whisker plots in <xref ref-type=\"fig\" rid=\"f7\">Fig. 7</xref>.</p><fig id=\"f7\" orientation=\"portrait\" position=\"float\"><label>Fig. 7</label><caption><p>Box-whisker plots of overall classification performance of classification based on static QPI, time-lapse QPI (value-based and feature-based approach): (a) accuracy, (b) precision, (c) recall, and (d) <inline-formula><mml:math id=\"math108\"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math></inline-formula>-score. Wilcoxon signed rank test was used for the statistical analysis. Symbols indicating significance are placed above (*<inline-formula><mml:math id=\"math109\"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.05</mml:mn></mml:mrow></mml:math></inline-formula>, **<inline-formula><mml:math id=\"math110\"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.01</mml:mn></mml:mrow></mml:math></inline-formula>, and ***<inline-formula><mml:math id=\"math111\"><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0.001</mml:mn></mml:mrow></mml:math></inline-formula>).</p></caption><graphic xlink:href=\"JBO-025-086502-g007\"/></fig><p>Several conclusions can be drawn from the results of the classification. The classification based on time-lapse QPI using either value-based or feature-based approach outperforms the classification based on static QPI, which does not consider the temporal information. Hence, taking into account the time information appears to improve the classification of the two cell phenotypes by nearly 9% in terms of accuracy. However, when it comes to the classification based on time-lapse QPI, the feature-based approach outperforms the value-based approach. The low-performance values in the value-based approach can be due to many factors, but mainly a consequence of the features, which are in this case the raw time series data, not fully representing the cell behavior. The other possibility is the increased sensitivity of this approach to the amount of noise in the time series.</p><p>Although the performance of the classification based on time-lapse QPI using the feature-based approach was relatively high, further improvement could be achieved by enlargement of the time-lapse QPI dataset, which would allow the classification algorithms to improve the training based on more extensive data.</p><p>Continued QPI-based classification of cell phenotype during EMT, beyond the epithelial and mesenchymal states, may allow for further understanding of this cell identity switch and thus cancer progression. Accordingly, even though the methodology was assessed using analysis of one specific cellular process, we suggest this analysis will be informative in the study of other dynamic cellular phenomena such as monitoring of cell cycle progression, cell death, and cellular response to external stimuli.</p></sec><sec id=\"sec4\"><label>4</label><title>Conclusions</title><p>We have proposed a new methodology for cell classification based on the time-lapse QPIs using cells undergoing EMT as the biological system of focus. We have applied several supervised classification algorithms to differentiate between two distinct cell morphologies. Our findings show that the extraction of the time-lapse features representing dynamic cell behavior outperforms analysis based solely on the single-time-point QPIs, which indicates the importance of incorporating temporal information into the classification process. Despite the challenging time-lapse feature extraction, the proposed approach provides a novel, yet efficient way to classify the cells in QPIs with promising performance results. This approach could improve the monitoring of live cell behavior in an automated fashion and we believe that exploiting the methodology in QPI could contribute to promoting the DHM as an analysis tool and potentially a standard diagnostic technique used in biology and medicine.</p></sec></body><back><ack><title>Acknowledgments</title><p>The work was supported by the Grant Agency of the Czech Republic (No. 18-01396S); Ministry of Education Youth and Sports of the Czech Republic (Nos. LQ1601, CEITEC 2020, and LM2015062 Czech BioImaging); and the Specific Research grant of Brno University of Technology (No. FSI-S-17-4506). We would also like to acknowledge Modernization and support of research activities of the National Infrastructure for Biological and Medical Imaging (No. CZ.02.1.01/0.0/0.0/16_013/0001775) funded by OP RDE. C. T. Vincent and B. B. Carson were supported by grants awarded to C. T. Vincent from Swedish Research Council (V. R.) and the Swedish Cancer Society (Cancerfonden).</p></ack><notes notes-type=\"conflict-of-interest\"><title>Disclosures</title><p>R. Chmelik reports patents for the CCHM optical system licensed to commercial entities (TESCAN ORSAY HOLDING a.s). This research was completed without their participation, knowledge, or financial support, and data were acquired and processed by co-authors unaffiliated with any commercial entity.</p></notes><ref-list><title>References</title><ref id=\"r1\"><label>1.</label><mixed-citation publication-type=\"book\"><person-group person-group-type=\"author\"><name><surname>Bishop</surname><given-names>C. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"discussion\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Oncol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Oncol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Oncol.</journal-id><journal-title-group><journal-title>Frontiers in Oncology</journal-title></journal-title-group><issn pub-type=\"epub\">2234-943X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850422</article-id><article-id pub-id-type=\"pmc\">PMC7431881</article-id><article-id pub-id-type=\"doi\">10.3389/fonc.2020.01329</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Oncology</subject><subj-group><subject>Opinion</subject></subj-group></subj-group></article-categories><title-group><article-title>What Does the “AKT” Stand for in the Name “AKT Kinase”? Some Historical Comments</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Xie</surname><given-names>Jiuyong</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/36243/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Weiskirchen</surname><given-names>Ralf</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/24872/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Physiology and Pathophysiology, Rady Faculty of Health Sciences, Max Rady College of Medicine, The University of Manitoba</institution>, <addr-line>Winnipeg, MB</addr-line>, <country>Canada</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Institute of Molecular Pathobiochemistry, Experimental Gene Therapy and Clinical Chemistry (IFMPEGKC), RWTH University Hospital Aachen</institution>, <addr-line>Aachen</addr-line>, <country>Germany</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Dario Palmieri, The Ohio State University, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Michela Ranieri, New York University, United States; Arturo Orlacchio, The Ohio State University, United States</p></fn><corresp id=\"c001\">*Correspondence: Jiuyong Xie <email>xiej@umanitoba.ca</email></corresp><corresp id=\"c002\">Ralf Weiskirchen <email>rweiskirchen@ukaachen.de</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Molecular and Cellular Oncology, a section of the journal Frontiers in Oncology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>1329</elocation-id><history><date date-type=\"received\"><day>25</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Xie and Weiskirchen.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Xie and Weiskirchen</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><kwd-group><kwd>AKT</kwd><kwd>kinase</kwd><kwd>name</kwd><kwd>origin</kwd><kwd>oncogene</kwd><kwd>AKR mice</kwd><kwd>retrovirus</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Natural Sciences and Engineering Research Council of Canada<named-content content-type=\"fundref-id\">10.13039/501100000038</named-content></funding-source></award-group><award-group><funding-source id=\"cn002\">Deutsche Forschungsgemeinschaft<named-content content-type=\"fundref-id\">10.13039/501100001659</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"0\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"11\"/><page-count count=\"2\"/><word-count count=\"1588\"/></counts></article-meta></front><body><p>The Akt serine/threonine kinase family is comprised of three highly homologous isoforms, namely Akt1 (PKBα, OMIM: <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"164730\">164730</ext-link>), Akt2 (PKBβ, OMIM: <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"164731\">164731</ext-link>), and Akt3 (PKBγ, OMIM: <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"611223\">611223</ext-link>). They are stimulated by a large variety of extracellular stimuli. Individual AKT members have a widely diverse repertoire of downstream effects in different settings by targeting over 100 different substrates (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). The origin of “Akt” in the “Akt kinase” (also called protein kinase B or PKB) is explained here based on the original research papers describing the related information tracing back to 1933.</p><p>Prompted by the “a serine/threonine protein kinase” put for the abbreviation AKT kinase by a student, we searched for the proper meaning of “AKT” but no clear answer was found, to our surprise. Further search in the literature resulted in the following findings:</p><list list-type=\"order\"><list-item><p>The 1987 paper by Dr. Stephen P. Staal of the Johns Hopkins Oncology Centre, on “<italic>Molecular cloning of the akt oncogene and its human homologues AKT1 and AKT2: Amplification of AKTI in a primary human gastric adenocarcinoma</italic>” (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>), mentioned “the isolation of a directly transforming retrovirus, AKT8 from a spontaneous thymoma of an AKR mouse.” Going back 10 years, it is further mentioned that the initial isolate of the virus strain T-8 was from “an <italic>in vitro</italic> thymoma cell line, AKT-8, from a spontaneously lymphomatous AKR/J mouse” (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). The “thymoma” interpretation for the virus name AKT8 was also noted later as “for AKR Thymoma #8” by Bellacosa et al. in a 2005 review (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Therefore, the “AK” was likely carried from the “AKR” of the mouse name, and the “T” was for the word “thymoma” describing the cellular source of the retrovirus, though it could also remind us of the “transforming” ability of the virus. The viral oncogene isolated from the AKT-8 was named v-<italic>akt</italic>. Therefore, the letters of the gene likely stand for the same.</p></list-item><list-item><p>The name AKR for the mouse strain was specifically explained by Dr. Clara J. Lynch of the Rockefeller Institute for Medical Research in a 1954 paper “<italic>The R.I.L. strain of mice: its relation to the leukemic AK strain and AKR substrains</italic>” (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). She explained: “The appellation AKR has now been adopted for the substrains to indicate the derivation of the random-bred colony from AK stock and the subsequent brother × sister breeding at the Rockefeller Institute,” in line with the recommendations by the Committee on Standardized Nomenclature for Inbred Strains of Mice (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Therefore, the “R” in “AKR” should stand for Rockefeller Institute. Dr. Lynch also mentioned the “AK stock of Dr. J. Furth,” suggesting that the letters “AK” was from Dr. Furth. In addition, she mentioned in her manuscript “From a random-bred stock in the laboratory of Dr. Cornelius P. Rhoads at the Rockefeller Institute, brother × sister inbreeding of lines selected for leukemia was begun by Katherine B. Rhoads” and further “Data on the early generations, presented through the courtesy of Dr. C. P. Rhoads and K. B. Rhoads, are given in text-Figure 1” (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). So interestingly the “R” in “AKR” coincides with the letter R in “random” or “Rhoads.”</p></list-item><list-item><p>The meaning of “AK” was explained in a 1933 paper “<italic>Experimental studies on lymphomatosis of mice</italic>” by Dr. J. Furth et al. of the Cornell University Medical College and the University of Pennsylvania (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). It said: “….mice of three different stocks bred by us and designated as A, R, and S,” followed later by “…. the inbred families (of each stock) are designated by a second small letter added to the capitals A, R, and S respectively (e.g., Aa, Ab, etc., Ra, Rb, etc.).” One of the transmissible strains is named Ak30, as described later in the paper. The meanings of A, R, etc. were not found to be explained there. The authors simply explained “Mice colonies studied: The spontaneous cases of lymphomatosis that will be described are derived from mice of three different stocks bred by us and designated A, R, and S. Stock A was purchased because it was claimed to yield many cancers, stock R because it was stated to be non-cancerous. No information was obtained concerning stock S.” So the stock A was taken because it's high susceptibility for tumor formation.</p></list-item><list-item><p>The full length v-<italic>ak</italic>t oncogene was cloned, sequenced and biochemically characterized as a protein kinase C-related serine-threonine kinase by the Staal group in 1991 (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Mammalian counterparts of this oncogene were isolated, sequenced and characterized independently by the Hemmings group in Switzerland and by the Woodgett group in England in the same year (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). The former isolated them by cDNA library screening from porcine kidney LLC-PK<sub>1</sub> cells using a porcine cAMP-PK probe, and then from human epithelial MCF-7 or lung fibroblast WI38 cell line (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>). The latter did so from human fibroblast using an amplified cDNA probe with degenerate oligonucleotide primers designed from regions conserved in the serine/threonine protein kinase catalytic domains (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). They were termed rac kinases (related to the A and C kinases) (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>) or PKB (for <underline>P</underline>rotein <underline>K</underline>inase <underline>B</underline> most similar to the PKC/PKA families) (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p></list-item></list><p>Taken together these points, it seems that the origin of the name for the mouse homologue AKT of the viral v-<italic>akt</italic> gene product could be at least interpreted as “a serine/threonine protein kinase encoded by the oncogene in the transforming retrovirus isolated from the <underline>t</underline>hymoma cell line AKT-8, which is derived from the Stock <underline>A</underline> Strain <underline>k</underline> AKR mouse originally inbred in the laboratory of Dr. C. P. Rhoads by K. B. Rhoads at the Rockefeller Institute.” Same interpretation applies to the human AKT kinases and genes.</p><sec id=\"s1\"><title>Author Contributions</title><p>JX and RW wrote the commentary together. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s2\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by a Discovery grant from the Canadian Natural Sciences & Engineering Research Council (NSREC) and a Research Chair Fund by the Manitoba Health Research Council to JX, and by the German Research Foundation (SFB/TRR57, P13 and Q3) and the Interdisciplinary Centre for Clinical Research within the Faculty of Medicine at the RWTH Aachen University (IZKF Aachen, O3-1) to RW.</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Manning</surname><given-names>BD</given-names></name><name><surname>Toker</surname><given-names>A</given-names></name></person-group>. <article-title>AKT/PKB signaling: navigating the network</article-title>. <source>Cell.</source> (<year>2017</year>) <volume>169</volume>:<fpage>381</fpage>–<lpage>405</lpage>. <pub-id pub-id-type=\"doi\">10.1016/j.cell.2017.04.001</pub-id><pub-id pub-id-type=\"pmid\">28431241</pub-id></mixed-citation></ref><ref id=\"B2\"><label>2.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Staal</surname><given-names>SP</given-names></name></person-group>. <article-title>Molecular cloning of the akt oncogene and its human homologues AKT1 and AKT2: amplification of AKT1 in a primary human gastric adenocarcinoma</article-title>. <source>Proc Natl Acad Sci USA.</source> (<year>1987</year>) <volume>84</volume>:<fpage>5034</fpage>–<lpage>7</lpage>. <pub-id pub-id-type=\"doi\">10.1073/pnas.84.14.5034</pub-id><pub-id pub-id-type=\"pmid\">3037531</pub-id></mixed-citation></ref><ref id=\"B3\"><label>3.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Staal</surname><given-names>SP</given-names></name><name><surname>Hartley</surname><given-names>JW</given-names></name><name><surname>Rowe</surname><given-names>WP</given-names></name></person-group>. <article-title>Isolation of transforming murine leukemia viruses from mice with a high incidence of spontaneous lymphoma</article-title>. <source>Proc Natl Acad Sci USA.</source> (<year>1977</year>) <volume>74</volume>:<fpage>3065</fpage>–<lpage>7</lpage>. <pub-id pub-id-type=\"doi\">10.1073/pnas.74.7.3065</pub-id><pub-id pub-id-type=\"pmid\">197531</pub-id></mixed-citation></ref><ref id=\"B4\"><label>4.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Bellacosa</surname><given-names>A</given-names></name><name><surname>Kumar</surname><given-names>CC</given-names></name><name><surname>Di Cristofano</surname><given-names>A</given-names></name><name><surname>Testa</surname><given-names>JR</given-names></name></person-group>. <article-title>Activation of AKT kinases in cancer: implications for therapeutic targeting</article-title>. <source>Adv Cancer Res.</source> (<year>2005</year>) <volume>94</volume>:<fpage>29</fpage>–<lpage>86</lpage>. <pub-id pub-id-type=\"doi\">10.1016/S0065-230X(05)94002-5</pub-id><pub-id pub-id-type=\"pmid\">16095999</pub-id></mixed-citation></ref><ref id=\"B5\"><label>5.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Lynch</surname><given-names>C J.</given-names></name></person-group>. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849223</article-id><article-id pub-id-type=\"pmc\">PMC7431882</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00768</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Aberrant Awake Spontaneous Brain Activity in Obstructive Sleep Apnea: A Review Focused on Resting-State EEG and Resting-State fMRI</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Wu</surname><given-names>Yue</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/925782/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Zhao</surname><given-names>Wenrui</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/524622/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Xinyuan</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/524617/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wan</surname><given-names>Xiaoyong</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/989623/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Lei</surname><given-names>Xu</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/230821/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University</institution>, <addr-line>Chongqing</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Key Laboratory of Cognition and Personality of Ministry of Education</institution>, <addr-line>Chongqing</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China</institution>, <addr-line>Chengdu</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Xi-Jian Dai, The Chinese University of Hong Kong, China</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Thomas Penzel, Charité—Universitätsmedizin Berlin, Germany; Lourdes DelRosso, University of Washington, United States</p></fn><corresp id=\"c001\">*Correspondence: Xu Lei <email>xlei@swu.edu.cn</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Sleep Disorders, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>768</elocation-id><history><date date-type=\"received\"><day>29</day><month>3</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Wu, Zhao, Chen, Wan and Lei.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Wu, Zhao, Chen, Wan and Lei</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>As one of the most common sleep-related respiratory disorders, obstructive sleep apnea (OSA) is characterized by excessive snoring, repetitive apnea, arousal, sleep fragmentation, and intermittent nocturnal hypoxemia. Focused on the resting-state brain imaging techniques, we reviewed the OSA-related resting-state electroencephalogram and resting-state functional magnetic resonance imaging (rsfMRI) studies. Compared with the healthy control group, patients with OSA presented increased frontal and central δ/θ powers during resting-state wakefulness, and their slow-wave activity showed a positive correlation with apnea–hypopnea index. For rsfMRI, the prefrontal cortex and insula may be the vital regions for OSA and are strongly related to the severity of the disease. Meanwhile, some large-scale brain networks, such as the default-mode network, salience network, and central executive network, play pivotal roles in the pathology of OSA. We then discussed the contribution of resting-state brain imaging as an evaluation approach for disease interventions. Finally, we briefly introduced the effects of OSA-related physiological and mental diseases and discussed some future research directions from the perspective of resting-state brain imaging.</p></abstract><kwd-group><kwd>obstructive sleep apnea</kwd><kwd>resting-state</kwd><kwd>electroencephalography</kwd><kwd>functional magnetic resonance imaging</kwd><kwd>brain activity</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"65\"/><page-count count=\"12\"/><word-count count=\"9051\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>As the most common sleep-related breathing disorder, obstructive sleep apnea (OSA) is characterized by excessive snoring, repetitive episodes of apnea, and arousal during various sleep stages. It can lead to severe sleep fragmentation and intermittent nocturnal hypoxemia (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>), which may result in excessive daytime sleepiness and increase the incidence of diabetes, hypertension, congestive heart failure, stroke, and cardiovascular disease (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>–<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Existing epidemiological studies indicate that OSA is highly prevalent; ~1 billion people in the 30- to 69-years age group may be affected by OSA, and the prevalence rate exceeds 50% (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Considering the striking prevalence of this disorder in the general population, the underlying neuro mechanism, however, remains largely unknown.</p><p>Over the last two decades, different kinds of techniques, including functional magnetic resonance imaging (fMRI), electroencephalography (EEG), positron emission tomography, magnetoencephalography, and functional near-infrared spectroscopy, have been widely used to investigate the neurophysiological characteristics of OSA. And the EEG-derived polysomnography (PSG) has been considered as the gold standard in the clinical diagnosis of sleep disorders. Among these techniques, EEG and fMRI can collect and analyze data more efficiently, which may have greater application value in the clinical diagnosis of diseases.</p><p>With considerable development and wide application of neuroimaging technology, more researchers use resting-state brain imaging to explore the dysfunction of the patient's brain (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Resting-state brain imaging, especially resting-state EEG (rsEEG) and resting-state fMRI (rsfMRI), was widely concerned for its convenience in operation and straightforward interpretation. During brain scanning, patients just need to lie down or sit quietly for about 5–10 min. The instructions invite the participants to stay relaxed in a state of mind wandering with eyes closed or keep on looking at a cross with eyes open (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). After the recording, the corresponding brain activity and functional connectivity (FC) can be obtained, which may help the clinicians to diagnose and treat the disease. There are some advantages for resting-state brain imaging. First, resting state requires no task-related stimulation, and it has limited requirements on patient's cooperation and interviewer's experience. It also reduced the influence of some irrelevant variables, for example, the familiarity for experiment materials. Second, the results are not dependent on the experimental paradigm, which is conducive to compare among multigroups or cross-center data (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Third, compared with whole-night PSG, it requires a relatively short time to record. Therefore, the research on resting-state brain imaging is becoming popular, and many big data platforms have been built from the fields of psychology, neuroscience, and clinical radiology (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p><p>The aim of our review is to summarize the research progress of OSA in the field of resting brain imaging and make suggestions for further research. First, we summarize different kinds of rsEEG and fMRI analytic methods used in the investigation of OSA. Second, we outline the main modality-specific results of OSA research in EEG and fMRI, respectively. Last but not least, current status and future directions of the research of OSA were prospected from the aspects of comorbidities of OSA and some new emerging techniques, such as EEG-fMRI, machine learning, and comorbidity.</p></sec><sec id=\"s2\"><title>Resting-State EEG and Resting-State fMRI</title><p>Neuroimaging techniques have made tremendous progress in the last two decades. Electroencephalography and fMRI are both non-invasive techniques, and more importantly, both have been installed in many research centers and hospitals. At present, rsEEG and rsfMRI are the most widely utilized techniques. Here we focus our review on these two modalities.</p><sec><title>Resting-State EEG</title><p>As a technique to record the electrophysiological activity of the brain, EEG possesses multiple advantages over other techniques, including high temporal resolution, non-invasiveness, and relatively lower costs. Many investigations employed rsEEG to explore its clinical values for diagnosing or treating OSA. Interestingly, both eyes-closed and eye-open conditions are usually recorded for rsEEG. However, the eyes-closed condition is more widely used, because data in this condition are less contaminated by eye-blinking artifact (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>).</p><p>As illustrated in <xref ref-type=\"fig\" rid=\"F1\">Figures 1A,B</xref>, there are two common analysis methods for rsEEG: power spectrum analysis (PSA) and EEG microstate analysis. Power spectrum analysis is employed to calculate the EEG power of different frequency bands. Electroencephalography signals can be converted from time domain to frequency domain by Fourier transformation, and the rhythms associated with specific neural functions can be extracted and quantified. These rhythms are mainly δ (1–4 Hz), θ (4–8 Hz), α (8–13 Hz), β (13–30 Hz), and γ (>30 Hz) (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Although some features of PSA are widely used in the researches of OSA, many other features, such as alpha peak frequency, left–right asymmetries, and scale-free properties, are rarely investigated in OSA-related studies.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Data analysis methods in resting-state neuroimaging. <bold>(A)</bold> EEG microstates; <bold>(B)</bold> EEG and fMRI power spectra, notice the difference in frequency ranges for each modality; <bold>(C)</bold> large-scale brain networks: DMN, SN, and CEN, and their key regions; <bold>(D)</bold> ReHo; <bold>(E)</bold> small-worldness. ReHo, regional homogeneity; ALFF, amplitude of low-frequency fluctuation; L, left; R, right; DMN, default-mode network; MPFC, medial prefrontal cortex; PCC, posterior cingulate cortex; AG, angular gyrus; SN, salience network; DACC, dorsal anterior cingulate cortex; AIns, anterior insula; CEN, central executive network; DLPFC, dorsolateral prefrontal cortex; PPC, posterior parietal cortex.</p></caption><graphic xlink:href=\"fneur-11-00768-g0001\"/></fig><p>Electroencephalography microstate analysis investigates brain activity at quasi-stable states of ~100-ms duration. Based on the clustering of the EEG topography over time, some recurring and stable topographies were identified during resting state (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Because the duration of the EEG microstate is similar to the duration of a single thought, it was assumed to represent the “atom” of thought (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Currently, EEG microstate analysis is employed to explore brain changes in mental diseases such as insomnia (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>), narcolepsy (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>), and schizophrenia (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). However, for our knowledge, there is no study investigating the changes of EEG microstates in patients with OSA.</p><p>At present, PSA is the most common analysis method in rsEEG study of OSA. In contrast, many other advanced EEG analysis methods, such as source location (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>), brain network analysis (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>), and detrended fluctuation analysis (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>), are rarely utilized for the studies of OSA. Meanwhile, these advanced analysis methods faced more difficulties in the diagnosis of OSA, because they have higher requirements for hardware, software, and experience. In the future, greater opportunity and challenge may lie in the application of these new methods for the study of OSA. Based on experience and existing research results, we believe that different research methods have different potentials in clinical application, PSA may have higher application value, and the clinical application value of microstate may be low.</p></sec><sec><title>Resting-State fMRI</title><p>Magnetic resonance imaging was utilized to collect signals of the whole brain with various scanning sequences. Resting-state fMRI is mostly based on the sequence of echoplanar imaging, which reflects the low-frequency spontaneous oscillations (typically 0.01–0.08 Hz) of blood oxygenation level–dependent (BOLD) signals. Brain regions with synchronous BOLD oscillations constitute a large-scale brain network. For neuroimaging of OSA, some large-scale brain networks receive more attention, such as central executive network (CEN), default-mode network (DMN), and salience network (SN) (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). As illustrated in <xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>, we listed these three networks and their key regions in the brain.</p><p>Most of the previous rsfMRI studies of OSA focused on the local properties (<xref ref-type=\"fig\" rid=\"F1\">Figures 1B,D</xref>), that is, the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo). The former measures the spontaneous fluctuations of BOLD signal, whereas the latter focuses on the similarity of the regional signals (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). An interesting topic is a relationship between the power spectrum of EEG and the power spectrum of fMRI. As illustrated in <xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>, the ALFF focused on a very slow oscillation in fMRI. In contrast, EEG has a wide range of spectrum. They may represent similar neural activity in the frequency range of 0.01 to 0.1 Hz.</p><p>Functional connectivity, another commonly used method in rsfMRI, focuses on the statistical correlation between signals in different brain regions. In fact, FC can be divided into two categories: first, seed-based analysis, a model-based region-of-interest (ROI) analysis, requires determining the ROIs in advance based on previous studies or other experiments. Then, a typical process is to calculate the correlation coefficient among the seed regions or with the whole brain (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Second, independent components analysis (ICA), a model-free analysis, separates BOLD signals into multiple sets of spatiotemporal components. For spatial ICA, the component is spatially independent of each other and constructs a large-scale brain network (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). In recent years, dynamic FC, an extension of static FC analysis, has been developed in rsfMRI. The duration of signal for estimating dynamic FC is a little short, usually <40 s. Dynamic FC may be a new method for the study of OSA (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>).</p><p>Graph theory analysis is occasionally adopted in OSA studies (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). It provides a relatively simple but powerful quantitative framework to describe whole human brain networks (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). For graph analysis, the nodes can be voxels, ROIs, or even large-scale brain networks. As illustrated in <xref ref-type=\"fig\" rid=\"F1\">Figure 1E</xref>, small-worldness is a frequently discussed parameter. With a large clustering coefficient and small average shortest path length, whole brain is statistically imitated as a small-world network.</p><p>At present, a growing body of analysis methods has been developed and applied in the study of OSA, which will undoubtedly promote the further exploration of the mechanism of this disease. More importantly, the reliability and validity of these methods are still worth to verify. Besides, based on experience and existing research results, we believe that different fMRI research indicators have different clinical research potentials. For example, graph theory and FC may have lower clinical potentials, whereas ALFF and ReHo may have higher application value.</p></sec></sec><sec id=\"s3\"><title>Resting-State Neuroimaging of OSA</title><p>In order to systematically investigate the application of resting brain imaging in OSA, we conducted our search in the Google Scholar, Scopus, and PubMed databases in April 2020 to systematically explore studies using rsEEG and rsfMRI in patients suffering from OSA. The language screening standard of this article is English. The keywords were “(functional magnetic resonance imaging OR fMRI), (electroencephalography OR EEG), (resting state OR rest) and (sleep-related breathing disorders OR sleep apnea OR OSA).” A total of 50 studies were retrieved from the database. Subsequently, we excluded studies that included other technology (<italic>n</italic> = 3), reviews (<italic>n</italic> = 2), and studies that were not related to the main topic of the present review (<italic>n</italic> = 21). The final results included 1,616 participants, with the age range from 4 to 89 years. Among all studies, 8 were about rsEEG (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>), and 16 were about rsfMRI (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>). There are more studies based on rsfMRI when compared with rsEEG.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Resting-state EEG studies of obstructive sleep apnea.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Number of P/C</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AHI of P</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Age of P/C</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Spectrum change (patients vs. controls)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Morisson et al. (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">21/10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">62.9 ± 26.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">44 ± 7/44 ± 6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑δ, θ activity(frontal)<break/> ↑(δ + θ)/(α + β) (frontal, central, parietal, occipital, temporal)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Morisson et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14/10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">62.8 ± 25.8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45 ± 6.4/44.2 ± 6.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑δ absolute activity in OSA (frontal) <break/> ↑(δ + θ)/(α + β) (frontal, central)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mathieu et al. (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(12/13)<break/> [young] <break/> (13 /14) <break/> [old]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">46.9 ± 20.3<break/> [young] <break/> /42.8 ± 24.7<break/> [old]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">38.2 ± 6.4/35.8 ± 8.9 <break/> 62.2 ± 5.6/60.2 ± 6.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑(δ + θ)/(α + β) (frontal, central, parietal, occipital, temporal) in both young and old groups</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Grenèche et al. (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12/8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">66.1 ± 11.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51.2 ± 2.5/49.4 ± 3.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑δ, θ, β power</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Xiromeritis et al. (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(28/35/68)/30</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10.4 ± 3(mild)/21.3 ± 4.1(moderate)/64.4(severe) ± 18.7(control)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">(46.2 ± 11.4/51.3 ± 8.5/49.6 ± 10)/46.7 ± 11.8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑θ, δ power (occipital, temporal, parietal)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Baril et al. (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12/12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">51.2 ± 23.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">47.9 ± 13.7/44.4 ± 9.5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No change</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">D'Rozario et al. (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8/9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">49.8 ± 24.7 46.62 ± 7.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">44.6 ± 8.4/27.8 ± 3.7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑δ power</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Zeidy Muñoz-Torres et al. (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">22 [men]/21<break/>[women]/0 [control]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">49.8 ± 24.7 46.62 ± 7.1<break/>[men]/53.87 ± 7.5 [women]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">54.4 ± 2.4 [men]/57.3 ± 2.6 [women]</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓α power <break/> ↓β-γ power (frontal, central, moderate OSA, men vs. women) <break/> ↓δ power (frontal, central, occipital, severe OSA, men vs. women)</td></tr></tbody></table><table-wrap-foot><p><italic>P, patients; C, controls; AHI, apnea–hypopnea index (events/h); PSA, power spectrum analysis; ↑, increased; ↓, decreased; (δ + θ)/(α + β), slowing ratio</italic>.</p></table-wrap-foot></table-wrap><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Resting-state fMRI studies of obstructive sleep apnea.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Number of P/C</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>AHI of P</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Age of P/C</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Change (patients vs. controls)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Zhang et al. (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24/21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">54.7 ± 19.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">44.6 ± 7.4/40.6 ± 11.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FC: ↑PCC(pDMN); <break/> ↓MPFC(aDMN), DLPFC(CEN)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Santarnecchi et al. (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19/19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">36.3 ± 13</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">43.2 ± 8/41 ± 6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ReHo: ↑thalamus, somatosensory, motor <break/> ↓right temporal, parietal, frontal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Peng et al. (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25/25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">60.6 ± 18.6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">39.4 ± 1.7/39.5 ± 1.6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ReHo: ↑right posterior cerebellum, right cingulate gyrus, lateral lenticular nucleus, putamen, insula <break/> ↓nodes of DMN</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Zhang et al. (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">24/21</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">44.6 ± 7.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">44.6 ± 7.4/40.6 ± 11.4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓FC between right AIns and the nodes of the DMN: MPFC, ACC, SFG, IPL, ITG</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Li et al. (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">25/25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">60.0 ± 18.6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">39.4 ± 1.7/ <break/> 39.5 ± 1.6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ALFF: ↓hubs of DMN</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Taylor et al. (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19/17</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6 ± 2(no or mild)/28 ± 2(moderate or severe)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58 ± 4/57 ± 4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">No change</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Park et al. (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">67/75</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">35.6 ± 23.5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">48.0 ± 9.2/47.1 ± 9.3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↓global efficiency, weighting clustering coefficient and nodal centralities</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Park et al. (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">69/82</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">35.6 ± 23.3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">48.3 ± 9.2/47.6 ± 9.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FC changes between insular and many brain regions, PFC, parietal, temporal, cingulate gyrus, basal ganglia, thalamus</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Li et al. (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">40/40</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">59.5 ± 20.9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">38.6 ± 8.1/39.3 ± 7.5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑FC between left IPL and right IPL, and between the MPFC and left and right IPL <break/> ↓right hippocampus and the PCC, MPFC, and left MTL</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Li et al. (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">36/40</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">56.5 ± 19.0</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">39.0 ± 8.1/38.8 ± 11.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DC ↑lenticular nucleus, the putamen, posterior cerebellar <break/> ↓PCC, IPL, left SFG</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chen et al. (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">30/25</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">62.5 ± 19.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">38.3 ± 8.4/39.5 ± 8.0</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑normalized characteristic path length, local efficiency <break/> ↓normalized clustering coefficient, small-worldness, global efficiency</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chen et al. (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">45/45</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58.7 ± 20.38</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">37.56 ± 8.86/37.84 ± 11.38</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑characteristic path length <break/> ↓normalized clustering coefficient, small-worldness, global efficiency <break/> ↓nodal centralities of DMN,SN,CEN</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">13</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chen et al. (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">46/46</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">58.26 ± 20.37</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">20–60</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑nodal DC in the ventral medial PFC and the right parahippocampal cortex <break/> ↓clustering coefficient, local efficiency, and nodal centralities in the left PCC and DLPFC</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">14</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Huang et al. (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">29/26</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">33.67 ± 21.75</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">39.62 ± 9.95/<break/>34.46 ± 9.97</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑characteristic path length <break/> ↓clustering coefficient, local efficiency, global efficiency</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">15</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Song et al. (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">70/89</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">36.0 ± 23.3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">48.3 ± 9.2/<break/>46.4 ± 9.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑FC among hippocampus, precuneus, PCC <break/> ↓FC nodes of DMN (IPL, AG)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">16</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Yu et al. (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">40/40</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">60.1 ± 20.45</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">37.0 ± 8.74</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">↑FC among left DA, anterior lobe of the cerebellum, among left VA, left IFG and left STG, between right VA and left IFG <break/> ↓FC between right DA and right PFC</td></tr></tbody></table><table-wrap-foot><p><italic>P, patients; C, controls; AHI, apnea–hypopnea index (events/h); ICA, independent component analysis; ReHo, regional homogeneity; FC, functional connectivity; DC, degree centrality; ↑, increased; ↓, decreased; DMN, default-mode network; aDMN, anterior DMN; pDMN, posterior DMN; MPFC, medial prefrontal cortex; PCC, posterior cingulate cortex; CEN, central executive network; DLPFC, dorsolateral prefrontal cortex; AIns, anterior insula; ACC, anterior cingulate cortex; SFG, superior frontal gyrus; IPL, inferior parietal lobe; ITG, inferior temporal gyrus; MTL, medial temporal lobe; ODI, oxygen desaturation index; MOG, Middle occipital gyrus; SMA, Supplementary motor area; MCC, middle cingulate cortex; MTG, medial temporal gyrus</italic>.</p></table-wrap-foot></table-wrap><p>Previous studies mostly focused on the changes in brain activity in OSA patients during sleep. In this article, the resting state was defined as the rest state of wakefulness, and the changes in brain activity in the non-sleeping process were focused on.</p><sec><title>Application of rsEEG in OSA</title><p>Until now, only seven studies use rsEEG to explore the pathophysiological mechanism of OSA. The features that have been applied in OSA-related resting-state studies could be classified into two categories: the whole band spectrum and the slow-wave activity. According to the spectrum analysis, we found both absolute power and relative power were equally concerned. In addition, slow-wave activity has shown a consistently increasing trend. For the correlation between rsEEG features and PSG-derived OSA severity indicators, we found there are no positive findings.</p><p><xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> summarizes the primary features that were frequently used in OSA studies. The height represents the number of articles for some common features. Both absolute power and relative power were equally concerned, and there were two articles that reported both of them. Another phenomenon is that slow-wave activity is widely reported, with eight articles in total. However, three articles have found an increase in the slowing ratio, three articles have found that both δ and θ are elevated, and only one article has found lower sigma power in men than women in the severe OSA group. In addition, two articles focus on the rise of δ, but not on the changes in θ. Rather than the correlation with PSG indicators, most studies are comparison between OSA patients and health control groups.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>The primary features in resting-state EEG studies of obstructive sleep apnea. The height of the squares of different colors represents the number of articles related to special features.</p></caption><graphic xlink:href=\"fneur-11-00768-g0002\"/></fig><p>For rsEEG, spectrum powers in multiple bands are significantly altered for the OSA patients. Grenèche et al. found an increase in the δ, θ, and β powers, suggesting that maintaining wakefulness requires more cortical activities for OSA patients (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Six rsEEG studies explored the difference of spectrum power between OSA patients and healthy control group (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>–<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Among them, the enhancement of δ activity is a convergent phenomenon. However, very few studies, for example, Baril et al. (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>), found no power differences between groups. Five studies found increased low-frequency activity (δ and θ) in the frontal and central regions in patients with OSA compared to healthy controls (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>–<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Four studies calculated the slowing ratio (ratio of slow frequencies to fast frequencies) between OSA patients and normal controls. After regressing some confound factors such as weight, age, and education level, these studies found that OSA patients presented a steady increase in the slowing ratio (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>–<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>). In addition, one study found gender differences in brain activity characteristics in OSA. δ, β, and γ powers are lower for men than for women (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). In summary, the increasing power of the low-frequency band, especially the δ rhythm in the frontal and the central areas, is a relatively stable pathological feature for OSA. We speculated that the slow-wave enhancement might be one of the essential criteria for OSA diagnosis and treatment in the future.</p><p>To further explore the physiological and psychological mechanisms under abnormal EEG activity in OSA patients, some researchers investigated the relationship between the features of rsEEG and the severity of OSA. Positive correlations between δ and θ relative power and apnea–hypopnea index (AHI) were revealed in patients with moderate to severe OSA (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Another group reported that the slowing ratio in severe OSA patients was positively correlated with arousal index (ArI) and AHI (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Not only the slowing ratio, some researchers even found that AHI and α power were positively correlated in OSA patients, and oxygen desaturation index (ODI) was positively correlated with θ and α power. In addition, they found that daytime alertness efforts were related to ArI, and daytime sleepiness was related to ODI (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Other studies found no significant correlation between OSA severity and any rsEEG rhythm (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>, <xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Only two studies found a significant correlation between subjective sleepiness and rsEEG power. And regrettably, the results were inconsistent. In one study, Epworth Sleepiness Scale (ESS) was positively correlated with the δ, θ, and α relative powers in eyes-open condition, and the similar correlation was identified with the δ and α relative powers in eyes-closed condition (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Another study reported that the ESS was negatively correlated with δ and θ relative powers and positively correlated with α relative power, in eyes-closed and eyes-open conditions separately (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). In addition, no significant differences have been found between genders related to sleep and respiratory factors (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>).</p><p>In summary, the current results of PSA of rsEEG were relatively consistent. A predominant phenomenon was the increased low-frequency activity, especially in the frontal and central regions. Furthermore, this increased activity was related to daytime sleepiness, which may be caused by long-term hypoxia at night. However, no robust association was found between various indicators of OSA severity and the power of different rsEEG bands. Because of the small number of literature (only eight), a large-scale comparison is impossible in our current review.</p><p>Current studies of rsEEG in OSA patients faced some common problems, such as a small number of samples and lack of application of PSG. More importantly, many advanced analyses require more electrodes, so the high-density EEG devices may be necessary. However, the collection of rsEEG data using high-density EEG is a heavy burden for patients, especially patients with severe OSA. It is worth mentioning that PSG cannot completely replace high-density EEG, because the latter has a large number of features and wide coverage of electrodes. For OSA patients, the future study may further consider EEG source imaging based on high-density EEG and may reveal more physiological information with high spatial resolution (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Besides, the current studies on the resting-state of OSA patients focus on the differences between patients and normal people. Perhaps it may be considered to conduct research based on gender differences, age differences, and different high-risk inducers (such as drinking, obesity, etc.). At the same time, the changes in brain electrical activity of the same patient group in three different states of daytime resting-state, daytime sleepiness state, and night sleep state may also be very interesting.</p></sec><sec><title>Application of rsfMRI in OSA</title><p>The characteristics that have been applied in OSA-related resting-state studies could be generally classified into three categories: the local features, FC, and the graph theory. <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref> summarizes the primary features that were frequently used in OSA studies. There were three articles that used local features, such as ALFF and ReHo. In addition, we found that FC and ROI-based FC were widely employed to measure the aberrant brain synchronous activity of OSA patients. Graph theory is also increasingly utilized, and it is powerful for investigating the topological properties of the large-scale brain networks (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). In addition, there were three articles that used local features, such as ALFF and ReHo.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>The primary features in resting-state fMRI studies of disorders of consciousness. The height of the squares of different colors represents the number of articles related to the contents of the square. In conclusion, the interpretation of the other column charts is consistent with the <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>.</p></caption><graphic xlink:href=\"fneur-11-00768-g0003\"/></fig><p>For FC analysis, large-scale brain networks, especially the DMN, SN, and CEN, were the focus of the rsfMRI study of OSA (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). The DMN consists of the posterior cingulate cortex (PCC), precuneus, medial prefrontal cortex (MPFC), inferior parietal lobe (IPL), hippocampus, and angular gyrus (AG) and can be further divided into two modules, namely, the anterior DMN and posterior DMN. The main characteristic of the DMN is that it is inhibited during the goal-oriented task, but highly active at rest (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B22\" ref-type=\"bibr\">22</xref>). The CEN mainly includes the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC), and it is thought to be related to cognitive processes such as decision-making and working memory (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). The SN mainly includes the dorsal anterior cingulate and anterior insula (AIns), as well as some subcortical regions, such as the thalamus, striatum, and amygdala. Most of these brain regions are involved in emotions and goal-directed responses. During the execution of specific cognitive tasks, the above three brain networks always cooperate, and the moderation of the SN between the DMN and CEN may be the physiological mechanism of transition from resting state to cognitive processing state (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>).</p><p>From the perspective of the large-scale brain networks, functional abnormalities of DMN network were often reported in rsfMRI studies of OSA. Six studies have reported abnormal internal connectivity of the DMN, including changes in global and local characteristics of the DMN, FC, and modulation structure (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B35\" ref-type=\"bibr\">35</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>, <xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>). Among them, the decrease in FC between the anterior DMN and other nodes of DMN is a relatively consistent result. In contrast, it is still controversial whether the FC within the posterior DMN is abnormal or not (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>).</p><p>Not only the DMN but also the CEN and the SN networks were reported that they had the abnormal FCs in OSA patients. A dominate change is the FC between the prefrontal lobe and insula. A study led by Zhang found reduced internal FC of DLPFC in OSA patients. In addition, Yu et al. found reduced FC between the right dorsal amygdala (DA) and right PFC (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>, <xref rid=\"B48\" ref-type=\"bibr\">48</xref>). Three studies have found that insula, one of the key brain regions of the SN, has impaired or even broken FC with many other brain regions, including prefrontal cortex (PFC), parietal lobe, temporal lobe, and cingulate gyrus (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>). For example, a study suggested that the FCs between the right insula and multiple nodes of DMN were decreased. And the similar decrease can be observed in the FCs between the hippocampus and the dorsum medial thalamus, parahippocampal gyrus, and insula, which partly explained the declined working memory ability of OSA patients (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). In addition, the caudate nucleus has an abnormal FC with several nodes in the DMN, especially with the IPL and AG (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). In addition, abnormal FC was revealed in the amygdala subregion, as well as in the left DA and anterior cerebellum (including 4/5 vermis), left ventrolateral amygdala (VA), left inferior frontal gyrus (IFG), and left superior temporal gyrus (STG), and enhanced FC was identified between the right VA and left IFG (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). An analysis of ReHo indicators across the whole brain yielded similar results in the amygdala (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>).</p><p>Graph theory was utilized occasionally in the rsfMRI studies of OSA. The main work came from the groups of Park, Chen, and others. In Park's study, he and his colleagues found the decrease in global efficiency, weighted clustering coefficient, and node attribute of whole-brain region in OSA patients (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Compared to healthy controls, OSA patients showed decreased clustering coefficient and local efficiency, increased characteristic path length, and decreased node degree of left PCC and dorsal medial PFC, and the node degree of ventral PFC and right parahippocampal gyrus increased (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>, <xref rid=\"B46\" ref-type=\"bibr\">46</xref>). In addition, Chen et al. also found that changes in global and local network properties and changes in the properties of several major nodes reflect the abnormal connection between the three major networks and may be related to cognitive impairment (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). These studies are consistent with the results of previous studies on other indicators. Researchers speculated that chronic nocturnal hypoxemia leads to changes in small-world characteristics of patients' brain networks, which have certain effects on whole-brain function. Although the results of graph theory metrics are sometimes difficult to interpret from behavioral or pathophysiological points of view, the study of patients' small-world network properties and the evolution of their patterns may be one of the indicators to distinguish OSA severity.</p><p>In summary, the FC associated with the three large-scale brain networks and their major nodes is significantly altered for OSA patients. The decrease in the strength of connections between different networks may explain the decline in the related functions, whereas the enhancement of FC within some network nodes can be understood as functional compensation to some extent. The evidence from graph theory analysis, such as the increase in characteristic path length and the decrease in global efficiency, is also the evidence for the above opinion. Therefore, reduced FC between large-scale networks and enhanced FC within networks may be utilized as stable biomarkers of OSA disease, which is the distinctive evolution pattern of OSA's influence on brain function. In the future, with the support of more evidence, these FC-based biomarkers may be applied in the screening and diagnosis of diseases. In addition, the current research on OSA mainly focuses on the static local indicators and the calculation of the whole-brain network. Perhaps we can further focus on the dynamic changes of the above biomarkers and the more microscopic differences of these biomarkers in different frequency bands. At the same time, because it is difficult for OSA patients to stay awake for a long time, it is particularly important to explore the transition from wakefulness to daytime sleepiness in OSA patients.</p></sec><sec><title>RsfMRI of OSA: Relationship With Severity of Disease</title><p>To better understand the relationship between abnormal neuroimaging features and the severity of the disease, investigating the relationship between direct physiology indicators of OSA and quality features of brain imaging is undoubtedly quite important. Physiological indicators from PSG are more representative of OSA severity, especially AHI and ODI. For example, a significant positive correlation between the connections of brain and ODI was found (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). Other researchers, such as Park, demonstrated that the function of the connection of the left insula and precuneus, IFG, cingulate central operculum, and IFG is positively correlated with AHI, and the connections in the left insula and bilateral sense–motor areas, left middle temporal gyrus, left middle temporal gyrus, left anterior central gyrus, right posterior hippocampus, and right cerebellum were negatively correlated with AHI (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Zhang et al. found that the FC between the right AIns and MPFC was positively correlated with AHI, negatively correlated with the lowest saturation of blood oxygen (Sa<sc>o</sc><sub>2</sub>), and the internal FC of the right DLPFC was negatively correlated with AHI (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). The right DLPFC internal FC was negatively correlated with AHI (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). A Canadian group suggested that the FC of a small number of voxels in the right AIns is positively correlated with AHI (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>), and decreased FCs between the right and left hippocampus and bilateral dorsum medial thalamus were negatively correlated with AHI (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>).</p><p>Topological properties of brain networks were also correlated with AHI; for example, normalized clustering coefficient was negatively correlated with AHI, and normalized characteristic path length was positively correlated with AHI (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). In a word, according to the physiological indicators of PSG, we can verify the key role of PFC and insula in OSA research, which are closely related to the severity of OSA. Further research on PFC and insula may provide help for a more detailed diagnosis of OSA severity.</p></sec></sec><sec id=\"s4\"><title>Current Status and Future Directions</title><p>Currently, based on resting-state neuroimaging, the abnormal brain functions of OSA patients were detectable by both the EEG and fMRI techniques. However, clinical research is more challenging to control extraneous variables, and there are still many uncertainties in the study of the brain function of OSA patients. Therefore, many assumptions and research results need to be verified by more rigorous experiments and more exploratory discoveries. In the following part, the future directions of OSA research were discussed with several perspectives, including OSA-related interventions, the contribution of simultaneous EEG-fMRI, machine learning, and comorbidity.</p><sec><title>OSA-Related Interventions</title><p>Continuous positive airway pressure (CPAP) is the most effective and widely used treatment of OSA. The mechanism of CPAP is elevating collapsed upper airway tissue to prevent airway obstruction (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). It is known that CPAP recovers both AHI and oxygen saturation in OSA patients. However, the evidence from resting-state neuroimaging is very limited. A Korean group found that the slowing ratio in the whole-brain area decreased after treatment (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). Several studies have pointed out that the main finding on the rsEEG is the reduction of θ power (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>). For δ power, a study led by Xiromeritis et al. (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>) found a significant increase in δ power after treatment, whereas another study found the contrary conclusion (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). We speculate that different phases of CPAP intervention, potential individual variations, and patient tolerance may be the key reasons for the observed different response in resting-state neuroimaging. On the one hand, rsfMRI study that recorded both pre- and post-CPAP treatment is scarce. On the other hand, the current results of rsEEG are always inconsistent. Thus, it is worth discussing whether FCs within any large-scale brain network or any small-world network properties are improved after treatment. Furthermore, differences in treatment duration and frequency should be fully considered for the influence of CPAP treatment.</p><p>In conclusion, the various indicators of resting-state brain imaging can be used as a criterion for evaluating the efficacy before and after the intervention and promote a more in-depth understanding of the physiological mechanisms of the disease. Therefore, the application of resting-state neuroimaging is worthy of expectation for the development of clinical interventions.</p></sec><sec><title>EEG-fMRI</title><p>Obstructive sleep apnea affects patients' daytime behavior by affecting their sleep quality. Therefore, it is undoubtedly necessary to research OSA patients' sleep states, such as sleep quality, sleep structure, and the brain activity during sleep, which requires simultaneous PSG recording during fMRI scanning. Combined with the high spatial resolution of fMRI and the high temporal precision of EEG, simultaneous EEG-fMRI provides evidence from two perspectives, electrophysiology and blood oxygen metabolism, to explore abnormal brain activity in patients with OSA (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>).</p><p>With the development of experimental facilities, such as MRI, the combination of conventional MR hardware and advanced scan sequences makes the repetition time reduce to 400 ms or even less. Previous studies have discussed the temporal correlation between EEG waveform and BOLD signal (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>), suggesting that there are similarities between the characteristics of brain network patterns and spontaneous oscillations of α rhythms. Simultaneous EEG-fMRI may help researchers better understand the physiological mechanisms of OSA disease. And if we can find a certain relationship between the data of the two modalities on OSA, it will be of great reference value for the screening and diagnosis of diseases and even the subsequent intervention. However, because of the small number of relevant experimental studies, clinical application of this technology in diseases other than epilepsy still needs more exploration and attempts.</p></sec><sec><title>Machine Learning</title><p>Most of the existing studies identify or classify OSA patients by the characteristics of the EEG changes produced by the patients during the sleep stage. In addition, the signal of rsEEG indicates the abnormal electrophysiological activity of patients in different frequency bands (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). Therefore, the slowing ratio calculated by PSA may be used to screen OSA patients before routine medical diagnosis in the future (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>). A related short period of rsEEG measurement (usually ~5 min) can greatly reduce the workload of routine PSG diagnosis and save medical resources. However, its calculation and the norm for health people need to first be constructed.</p><p>In the field of diagnosis of clinical diseases, it becomes increasingly popular to achieve high-efficiency auxiliary diagnosis by the technology of machine learning. If this technology becomes established as part of routine clinical diagnosis, the resting-state neuroimaging may become an immensely valuable tool for clinical practice. Resting-state fMRI can measure patients' brain FC and also can further measure the small-world network parameters and topological properties. Multivoxel pattern analysis (MVPA) using algorithms, such as linear discriminant analysis and support vector machine, can more accurately distinguish the differences in activation patterns of brain regions in different states (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). By combining the MVPA and the resting-state information, patients and normal people can be distinguished (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). Similar work has been done in other diseases to identify some effective biomarkers in patients with Alzheimer disease (AD) (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>), bipolar disorder, and major depressive disorder (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). For example, a noteworthy study evaluated four major features by analyzing abnormal resting-state FC and achieved a classification accuracy of 76.1% in distinguishing major depression from healthy controls (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>). Therefore, the development of the above technology may play a role in the rapid diagnosis of OSA and the classification of subtypes in the future.</p></sec><sec><title>OSA-Related Comorbidity</title><p>For the middle-aged and elderly, impairment of daily function due to cognitive impairment may have a significant impact on the quality of life, whether it is caused by OSA or its associated complications. Among them, the decline in the attention, executive function and working memory ability of the elderly is particularly obvious (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Evidence from fMRI shows that the decreased activation of the anterior cingulate gyrus, dorsal frontal cortex, and PPC in patients with OSA leads to decreased working memory performance (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). The decrease in cingulate gyrus activity may be related to the execution of attention. Therefore, for the study of OSA patients, identifying the specific effect of comorbidities is very important. Besides, the interactions among the comorbidities and OSA are other important question. At present, some literature has reported the association between OSA and AD (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>) or related biomarkers (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>). Notably, because OSA patients tend to have multiple complications, it is difficult to fully control each of these complications in clinical practice, making it difficult to distinguish the neural substrate of cognitive impairment. Despite the difficulties, some studies also have tried to explore this aspect. After specifically recruiting OSA patients without complications, a study found that there was no significant difference in cognitive performance between the patient group and the healthy control group (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>). Although this study suggests that OSA patients' cognitive impairment may not originate from OSA itself, it is difficult to control the influence of intelligence, education, and other confound factors. And some of the comorbidities may be caused by damage to brain areas caused by chronic hypoxemia and poor sleep quality. Therefore, the traceability of cognitive impairment may also be closely related to the severity of OSA, and the complex interactions between them are difficult to distinguish and quantify. Thus, the above conclusions require a lot of research and large sample studies.</p></sec></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>This article summarizes the literature on rsEEG and rsfMRI studies of OSA so far and provides some suggestions for the better intervention of OSA deterioration and alleviating its impairment on cognitive functions. In summary, evidence from the rsEEG focused on the increased power of δ and θ in the frontal and central regions of the patient, whereas evidence from rsfMRI mainly found functional abnormalities within and between the three large-scale brain networks, that is, the DMN, the SN, and the CEN.</p><p>In terms of intervention therapy, we need to pay more attention to the following aspects: reducing the diagnosis cost of patients and improving the unhealthy lifestyles of patients. In terms of the physiological mechanism of diseases, the existing rsEEG research focuses more on the specific rhythms and the abnormity of power, but lacks the research on the connections in various brain regions, so methods such as functional connection and EEG source localization are required. Meanwhile, most of rsfMRI studies pay attention to abnormal activity of large-scale brain networks, and less research has related these abnormalities with the severity of the disease. In addition, the existing studies have not combined the evidence of both EEG and fMRI to explain the pathology of OSA. It is expected that this review will promote our understanding of the resting-state characteristics of neuroimaging studies in OSA.</p></sec><sec id=\"s6\"><title>Author Contributions</title><p>XL, WZ, and XW contributed conception and design of the study. YW performed the statistical analysis and wrote the first draft of the manuscript. XC wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.</p></sec><sec id=\"s7\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This research was supported by grants from the National Nature Science Foundation of China (31971028), Major Project of Medicine Science and Technology of PLA (AWS17J012), and Chongqing Research Program of Basic Research and Frontier Technology (cstc2017jcyjAX0110).</p></fn></fn-group><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Plant Sci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Plant Sci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Plant Sci.</journal-id><journal-title-group><journal-title>Frontiers in Plant Science</journal-title></journal-title-group><issn pub-type=\"epub\">1664-462X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849748</article-id><article-id pub-id-type=\"pmc\">PMC7431883</article-id><article-id pub-id-type=\"doi\">10.3389/fpls.2020.01206</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Plant Science</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Similar Arbuscular Mycorrhizal Fungal Communities in 31 Durum Wheat Cultivars (<italic>Triticum turgidum</italic> L. var. durum) Under Field Conditions in Eastern Canada</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Stefani</surname><given-names>Franck</given-names></name><xref ref-type=\"aff\" rid=\"aff1\">\n<sup>1</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/431118\"/></contrib><contrib contrib-type=\"author\"><name><surname>Dupont</surname><given-names>Sarah</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Laterrière</surname><given-names>Mario</given-names></name><xref ref-type=\"aff\" rid=\"aff3\">\n<sup>3</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/1050630\"/></contrib><contrib contrib-type=\"author\"><name><surname>Knox</surname><given-names>Ron</given-names></name><xref ref-type=\"aff\" rid=\"aff4\">\n<sup>4</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/812592\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ruan</surname><given-names>Yuefeng</given-names></name><xref ref-type=\"aff\" rid=\"aff4\">\n<sup>4</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/526910\"/></contrib><contrib contrib-type=\"author\"><name><surname>Hamel</surname><given-names>Chantal</given-names></name><xref ref-type=\"aff\" rid=\"aff3\">\n<sup>3</sup>\n</xref></contrib><contrib contrib-type=\"author\"><name><surname>Hijri</surname><given-names>Mohamed</given-names></name><xref ref-type=\"aff\" rid=\"aff2\">\n<sup>2</sup>\n</xref><xref ref-type=\"aff\" rid=\"aff5\">\n<sup>5</sup>\n</xref><xref ref-type=\"author-notes\" rid=\"fn001\">\n<sup>*</sup>\n</xref><uri xlink:type=\"simple\" xlink:href=\"https://loop.frontiersin.org/people/244954\"/></contrib></contrib-group><aff id=\"aff1\">\n<sup>1</sup>\n<institution>Ottawa Research and Development Centre of Agriculture and Agri-Food Canada</institution>, <addr-line>Ottawa, ON</addr-line>, <country>Canada</country>\n</aff><aff id=\"aff2\">\n<sup>2</sup>\n<institution>Département de Sciences Biologiques, Institut de Recherche en Biologie Végétale, Université de Montréal</institution>, <addr-line>Montréal, QC</addr-line>, <country>Canada</country>\n</aff><aff id=\"aff3\">\n<sup>3</sup>\n<institution>Quebec Research and Development Centre of Agriculture and Agri-Food Canada</institution>, <addr-line>Quebec, QC</addr-line>, <country>Canada</country>\n</aff><aff id=\"aff4\">\n<sup>4</sup>\n<institution>Swift Current Research and Development Centre of Agriculture and Agri-Food Canada</institution>, <addr-line>Swift Current, SK</addr-line>, <country>Canada</country>\n</aff><aff id=\"aff5\">\n<sup>5</sup>\n<institution>AgroBioSciences, Mohammed VI Polytechnic University</institution>, <addr-line>Ben Guerir</addr-line>, <country>Morocco</country>\n</aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Sergio Saia, Council for Agricultural and Economics Research (CREA), Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Valentina Fiorilli, University of Turin, Italy; Manuela Giovannetti, University of Pisa, Italy</p></fn><corresp id=\"fn001\">*Correspondence: Mohamed Hijri, <email xlink:href=\"mailto:mohamed.hijri@umontreal.ca\" xlink:type=\"simple\">mohamed.hijri@umontreal.ca</email>\n</corresp><fn fn-type=\"other\" id=\"fn002\"><p>This article was submitted to Plant Symbiotic Interactions, a section of the journal Frontiers in Plant Science</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1206</elocation-id><history><date date-type=\"received\"><day>18</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>24</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Stefani, Dupont, Laterrière, Knox, Ruan, Hamel and Hijri</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Stefani, Dupont, Laterrière, Knox, Ruan, Hamel and Hijri</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Wheat is among the important crops harnessed by humans whose breeding efforts resulted in a diversity of genotypes with contrasting traits. The goal of this study was to determine whether different old and new cultivars of durum wheat (<italic>Triticum turgidum</italic> L. var. <italic>durum</italic>) recruit specific arbuscular mycorrhizal (AM) fungal communities from indigenous AM fungal populations of soil under field conditions. A historical set of five landraces and 26 durum wheat cultivars were field cultivated in a humid climate in Eastern Canada, under phosphorus-limiting conditions. To characterize the community of AMF inhabiting bulk soil, rhizosphere, and roots, MiSeq amplicon sequencing targeting the 18S rRNA gene (SSU) was performed on total DNAs using a nested PCR approach. Mycorrhizal colonization was estimated using root staining and microscope observations. A total of 317 amplicon sequence variants (ASVs) were identified as belonging to Glomeromycota. The core AM fungal community (i.e., ASVs present in > 50% of the samples) in the soil, rhizosphere, and root included 29, 30, and 29 ASVs, respectively. ASVs from the genera <italic>Funneliformis</italic>, <italic>Claroideoglomus</italic>, and <italic>Rhizophagus</italic> represented 37%, 18.6%, and 14.7% of the sequences recovered in the rarefied dataset, respectively. The two most abundant ASVs had sequence homology with the 18S sequences from well-identified herbarium cultures of <italic>Funneliformis mosseae</italic> BEG12 and <italic>Rhizophagus irregularis</italic> DAOM 197198, while the third most abundant ASV was assigned to the genus <italic>Paraglomus</italic>. Cultivars showed no significant difference of the percentage of root colonization ranging from 57.8% in Arnautka to 84.0% in AC Navigator. Cultivars were generally associated with similar soil, rhizosphere, and root communities, but the abundance of <italic>F. mosseae</italic>, <italic>R. irregularis</italic>, and <italic>Claroideoglomus</italic> sp. sequences varied in Eurostar, Golden Ball, and Wakooma. Although these results were obtained in one field trial using a non-restricted pool of durum wheat and at the time of sampling, that may have filtered the community in biotopes. The low genetic variation between durum wheat cultivars for the diversity of AM symbiosis at the species level suggests breeding resources need not be committed to leveraging plant selective influence through the use of traditional methods for genotype development.</p></abstract><kwd-group><kwd>arbuscular mycorrhizal fungal communities</kwd><kwd>durum wheat cultivars</kwd><kwd>plant breeding</kwd><kwd>symbiosis</kwd><kwd><italic>Triticum turgidum</italic> var. <italic>durum</italic></kwd></kwd-group><counts><fig-count count=\"5\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"68\"/><page-count count=\"15\"/><word-count count=\"8147\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Durum wheat (<italic>Triticum turgidum</italic> L. var. <italic>durum</italic> Desf.) is a major crop in Canada with an average annual production of 5.96 million tonnes from 2015 to 2019 (<xref rid=\"B58\" ref-type=\"bibr\">Statistics Canada, 2020</xref>), establishing Canada as the largest exporter of durum wheat in the world. The flour derived from durum grains is mostly used for the production of pasta, semolina bourghul, and breads. Durum wheat originates from the Fertile Crescent and was the major cultivated form of tetraploid wheat during the Hellenistic period ca. 2300 BP (<xref rid=\"B21\" ref-type=\"bibr\">Feldman and Kislev, 2008</xref>). It was introduced into Western Canada in the late 19<sup>th</sup> century where 80% of the production occurred in the Brown and Dark Brown soil zones (<xref rid=\"B48\" ref-type=\"bibr\">McCaig and Clarke, 1995</xref>). Farmers increased cultivation of durum wheat in Western Canada in the 1960s because it was less susceptible to stem rust compared to bread wheat varieties cultivated at that time. The first developed cultivar in Canada was Stewart 63 released in 1963. <xref rid=\"B48\" ref-type=\"bibr\">McCaig and Clarke (1995)</xref> estimated that the development of new cultivars through the Canadian durum breeding programs for the period 1960 to 1990 increased yields by about 25% compared to foreign cultivars available prior to Stewart 63. Gluten content, cadmium concentration, resistance to fungal pathogens (<italic>Fusarium</italic> head blight, leaf, and stem rust) and insect pests (wheat stem sawfly, wheat midge) were the main traits considered for developing new varieties (<xref rid=\"B14\" ref-type=\"bibr\">Dexter, 2008</xref>; <xref rid=\"B12\" ref-type=\"bibr\">Clarke et al., 2010</xref>).</p><p>The selection pressure applied in the 20<sup>th</sup> century on the new varieties of durum wheat was therefore driven by commercial purposes and high performance in high input systems (fertilizers, pesticides). For a long time, plants were considered as autonomous individuals and, as a consequence, breeding approaches completely overlook the complex microbial context of the soil environment in which crops grow. More specifically, breeding approaches do not take into account the performance of the newly developed varieties to recruit root mutualists.</p><p>The association of roots with microorganisms relies on intricate molecular crosstalk which results from long-term co-evolution between plant hosts and microbial partners (<xref rid=\"B39\" ref-type=\"bibr\">Lambers et al., 2009</xref>). Modification of the root exudates following domestication and breeding can shape different microbial communities. Selective breeding in wild emmer, domesticated emmer, and modern durum wheat has triggered changes in root exudate composition (<xref rid=\"B33\" ref-type=\"bibr\">Iannucci et al., 2017</xref>). Less is known on the effect of selective breeding on the microbial associations of durum than on common wheat (<italic>Triticum aestivum</italic> L.), its hexaploid relative. High-throughput sequencing of rhizospheric bacterial communities associated with different winter wheat cultivars showed a line effect on the structure of the bacterial communities, suggesting that these communities could be manipulated by wheat breeding (<xref rid=\"B15\" ref-type=\"bibr\">Donn et al., 2015</xref>; <xref rid=\"B46\" ref-type=\"bibr\">Mahoney et al., 2017</xref>).</p><p>Among beneficial root associates, arbuscular mycorrhizal fungi (phylum Glomeromycota) coevolve with plants since ~980 Ma – 600 Ma. Arbuscular mycorrhizal (AM) fungi colonize the root system of 72% of vascular plants (<xref rid=\"B7\" ref-type=\"bibr\">Brundrett and Tedersoo, 2018</xref>) where they form highly branched fungal structures called arbuscules (<xref rid=\"B56\" ref-type=\"bibr\">Smith and Read, 2010</xref>). Arbuscules are the sites for nutrient exchanges between both partners. AM symbionts obtain plant carbon and, in exchange, release mineral nutrients absorbed from the soil. Despite their limited species richness (334 species described so far, <uri xlink:type=\"simple\" xlink:href=\"http://www.amf-phylogeny.com\">www.amf-phylogeny.com</uri>), AM fungi, together with their associated microbiota, provide a range of essential services, from drought stress mitigation and disease prevention, to plant nutrient and water uptake, and the maintenance of biological soil fertility (<xref rid=\"B25\" ref-type=\"bibr\">Gianinazzi et al., 2010</xref>; <xref rid=\"B62\" ref-type=\"bibr\">Turrini et al., 2018</xref>). Fine tuning the interaction between the naturally occurring AM fungal communities and crop plants through plant breeding and appropriate agronomy, could improve the sustainability of agroecosystems (<xref rid=\"B1\" ref-type=\"bibr\">Bakker et al., 2012</xref>; <xref rid=\"B23\" ref-type=\"bibr\">Gan et al., 2015</xref>; <xref rid=\"B31\" ref-type=\"bibr\">Hijri, 2016</xref>).</p><p>Wheat has long been recognized as a crop with mixed responses to AM fungi, from negative, neutral, to positive effects. <xref rid=\"B30\" ref-type=\"bibr\">Hetrick et al. (1993)</xref> found strong dependence of winter wheat on mycorrhiza in cultivars released prior to 1950, but more variable responses in recently released cultivars. The authors suggested that cultivars released after 1950 had reduced dependence on mycorrhizae due to breeding performed under high fertility conditions. The impact of breeding on the mycorrhiza of durum wheat is less clear. In a ‘proof of concept’ experiment conducted under greenhouse conditions by the Canadian Government durum wheat breeding program (<xref rid=\"B55\" ref-type=\"bibr\">Singh et al., 2012</xref>), plant growth response to the model AM fungus <italic>R. irregularis</italic> DAOM 197198 varied among five cultivars (AC Morse, Commander, DT 710, Strongfield, Mongibelllo). Then, a thorough examination of the AM symbiosis formed between <italic>R. irregularis</italic> DAOM 197198 and five landraces and 27 modern cultivars (Canadian historical set) revealed that breeding had inconsistent effects on mycorrhiza development in durum wheat under greenhouse conditions. It led to the identification of cultivars with unimproved patterns of regulation of symbiotic development (e.g.: Commander, AC Pathfinder), and in a few cases, to cultivars (e.g.: Hercules, Wascana, Eurostar) with crippled regulation and poor plant performance in soil with high fertility (<xref rid=\"B16\" ref-type=\"bibr\">Ellouze et al., 2016</xref>). In a study that investigated the impact of these 32 cultivars on the structure of the AM fungal community in two field trials in the Canadian Prairies (Swift Current and Regina), <xref rid=\"B17\" ref-type=\"bibr\">Ellouze et al. (2018)</xref> reported significantly different relative abundance of the genus <italic>Paraglomus</italic> in the cultivars Ramsey (11%) and Strongfield (93.7%) and a significant effect of the cultivars on the structure of the AM fungal community in the rhizosphere, but not in the roots. However, this study was performed in the semiarid zone of Canadian prairie where moisture shortage could mask possible selective effects of cultivars on the AM fungal communities colonizing plant roots. Variation in soil moisture, the factor shaping the prairie ecosystems and more dramatically so the semiarid prairie (<xref rid=\"B29\" ref-type=\"bibr\">Hamel et al., 2006</xref>), was a confounding influence in this study. Moreover, the sequencing depth per sample was low with an average of 168 AM fungal pyrosequences per sample.</p><p>In order to overcome the abovementioned pitfalls and to discern possible differences in AM fungal community composition and root colonization percentages between genetically diverse durum wheat cultivars, a field trial seeded with five landraces and 26 cultivars released at different times in the history of durum wheat breeding was set up under a humid climate in Eastern Canada and the AM fungal community was thoroughly characterized by high-throughput sequencing. Based on the results from previous studies, we hypothesised that different field grown durum wheat cultivars associate with distinct AM fungal communities. The V3-V4 region of the nuclear 18S rRNA gene of AM fungi was sequenced to describe AM fungal communities located in bulk soil, rhizosphere soil, and roots, at anthesis.</p></sec><sec sec-type=\"materials|methods\" id=\"s2\"><title>Materials and Methods</title><sec id=\"s2_1\"><title>Experimental Field-Trial</title><p>The experimental field was set up in 2016 nearby the city of Lévis (Québec, Canada, GPS coordinates: 46°47′40′′N; 71°08′05′′W). The region is featured by a growing season of 140 to 150 days, a cool and humid climate with average temperatures of 12.5°C, 16.9°C, and 19.1°C in May, June, and July, respectively, according to the Environment Canada weather station (<uri xlink:type=\"simple\" xlink:href=\"https://climate.weather.gc.ca\">https://climate.weather.gc.ca</uri>) located at 3.5 km from the experimental field. These temperatures are similar to those recorded for the same months during the period 1981 to 2010 (11.0°C, 16.5°C, and 19.3°C).</p><p>The soil was a well-drained Saint-André gravelly loam (fragic, humo-ferric podzol or mixed, frigid typic dystrochrept, <xref rid=\"B57\" ref-type=\"bibr\">Soil Classification Working Group, 1998</xref>). Physical and chemical properties of soil are provided in <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Table S1</bold>\n</xref> as supplementary material. The field was previously used to grow switchgrass (<italic>Panicum virgatum</italic> L.) in 2014 to 2015. Glyphosate-Roundup® was applied, and the field was tilled in fall 2015. Harrowing and fertilization were carried out on May 11, 2016. The plots at time of sowing received 90 kg/ha of nitrogen (N) as calcium ammonium nitrate (27-0-0) and 37 kg/ha potassium (K) as potassium chloride (0-0-60). Phosphorous (P) fertilization was not applied in order to make P resource a limiting factor to favour the mycorrhizal association. Five landraces and 26 durum wheat cultivars (details about each cultivar is provided in <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Table S2</bold>\n</xref>) were seeded at a density of 118 seeds/m<sup>2</sup> using a 4-row cereal plot seeder on 12 May 2016. Seeds were obtained from the collection at Agriculture and Agri-Food Canada, Swift Current, SK. Each plot was 1 m × 1.7 m and four rows per plot were seeded with one out of the 31 cultivars. DyVel® herbicide (Dicamba) was applied at 1.25 L/ha on 7 June 2016 for weed control.</p><p>The field trial was arranged in a randomized complete block design, with four blocks, 31 cultivars per block, representing a total of 124 plots (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S1</bold>\n</xref>). Each block was layered in two rows of plots and a guard plot was seeded with cultivar AAC Cabri at both ends of each row.</p></sec><sec id=\"s2_2\"><title>Field Sampling</title><p>To characterize the AM fungal community associated with durum wheat, three biologically relevant compartments were sampled: bulk soil, rhizosphere, and roots. Sampling was performed at anthesis, on July 8 and 12, 2016, as follows: six plants were randomly selected from within rows 2 and 3 of each plot and dug out with a spade. The aboveground portion of each plant was cut off and discarded, and the root system was stored at 4°C in a cooler in the field and then at 4°C in a laboratory fridge until processing. Six soil cores were collected between rows 3 and 4 using a soil probe (2.5 cm in diameter, 15 cm long). For each plot, the six soil cores were sieved (mesh size 2 mm) and combined into a single composite sample. The root system of each of the six plants per plot was gently shaken to collect the rhizosphere soil. The six rhizosphere soil samples were sieved (mesh size 0.5 mm) and combined into a single composite sample. Finally, the root system of each of the six plants per plot was combined into a composite sample and rinsed, and the fine roots (≤ 1 mm thick) were cut into 1- to 2-cm-long fragments. Three subsamples were collected from each pool of root fragments: two subsamples were transferred into two plastic Shandon™ tissue cassettes (Thermo Scientific™) for root colonization analysis and one subsample was stored in 1.5 ml tubes. Composite samples of bulk soil, rhizosphere soil, and roots were stored at −80°C.</p></sec><sec id=\"s2_3\"><title>Root Colonization Analysis</title><p>The tissue cassettes containing the root fragments were stored in tap water acidified with a few drops of white vinegar at 4°C until all samples were processed. Root fragments were stained using the “ink and vinegar” technique (<xref rid=\"B64\" ref-type=\"bibr\">Vierheilig and Piché, 1998</xref>). Cassettes were boiled in 10% w/v KOH solution for 3 min, boiled in a 5% ink (Shaeffer black) and white vinegar solution for 3 min, soaked in acidified tap water for 20 min and stored in a 50% glycerol solution. Root samples were examined under a Zeiss Discovery V20 stereomicroscope coupled with an AxioCam ICC 5 camera (Carl Zeiss, Oberkochen, Germany). ZEN pro software v2012 (Carl Zeiss, Oberkochen, Germany) was used to digitize and visualize the root fragments. The percent of root length colonized was evaluated using the gridline intersection method (<xref rid=\"B26\" ref-type=\"bibr\">Giovannetti and Mosse, 1980</xref>). A total of 39,116 intersects were recorded. Colonization percentage was calculated as the ratio of colonized intersects divided by the total number of intersects and multiplied by 100.</p></sec><sec id=\"s2_4\"><title>DNA Extraction</title><p>The UltraClean™ soil DNA Isolation Kit (MoBio, Laboratories, Carlsbad, CA) was initially used to isolate DNA from soil samples (48 out of 124). However, due to MoBio Laboratories not manufacturing anymore that kit during the wet lab stage of the study, the PowerSoil™ DNA Isolation Kit (Qiagen, Hilden, Germany) was then used to isolate DNA for the remaining soil samples and the rhizosphere samples. <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Table S3</bold>\n</xref> provides the list of the soil samples which were analysed with one or the other kit and the absence of difference between the AM fungal communities recovered with each kit is shown in supplementary material <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Table S4</bold>\n</xref>. The manufacturer’s instructions for both kits were followed, except that soil and rhizosphere DNA was eluted in 50 µl for the PowerSoil kit. DNA extractions from soil and rhizosphere samples were performed in duplicate and the duplicates were pooled.</p><p>Root fragments were put in 2-ml tubes containing Tungsten Carbide Beads of 3 mm, cooled in liquid nitrogen and placed immediately in a TissueLyser II instrument (Qiagen) for crushing mechanically the roots. A DNeasy® Plant Mini Kit (Qiagen, Valencia, CA) was used following the manufacturer’s instructions except that the final elution was done in 75 μl instead of 100 μl, and the flow-through from the first elution was reused for the second elution rather than using fresh elution buffer. The quantity and quality of the DNA extracts were first assessed on 1.5% agarose gel stained with GelRed<sup>®</sup> (1:10000, Biotium, USA), run at 70 V for 60 min, and visualized using the Gel-Doc system (Bio-Rad Laboratories, Mississauga, ON). The quantity and quality of the DNA extracts were also assessed by means of Qubit Fluorometer 2.0 (Life Technologies, Burlington, ON, Canada), using the Qubit dsDNA HS assay kit. DNA extracts were stored at –20°C until use.</p></sec><sec id=\"s2_5\"><title>DNA Amplification and Illumina Library Preparation</title><p>The AM fungal communities were characterized using the primer pair AML1/AML2 (<xref rid=\"B41\" ref-type=\"bibr\">Lee et al., 2008</xref>) which targets the V3-V4-V5 variable regions of the nuclear 18S small subunit (SSU) ribosomal RNA gene. The amplification was performed in 20 μl of reaction mix in triplicate as follows: 1 μl of gDNA, 200 μM of each dNTP, 2 mM of Mg<sup>2+</sup>, 0.8 μM of each primer, and 2.5 U of Q5 HighFidelity DNA Polymerase (NEBNext<sup>®</sup> Q5 Hot Start HiFi PCR Master Mix). The thermocycling conditions were as follows: initial denaturation at 98°C for 30 s, 20 cycles at 98°C for 10 s, 64°C for 30 s, 65°C for 60 s, and final extension performed at 65°C for 5 min. The DNA was amplified in a Biometra TProfessional thermocycler (Biometra GmbH, Goettingen, Germany). The three amplicon replicates were pooled and purified using the QIAquick PCR Purification Kit (Qiagen, Valencia, CA) and eluted in 50 μl of elution buffer. This step is important to prevent interactions between the remaining primers during nested PCR. PCR products were visualized in a GelRed stained 1.5% agarose gel.</p><p>In order to comply with the sequencing length capacity of Illumina MiSeq<sup>®</sup> Reagent Kit v3 (2 × 300 bp), a new primer pair yielding a 490-bp-length amplicon (including primers) was designed to target the V3-V4 region of the nuclear 18S rRNA gene: nu-SSU-0450-5′ (5′- CGCAAATTACCCAATCCC-3′) and nu-SSU-0899-3′ (5′-ATAAATCCAAGAATTTCACCTC-3′). Primers were named according to the primer nomenclature system of <xref rid=\"B24\" ref-type=\"bibr\">Gargas and DePriest (1996)</xref>. The number in the primer name refers to the 5′ end position of the primer on the 18S sequence standard of <italic>Saccharomyces cerevisiae</italic> (GenBank accession Z75578). Primers were designed based on the guidelines provided by Integrated DNA Technologies (IDT Inc., San Diego, CA USA). Thermodynamic features of each primer are provided as supplementary material (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Table S5</bold>\n</xref>). Purified PCR products amplified with AML1/AML2 were used as templates for nested PCR. A one to three bp “heterogeneity spacer” was introduced between the 3′ end of the adapter and the 5′ end of the primer pair nu-SSU-0450-5′/nu-SSU-0899-3′ to dampen the effect of the low sequence diversity issue of the MiSeq platform (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Table S6</bold>\n</xref>, <xref rid=\"B19\" ref-type=\"bibr\">Fadrosh et al., 2014</xref>). The recipe for the amplification reaction was similar to the first-round PCR, except for the primer concentration which was 0.5 μM. The thermocycling conditions were as for the first round PCR except for the number of cycles which was reduced to 15 and the annealing temperature which was 59°C. The nested PCR was performed in triplicate and verified by electrophoresis on a GelRed-stained 1.5% agarose gel. Replicates were pooled.</p><p>Library preparation followed the protocol described in <xref rid=\"B59\" ref-type=\"bibr\">Stefani et al. (2020)</xref>. Briefly, the PCR products from the nested PCR were purified using Agencourt AMPure<sup>®</sup> XP beads (Beckman Coulter Inc., Indianapolis, IN, USA), normalized to 1 to 2 ng/μl with the SequalPrep™ Normalization Plate kit (ThermoFisher Scientific) and indexed using the Nextera index kit (Illumina, San Diego, CA, USA). Indexed amplicons were then purified and normalized. Purified indexed amplicons were quantified by qPCR using the LightCycler<sup>®</sup> 480 system (Roche Molecular Systems Inc., Branchburg, NJ, USA) with the KAPA library quantification kit for Illumina platforms (KAPA Biosystems, MA, USA) in order to determine the volume of each sample to make up a 1-nM amplicon pool for library preparation.</p><p>Paired-end sequencing (2 × 300 bp) was carried out using the Illumina MiSeq<sup>®</sup> sequencer for 500 cycles at the Molecular Technologies Laboratory of Agriculture and Agri-Food Canada Ottawa Research and Development Centre.</p></sec><sec id=\"s2_6\"><title>Bioinformatic Analyses</title><p>The bioinformatic workflow is illustrated in <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S2</bold>\n</xref> and its impact on the sequence dataset is described in the supplementary material. The raw demultiplexed sequences were processed in QIIME 2 v2020.2.0 (<xref rid=\"B5\" ref-type=\"bibr\">Bolyen et al., 2019</xref>). Paired-end sequences were denoised, dereplicated, and filtered for chimeras using the DADA2 plugin (<xref rid=\"B8\" ref-type=\"bibr\">Callahan et al., 2016</xref>), as implemented in QIIME 2. Sequences were trimmed in order to include only bases with quality scores > 35. The first 18 and 22 nucleotides of the 5′ end of the forward and reverse sequences, respectively, were trimmed. The 3′ end of the forward and reverse sequences was truncated at positions 266 and 261, respectively. Reads with number of expected errors higher than 1 were discarded. The number of sequences used to train the error model was set to 200,000. Amplicon sequence variants with a frequency of less than 0.1% of the mean sample depth were considered rare ASVs and removed. This threshold represents the MiSeq bleed-through between runs as reported by Illumina (<uri xlink:type=\"simple\" xlink:href=\"https://github.com/LangilleLab/microbiome_helper/wiki/Amplicon-SOP-v2-(qiime2-2020.2))\">https://github.com/LangilleLab/microbiome_helper/wiki/Amplicon-SOP-v2-(qiime2-2020.2))</uri>. De novo clustering using a threshold of 100% similarity was performed using <italic>vsearch</italic> (<xref rid=\"B52\" ref-type=\"bibr\">Rognes et al., 2016</xref>), as implemented in QIIME 2. This step was required because the use of degenerate fusion primers (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Table S6</bold>\n</xref>) introduced one to three extra nucleotides in read length and the use of DADA2 (as implemented in QIIME 2) would have produced different ASVs for identical sequences of variable length. <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S3A</bold>\n</xref> shows that the ASV richness in samples from each compartment was saturated with a sequencing depth of 5,000. <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S3B</bold>\n</xref> showed that the number of analysed samples was appropriate to characterise the field resident AM fungal communities. Each sample was rarefied to 5,000 sequences which retained 1,680,000 (32.9%) sequences in 336 (90.3%) samples and 303 (95.6%) of the amplicon sequence variants (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S4</bold>\n</xref>). The taxonomic identification of each ASV was performed using a backbone phylogenetic tree as described in <xref rid=\"B59\" ref-type=\"bibr\">Stefani et al. (2020)</xref>. The taxonomic assignment of each ASV is provided in <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Table S7</bold>\n</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S5</bold>\n</xref>.</p></sec><sec id=\"s2_7\"><title>Alpha-Diversity Analyses</title><p>AM fungal diversity was estimated <italic>via</italic> the number of ASVs as a proxy of species richness. Venn diagrams were produced using the package <italic>venndiagram</italic> v1.6.20 (<xref rid=\"B10\" ref-type=\"bibr\">Chen and Boutros, 2011</xref>; <xref rid=\"B11\" ref-type=\"bibr\">Chen, 2018</xref>). The matrix used to calculate the relative abundance of the main AM fungal clades per block and per compartment was obtained using the rarefied ASV table. The ASV table was converted into a “biom” file, and the taxonomic information was added using the command <italic>biom add-metadata</italic>. Then the command <italic>collapse_samples.py</italic> (QIIME 1 v1.9.1) was used to combine repetitions from each block per microbiome. The barplots were produced using the R package <italic>ggplot2</italic> v3.3.0 (<xref rid=\"B66\" ref-type=\"bibr\">Wickham, 2016</xref>). The core AM fungal community was calculated on the rarefied datasets (raw abundance) using the function <italic>core</italic> and <italic>plot_core</italic> from R package <italic>microbiome</italic> v1.8.0. (<xref rid=\"B38\" ref-type=\"bibr\">Lahti and Shetty, 2019</xref>). Detection and prevalence thresholds were set to 0 and 50, respectively. Within-sample (alpha) diversity was calculated using the sample size- and coverage-based rarefaction and extrapolation (R/E) of the Hill numbers of species, i.e., richness (q = 0), Shannon diversity (q = 1, the exponential of Shannon entropy), and Simpson diversity (q = 2, the inverse of Simpson concentration), as implemented in the R package <italic>iNEXT</italic> version 2.0.19 (<xref rid=\"B9\" ref-type=\"bibr\">Chao et al., 2014</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Hsieh et al., 2016</xref>). The Faith’s phylogenetic diversity index (<xref rid=\"B20\" ref-type=\"bibr\">Faith and Baker, 2006</xref>) was calculated using the QIIME 2 command <italic>qiime diversity alpha-phylogenetic</italic> on a RAxML phylogenetic tree that included 303 AM fungal ASVs as described in <xref rid=\"B59\" ref-type=\"bibr\">Stefani et al. (2020)</xref>. Heatmaps showing the relative abundance of the core ASVs by cultivar for each compartment were produced using the R package <italic>superheat</italic> v0.1.0 (<xref rid=\"B2\" ref-type=\"bibr\">Barter and Yu, 2017</xref>). Principal component analysis was visualized using the function <italic>fviz_pca_var</italic> from the R package <italic>factoextra</italic> v1.0.7 (<xref rid=\"B35\" ref-type=\"bibr\">Kassambara and Mundt, 2020</xref>).</p></sec><sec id=\"s2_8\"><title>Statistical Analyses</title><p>Linear models were used to investigate the effects of durum wheat cultivar and compartment (i.e. bulk soil, rhizosphere soil, and root) on the structure of AM fungal community. In order to analyse data sharing a similar distribution, variables (ASVs) with > 50% of non-zero values (i.e. core AM fungal community represented by 29 ASVs, hereafter identified as category ASVs<sub>50+</sub>), and variables with 10% to 50% of non-zero values (47 ASVs, category ASVs<sub>10-50</sub>) were analysed separately. Variables with less than 10% of non-zero values (227 ASVs, category ASVs<sub>10-</sub>) were ignored.</p><p>For the category ASVs<sub>50+</sub>, a principal component analysis (PCA) was realized on the correlation matrix with a varimax rotation. The function <italic>PCA</italic> from the R package <italic>FactoMineR</italic> v2.3, (<xref rid=\"B40\" ref-type=\"bibr\">Le et al., 2008</xref>) was used, with data scale to unite variance and nine principal components (74.6% of cumulative variance) retained based on the Kaiser criterion (i.e. with an eigenvalue higher than one). In order to avoid running the linear mixed effects analysis on 29 ASVs, a single ASV per principal component was selected with a saturation coefficient close to +1 or −1 (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S6</bold>\n</xref>). The linear mixed effects analysis was performed to investigate the relationship between the abundance of sequences and durum wheat cultivars (31), compartments (3) and ASVs (9). Cultivars, compartments, and ASVs (with interaction terms) were set as fixed factors and block, block × cultivar, and block × cultivar × compartment were set as random factors. A squared root transformation was performed on the outcome value to respect the assumptions of the model. Means are presented on the original scale, while the <italic>p</italic> values come from the model on transformed data. For the category ASVs<sub>10-50</sub>, ASVs were dichotomized as null or non-null values. Principal component analysis was realized on the tetrachoric correlation matrix. Three dimensions (58.9% of cumulative variance) were retained based on variance criteria (one dimension should explain > 10% variance), with eigenvalues ranking from 0.83 to 0.25. Again, a single ASV showing the highest positive or negative saturation coefficient was selected per dimension, leading to the selection of ASV021, ASV030 and ASV031. A generalized linear mixed model (GLMM), with a logit link, was then performed using the same technique as for the LMM. Finally, linear mixed models were also used to investigate the effects of cultivars and compartments on diversity indices (ASV richness, Shannon and Simpson diversity, Faith’s phylogenetic diversity index), with random effects for block and block × cultivar. Heterogeneous variances were modelled for Shannon diversity values. Linear mixed models were also used to test the effects of cultivars on the colonisation rate, with a random effect for block. In all LMM and GLMM, multiple comparisons using Tukey adjustment were done for significant effects.</p><p>Statistical analyses were done using R v3.6.3 (<xref rid=\"B50\" ref-type=\"bibr\">R Core Team, 2020</xref>), and the following packages: <italic>car</italic>\n<italic>v3.0-7</italic> (<xref rid=\"B22\" ref-type=\"bibr\">Fox and Weisberg, 2019</xref>), <italic>emmeans v1.4.6</italic> (<xref rid=\"B43\" ref-type=\"bibr\">Lenth, 2020</xref>), <italic>factoextra v1.0.7</italic> (<xref rid=\"B35\" ref-type=\"bibr\">Kassambara and Mundt, 2020</xref>), <italic>MASS v7.3-51.5</italic> (<xref rid=\"B63\" ref-type=\"bibr\">Venables and Ripley, 2002</xref>), <italic>mgcv v1.8-31</italic> (<xref rid=\"B67\" ref-type=\"bibr\">Wood, 2004</xref>), <italic>moments v0.14</italic> (<xref rid=\"B37\" ref-type=\"bibr\">Komsta and Novomestky, 2015</xref>), <italic>nlme v3.1-144</italic> (<xref rid=\"B49\" ref-type=\"bibr\">Pinheiro et al., 2020</xref>), <italic>psych v1.9.12.31</italic> (<xref rid=\"B51\" ref-type=\"bibr\">Revelle, 2020</xref>), <italic>reshape v0.8.8</italic> (<xref rid=\"B65\" ref-type=\"bibr\">Wickham, 2007</xref>), and <italic>sjmisc v2.8.4</italic> (<xref rid=\"B44\" ref-type=\"bibr\">Lüdecke, 2018</xref>).</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec id=\"s3_1\"><title>Characterization of the AM Fungal Community Associated With Durum Wheat</title><p>A total of 303 ASVs belonging to Glomeromycota were recovered in the rarefied dataset. About 50% of the 303 ASVs were shared between the bulk soil, rhizosphere soil, and root samples (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1A</bold>\n</xref>). The number of ASVs recorded was relatively similar between soil (242 ASVs), rhizosphere (226 ASVs) and root (214 ASVs) compartments. The AM fungal community was dominated by the genera <italic>Funneliformis, Claroideoglomus, Paraglomus</italic>, and <italic>Rhizophagus</italic> (<xref ref-type=\"fig\" rid=\"f1\">\n<bold>Figure 1B</bold>\n</xref>). Sequences from the genus <italic>Funneliformis</italic> were the most abundant (19 ASVs), with a relative abundance ranging from 30% in roots to 45% in soil. ASV001 was the most abundant (21.6% of sequences) and the sequences were homologous to the 18S sequence of <italic>F. mosseae</italic> BEG12. The clade Claroideoglomus-7 was the second most abundant in bulk soil (12%) and rhizosphere soil (22%). It included 9 ASVs with sequence similarities close to the species <italic>C. claroideum, C. etunicatum, C. lamellosum</italic>, and <italic>C. luteum</italic> (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S5</bold>\n</xref>). The clade Paraglomus-1 (8 ASVs) was the third most abundant in bulk soil (11%) and rhizosphere soil (11%). A BLAST search for these 8 ASVs showed that their sequences were closely related to 18S sequences assigned to <italic>P. occultum</italic> (MN793990) and <italic>P. laccatum</italic> (MN517120). However, these ASVs did not cluster with the 18S sequences from well identified herbarium cultures of <italic>P. occultum</italic> (HA771 and IA702, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S5</bold>\n</xref>). The clade Archaeospora-1 (14 ASVs) was well represented in rhizosphere soil with an average relative abundance of 10%. It is interesting to observe the increasing abundance of the clade Rhizophagus-1 (21 ASVs) from bulk soil (4.1%), to rhizosphere soil (9.5%), to roots (30.6%). The clade Rhizophagus-1 included sequences of well-identified herbarium cultures of species <italic>R. irregularis</italic>, <italic>R. vesiculifer</italic>, and <italic>R. fasciculatum</italic> (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S5</bold>\n</xref>).</p><fig id=\"f1\" position=\"float\"><label>Figure 1</label><caption><p>\n<bold>(A)</bold> Venn diagram showing overlap of amplicon sequence variants (ASVs) between soil, rhizosphere, and root compartments. The size of the circles is proportional to the number of ASVs recorded in each compartment. Each sample was randomly subsampled to a common sequencing depth of 5000 sequences. <bold>(B)</bold> Taxonomic profile of AM fungi recovered in soil, rhizosphere and root compartments for each block. The clades with a relative abundance < 1% are not shown.</p></caption><graphic xlink:href=\"fpls-11-01206-g001\"/></fig><p>The core AM fungal community (i.e. ASVs present in > 50% of the samples) included 29, 30 and 29 ASVs for the bulk soil, rhizosphere soil, and root compartments, respectively (<xref ref-type=\"fig\" rid=\"f2\">\n<bold>Figure 2</bold>\n</xref>) and 29 ASVs (category ASVs<sub>50+</sub>) when the three compartments were considered together. These ASVs were assigned to 8 genera (<italic>Archaeospora, Claroideoglomus, Diversispora, Dominikia, Funneliformis, Glomus, Paraglomus, Rhizophagus</italic>), and represented five families (Archaeosporaceae, Claroideoglomeraceae, Diversisporaceae, Glomeraceae, Paraglomeraceae), four orders (Archaeosporales, Diversisporales, Glomerales, Paraglomerales) and three classes (Archaeosporomycetes, Glomeromycetes, and Paraglomeromycetes).</p><fig id=\"f2\" position=\"float\"><label>Figure 2</label><caption><p>Core community of arbuscular mycorrhizal fungi recovered in soil (29 ASVs), rhizosphere (30 ASVs) and root (29 ASVs) samples. The top five amplicon sequence variants recovered in root samples are highlighted to emphasize their ranking in rhizosphere and soil samples. ASVs are ranked on the ordinate axis from the most (top) to the least (bottom) prevalent in each compartment while they are ordered by ascending relative abundance on the abscissa axis.</p></caption><graphic xlink:href=\"fpls-11-01206-g002\"/></fig></sec><sec id=\"s3_2\"><title>Cultivar and Compartments Effects on AM Fungal Community</title><p>The lowest percentages of root colonisation were recorded in Arnautka (57.8% ± 14.8), Hercules (59.8% ± 6.7) and AC Pathfinder (62.8% ± 10.4) while the highest percentages were observed in roots from Transcend (77.3% ± 19), Enterprise (79.8% ± 7.3) and AC Navigator (84% ± 17.5, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S7</bold>\n</xref>). However, the effect of cultivars on the percentages of root colonisation was not significant (<italic>F</italic>\n<sub>30,90</sub> = 1.02, <italic>p</italic> = 0.45).</p><p>The average ASV richness per cultivar and compartment ranged between 50 and 100 (<xref ref-type=\"fig\" rid=\"f3\">\n<bold>Figure 3</bold>\n</xref>). Only cultivar Quilafen consistently showed low average ASV richness for each compartment (57, 49 and 46 for bulk soil, rhizosphere soil, and roots, respectively). No clear relationship in the average ASVs richness between compartment was observed for the other cultivars. For example, 57 ASVs were recorded in bulk soil samples under Arnautka while 97 ASVs were recorded in its roots. High number of ASVs in root samples does not mean high percentage of root colonisation because the percentage of root colonization was lowest in Arnautka (57.8% ± 14.8). The opposite situation was observed in Quilafen which had the lowest average number of ASVs in roots, and the fifth highest average rate of root colonization (76% ± 7.8).</p><fig id=\"f3\" position=\"float\"><label>Figure 3</label><caption><p>Diversity measured in soil, rhizosphere and root samples for each of the five landraces and 26 durum wheat cultivars. The ordinate displays Hill numbers of three orders: ASV richness (q = 0), Shannon diversity (q = 1), and Simpson diversity (q = 2), and Faith’s Phylogenetic diversity. Cultivars are sorted according to the mean ASV richness observed in root samples, from the lowest to the highest. Error bars represent estimated bootstrap standard error.</p></caption><graphic xlink:href=\"fpls-11-01206-g003\"/></fig><p>No significant effect of cultivars (<italic>F</italic>\n<sub>30,90</sub> = 1.23, <italic>p</italic> > 0.05) and compartments (<italic>F</italic>\n<sub>2,150</sub> = 1.90, <italic>p</italic> > 0.05) was observed on ASV richness (<xref ref-type=\"fig\" rid=\"f3\">\n<bold>Figure 3</bold>\n</xref>). No significant effect of cultivars (<italic>F</italic>\n<sub>30,90</sub> = 1.15, <italic>p</italic> > 0.05) and compartments (<italic>F</italic>\n<sub>2,150</sub> = 2.38, <italic>p</italic> > 0.05) was observed on Faith’s phylogenetic diversity index. A compartment effect was found significant on Shannon (<italic>F</italic>\n<sub>2,150</sub> = 17.7, <italic>p</italic> < 0.0001) and Simpson diversity (<italic>F</italic>\n<sub>2,150</sub> = 34.2, <italic>p</italic> < 0.0001). The Tukey <italic>post hoc</italic> tests showed that estimated marginal means of both indices were significantly lower (<italic>p</italic> < 0.0001) in roots than in bulk soil and rhizosphere soil samples (data not shown), which indicates a degree of dominance in the AM fungal community recorded in the root samples.</p><p>The heatmaps of the relative abundance of the 29 core ASVs by compartment and cultivar did not show major differences between cultivars for most of the ASVs (<xref ref-type=\"fig\" rid=\"f4\">\n<bold>Figure 4</bold>\n</xref>). A clear shift in the relative abundance of ASV001 (Funneliformis-1) was visible in each compartment but it did not involve the same groups of cultivars, with exception of Eurostar. The heatmap based on the root compartment clearly showed an antagonist behavior between ASV001 and ASV002 (Rhizophagus-1) for two clusters of 9 and 22 cultivars. The dendrograms of core ASVs community profiles were statistically not similar, according to the Bray-Curtis distance (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Table S8</bold>\n</xref>).</p><fig id=\"f4\" position=\"float\"><label>Figure 4</label><caption><p>Heatmaps showing the relative abundance of each of the 29-core amplicon sequence variants recorded in soil, rhizosphere, and root compartments for the five landraces and 26 durum wheat cultivars. Left: RAxML phylogeny of 29 core ASVs, black circles on the nodes represent bootstrap values > 70. The scale represents the branch length corresponding to expected substitutions per site. Top: cladogram showing the relationships between each cultivar based on the distance matrix calculated on ASV relative abundance.</p></caption><graphic xlink:href=\"fpls-11-01206-g004\"/></fig><p>The PCA performed on the 29 core ASVs (category ASVs<sub>50+</sub>) showed that ASVs from the genera <italic>Dominikia</italic> and <italic>Funneliformis</italic> were highly correlated with the first and second principal component, respectively (<xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figures 5A</bold>\n</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>S6</bold>\n</xref>). ASVs from the genera <italic>Rhizophagus</italic> (ASV002), <italic>Claroideoglomus</italic> (ASV010), <italic>Scutellospora</italic> (ASV32) and <italic>Paraglomus</italic> (ASV003) were correlated with the fourth, sixth, seventh, and eighth principal component, respectively (<xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S6</bold>\n</xref>). The linear mixed model analysis performed on one representative ASV per principal component showed that the two-way interactions cultivar × ASV (<italic>F</italic>\n<sub>240,1944</sub> = 1.4, <italic>p</italic> = 0.0003) and compartment × ASV (<italic>F</italic>\n<sub>16,1944</sub> = 71.4, <italic>p</italic> < 0.0001) were significant. The pairwise comparisons of estimated marginal means between cultivars were significantly different (<italic>p</italic> < 0.05) for ASV001 (Funneliformis-1), ASV002 (Rhizophagus-1), and ASV010 (Claroideoglomus-5), following Tukey’s correction. For ASV001, the estimated marginal mean calculated for Eurostar was significantly inferior to Commander, Macoun, and Plenty (<xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figure 5B</bold>\n</xref>). The same differences between these cultivars are expected for ASV005 and ASV012 since they are negatively correlated on principal component 2 (r = −0.925 and r = −0.917, respectively), similarly to ASV001 (r = −0.925, <xref ref-type=\"supplementary-material\" rid=\"SM1\">\n<bold>Figure S6</bold>\n</xref>). For ASV002, the estimated marginal means calculated for AC Napoleon, Lakota, and Mindum were significantly inferior to Wakooma. For ASV010, the estimated marginal means calculated for AC Avonlea, AC Pathfinder, CDC Verona, Eurostar, Golden Ball, Hercules, Macoun, Mindum, Strongfield were significantly inferior to cultivar Kubanka. Finally, the abundance recorded for six out of nine ASVs were significantly different between the compartments (<xref ref-type=\"fig\" rid=\"f5\">\n<bold>Figure 5C</bold>\n</xref>). Only ASV002 (Rhizophagus-1) was significantly more abundant in roots than in rhizosphere and bulk soil. ASV003 (Paraglomus-1), ASV008 (Funneliformis-2), and ASV010 (Claroideoglomus-5) followed the opposite dynamic since their abundance were significantly lower in roots compared to rhizosphere and/or soil. ASV001 (Funnliformis-1) was significantly less abundant in rhizosphere compared to soil and roots while ASV004 (Claroideoglomus-7) showed the opposite pattern. Finally, the generalized linear mixed model analysis performed on the category ASVs<sub>10-50</sub> showed no significant difference (data not shown).</p><fig id=\"f5\" position=\"float\"><label>Figure 5</label><caption><p>\n<bold>(A)</bold> Principal component analyses on 29 ASVs with non-zero values in > 50% of the samples across the three compartments (category ASV<sub>50+</sub>). The first two principal components explained 33.1% of variance. <bold>(B)</bold> Boxplots showing the abundance of the three ASVs for which significant differences were recorded between the pairwise comparisons of the 31 cultivars of durum wheat. Boxplots with a different number indicate significant differences between cultivars (<italic>p</italic> < 0.05). Boxplots were ordered by increasing mean abundance. <bold>(C)</bold> Boxplots showing the abundance of the six ASVs for which the significant differences were found between soil, rhizosphere and root compartments. Non-significant differences are not shown, ** and *** indicate <italic>p</italic> values < 0.01 and < 0.001, respectively.</p></caption><graphic xlink:href=\"fpls-11-01206-g005\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><sec id=\"s4_1\"><title>AM Fungal Communities Associated With Durum Wheat</title><p>Results from this study provide an in-depth overview of the AM fungal communities associating with wheat genotypes representative of Canadian durum, in a humid climate. The core AM fungal community associated with durum wheat recorded in Eastern Canada was relatively similar to that recorded in the Canadian prairie (<xref rid=\"B17\" ref-type=\"bibr\">Ellouze et al., 2018</xref>). Results from the current study and from <xref rid=\"B17\" ref-type=\"bibr\">Ellouze et al. (2018)</xref> showed that the genus <italic>Funneliformis</italic> was core for durum wheat. In the current study, a sequence homologous to that from the culture of <italic>Funneliformis mosseae</italic> BEG12 was the most abundant in soil, rhizosphere, and root compartments. In <xref rid=\"B17\" ref-type=\"bibr\">Ellouze et al. (2018)</xref>, <italic>Funneliformis</italic> (50.3% of the reads), <italic>Claroideoglomus</italic> (10.7%) and <italic>Dominikia</italic> (2.9%) were the main genera recorded in soil and root samples associated with five landraces and 32 durum wheat cultivars grown on the Brown and Black soils in the Canadian prairie. In the current study, the genera <italic>Claroideoglomus</italic> and <italic>Paraglomus</italic> were also part of the five most abundant genera recorded in each compartment. The genus <italic>Dominikia</italic> was also included in the core AM fungal community with a relative abundance ranging from 4% to 6% across the three sampled compartments. A sequence from an unknown <italic>Archaeospora</italic> species (98.5% of pairwise similarity with the 18S sequence of the herbarium culture of <italic>A. trappei</italic> NB112) was part of the 10 most abundant sequences in bulk soil, rhizosphere soil, as well as in roots. <xref rid=\"B17\" ref-type=\"bibr\">Ellouze et al. (2018)</xref> reported the genus <italic>Archaeospora</italic> was abundant in root samples from the Brown soil site only. None of the ASVs were assigned to the genus <italic>Gigaspora</italic>. It has been previously observed that taxa from <italic>Gigaspora</italic> and <italic>Scutellospora</italic> tended to disappear in agricultural field under conventional management such as tillage (<xref rid=\"B28\" ref-type=\"bibr\">Hamel et al., 1994</xref>; <xref rid=\"B4\" ref-type=\"bibr\">Boddington and Dodd, 2000</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Kabir, 2005</xref>). Here the experimental field was tilled in fall 2015 and harrowed in spring 2016. A total of 317 ASVs was recorded in the current study while 190 AM fungal OTUs were associated with durum wheat in the Prairies using a similarity threshold of 97% to cluster 18S AM fungal sequences. As discussed in <xref rid=\"B59\" ref-type=\"bibr\">Stefani et al. (2020)</xref> and in <xref rid=\"B54\" ref-type=\"bibr\">Schlaeppi et al. (2016)</xref>, 18S sequences from different AM fungal species are lumped together at a similarity threshold of 99%, making the ASVs approach the least inaccurate as a proxy for species for datasets based on the 18S sequences of the nuclear ribosomal DNA. However, using ASVs based on 18S sequences as a proxy for AM fungal species could overestimate species richness. For instance, within the clade Rhizophagus-1, 10 ASVs were closely related to the sequences from cultures of <italic>R. irregularis</italic> (DAOM 197198, MUCL 43195, and W4533). The pairwise similarities between these 10 ASVs ranged from 98.9% (5 diverging nucleotides) to 99.8% (1 diverging nucleotide). <xref rid=\"B45\" ref-type=\"bibr\">Maeda et al. (2018)</xref> determined that the genome of <italic>R. irregularis</italic> DAOM 181602 was characterized by ten rDNA paralogs which the mean intra-genomic similarity was 99.91% (SD = 0.06) for the 18S gene. Therefore, it is possible that 490 bp long 18S fragments diverging by a single nucleotide could represent paralogs from the same species. Similarly, ten ASVs closely related to the sequences from well characterised cultures of <italic>F. mosseae</italic> (BEG12, FL126, DAOM 212595) had pairwise similarities ranging from 99.6% to 99.8%, i.e. two to one nucleotide of divergence, respectively. With exception to <italic>R. irregularis</italic>, the number of rDNA paralogs and their intra-genomic variability remain unknown in the other species of AM fungi. While it is undetermined if a unique similarity threshold can be applied across the phylum Glomeromycota to accurately recognise species, a threshold ranging from 99.5% to 100% seems to be safe for not lumping into the same contig 18S sequences from closely related species.</p><p>ASV002 (100% of pairwise similarity with <italic>R. irregularis</italic> DAOM 181602/DAOM 197198) was the second most abundant sequence recorded in the root samples, while it was ranked in 11<sup>th</sup> position in bulk soil. Its relative abundance in roots was significantly more important than in rhizosphere and bulk soil compartments. This clearly shows the preferential selection of the host plant for <italic>R. irregularis</italic>. Compared to <italic>Glomus custos</italic> and <italic>G. aggregatum</italic>, <xref rid=\"B36\" ref-type=\"bibr\">Kiers et al. (2011)</xref> showed that <italic>R. irregularis</italic> (identified in the publication as <italic>G. intraradices</italic>) was the most cooperative species as it provided to its host the best rate of nutrient exchange (more phosphorus for less carbon), resulting in a host preference for resources allocation to <italic>R. irregularis</italic>. This makes <italic>R. irregularis</italic> a strong competitor in <italic>in vivo</italic> or <italic>in vitro</italic> system (<xref rid=\"B18\" ref-type=\"bibr\">Engelmoer et al., 2014</xref>), but also under field conditions as suggested by the data from the current study. Among the five most abundant sequences recorded in roots, two were from the genus <italic>Rhizophagus</italic> and two from the genus <italic>Funneliformis</italic>.</p><p>ASVs from all four AM fungal orders were recovered, showing that the nested PCR approach based on the primer set AML1/AML2 and the new primer set nu-SSU-0450-5′/nu-SSU-0899-3′ is able to target all AM fungal taxa. Moreover, non-AM fungal sequences represented only 6.1% of the sequences obtained after the quality filtering. <xref rid=\"B3\" ref-type=\"bibr\">Berruti et al. (2017)</xref> used a similar approach with a nested PCR based on the primer set AML1/AML2 for the first round PCR and the primer set AMADf/AMDGR (<xref rid=\"B53\" ref-type=\"bibr\">Sato et al., 2005</xref>) for the second round PCR to characterize the AM fungal community in roots and soils of three mountain vineyards. Both primer sets nu-SSU-0450-5′/nu-SSU-0899-3′ and AMADf/AMDGR target the V3-V4 regions of the 18S and are able to recover all known AM fungal lineages when used in combination with the AM fungi specific primer set AML1/AML2 (<xref rid=\"B41\" ref-type=\"bibr\">Lee et al., 2008</xref>). The new primer set nu-SSU-0450-5′/nu-SSU-0899-3′ amplifies a fragment slightly longer (490 bp) than the primer set AMADf/AMDGR (423 bp). This length is compatible with 2 × 300 paired-end sequencing and allows a 37 bp of overlap between forward and reverse reads once they were truncated in 3′ position due to decrease in quality.</p></sec><sec id=\"s4_2\"><title>Cultivar Impact on AM Fungal Communities</title><p>To our knowledge, results from this study provide the most comprehensive characterisation of the AM fungal communities associated with durum wheat under field conditions. However, our results were obtained in one field trial using a non-restricted pool of durum wheat and at the time of sampling, that may have filtered the community in bulk soils, rhizosphere soils, and roots. A total of 317 ASVs were recorded representing the four AM fungal orders, thus a filtering effect on the AMF pool due to a single site and sampling time is unlikely. The levels of AM fungal diversity were similar between each cultivar, in all three compartments examined. However, the results clearly show a differential affinity of some cultivars for ASVs related to <italic>F. mosseae</italic> (ASV001, ASV005, and ASV010), <italic>R. irregularis</italic> (ASV002), and <italic>Claroideoglomus</italic> sp. (ASV010). ASV001 and ASV002 were the most abundant in the whole dataset and were assigned to genera previously identified as predominant in the AM fungal community associated with durum wheat growing in the dry environment of the Canadian prairie (<xref rid=\"B17\" ref-type=\"bibr\">Ellouze et al., 2018</xref>). Cultivars with strong affinity for <italic>F. mosseae</italic> had less affinity for <italic>R. irregularis</italic> and vice and versa. Indeed, the abundance of ASV001 was the lowest in cultivars Eurostar, Wakooma, and Golden Ball while the abundance of ASV002 was among the highest for these cultivars. Moreover, the responsiveness of cultivars Eurostar and Golden Ball to ASV010 (<italic>Claroideoglomus</italic> sp.) was limited compared to the other cultivars. Overall results showed that the genotypic differences between the five landraces and 26 durum wheat cultivars had only a minor impact on the structure of the AM fungal community. This suggests that the symbiotic signalling system (<xref rid=\"B6\" ref-type=\"bibr\">Bonfante and Requena, 2011</xref>) and the molecules (i.e. flavonoids, strigolactones, <xref rid=\"B60\" ref-type=\"bibr\">Steinkellner et al., 2007</xref>) released by the durum wheat cultivars to initiate the mutualistic interaction with AM fungi are well conserved for each genotype and that the set of genes involved with the recognition of the Myc-factors (pre-physical contact stage) and with the establishment of the mycorrhization (post-physical contact stage) were only marginally altered through the breeding. <xref rid=\"B61\" ref-type=\"bibr\">Tian et al. (2019)</xref> showed that 2360 genes were differentially expressed in the roots of <italic>Triticum aestivum</italic> under the influence of the molecular signals produced by <italic>R. irregularis</italic>. <xref rid=\"B68\" ref-type=\"bibr\">Zhao et al. (2014)</xref> showed that the orchid mycorrhizae trigger in the host the induction of various genes involved with cell wall modification or defence-related phytohormone and phosphate transport. It is possible that the genotypic differences between durum wheat cultivars lead to slightly different molecular interactions with some AM taxa. This could result in less compatible partners featured by a less abundant fungal biomass, leading to less sequence count.</p><p>Despite Arnautka, Hercules, and AC Pathfinder cultivars were less colonized than Transcend, Enterprise, and AC Navigators ones, the analysis of the percentages of root colonisation showed non-significant variation within durum wheat cultivars. However, a spread of 26% between the cultivars showing the lowest and highest percentages of colonisation was observed. Because the phenotypic variation of these cultivars has not been characterized so far, one cannot exclude that some genotypes have had variable phenotypical traits (such as root branching, biomass, shoot branching) that could result in different level of root colonization. The non-significant differences observed in the percentages of root colonisation between cultivars grown under field conditions contrasts with experiments performed in greenhouse with commercial inoculum of <italic>R. irregularis</italic> DAOM 197198 (<xref rid=\"B55\" ref-type=\"bibr\">Singh et al., 2012</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Ellouze et al., 2016</xref>). <xref rid=\"B55\" ref-type=\"bibr\">Singh et al. (2012)</xref> inoculated five cultivars of durum wheat under low and medium fertility conditions. The type of cultivar was identified as having a significant effect on the percentages of root colonization at both low and medium soil fertility, and the cultivars showing percentages of root colonisation significantly lower or higher varied according to the levels of fertility. At low and medium fertility, the cultivar “Commander” had the highest and lowest percentages of colonisation respectively. The same trend was observed with “Commander” and “Pathfinder” in <xref rid=\"B16\" ref-type=\"bibr\">Ellouze et al. (2016)</xref>. In the current study, the cultivar “Commander” had the fifth lowest average colonisation percentage. In a pot trial carried out in greenhouse and involving a set of 94 bread wheat genotypes, <xref rid=\"B42\" ref-type=\"bibr\">Lehnert et al. (2017)</xref> reported significant genotypic differences with regard to root colonization with a blend of three AM species (<italic>Rhizophagus intraradices, Claroideoglomus claroideum</italic>, and <italic>C. etunicatum</italic>). The authors also identified 30 significant markers (representing six quantitative trait loci (QTL) regions) associated with root colonization, and they estimated that the heritability for root colonization was moderate. This suggests it is possible to improve root colonization by breeding. Similarly, <xref rid=\"B13\" ref-type=\"bibr\">De Vita et al. (2018)</xref> investigated the percentages of root colonisation and its genetic basis in the plant host by inoculating 108 durum wheat cultivars with <italic>F. mosseae</italic> and <italic>R. irregularis</italic>. They identified seven putative QTL associated with mycorrhizal susceptibility with each AM species and reported high variability in the percentage of root colonisation. These results suggest a complex genetic control of root colonisation.</p><p>Surprisingly, the percentages of root colonisation were very different between greenhouse experiments (<xref rid=\"B55\" ref-type=\"bibr\">Singh et al., 2012</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Ellouze et al., 2016</xref>; <xref rid=\"B13\" ref-type=\"bibr\">De Vita et al., 2018</xref>) and the current field-based study while the same approach based on gridline intersect method (<xref rid=\"B26\" ref-type=\"bibr\">Giovannetti and Mosse, 1980</xref>) was used. Indeed, the average colonisation percentage recorded across all the cultivars was high (71%), with a spread of 26% between the cultivars showing the lowest (Arnautka) and highest (AC Navigator) percentages of colonisation. The percentages of root colonisation recorded in <xref rid=\"B55\" ref-type=\"bibr\">Singh et al. (2012)</xref>; <xref rid=\"B16\" ref-type=\"bibr\">Ellouze et al. (2016)</xref> and <xref rid=\"B13\" ref-type=\"bibr\">De Vita et al. (2018)</xref> ranged between 5% and 45%. In their study on the effect of domestication on AM association at different fertility regimes, <xref rid=\"B47\" ref-type=\"bibr\">Martín-Robles et al. (2017)</xref> found better AM symbiotic development at low P fertility levels, in both domesticated crops and wild progenitors. However, P fertility was limited either in the greenhouse experiments as in the current field study. High percentages of root colonization here likely reflect other field conditions conducive to AM symbiotic development. The colonization percentages reported here are in line with what <xref rid=\"B27\" ref-type=\"bibr\">Graham and Abbott (2000)</xref> observed from “aggressive colonizers” at low P fertility (50–89% of root length colonization). In their case the aggressive colonizers included species such as <italic>Scutellospora calospora, Glomus invermaium, Acaulospora laevis</italic>, and <italic>Gigaspora decipiens</italic> inoculated onto specimens of Kulin wheat. These species triggered growth depression and reduced sucrose concentration in roots.</p></sec></sec><sec id=\"s5\"><title>Conclusion</title><p>Using deep 18S rDNA sequencing, the AM communities associating with the historical set of durum wheat genotypes in the field under an humid climate were comprehensively characterised and allowed to detect minor impacts of the cultivars on the structure of the AM fungal community. The hypothesis that different cultivars host distinct AM fungal communities is not supported in durum wheat, contrary to what some previous studies using other plant species have suggested. The genetic variation among durum wheat genotypes seems to be too narrow to select for specific plant-AM fungal associations from field resident AM fungal communities, using traditional breeding techniques. However, few cultivars had a differential responsiveness to <italic>F. mosseae</italic>, <italic>R. irregularis</italic>, and <italic>Claroideoglomus</italic> sp. Because the field trial was performed in a humid climate in Eastern Canada, results were not influenced by variation in soil moisture. This field trial along with the ones performed in the Canadian prairie examined the AM associations formed between durum wheat genotypes and resident AM fungi. In these three ecoregions, <italic>F. mosseae</italic> and <italic>R. irregularis</italic> were the main taxa recruited by durum wheat.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The Illumina data generated in this study were deposited in the NCBI Sequence Read Archive and are available under project number PRJNA645613.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>CH designed the project and supervised field work. RK and YR designed the project and provided the biological material. SD performed the sampling, the DNA isolation and estimated the root length colonization. FS designed the primers nu-ssu-0450-5′/nu-ssu-0899-3′. FS and MH supervised laboratory work. FS and ML analyzed the data. ML performed data visualization. FS wrote the paper. MH and CH edited and provided critical review of the manuscript. All authors contributed to the article and approved the submitted version.</p></sec><sec sec-type=\"funding-information\" id=\"s8\"><title>Funding</title><p>This work was supported by funding from the NSERC Discovery grant (RGPIN-2018-04178, MH) and from the Agriculture and Agri-Food Canada through the projects J-000617 (CH) and J-002272 (FS).</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><title>Acknowledgments</title><p>The authors are grateful to Dr Pierre-Luc Chagnon and Dr Marc St-Arnaud for insightful comments on an early draft of the manuscript; Réjean Desgagnés and Benoît Bérubé for help in field work; David Gagné for bioinformatic support; Lisa Koziol and Julie Chapados from the Molecular Technology Laboratory (Ottawa-RDC, AAFC), for MiSeq library preparation and Illumina sequencing. The authors thank the two reviewers for their useful comments.</p></ack><sec id=\"s10\" sec-type=\"supplementary-material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fpls.2020.01206/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fpls.2020.01206/full#supplementary-material</ext-link>\n</p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"DataSheet_1.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM2\"><media xlink:href=\"Table_1.docx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\">\n<person-group person-group-type=\"author\"><name><surname>Bakker</surname><given-names>M. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"methods-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Med (Lausanne)</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Med (Lausanne)</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Med.</journal-id><journal-title-group><journal-title>Frontiers in Medicine</journal-title></journal-title-group><issn pub-type=\"epub\">2296-858X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850928</article-id><article-id pub-id-type=\"pmc\">PMC7431884</article-id><article-id pub-id-type=\"doi\">10.3389/fmed.2020.00519</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Medicine</subject><subj-group><subject>Methods</subject></subj-group></subj-group></article-categories><title-group><article-title>Care for Critical Ill Patients With COVID-19: Establishment of a Temporary Intensive Care Unit in an Isolated Hospital</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Peng</surname><given-names>Milin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/968516/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Qian</surname><given-names>Zhaoxin</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Lina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Critical Care Medicine, Xiangya Hospital, Central South University</institution>, <addr-line>Changsha</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University</institution>, <addr-line>Changsha</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Cardiovascular, Xiangya Hospital, Central South University</institution>, <addr-line>Changsha</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Jiapeng Huang, University of Louisville, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Massimiliano Sorbello, Gaspare Rodolico Hospital, Italy; Andre M. Japiassu, Oswaldo Cruz Institute, Oswaldo Cruz Foundation (Fiocruz), Brazil</p></fn><corresp id=\"c001\">*Correspondence: Lina Zhang <email>zln7095@163.com</email></corresp><corresp id=\"c002\">Zhaoxin Qian <email>xyqzx@csu.edu.cn</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Intensive Care Medicine and Anesthesiology, a section of the journal Frontiers in Medicine</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†These authors have contributed equally to this work</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>11</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>7</volume><elocation-id>519</elocation-id><history><date date-type=\"received\"><day>02</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Peng, Qian and Zhang.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Peng, Qian and Zhang</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The current global spread of COVID-19, a highly contagious disease, has challenged healthcare systems, and placed immense burdens on medical staff globally. With a sharp increase in the number of newly confirmed cases and the rapid progression of the disease into a critically ill state, overstretched critical care units have had to contend with a shortage of beds, specialist personnel, and medical resources. Temporary intensive care units (ICUs) were therefore set up in isolated hospitals to provide the required standardized care for all severe cases. The current paper describes the authors' experience of setting up and managing such an ICU in Wuhan, Hubei Province, China, from the identification of critically ill COVID-19 patients through to the arranging and equipping of the unit, providing training and protection for staff, and standardizing all aspects of care.</p></abstract><kwd-group><kwd>COVID-19</kwd><kwd>management</kwd><kwd>severe</kwd><kwd>temporary</kwd><kwd>intensive care unit</kwd></kwd-group><counts><fig-count count=\"6\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"16\"/><page-count count=\"8\"/><word-count count=\"3650\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is highly contagious. In recognition of the global threat it poses, on March 11, 2020, the World Health Organization (WHO) declared COVID-19 to be a pandemic. By July 22, 2020, the total officially confirmed cases in China reached 86,152 with 4,653(5.4%) having died since the outbreak began in December 2019 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://covid19.who.int/\">https://covid19.who.int/</ext-link>). COVID-19 has high mortality throughout the world, being especially high in Italy (<italic>n</italic> = 236,076, mortality with 32,867/236,076), Spain (<italic>n</italic> = 264,836, mortality with 28,422/264,836), Russia (<italic>n</italic> = 783,328, mortality with 12,580/783,328), Brazil (<italic>n</italic> = 2,098,389, mortality with 79,488/2,098,389), the United States (<italic>n</italic> = 3,748,248, mortality with 139,964/3,748,248), and South Africa (<italic>n</italic> = 373,628, mortality with 5,173/373,628) (<ext-link ext-link-type=\"uri\" xlink:href=\"https://covid19.who.int/\">https://covid19.who.int/</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.epicentro.iss.it/en/coronavirus/bollettino/Infografica_10giugno%20ENG.pdf\">https://www.epicentro.iss.it/en/coronavirus/bollettino/Infografica_10giugno%20ENG.pdf</ext-link>). The high incidence and mortality of COVID-19 puts pressure on the need for urgent and special requirements for global medical resources and infrastructures.</p></sec><sec id=\"s2\"><title>Purpose and Significance of a Temporary COVID-19 ICU</title><p>Data provided by the Chinese Center for Disease Control and Prevention revealed a total of 44,672 confirmed COVID-19 cases in China by February 11, 2020. Of these, the majority (87%) were aged between 30 and 79, while 3% were aged 80 or more. A total of 14% of these patients were, or had been, severely ill, and 5% required critical care (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Data from a single-center, retrospective study of 52 critically ill COVID-19 patients showed that 61.5% died at 28 days, 71% required mechanical ventilation, and 67% had acute respiratory distress syndrome (ARDS). Patients who died from the disease were recorded longer lingering time from onset of symptoms to ICU admission than those who survived (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>).</p><p>As the number of newly-confirmed and critical COVID-19 cases rose sharply from December 2019 onwards in Wuhan, isolated hospitals charged with caring for such patients had to solve immense challenges in terms of finding sufficient bed space, personnel, and resources, particularly in relation to the provision of care for the critically ill patients (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). As COVID-19 is characterized by a high rate of contagion and rapid progression coupled with a high mortality rate, especially in the case of critical patients, it was essential to identify potentially critical cases at the earliest possible stage so they could be transferred to ICU timely to achieve necessary respiratory or circulation support in order to reduce mortality rates (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>).</p><p>Given the shortage of critical care facilities and resources in isolated hospitals (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>), the establishment of temporary ICUs in such hospitals was an essential part of the infrastructure for providing care to critically ill COVID-19 patients. The government thus made the decision to allocate an estimated 2,500 beds in three general hospitals in the city of Wuhan as temporary isolated wards for the treatment of severely infected patients. Over 3,000 medical personnel, including up to 1,000 ICU doctors and nurses, were called together from elsewhere in China to form centralized, specialist rescue teams in each isolated hospital. Each member of the teams represented a different department; thus, uniform and normative team management and patient care were challenging. Existing medical resources were leveraged to meet the needs of these ICUs, and the training of non-ICU staff was also critically important.</p><p>Over the course of a month in Wuhan, after the temporary ICU was established, a total of 157 critically ill patients were admitted into our temporary ward; they had characteristics of a mean age at 62 years old, a mean hospital stay at 16.01 days, 14.0% requiring invasive or non-invasive mechanical ventilation, and 3.82% died at discharge. Hence, the current paper shares best practices gleaned from the establishment and management of temporary ICUs in isolated hospitals during the epidemic in Wuhan; the centralization of severe confirmed cases enabled reintegration and maximum use of existing medical resources, thus facilitating effective professional treatments for COVID-19 patients.</p></sec><sec id=\"s3\"><title>Establishment of a Temporary COVID-19 ICU</title><p>On February 7, 2020, a national medical team from Xiangya Hospital, Hunan, took over the 51-bed ophthalmic ward in the Union Hospital Tongji Medical College Huazhong University of Science and Technology, Wuhan, in response to the insufficient number of ICU beds in isolated hospitals to cope with the rapidly increasing numbers of cases presenting with COVID-19. Combining the characteristics of the ward and taking into consideration severe cases, we planned to establish a temporary ICU in the Ophthalmic Ward; however, given its previous use, not all beds were supplied with sufficient oxygen pressure to run the necessary ventilators.</p><sec><title>Early Identification and Sorting of Critical Cases</title><p>The first stage in the care of COVID-19 patients is to identify those who are critically ill. For this purpose, we modified the National Early Warning Score (News) (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>) and SOFA score and added two further predicted risk factors for severe COVID-19 infection: age ≥ 66 years old (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>) and persistent lymphocytopenia (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>, <xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B9\" ref-type=\"bibr\">9</xref>) (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). Post-admission, critically ill patients were sorted into one of four risk categories on the basis of their scores on the severity grading scales: mild, moderate, critical, and severe critical. It was important to recognize that critically ill patients who presented with silent hypoxemia (severe hypoxemia without signs of respiratory distress), particularly older ones, required vigilant monitoring. Initially, due to the lack of severe respiratory distress symptoms at the early stage of COVID-19 in some severe confirmed patients, clinicians might delay tracheal intubation, and invasive ventilation. This postponing would bring disastrous consequences for these patients. Hence, early identification of severe critical patients was crucial in enabling personnel to offer optimized treatment within the temporary ICU in the shortest time.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Early classification of critically COVID-19 patients. BP, blood pressure, level of consciousness; A, Alert; V, Responds to voice; P, Responds to pain; U, Unresponsive.</p></caption><graphic xlink:href=\"fmed-07-00519-g0001\"/></fig></sec><sec><title>Dividing the Ward Into Sectors</title><p>The ward was divided into three sectors according to the results of the severity grading scales to facilitate clinical management (<xref ref-type=\"fig\" rid=\"F2\">Figures 2</xref>, <xref ref-type=\"fig\" rid=\"F3\">3</xref>).</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Dividing a ward into sectors. <bold>(A)</bold> After the ophthalmology ward was transformed into a temporary ICU, beds were arranged as follows: the first sector, consisting of 12 beds, made up the temporary ICU (room four with six beds; room three with three beds; and room five with three beds); the rooms shown in orange indicate the second sector, comprising 25 beds; the rooms shown in blue indicate the third sector, comprising 14 beds. <bold>(B)</bold> A team led by specialized ICU physicians viewing critically ill COVID-19 patients in a temporary ICU.</p></caption><graphic xlink:href=\"fmed-07-00519-g0002\"/></fig><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Early sorting of critically ill COVID-19 patients according to a modified Early Warning Score in a temporary ICU.</p></caption><graphic xlink:href=\"fmed-07-00519-g0003\"/></fig><p>The first sector, containing 12 beds supplied with sufficient oxygen pressure to run ventilators, was designated for the management of severe critically ill patients, including those requiring invasive ventilator support or prone ventilation, exhibiting hemodynamic instability. This sector was located close to the nurses' and doctors' workstations, enabling staff to monitor patients closely and provide immediate attention. The beds were placed at least two meters apart to prevent cross-infection. <xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref> displays the special medical equipment required in the temporary ICU, including non-invasive and invasive ventilators, high-flow nasal cannula (HFNC), renal replacement machines, extracorporeal membrane oxygenation machines, videobronchoscopes (with external monitor), equipment to monitor central venous pressure (CVP) or invasive arterial blood pressure, ultrasound machines, videolaryngoscopes, infusion or syringe pumps, vibrating expectoration machines, and closed tracheal suction catheters.</p><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>Summary of the requirements for special medical equipment in a temporary ICU caring for critically ill COVID-19 patients.</p></caption><graphic xlink:href=\"fmed-07-00519-g0004\"/></fig><p>The second sector of the temporary ICU consisted of a ring of 25 beds outside the critical care sector and was designated for the care of moderate to critical patients, typically requiring low-parameter high flow oxygen therapy with a maximum flow rate of 40 L/min and inspired oxygen (FiO2) of 50%, or mask oxygen support with a maximum flow of 5 L/min. The third part, which consisted of 14 beds around the perimeter of the ward, was for the use of patients identified as moderate, or who were in the recovery stage or requiring nasal catheter oxygen.</p></sec><sec><title>COVID-19 Infection Special Intensive Care Teams Establish</title><p>With insufficient trained ICU personnel to meet demand, we set up a multidisciplinary intensive care team led by senior intensive care specialists, and an intensive nurse team led by experienced intensive care nurses. Our first task was to evaluate isolation conditions, after which we divided the ward into inner and outer zones to give workers a space in which they could rest safely. Since every patient under our care was suffering from the same disease, we then took steps to standardize treatments by establishing protection strategy for each healthcare operation, for example routine medical care, tracheal intubation, and percutaneous tracheotomy.</p></sec><sec><title>Protecting Patients and Health Workers</title><p>Preventing a nosocomial outbreak of COVID-19 through transmission from patients to healthcare workers was of vital importance. To this end, every health worker had to be equipped with the correct personal protective equipment (PPE) and given training in its use before they could start caring for patients. After donning a medical protective mask, every worker was required to ask another to check if it was sufficiently tight, and, after they took their PPE off, they used quick-drying hand sanitizer. Different levels of PPE were assigned to healthcare workers according to the severity of the patients under their care, as showed in <xref ref-type=\"fig\" rid=\"F5\">Figure 5</xref>. Workers administering oxygen therapy to confirmed COVID-19 patients in the third sector wore level-II protection consisting of protective clothing, gloves, shoe covers, head covers, a N95 mask, and protective (anti-fog) glasses, and they also carried out hand hygiene measures. Personnel carrying out operations among patients that might be associated with aerosol generating procedures, like sampling or sputum suction, wore level III-1 protection consisting of all the above items plus a waterproof isolation gown, and a face shield and also carried out the hand hygiene procedures. Personnel caring for sector-two patients, suffering from severe respiratory distress but not requiring invasive ventilation treatment, were also obliged to use level III-1 PPE (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). According to recent recommendations (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>), the highest level of PPE, level III-2, which included an air-purifying respirators (PAPRs), was reserved for high risk operations including tracheal intubation, tracheotomy, or fiberoptic bronchoscopy.</p><fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p>Standard personal protective equipment (PPE) required for normal critical care with COVID-confirmed patients in a temporary ICU. The required standard of PPE is based on the guidelines for the prevention and control of COVID-19 infection in medical institutions laid out by the government in China. Doctors have different scales of PPE to care for severe confirmed patients in different situations. <bold>(A)</bold> A doctor with level-II PPE at the nurses' station; <bold>(B)</bold> A doctor with level III-1 PPE for ultrasonography in a temporary ICU; <bold>(C)</bold> Two doctors with level III-2 PPE preparing for percutaneous tracheostomy in a temporary ICU.</p></caption><graphic xlink:href=\"fmed-07-00519-g0005\"/></fig></sec><sec><title>Education and Training of Health Workers</title><p>It is estimated that a considerable proportion of healthcare workers have been infected since the COVID-19 outbreak (for example, infected rates of health workers were 28603/236076 (12.1%) in Italy (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.epicentro.iss.it/en/coronavirus/bollettino/Infografica_10giugno%20ENG.pdf\">https://www.epicentro.iss.it/en/coronavirus/bollettino/Infografica_10giugno%20ENG.pdf</ext-link>). Minimizing the risk of nosocomial outbreak amplification and protecting healthcare workers are of critical importance (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Widespread use of recommended barrier precautions in isolated wards must be of highly priority. The temporary ICUs set up to manage the epidemic in China established protocols for both the prevention of infection and contingency management, including PPE regulations, procedures for entering and leaving quarantined zones, emergency plans to cope with occupational exposure, checks on the hygiene standards observed by healthcare workers, and management of preventive medication. It is imperative that ensuring every healthcare worker is properly trained, continually protected against droplet infection, and versed in all the necessary checks and precautions, including the use of PPE. This is vital in the battle against a highly infectious disease such as COVID-19.</p></sec></sec><sec id=\"s4\"><title>Management</title><p>Data from a follow-up study of 99 COVID-19 cases, dated January 25, 2020, reveal that 11% died, 9% required invasive mechanical ventilation, and 3% required extracorporeal membrane oxygenation (ECMO) (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Among the significant findings was that before January 30, 2020, only about 25% of patients who died from COVID-19 received invasive mechanical ventilation or ECMO, and that HFNC and/or non-invasive ventilation (NIV) were used for an average of 6 (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>) days before intubation or death (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). It can be inferred that invasive mechanical ventilation was delayed, possibly due to a lack of the necessary equipment, specialist staff, or areas in which the procedure could be carried out or to a fear of infection during the operation of trachea intubation.</p><p>COVID-19 is an infectious disease characterized as an acute hypoxemic respiratory insufficiency or failure which requires the use of oxygen and ventilation therapies. Most infected patients suffer mild symptoms and are self-healing; however, in severe cases, progression is rapid and can lead to ARDS, septic shock, metabolic acidosis, and coagulopathy (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>), especially when combined with old age, comorbidities, or persistent lymphocytopenia, which is difficult to correct. As standardizing the management of care is a vital element in improving survival rates, the National Health Commission of the People's Republic of China (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.nhc.gov.cn/yzygj/s7653p/202002/d4b895337e19445f8d728fcaf1e3e13a.shtml\">http://www.nhc.gov.cn/yzygj/s7653p/202002/d4b895337e19445f8d728fcaf1e3e13a.shtml</ext-link>) and the WHO (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.who.int/docs/default-source/coronaviruse/clinical-management-of-novel-cov.pdf\">https://www.who.int/docs/default-source/coronaviruse/clinical-management-of-novel-cov.pdf</ext-link>) have established a protocol for the treatment of COVID-19. Timely and effective airway management and maximized first-pass success rate of airway operation for COVID-19 patients are recommended (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>).</p><p>For patients in the third sector of the temporary ICU (who have mild symptoms), healthcare workers must give supportive care and ensure monitoring at 6-h intervals of the vital symptoms, such as breathing rate, oxygen saturation (SpO2), and heart rate. Oxygen therapy should be initiated at 5 L/min and titrated to SpO2 ≥ 92% in COVID-19 patients. For patients with high risk factors (≥66 years old and comorbidities), nurses must ensure close monitoring of vital symptoms at 4-h intervals. In the following conditions, patients must be swiftly transferred to the second (moderate) sector, in case they need HFNC oxygenation or NIV: breathing rate ≥24/min, SpO2 <92% with oxygen therapy at 5 L/min, or heart rate >130/min, or persistent lymphocytopenia. Throughout treatment, conservative intravenous fluid strategies must be strictly implemented unless septic shock occurs.</p><p>In the case of severe patients with persistent respiratory distress, a respiratory rate >30/min, oxygenation index <150 mmHg, or showing no improvement after HFNC or NIV, continuous monitoring is necessary from an experienced ICU team. The vital symptoms must be monitored after HFNC or NIV treatment at intervals of no more than 2 h (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). If there is no improvement after 2 h, or the patient's condition worsens, the team should consider early invasive ventilation (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>), followed by lung-protective ventilation strategies and early prone positioning during mechanical ventilation for more than 12 hours per day (<xref ref-type=\"fig\" rid=\"F6\">Figure 6</xref>).</p><fig id=\"F6\" position=\"float\"><label>Figure 6</label><caption><p>The management algorithm based on the clinical severity of critically ill COVID-19 patients. HFNC, high flow nasal cannula; NIV, non-invasive ventilation.</p></caption><graphic xlink:href=\"fmed-07-00519-g0006\"/></fig></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>The sudden outbreak of the highly contagious disease now known as COVID-19 in China in December 2019 has impacted healthcare system and societies more widely across the world. Should another virus of this type emerge, it will be vital to first control the source of infection and second prevent transmission. Moreover, the identification, sorting, and management of infected patients in different isolated sectors according to level of severity is crucial in ensuring full use is made of the medical resources available. Public anxiety around COVID-19 centers on the large numbers of critically ill patients and high death rates. In this regard, setting up a temporary ICU in isolated hospitals, run by multidisciplinary staff under the leadership of intensive care specialists and nurses, is crucial not only in caring for critically ill patients and bringing down mortality rates, but also in allaying public fear.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>MP contributed to the literature search, figures, writing of the original draft, and project administration of the manuscript. ZQ and LZ contributed equally to the conceptualization, data and resource curation, supervision, validation, and writing, reviewing, and editing of the research. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We would like to thank all the health workers for their efforts in our Temporary ICU.</p></ack><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Wu</surname><given-names>Z</given-names></name><name><surname>McGoogan</surname><given-names>JM</given-names></name></person-group>. <article-title>Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention</article-title>. <source>JAMA. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurosci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurosci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Neuroscience</journal-title></journal-title-group><issn pub-type=\"ppub\">1662-4548</issn><issn pub-type=\"epub\">1662-453X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848559</article-id><article-id pub-id-type=\"pmc\">PMC7431885</article-id><article-id pub-id-type=\"doi\">10.3389/fnins.2020.00785</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neuroscience</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>PT320, Sustained-Release Exendin-4, Mitigates L-DOPA-Induced Dyskinesia in a Rat 6-Hydroxydopamine Model of Parkinson’s Disease</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Yu</surname><given-names>Seong-Jin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/964662/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Shuchun</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Yang</surname><given-names>Yung-Yung</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Glotfelty</surname><given-names>Elliot J.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/999543/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Jung</surname><given-names>Jin</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Kim</surname><given-names>Hee Kyung</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Choi</surname><given-names>Ho-Il</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Choi</surname><given-names>Doo-Sup</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/100733/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Hoffer</surname><given-names>Barry J.</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Greig</surname><given-names>Nigel H.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/23353/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Yun</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/936954/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Center for Neuropsychiatric Research, National Health Research Institutes</institution>, <addr-line>Zhunan</addr-line>, <country>Taiwan</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Drug Design and Development Section, Translational Gerontology Branch, Intramural Research Program, National Institute on Aging, National Institutes of Health</institution>, <addr-line>Baltimore, MD</addr-line>, <country>United States</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Department of Neuroscience, Karolinska Institutet</institution>, <addr-line>Stockholm</addr-line>, <country>Sweden</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Peptron Inc.</institution>, <addr-line>Daejeon</addr-line>, <country>South Korea</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Departments of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science</institution>, <addr-line>Rochester, MN</addr-line>, <country>United States</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Department of Neurosurgery, Case Western Reserve University School of Medicine</institution>, <addr-line>Cleveland, OH</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Cesar V. Borlongan, University of South Florida, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Vanessa Castelli, University of L’Aquila, Italy; Jea Young Lee, University of South Florida, United States</p></fn><corresp id=\"c001\">*Correspondence: Yun Wang, <email>ywang@nhri.edu.tw</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Neuropharmacology, a section of the journal Frontiers in Neuroscience</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>14</volume><elocation-id>785</elocation-id><history><date date-type=\"received\"><day>30</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>03</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Yu, Chen, Yang, Glotfelty, Jung, Kim, Choi, Choi, Hoffer, Greig and Wang.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Yu, Chen, Yang, Glotfelty, Jung, Kim, Choi, Choi, Hoffer, Greig and Wang</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><sec><title>Background</title><p>We previously demonstrated that subcutaneous administration of PT320, a sustained-release (SR) form of exendin-4, resulted in the long-term maintenance of steady-state exenatide (exendin-4) plasma and target levels in 6-hydroxydopamine (6-OHDA)-pretreated animals. Additionally, pre- or post-treatment with PT320 mitigated the early stage of 6-OHDA-induced dopaminergic neurodegeneration. The purpose of this study was to evaluate the effect of PT320 on L-3,4-dihydroxyphenylalanine (L-DOPA)-induced abnormal involuntary movements (AIMs) in the rat 6-OHDA model of Parkinson’s disease.</p></sec><sec><title>Methods</title><p>Adult male Sprague–Dawley rats were unilaterally lesioned in the right medial forebrain bundle by 6-OHDA. L-DOPA and benserazide were given daily for 22 days, starting from 4 weeks after lesioning. PT320 was co-administered weekly for 3 weeks. AIM was evaluated on days 1, 16, and 22 after initiating L-DOPA/benserazide + PT320 treatment. Brain tissues were subsequently collected for HPLC measurements of dopamine (DA) and metabolite concentrations.</p></sec><sec><title>Results</title><p>L-DOPA/benserazide increased AIMs of limbs and axial as well as the sum of all dyskinesia scores (ALO) over 3 weeks. PT320 significantly reduced the AIM scores of limbs, orolingual, and ALO. Although PT320 did not alter DA levels in the lesioned striatum, PT320 significantly attenuated 6-OHDA-enhanced DA turnover.</p></sec><sec><title>Conclusion</title><p>PT320 attenuates L-DOPA/benserazide-induced dyskinesia in a 6-OHDA rat model of PD and warrants clinical evaluation to mitigate Parkinson’s disease in humans.</p></sec></abstract><kwd-group><kwd>Parkinson’s disease</kwd><kwd>levodopa</kwd><kwd>L-DOPA-induced dyskinesia</kwd><kwd>glucagon-like peptide-1</kwd><kwd>exendin-4</kwd><kwd>PT320</kwd><kwd>PT302</kwd><kwd>exenatide</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">National Institute on Aging<named-content content-type=\"fundref-id\">10.13039/100000049</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"4\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"47\"/><page-count count=\"9\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>Levodopa, also known as L-3,4-dihydroxyphenylalanine (L-DOPA), is the precursor of dopamine (DA) and is currently the most commonly used medication for Parkinson’s disease (PD). The use of L-DOPA elevates dopamine (DA) synthesis in the lesioned substantia nigra and restores motor functions in PD patients. However, chronic administration of L-DOPA is often associated with abnormal involuntary movements (AIMs), also called levodopa-induced dyskinesia (LID) in PD patients. Early clinical studies have shown that 20–50% of PD patients developed dyskinesia within 5 years after the initiation of L-DOPA treatment (<xref rid=\"B42\" ref-type=\"bibr\">Rascol et al., 2000</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Manson et al., 2012</xref>; <xref rid=\"B8\" ref-type=\"bibr\">Bjornestad et al., 2016</xref>). The severity of dyskinesia positively correlates with disease duration, Hoehn–Yahr stage, and duration of L-DOPA treatment (<xref rid=\"B36\" ref-type=\"bibr\">Nicoletti et al., 2016</xref>). Other studies also suggest that the disease severity and dose of L-DOPA are more important than the duration of L-DOPA treatment for the development of LID (<xref rid=\"B37\" ref-type=\"bibr\">Nutt et al., 2010</xref>; <xref rid=\"B12\" ref-type=\"bibr\">Espay et al., 2018</xref>).</p><p>LID has also been established in experimental animals. Chronic administration of L-DOPA to unilaterally 6-OHDA-lesioned rats has been widely used to examine AIMs (<xref rid=\"B30\" ref-type=\"bibr\">Lundblad et al., 2002</xref>). Similar to the PD patients, LID in the lesioned rats significantly correlates with the dose of L-DOPA and the magnitude of DA depletion (<xref rid=\"B41\" ref-type=\"bibr\">Putterman et al., 2007</xref>).</p><p>DA is a key neurotransmitter modulating normal movement. DA, released from the A9 neurons of the substantia nigra pars compacta (SNc) DA-ergic neurons, interacts with GABA-ergic medium spiny neurons (MSNs) within the dorsal striatum mainly comprised of caudate and putamen. There are two classical striatopallidal pathways (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). DA differentially inhibits the indirect GPe (external segment of globus pallidus) pathway through D2 receptors (D2R)-expressing MSNs, while it activates the direct GPi (internal segment of globus in primates or entopeduncular nucleus in rodents) pathway through D1R-expressing MSNs (<xref rid=\"B11\" ref-type=\"bibr\">Durieux et al., 2011</xref>; <xref rid=\"B15\" ref-type=\"bibr\">Gerfen and Surmeier, 2011</xref>). These interactions result in activation of GPe and suppression of neuronal activity in subthalamic nucleus (STN) and GPi, which further regulates thalamic neuronal activity and facilitates movement (<xref rid=\"B35\" ref-type=\"bibr\">Nambu et al., 2002</xref>). In pathological conditions, such as PD, reduction of dopaminergic innervation to caudate and putamen leads to overactivity of GABA-ergic inputs to GPe, which then suppresses the inhibitory outputs from the GPe to STN (<xref rid=\"B40\" ref-type=\"bibr\">Petri et al., 2013</xref>), activates STN and GPi neurons, and reduces neuronal firing in the thalamus. DA denervation also activates the GPi neurons through the direct striatopallidal pathway. Lesioning the STN or GPi induces marked functional improvement in 6-OHDA-lesioned rats (<xref rid=\"B45\" ref-type=\"bibr\">Touchon et al., 2004</xref>), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) -treated monkeys (<xref rid=\"B7\" ref-type=\"bibr\">Bergman et al., 1990</xref>), and PD patients (<xref rid=\"B6\" ref-type=\"bibr\">Baron et al., 2000</xref>). On the other hand, L-DOPA or DA agonists can overstimulate DA receptors in the direct and indirect pathways in the lesioned brain, reduce neuronal firing in the STN and GPi while activating the thalamus, and result in increasing involuntary movements in MPTP-treated monkeys (<xref rid=\"B38\" ref-type=\"bibr\">Papa et al., 1999</xref>) and PD patients (<xref rid=\"B33\" ref-type=\"bibr\">Merello et al., 1999</xref>). Besides the interaction with the striatopallidal pathway, several other mechanisms have been suggested for LID (<xref rid=\"B20\" ref-type=\"bibr\">Jenner, 2008</xref>).</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Two classical striatopallidal pathways of external segment of globus pallidus (GPe) and internal segment of globus (GPi) regulate neuronal activity in STN and thalamus as well as movement. MSN, medium spiny neurons; STN, subthalamic nucleus.</p></caption><graphic xlink:href=\"fnins-14-00785-g001\"/></fig><p>Since AIM is mainly induced after chronic administration of L-DOPA or DA-ergic agonists, agents that are non-DA-ergic molecules that possess less L-DOPA side effects are being increasingly studied for PD treatment (<xref rid=\"B13\" ref-type=\"bibr\">Fox et al., 2008</xref>). We and others previously demonstrated that the endogenous incretin glucagon-like peptide-1 (GLP-1) as well as exendin-4 (also known as exenatide), a long-acting GLP-1 receptor (GLP-1R) agonist approved for the treatment of type 2 diabetes mellitus (T2DM) (<xref rid=\"B10\" ref-type=\"bibr\">Drucker, 2018</xref>; <xref rid=\"B14\" ref-type=\"bibr\">Gentilella et al., 2019</xref>), protect tyrosine hydroxylase immunoreactivity (TH-IR) in primary ventromesenchephalic neurons from 6-OHDA lesioning. Infusion of exendin-4 into the lateral ventricle also mitigated the loss of TH-IR, preserved DA levels in the SNc, and improved the behavioral function of mice receiving MPTP (<xref rid=\"B26\" ref-type=\"bibr\">Li et al., 2009</xref>). Such GLP-1R-mediated protection has been broadly found across animal models of PD (<xref rid=\"B22\" ref-type=\"bibr\">Kim et al., 2017</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Athauda and Foltynie, 2018</xref>; <xref rid=\"B19\" ref-type=\"bibr\">Holscher, 2020</xref>) as well as in other neurodegenerative disorders (<xref rid=\"B16\" ref-type=\"bibr\">Glotfelty et al., 2019</xref>). Importantly, clinical studies have demonstrated that PD patients taking exendin-4 for 1 year had better motor skills than those on placebo (<xref rid=\"B4\" ref-type=\"bibr\">Athauda et al., 2017</xref>, <xref rid=\"B3\" ref-type=\"bibr\">2019</xref>). Besides its neuroprotective effects, exendin-4 has been reported to reduce LID in rats, with repeated administration of exendin-4 starting from the seventh day after 6-OHDA-lesioning resulting in lowering L-DOPA (10 mg/kg/day)-mediated AIM scores in 6-OHDA-lesioned rats (<xref rid=\"B1\" ref-type=\"bibr\">Abuirmeileh et al., 2012</xref>). These data suggest that activation of the GLP-1R can reduce the progression of DA degeneration and LID.</p><p>Major limitations of GLP-1R agonists, such as GLP-1, for clinical use are their relatively short half-life and, as peptide-based drugs, limited brain uptake (<xref rid=\"B17\" ref-type=\"bibr\">Glotfelty et al., 2020</xref>). A key amino acid change at the N-terminal of GLP-1 prevents breakdown by dipeptidyl peptidase-4 (DPP4) to extend its half-life from 1.5 min to 2.4 h for exendin-4 (<xref rid=\"B10\" ref-type=\"bibr\">Drucker, 2018</xref>). This results in the twice-daily clinical formulation <italic>Byetta</italic> that is considered the short-acting drug version. In contrast, the application of sustained-release (SR) technology to exendin-4 provides the capability to continuously release the same peptide present in <italic>Byetta</italic> over weeks to months after a single acute subcutaneous (s.c.) administration, resulting in the longer-term formulations PT320 (1 or 2 weeks) and <italic>Bydureon</italic> (1-week administration). Such technology provides the opportunity to optimize the beneficial potential of drug treatment for a chronic disorder by maintaining steady-state plasma levels as a source to maintain the brain target concentration (<xref rid=\"B27\" ref-type=\"bibr\">Li et al., 2019</xref>).</p><p>We recently reported that systemic administration of PT320 (also called PT302), SR exendin-4, given once every 2 weeks to unilaterally 6-OHDA-lesioned rats, provides sustained plasma exendin-4 levels (<xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2018</xref>). Pre- and post-treatment with PT320 significantly reduced methamphetamine-induced rotation and increased TH-IR in the lesioned SNc and striatum in these unilaterally 6-OHDA-lesioned rats. Furthermore, there was a significant correlation between exendin-4 plasma levels and TH-IR in the 6-OHDA-lesioned side SNc and striatum. These data suggest that PT320 provides long-lasting exendin-4 release and reduces dopaminergic neurodegeneration in this experimental model of PD. The use of PT320 in LID, however, has not been examined previously.</p><p>The purpose of this study was to evaluate the effect of PT320 on the L-DOPA/benserazide-mediated dyskinesia in a rat 6-OHDA model of Parkinsonism. Three doses of PT320 were administered over 3 weeks together with daily L-DOPA/benserazide (a peripherally acting aromatic L-amino acid decarboxylase inhibitor). We found that PT320 normalized DA turnover in the striatum and reduced LID behavior in these lesioned animals. Our data support the future clinical use of PT320 as a co-treatment with L-DOPA for PD.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Animals</title><p>Adult male Sprague–Dawley rats were used for this study. Experimental procedures followed the guidelines of the “Principles of Laboratory Care” (National Institutes of Health publication No. 86-23, 1996) and were approved by the Animal Care and Use Committee. Rats were fed with a regular chow diet and kept on a 12 h light/dark cycle at 25 ± 2°C. Animals were randomly assigned into four groups: Group 1 (sham operated); Group 2 (6-OHDA lesioned, no L-DOPA); Group 3 (lesioned + L-DOPA/benserazide + vehicle); and Group 4 (lesioned + L-DOPA/benserazide + PT320).</p></sec><sec id=\"S2.SS2\"><title>PT320</title><p>PT320 (previously termed PT302) is an SR formulation of exendin-4 (exenatide). Powdered PT320 used in our study (Lot PT3025014) was of clinical-grade material, similar to that used in prior human studies (<xref rid=\"B18\" ref-type=\"bibr\">Gu et al., 2014</xref>), and contained a mixture of polymers (98%) and exendin-4 (2%). Specifically, exendin-4 was incorporated into poly(lactic-co-glycolic acid) (PLGA) microspheres of 20 μm diameter utilizing a proprietary ultrasonic spray drying process (SmartDepot<sup>TM</sup>, <xref rid=\"B39\" ref-type=\"bibr\">Peptron Inc, 2020</xref>) together with the use of an L-lysine coating to regulate the initial release burst of peptide (<xref rid=\"B27\" ref-type=\"bibr\">Li et al., 2019</xref>). The composition of the diluent used to prepare the PT320 suspension was 0.5% carboxymethylcellulose sodium, 5.0% D-mannitol, and 0.1% Tween 80 (pH 6.66) in sterile, double-distilled water as also used when PT320 was administered to humans. PT320 was freshly prepared in diluent within an hour of administration, maintained on wet ice (4°C), and thoroughly mixed (by vortex) immediately before each injection.</p></sec><sec id=\"S2.SS3\"><title>6-OHDA Lesioning and Drug Treatment</title><p>Thirty minutes prior to surgery, rats were given desipramine intraperitoneally (25 mg/kg; i.p.) to block noradrenergic uptake of 6-OHDA. Animals were anesthetized with 3% isoflurane. 6-OHDA (3 μg/μl × 2.5 μl dissolved in 0.1% ascorbic acid) was stereotactically injected into the right medial forebrain bundle (coordinates: −3.6 mm rostral and 1.6 mm lateral to bregma, 7.5 mm below the skull) at 0.25 μl/min over a 10 min period. Animals were allowed to recover for 3 weeks following the 6-OHDA lesion and then received drug treatment. Specifically, L-DOPA, dissolved in saline together with benserazide (15 mg/kg), was administered i.p. at a dose of 6 mg/kg/day for 22 days. PT320 (100 mg/kg, containing 2 mg/kg exendin-4 clinical-grade material) was administered subcutaneously once a week (three times in total) at 1 h before L-DOPA/benserazide administration. The timeline of the experiment is shown in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>.</p><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Timeline of drug treatment. Animals were screened by a stepping test at 3 weeks (W3) after unilateral 6-hydroxydopamine (6-OHDA) lesioning in the right medial forebrain bundle (Rt MFB). L-3,4-dihydroxyphenylalanine/benserazide was administered daily (i.p., L-DOPA: 6 mg/kg/day + benserazide 15 mg/kg/day) for 22 days. PT320 (100 mg/kg containing 2 mg/kg exendin-4) was administered subcutaneously once a week (W4, W5, and W6). Behavioral (Beh) evaluations of AIMs were examined on day 1 (D1), D16, and D22 after initiating L-DOPA/benserazide treatment (D0).</p></caption><graphic xlink:href=\"fnins-14-00785-g002\"/></fig></sec><sec id=\"S2.SS4\"><title>Behavioral Tests</title><list list-type=\"simple\"><list-item><label>(1)</label><p>A “Stepping test” was used to screen the success of lesioning at 3 weeks following the surgery. This was used rather than using methamphetamine-induced rotation to avoid baseline shifts due to sensitization. Briefly, the experimenter took the rat with one hand holding both hindlimbs and the other hand holding one of the forelimbs. The free paw was placed in contact with a flat surface. The experimenter then moved the animal slowly sideways in forward and backward directions. The number of adjusting steps taken by the rat was counted for both paws in the backward and forward direction. All animals displaying limb hypokinesia contralateral to the lesion were selected for subsequent study.</p></list-item><list-item><label>(2)</label><p>L-dopa/benserazide-induced dyskinesia (LID) was examined by the abnormal involuntary movements (AIMs) test. Animals were placed in clear Perspex boxes (22 cm × 34 cm × 20 cm). Each rat was observed for 1 min at 30 min intervals following L-DOPA/benserazide administration over a 3 h period. Three subtypes of AIMs were assessed: (i) limb – random uncontrollable movements of forelimb contralateral to the lesion; (ii) orolingual – excess chewing and jaw movements with protrusion of the tongue; and (iii) axial – dystonic postures or choreiform twisting of the neck and upper body toward the contralateral side. The ALO score is the sum of all AIMs (axial, limb, and orolingual) scores. The severity of each AIM was scored non-parametrically between 1 and 4, based upon the following criteria:</p></list-item></list><p>1 = present for less than 30 s</p><p>2 = present for more than 30 s</p><p>3 = present throughout a minute but suppressed by external stimuli</p><p>4 = present throughout a minute but not suppressible by external stimuli</p></sec><sec id=\"S2.SS5\"><title>HPLC Analysis and Electrochemical Detection</title><p>After final behavioral testing, animals were euthanized; their brains were quickly removed, placed in an ice-cold glass dish, and rapidly dissected on ice. Both lesioned and non-lesioned side striata were dissected, placed in an Eppendorf tube, frozen on dry ice, and stored at < −70°C before HPLC analysis. On the day of biochemical analysis, tissues were homogenized in 200 μl of 0.1 N perchloric acid (HClO<sub>4</sub>), sonicated, and centrifuged at 13,000 rpm for 30 min at 4°C. Aliquots (50 μl) of the supernatants were diluted in HCLO4 (1:4 v/v) before the injection into the HPLC system. The tissue concentrations of DA and metabolites were measured by HPLC coupled to the coulometric detection system. The mobile phase of the HPLC system was composed of methanol (7%), NaH<sub>2</sub>PO<sub>4</sub> (70 mM), triethylamine (100 μl/L), EDTA (0.1 mM), and sodium octyl sulfate (100 mg/L) diluted in deionized water (pH 4.2, adjusted with orthophosphoric acid). It was filtered (0.22 μm) before its introduction in the system. The mobile phase was delivered through the HPLC column (Hypersyl, C18, 15 cm × 4.6 mm, particle size 5 μm) at a flow rate of 1.2 ml/min using an HPLC pump. The column was protected by a Brownlee–Newgard precolumn (RP-8, 15 × 3.2 mm, 7 μm.). The injection of the samples (10 μl) was carried out by a manual injection valve (Rheodyne, model 7725i) equipped with a loop of 20 μl. The compounds exited the column at different retention times and passed into the coulometric detection cell (Cell 5014, ESA) equipped with two electrodes. The potential of these two electrodes was fixed via the coulometric detector at + 350 mV (oxidation) and −270 m (reduction), respectively. The calibration curves were performed once the peaks in a standard solution (1 ng/10 μl) were well separated in the chromatogram. Calibration curves were performed using three concentrations of DA, DOPAC, and HVA injected three times each with an acceptable <italic>r</italic> = 0.99. Standard solutions were used before each series of 10/12 samples to verify the correspondence of the chromatographic conditions to both the elution time and quantities calculated from the calibration curves. The overall sensitivity for the compounds ranged from 2 pg/10 μl for DA to 18 pg/10 μl for HVA with a signal/noise ratio of 3:1.</p></sec><sec id=\"S2.SS6\"><title>Statistical Analysis</title><p>Data are presented as mean ± s.e.m. Linear regression, one or two-way ANOVA, and <italic>post hoc</italic> Newman–Keuls tests were used for statistical comparisons, with a significance level of <italic>p</italic> < 0.05.</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>PT320 Reduces L-DOPA/Benserazide-Induced Abnormal Involuntary Movements</title><p>A total of 24 rats received unilateral 6-OHDA lesioning. Of these, 12 rats received daily L-DOPA/benserazide only, and the other 12 rats received daily L-DOPA/benserazide + weekly PT320 for 3 weeks. LID was examined on days 1, 16, and 22 after the initiation of L-DOPA/benserazide treatment (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>). A significant correlation was found between the duration of L-DOPA/benserazide treatment and overall AIM scores(<xref ref-type=\"fig\" rid=\"F3\">Figure 3A1</xref>: ALO, <italic>p</italic> = 0.018, <italic>R</italic> = 0.398). The AIMs on limb (<italic>p</italic> = 0.018, <italic>R</italic> = 0.398) (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B1</xref>) and axial (<italic>p</italic> = 0.026, <italic>R</italic> = 0.370) (<xref ref-type=\"fig\" rid=\"F3\">Figure 3C</xref>) were also significantly correlated with days of L-DOPA/benserazide treatment. In contrast, in the animals receiving PT320, the AIM score was not significantly correlated with the duration of L-DOPA/benserazide treatment (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A1</xref>, ALO, <italic>p</italic> = 0.081; B1: limb, <italic>p</italic> = 0.068; C: axial, <italic>p</italic> = 0.051). These data suggest that L-DOPA/benserazide treatment time-dependently increased dyskinesia, which was attenuated by PT320.</p><fig id=\"F3\" position=\"float\"><label>FIGURE 3</label><caption><p>PT320 reduces L-DOPA/benserazide-induced abnormal involuntary movements (AIMs) in unilaterally 6-OHDA-lesioned rats. <bold>(A)</bold> The dyskinesia (<bold>A1</bold>: ALO; <bold>B1</bold>: limbs; and <bold>C:</bold> axial) significantly correlates with the duration of L-DOPA/benserazide treatment. PT320 administration significantly reduced <bold>(A2)</bold> ALO, <bold>(B2)</bold> limb, and <bold>(D)</bold> orolingual AIM scores.</p></caption><graphic xlink:href=\"fnins-14-00785-g003\"/></fig><p>Next, the interaction of PT320 and AIMs was analyzed by a two-way ANOVA with a Newman–Keuls <italic>post hoc</italic> test. The ALO was significantly reduced by PT320 [<italic>p</italic> = 0.031, <italic>F</italic><sub>(</sub><sub>1, 66</sub><sub>)</sub> = 4.858] (<xref ref-type=\"fig\" rid=\"F3\">Figure 3A2</xref>). PT320 also significantly reduced limb [<italic>p</italic> = 0.036, <italic>F</italic><sub>(</sub><sub>1, 64</sub><sub>)</sub> = 4.582] (<xref ref-type=\"fig\" rid=\"F3\">Figure 3B2</xref>) and orolingual AIM scores [<italic>p</italic> = 0.008, <italic>F</italic><sub>(</sub><sub>1, 66</sub><sub>)</sub> = 7.418] (<xref ref-type=\"fig\" rid=\"F3\">Figure 3D</xref>).</p></sec><sec id=\"S3.SS2\"><title>PT320 Normalized DA Turnover in the Lesioned Striatum</title><p>Lesioned and non-lesioned side striata were collected from 46 rats for HPLC analysis (sham, <italic>n</italic> = 10; lesioned, <italic>n</italic> = 12; lesioned + L-DOPA/benserazide, <italic>n</italic> = 12; and lesioned + L-DOPA/benserazide + PT320, <italic>n</italic> = 12). An averaged 84.6 ± 5.7% reduction of DA was found in the lesioned striatum (<italic>n</italic> = 36). DA levels were significantly reduced in the lesioned side striatum, compared to the non-lesioned side striatum of rats receiving L-DOPA/benserazide (<italic>p</italic> < 0.001) (<xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref>), L-DOPA/benserazide + PT320 treatment (<italic>p</italic> < 0.001), or without L-DOPA/benserazide treatment (<italic>p</italic> < 0.001). In contrast, no difference was found in the control animals receiving sham surgery (<italic>p</italic> = 0.930). DA levels on the lesioned (right) side striatum of all 6-OHDA-lesioned animals were further analyzed by a two-way ANOVA. L-DOPA/benserazide or L-DOPA/benserazide + PT320 treatment did not alter DA levels in the lesioned striatum (L-DOPA/benserazide + 6-OHDA vs. 6-OHDA: <italic>p</italic> = 0.844; L-DOPA/benserazide + PT320 + 6-OHDA vs. 6-OHDA, <italic>p</italic> = 0.491; L-DOPA/benserazide + PT320 + 6-OHDA vs. L-DOPA/benserazide + 6-OHDA, <italic>p</italic> = 0.649) (<xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref>). DOPAC and HVA levels were also significantly reduced on the lesioned side striatum after 6-OHDA lesioning (<italic>p</italic> < 0.001). PT320 or L-DOPA/benserazide treatment did not significantly alter DOPAC or HVA levels in the lesioned side striatum (<italic>p</italic> > 0.399).</p><fig id=\"F4\" position=\"float\"><label>FIGURE 4</label><caption><p>Dopamine (DA) and DA turnover in the striatum. <bold>(A)</bold> 6-OHDA lesioning significantly reduced DA levels (*<italic>p</italic> < 0.001) in the lesioned striatum (red bars). L-DOPA/benserazide or L-DOPA/benserazide + PT320 treatment did not alter DA after lesioning (<italic>p</italic> = 0.844, 6-OHDA vs. L-DOPA/benserazide + 6-OHDA; <italic>p</italic> = 0.491, 6-OHDA vs. L-DOPA/benserazide + PT320 + 6-OHDA). <bold>(B)</bold> DA turnover was examined by comparing DA metabolite levels (DOPAC + HVA) with DA. DA turnover was enhanced by the 6-OHDA lesion (*<italic>p</italic> < 0.001, sham vs. 6-OHDA). PT320 significantly reduced DA turnover in the lesioned striatum (*<italic>p</italic> = 0.028, 6-OHDA + L-DOPA/benserazide vs. 6-OHDA + L-DOPA/benserazide + PT320). 6-OHDA lesioning significantly increased <bold>(C)</bold> DOPAC/DA (<sup>#</sup><italic>p</italic> = 0.019, lesioned vs. non-lesioned striatum) and <bold>(D)</bold> HVA/DA ratio (<sup>#</sup><italic>p</italic> < 0.001, lesioned vs. non-lesioned striatum; *<italic>p</italic> < 0.001, sham vs. 6-OHDA). HVA/DA ratio was significantly reduced by PT320 in the lesioned animals receiving L-DOPA (*<italic>p</italic> = 0.006, 6-OHDA + L-DOPA/benserazide vs. 6-OHDA + L-DOPA/benserazide + PT320). <sup>#</sup>Significant difference between the lesioned and non-lesioned side striatum; *significant difference among groups. Two-way ANOVA + <italic>post hoc</italic> Newman–Keuls test.</p></caption><graphic xlink:href=\"fnins-14-00785-g004\"/></fig><p>DA turnover was examined by comparing 3,4-dihydroxyphenylacetic acid (DOPAC) + homovanillic acid (HVA) with DA (<xref ref-type=\"fig\" rid=\"F4\">Figure 4B</xref>). 6-OHDA lesioning significantly increased DA turnover in the striatum (sham vs. 6-OHDA, <italic>p</italic> < 0.001). A similar response was found in animals receiving L-DOPA/benserazide (sham vs. 6-OHDA + L-DOPA/benserazide, <italic>p</italic> = 0.004). Importantly, PT320 significantly reduced DA turnover in the lesioned striatum (<italic>p</italic> = 0.028, 6-OHDA + L-DOPA/benserazide vs. 6-OHDA + L-DOPA/benserazide + PT320) (<xref ref-type=\"fig\" rid=\"F4\">Figure 4B</xref>). 6-OHDA lesioning significantly increased DOPAC/DA (<italic>p</italic> = 0.019, lesioned vs. non-lesioned striatum) (<xref ref-type=\"fig\" rid=\"F4\">Figure 4C</xref>) as well as the HVA/DA ratio (<italic>p</italic> < 0.001, lesioned vs. non-lesioned striatum; <italic>p</italic> < 0.001, sham vs. 6-OHDA) (<xref ref-type=\"fig\" rid=\"F4\">Figure 4D</xref>). Similar to the DA turnover noted above, the HVA/DA ratio was significantly reduced by PT320 in the lesioned striatum (<sup>∗</sup><italic>p</italic> = 0.006, 6-OHDA + L-DOPA/benserazide vs. 6-OHDA + L-DOPA/benserazide + PT320).</p></sec><sec id=\"S3.SS3\"><title>Interaction of AIM and DA Turnover</title><p>All lesioned animals receiving L-DOPA/benserazide (with or without PT320) were pooled for the correlation analysis. ALO score on day 22 was significantly correlated with lesioned side DA turnover [ALO = 3.384 + (12.465 <sup>∗</sup> DA turnover), <italic>p</italic> = 0.023, <italic>R</italic> = 0.471, <italic>n</italic> = 23].</p></sec></sec><sec id=\"S4\"><title>Discussion</title><p>LID was examined in unilaterally 6-OHDA−lesioned rats during a 22-day L-DOPA/benserazide treatment. L-DOPA/benserazide or PT320 was administered to animals with an 84% reduction in DA. We found that L-DOPA/benserazide increased AIMs on limbs and axial parameters as well as ALO, the sum of all dyskinesia scores, over 3 weeks. Treatment with PT320 reduced the ALO AIM score and normalized DA turnover. The main finding of this study is that PT320 attenuates LID in PD-like animals.</p><p>PT320 is a new SR formulation that provides controlled, continuous release of clinical-grade exendin-4 following s.c. administration across mice (<xref rid=\"B5\" ref-type=\"bibr\">Bader et al., 2019</xref>), rats (<xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2018</xref>), non-human primates (<xref rid=\"B27\" ref-type=\"bibr\">Li et al., 2019</xref>), and humans (<xref rid=\"B18\" ref-type=\"bibr\">Gu et al., 2014</xref>). In this regard, a regulated, initial rapid-release burst provides therapeutic levels of drug in plasma within a few hours, as exendin-4 is liberated from the surface of the injected PLGA microspheres. This is followed by slower secondary and tertiary release phases associated with microsphere hydration that creates an <italic>in situ</italic> matrix drug reservoir from which hydrolysis and erosion of the PLGA polymer subsequently occurs, and results in steady-state exendin-4 release and the long-term maintenance of therapeutic drug levels (<xref rid=\"B43\" ref-type=\"bibr\">Schwendeman et al., 2014</xref>; <xref rid=\"B47\" ref-type=\"bibr\">Wan and Yang, 2016</xref>).</p><p>The continuous release of exendin-4 from SR formulations, such as PT320, as a mechanism to maintain therapeutic drug levels, differs from long-acting GLP-1R agonists that are either covalently linked or bind to large proteins, such as the Fc fragment of human IgG4 (dulaglutide) or human albumin (albiglutide and semiglutide) to reduce clearance and, thereby, maintain GLP-1R agonist levels in plasma. Whereas the brain uptake of Exendin-4 has been reported as approximately 1–2% of its concomitant plasma level across rodent and human studies (<xref rid=\"B4\" ref-type=\"bibr\">Athauda et al., 2017</xref>; <xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2018</xref>; <xref rid=\"B5\" ref-type=\"bibr\">Bader et al., 2019</xref>; <xref rid=\"B34\" ref-type=\"bibr\">Mullins et al., 2019</xref>), that of protein-linked GLP-1R agonists remains unknown but is likely exceedingly low (<xref rid=\"B21\" ref-type=\"bibr\">Kim et al., 2010</xref>). Notably, PT320 is currently in Phase IIa clinical trials to evaluate its efficacy and safety in patients with early Parkinson’s disease (<ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.gov/\">ClinicalTrials.gov</ext-link> Identifier: NCT04269642), as exendin-4 provided via PT320 s.c. administration resulted in substantially greater brain penetration than twice-daily administration of immediate-release exendin-4 (<xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2018</xref>; <xref rid=\"B5\" ref-type=\"bibr\">Bader et al., 2019</xref>).</p><p>We previously reported that PT320, given before or 6 days after 6-OHDA lesioning, significantly improved TH-IR in the lesioned striatum and SNc, and attenuated methamphetamine-induced rotation (<xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2018</xref>). In this study, PT320 was given at 4 weeks after 6-OHDA lesioning. Using HPLC analysis, we found that delayed PT320 treatment did not alter DA levels in the lesioned striatum when the lesion was close to complete. These data suggest that while PT320 reduces the progression of DA degeneration, the protective response to PT320 requires early treatment in this PD model and, on translating this to humans, should best be initiated during the early disease course.</p><p>In contrast to its protective effect against DA degeneration in the early stages of PD (<xref rid=\"B9\" ref-type=\"bibr\">Chen et al., 2018</xref>), PT320 reduces L-DOPA/benserazide-induced AIMs at 7 weeks after lesioning. Chronic L-DOPA/benserazide treatment for 3 weeks significantly increased AIM (LID). PT320 significantly reduced the AIM score and its correlation with L-DOPA/benserazide treatment in these 6-OHDA rats. We found that the increase in ALO scores significantly correlated with reduced DA turnover, but not DA or its metabolites in the lesioned striatum. Similar findings have been reported in that PD patients with dyskinesia had higher DA turnover in the putamen (<xref rid=\"B28\" ref-type=\"bibr\">Lohle et al., 2016</xref>) and HVA/DA in cerebrospinal fluid (<xref rid=\"B29\" ref-type=\"bibr\">Lunardi et al., 2009</xref>) than those without dyskinesia. Associated with the reduction of the AIM score, animals receiving PT320 here also had lower DA turnover. Taken together, these data support the interaction of AIM and DA turnover. DA turnover, but not the levels of DA, HVA, or DOPAC, may be a good biomarker for AIM. The mechanisms underlying DA turnover and LID, however, require further investigation.</p><p>Besides LID, DA graft-induced dyskinesia (GID) has been reported in selective PD cases (<xref rid=\"B31\" ref-type=\"bibr\">Ma et al., 2002</xref>) and in animals receiving amphetamine injection (<xref rid=\"B44\" ref-type=\"bibr\">Smith et al., 2012</xref>). However, GID was not found in MPTP-treated monkeys receiving intraputamenal grafts of fetal dopaminergic cells (<xref rid=\"B23\" ref-type=\"bibr\">Kordower et al., 2017</xref>). Furthermore, embryonic dopamine neuronal grafts improved L-DOPA-mediated AIM and normalized preproenkephalin and prodynorphin expression in the indirect and direct pathway in 6-OHDA-lesioned rats (<xref rid=\"B25\" ref-type=\"bibr\">Lee et al., 2000</xref>). These data suggest that differential mechanisms of dyskinesia may be involved in LID and GID. The use of PT320 in preventing GID requires further investigation. In the light of the positive actions of PT320 on LID in the current study, the evaluation of PT320 in preventing GID warrants investigation as our understanding and present treatment options of GID are strictly limited (<xref rid=\"B46\" ref-type=\"bibr\">Tronci et al., 2015</xref>; <xref rid=\"B24\" ref-type=\"bibr\">Lane, 2019</xref>).</p><p>PT320 significantly reduced DA turnover in the 6-OHDA-lesioned striatum. On the other hand, PT320 or L-DOPA/benserazide did not alter DA, DOPAC, or HVA levels in the lesioned striatum. The “normalization” of DA turnover in the 6-OHDA lesioned animals treated with PT320 suggests increased conversion of extracellular DA to DA metabolites in these animals. The presynaptic terminals of nigrostriatal DA fibers have D2 autoreceptors whose stimulation by extracellular DA inhibits DA synthesis and release, and subsequent metabolism to HVA and DOPAC. The reduction in turnover implies more extracellular DA availability in response to activation of these inhibitory D2 autoreceptors by DA after PT320 treatment. This would be readily reflected in DA turnover or the HVA/DA ratio, but not in DA levels.</p></sec><sec id=\"S5\"><title>Conclusion</title><p>In conclusion, our data support the notion that PT320 reduced the L-DOPA/benserazide−mediated AIM in a 6-OHDA rat model of PD, likely through the modulation of DA turnover in the lesioned brain. Our studies further emphasize the potential utility of PT320 in the treatment of clinical PD, for which clinical trials are ongoing, and highlight the opportunity to mitigate LID, a common and disabling feature of L-DOPA treatment that can reduce its beneficial effects.</p></sec><sec sec-type=\"data-availability\" id=\"S6\"><title>Data Availability Statement</title><p>All datasets presented in this study are included in the article/supplementary material.</p></sec><sec id=\"S7\"><title>Ethics Statement</title><p>The animal study was reviewed and approved by the National Health Research Institutes.</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>S-JY, SC, and Y-YY analyzed the data. EG, JJ, HK, H-IC, and D-SC developed the dosing, provided the materials, and performed the experiments. BH, NG, H-IC, and D-SC conceptualized the study. YW and S-JY wrote the initial draft. All authors edited the manuscript.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>JJ, S-JY, and H-IC are employees of Peptron Inc. D-SC is a scientific advisor to Peptron Inc. The Intramural Research Program of the National Institute on Aging, N.I.H., and Peptron Inc. have a Cooperative Research and Development Agreement to develop Exendin-4 as a treatment strategy for neurodegenerative disorders for which NIA and Peptron Inc. hold patent rights via the work of H-IC and NG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling Editor is currently organizing a Research Topic with one of the authors, NG.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This research was supported in part by (i) the Technological Innovation R&D Program (S2174574) funded by the Small and Medium Business Administration (South Korea); (ii) Peptron Inc., Daejeon, South Korea; (iii) the Bio and Medical Technology Development Program (NRF-2014M3A9B5073868) of the National Research Foundation (NRF) funded by the Ministry of Science, ICT and Future Planning (MSIP) of Korea; (iv) the Intramural Research Program of the National Institute on Aging, National Institutes of Health, United States; (v) USPHS, NIH, NS 094152-03S1; (vi) National Health Research Institutes, Taiwan; and (vii) the Ministry of Science and Technology, Taiwan (MOST 108-2320-B-400-013).</p></fn></fn-group><ack><p>We acknowledge the input of MOTAC, Bordeaux, France, and Ms. Jae-Nam Yun of the Peptron Inc., Daejeon, South Korea.</p></ack><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Abuirmeileh</surname><given-names>A.</given-names></name><name><surname>Harkavyi</surname><given-names>A.</given-names></name><name><surname>Rampersaud</surname><given-names>N.</given-names></name><name><surname>Lever</surname><given-names>R.</given-names></name><name><surname>Tadross</surname><given-names>J. 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Med (Lausanne)</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Med (Lausanne)</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Med.</journal-id><journal-title-group><journal-title>Frontiers in Medicine</journal-title></journal-title-group><issn pub-type=\"epub\">2296-858X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850929</article-id><article-id pub-id-type=\"pmc\">PMC7431891</article-id><article-id pub-id-type=\"doi\">10.3389/fmed.2020.00524</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Medicine</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Early Epidemiological Features of COVID-19 in Nepal and Public Health Response</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Dhakal</surname><given-names>Santosh</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/518908/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Karki</surname><given-names>Surendra</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/998456/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health</institution>, <addr-line>Baltimore, MD</addr-line>, <country>United States</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Epidemiology and Public Health, Himalayan College of Agricultural Sciences and Technology</institution>, <addr-line>Kirtipur</addr-line>, <country>Nepal</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Zisis Kozlakidis, International Agency For Research On Cancer (IARC), France</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Mohamed Izham Mohamed Ibrahim, Qatar University, Qatar; Meghnath Dhimal, Nepal Health Research Council, Nepal</p></fn><corresp id=\"c001\">*Correspondence: Surendra Karki <email>karkisuren@fulbrightmail.org</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Infectious Diseases - Surveillance, Prevention and Treatment, a section of the journal Frontiers in Medicine</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>11</day><month>8</month><year>2020</year></pub-date><!-- PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type=\"epub\"/>. --><volume>7</volume><elocation-id>524</elocation-id><history><date date-type=\"received\"><day>06</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>27</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Dhakal and Karki.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Dhakal and Karki</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), was first reported in late 2019 from Wuhan, China. Considering COVID-19's alarming levels of spread and severity, the World Health Organization (WHO) declared a global pandemic on March 11, 2020. The first case of COVID-19 in Nepal was reported on January 23, 2020. The Government of Nepal implemented different public health measures to contain COVID-19, including border closures and a countrywide lockdown. We collected the daily data provided by the Ministry of Health and Population (MoHP) of the Government of Nepal and illustrated the early epidemiological characteristics of COVID-19 in Nepal. By May 31, 2020, 1,572 cases and eight deaths were reported in Nepal associated with COVID-19. The estimate of prevalence for COVID-19 among tested populations was 2.25% (95% CI: 2.15–2.37%) and case-fatality rate was 0.5%. The majority of the cases were young males (<italic>n</italic> = 1,454, 92%), with overall average age being 30.5 years (ranging from 2 months to 81 years) and were mostly asymptomatic. There were only five cases from three districts until the end of March, but cases surged from April and spread to 57 out of 77 districts of Nepal by the end of May 2020 despite the continuous lockdown. Most of these cases are from the southern plains of Nepal, bordering India. As the effect of COVID-19 is expected to persist longer, the Government of Nepal should make appropriate strategies for loosening lockdowns in a phase-wise manner while maintaining social distancing and personal hygiene and increasing its testing, tracking, and medical capacity.</p></abstract><kwd-group><kwd>severe acute respiratory syndrome coronavirus-2</kwd><kwd>coronavirus disease 2019</kwd><kwd>epidemiology</kwd><kwd>public health response</kwd><kwd>Nepal</kwd></kwd-group><counts><fig-count count=\"4\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"48\"/><page-count count=\"8\"/><word-count count=\"5365\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Coronaviruses (CoVs) are enveloped, positive-sense, single-stranded RNA viruses with a comparatively larger genome size (30 Kb), belonging to the order <italic>Nidovirales</italic>, family <italic>Coronaviridae</italic>, and subfamily <italic>Coronavirinae</italic> (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). The subfamily is further divided into four genera: alpha, beta, gamma, and delta coronaviruses. Those infecting mammals fall within alpha and beta CoVs (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>). When contracted by farm animals, CoVs are known to cause severe economic losses for a considerable time. Transmissible Gastroenteritis Virus (TGEV) and Porcine Epidemic Diarrhea Virus (PEDV) in pigs, and Bovine Coronaviruses (BCoVs) in cattle are a few such examples (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B4\" ref-type=\"bibr\">4</xref>). The PEDV outbreak in the US pig industry in 2013 was characterized by severe gastroenteritis in piglets. This outbreak killed over 7 million pigs within a year, which was 10% of the total pig population in the US (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). CoVs also cause Infectious Bronchitis in poultry, resulting in huge economic losses in the poultry industry. They are also transmissible to dogs and cats. In humans, CoVs (HCoV-NL63, HCoV-229E, HCoV-OC43, and KHU1) were traditionally known to cause mild respiratory infections until the emergence of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). SARS-CoV emerged from Guangdong Province, China, in November 2002 and spread rapidly to at least 27 countries, leading to over 8,000 reported cases and over 750 deaths, with about a 10% case-fatality rate (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Within a decade of the SARS-CoV epidemic, another novel coronavirus infection was reported from Saudi Arabia, in June 2012 (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). This virus, later named Middle East Respiratory Syndrome Coronavirus (MERS-CoV), had around a 34% case-fatality rate and resulted in a total of nearly 2,500 laboratory-confirmed cases and over 850 associated deaths from 27 countries as of November 2019 (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). In December 2019, a series of viral pneumonia cases were reported from Wuhan, Hubei Province, China. The causative agent, a novel beta coronavirus, was first named as 2019 novel coronavirus (2019-nCoV) (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). 2019-nCoV was ultimately renamed as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the disease, characterized by symptoms including fever, shortness of breath, cough, fatigue, and pneumonia, was named Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO) (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>).</p><p>On 30th January 2020, the WHO declared that the COVID-19 outbreak constituted a Public Health Emergency of International Concern (PHEIC), which was eventually declared a global pandemic on 11th March 2020, owing to the alarming levels of spread and severity of COVID-19 (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). It is most likely that, similar to SARS and MERS coronaviruses, SARS-CoV-2 also originated from bat reservoirs. Studies have shown around an 80% genetic sequence homology between SARS-CoV-2 and SARS-CoV, while the resemblance of SARS-CoV-2 with bat coronaviruses is over 95% (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Bat coronaviruses require intermediate animal hosts before the spillover occurs in humans. For SARS-CoV and MERS-CoV, palm civet and dromedary camels, respectively, were found to serve as intermediate hosts (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Pangolin coronaviruses had over a 90% similarity to SARS-CoV-2 but evidence contrasts regarding the possibility of pangolins being the intermediate host (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). SARS-CoV-2 can infect animals including ferrets, domestic cats, tigers, and rhesus macaques either naturally or experimentally, but the actual intermediate host which contributed in virus transmission dynamics is not known yet (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>–<xref rid=\"B17\" ref-type=\"bibr\">17</xref>).</p><p>As per the WHO's situation report from 31st May 2020, more than 5.9 million cases of COVID-19 were reported globally, with over 365,000 deaths (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). The US alone has reported more than 1.7 million cases and over 100,000 deaths (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). Other countries most severely affected with higher number of cases of COVID-19 include Brazil, Russia, Spain, the UK, India, Italy, Peru, Germany, and Turkey (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Early epidemiological studies from China and the US indicated that older age and patients with underlying health conditions were at greater risk of hospitalization, intensive care unit (ICU) admission, and death due to COVID-19 (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). A recent retrospective study from New York also showed that older age and chronic pulmonary and cardiac diseases were independently associated with in-hospital mortality with COVID-19 (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>).</p><p>As of May 31, 2020, COVID-19 has been reported from 10 of 11 member countries in the WHO South-East Asia region. The highest number of cases have been reported from India, Bangladesh, and Indonesia (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). In Nepal, the first case of COVID-19 was officially reported on 23rd January 2020 in a 32-year-old man who returned from Wuhan, China (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). The second case was detected after two months on 23rd March. By May 31, 1,572 cases and eight deaths were reported from Nepal (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). The increasing situation of COVID-19 will be challenging for countries like Nepal where the health infrastructure is fragile and less equipped. In Nepal, there are only 194 hospitals with ICU facilities, with a capacity of 26,930 hospital beds, 3,076 isolation beds, 1,595 ICU beds, and 840 ventilators. In total, 111 hospitals run COVID-19 clinics while 13 hospitals are designated as level-I COVID-19 hospitals, 12 hospitals as level-II COVID-19 hospitals, and three hospitals as level-III COVID hospitals (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). In this article, we describe the early epidemiological features of COVID-19 in Nepal, its spatiotemporal distribution, the public health response taken by the Government of Nepal, and the way forward.</p></sec><sec sec-type=\"methods\" id=\"s2\"><title>Methods</title><sec><title>Study Design</title><p>This is a descriptive epidemiological study to highlight the early epidemiological features of COVID-19 cases in Nepal.</p></sec><sec><title>Study Area</title><p>Nepal is a landlocked country surrounded by India in the south, east, and west and China in the north. Nepal has a population of around 30 million (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). Politically, Nepal is divided into seven provinces, 77 districts, and 753 local bodies. Geographically, it is divided into Terai (southern plains bordering to India), hills, and mountains (Himalayan range).</p></sec><sec><title>Data Collection</title><p>The COVID-19 cases and Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) testing data for this study were compiled using the publicly available official situation reports of the Ministry of Health and Population (MoHP) of the Government of Nepal (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). The MoHP made these data public through daily press meets and through national television broadcasts and MoHP's social media page. The daily situation reports are available from MoHP's website: <ext-link ext-link-type=\"uri\" xlink:href=\"https://drive.google.com/drive/folders/1QhLMbT76t6Zu1sFy5qlB5aoDbHVAcnHx\">https://drive.google.com/drive/folders/1QhLMbT76t6Zu1sFy5qlB5aoDbHVAcnHx</ext-link>. This study includes data from January 23, 2020 to May 31, 2020 to understand the early epidemiological features of COVID-19 cases in Nepal. COVID-19 cases in Nepal, as defined by the MoHP, included any individual who had RT-PCR tested positive for SARS-CoV-2 virus infection.</p></sec><sec><title>Statistical Analysis</title><p>The daily data were collated in Microsoft Excel 2016. The graphs were created using the same version of Microsoft Excel. Descriptive statistics of the age distribution of confirmed COVID-19 cases such as the mean, median, minimum, maximum, standard deviation, and quartiles were calculated using the Epi Info version 7.2.3.1 developed by the Center for Disease Control and Prevention (CDC) of the United States (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cdc.gov/epiinfo/index.html\">https://www.cdc.gov/epiinfo/index.html</ext-link>).</p><p>The choropleth maps, which helps to show the spatial patterns by shading the geographical areas in different colors, were created using the open-access software, QGIS version 3.10.3 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.qgis.org/en/site/\">https://www.qgis.org/en/site/</ext-link>) to show the spatial distribution of COVID-19 cases in Nepal in three time periods (January-March; April and May 2020). We aggregated January-March as there were only a few cases (five) in total by the end of March.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><p>The number of COVID-19 confirmed cases in Nepal reached 1,572 by May 31, 2020, after it was first confirmed in the country on January 23, 2020. The first case included a student who had returned from Wuhan, China, who, being aware of the coronavirus outbreak, visited the hospital in Kathmandu for a medical check-up (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>). Among the total infected, 220 individuals were discharged from the hospital after testing negative. The epidemic curve based on daily data showed that cases have started rapidly rising since May 2020 (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). During this same period, 69,587 samples were tested using RT-PCR in 20 laboratories distributed across the country, with the majority of the tests being conducted at the National Public Health Laboratory (NPHL) based in the capital city, Kathmandu (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). This early epidemiological data indicates that the prevalence of COVID-19 among the tested individuals in Nepal was 2.25% (95% CI: 2.15–2.37%) (<italic>n</italic> = 1,572/69,587). More than 95% of these cases were asymptomatic. The tested individuals were mostly people who came in contact with the confirmed cases identified through contact tracing or those in quarantine set up by the government who had returned from foreign countries, the majority of whom returned from India. The earlier cases in Nepal up to mid-April 2020 had a travel history from countries such as China, France, Qatar, Belgium, the United Arab Emirates, the United Kingdom, and Saudi Arabia. All cases after mid-April were either linked to people coming from India via land or people contracting the virus locally, as all international flights were closed effective from March 23, 2020.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Epidemic curve of COVID-19 cases in Nepal.</p></caption><graphic xlink:href=\"fmed-07-00524-g0001\"/></fig><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Cumulative and daily RT-PCR testing for COVID-19 diagnosis in Nepal.</p></caption><graphic xlink:href=\"fmed-07-00524-g0002\"/></fig><sec><title>Age and Gender Distribution</title><p>Among the 1,572 confirmed cases, eight people (0.5%) had died from COVID-19 in Nepal by May 31, 2020. The first COVID-19 death was reported on May 16, 2020, in a new mother who gave birth to her child on May 6th, 2020 in a hospital in Kathmandu and was discharged. She went to her home in Sidhupalchok district, around 4-h bus travel from Kathmandu, and later developed signs of fever and respiratory difficulties and ultimately died on May 16, 2020. The majority of the other deaths were in quarantine and confirmed as COVID-19 after their deaths.</p><p>The majority of the cases confirmed in Nepal were young males (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>). 92% (<italic>n</italic> = 1,454/1,572) of the total cases were males and only 8% (<italic>n</italic> = 118/1,572) were females (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>). This is not surprising as this population was tested most given their higher proportion in quarantine. The average age among the overall cases was 30.5 years (Range: 2 months to 81 years). Disaggregation by gender showed that the average age among the males was 30.4 years (Range: 2 months to 74 years) while the average age among the females was 30.8 years (Range: 4 months to 81 years), showing no statistical difference between the average ages by gender (<italic>p</italic> = 0.82). The detailed descriptive statistics of overall age distribution and gender are shown in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Age and gender-wise distribution of COVID-19 cases in Nepal.</p></caption><graphic xlink:href=\"fmed-07-00524-g0003\"/></fig><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Age distribution of COVID-19 cases in Nepal<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>*</sup></xref>.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>COVID-19</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Mean</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Std. Dev</bold>.</th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Median</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>1st quartile</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>3rd quartile</bold></th></tr><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>cases</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>(Years)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>(Years)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>(Years)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>(25%)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>(75%)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">All (<italic>N</italic> = 717)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">37</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Male (<italic>N</italic> = 616)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">12.3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">36.5</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Female (<italic>N</italic> = 101)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30.8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43</td></tr></tbody></table><table-wrap-foot><fn id=\"TN1\"><label>*</label><p><italic>Out of 1,572 cases by May 31, 2020, exact age of 717 cases were made public while age of remaining cases were provided in ranges</italic>.</p></fn></table-wrap-foot></table-wrap></sec><sec><title>Spatial Patterns</title><p>The spatial pattern of COVID-19 cases in Nepal showed that, up to the end of March 2020, cases were reported only from three districts—Kathmandu, Baglung, and Kailali—out of 77 districts of Nepal. The number of districts affected increased to 12 by the end of April 2020 (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>). Most of these districts had sporadic cases, except for Udayapur district in the eastern part of Nepal, where a cluster of cases (<italic>n</italic> = 28) was reported from one small village (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>). The number of districts affected substantially increased and reached 57 out of 77 districts by the end of May 2020 (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>). The majority of the cases were observed in the southern plains of Nepal bordering India in Provinces 1, 2, and 5. Five districts, namely Jhapa, Parsa, Rautahat, Banke, and Kapilvastu reported more than 100 confirmed cases (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>). The province-wise distribution shows that Province 2 had the highest number of confirmed cases (<italic>n</italic> = 624 out of 1,572), followed by Province 5 (<italic>n</italic> = 565 out of 1,572), Province 1 (<italic>n</italic> = 165 out of 1,572), Karnali province (<italic>n</italic> = 123 out of 1,572), Bagmati province (<italic>n</italic> = 45 out of 1,572), Sudur Pashchim province (<italic>n</italic> = 27 out of 1,572), and Gandaki province (<italic>n</italic> = 23 out of 1,572).</p><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>Spatial distribution of COVD-19 cases in Nepal.</p></caption><graphic xlink:href=\"fmed-07-00524-g0004\"/></fig></sec><sec><title>Public Health Measures Adopted by the Government</title><p>The Government of Nepal closed all international flights and its international borders on March 23, 2020, after the second COVID-19 case was recorded in Nepal. A day after this, Nepal enforced a nation-wide lockdown on March 24, 2020, which has been extended continuously and has been in effect up to June 14, 2020. The total length of continuous lockdown shall reach 83 days by June 14, 2020. The lockdown modality after June 14, 2020, is not clear at the moment of drafting this manuscript. The government has been using both RT-PCR and antibody-based rapid diagnostic tests (RDT) in parallel to diagnose or screen probable patients. However, only RT-PCR has been considered the confirmatory test. The government increased the number of RT-PCR testing laboratories from one to 20, including four veterinary laboratories. By May 31, 2020, the government had used 111,109 RDT tests to screen people in quarantine or other suspected areas. It has been a challenge for all three tiers of the Government, Federal, Provincial, and Local, to manage the large influx of Nepalese people wanting to return home from India. A small subset of people who have already entered Nepal has been kept in quarantine, which is often reported to be poorly managed due to limited resources. Up till now, the government has been isolating all COVID-19 cases in designated COVID-19 hospitals, irrespective of their clinical situation. This has overwhelmed hospitals with patients who do not need immediate medical attention.</p></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>This study was conducted to describe the spatiotemporal patterns and early epidemiological features of COVID-19 cases in Nepal from January 23 to May 31, 2020. The findings show that the vast majority of the cases in Nepal were young males and the case fatality rate was 0.5%. The disease was rapidly spreading and reached 57 out of 77 districts from all seven provinces by the end of May 2020.</p><p>The strict lockdown, meticulous testing and tracking, and massive isolation of people helped China to reduce the effects of the COVID-19 pandemic (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Precise and widespread contact tracing and testing, including of asymptomatic individuals, together with social distancing led Taiwan to control COVID-19 in a fascinating way (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B31\" ref-type=\"bibr\">31</xref>). Similar intensive measures were also successfully used by South Korea to reduce COVID-19-associated casualties (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Likewise, Vietnam, a country of 97 million people with limited resources, has been successful in limiting the spread of COVID-19 through a strong response system, including quick strategic testing and aggressive contact tracing (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). Nepal closed its international borders and enforced a country-wide lockdown early on, when only two cases were identified. The non-pharmaceutical interventions, including border control, lockdown, social distancing, and personal hygiene, helped Nepal in preventing the spread of SARS-CoV-2 during the initial days. However, later on, the effectiveness of the countrywide lockdown has not been observed, as the number of cases surged from 57 cases up to April to 1,572 by the end of May 2020 (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). One major contributor to this surge has been the return of daily wage migrant workers from India (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>), where the cases of COVID-19 has been rapidly increasing since April 2020 (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). As Nepal shares its open border with India, citizens desperate to return home found different ways to return to Nepal, including swimming across the Mahakali river bordering two countries (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). There was also significant in-country movement of people wanting to return to their hometown as their livelihood sources in cities were compromised due to the lockdown.</p><p>Based on the available data, we estimated the COVID-19 prevalence in Nepal to be 2.25% (n =1,572/69,587). However, it may not represent the actual COVID-19 prevalence because samples from COVID-19 positive individuals are tested at least twice before declaring them COVID-19 negative and added in total numbers, without separating them. This prevalence also might not represent national level prevalence as samples from random populations have not been tested. Early studies reported that COVID-19 patients in Nepal showed few or no symptoms at all (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>, <xref rid=\"B36\" ref-type=\"bibr\">36</xref>). The situation updates of the Ministry of Health and Population (MoHP) also indicates that most of the confirmed cases are found through active surveillance and contact tracing rather than patients visiting hospitals with symptoms (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). This is in contrary to what is observed in other countries. The reported death rate (0.5%, n =8/1,572) also appears lower in comparison to the case-fatality rates reported from other countries. As per the mortality analysis carried out by Johns Hopkins University, among the 20 countries most severely affected with COVID-19 as of June 5, 2020, the case-fatality rate is highest in France (15.3%) and lowest in Chile (1.1%) (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). Nepal's neighboring countries, including China, India, Pakistan, and Bangladesh, have 5.5, 2.8, 2.1, and 1.4% case-fatality rates, respectively (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). Though the case fatality rates seem lower, it should not contribute to the relaxing of ongoing pandemic mitigation efforts by the Government of Nepal. The complete genome sequencing of the first SARS-CoV-2, isolated from Nepal, showed more than a 99% sequence homology with viruses isolated from Wuhan, China (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>). Further studies are necessary to determine the origin and nature of SARS-CoV-2 circulating in Nepal. Importantly, the true burden of COVID-19 in South Asia, including Nepal, is difficult to estimate due to the low amount of testing and poor documentation (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Moreover, as of May 31, 2020, the WHO classified the transmission pattern in Nepal as sporadic (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>), which means Nepal has not yet observed the larger outbreaks of community-level transmission or the peak of the disease, which might be on its way. There are early signs of it as the WHO Nepal office has indicated that there is some evidence of secondary community transmission and a cluster of cases have been observed in four out of seven provinces of Nepal (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>).</p><p>Nepal represents a real scenario of low- and middle- income countries (LMICs) where pandemic mitigation efforts are impacted largely by the lack of medical supplies and infrastructure. This includes personal protective equipment (PPE) and ventilators, the limitation of well-trained manpower, the unavailability of enough diagnostic kits; a lack of a proper coordination mechanisms among stakeholders, and poor reporting and documentation of cases (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>–<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). This pandemic has taught Nepal that it should invest more in research and development in the public health sector, besides the current primary focus on curative medicine. Current use of the laboratory facilities developed by the veterinary sector, to tackle with periodic disease outbreaks in animals including avian influenza viruses (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>), for COVID-19 diagnostic purposes further highlights the necessity of intersectoral collaboration in pandemic mitigation efforts. A multisectoral and collaborative one-health approach including animal health, human health, and environmental health professionals (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>) will not only be effective in managing the ongoing COVID-19 pandemic control but also will allow for better preparedness against future outbreaks and other imminent problems, such as antimicrobial resistance in Nepal.</p><p>COVID-19 has geographically expanded and affected all age groups in Nepal. As of June 6, 2020, the total number of cases and deaths have reached 3,235 and 13, respectively, from 69 out of 77 districts of Nepal (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). The Government of Nepal has been using lockdown as one of its major weapons against COVID-19. If enforced correctly, lockdown measures can effectively reduce the spread of the virus (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). However, the enforcement of a lockdown will likely be less effective if it is continued for long periods of time. Besides this tactic, the government should also consider and be prepared for managing the socio-cultural, economical, and psychological burdens of the lockdown, if it will be continued further. It will be very challenging for countries like Nepal to opt for indefinite lockdown measures given their limited resources and vulnerable socio-economic status.</p><sec><title>Strength and Limitations</title><p>The strength of this study is that it uses the daily data made public by the MoHP and provides early epidemiological features of COVID-19 cases in Nepal. This study will provide a baseline to compare the epidemiological features of COVID-19 cases in Nepal in the future, as the pattern might change with progression in infection. As only RT-PCR confirmed cases were included in the study, the data is reliable and provides useful information regarding the spatiotemporal patterns of COVID-19 cases in Nepal. However, this study has some limitations, such as the prevalence calculated in this study perhaps being an underestimation as the number of individuals tested is lower than the total samples tested. In addition, the estimated prevalence is only a reflection of those who are tested rather than the true prevalence at the population level.</p></sec></sec><sec id=\"s5\"><title>Conclusion and Recommendation</title><p>This study provides an overview of the spatiotemporal patterns and early epidemiological features of COVID-19 cases in Nepal. There were 1,572 cases and eight deaths associated with COVID-19 in Nepal by the end of May 2020. The estimate of prevalence for COVID-19 among the tested population was 2.25% and case-fatality rate was 0.5%. The majority of the cases were young and were mostly asymptomatic. The disease had spread to 57 out of 77 districts of Nepal by the end of May 2020, despite the continuous lockdown.</p><p>Moving forward, it would be better to identify high-, medium-, and low-risk areas and make appropriate plans for loosening lockdowns in a phase-wise manner to return toward the state of “new normal.” As the effect of COVID-19 is likely to persist longer (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>), practice of social distancing and good personal hygiene, including the use of face masks, continuous scrutiny at the porous Indian border, increased testing, tracking, and medical capacity, and proper quarantine of cases and high-risk groups should continue in Nepal.</p></sec><sec sec-type=\"data-availability\" id=\"s6\"><title>Data Availability Statement</title><p>All datasets generated for this study are included in the article/supplementary material.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>SD and SK conceived the idea and designed the study and prepared the first draft and revised it. SK collected and analyzed the data. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849227</article-id><article-id pub-id-type=\"pmc\">PMC7431892</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00774</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Feasibility and Safety of Paclitaxel-Coated Balloon Angioplasty for the Treatment of Intracranial Symptomatic In-Stent Restenosis</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Xu</surname><given-names>Haowen</given-names></name><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/635157/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Fu</surname><given-names>Xiaojie</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/692929/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Yuan</surname><given-names>Yongjie</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Quan</surname><given-names>Tao</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Zibo</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Han</surname><given-names>Kaihao</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Liu</surname><given-names>Guo</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Guan</surname><given-names>Sheng</given-names></name><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref></contrib></contrib-group><aff><institution>Department of Neurointerventional Radiology, First Affiliated Hospital of Zhengzhou University</institution>, <addr-line>Zhengzhou</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Diogo C. Haussen, Emory University, United States</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Priyank Khandelwal, Rutgers University, United States; Shyian Jen, MultiCare Health System, United States</p></fn><corresp id=\"c001\">*Correspondence: Xiaojie Fu <email>xjfu@zzu.edu.cn</email></corresp><corresp id=\"c002\">Sheng Guan <email>gsradio@126.com</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Endovascular and Interventional Neurology, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>774</elocation-id><history><date date-type=\"received\"><day>07</day><month>12</month><year>2019</year></date><date date-type=\"accepted\"><day>24</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Xu, Fu, Yuan, Quan, Wang, Han, Liu and Guan.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Xu, Fu, Yuan, Quan, Wang, Han, Liu and Guan</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Objective:</bold> Symptomatic in-stent restenosis (sISR) is the major cause of medium- or long-term cerebral infarctions in patients who underwent percutaneous transluminal angioplasty and stenting for severe intracranial atherosclerotic stenosis. This study aims to evaluate the feasibility and safety of paclitaxel-coated balloon (PCB) angioplasty for the treatment of intracranial sISR.</p><p><bold>Methods:</bold> We report 11 cases of PCB angioplasty for intracranial sISR. Lesion locations and number were as follows: intracranial internal carotid artery (<italic>n</italic> = 4), M1 segment of middle cerebral artery (MCA) (<italic>n</italic> = 1), V4 segment of vertebral artery (<italic>n</italic> = 6). The technical success rate, periprocedural complications, and short-term outcome were retrospectively analyzed.</p><p><bold>Results:</bold> All procedures were successfully performed without periprocedural complication. Asymptomatic vessel dissection after PCB inflation occurred in one case. Postprocedural diffusion-weighted imaging (DWI) showed new asymptomatic ipsilateral infarction in one case. All 11 cases did not experience ipsilateral stroke or death within 30 days or ischemic stroke in the territory of the target artery between 31 and 90 days after procedure.</p><p><bold>Conclusion:</bold> This preliminary study indicates that PCB angioplasty is feasible and safe for the treatment of intracranial sISR. Further studies are needed to clarify its efficiency and long-term outcome.</p></abstract><kwd-group><kwd>intracranial atherosclerotic stenosis</kwd><kwd>in-stent restenosis</kwd><kwd>drug-coated balloon</kwd><kwd>angioplasty</kwd><kwd>stroke</kwd></kwd-group><counts><fig-count count=\"1\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"22\"/><page-count count=\"6\"/><word-count count=\"4206\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Intracranial atherosclerotic stenosis (ICAS) responsible for 33–37% of acute ischemic strokes in Asian populations (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). WASID (Warfarin-Aspirin Symptomatic Intracranial Disease) trial demonstrated that more than 20% of medical-treated symptomatic ICAS patients had poor outcomes, driving rapid development in endovascular treatment (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>). Percutaneous transluminal angioplasty and stenting (PTAS) has been evolving as a potential treatment for ICAS patients with recurrent stroke despite medical treatment. However, the use of PTAS in ICAS became increasingly debated since the publish of SAMMPRIS (Stenting and Aggressive Medical Management for the Prevention of Recurrent Stroke Intracranial Stenosis) trial (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>) and VISSIT (Vitesse Intracranial Stent Study for the Ischemic Therapy) trial (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Both trials indicated that symptomatic ICAS patients treated with stenting had significantly higher periprocedural morbidity and mortality than that treated with aggressive medical management (AMM).</p><p>However, SAMMPRIS trial still demonstrated that 12.2% of patients with symptomatic severe ICAS developed ipsilateral stroke or death within 1 year despite AMM treatment (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>), suggesting significant need for alternative treatment strategies. In 2019, the WEAVE (Wingspan Stent System Post Market Surveillance) trial (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>) reported that with precise patient selection following the on-label usage guidelines, a low periprocedural complication rate (2.4%) of Wingspan stenting for ICAS could be achieved by experienced interventionalists. This joyful result demonstrated that PTAS is a promising therapy for symptomatic ICAS patients who are refractory to AMM.</p><p>High in-stent restenosis (ISR) rate is one of the discouraging results of intracranial stenting. In the SAMMPRIS trial, during a median follow-up of 35 months, various degrees of ISR were found in 66.7% of patients with infarction and 80% of patients with transient ischemic attack (TIA) who received adequate vascular imaging examination (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). The 1-, 2-, and 3-years rates for symptomatic ISR (sISR) of patients treated with Wingspan stenting were 9.6, 11.3, and 14%, respectively (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Symptomatic ISR is the major cause of medium- or long-term ipsilateral stroke after intracranial stenting. Bare balloon angioplasty and restenting are the two mostly reported interventional strategies to deal with sISR, but the restenosis rate is still high, and the efficiency remains unknown. The application of drug-coated balloons (DCBs, mostly paclitaxel-coated) angioplasty has been proven as a promising effective method to prevent and treat sISR in coronary and peripheral arteries in abundant studies (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>–<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). The use of DCB angioplasty for intracranial sISR was reported in few case reports. In this study, we evaluated the feasibility and safety of paclitaxel-coated balloon (PCB) angioplasty for the treatment of intracranial sISR.</p></sec><sec sec-type=\"methods\" id=\"s2\"><title>Methods</title><sec><title>Patient Selection</title><p>We conducted a retrospective review of ICAS patients treated with PTAS in our center (including Heyi, Zhengdong, and Huiji Branch Hospitals) from January 2018 to July 2019. Patients who developed sISR and treated with PCB angioplasty were retrospectively analyzed. Patients treated with PTAS and had any of the following events were identified (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>): (1) ischemic stroke in the territory of the stenting artery, (2) cerebral infarction with transient signs in the territory, or (3) TIA was associated with the territory. In-stent restenosis was determined by digital subtraction angiography (DSA) and defined as >50% stenosis within or immediately adjacent (within 5 mm) of the implanted stent and >20% absolute luminal loss (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Symptomatic ISR was defined as probable or definite ISR-associated ischemic symptoms in the territory (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>).</p><p>The criteria are as follows. Inclusion criteria were as follows: (1) 18–80 years old; (2) intracranial sISR; (3) baseline modified Rankin score <3; (4) patient understands the purpose and requirements of this therapy and has provided informed consent. Exclusion criteria were as follows: (1) intracranial or extracranial arterial dissection, moyamoya disease, vasculitis, radiation-induced vasculopathy, fibromuscular dysplasia; (2) a severe neurological deficit that renders the patient incapable of living independently; (3) dementia or psychiatric problem that prevents the reliable follow-up; and (4) comorbid conditions that may limit survival to <5 years.</p><p>A total of 151 ICAS patients who received successful intracranial stenting in our center (including Heyi, Zhengdong, and Huiji Branch Hospitals) were retrospectively reviewed; 85.4% (129/151) of them had valid angiographic follow-ups, and 15 patients (11.6% in 129) developed sISR. In the 15 patients with sISR, three of them rejected PCB angioplasty; one patient did not meet the criteria because of lung cancer, and 11 patients were finally included. All patients or their authorized family members were fully informed the benefits and risks of endovascular treatment and off-label use of the PCB. Clinical and imaging data of the subjects were retrospectively analyzed. This study was approved by the ethics committee of the First Affiliated Hospital of Zhengzhou University (approval no. 2019-KY-195). The privacy of patients was strictly protected.</p></sec><sec><title>Procedure</title><p>The preprocedural management included physical examination, brain magnetic resonance (MR) imaging, or high-resolution MR imaging of the target artery, Mini Mental State Examination (MMSE) score before and after procedure, and dual antiplatelet therapy with 100 mg of aspirin and 75 mg of clopidogrel daily at least 5 days.</p><p>Paclitaxel-coated balloon angioplasty was performed under general anesthesia. Heparin was titrated during the procedure to maintain activated clotting time between 250 and 300 s. The ISR grade was assessed according to the WASID trial. In this study, we used SeQuent Please (B. Braun, Berlin, Germany) to treat intracranial sISR. The paclitaxel-loading dosage is 3 <italic>u</italic>g/mm<sup>2</sup> and 16% of which will finally be implanted in the vessel wall. SeQuent Please is relatively rigid, which makes it challenging to use in patients with tortuous intracranial vasculature and requires rapid navigation and location to the target lesion; therefore, we used intracranial support catheter (5F or 6F Navien, ev3, Irvine, CA, USA) in most cases to support use of the PCB. Via the guiding catheter or Navien, the ISR lesion was crossed with a 0.014-inch Synchro microguidewire (Stryker Neurovascular, Salt Lake City, UT, USA) and predilated with bare balloon (Gateway balloon; Boston Scientific, Maple Grove, MN, USA). The bare balloon was then exchanged for a similarly sized PCB and centered across the lesion within 90 s. The DCB was then slowly inflated and kept at work pressure for 60 s. The ISR degree of residual stenosis was confirmed by DSA after the PCB withdrawn. The technical success of PCB angioplasty was defined as less 50% residual stenosis and stable antegrade perfusion (2b/3a) with no vessel dissection, perforation, or distal embolization (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). If the residual stenosis was more than 50% or there were vessel dissection, stent placement could be considered.</p><p>After the procedure, patients were typically monitored in neuro critical care units for 24 h. Postprocedurally, the systolic blood pressure was kept under 130 mm Hg. All patients who underwent PCB angioplasty were continued on 100 mg aspirin and 75 mg clopidogrel daily for 3 months and 100 mg of aspirin daily thereafter. Postprocedural MR imaging and MR angiography were performed within 2 weeks after treatment. The clinical follow-up was scheduled for 1, 3, 6, and 12 months and yearly thereafter.</p></sec><sec><title>Data Collection and Analysis</title><p>The following data were collected: demographic characters of all patients such as age, sex, location of the target artery, date of last stent implantation, periprocedural complications of last stenting procedure, MMSE scores before and after the procedure, date and feature of the symptomatic neurological symptoms, Mori classification of ISR (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>), degree of ISR, size of bare, and PCB used in the angioplasty, residual stenosis after dilation, and occurrence, type, and severity of all periprocedural complications.</p><p>The feasibility of PCB angioplasty for the treatment of intracranial ISR was determined by the following: (1) if the PCB can be safely transferred to the target lesion within 90 s despite significant tortuous access; (2) if the PCB can be safely inflated in target vessel for 60 s; (3) if the ISR grade was safely improved after PCB angioplasty. Safety of PCB angioplasty in intracranial ISR was determined by the following: (1) there was no hemorrhagic stroke due to microguidewire/microcatheter perforation or vessel rupture or hyperperfusion injury within 30 days after procedure; (2) there was no ischemic stroke due to distal embolization, perforator occlusion, and vessel dissection; (3) there was no stroke or death within 30 days after PCB angioplasty or ischemic stroke in the territory of the target artery between 31 and 90 days after procedure (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Descriptive statistical methods were applied in this study.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>Patient Characteristics</title><p>Eleven patients underwent PCB angioplasty for intracranial sISR, and their data were retrospectively analyzed in this study; 90.1% (10/11) of the patients were male. Ages ranged from 40 to 71 years with a mean of 56.0 years. The prevalence of dyslipidemia, hypertension, and diabetes was 72.7% (8/11), 36.4% (4/11), and 36.4% (4/11); 45.5% (5/11) of the sISR located in anterior cerebral circulation. The baseline characteristics are presented in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>.</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Baseline demographic characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Baseline demographic characteristics (<italic>n</italic> = 13)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>n</italic> (%)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Male, <italic>n</italic> (%)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (90.1)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age, mean ±<italic>SD</italic> (range), years</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">56.0 ± 10.2 (40–71)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hyperlipidemia</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8 (72.7)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hypertension</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (36.4)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Diabetes mellitus</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (36.4)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Body mass index (18–24)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (54.5)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Smoker (former or current)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9 (81.8)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Moderate or vigorous exercise</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (36.4)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Valid aggressive medical management</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3 (27.3)</td></tr></tbody></table></table-wrap></sec><sec><title>Standard-Reaching Rate of AMM</title><p>All subjects received AMM after the first PTAS treatment. On the baseline of this study, 36.4% (4/11) of the subjects had low-density lipoprotein (LDL) level >1.8 mg/dL, 36.4% (4/11) had hypertension (with systolic/diastolic blood pressure >140/90 mm Hg), 18.2% (2/11) had poor blood glucose control (with blood glycated hemoglobin level >6.0%), 18.2% (2/11) were current smokers, and 36.4% (4/11) lack moderate or vigorous exercise. In total, 27.3% (3/11) of the subjects achieved the goal of AMM (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p></sec><sec><title>Technique and Clinical Outcome</title><p>In a total of 11 PCB angioplasty cases, all PCBs were transferred to the target lesion within 90 s and inflated for at least 60 s. Paclitaxel-coated balloon angioplasty was technically successful in 90.1% (10/11) of patients. Asymptomatic vessel dissection after PCB inflation occurred in one patient (9.1%). No distal embolization or snowplow effect was seen during the navigation, location, inflation, deflation, and withdrawal of the PCB catheter. The preprocedure stenosis was 76.4 ± 8.3%, and postprocedure stenosis was 19.5 ± 9.6% (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>); 9.1% (1/11) of the subjects had asymptomatic ipsilateral infarction based on MR imaging after procedure. There was no symptomatic stroke or death within 30 days after DCB angioplasty or ischemic stroke in the territory of the target artery between 31 and 90 days after procedure. The lesion and procedural characteristics of the 11 patients are presented in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>. The PCB angioplasty procedure in a patient with sISR was presented in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>.</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Lesion and procedural characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Lesion and procedural characteristics (<italic>n</italic> = 13)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold><italic>n</italic> (%)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Lesion location</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Internal carotid artery</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4 (36.4)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Middle carotid artery</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (9.1)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Vertebral artery</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6 (54.5)</td></tr><tr><td valign=\"top\" align=\"left\" colspan=\"2\" rowspan=\"1\"><bold>Stenosis degree, Mean</bold>\n<bold>±</bold><italic><bold>SD</bold></italic><bold>, %</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   Before procedure</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">76.4 ± 8.3</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">   After DCB angioplasty</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19.5 ± 9.6</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Technique success</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">10 (90.1)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Symptomatic ipsilateral infarction</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Asymptomatic ipsilateral infarction</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (9.1)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Vessel dissection</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1 (9.1)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Perforation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Distal embolization</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Decreased MMSE score</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0 (0)</td></tr></tbody></table></table-wrap><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Paclitaxel-coated balloon angioplasty for intracranial sISR. <bold>(A)</bold> Symptomatic severe vertebral artery atherosclerotic stenosis despite of aggressive medical management. <bold>(B)</bold> Angiography results after PTAS using wingspan stent. <bold>(C)</bold> Symptomatic ISR because of the discontinuance of aggressive medical management. <bold>(D)</bold> Drug-coated balloon dilatation. <bold>(E)</bold> Angiographic result after the DCB angioplasty. <bold>(F)</bold> Brain DWI result on day 5 after procedure presented no new infarction.</p></caption><graphic xlink:href=\"fneur-11-00774-g0001\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>In this single-center retrospective pilot study, we found that PCB angioplasty was feasible and safe in the treatment of patients with intracranial sISR.</p><p>High periprocedural complication rate and intracranial ISR are the two main factors that limit the use of PTAS in treatment of ICAS. As discussed before, the WEAVE trial demonstrated that with experienced interventionalists, precise patient selection, and on-label usage guidelines, PTAS has excellent safety profile and a low periprocedural complication rate (2.4%) in ICAS patients (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>), suggesting the urgent need for intracranial ISR research.</p><p>The researches that focus on intracranial ISR are limited. The reported prevalence of intracranial ISR ranges from 14.4 to 30% (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B14\" ref-type=\"bibr\">14</xref>). In the stenting arm of SAMMPRIS trial, of 183 patients without a periprocedural primary endpoint, intracranial ISR was found in 70.6% (24/34) of patients with symptomatic infarction or TIA during a median follow-up of 35.0 months. Symptomatic ISR occurred in at least one of seven patients by 3 years of follow-up and was likely responsible for the majority of non-procedural cerebral infarctions (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). In a prospective study of 226 Chinese ICAS patients treated with PTAS, during a median follow-up of 10.1 months, 25.2% (<italic>n</italic> = 57) patients developed intracranial ISR and 26.3% (15/57) of which were symptomatic (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). These studies indicate that intracranial ISR is a key risk factor that affects the long-term outcome of ICAS patients treated with PTAS.</p><p>The mechanism of intracranial ISR remains unknown. Unlike the ISR research in cardiac and peripheral vessels, the basic study of intracranial ISR is relatively few. Clinical prospective studies have demonstrated that age, diabetes mellitus, stent type, lesion location, and history of smoking are risk factors in the development of ISR after intracranial stenting, which are similar with cardiac and peripheral ISR (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>, <xref rid=\"B17\" ref-type=\"bibr\">17</xref>). In this study, only three of the 11 patients (27.3%) achieved the goal of AMM since the last PTAS, indicating that patient compliance may also affect the progress of intracranial ISR. In the SAMMPRIS trial, even under the rigorous follow-up strategy, more than 30% patients cannot achieve target blood pressure and LDL level. The poor adherence and low goal-achieving rate of AMM are indeed concerning problems, indicating more efforts should be done in postdischarge treatment. Early elastic return, relocation of axially transmitted plaque, reorganization of thrombus, neointima formation, vascular remodeling, neoatherosclerosis, platelet aggregation resolution (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>), and inflammation are the pathogenic mechanisms that underlie peripheral ISR (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). However, considering the difference between cerebral vascular and peripheral vascular, the intrinsic mechanism of intracranial ISR still needs further research.</p><p>The optimal management of patients with intracranial sISR remains unclear (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Currently, the most reported methods include medical and interventional treatment. Although small sample studies showed that dual antiplatelet and stain therapy may be effective in intracranial sISR patients (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>), the long-term efficiency of medical treatment is uncertain. Interventional treatment includes balloon angioplasty and restenting. Wu et al. (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>) used balloon angioplasty and restenting to treat 21 patients with intracranial sISR; one patient experienced perforator stroke after procedure, and one patient had acute cerebral infarction during follow-up; 90.5% (19/21) patients had alleviated ISR grade and good outcome. Cardiac studies demonstrated that bare balloon angioplasty and restenting have a high restenosis rate in the treatment of ISR (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>); the efficiency of its use for intracranial sISR needs further research (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>).</p><p>Drug-coated balloon angioplasty has been officially recommended to treat coronary sISR. The balloon-carried drug, usually paclitaxel, can effectively inhibit smooth muscle cells proliferation and migration by irreversibly stabilizing intracellular microtubules (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Some studies have reported the intracranial use of DCB angioplasty. Vajda et al. (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>) reported that predilatation with SeQuent Please PCB followed by the deployment of Enterprise stent could significantly decrease the intracranial ISR rate to 3% in ICAS patients during average 8.9 months' follow-up. Predilatation using a conventional percutaneous transluminal coronary angioplasty (PTCA) balloon (Ryujin Plus Terumo) was performed in 13 cases (24%). The DCB angioplasty was attempted in 51 cases, and 23.6% failed (12 cases). The authors claimed the failure to difficult anatomy combined with the shaft thickness and the rigidity of the DCB-tip (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Gruber et al. (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>) compared the safety and efficacy between Neuro Elutax SV PCB angioplasty and routine PTAS in the treatment of symptomatic ICAS; they found both safety and efficacy were similar (complication rate: 0 vs. 18%, <italic>P</italic> = 0.21; technical success: 63 vs. 64%, <italic>P</italic> = 0.0.96, in DCB and PTAS groups, respectively). The DCB failure occurred in one case because of the difficult local anatomical conditions (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). In 2011, Zsolt Vajda et al. (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>) first reported the use of DCB angioplasty for neurovascular ISR. They found the recurrent stenosis rate (9%) of DCB angioplasty arm was significantly lower than that of bare balloon angioplasty (50%). In four of 47 cases (8%), the DCB could not be navigated through the in-stent stenotic lesion, and the treatment of these lesions was finally performed with a conventional balloon (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). This encouraging result showed that DCB was a promising technique to treat intracranial sISR, but since then, few studies have further reported this technical development.</p><p>In this study, we retrospectively analyzed the feasibility and safety of PCB angioplasty in patients with intracranial sISR. We used PCB catheter, which has been reported for the intracranial use (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Difficult local anatomical conditions combined with the rigidity of the DCB-tip are the most reported reasons that lead to the failure of PCB angioplasty in intracranial vascular (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>–<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). In our study, we found that with the help of intracranial support catheter and proper patient selection, PCB can be safely navigated to the target artery, even the distal portion of M1segment of middle cerebral artery. The technical success was achieved in 90.1% (10/11) of patients. One patient had asymptomatic vessel dissection after PCB inflation, which may be related to the stiffness of PCB catheter. This patient was treated with dual antiplatelet and stain therapy and had no symptoms during the 3-months follow-up. One patient had asymptomatic ipsilateral infarction after the procedure, which may be related to the microembolus during the angioplasty. No patients had decreased MMSE on day 5 after the procedure. There was no symptomatic stroke or death within 30 days or ischemic stroke in the territory of the target artery between 31 and 90 days after procedure.</p><p>This study has some important limitations. This descriptive study has a small sample size and lack of further follow-up. Based on the results of this study, we are enrolling more subjects and will continue the follow-up to investigate the long-term outcome of PCB angioplasty in intracranial sISR. This study showed the feasibility and safety of PCB angioplasty in patients with intracranial sISR. Further studies are needed to clarify its efficiency and long-term outcome.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>The datasets generated for this study are available on request to the corresponding author.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Approval No. 2019-KY-195). The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>HX and XF wrote this manuscript. SG approved the final submission. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"systematic-review\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849238</article-id><article-id pub-id-type=\"pmc\">PMC7431893</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00792</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Systematic Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Properties of Pain Assessment Tools for Use in People Living With Stroke: Systematic Review</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Edwards</surname><given-names>Sophie Amelia</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/938428/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ioannou</surname><given-names>Antreas</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1014365/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Carin-Levy</surname><given-names>Gail</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/965273/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Cowey</surname><given-names>Eileen</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/964411/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Brady</surname><given-names>Marian</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/868486/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Morton</surname><given-names>Sarah</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Sande</surname><given-names>Tonje A.</given-names></name><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Mead</surname><given-names>Gillian</given-names></name><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Quinn</surname><given-names>Terence J.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/96626/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Institute of Cardiovascular and Medical Sciences, University of Glasgow</institution>, <addr-line>Glasgow</addr-line>, <country>United Kingdom</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Internal Medicine Department, Nicosia General Hospital</institution>, <addr-line>Strovolos</addr-line>, <country>Cyprus</country></aff><aff id=\"aff3\"><sup>3</sup><institution>School of Health Sciences, Queen Margaret University</institution>, <addr-line>Edinburgh</addr-line>, <country>United Kingdom</country></aff><aff id=\"aff4\"><sup>4</sup><institution>School of Medicine, University of Glasgow</institution>, <addr-line>Glasgow</addr-line>, <country>United Kingdom</country></aff><aff id=\"aff5\"><sup>5</sup><institution>NMAHP Research Unit, Glasgow Caledonian University</institution>, <addr-line>Glasgow</addr-line>, <country>United Kingdom</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Centre for Clinical Brain Sciences, University of Edinburgh</institution>, <addr-line>Edinburgh</addr-line>, <country>United Kingdom</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Centre for Medical Informatics, Usher Institute, University of Edinburgh</institution>, <addr-line>Edinburgh</addr-line>, <country>United Kingdom</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Valerie Moyra Pomeroy, University of East Anglia, United Kingdom</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Jelle Demeestere, University Hospitals Leuven, Belgium; Anna Danielsson, University of Gothenburg, Sweden</p></fn><corresp id=\"c001\">*Correspondence: Terence J. Quinn <email>terry.quinn@glasgow.ac.uk</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Stroke, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>792</elocation-id><history><date date-type=\"received\"><day>01</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>25</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Edwards, Ioannou, Carin-Levy, Cowey, Brady, Morton, Sande, Mead and Quinn.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Edwards, Ioannou, Carin-Levy, Cowey, Brady, Morton, Sande, Mead and Quinn</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><bold>Background:</bold> Pain is a common problem after stroke and is associated with poor outcomes. There is no consensus on the optimal method of pain assessment in stroke. A review of the properties of tools should allow an evidence based approach to assessment.</p><p><bold>Objectives:</bold> We aimed to systematically review published data on pain assessment tools used in stroke, with particular focus on classical test properties of: validity, reliability, feasibility, responsiveness.</p><p><bold>Methods:</bold> We searched multiple, cross-disciplinary databases for studies evaluating properties of pain assessment tools used in stroke. We assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies tool. We used a modified harvest plot to visually represent psychometric properties across tests.</p><p><bold>Results:</bold> The search yielded 12 relevant articles, describing 10 different tools (<italic>n</italic> = 1,106 participants). There was substantial heterogeneity and an overall high risk of bias. The most commonly assessed property was validity (eight studies) and responsiveness the least (one study). There were no studies with a neuropathic or headache focus. Included tools were either scales or questionnaires. The most commonly assessed tool was the Faces Pain Scale (FPS) (6 studies). The limited number of papers precluded meaningful meta-analysis at level of pain assessment tool or pain syndrome. Even where common data were available across papers, results were conflicting e.g., two papers described FPS as feasible and two described the scale as having feasibility issues.</p><p><bold>Conclusion:</bold> Robust data on the properties of pain assessment tools for stroke are limited. Our review highlights specific areas where evidence is lacking and could guide further research to identify the best tool(s) for assessing post-stroke pain. Improving feasibility of assessment in stroke survivors should be a future research target.</p><p><bold>Systematic Review Registration Number:</bold> PROSPERO CRD42019160679</p><p>Available online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019160679\">https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019160679</ext-link>.</p></abstract><kwd-group><kwd>stroke</kwd><kwd>stroke care</kwd><kwd>pain</kwd><kwd>assessment</kwd><kwd>evaluation</kwd><kwd>psychometric</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"38\"/><page-count count=\"10\"/><word-count count=\"6022\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Pain is a common problem after stroke (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Estimates of the frequency of pain vary across studies, depending on the population assessed and whether the focus is incident or prevalent pain. Large cohorts of mild to moderate stroke survivors suggest pain incidence of around 10% (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>), while in smaller cohorts figures range from 30% during the first months (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>), to 48% at 1 year (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>) and 43% at 10 years (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>) after stroke.</p><p>Post-stroke pain is associated with disability and reduced quality of life (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). It is independently associated with fatigue (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>), depression (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>) and has been strongly linked with suicidality (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>, <xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Pain after stroke can have a variety of etiologies and manifestations, including: shoulder pain, headache, neuropathic pain and exacerbation of pre-existing pain. Pain symptoms can present at any point during stroke recovery and may progress to chronic pain if not recognized and treated appropriately.</p><p>The first step in managing post-stroke pain is recognition and measurement. However, management of pain has not always been given the same priority as other aspects of stroke care such as instituting secondary prevention (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Pain assessment is a complicated task made more challenging in the context of stroke. Since pain is a subjective experience, self-report scales and questionnaires are the most commonly employed pain assessment tools in clinical practice and pain may be part of a more general health related quality of life assessment (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>). However, stroke impairments such as cognitive decline and communication issues may make it difficult for stroke survivors to communicate the presence and experience of pain using these tools (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Other impairments such as visual issues or loss of motor skills may further complicate the use of self-completion questionnaires or visual analog scales.</p><p>Accepting these caveats, there is a range of pain assessment tools available that could be used with stroke survivors. Some are generic, some are specific to a certain pain syndrome and some are developed exclusively for stroke. At present there is no consensus on the best approach to assessing post-stroke pain and no standardized tool is recommended for research or practice (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). In the absence of a gold standard pain assessment in stroke survivors and with the great variety of assessment tools available, clinicians may struggle to know the most appropriate approach for their patients. The choice of assessment tools should be guided by evidence, particularly, the psychometric properties of the pain assessment tools available. Classical test features such as validity and responsiveness have been described for certain pain tools, however, equally important are end-user evaluations such as acceptability and feasibility within the person's healthcare setting.</p><p>A summary of psychometric properties of pain assessment tools could help clinicians and researchers choose the most appropriate measure, highlighting strengths and limitations and also showing where new evidence is needed. Thus, we conducted a systematic review to compare methods of pain assessment following stroke with a particular focus on properties of validity, reliability, feasibility, and responsiveness.</p></sec><sec sec-type=\"methods\" id=\"s2\"><title>Methods</title><p>We performed a systematic review, following best practice (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>) and where appropriate Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidance (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Two assessors (SE, TQ) performed all aspects of title selection, data extraction and analyses with disagreements resolved through discussion.</p><p>As our focus was test properties, we structured our review question using the format recommended for test accuracy evidence synthesis (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>).</p><list list-type=\"bullet\"><list-item><p>Index test: Any measure of pain that gives an objective read out.</p></list-item><list-item><p>Reference standard: Any measure that provides data on the classical test properties of interest namely validity, reliability, feasibility and responsiveness.</p></list-item><list-item><p>Condition: Stroke of any kind and at any stage in stroke journey.</p></list-item><list-item><p>Setting: Any healthcare setting.</p></list-item></list><sec><title>Search Strategy</title><p>We searched the following databases, chosen to represent the various disciplines that may assess post-stroke pain: Medline (Ovid), Embase (Ovid), CINAHL (EBSCO) and PsychInfo (EBSCO). All were searched from inception to 1st May 2020. Search concepts were “stroke” and “pain” and “assessment.” We used validated search filters for “stroke” and “pain,” taken from the relevant Cochrane review group (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Materials</xref>). We complemented our search by contacting members of an international stroke pain research group to ensure we had not missed relevant studies.</p><p>We screened titles, abstracts and then full text to inform decisions on inclusion. Forward and backward citation searching was conducted for relevant studies using Web of Science functionality. As a test of search validity, we pre-specified two papers (one original research and one review) that should be returned on our literature search (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>). As a further test we cross-checked our included papers with a systematic review of pain assessment in aphasia, recognizing that the topics were distinct but were likely to have considerable overlap (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p></sec><sec><title>Selection Criteria</title><p>The population of interest was adult stroke survivors at any stage of recovery. We did not include traumatic brain injury. If a mixed population was included, stroke had to represent more than 75% of the group. The test of interest was any form of pain assessment, including scales, questionnaires, observations and other patient reported outcome measures. Outcomes of interest were psychometric properties of the tools as defined below. We included studies of any quantitative design, conducted in any healthcare setting, noting setting as part of our data extraction. We only included studies published in peer reviewed journals but applied no other restrictions.</p></sec><sec><title>Data Collection Process and Data Items</title><p>We designed and piloted a bespoke data collection form. We used the research paper that informed our internal validation for piloting (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>).</p><p>We collected data on the following:</p><p>Study details: publication date, country, study design (<italic>i.e.</italic>, cross-sectional, prospective, retrospective), psychometric properties assessed (validity, feasibility, intra/inter-reliability, responsivity), sample size.</p><p>Stroke details: stroke classification (for example ischaemic or haemorrhagic), time since stroke, setting (classified as: acute stroke unit, rehabilitation, outpatient, community, using descriptions in the original paper), inclusion/exclusion criteria in original study, noting if there were specific exclusions relating to language or cognition.</p><p>Pain assessment: type of pain (see below), method(s) of pain assessment (<italic>i.e.</italic>, pain scales, questionnaires, stroke specific or generic), pain assessor(s) (<italic>i.e.</italic>, researcher or clinical discipline). For articles comparing multiple methods of pain assessment, we included all tools and recorded the primary pain assessment tool.</p></sec><sec><title>Categorization of Pain Syndromes</title><p>We categorized pain using the following pre-specified labels: neuropathic, nociceptive (noting the site <italic>i.e.</italic>, lower limb), headache or experimental (<italic>i.e.</italic>, investigator induced pain). We classified stroke shoulder pain as a distinct category as it can include both nociceptive and neuropathic elements. Our pain classification was based on the description in the original paper. Where the nature of the pain syndrome was not clear, two reviewers (SE, TQ) discussed and came to consensus. For some papers, lack of detail precluded applying any label with certainty, and these were categorized as “non-specified.”</p></sec><sec><title>Psychometric Properties</title><p>We were interested in the following psychometric properties: validity, reliability, feasibility, responsiveness. These were defined as (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>):</p><list list-type=\"bullet\"><list-item><p>Validity: the extent to which an instrument measures what is intended, in this case, is the tool a measure of pain? The concept of “accuracy” would be included as a measure of validity.</p></list-item><list-item><p>Reliability: the internal consistency of an instrument, and the degree to which it is free from error on repeated. We included measures of inter-observer, intra-observer and internal reliability.</p></list-item><list-item><p>Feasibility: usability, and acceptability of an instrument from the perspective of assessors and those being assessed.</p></list-item><list-item><p>Responsiveness: the ability of the instrument to distinguish clinically important changes over time.</p></list-item></list><p>On initial scoping it became clear that a traditional quantitative meta-analysis would not be possible, due to the substantial clinical heterogeneity across studies in terms of populations assessed, methods used, nature of pain assessments and psychometric properties described. To allow cross-study comparisons, we created summary measures of the study findings at the level of the psychometric property studied. Our categorization was based on the conclusions of the original paper and was agreed by consensus of two assessors (SE, TQ). We classified results as positive, neutral or inconclusive.</p></sec><sec><title>Risk of Bias</title><p>We assessed risk of bias for included studies at the outcome level. Two (SE, TQ) investigators individually assessed papers and agreed final grading. No single quality assessment tool would be suitable for the variety of methodologies that were included in our eligible papers. We elected to use the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>). QUADAS-2 is designed for assessing studies of test accuracy and uses a framework suited to our review with assessment of bias and applicability across four domains: patient selection, index tests, reference standard, flow and timing (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). As recommended, we took the original QUADAS-2 anchoring statements and modified to suit our review (modified domain questions included in <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Materials</xref>). We used robvis R package software to create summary “traffic light” plots (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Due to the limited number of studies and heterogeneity in summary measures we did not perform quantitative assessment for publication bias.</p></sec><sec><title>Evidence Synthesis</title><p>We created two summary tables (<xref rid=\"T1\" ref-type=\"table\">Tables 1</xref>, <xref rid=\"T2\" ref-type=\"table\">2</xref>): the first describes key characteristics of the included articles and the second summarizes their quantitative results. Our data were heterogeneous and required representation of differing constructs across various axes. To allow a visual representation that included pain syndrome, pain assessment tool and results of psychometric testing across various constructs we developed a visual plot using a modified harvest plot (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). We first created a matrix that plotted results by pain assessment tool (we created space in the plot for subcategorising by pain scales and questionnaires) against each psychometric property of interest. We color-coded according to pain type with one unit of plot space per study/experiment and then assigned the results of the study as positive (above a horizontal line of no effect), neutral (below the line) or inconclusive (crossing the line).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Key Characteristics of included papers.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Author/s</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study design</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Psychometric properties assessed</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Number included</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Age (years) (mean, SD)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Stroke setting</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Exclusion criteria</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Type of pain</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Pain assessment tool</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Pain assessor</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1. Benaim (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cross-sectional</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Validity, reliability</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">127</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">63 ± 8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rehabilitation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cognitive impairments, psychiatric disorders</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Shoulder pain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FPS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2. Chuang (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prospective</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reliability</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">50</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">52.6 ± 11.0</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Outpatient</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">other acute pain conditions, major medical problems, psychological impairments, aphasia</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Arm/shoulder pain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">v-NPRS-FPS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Clinical staff (rehabilitation physicians)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3. Dogan (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case control</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Validity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">60 including non-stroke control (<italic>n</italic> = 30)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">64.2 ± 9.42</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rehabilitation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pre-existing pain conditions, cognitive impairment, aphasia</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Shoulder pain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FPS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4. Korner-Bitensky (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cross-sectional</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Validity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">90</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not available</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rehabilitation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">cognitive impairments, central post-stroke pain syndrome</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Experimental (thermal)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10-cm v-VAS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Clinical staff (SLT), researcher</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5. Price (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Case control</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Feasibility, validity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">144 including non-stroke controls (<italic>n</italic> = 48)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">72.5 mean</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Acute stroke unit</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reduced conscious level or dysphasic</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Experimental (pressure)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">v/m/h-VAS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Researcher</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6. Smith (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Feasibility</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">388</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">77 (IQR:66–86)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Acute stroke unit</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">subsequent strokes</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not specified</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FPS and/or NRS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Clinical staff (Nurses)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7. Roosink (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cross-sectional</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Validity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">57.5 ± 7. 5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rehabilitation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">other chronic pain conditions, neurological deficits</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Shoulder pain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DN4</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unknown</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8. Turner-Stokes (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cross-sectional</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Validity, reliability, feasibility</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">49</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">52.6 ± 3.1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rehabilitation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">not specified</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Shoulder pain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AbilityQ, ShoulderQ</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Researcher</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9. Turner-Stokes (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Responsiveness</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">47.2 ± 2.2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Rehabilitation</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">not specified</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Shoulder pain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AbilityQ, ShoulderQ</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Clinical staff (Nurses)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10. Mandysová (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cross-sectional</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Validity, reliability, feasibility</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">80</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">71.0 ± 13.7 (range 22–94)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Acute stroke unit</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reduced conscious level</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not specified</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS/NRS, NRS, FPS-R</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Researcher</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11. Pomeroy (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prospective</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reliability</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">33</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">74 (range 57–89)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Community</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">reduced conscious level, other pain conditions, no irregular pain medication, no neurological/MSK disorders</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Shoulder pain</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10-cm v-VAS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Clinical staff (physiotherapist)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12. Soares (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cross-sectional</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Reliability, validity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">36</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">61 median (range 46–71.75)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Acute stroke unit</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">neurological disorders</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Experimental (mechanical)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PACSLAC-II</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Clinical staff (Neurology nurses)</td></tr></tbody></table><table-wrap-foot><p><italic>Study design and setting were categorized and agreed by two raters (SE, TQ)</italic>.</p><p><italic>FPS, Faces Pain Scale; NRS/NPRS, Numerical Rating Scale; VAS, Visual Analog Scale, v-/m-/h-, vertical/mechanical/horizontal</italic>.</p><p><italic>NPRS-FPS and VAS/NRS indicate combined versions of scales DN4, neuropathic pain diagnostic questionnaire; PACSLAC-II, Pain Assessment Scale for Seniors with Severe Dementia-II</italic>.</p><p><italic>SLT, Speech and Language Therapy</italic>.</p><p><italic>N.B. more comprehensive version of table is available in <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Materials</xref></italic>.</p></table-wrap-foot></table-wrap><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Summary of results from included articles.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Author/s</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Pain assessment (comparator)</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Results</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1. Benaim (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FPS (VAS, VRS)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Validity</underline></italic>: Correlation of FPS with VAS and VRS in both left and right hemisphere stroke (<italic>r</italic> = 0.65–0.82)<break/> • <italic><underline>Reliability</underline></italic>:<break/> • <italic><underline>Inter-rater:</underline>K</italic>:0.64 (SE = 0.11) and <italic>K:</italic>0.44 (0.09) in left and right hemisphere stroke respectively.<break/> • <italic><underline>Intra-rater:</underline>K</italic>:0.74 (0.13) and <italic>K</italic>:0.53 (0.10) in left and right hemisphere stroke respectively.<break/> • <italic><underline>Feasibility</underline></italic>: FPS was preferred in left hemisphere stroke, VAS was preferred in right hemisphere stroke.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">2. Chuang (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">v-NPRS-FPS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Reliability (intra-rater):</underline></italic>ICC=0.82 (SE=0.81), [smallest real difference = 1.87].<break/> • No significant systematic bias between repeated measurements for NPRS-FPS.<break/> • High level of stability and minimal temporal variation, range of limits of agreement (−2.50 to 1.90)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">3. Dogan (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FPS (VAS, LPS, NRS)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Validity</underline></italic>: Correlation of FPS with other pain scales in both groups (<italic>r</italic> = 0.95–0.97 and 0.67–0.93, respectively).</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4. Korner-Bitensky (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10-cm v-VAS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Validity</underline></italic>: No between group difference in pain discrimination (<italic>p =</italic> 0.75).<break/> • Repeated-measures ANOVA revealed no effect of group.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">5. Price (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">v/m/h-VAS FPRS, NRS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Feasibility</underline></italic>: Range (proportion) of stroke survivors able to complete various versions of VAS 65–47% (<italic>P</italic> < 0.001 in comparison to non-stroke controls)<break/> • Range (proportion) of more severe stroke (TACS) able to complete various versions of VAS 28–14% (<italic>P</italic> < 0.001 in comparison to other strokes)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">6. Smith (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">FPS, NRS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Feasibility</underline></italic>: 13.4% individuals unable to provide a meaningful response to either FPS or NRS.<break/> • <italic><underline>Validity</underline></italic>: Maximum NRS values correlated with length of stay (<italic>r</italic> = 0.29, <italic>P</italic> < 0.0001), stroke severity (<italic>r</italic> = 0.212, <italic>P</italic> = 0.0008), and number of sites of pain (<italic>r</italic> = 0.20, <italic>P</italic> = 0.007).</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">7. Roosink (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">DN4 (NRS)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Validity</underline></italic>: DN4+ classified patients reported: constant pain [DN4+:<italic>n</italic> = 4 (44%); DN4-:<italic>n</italic> = 0] higher pain intensity [DN4+ = 4.7 (SD = 2.9); DN4- = 2.5 (SD = 2.4)] higher impact of pain on daily living DN4+ = 5.9 (SD = 4.8), DN4- = 2.0 (SD = 2.6) more frequent loss of cold sensation [DN4+: <italic>n</italic> = 7 (78%); DN4-: <italic>n</italic> = 2 (20%)]<break/> • Signs and symptoms suggestive of neuropathic or nociceptive pain corresponded to DN4+ and DN4- respectively.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">8. Turner-Stokes (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AbilityQ, ShoulderQ (VAS)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Validity:</underline></italic> VAS agreement ± 1 on a 10-point scale was 36–59% with intraclass correlation coefficients 0.50–0.60 (<italic>p</italic> < 0.01).<break/> • <italic><underline>Reliability:</underline></italic> Agreement for individual questions 55–88%; <italic>K:</italic>0.07–0.79<break/> • Repeatability of ShoulderQ 36–72%, <italic>K:</italic> 0.16–0.56.<break/> • <italic><underline>Feasibility:</underline> N</italic> = 31 (63%) required help in completing AbilityQ.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">9. Turner-Stokes (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AbilityQ, ShoulderQ (VGRS)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><italic><underline>Responsiveness</underline></italic></italic>: Changes on VGRS associated with verbal reports of improvement (<italic>r</italic>: 0.67, <italic>P</italic> < 0.001).<break/> • Responders demonstrated significant change in VGRS and verbal scores, whereas non-responder group did not.<break/> • A change in summed VGRS score of ≥3 showed 77% sensitivity and 91.3% specificity for identifying responders, with a positive predictive value of 93.3%. Summed VGRS scores of ≤2 had a negative predictive value of 73.3%.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10. Mandysová (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VAS/NRS, NRS, FPS-R</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Validity:</underline> n</italic> = 19 (24%) reported pain using at least one scale.<break/> • Spearman correlation was 0.997 (<italic>p</italic> < 0.001) between VAS/NRS and NRS.<break/> • <italic><underline>Feasibility:</underline></italic> NRS had the highest preference ranking (ranking first or second in 75% cases).</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">11. Pomeroy (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">10-cm v-VAS</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic><underline>Inter-rater reliability:</underline></italic>ICC:0.79 for intensity, 0.75 for frequency and 0.62 for affective response.<break/> • Wide limits of agreement and significant rater bias reported for 6/27 ratings.<break/> • <italic><underline>Intra-rater reliability</underline></italic>:ICC:0.70 for intensity, 0.77 for frequency and 0.69 for affective response.</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">12. Soares (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PACSLAC-II</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">• <italic>Validity</italic>: PACSLAC-II differentiated 4.5-lb stimulus versus 2-lb (<italic>p</italic> = 0.03) or 0lb (<italic>p</italic> = 0.05).<break/> • <underline><italic>Reliability (internal)</italic></underline>: Cronbach α:0.87, 0.94, and 0.96 for weights of 0, 2, and 4.5 lb, respectively.</td></tr></tbody></table><table-wrap-foot><p><italic>FPS, Faces Pain Scale; NRS/NPRS, Numerical Rating Scale; VAS, Visual Analog Scale; LPS, Likert Pain Scale; FPRS, Four-point rating scale; v-/m-/h-, vertical/mechanical/horizontal, visual graphic rating scale (VGRS); NPRS-FPS and VAS/NRS indicate combined versions of scales DN4=neuropathic pain diagnostic questionnaire (DN4+, neuropathic pain reported; DN4-, no neuropathic pain reported); PACSLAC-II, Pain Assessment Scale for Seniors with Severe Dementia-II</italic>.</p></table-wrap-foot></table-wrap></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><p>The primary search yielded 2,851 articles, with 12 (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>–<xref rid=\"B33\" ref-type=\"bibr\">33</xref>) papers (<italic>n</italic> = 1,106 participants) meeting the inclusion criteria (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>). Our search results suggested a valid search as they included the two pre-selected papers and had all the relevant studies from the previous aphasia review. The number of participants in eligible papers ranged from 19 to 388. The most commonly employed design was cross-sectional (<italic>n</italic> = 6) with the majority of studies (<italic>n</italic> = 6) conducted in a rehabilitation setting (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>, <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Materials</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>PRISMA Flow chart for selection of studies for systematic review. The first search was performed on 31st July 2019; to ensure the review was up to date we ran a repeat search on 08/05/2020. The PRISMA contains an aggregate of both searches.</p></caption><graphic xlink:href=\"fneur-11-00792-g0001\"/></fig><p>In total, 10 different pain scales and questionnaires were assessed across the 12 studies (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). These were: Visual Analog Scale (VAS [differing scales described as VAS]), the Faces Pain Scale (including a revised version), Numerical Rating Scale, and various combinations of these; the Pain Assessment Scale for Seniors with Severe Dementia-II (PACSLAC-II), and three questionnaires: AbilityQ, ShoulderQ and the neuropathic pain diagnostic questionnaire (DN4). Of the included assessments, only the ShoulderQ was developed specifically for stroke. The Faces Pain Scale was the most commonly reported, with a version of this scale used in six of the 12 studies.</p><p>Where a pain category was described, the most commonly studied was shoulder pain. Neuropathic pain and Headache were not studied, except possibly in those papers that did not differentiate pain type. There was heterogeneity in the tools assessed for each pain category, with no pain category having more than two studies using a common tool (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>).</p><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>Cross-tabulation of pain assessment tool and post stroke pain syndrome.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" colspan=\"10\" style=\"border-bottom: thin solid #000000;\" rowspan=\"1\"><bold>Pain assessment tool</bold></th></tr><tr><th rowspan=\"1\" colspan=\"1\"/><th rowspan=\"1\" colspan=\"1\"/><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>VAS</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>VAS-NRS</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>FPS</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>FPS-NRS</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>NRS</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>VRS</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>ShoulderQ</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>PACSLAC-11</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>DN4</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Post-stroke pain syndrome</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Shoulder/arm pain</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Experimental</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Not specified</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Neuropathic</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Headache</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</td></tr></tbody></table><table-wrap-foot><p><italic>Each value represents use of a pain assessment tool by according to a post-stroke pain syndrome</italic>.</p><p><italic>FPS, Faces Pain Scale; NRS/NPRS, Numerical Rating Scale; VAS, Visual Analog Scale; LPS, Likert Pain Scale; FPRS, Four-point rating scale; v-/m-/h-, vertical/mechanical/horizontal, visual graphic rating scale (VGRS), NPRS-FPS and VAS/NRS indicate combined versions of scales DN4, neuropathic pain diagnostic questionnaire (DN4+, neuropathic pain reported; DN4-, no neuropathic pain reported); PACSLAC-II, Pain Assessment Scale for Seniors with Severe Dementia-II</italic>.</p></table-wrap-foot></table-wrap><p>There was a high risk of bias detected in the majority of included papers (<italic>n</italic> = 8; <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). Highest risk of bias and issues with generalisability was seen for the domain of patient selection (<italic>n</italic> = 10; judged high risk). This was due to exclusion of patients for whom pain assessment would be expected in clinical practice, including those with pre-stroke pain (<italic>n</italic> = 5 papers), aphasia (<italic>n</italic> = 3) and cognitive impairment (<italic>n</italic> = 3). There was poor reporting of study methods relevant to the risk of bias assessment, particularly around blinding of results when a study compared scales. Only four papers were judged to have overall low risk of bias (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>, <xref rid=\"B32\" ref-type=\"bibr\">32</xref>, <xref rid=\"B33\" ref-type=\"bibr\">33</xref>).</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Traffic Light plot for risk of bias in individual studies.</p></caption><graphic xlink:href=\"fneur-11-00792-g0002\"/></fig><p>We created a visual synthesis of the psychometric properties of the tools used to assess pain as a modified harvest plot (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>). The harvest plot approach allows visual display of data across several axes in one figure. We represented each study as a single unit (square), and color coded based on pain type. A horizontal line that bisected each row was a line of uncertain effect, if a study claimed that the psychometric property of interest was “good” i.e., acceptable for clinical use then the study was placed above the line, if the paper reported that the study was “poor” i.e., would not be suitable it was placed below the line.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Harvest plot of psychometric evaluation of pain scale according to the 12 included studies. <italic>Each unit represents a differing study. Color coding is used to represent differing pain types. Position around horizontal line describes paper conclusions regarding the property of interest, where above the line indicates “good,” below the line indicates “poor” and on the line indicates “uncertain.” Full description given in main manuscript. VAS, Visual Analog Scale; NRS, Numerical Rating Scale; FPS, Faces Pain Scale; VRS, Visual Rating Scale; ShoulderQ, Shoulder pain questionnaire; PACSLAC-II, Pain Assessment Scale for Seniors with Severe Dementia-II; DN4, neuropathic pain diagnostic</italic> questionnaire.</p></caption><graphic xlink:href=\"fneur-11-00792-g0003\"/></fig><p>All psychometric domains of interest were reviewed by at least one study, although the statistical approach to these assessments varied. Validity was the psychometric property evaluated most frequently (<italic>n</italic> = 8), and responsiveness was only considered by one study. In general the pain scales assessed were judged to be valid measures by the authors of the studies, with only two studies reporting concerns around validity (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>). A version of the Faces Pain Scale was the most commonly assessed, with evaluations of validity (<italic>n</italic> = 3), reliability (<italic>n</italic> = 3), and feasibility (<italic>n</italic> = 2). However, results were conflicting, for example feasibility of FPS was assessed as good, neutral and poor across the studies (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>).</p></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>We aimed to systematically review the psychometrics of pain assessment tools when used with stroke survivors. We found a limited literature with substantial heterogeneity in the tools used, the research methods employed and the properties assessed. The available data were limited by risk of bias and modest sample sizes. Thus, we are unable to recommend a preferred tool based on published psychometric properties. However, through our evidence synthesis, we have highlighted important evidence gaps that can inform the direction of future research activity in the pain assessment space.</p><p>Our mapping of the evidence using the harvest plot demonstrates the many limitations in the evidence base. Of the four key psychometric properties, there was little information on reliability, and responsiveness. Even where there was a portfolio of papers on a single tool it was difficult to draw conclusions. There were more studies on visual scales than questionnaires, with few studies using a scale specifically developed for stroke and no studies with a neuropathic or headache pain focus.</p><p>Our findings of inconsistent and inconclusive evidence are not unique to stroke. A previous review of pain assessment in aphasia concluded that “a feasible, reliable and valid pain assessment instrument is not yet available” (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>). Dementia is another clinical condition where pain is common but potentially difficult to assess. Although there is more published literature on dementia pain assessment tools (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>), conclusions of reviews are similar “limited evidence about reliability, validity and clinical utility” (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). This seems a missed opportunity, as well as the clinical importance of looking for pain, quantitative pain assessment could be a useful research outcome (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>).</p><p>Our assessment of risk of bias suggests common areas of concern particularly around reporting and generalisability. Exclusion of stroke survivors with aphasia, dementia or comorbidity threatens the external validity of study results. Similar exclusions have been demonstrated in other aspects of stroke assessment (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>). Certain scales may not be suitable for all stroke impairments, but simply excluding those people who may struggle to complete an assessment creates bias in any resulting estimates (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>).</p><p>Our review has several strengths. We performed a comprehensive search, followed best practice guidance and embedded internal validation steps. Given the disparate nature of relevant studies, we used non-traditional methods for evidence synthesis and assessment of quality. There are limitations to our approach. Despite internal and external validity steps we may have missed relevant papers. We were not able to perform quantitative meta-analysis either at an aggregate level or at the level of differing pain types, but instead used a relatively novel method of visual data synthesis. Our modified harvest plot approach gives a summary of the totality of the data across various axes, allowing for visual comparisons across tools. This approach could be applied in other complex reviews with substantial heterogeneity in the supporting literature.</p><p>Despite the prevalence of post-stroke pain, studies describing the best way to assess for this problem are limited in number and quality. Our evidence mapping and quality assessments highlight particular pain syndromes and tests that have no empirical evidence base. No pain assessment had sufficient data to be considered definitive and further, robust research for any pain tool would be a welcome addition.</p><p>In light of this uncertainty what conclusions can be made? Patient based scales, such as faces pain scale, seem to have the most supporting evidence and are a valid means to assess pain. Our review suggests there are many evidence gaps requiring future research, but methods to improve feasibility of assessment seem an important target.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>All datasets presented in this study are included in the article/<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Material</xref>.</p></sec><sec id=\"s6\"><title>Author Contributions</title><p>SE contributed to all aspects of searching, data extraction and analysis, provided critical review, and contributed to draft manuscripts. AI assisted with data extraction, provided critical review, and contributed to draft manuscripts. GC-L provided critical review, assisted with formatting, and contributed to draft manuscripts. EC provided critical review, assisted with formatting, and contributed to draft manuscripts. MB provided critical review, expert aphasia advice, and contributed to draft manuscripts. SM provided critical review and contributed to draft manuscripts. TS provided critical review and contributed to draft manuscripts. GM devised the study question, coordinated the team, and contributed to draft manuscripts. TQ provided critical review and contributed to draft manuscripts. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s7\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We acknowledge the support of the Pain in Stroke Research Group.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> SE was supported by a University of Glasgow studentship; the Pain in Stroke Research Group is supported by a British Association of Physicians and National Institute of Health Research Grant.</p></fn></fn-group><sec sec-type=\"supplementary-material\" id=\"s8\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fneur.2020.00792/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fneur.2020.00792/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Table_1.DOCX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM2\"><media xlink:href=\"Table_2.DOC\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM3\"><media xlink:href=\"Data_Sheet_1.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM4\"><media xlink:href=\"Data_Sheet_2.PDF\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Harrison</surname><given-names>RA</given-names></name><name><surname>Field</surname><given-names>TS</given-names></name></person-group>. <article-title>Post stroke pain: identification, assessment, and therapy</article-title>. <source>Cerebrovasc Dis.</source> (<year>2015</year>) <volume>39</volume>:<fpage>190</fpage>–<lpage>201</lpage>. <pub-id pub-id-type=\"doi\">10.1159/000375397</pub-id><pub-id pub-id-type=\"pmid\">25766121</pub-id></mixed-citation></ref><ref id=\"B2\"><label>2.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>O'Donnell</surname><given-names>MJ</given-names></name><name><surname>Diener</surname><given-names>H-C</given-names></name><name><surname>Sacco</surname><given-names>RL</given-names></name><name><surname>Panju</surname><given-names>AA</given-names></name><name><surname>Vinisko</surname><given-names>R</given-names></name><name><surname>Yusuf</surname><given-names>S</given-names></name></person-group>. <article-title>Chronic pain syndromes after ischemic stroke</article-title>. <source>Stroke</source>. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Immunol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Immunol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Immunol.</journal-id><journal-title-group><journal-title>Frontiers in Immunology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-3224</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849575</article-id><article-id pub-id-type=\"pmc\">PMC7431894</article-id><article-id pub-id-type=\"doi\">10.3389/fimmu.2020.01666</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Immunology</subject><subj-group><subject>Mini Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Host Lipid Rafts as the Gates for <italic>Listeria monocytogenes</italic> Infection: A Mini-Review</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Tsai</surname><given-names>Yu-Huan</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/961893/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Wei-Lin</given-names></name><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/1043681/overview\"/></contrib></contrib-group><aff><institution>Laboratory of Host–Microbe Interactions and Cell Dynamics, Institute of Microbiology and Immunology, National Yang-Ming University</institution>, <addr-line>Taipei</addr-line>, <country>Taiwan</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Chih-Ho Lai, Chang Gung University, College of Medicine, Taiwan</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Fabrizia Stavru, CNRS UMR2001 Microbiologie Intégrative et Moléculaire, Institut Pasteur, France; Stephanie M. Seveau, The Ohio State University, United States</p></fn><corresp id=\"c001\">*Correspondence: Yu-Huan Tsai <email>yuhuan.tsai@ym.edu.tw</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>1666</elocation-id><history><date date-type=\"received\"><day>25</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>22</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Tsai and Chen.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Tsai and Chen</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p><italic>Listeria monocytogenes</italic> is a Gram-positive foodborne bacterial pathogen capable of interacting and crossing the intestinal barrier, blood–brain barrier, and placental barrier to cause deadly infection with high mortality. <italic>L. monocytogenes</italic> is an intracellular pathogen characterized by its ability to enter non-phagocytic cells. Expression of the cytolysin listeriolysin O has been shown to be the main virulence determinant <italic>in vitro</italic> and <italic>in vivo</italic> in mouse models. <italic>L. monocytogenes</italic> can also perform cell-to-cell spreading using actin-rich membrane protrusions to infect neighboring cells, which also constitutes an important strategy for infection. These events including entry into host cells, interaction between listeriolysin O and host plasma membrane, and bacterial cell-to-cell spreading have been demonstrated to implicate the cholesterol-rich lipid rafts or molecules in these microdomains in the host plasma membrane <italic>in vitro</italic> with tissue culture models. Here we review the contribution of lipid rafts on plasma membrane to <italic>L. monocytogenes</italic> infection.</p></abstract><kwd-group><kwd><italic>Listeria monocytogenes</italic></kwd><kwd>listeriosis</kwd><kwd>lipid rafts</kwd><kwd>intracellular bacteria</kwd><kwd>listeriolysin O</kwd><kwd>internalin</kwd><kwd>cell-to-cell spreading</kwd></kwd-group><funding-group><award-group><funding-source id=\"cn001\">Ministry of Science and Technology, Taiwan<named-content content-type=\"fundref-id\">10.13039/501100004663</named-content></funding-source></award-group></funding-group><counts><fig-count count=\"1\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"80\"/><page-count count=\"6\"/><word-count count=\"4966\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Human listeriosis is a foodborne disease caused by the intracellular pathogen <italic>Listeria monocytogenes</italic>. Upon ingestion of contaminated food by the host, <italic>L. monocytogenes</italic> interacts and traverses the intestinal epithelium to reach the lamina propria, followed by dissemination into lymph and bloodstream toward the liver and spleen, where the bacteria replicate. <italic>L. monocytogenes</italic> can further cross the blood–brain barrier to induce meningoencephalitis and invade the placenta and result in fetal infection, stillbirth, abortion, and neonatal infection (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B2\" ref-type=\"bibr\">2</xref>). <italic>L. monocytogenes</italic> has been used as a model microorganism in studying host–microbe interactions since the late 1980s. Efforts have been made to unveil how this facultative intracellular pathogen enters into cultured non-phagocytic epithelial cells, escapes from the internalization vacuole, and spreads from the infected cell to another (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). Interaction of <italic>L. monocytogenes</italic> surface protein InlA and InlB with corresponding host receptors human E-cadherin (hEcad) and human c-Met at the plasma membrane leads to host actin polymerization and septin assembly, followed by internalization of bacteria in a vacuole (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). The <italic>L. monocytogenes</italic>-containing vacuole, especially in phagocytic cells, is subsequently lysed by the bacterial pore-forming toxin listeriolysin O (LLO), resulting in bacterial escape from the vacuole (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). Within the host cell cytosol, the bacterial surface protein ActA contributes to <italic>L. monocytogenes</italic> intracellular motility via host actin polymerization and actin comet tail formation, followed by the induction of membrane protrusions and infection of neighboring cells (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). In the neighboring cells, <italic>L. monocytogenes</italic> will then be located in a double-membrane vacuole, in which bacterial phospholipases PlcA and PlcB, together with LLO, are required for vacuole lysis and bacterial escape into cytosol for infection propagation (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). The membrane damaging property of LLO is also involved in multiple modulation activities during infection, such as Arp2/3-dependent F-actin remodeling that promotes bacterial internalization, changes in histone modification and thus modulation of host gene expression, desumoylation of host proteins, induction of mitochondrial fission, increase of endoplasmic reticulum (ER) stress, and lysosomal permeabilization (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>–<xref rid=\"B19\" ref-type=\"bibr\">19</xref>).</p><p>Plasma membranes have been described as a fluid mosaic interface containing various lipid species in two asymmetric leaflets with plenty of floating proteins (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). The observations that cell membranes can be separated into detergent-sensitive and detergent-resistant fractions suggested the presence of distinct membrane sub-compartments in cell membranes. The clusters of lipids in a more ordered state with relatively saturated lipids and glycosylated lipids are referred to as lipid rafts, as compared to the disordered membrane domains with unsaturated lipids (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Lipid rafts are cholesterol- and sphingolipid-enriched microdomains with ordered assemblies of proteins and lipids in cell membranes (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>). These rafts are heterogeneous and dynamic, and have the potential to form large domains (>300 nm) upon clustering induced by protein–protein and protein–lipid interactions (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). The organization of lipid rafts can also be regulated and mobilized by cortical actin filaments via specific interactions between actin and membrane adaptor proteins (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>). Lipid rafts have been suggested to be implicated in membrane protein signaling, membrane trafficking, and host–microbe interactions (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Compartmentalization of cellular signaling in membrane domains is important to regulate maturation of immune cells. It was demonstrated that T cell receptors and B cell receptors were found in detergent-sensitive membrane in resting stage, but shifted to detergent-resistant fractions upon receptor activation (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>–<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). This suggests that the translocation of receptors involved in antigen presentation to lipid rafts is associated with active signaling in these immune cells. Lipid rafts are also enriched in caveolae, which are flask-shaped pits with a size of 50–80 nm in the cell membrane. Caveolae are associated with expression of caveolin, which is responsible for stabilization of caveolar structure and the internalization of extracellular materials into caveolae (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). While host plasma membranes are the first barrier for the invasion of intracellular pathogens, the observation that host receptors are clustered in the lipid rafts and enrichment of cholesterol on microbe-containing vacuoles highlights the importance of lipid rafts in pathogen–host interactions (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). Here we review and discuss the implication of lipid rafts in the interaction between <italic>L. monocytogenes</italic> and host cells at different interfaces (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p><italic>L. monocytogenes</italic> harnesses host lipid rafts for infection. <bold>(A)</bold> Extracellular <italic>L. monocytogenes</italic> secretes the cytolysin LLO, which binds cholesterol and inserts into host cell membrane in lipid raft domains. The interaction between the endocytosis adaptor protein Ap2a2 and inserted LLO results in endocytosis of LLO to prevent LLO-induced plasma membrane damage. <bold>(B)</bold> During bacterial entry, the <italic>L. monocytogenes</italic> surface protein InlA acts as an adhesin binding to host E-cadherin in lipid raft domains. Binding of <italic>L. monocytogenes</italic> InlB to host c-Met triggers PI3-K activation, which catalyzes the production of PIP3 from PIP2 in lipid raft domains, leading to actin polymerization and internalization of E-cadherin-bound bacteria. <bold>(C)</bold> In the process of cell-to-cell spreading, LLO damages the plasma membrane of the infected cells to induce PS inversion in the lipid rafts. Neighboring macrophages engulf PS-positive protrusion structures by the PS receptor TIM4. Neighboring non-phagocytic cells internalize <italic>L. monocytogenes</italic> membrane protrusions by caveolin-dependent endocytosis in lipid rafts. LLO, listeriolysin O; PIP2, phosphatidylinositol-4,5-bisphosphate; PIP3, phosphatidylinositol-3,4,5-triphosphate; PI3-K, phosphoinositide 3-kinase; PS, phosphatidylserine.</p></caption><graphic xlink:href=\"fimmu-11-01666-g0001\"/></fig></sec><sec id=\"s2\"><title>LLO, a Multifunctional Cytolysin Targeting Lipid Rafts</title><p>LLO is crucial for full virulence of <italic>L. monocytogenes</italic> in both <italic>in vitro</italic> tissue culture systems and <italic>in vivo</italic> animal models (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>–<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). LLO is a secreted protein of 56 kDa molecular weight belonging to the family of cholesterol-dependent cytolysins (CDCs), which represent the largest family of pore-forming toxins that form large pores (up to 35 nm) produced by different bacterial species (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>–<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Preincubation of LLO with cholesterol abolished cytolytic activity, suggesting the importance of cholesterol binding in lipid rafts for cytolysis (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Differing from other CDC members, LLO exhibits optimal binding to cholesterol-containing membranes at pH 5.5, and this binding decreases at neutral and basic pH. Nevertheless, high cholesterol levels, corresponding to the concentration range of cholesterol found in lipid rafts, can restore LLO binding to membranes at suboptimal pH (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). This is explained by the presence of an acidic triad in the transmembrane domain, which functions as a pH sensor and triggers premature denaturation of LLO at neutral pH at 37°C, thereby allowing pore formation to occur mainly at acidic pH (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>–<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). The pH dependence of LLO limits cytolytic activity to acidic vesicles and prevents damage in the host cytosol, which is a niche for <italic>L. monocytogenes</italic> replication. Accordingly, replacement of LLO by pH-insensitive CDCs such as perfringolysin O from <italic>Clostridium perfringens</italic> allowed phagosomal escape of <italic>L. monocytogenes</italic>, but led to decreased infection efficiency <italic>in vitro</italic> in a plaque assay (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). Cholesterol in the lipid rafts not only provides an initial binding site for LLO, <italic>in vitro</italic> studies with high-speed atomic force microscopy (HS-AFM) further demonstrated that in acidic environments LLO can produce arc pores in the membrane as a lineactant, and therefore creates large-scale defects for bacterial escape from phagocytic vacuole (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>).</p><p>In addition to targeting of cholesterol-rich domains to damage the host cell membrane, the PEST-like sequence at the N-terminus of LLO interacts with the endocytosis adaptor Ap2a2, a lipid-raft associated protein (<xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>) (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>, <xref rid=\"B49\" ref-type=\"bibr\">49</xref>). This interaction facilitates clathrin-dependent endocytosis of plasma membrane-associated LLO and removes these pore-forming toxins from the plasma membrane, thereby preventing its cytotoxicity to the infected cell and enhancing <italic>L. monocytogenes</italic> virulence during infection <italic>in vivo</italic> (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>). However, this clathrin-dependent endocytosis of LLO-associated membrane may not contribute to membrane repair after pore formation as endocytic proteins are not recruited to the membrane damage sites (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). Instead, LLO-induced membrane damage can result in influx of intracellular calcium, which subsequently activates TMEM16F lipid scramblase, leading to membrane blebbing and extracellular vesicle release to repair plasma membrane damage (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>). Although less characterized, LLO was demonstrated to induce clustering of GPI-anchored proteins CD14 and CD24 on the surface of murine macrophages J774, while the non-lipid-raft marker transferrin receptor was not affected (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>).</p></sec><sec id=\"s3\"><title><italic>L. monocytogenes</italic> Harnesses Lipid Rafts Proteins for Entry Into Host Cells</title><p><italic>L. monocytogenes</italic> uses its surface internalin proteins InlA and InlB to bind hEcad and human c-Met, respectively. These interactions result in cytoskeleton rearrangement, thereby bacterial entry into non-phagocytic cells through a zipper mechanism (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>) (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>, <xref rid=\"B54\" ref-type=\"bibr\">54</xref>). Treatment of phosphoinositide 3-kinase (PI3-K) inhibitors wortmannin and LY294002, respectively, abolished InlA- and InlB-dependent invasion into different host cells, showing the importance of PI3-K activity in internalin-mediated entry (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>) (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>, <xref rid=\"B56\" ref-type=\"bibr\">56</xref>). Depletion of cholesterol by methyl-β-cyclodextrin reduced InlA- and InlB-dependent <italic>L. monocytogenes</italic> internalization into non-phagocytic cells, indicating the involvement of lipid rafts in internalin-mediated internalization (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). This is further supported by the observation that multiple lipid raft markers, such as glycosylphosphatidylinositol-linked proteins, a myristoylated and palmitoylated peptide, and the ganglioside GM1 were recruited at the bacterial entry site (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). While InlA–Ecad interaction and Ecad recruitment at the entry site were cholesterol-dependent, cholesterol depletion did not affect InlB interaction with c-Met, the recruitment of c-Met at the entry site, and c-Met downstream signaling (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). Nevertheless, cholesterol depletion abrogates InlB-mediated actin polymerization (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>). The implication of lipid rafts in InlA-mediated entry was further demonstrated by the observation that caveolin was recruited to the bacterial entry sites and was required for the internalization in an InlA-dependent manner (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). While InlB is dispensable for bacterial entry into LS174T intestinal epithelial cells that show constitutively activated PI3-K activity, this protein is necessary for InlA-dependent entry into cells that do not exhibit constitutive PI3-K activity (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>). Together, while InlB-induced c-Met phosphorylation does not depend on cholesterol and lipid rafts, subsequent PI3-K activation and Rac1-induced actin polymerization at the bacterial entry site occur within lipid rafts and require their integrity (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>, <xref rid=\"B59\" ref-type=\"bibr\">59</xref>). InlA acts as an adhesion molecule to Ecad in the detergent-resistant lipid rafts, thereby triggering internalization of <italic>L. monocytogenes</italic> dependent on PI3-K activation and caveolin-mediated endocytosis (<xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>) (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>–<xref rid=\"B60\" ref-type=\"bibr\">60</xref>).</p></sec><sec id=\"s4\"><title>The Role of Lipid Rafts in <italic>L. monocytogenes</italic> Cell-to-Cell Spreading</title><p>Following lysis of the phagocytic vacuole in host cells, the <italic>L. monocytogenes</italic> surface protein ActA can polymerize actin in cytosol, which allows formation of bacteria-containing membrane protrusions, the structures that are later internalized by neighboring cells to result in cell-to-cell spreading of <italic>L. monocytogenes</italic> (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>) (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Internalization of <italic>L. monocytogenes</italic> membrane protrusions was demonstrated to be exploited by efferocytosis, by which the apoptotic cells are removed by macrophages (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>, left) (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). In both phagocytic and non-phagocytic cells, ActA-mediated actin-based motility was proposed to allow close apposition of bacteria to the cell membrane, where secreted LLO may damage cell membrane and induce externalization of phosphatidylserine (PS), a hallmark of apoptotic cells at cell membrane (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). The PS-positive protrusion structures containing <italic>L. monocytogenes</italic> are subsequently recognized by the PS-binding receptor TIM-4 on macrophages to facilitate phagocytic uptake and cell–cell spreading (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>). While methyl-β-cyclodextrin-mediated cholesterol depletion was demonstrated to reduce PS externalization and phagocytosis of apoptotic cells, this suggests that lipid raft integrity could be important for efferocytosis-mediated <italic>L. monocytogenes</italic> spreading (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>–<xref rid=\"B65\" ref-type=\"bibr\">65</xref>). <italic>L. monocytogenes</italic> cell-to-cell spreading is not limited to transfer to phagocytic cells (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>). Dhanda et al. further investigated the membrane invagination in neighboring non-phagocytic cells (<xref ref-type=\"fig\" rid=\"F1\">Figure 1C</xref>, right) (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). While caveolin-based and lipid raft-dependent endocytosis is supposed to be a process that internalizes extracellular material into bulb-shaped caveolae no larger than 100 nm, <italic>L. monocytogenes</italic> membrane protrusions triggered the recruitment of caveolar proteins and PS in a neighboring cell (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). Knock-down of caveolin-1 reduced invagination length in the neighboring cells and <italic>L. monocytogenes</italic> cell-to-cell spreading without detectable effect on the length of the actin comet tail and protrusion in initial infected cells (<xref rid=\"B66\" ref-type=\"bibr\">66</xref>). This suggests that caveolin can mediate engulfment of large materials, such as <italic>L. monocytogenes</italic>-containing membrane protrusions, which was not supposed to be achieved based on the size of caveolae. Collectively, due to the importance of lipid rafts in both efferocytosis and caveolae structure, efficient <italic>L. monocytogenes</italic> cell-to-cell spreading may require lipid raft integrity (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>, <xref rid=\"B67\" ref-type=\"bibr\">67</xref>). Further studies disrupting these membrane microdomains in <italic>L. monocytogenes</italic> cell-to-cell spreading are needed to directly address the role of lipid rafts in this process.</p></sec><sec id=\"s5\"><title>Concluding Remarks</title><p>Our understanding of lipid rafts has been improved by the development of biochemical and microscopy tools (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>, <xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Depletion of membrane cholesterol by methyl-β-cyclodextrin constitutes the most common approach to disrupt membrane lipid rafts to investigate their biological function (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>). However, methyl-β-cyclodextrin exhibits pleiotropic effects beyond lipid raft disruption such as inhibition of clathrin-mediated endocytosis (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>–<xref rid=\"B71\" ref-type=\"bibr\">71</xref>). The models where lipid raft components are genetically deficient may be applied to more specifically address the function of lipid rafts (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>–<xref rid=\"B74\" ref-type=\"bibr\">74</xref>). On the other hand, advance in microscopy techniques allows for visualizing the lipid rafts from model membranes to living cells <italic>in vitro</italic> (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). Recent advances suggest that pathogens may behave differently and adopt a distinct strategy to interact with the host between <italic>in vitro</italic> cell culture and <italic>in vivo</italic> animal models. For example, while <italic>L. monocytogenes</italic> interacts with enterocytes and access to the cytosol in cell culture systems, it targets goblet cells in the small intestine and performs transcytosis to cross intestinal epithelium followed by systemic infection in humanized mouse models (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>, <xref rid=\"B75\" ref-type=\"bibr\">75</xref>, <xref rid=\"B76\" ref-type=\"bibr\">76</xref>). Conditional genetic knock-out mouse models, where lipid rafts are disrupted in villin-expressing intestinal epithelium, may provide a clue regarding the role of lipid rafts in <italic>L. monocytogenes</italic> crossing of intestinal barrier (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>). Stem cell-derived organoids have been shown to recapitulate the complexity of a local tissue <italic>in vitro</italic> in culture systems (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>). While <italic>L. monocytogenes</italic> was demonstrated to successfully infect intestinal organoids, coupling of super-resolution optical microscopy methods may allow visualizing the organization of lipid rafts at the interface between <italic>L. monocytogenes</italic> and host cells relevant to the <italic>in vivo</italic> environment (<xref rid=\"B79\" ref-type=\"bibr\">79</xref>, <xref rid=\"B80\" ref-type=\"bibr\">80</xref>). Together, future studies addressing the role of lipid rafts <italic>in vivo</italic> and visualizing these nanoscale membrane domains at the interface between <italic>L. monocytogenes</italic> and host in tissues will provide insight into how lipid rafts are implicated in the pathophysiology of <italic>L. monocytogenes</italic> infection.</p></sec><sec id=\"s6\"><title>Author Contributions</title><p>Y-HT: conceived and designed the study, and wrote and edited the paper. W-LC: reviewed the literature and edited the paper. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s7\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We would like to thank all the members of the Laboratory of Host–Microbe Interactions and Cell Dynamics.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by a grant from the Ministry of Science and Technology, Taiwan (MOST106-2321-B-182-004-MY3) to Y-HT. 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rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Durand</surname><given-names>O.</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Textoris</surname><given-names>L.</given-names></name><xref ref-type=\"aff\" rid=\"Aff30\">30</xref></contrib><contrib contrib-type=\"author\"><name><surname>Dubost</surname><given-names>T.</given-names></name><xref ref-type=\"aff\" rid=\"Aff30\">30</xref></contrib></contrib-group></collab></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, INSERM U1116, Médecine Intensive et Réanimation Brabois, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Centre Universitaire de Médecine du Sport et Activité Physique Adaptée, Explorations Fonctionnelles Respiratoires, EA DevAH, Département de Physiologie, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Centre Universitaire de Médecine du Sport et Activité Physique Adaptée, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Médecine Vasculaire, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>Faculté de Médecine de Nancy, CHRU-Nancy, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, ORL et Chirurgie Cervico-Faciale, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff7\"><label>7</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Chirurgie Cardio-Vasculaire et Transplantations, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff8\"><label>8</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Cardiologie Médicale, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff9\"><label>9</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Unité Médicochirurgicale de Chirurgie Viscérale et Cancérologie, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff10\"><label>10</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Chirurgie Digestive et Générale, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff11\"><label>11</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Biologie Médicale, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff12\"><label>12</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Hépato-Gastro-Entérologie, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff13\"><label>13</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Urologie, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff14\"><label>14</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Chirurgie de la main, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff15\"><label>15</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Pédiatrie, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff16\"><label>16</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Chirurgie Orthopédique Infantile, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff17\"><label>17</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Néphrologie, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff18\"><label>18</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Anatomopathologie, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff19\"><label>19</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Chirurgie Orthopédique, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff20\"><label>20</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Gynécologie Obstétrique, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff21\"><label>21</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Département de Médecine Générale, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff22\"><label>22</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Rééducation Fonctionnelle, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff23\"><label>23</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Ophtalmologie, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff24\"><label>24</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Nutrition, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff25\"><label>25</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Chirurgie Maxillo-Faciale et Stomatologique, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff26\"><label>26</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Chirurgie Vasculaire et Endoluminale, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff27\"><label>27</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Consultations Pathologies Professionnelles, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff28\"><label>28</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Chirurgie Infantile Viscérale, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff29\"><label>29</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, Médecine Nucléaire, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff><aff id=\"Aff30\"><label>30</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.29172.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2194 6418</institution-id><institution>CHRU-Nancy, INSERM U1116, Médecine Intensive et Réanimation Brabois, </institution><institution>Université de Lorraine, </institution></institution-wrap>F-54000 Nancy, France </aff></contrib-group><pub-date pub-type=\"epub\"><day>18</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>18</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>24</volume><elocation-id>509</elocation-id><history><date date-type=\"received\"><day>2</day><month>6</month><year>2020</year></date><date date-type=\"accepted\"><day>1</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/publicdomain/zero/1.0/\">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated in a credit line to the data.</license-p></license></permissions><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><p id=\"Par1\">To the Editor:</p><p id=\"Par2\">Prone position ventilation has been shown to improve oxygenation and survival in patients with severe acute respiratory distress syndrome (ARDS) [<xref ref-type=\"bibr\" rid=\"CR1\">1</xref>]. Facing the coronavirus disease 2019 (COVID-19) pandemic, prone positioning (PP) is of crucial importance to treat severe ARDS patients [<xref ref-type=\"bibr\" rid=\"CR2\">2</xref>]. Nevertheless, the high number of ICU admissions quickly overwhelmed the ability of the daily ICU team to place patients in PP, a complex and time-consuming maneuver. Thus, we created a dedicated medical team with reassigned volunteers to cope with the large number of patients requiring PP.</p><p id=\"Par3\">PP Team consisted of five volunteers: a senior medical non-intensivist physician placed at the patient’s head to secure the endotracheal tube and four medical fellows or medical students placed at each side of the bed. For patients treated with VV-ECMO, a supplementary physician was added to secure the lines. Since PP is a complex procedure and has many potential adverse events requiring adequate and well-trained staff, volunteers received previously a theoretical training and a hands-on ad hoc training session. PP teams followed the guidelines for PP placement [<xref ref-type=\"bibr\" rid=\"CR1\">1</xref>].</p><p id=\"Par4\">This retrospective observational study was performed in our extended ICU (from 22 to 46 beds), from the first day of deployment of PPT (March 23 to April 23, 2020).</p><p id=\"Par5\">The main characteristics and outcomes of prone positioned patients (<italic>n</italic> = 63) are presented in Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>. A total of 367 placements in a prone or supine position were performed during the 1-month study period (Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>).\n<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Initial characteristics and outcome of the prone positioned ARDS COVID-19 population (data are expressed as median (interquartile range, IQR) or number (%) as appropriate. After visual assumption of normality, Wilcoxon rank tests were applied for continuous variables. For categorical variables, Fisher’s exact or chi<sup>2</sup> tests were applied as appropriate)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Variables</th><th><bold><italic>n</italic></bold>, total available</th><th>Total, <bold><italic>n</italic></bold>(%) or median (IQR) (<bold><italic>n</italic></bold> = 63)</th><th><bold><italic>n</italic></bold>, available for ICU survivors (<bold><italic>n</italic></bold> = 46) *</th><th>ICU survivors, <bold><italic>n</italic></bold>(%) or median (IQR) *</th><th><bold><italic>n</italic></bold>, available for ICU non-survivors (<bold><italic>n</italic></bold> = 16)*</th><th>ICU non-survivors, <bold><italic>n</italic></bold>(%) or median (IQR) *</th><th><bold><italic>p</italic></bold></th></tr></thead><tbody><tr><td colspan=\"8\"><bold>Demographic data</bold></td></tr><tr><td> Female ratio</td><td>63</td><td>15 (24%)</td><td>46</td><td>10 (22%)</td><td>16</td><td>5 (31%)</td><td>0.50</td></tr><tr><td> Age (years)</td><td>63</td><td>64 (56–70)</td><td>46</td><td>62 (54–69)</td><td>16</td><td>67 (64–74)</td><td>0.045</td></tr><tr><td> Weight (kg)</td><td>61</td><td>89 (75–103)</td><td>45</td><td>90 (80–103)</td><td>15</td><td>89 (73–106)</td><td>0.68</td></tr><tr><td> Body mass index (kg/m<sup>2</sup>)</td><td>61</td><td>30 (25–36)</td><td>45</td><td>30 (26–35)</td><td>15</td><td>29 (25–46)</td><td>0.87</td></tr><tr><td> SAPS II</td><td>63</td><td>42 (31–57)</td><td>46</td><td>37 (27–57)</td><td>16</td><td>46 (42–59)</td><td>0.030</td></tr><tr><td colspan=\"8\"><bold>Medical history</bold></td></tr><tr><td> Diabetes mellitus</td><td>63</td><td>17 (27%)</td><td>46</td><td>11 (24%)</td><td>16</td><td>5 (31%)</td><td>0.74</td></tr><tr><td> Hypertension</td><td>63</td><td>30 (48%)</td><td>46</td><td>19 (41%)</td><td>16</td><td>10 (62%)</td><td>0.14</td></tr><tr><td> Chronic respiratory disease</td><td>63</td><td>16 (25%)</td><td>46</td><td>13 (28%)</td><td>16</td><td>3 (19%)</td><td>0.53</td></tr><tr><td> Chronic immunosuppression†</td><td>63</td><td>5 (8%)</td><td>46</td><td>2 (4%)</td><td>16</td><td>3 (19%)</td><td>0.10</td></tr><tr><td> Chronic Cardiovascular disease</td><td>63</td><td>16 (25%)</td><td>46</td><td>12 (26%)</td><td>16</td><td>3 (19%)</td><td>0.74</td></tr><tr><td> Chronic kidney disease</td><td>63</td><td>3 (5%)</td><td>46</td><td>2 (4%)</td><td>16</td><td>1 (6%)</td><td>1.00</td></tr><tr><td colspan=\"8\"><bold>Respiratory parameters</bold></td></tr><tr><td> Static compliance (ml/cmH<sub>2</sub>O) before first prone positioning</td><td>46</td><td>33 (23–42)</td><td>35</td><td>35 (27–44)</td><td>11</td><td>22 (18–36)</td><td>0.036</td></tr><tr><td> PaO<sub>2</sub>/F<sub>I</sub>O<sub>2</sub> ratio before first prone positioning</td><td>63</td><td>92 (70–117)</td><td>46</td><td>96 (70–120)</td><td>16</td><td>86 (64–111)</td><td>0.54</td></tr><tr><td> Number of prone positioning per patient</td><td>63</td><td>3 (2–6)</td><td>46</td><td>3 (2–6)</td><td>16</td><td>4 (3–8)</td><td>0.19</td></tr><tr><td colspan=\"8\"><bold>Events in ICU</bold></td></tr><tr><td> Vasopressors administered</td><td>63</td><td>38 (60%)</td><td>46</td><td>23 (50%)</td><td>16</td><td>14 (88%)</td><td>0.008</td></tr><tr><td> VV-ECMO</td><td>63</td><td>14 (22%)</td><td>46</td><td>11 (24%)</td><td>16</td><td>3 (19%)</td><td>1.00</td></tr><tr><td> Renal replacement therapy</td><td>63</td><td>8 (13%)</td><td>46</td><td>4 (9%)</td><td>16</td><td>4 (25%)</td><td>0.19</td></tr><tr><td> ICU length of stay (days)</td><td>62</td><td>19 (14–31)</td><td>46</td><td>20 (15–32)</td><td>16</td><td>16 (12–28)</td><td>0.24</td></tr></tbody></table><table-wrap-foot><p><sup>*</sup>1 patient still in ICU</p><p><sup>†</sup>Representing active cancer medical history or chronic immunosuppressor therapies</p></table-wrap-foot></table-wrap><table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Prone and supine placements and reported adverse events during the procedure on the study period. (data are expressed as mean ± standard deviation or number (percentage)). The 367 placements represent the placement in prone or supine positions</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Prone/Supine positioning placements, <bold><italic>n</italic></bold> (%) or mean ± SD (<bold><italic>n</italic></bold> = 367)</th></tr></thead><tbody><tr><td colspan=\"2\"><bold>Number of placements performed</bold></td></tr><tr><td> Daily</td><td>11.5 ± 3.4</td></tr><tr><td> First 2-day period</td><td>7 ± 1.4</td></tr><tr><td> Acme 2-day period</td><td>20 ± 4.2</td></tr><tr><td> Last 2-day period</td><td>5 ± 0</td></tr><tr><td> Under VV-ECMO</td><td>124 (34%)</td></tr><tr><td colspan=\"2\"><bold>Adverse events recorded during placements</bold></td></tr><tr><td colspan=\"2\"> <bold>Major</bold></td></tr><tr><td>  Cardiac arrest</td><td>0</td></tr><tr><td>  Unscheduled extubation</td><td>0</td></tr><tr><td>  Severe desaturation (SpO<sub>2</sub> < 85%)*</td><td>5 (1%)</td></tr><tr><td colspan=\"2\"> <bold>Minor</bold></td></tr><tr><td>  Accidental device removing or disconnection†</td><td>6 (2%)</td></tr></tbody></table><table-wrap-foot><p>*Needing medical intervention</p><p>†Minor: one epistaxis following accidental removing of naso-gastric tube, four incidental disconnections of ventilator lines, one incidental removing of central venous catheter</p></table-wrap-foot></table-wrap></p><p id=\"Par6\">This specific medical team of trained non-intensivist volunteers was able to manage this delicate PP task without any major adverse events such as cardiac arrest or unscheduled extubation when compared to the relatively high incidence (respectively 6.8 and 13.3%) observed in Guérin et al. study [<xref ref-type=\"bibr\" rid=\"CR1\">1</xref>]. Our studied population was comparable to already published series of severe ARDS, and we found a similar mortality (26%) despite a lower initial P/F ratio and COVID-19 association [<xref ref-type=\"bibr\" rid=\"CR1\">1</xref>]. Interestingly, we recorded a greater survival rate than reported by Richardson et al. in the New York area at their edge of COVID-19 pandemic, but they did not detail the use of PP [<xref ref-type=\"bibr\" rid=\"CR3\">3</xref>].</p><p id=\"Par7\">This innovative management allowed three major benefits: (i) critical relief of permanent intensive care team’s workload; (ii) reduction of the nurse-to-patient ratio, permitting also the reassignment of critical care nurses to newly created ICUs; and (iii) devoid of any self-censorship for fear of overwork and burn-out, intensivist physicians were able to strictly follow PP guideline recommendations, ensuring the best standard of care for ARDS patients.</p><p id=\"Par8\">Since the pathophysiology is poorly understood [<xref ref-type=\"bibr\" rid=\"CR4\">4</xref>, <xref ref-type=\"bibr\" rid=\"CR5\">5</xref>], the specific role of PP among the optimal management for COVID-19 patients with ARDS, in order to reduce mortality needs to be addressed.</p></body><back><fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><ack><title>Acknowledgements</title><p><bold>The DV-Team group</bold></p><p>Collaborators:</p><p>S. Barde<sup>1</sup>, A. Didelot<sup>1</sup>, B. Chenuel<sup>1</sup>, P. Zieminski<sup>2</sup>, M. Lorcin<sup>3</sup>, C. Schweitzer<sup>3</sup>, R. Karunna<sup>4</sup>, V. Moulin<sup>3</sup>, M. Huet<sup>3</sup>, F. Lacour<sup>3</sup>, P. Rodriguez<sup>3</sup>, D. Grandmougin<sup>5</sup>, Y. Liu<sup>5</sup>, P.A. Metzdorf<sup>6</sup>, M. Costa<sup>3</sup>, T. Fouquet<sup>7</sup>, A. Germain<sup>8</sup>, H. Chanty<sup>8</sup>, J. Levy<sup>9</sup>, A. Didier<sup>8</sup>, J. Lawton<sup>10</sup>, C. Parietti-Winkler<sup>4</sup>, A. Manuguerra<sup>11</sup>, C. Mazeaud<sup>11</sup>, C. Gaulier<sup>7</sup>, C. Rumeau<sup>4</sup>, L. Bourson<sup>10</sup>,M. Cholley-Roulleau<sup>12</sup>, A. Ionescu<sup>5</sup>, A. Gatin<sup>13</sup>, M. Perez<sup>7</sup>, A. Schwanké<sup>7</sup>, G. Lauria<sup>5</sup>, L. Freysz<sup>6</sup>, T. Cuinet<sup>14</sup>, J. Chauvelot<sup>4</sup>, C. Mottola<sup>15</sup>, T. Toussaint<sup>3</sup>, L. Dechambenoit<sup>3</sup>, F. Lagrange<sup>11</sup>, C. Mathieu<sup>3</sup>, C. Clément<sup>3</sup>, H. Benamron<sup>4</sup>, L. Dubouis<sup>16</sup>, H. Kremer<sup>17</sup>, L. Cabanel<sup>18</sup>, M. Falcetta<sup>3</sup>, V. Gorzkowski<sup>4</sup>, P. Campoli<sup>16</sup>, J. Cavailhes<sup>17</sup>, J. Zavoli<sup>19</sup>, C. Nominé-Criqui<sup>7</sup>, J. Felloni<sup>20</sup>, V. Cloché-Fouquet<sup>21</sup>, F.G. Midon<sup>17</sup>, G. Vaz<sup>17</sup>, D. Valentin<sup>3</sup>, V. Perkovic<sup>3</sup>, A. Courandon<sup>3</sup>, Y. Briot<sup>3</sup>, A. Epin<sup>22</sup>, K. Dreller<sup>13</sup>, L. Florion<sup>3</sup>, C. Larose<sup>3</sup>, M. Barron<sup>23</sup>, C. Sadoul<sup>24</sup>, M. Gimbert<sup>25</sup>, M. Fernandez<sup>17</sup>, T. Thomas<sup>3</sup>, P. Bichet<sup>3</sup>, N. Petkunaite<sup>25</sup>, S. Brahami<sup>4</sup>, D. Nguyen<sup>4</sup>, G. Vaz<sup>17</sup>, A. Schaefer<sup>13</sup>, C. Fabbri<sup>3</sup>, C. Ferri<sup>17</sup>, A. Gegout<sup>3</sup>, A. Poncy<sup>19</sup>., R. Delaplace<sup>26</sup>, M. Ammisaid<sup>5</sup>, J. Rebois<sup>4</sup>, B. Vendeville<sup>3</sup>, C. Dubroux<sup>21</sup>, R. Raynaud<sup>11</sup>, S. Moog<sup>3</sup>, C. Cottez<sup>7</sup>, L. Woirhaye<sup>3</sup>, J. Menet<sup>21</sup>, A.C. Madkaud<sup>21</sup>, L. Naisseline<sup>3</sup>, C. Mathieu<sup>3</sup>, T. Raze<sup>3</sup>, F. Violon<sup>27</sup>, M. Meiers<sup>17</sup>, D. Albanesi<sup>3</sup>, O. Durand<sup>3</sup>, L. Textoris<sup>28</sup>, T Dubost<sup>28</sup></p><p>1- Université de Lorraine, CHRU-Nancy, Centre Universitaire de Médecine du Sport et Activité Physique Adaptée, F-54000 Nancy, France.</p><p>2- Université de Lorraine, CHRU-Nancy, Médecine Vasculaire F-54000 Nancy, France.</p><p>3- Université de Lorraine, Faculté de Médecine de Nancy, CHRU-Nancy, F-54000 Nancy, France.</p><p>4- Université de Lorraine, CHRU-Nancy, ORL et Chirurgie Cervico-Faciale, F-54000 Nancy, France.</p><p>5- Université de Lorraine, CHRU-Nancy, Chirurgie Cardio-Vasculaire et Transplantations, F-54000 Nancy, France.</p><p>6- Université de Lorraine, CHRU-Nancy, Cardiologie Médicale, F-54000 Nancy, France.</p><p>7- Université de Lorraine, CHRU-Nancy, Unité Médicochirurgicale de Chirurgie Viscérale et Cancérologie, F-54000 Nancy, France.</p><p>8- Université de Lorraine, CHRU-Nancy, Chirurgie Digestive et Générale, F-54000 Nancy, France.</p><p>9- Université de Lorraine, CHRU-Nancy, Biologie Médicale, F-54000 Nancy, France.</p><p>10- Université de Lorraine, CHRU-Nancy, Hépato-Gastro-Entérologie, F-54000 Nancy, France.</p><p>11- Université de Lorraine, CHRU-Nancy, Urologie, F-54000 Nancy, France.</p><p>12- Université de Lorraine, CHRU-Nancy, Chirurgie de la main, F-54000 Nancy, France.</p><p>13- Université de Lorraine, CHRU-Nancy, Pédiatrie, F-54000 Nancy, France.</p><p>14- Université de Lorraine, CHRU-Nancy, Chirurgie Orthopédique Infantile, F-54000 Nancy, France.</p><p>15- Université de Lorraine, CHRU-Nancy, Néphrologie, F-54000 Nancy, France.</p><p>16- Université de Lorraine, CHRU-Nancy, Anatomopathologie, F-54000 Nancy, France.</p><p>17- Université de Lorraine, CHRU-Nancy, Chirurgie Orthopédique, F-54000 Nancy, France.</p><p>18- Université de Lorraine, CHRU-Nancy, Gynécologie Obstétrique, F-54000 Nancy, France.</p><p>19- Université de Lorraine, CHRU-Nancy, Département de Médecine Générale, F-54000 Nancy, France.</p><p>20- Université de Lorraine, CHRU-Nancy, Rééducation Fonctionnelle, F-54000 Nancy, France.</p><p>21- Université de Lorraine, CHRU-Nancy, Ophtalmologie, F-54000 Nancy, France.</p><p>22- Université de Lorraine, CHRU-Nancy, Nutrition. F-54000 Nancy, France.</p><p>23- Université de Lorraine, CHRU-Nancy, Chirurgie Maxillo-Faciale et Stomatologique, F-54000 Nancy, France.</p><p>24- Université de Lorraine, CHRU-Nancy, Chirurgie Vasculaire et Endoluminale, F-54000 Nancy, France.</p><p>25- Université de Lorraine, CHRU-Nancy, Consultations Pathologies Professionnelles. F-54000 Nancy, France.</p><p>26- Université de Lorraine, CHRU-Nancy, Chirurgie Infantile Viscérale. F-54000 Nancy, France.</p><p>27- Université de Lorraine, CHRU-Nancy, Médecine Nucléaire. F-54000 Nancy, France.</p><p>28- Université de Lorraine, CHRU-Nancy, INSERM U1116, Médecine Intensive et Réanimation Brabois, F-54000 Nancy, France</p></ack><notes notes-type=\"author-contribution\"><title>Authors’ contributions</title><p>Data acquisition: AK. Data analysis: AK. Data interpretation: all authors. Manuscript drafting and revising: all authors. The authors read and approved the final manuscript.</p></notes><notes notes-type=\"funding-information\"><title>Funding</title><p>There was no financial funding.</p></notes><notes notes-type=\"data-availability\"><title>Availability of data and materials</title><p>All data generated or analyzed during this study are included in this published article. The data used to support the findings of this study are available from the corresponding author upon request.</p></notes><notes id=\"FPar1\"><title>Ethics approval and consent to participate</title><p id=\"Par9\">Not applicable.</p></notes><notes id=\"FPar2\"><title>Consent for publication</title><p id=\"Par10\">Not applicable.</p></notes><notes id=\"FPar3\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par11\">The authors declare that they have no competing interests related to the present publication.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Guérin</surname><given-names>C</given-names></name><name><surname>Reignier</surname><given-names>J</given-names></name><name><surname>Richard</surname><given-names>JC</given-names></name><name><surname>Beuret</surname><given-names>P</given-names></name><name><surname>Gacouin</surname><given-names>A</given-names></name><name><surname>Boulain</surname><given-names>T</given-names></name><etal/></person-group><article-title>Prone positioning in severe acute respiratory distress syndrome</article-title><source>N Engl J Med</source><year>2013</year><volume>368</volume><fpage>2159</fpage><lpage>2168</lpage><pub-id pub-id-type=\"doi\">10.1056/NEJMoa1214103</pub-id><pub-id pub-id-type=\"pmid\">23688302</pub-id></element-citation></ref><ref id=\"CR2\"><label>2.</label><mixed-citation publication-type=\"other\">Carsetti A, Damia Paciarini A, Marini B, et al. Prolonged prone position ventilation for SARS-CoV-2 patients is feasible and effective. Crit Care. 2020;24:225.</mixed-citation></ref><ref id=\"CR3\"><label>3.</label><mixed-citation publication-type=\"other\">Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, the Northwell COVID-19 Research Consortium. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York city area. JAMA. 2020. 10.1001/jama.2020.6775.</mixed-citation></ref><ref id=\"CR4\"><label>4.</label><mixed-citation publication-type=\"other\">Gattinoni L, Chiumello D, Caironi P, Busana M, Romitti F, Brazzi L, et al. COVID-19 pneumonia: different respiratory treatments for different phenotypes? Intensive Care Med. 2020;46:1099–1102. 10.1007/s00134-020-06033-2.</mixed-citation></ref><ref id=\"CR5\"><label>5.</label><mixed-citation publication-type=\"other\">Gattinoni L, Chiumello D, Rossi S. COVID-19 pneumonia: ARDS or not? 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] |
[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Virol J</journal-id><journal-id journal-id-type=\"iso-abbrev\">Virol. J</journal-id><journal-title-group><journal-title>Virology Journal</journal-title></journal-title-group><issn pub-type=\"epub\">1743-422X</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32811514</article-id><article-id pub-id-type=\"pmc\">PMC7431901</article-id><article-id pub-id-type=\"publisher-id\">1396</article-id><article-id pub-id-type=\"doi\">10.1186/s12985-020-01396-w</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Short Report</subject></subj-group></article-categories><title-group><article-title>Intranasal exposure of African green monkeys to SARS-CoV-2 results in acute phase pneumonia with shedding and lung injury still present in the early convalescence phase</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Cross</surname><given-names>Robert W.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Agans</surname><given-names>Krystle N.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Prasad</surname><given-names>Abhishek N.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Borisevich</surname><given-names>Viktoriya</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Woolsey</surname><given-names>Courtney</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Deer</surname><given-names>Daniel J.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Dobias</surname><given-names>Natalie S.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Geisbert</surname><given-names>Joan B.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fenton</surname><given-names>Karla A.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0003-0858-1877</contrib-id><name><surname>Geisbert</surname><given-names>Thomas W.</given-names></name><address><email>twgeisbe@utmb.edu</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.176731.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 1547 9964</institution-id><institution>Department of Microbiology and Immunology, </institution><institution>University of Texas Medical Branch, </institution></institution-wrap>Galveston, TX 77555 USA </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.176731.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 1547 9964</institution-id><institution>Galveston National Laboratory, </institution><institution>University of Texas Medical Branch, </institution></institution-wrap>Galveston, TX 77555 USA </aff></contrib-group><pub-date pub-type=\"epub\"><day>18</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>18</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>17</volume><elocation-id>125</elocation-id><history><date date-type=\"received\"><day>27</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>11</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/publicdomain/zero/1.0/\">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated in a credit line to the data.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">We recently reported the development of the first African green monkey (AGM) model for COVID-19 based on a combined liquid intranasal (i.n.) and intratracheal (i.t.) exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we followed up on this work by assessing an i.n. particle only route of exposure using the LMA mucosal atomization device (MAD). Six AGMs were infected with SARS-CoV-2; three animals were euthanized near the peak stage of virus replication (day 5) and three animals were euthanized during the early convalescence period (day 34). All six AGMs supported robust SARS-CoV-2 replication and developed respiratory disease. Evidence of coagulation dysfunction as noted by a transient increases in aPTT and circulating levels of fibrinogen was observed in all AGMs. The level of SARS-CoV-2 replication and lung pathology was not quite as pronounced as previously reported with AGMs exposed by the combined i.n. and i.t. routes; however, SARS-CoV-2 RNA was detected in nasal swabs of some animals as late as day 15 and rectal swabs as late as day 28 after virus challenge. Of particular importance to this study, all three AGMs that were followed until the early convalescence stage of COVID-19 showed substantial lung pathology at necropsy as evidenced by multifocal chronic interstitial pneumonia and increased collagen deposition in alveolar walls despite the absence of detectable SARS-CoV-2 in any of the lungs of these animals. These findings are consistent with human COVID-19 further demonstrating that the AGM faithfully reproduces the human condition.</p></abstract><kwd-group xml:lang=\"en\"><title>Keywords</title><kwd>Coronavirus</kwd><kwd>SARS-CoV-2</kwd><kwd>COVID-19</kwd><kwd>Nonhuman primate</kwd><kwd>Animal models</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">http://dx.doi.org/10.13039/100008013</institution-id><institution>University of Texas Medical Branch at Galveston</institution></institution-wrap></funding-source><award-id>N/A</award-id></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par12\">The unprecedented pandemic of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had devastating effects on public health and the global economy. Considerable resources have been allocated by governments, philanthropic organizations, and private companies in an attempt to expedite the development of vaccines and treatments to combat COVID-19. With the rapid development of 24 preventative vaccines in clinical evaluation [<xref ref-type=\"bibr\" rid=\"CR1\">1</xref>], and nearly 200 more in the pipeline [<xref ref-type=\"bibr\" rid=\"CR2\">2</xref>], coupled with the availability of nearly 300 candidate antivirals and disease modulators [<xref ref-type=\"bibr\" rid=\"CR2\">2</xref>] it is impossible to investigate the safety and efficacy of all of these various interventions in humans. Both small animal models and nonhuman primates (NHP) may prove valuable in triaging the most promising medical countermeasures prior to use in humans. Hamsters and ferrets are currently being used as immunocompetent small animal models of COVID-19 [<xref ref-type=\"bibr\" rid=\"CR3\">3</xref>–<xref ref-type=\"bibr\" rid=\"CR5\">5</xref>] while several NHP models have been quickly developed [<xref ref-type=\"bibr\" rid=\"CR6\">6</xref>–<xref ref-type=\"bibr\" rid=\"CR12\">12</xref>]. Among the nonhuman primate models evaluated the African green monkey (AGM) appears to best recapitulate the most salient features of human COVID-19 [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>–<xref ref-type=\"bibr\" rid=\"CR12\">12</xref>].</p><p id=\"Par13\">We recently reported the development of the first AGM model for COVID-19 and showed that back-challenge of animals with SARS-CoV-2 5 weeks after initial exposure resulted in protection from reinfection [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>]. In this study the AGMs were exposed to SARS-CoV-2 by a combination of the intranasal (i.n.) and intratracheal (i.t.) routes with the virus delivered in liquid media. As a natural extension of this initial work we sought to assess the pathogenesis of SARS-CoV-2 in AGMs exposed by the i.n. route only using the LMA Mucosal Atomization Device (MAD). Previous studies with another respiratory virus, Nipah virus, showed that there were no major differences in disease pathogenesis when virus was delivered to AGMs by a combined liquid-based i.n. and i.t. delivery [<xref ref-type=\"bibr\" rid=\"CR13\">13</xref>] or by the LMA MAD system [<xref ref-type=\"bibr\" rid=\"CR14\">14</xref>]. The LMA MAD was developed for the efficient and safe delivery of test particles and is currently employed to administer US FDA approved drugs for i.n. delivery. The LMA MAD delivers atomized particles that range in size from 30 to 100 μm, which is highly consistent with the size of droplets exhaled by humans due to coughing [<xref ref-type=\"bibr\" rid=\"CR15\">15</xref>]. In addition, in our previous work as the AGMs were back challenged with SARS-CoV-2 it was impossible to assess tissue pathology during convalescence after primary challenge. Here, we focused on assessing the pathogenesis of SARS-CoV-2 infection in AGMs when administered as 30 to 100 μm particles and on evaluating virus shedding and lung pathology during early convalescence.</p></sec><sec id=\"Sec2\"><title>Materials and methods</title><sec id=\"Sec3\"><title>Virus</title><p id=\"Par14\">The virus (SARS-CoV-2/INMI1-Isolate/2020/Italy) was isolated on January 30, 2020 from the sputum of the first clinical case in Italy, a tourist visiting from the Hubei province of China that developed respiratory illness while traveling [<xref ref-type=\"bibr\" rid=\"CR16\">16</xref>]. The virus was initially passaged twice (P2) on Vero E6 cells; the supernatant and cell lysate were collected and clarified following a freeze/thaw cycle. This isolate is certified mycoplasma and Foot-and-Mouth Disease virus free. The complete sequence was submitted to GenBank (MT066156) and is available on the GISAID website (BetaCoV/Italy/INMI1-isl/2020: EPI_ISL_410545) upon registration. For in vivo challenge, the P2 virus was propagated on Vero E6 cells and the supernatant was collected and clarified by centrifugation making the virus used in this study a P3 stock.</p></sec><sec id=\"Sec4\"><title>Animal challenge</title><p id=\"Par15\">SARS-CoV-2 seronegative AGMs (<italic>Chlorocebus aethiops</italic>) (6 females) (St Kitts origin, Worldwide Primates, Inc.) were randomized into two cohorts where one group (<italic>n</italic> = 3) was scheduled for euthanasia at 5 dpi and the other at 34 dpi. Animals were anesthetized with ketamine and inoculated with a target dose of 3.0 × 10<sup>6</sup> PFU of SARS-CoV-2 (SARS-CoV-2/INMI1-Isolate/2020/Italy) using the LMA MAD, with the dose being equally divided between each nostril. All animals were longitudinally monitored for clinical signs of illness including temperature (measured by surgically implanted DST micro-T small implantable thermo loggers (Star-Oddi, Gardabaer, Iceland)), respiration quality, and clinical pathology. All measurements requiring physical manipulation of the animals were performed under sedation by ketamine. Mucosal swabs were obtained using sterile swabs inserted into the mucosal cavity, gently rotated to maximize contact with the mucosal surface, and deposited into 2.0 mL screw-top tubes containing sterile MEM media supplemented to 2% with FBS.</p></sec><sec id=\"Sec5\"><title>RNA isolation from SARS-CoV-2-infected AGMs</title><p id=\"Par16\">On specified procedure days (days 0, 2, 3, 4, 5, 7, 12, 15, 21, 28, 34), 100 μl of blood was added to 600 μl of AVL viral lysis buffer (Qiagen) for virus inactivation and RNA extraction. Following removal from the high containment laboratory, RNA was isolated from blood and swabs using the QIAamp viral RNA kit (Qiagen).</p></sec><sec id=\"Sec6\"><title>Detection of SARS-CoV-2 load</title><p id=\"Par17\">RNA was isolated from blood and mucosal swabs and assessed using the CDC SARS-CoV-2 N2 assay primers/probe for reverse transcriptase quantitative PCR (RT-qPCR) [<xref ref-type=\"bibr\" rid=\"CR17\">17</xref>]. SARS-CoV-2 RNA was detected using One-step probe RT-qPCR kits (Qiagen) run on the CFX96 detection system (Bio-Rad), with the following cycle conditions: 50 °C for 10 min, 95 °C for 10 s, and 45 cycles of 95 °C for 10 s and 55 °C for 30 s. Threshold cycle (<italic>C</italic><sub><italic>T</italic></sub>) values representing SARS-CoV-2 genomes were analyzed with CFX Manager Software, and data are presented as GEq. To generate the GEq standard curve, RNA was extracted from supernatant derived from Vero E6 cells infected with SARS-CoV-2/INMI1-Isolate/2020/Italy was extracted and the number of genomes was calculated using Avogadro’s number and the molecular weight of the SARS-CoV-2 genome.</p><p id=\"Par18\">Infectious virus was quantitated by plaque assay on Vero E6 cells (ATCC CRL-1586) from all blood plasma and mucosal swabs, and bronchoalveolar lavage (BAL) samples. Briefly, increasing 10-fold dilutions of the samples were adsorbed to Vero E6 cell monolayers in duplicate wells (200 μl). Cells were overlaid with EMEM medium plus 1.25% Avicel, incubated for 2 days, and plaques were counted after staining with 1% crystal violet in formalin. The limit of detection for this assay is 25 PFU/ml.</p></sec><sec id=\"Sec7\"><title>Hematology and serum biochemistry</title><p id=\"Par19\">Total white blood cell counts, white blood cell differentials, red blood cell counts, platelet counts, hematocrit values, total hemoglobin concentrations, mean cell volumes, mean corpuscular volumes, and mean corpuscular hemoglobin concentrations were analyzed from blood collected in tubes containing EDTA using a Vetscan HM5 hematologic analyzer (Abaxis). Serum samples were tested for concentrations of albumin, amylase, alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), blood urea nitrogen (BUN), calcium, creatinine (CRE), C-reactive protein (CRP), gamma-glutamyltransferase (GGT), glucose, total protein, and uric acid by using a Piccolo point-of-care analyzer and Biochemistry Panel Plus analyzer discs (Abaxis). Partial pressures of CO<sub>2</sub> and O<sub>2</sub> were obtained using an iSTAT Alinity hematological analyzer (Abbott).</p></sec><sec id=\"Sec8\"><title>Serum neutralization assay</title><p id=\"Par20\">Neutralization titers were calculated by determining the dilution of serum that reduced 50% of plaques (PRNT<sub>50</sub>). A standard 100 PFU amount of SARS-CoV-2 was incubated with two-fold serial dilutions of serum samples for 1 hour. The virus-serum mixture was then used to inoculate Vero E6 cells for 60 min. Cells were overlaid with EMEM medium plus 1.25% Avicel, incubated for 2 days, and plaques were counted after staining with 1% crystal violet in formalin.</p></sec><sec id=\"Sec9\"><title>ELISA</title><p id=\"Par21\">SARS-CoV-2-specific IgG antibodies to nucleoprotein were measured in sera by ELISA at the indicated time points. Nucleoprotein ELISA kits were kindly provided by Zalgen Labs, LLC. Sera were initially diluted 1:100 and then two-fold through 1:25,600 in 4 in (1 x PBS with 0.02% Tween-20). After a one-hour incubation, plates were washed six times with wash buffer (1 x PBS with 0.2% Tween-20) and incubated for an hour with a 1:5000 dilution of horseradish peroxidase conjugated anti-primate IgG antibody (Fitzgerald Industries International; Cat: 43R-IG020HRP). Tetramethylbenzidine was used to develop the reaction; the reaction was stopped with methane-sulfonic acid and plates were read at a wavelength of 450 nm. Absorbance values were normalized by blank-subtracting values from wells incubated with sera from a SARS-CoV-2-naïve animal at the corresponding serum dilution. End-point titers were defined as the reciprocal of the last adjusted serum dilution with a value ≥0.20.</p></sec><sec id=\"Sec10\"><title>Histopathology and immunohistochemistry</title><p id=\"Par22\">Necropsy was performed on all subjects euthanized at 5 dpi and 34 dpi. Tissue samples of all major organs were collected for histopathologic and immunohistochemical (IHC) examination and were immersion-fixed in 10% neutral buffered formalin for > 7 days. Specimens were processed and embedded in paraffin and sectioned at 5 μm thickness. For IHC, specific anti-SARS immunoreactivity was detected using an anti-SARS nucleocapsid protein rabbit primary antibody at a 1:800 dilution for 60 min (Novusbio). The tissue sections were processed for IHC using the ThermoFisher Scientific Lab Vision Autostainer 360 (ThermoFisher Scientific). Secondary antibody used was biotinylated goat anti-rabbit IgG (Vector Laboratories) at 1:200 for 30 min followed by Vector Streptavidin Alkaline Phosphatase at a dilution of 1:200 for 20 min (Vector Laboratories). Slides were developed with Bio-Red (Biopath) for 7 min and counterstained with hematoxylin for 1 minute. For IHC, specific anti-fibrin was detected using an anti-fibrin monoclonal mouse primary antibody at a 1:3200 dilution for 60 min (Sekisui Diagnostics). The tissue sections were processed for IHC using the ThermoFisher Scientific Lab Vision Autostainer 360 (ThermoFisher Scientific). Secondary antibody used was biotinylated goat anti-mouse IgG (Vector Laboratories) at 1:200 for 30 min followed by Vector Streptavidin Alkaline Phosphatase at a dilution of 1:200 for 20 min (Vector Laboratories). Slides were developed with Bio-Red (Biopath Laboratories) for 7 min and counterstained with hematoxylin for 1 minute. Tissues were stained following package instructions for collagen with the Trichrome One-Step Blue & Red Stain Kit (American MasterTech Scientific Laboratory Supplies).</p></sec></sec><sec id=\"Sec11\"><title>Results</title><sec id=\"Sec12\"><title>SARS-CoV-2 experimental infection of African green monkeys using the LMA MAD</title><p id=\"Par23\">We challenged six healthy, adult AGMs with a target dose of 3.0 × 10<sup>6</sup> PFU of SARS-CoV-2 (SARS-CoV-2/INMI1-Isolate/2020/Italy) via intranasal inoculation with the LMA MAD (actual delivered dose of 2.8 × 10<sup>6</sup> PFU). Three animals were euthanized at 5 days post-infection (dpi) which is thought to be the approximate time point of peak disease in AGMs [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>], while the remaining three animals were euthanized at 34 dpi during early convalescence. Blood and mucosal swabs were sampled from all animals on days 0, 2, 3, 4, 5, and additionally on days 7, 9, 12, 15, 21, 28, and 34 for AGM-4, AGM-5, and AGM-6. BAL fluid collection was performed on days − 8, 3, and 5 for all animals, as well as 7 dpi for AGM-4, AGM-5 and AGM-6. Consistent with our previous report describing the development of the combined intranasal and intratracheal SARS-CoV-2 challenge model in AGMs [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>], we did not observe overt signs of clinical illness in any AGMs in this study, other than decreased appetite or brief (single day) anorexia (Supp Table <xref rid=\"MOESM2\" ref-type=\"media\">1</xref>). Temperature was longitudinally monitored in 15 min increments for the entire study duration using surgically implanted temperature loggers; several animals (AGM-4, AGM-6) experienced brief (< 2 h) periods of mildly elevated temperatures at 3 dpi, and two animals (AGM-2, AGM-3) exhibited an abnormal temperature cycling pattern at 3 dpi (Supp Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>).</p><p id=\"Par24\">As in our previous report, transient shifts in leukocyte populations, predominately manifested as lymphocytopenia (5/6 animals), thrombocytopenia (3/6 animals), and granulocytosis (defined by neutrophilia, eosinophilia, and/or basophilia) (6/6 animals) were observed, while markers for renal (BUN, CRE) and hepatic function (ALT, AST, ALP, GGT) remained unchanged for the most part, with the exception of mild (≤ 2-fold) increases in ALT (2/6 animals), and mild to moderate (1 to 16-fold) increases in CRP, a marker of acute systemic inflammation (5/6 animals) (Supp Table <xref rid=\"MOESM2\" ref-type=\"media\">1</xref>), although statistical significance was not reached for most parameters at most time points (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>). In addition, hypercapnia (defined here as ≥4 mmHg increase in dissolved CO<sub>2</sub>) was observed in 3/6 animals (Supp Table <xref rid=\"MOESM2\" ref-type=\"media\">1</xref>), which as we observed previously [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>], appeared to follow a biphasic pattern (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, data shown as fold-change from baseline]).\n<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Hematological features of SARS-CoV-2 infection in AGMs. Blood gas (<bold>a</bold>, <bold>b</bold>), selected leukocyte populations (<bold>c-g</bold>), and coagulation assays (<bold>h-j</bold>) are shown. For parameters where fold change is used, fold change was determined by baseline (0 dpi) subtraction of each time point for each animal. Statistical significance was determined in Graphpad Prism 8.4.3 by mixed-effects analysis with the Geisser-Greenhouse correction without the assumption of sphericity, with multiple comparisons made using Dunnett’s post-hoc test and all comparisons made to baseline values (0 dpi). Asterisks denote significance: * = <italic>p</italic> ≤ 0.05, ** = <italic>p</italic> ≤ 0.01, *** = <italic>p</italic> ≤ 0.001. Two-tailed <italic>p</italic>-values were computed for all comparisons</p></caption><graphic xlink:href=\"12985_2020_1396_Fig1_HTML\" id=\"MO1\"/></fig></p><p id=\"Par25\">All animals exhibited normal prothrombin times (PT) as compared to their individual baseline values; however, mild to moderate prolongation of the activated partial thromboplastin time (aPTT) was also observed in all animals through the acute phase of disease, most prominently in AGM-1 and AGM-2, indicating possible disorder of the intrinsic coagulation pathway (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>h, i); this was mirrored by increased levels of circulating fibrinogen (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>j). We previously showed that the pathways connected to IL-6 production are activated during SARS-CoV-2 infection of AGMs [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>], indicating possible mechanisms of coagulopathy in the current study.</p><p id=\"Par26\">All animals seroconverted, with weakly neutralizing titers (as quantified by PRNT<sub>50</sub>) being detected as early as 5 dpi and gradually increasing in potency by 34 dpi, with terminal neutralizing antibody titers ranging from ~ 1:16–1:128 (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>a-e). We next quantified SARS-CoV-2 nucleoprotein specific IgG by ELISA (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>f). Seroconversion was not detected until day 15 in two animals (AGM-4 & AGM-5). Interestingly, not until 34 dpi was a modest level (1:800) of seroconversion detected in the third animal.\n<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Serum neutralization and binding antibody titers in SARS-CoV-2 infected AGMs. Total anti-SARS-CoV-2 serum neutralization activity was determined for each animal by PRNT<sub>50</sub> at the indicated time points (<bold>a-e</bold>). Anti-SARS-CoV-2 N protein specific IgG endpoint titers were quantified for each animal and time point by ELISA (<bold>f</bold>)</p></caption><graphic xlink:href=\"12985_2020_1396_Fig2_HTML\" id=\"MO2\"/></fig></p></sec><sec id=\"Sec13\"><title>Quantification of viral load in blood, mucosal swabs, and lungs</title><p id=\"Par27\">Viral RNA (vRNA) was purified from whole blood, oral, nasal and rectal mucosa, and BAL fluid from all collection days, as well as from lung tissue harvested at necropsy. As we previously reported [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>], we were unable to detect SARS-CoV-2 vRNA in whole blood by RT-qPCR, nor were we able to recover infectious virus in the plasma fraction by plaque assay, confirming a lack of either cell-associated or freely-circulating virus in the peripheral blood. SARS-CoV-2 vRNA and infectious virus was detected in the nasal mucosa from all animals as early as 2 dpi, with vRNA persisting in a single animal up to 15 dpi (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>a, b). Likewise, vRNA was detected in oral swabs from all animals beginning 2–3 dpi before falling below the limit of detection by 7 dpi, while low quantities of infectious virus (1–2 log<sub>10</sub> PFU/mL) were only isolated from three animals (AGM-4, AGM-5, and AGM-6) (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>c, d). Remarkably, vRNA was transiently shed from the lower gastrointestinal tract up to 28 dpi (AGM-4 and AGM-6), although infectious virus could only be recovered from the rectal swab of a single animal (AGM-3) 4–5 dpi (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>e, f). vRNA was detected in BAL fluid from 4/6 animals 3 dpi and up to 7 dpi in all three animals held past 5 dpi, while infectious virus was recovered from 3/6 animals (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>g, h). Detectable quantities of vRNA were absent from lungs harvested during necropsy of AGMs euthanized 34 dpi, while 6–9 log<sub>10</sub> GEq/g were detected from all three animals euthanized at 5 dpi (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>i).\n<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Quantification of SARS-CoV-2 infectious virus and vRNA in mucosal swabs, BAL fluid, and lung tissue. Viral load was quantified by detection of SARS-CoV-2 vRNA by RT-qPCR (<bold>a</bold>, <bold>c</bold>, <bold>e</bold>, <bold>g</bold>, <bold>i</bold>) or plaque titration (<bold>b</bold>, <bold>d</bold>, <bold>f</bold>, <bold>h</bold>). The limit of detection for each assay is indicated by horizontal dashed line (1000 GEq/mL for RT-qPCR, 25 PFU/mL for plaque titration). For both assays, data shown is the mean of two technical replicates of the same biological sample. Arrow in (<bold>g</bold>) indicates day of challenge. For panel I, RUL: right upper lung; RML: right middle lung; RLL: right lower lung; LUL: left upper lung; LML: left middle lung; LLL: left lower lung</p></caption><graphic xlink:href=\"12985_2020_1396_Fig3_HTML\" id=\"MO3\"/></fig></p></sec><sec id=\"Sec14\"><title>Gross pathology, histopathology, and immunohistochemistry</title><p id=\"Par28\">Necropsy was performed on all animals following euthanasia, and lungs were collected for gross examination and histopathological analysis. Consistent with our previous study utilizing a combined i.n. and i.t. inoculation route [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>], all AGMs displayed varying degrees of pulmonary consolidation with hyperemia and hemorrhage, characterized by depressed and patchy dark red to light pink regions (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref><bold>, arrows</bold>). In all AGMs, the most severe lesions were located in the dorsal aspects of the lower lung lobes. A board-certified veterinary pathologist approximated lesion severity for each lung lobe (Supp Table <xref rid=\"MOESM3\" ref-type=\"media\">2</xref>). All AGMs at 5 dpi also had segmentally flaccid and gas distention of small intestines. There were no other significant gross lesions.\n<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Gross lung pathology in AGMs infected with SARS-CoV-2. Dorsal view of lungs from AGM-1 (<bold>a</bold>), AGM-2 (<bold>b</bold>) and AGM-3(<bold>c</bold>) euthanized at 5 dpi with SARS-CoV-2 exhibiting mild to moderate locally extensive pulmonary consolidation with hyperemia and hemorrhage. Dorsal view of lungs from AGM-4 (<bold>d</bold>), AGM-5 (<bold>e</bold>) and AGM-6 (<bold>f</bold>) euthanized at 34 dpi with SARS-CoV-2 exhibiting mild to marked locally extensive pulmonary consolidation with hyperemia and hemorrhage. Dorsal view of control lungs with no significant lesions from SARS-CoV-2 negative AGM (<bold>g</bold>)</p></caption><graphic xlink:href=\"12985_2020_1396_Fig4_HTML\" id=\"MO4\"/></fig></p><p id=\"Par29\">Histologically, all three AGMs euthanized at 5 dpi developed mild multifocal neutrophilic bronchointerstitial pneumonia (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>a-e, o). Histologic features include acute inflammation centered within the airways of terminal bronchioles with occasional flooding of adjacent alveolar spaces with neutrophils, macrophages, fibrin, edema, hemorrhage, mucous and rarely multinucleated giant cells <bold>(5A, B)</bold>. In lesser-affected regions alveolar septate were expanded with mixed inflammatory cells and alveolar spaces contain increased numbers of alveolar macrophages with scattered red blood cells. Ulcerative tracheobronchitis was also present in all three AGMs and characterized by multifocal epithelial erosion associated with underlying hemorrhage, fibrin accumulation and infiltrating acute inflammation. Polymerized fibrin, highlighted by IHC, colocalized with acute inflammation within the bronchial lumen, alveolar spaces, alveolar walls and ulcerated regions of the trachea and bronchus (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>c). Fibrin was also present within medium and small caliber vessels but was not associated with an obvious adherent thrombus. Trichrome stain of representative lung sections identified modest collagen deposition within multifocal regions of alveolar septae (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>d). IHC for SARS-CoV-2 antigen was positive in all three AGMs associated with pulmonary lesions. Positive IHC labeling was noted diffusely within the cytoplasm of respiratory epithelium of the bronchus (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>o) and less in type I and type II pneumocytes (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>e).\n<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Comparative pulmonary histologic lesions in AGMs infected with SARS-CoV-2. Representative tissues of AGM from 5 dpi (<bold>a</bold>-<bold>e</bold> & <bold>o</bold>) and 34 dpi (<bold>f</bold>-<bold>j</bold>). SARS-CoV-2 naïve tissues from an AGM (<bold>k</bold>-<bold>n</bold>). H&E staining at low magnification (20x) (<bold>a</bold>, <bold>f</bold>, & <bold>k</bold>) and higher magnification (40x) (<bold>b</bold>, <bold>g</bold>, <bold>l</bold>, and <bold>f</bold> inset) of pulmonary alveolar septae and alveolar spaces. Moderate neutrophilic bronchiolitis and alveolitis and mild interstitial pneumonia with congestion (<bold>a</bold> & <bold>b</bold>) Moderate lymphohistocytic interstitial pneumonia with congestion, mild alveolar wall fibrosis (<bold>f</bold> & <bold>g</bold>) and moderate perivascular lymphocytic cuffs (<bold>f</bold> inset). No significant lesions (<bold>k</bold> & <bold>l</bold>). IHC for anti-fibrin antigen (red) (<bold>c</bold>, <bold>h</bold> & <bold>m</bold>). Alveolar spaces are partially to completely flooded with fibrin (<bold>c</bold>) Minimal intravascular fibrin labeling (<bold>h</bold>) and no significant fibrin immunolabeling (<bold>m</bold>). Trichrome special stain for collagen (blue) (<bold>d</bold>, <bold>i</bold> & <bold>n</bold>). Minimal to mild alveolar wall collagen deposition (<bold>d</bold>) moderate alveolar wall collagen deposition (<bold>i</bold>) and minimal collagen staining of alveolar wall basement membranes (<bold>n</bold>). IHC labeling for anti-SARS-CoV2 antigen (red) (<bold>e</bold>, <bold>j</bold> & <bold>o</bold>). IHC positive type I pneumoncytes (black arrows) and type II pneumoncytes (white arrow) localized with alveolar inflammation (<bold>e</bold>), No immonolabeling (<bold>j</bold>) and IHC positive labeling of respiratory epithelium of the bronchus (<bold>o</bold>). Images captured at 20x (<bold>m</bold>, <bold>d</bold>, <bold>i</bold>, <bold>n</bold>, & <bold>j</bold>) and 40x (<bold>c</bold>, <bold>h</bold>, <bold>e</bold>, & <bold>o</bold>)</p></caption><graphic xlink:href=\"12985_2020_1396_Fig5_HTML\" id=\"MO5\"/></fig></p><p id=\"Par30\">Histologically, all three AGMs euthanized at 34 dpi developed moderate multifocal chronic interstitial pneumonia Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>f-j). Histologic features include expansion of alveolar septae with macrophages, lymphocytes, and very rarely neutrophils (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>f, g). Wispy, pale eosinophilic, acellular material also multifocally expanded the alveolar walls and stained as immature collagen with trichrome staining (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>i). Polymerized fibrin was present within medium and small caliber vessels but was not associated with an obvious adherent thrombus (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>h). No immunolabeling for SARS-CoV-2 was noted with IHC in any of the examined tissue sections from this 34 dpi cohort (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>j).</p></sec></sec><sec id=\"Sec15\"><title>Discussion</title><p id=\"Par31\">We previously reported the development of the AGM as a promising animal model of human COVID-19 [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>]. Other studies have subsequently reported similar findings [<xref ref-type=\"bibr\" rid=\"CR11\">11</xref>, <xref ref-type=\"bibr\" rid=\"CR12\">12</xref>]. The focus of the current study was to assess a more natural route of human exposure, specifically an exposure mimicking an infection resulting from mucosal exposure to infectious droplets expelled from close quarter exposure to a sneeze, cough, or even speech in order to begin characterization of lung pathology in the early convalescence phase of COVID-19. The disease resulting from the i.n. MAD challenge was largely reflective of that observed with the combination of the i.t. and i.n. routes except it appeared to be somewhat milder in terms of length of any fever, less severe signs of pneumonia as evidenced by reduced alveolar flooding, and a lower prevalence of SARS-CoV-2 infection [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>]. However, the MAD-infected AGMs still developed virus-induced pneumonia and viral shedding was detected into the early convalescence period. While it appears that inclusion of direct i.t. instillation of SARS-CoV-2 as an exposure route may result in a more severe disease in AGMs, it is also possible that animal to animal variability may have contributed to the modest difference between the studies. SARS-CoV-2 infection of humans results in a wide spectrum of disease ranging from asymptomatic to severe and fatal disease so it is not unexpected that there could be variability among AGMs as well. While the current study employed female AGMs because of animal availability at the time the work was initiated, gender did not affect the outcome when compared to similar studies [<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>–<xref ref-type=\"bibr\" rid=\"CR12\">12</xref>].</p><p id=\"Par32\">Coagulation dysfunction is a consistent observation in human COVID-19 and has been associated with disease severity [<xref ref-type=\"bibr\" rid=\"CR18\">18</xref>–<xref ref-type=\"bibr\" rid=\"CR22\">22</xref>]. Here, we performed a limited analysis of blood clotting times (PT and aPTT) and circulating fibrinogen levels to begin to characterize the coagulopathy in SARS-CoV-2-infected AGMs. Transient increases in aPTT and in circulating fibrinogen levels were observed during the acute phase of infection. Increases in PT and/or aPTT have been linked to severe human COVID-19 cases in some but not all studies [<xref ref-type=\"bibr\" rid=\"CR18\">18</xref>–<xref ref-type=\"bibr\" rid=\"CR22\">22</xref>]. However, nearly all severe COVID-19 cases have been associated with high levels of fibrinogen [<xref ref-type=\"bibr\" rid=\"CR20\">20</xref>–<xref ref-type=\"bibr\" rid=\"CR22\">22</xref>].</p><p id=\"Par33\">Our findings regarding lung injury in the three AGMs that were euthanized at 34 dpi during early convalescence are consistent with the limited human COVID-19 studies that have been reported so far. For example, a recent study of fifty-seven COVID-19 patients in China was completed during the early convalescence phase, approximately 30 days after discharge [<xref ref-type=\"bibr\" rid=\"CR23\">23</xref>]. The study included 40 non-severe cases and 17 severe cases. Thirty-one patients (54.3%) had abnormal CT findings while abnormalities were detected in the pulmonary function tests in 43 (75.4%) of the patients. In a second human study, 21 patients recovering from COVID-19 (without severe respiratory distress during the disease course), had lung abnormalities visible on chest CT at 10 days after initial onset of symptoms [<xref ref-type=\"bibr\" rid=\"CR24\">24</xref>]. While other studies suggest that some of the abnormalities may be resolved over time [<xref ref-type=\"bibr\" rid=\"CR25\">25</xref>, <xref ref-type=\"bibr\" rid=\"CR26\">26</xref>] more research needs to be conducted in this area.</p><p id=\"Par34\">Regarding histopathology, human data is particularly sparse. One small study performed thoracoscopies with blebs resection and pleurectomies on performed on the 16th and 23rd days from symptoms onset of two patients [<xref ref-type=\"bibr\" rid=\"CR27\">27</xref>]. Despite well-known pulmonary damages induced during the acute phase of COVID-19, the late-phase gross and histological changes include nonspecific chronic reparative lesions, similarly to what we have described in the AGMs at 34 dpi. Grossly in the human study, there was non-specific diffuse pulmonary congestion, edema and hemorrhagic necrosis. Histologically, the main lesions were focused on alveolar damage with mildly thickened alveolar interstitial tissues with fibrosis and mononuclear cellular infiltration (lymphocytes, plasma cells and multinucleate giant cells). Intravascular hemorrhagic thrombosis was also noted in these specimens.</p><p id=\"Par35\">In summary, we have expanded on our previous development of the combined i.n. and i.t. inoculation model of SARS-CoV-2 in AGMs. Importantly, while AGMs challenged with SARS-CoV-2 via the LMA MAD exhibited apparently milder clinical illness and disease, hallmark features from our previous study were still apparent, notably the development of viral pneumonia during the acute phase. The AGM COVID-19 model should be useful in future studies to assess disease and develop interventions that improve recovery.</p></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec16\"><p>\n<supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"12985_2020_1396_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1: Figure S1</bold>: Longitudinal temperature monitoring of SARS-CoV-2 infected AGMs. Temperature was longitudinally monitored for all animals via surgically-implanted temperature loggers (see Materials and methods). Data shown for all animals begins 1 day prior to infection (− 1) and terminates in the morning of day 5 post-infection, when AGM-1, AGM-2, and AGM-3 were euthanized. Periods of elevated temperature are indicated in red. Arrows denote time of challenge.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"12985_2020_1396_MOESM2_ESM.docx\"><caption><p><bold>Additional file 2: Table S1</bold>: Clinical description and outcome of African green monkeys following SARS-CoV-2 challenge. Days after SARS-CoV-2 challenge are in parentheses. All reported findings are in comparison to baseline (d0) values. Decreased appetite is defined as some food but not all food consumed from the previous day. Anorexia is defined as no food consumed from the previous day. Lymphocytopenia, monocytopenia, erythrocytopenia, thrombocytopenia, neutropenia, eosinopenia, and basopenia are defined by a ≥ 35% drop in numbers of lymphocytes, monocytes, erythrocytes, platelets, neutrophils, eosinophils, and basophils, respectively. Lymphocytosis, monocytosis, neutrophilia, eosinophilia, and basophilia are defined by a 100% or greater increase in numbers of lymphocytes, monocytes, neutrophils, eosinophils, or basophils, respectively. Hyperglycemia is defined as a 100% or greater increase in levels of glucose. Hypoglycemia is defined by a ≥ 25% decrease in levels of glucose. Hypoalbuminemia is defined by a ≥ 25% decrease in levels of albumin. Hypoproteinemia is defined by a ≥ 25% decrease in levels of total protein. Hypoamylasemia is defined by a ≥ 25% decrease in levels of serum amylase. Hypocalcemia is defined by a ≥ 25% decrease in levels of serum calcium. Hypercapnia was defined as having a partial CO2 > 4 mmHg over d0 baseline values. (ALT) alanine aminotransferase, (AST) aspartate aminotransferase, (ALP) alkaline phosphatase, (CRE) Creatinine, (CRP) C-reactive protein, (Hct) hematocrit, (Hgb) hemoglobin.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"12985_2020_1396_MOESM3_ESM.tif\"><caption><p><bold>Additional file 3: Table S2</bold>: Gross lung lesion severity scores in AGMs infected with SARS-CoV-2</p></caption></media></supplementary-material></p></sec></sec></body><back><glossary><title>Abbreviations</title><def-list><def-item><term>aPTT</term><def><p id=\"Par2\">Activated partial thromboplastin time</p></def></def-item><def-item><term>AGM</term><def><p id=\"Par3\">African green monkey</p></def></def-item><def-item><term>BAL</term><def><p id=\"Par4\">Bronchoalveolar lavage</p></def></def-item><def-item><term>COVID-19</term><def><p id=\"Par5\">Coronavirus Disease 2019</p></def></def-item><def-item><term>IHC</term><def><p id=\"Par6\">Immunohistochemistry</p></def></def-item><def-item><term>i.n.</term><def><p id=\"Par7\">Intranasal</p></def></def-item><def-item><term>i.t.</term><def><p id=\"Par8\">Intratracheal</p></def></def-item><def-item><term>MAD</term><def><p id=\"Par9\">Mucosal Atomization Device</p></def></def-item><def-item><term>PFU</term><def><p id=\"Par10\">Plaque forming unit</p></def></def-item><def-item><term>PT</term><def><p id=\"Par11\">Prothrombin time</p></def></def-item><def-item><term>SARS-CoV-2</term><def><p id=\"Par111\">Severe acute respiratory syndrome coronavirus 2</p></def></def-item></def-list></glossary><fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> accompanies this paper at 10.1186/s12985-020-01396-w.</p></sec><ack><p>The authors would like to thank the UTMB Animal Resource Center for veterinary support for surgery to implant temperature data loggers and husbandry support of laboratory animals and Dr. Kevin Melody for assistance with animal studies. The virus used in this publication was kindly provided by the European Virus Archive goes Global (EVAg) project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 653316.</p></ack><notes notes-type=\"author-contribution\"><title>Authors’ contributions</title><p>RWC and TWG conceived and designed the study. DJD, JBG, and TWG performed the SARS-CoV-2 challenge experiments. RWC, DJD, CW, JBG, and TWG performed animal procedures and clinical observations. KNA and VB performed the clinical pathology assays. VB performed the SARS-CoV-2 infectivity assays. KNA optimized and performed the PCR. NSD optimized and performed the immunohistochemistry. CW performed ELISAs. KAF performed necropsies and analysis of the gross pathology, histopathology, and immunohistochemistry. All authors analyzed the clinical pathology, virology, and immunology data. RWC, ANP, KAF, and TWG, wrote the paper. The authors had access to all of the data and approved the final version of the manuscript.</p></notes><notes notes-type=\"funding-information\"><title>Funding</title><p>This study was supported by funds from the Department of Microbiology and Immunology, University of Texas Medical Branch at Galveston, Galveston, TX to TWG. Operations support of the Galveston National Laboratory was supported by NIAID/NIH grant UC7AI094660.</p></notes><notes notes-type=\"data-availability\"><title>Availability of data and materials</title><p>The data supporting the conclusions of this article are included within the article.</p></notes><notes id=\"FPar1\"><title>Ethics approval and consent to participate</title><p id=\"Par36\">All animal studies were approved by the University of Texas Medical Branch (UTMB) Institutional Animal Care and Use Committee and adhere to the NIH Guide for the Care and Use of Laboratory Animals.</p></notes><notes id=\"FPar2\"><title>Consent for publication</title><p id=\"Par37\">Not applicable.</p></notes><notes id=\"FPar3\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par38\">The authors declare no competing interests.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><mixed-citation publication-type=\"other\">World Health Organization. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849246</article-id><article-id pub-id-type=\"pmc\">PMC7431906</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00816</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Rosuvastatin Alleviates Intestinal Injury by Down-Regulating the CD40 Pathway in the Intestines of Rats Following Traumatic Brain Injury</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Hu</surname><given-names>Yangchun</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/949774/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Wang</surname><given-names>Xiaojian</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Ye</surname><given-names>Lei</given-names></name><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Chao</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Chen</surname><given-names>Weiwei</given-names></name></contrib><contrib contrib-type=\"author\"><name><surname>Cheng</surname><given-names>Hongwei</given-names></name></contrib></contrib-group><aff><institution>Department of Neurosurgery, First Affiliated Hospital of Anhui Medical University</institution>, <addr-line>Hefei</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Stefania Mondello, University of Messina, Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Meng-Liang, Department of Neurosurgery, Nanjing General Hospital of Nanjing Military Command, China; Edmund R. Hollis, Burke Medical Research Institute, United States</p></fn><corresp id=\"c001\">*Correspondence: Yangchun Hu <email>hycdoc@sohu.com</email></corresp><corresp id=\"c002\">Lei Ye <email>yelei8778@sina.cn</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Neurotrauma, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>816</elocation-id><history><date date-type=\"received\"><day>09</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>29</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Hu, Wang, Ye, Li, Chen and Cheng.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Hu, Wang, Ye, Li, Chen and Cheng</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Statins have been reported to suppress CD40 expression and nuclear factor (NF)-κB activation, which are both up-regulated in the intestines following traumatic brain injury (TBI)-induced intestinal injury. In this study, we aimed to investigate the effects of the statin rosuvastatin on post-TBI jejunal injury in rats, focusing on potential mechanisms involving the CD40/NF-κB signaling pathway. The jejunal CD40 expression was determined by western blotting. The DNA-binding activity of NF-κB was assessed by electrophoretic mobility shift assays (EMSAs). The tumor necrosis factor (TNF)-α and interleukin (IL)-1β levels were assessed by enzyme-linked immunosorbent assays (ELISAs). The severity of the jejunal mucosal injury was assessed by hematoxylin and eosin (HE) staining and histopathological evaluation. We found that the post-TBI upregulation of both CD40 expression and NF-κB activity in the jejunal tissues were significantly inhibited by rosuvastatin, while the post-TBI expression of TNF-α and IL-1β was significantly suppressed by rosuvastatin. In addition, rosuvastatin significantly ameliorated TBI-induced effects on the villus height, crypt depth, and villous surface area. Rosuvastatin suppressed TBI-induced intestinal injury in rats, which may be associated with the blockade of the CD40/NF-κB pathway.</p></abstract><kwd-group><kwd>rosuvastatin</kwd><kwd>CD40</kwd><kwd>NF-κB</kwd><kwd>intestinal injury</kwd><kwd>traumatic brain injury</kwd></kwd-group><counts><fig-count count=\"4\"/><table-count count=\"1\"/><equation-count count=\"0\"/><ref-count count=\"34\"/><page-count count=\"7\"/><word-count count=\"4212\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Traumatic brain injury (TBI) is a serious medical problem worldwide, with extremely high disability and mortality rates (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Although intensive investigations of TBI have been carried out, researchers have focused mainly on the pathophysiologic processes of the brain injury itself. However, it is also important to realize that extracranial complications following the initial brain injury might, to some extent, impede treatment efficacy, and recovery. Therefore, understanding the etiology and underlying mechanisms of post-TBI extracranial complications is important (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>).</p><p>Organ dysfunction, especially gastrointestinal dysfunction, has frequently been observed in TBI patients (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). In previous research, we demonstrated that TBI can induce marked damage to intestinal mucosal structures and barrier functions (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Additionally, we found that TBI up-regulated the intestinal expression of CD40, nuclear factor (NF)-κB, and pro-inflammatory cytokines, which may play pivotal roles in the pathogenesis of acute intestinal mucosal injury (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>–<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Several studies have suggested that the inflammatory responses mediated by NF-κB and other inflammatory cytokines are key factors in intestinal mucosal damage (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>–<xref rid=\"B10\" ref-type=\"bibr\">10</xref>). Notably, both <italic>in vivo</italic> and <italic>in vitro</italic> studies have reported that statins regulate CD40 expression (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>), block NF-κB activation (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>, <xref rid=\"B13\" ref-type=\"bibr\">13</xref>), and further exhibit anti-inflammatory properties, in addition to exhibiting lipid-lowering effects (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>).</p><p>The statin rosuvastatin, a new 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitor, has increased affinity for the active site of HMG-CoA reductase compared to other statins. The protective role of rosuvastatin in preventing ischemic injury has been clearly documented (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Yuji et al. reported that rosuvastatin reduced intestinal ischemia-reperfusion injury in animal models (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). This indicates that rosuvastatin is a potential candidate drug for protecting against post-TBI intestinal injury. Hence, in this study, we aimed to investigate whether rosuvastatin treatment could regulate the CD40/NF-κB signaling pathway, and tried to clarify the potential role of this pathway in post-TBI intestinal injury.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>Rat Model of TBI</title><p>Male Sprague-Dawley rats, weighing about 250–300 g, were purchased from the Experimental Animal Center of Anhui Medical University. They were housed at about 25°C in a controlled environment with 12 h of artificial light per day. They were randomized into three groups: the sham operation + normal saline group (SN, <italic>n</italic> = 6), TBI + normal saline group (TN, <italic>n</italic> = 6), and TBI + rosuvastatin group (TR, <italic>n</italic> = 6).</p><p>TBI was induced using a modified version of Feeney's weight-drop model technique (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). Briefly, the rats were anesthetized with 4% isoflurane and the anesthetic effect was maintained with 2% isoflurane (0.6 L/min) delivered by a small-animal anesthetic machine. Thereafter, a right parietal craniotomy (5 mm in diameter) was performed at 1 mm posterior and 2 mm lateral to the bregma. A steel rod (weighing 40 g with a flat end and a diameter of 4 mm) freely fell from a height of 25 cm onto the exposed intact cranial dura to produce a standardized parietal contusion. The rod was allowed to compress the tissue a maximum of 5 mm. The rats in the sham operation group were anesthetized, mounted in the stereotaxic apparatus and had their scalps cut and sutured but did not undergo trephination.</p><p>At 30 min after the TBI, rosuvastatin (AstraZeneca UK, Ltd., London, UK) was dissolved in isotonic normal saline and administered at 30 mg/kg intraperitoneally. In the SN group, an equivalent amount of saline was administered intraperitoneally under the same time condition.</p><p>All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.</p></sec><sec><title>Preparation of Jejunal Tissues</title><p>The rats were decapitated 24 h after the TBI to conduct tissue assays. A-3 cm segment of the mid-jejunum was obtained, flushed with ice-cold saline, and opened longitudinally. For the histopathological evaluation, jejunal tissues were immersed in 4% buffered formalin.</p></sec><sec><title>Western Blotting (WB) Analysis</title><p>The total proteins of the jejunal tissue homogenates were extracted using a radioimmunoprecipitation assay (RIPA) buffer kit (Beyotime Biotechnology, Shanghai, China) according to the manufacturer's protocol. They were then measured using a bicinchoninic acid (BCA) protein quantification kit (Thermo Scientific, Waltham, MA, USA), according to the manufacturer's protocol. The CD40 protein levels were assessed as previously described (<xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Briefly, the proteins were separated by 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis and then transferred to polyvinylidene difluoride membranes (Millipore Corp., Bedford, MA, USA). The membranes were blocked with 5% non-fat milk (w/v) dissolved in Tris-buffered saline with Tween 20 for 1 h at room temperature. The proteins were then labeled with the following primary antibodies: anti-CD40 antibody (1:100; Santa Cruz Biotechnology, CA, USA) and anti-glyceraldehyde 3-phosphate dehydrogenase (GAPDH) antibody (1:1,000; Sigma, St Louis, MO, USA) at 4°C overnight. This was followed by incubation with secondary goat anti-mouse IgG (H+L) antibody (peroxidase/horseradish peroxidase conjugated; 1:1,000; E-AB-1001; Elabscience, Wuhan, China) for 1.5 h at room temperature. The optical density of the resulting bands was determined using UN-SCAN-IT graph digitizing software (UT, USA), with densitometry values normalized to the GAPDH values.</p></sec><sec><title>Electrophoretic Mobility Shift Assay (EMSA)</title><p>We performed EMSA to detect the NF-κB DNA-binding activity as previously described (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Briefly, a consensus oligonucleotide probe containing the DNA-binding site for NF-κB (5′-AGTTGAGGGGACTTTCCCAGGC-3′) was end-labeled with [γ-<sup>32</sup>P]-ATP (Free Biotech, Beijing, China) using T4-polynucleotide kinase. Competitive reactions were performed by adding a 100-fold excess of unlabeled NF-κB consensus oligonucleotide. HeLa nuclear extract was used as the positive control. Data were expressed as arbitrary densitometry units (ADU) obtained from the densitometric scans.</p></sec><sec><title>Enzyme-Linked Immunosorbent Assay (ELISA)</title><p>The concentrations of tumor necrosis factor (TNF)-α and interleukin (IL)-1β in the jejunal tissue supernatants were determined using ELISA kits according to the manufacturer's protocols (TNF-α ELISA kit from Diaclone Research, Besançon, France; IL-1β ELISA kit from BioSource Europe SA, Nivelles, Belgium).</p></sec><sec><title>Histopathological Evaluation</title><p>The formalin-fixed jejunal tissues were embedded in paraffin, sectioned at 4-μm thickness with a microtome, and stained with hematoxylin and eosin (HE). The villus height, diameter of the middle of the villus, and crypt depth in the tissues were determined using an HPIAS-1000 image analysis system (Champion Image Engineering Company, Wuhan, China). The villous surface area was calculated on the basis of the following formula: surface area = πdh (d, villus diameter; h, villus height). At least 10 well-oriented crypt-villus units per sample were assessed and average values were calculated by an independent pathologist who was blind to the animal grouping.</p><p>Additionally, pathology grades were determined based on the following criteria, as described in a previous study (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>): 0: normal; 1: mild focal infiltration of the lamina propria; 2: mild infiltration of the lamina propria, multifocal, and mild glandular separation; 3: infiltration with multifocal mild edema; 4: mixed infiltration of the submucosa and lamina propria, extensive separation of glands, plaque enlargement, and edema.</p></sec><sec><title>Statistical Analysis</title><p>We used SPSS (version 19.0. IBM Co., Ltd., Armonk, NY, USA) for the statistical analysis. Each parameter was expressed as mean ± SD and compared between groups using Kruskal-Wallis test or one-way analysis of variance (ANOVA), followed by Tukey's <italic>post-hoc</italic> test. <italic>P</italic> < 0.05 was considered statistically significant.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>CD40 Protein Levels in the Jejunal Tissues</title><p>CD40 protein levels were measured by WB. As shown in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>, compared to the SN group, the level of CD40 protein significantly increased in the TN group (<italic>P</italic> < 0.01), while the rosuvastatin (in the TR group) significantly suppressed the CD40 protein level compared to that in the TN group (<italic>P</italic> < 0.05). The replicates of CD40 proteins in other 5 groups were provided as request (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Figure 1</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>Western blotting analysis of CD40 protein expression. <bold>(A)</bold> Representative western blots of CD40 levels in the jejunal tissues in the SN, TN, and TR groups. <bold>(B)</bold> Quantitative analysis of the western blotting results for CD40. CD40 expression was up-regulated following traumatic brain injury (TBI; TN vs. SN), but the increased level of CD40 was suppressed by rosuvastatin (TR vs. TN). Bars represent mean ± SD (<italic>n</italic> = 6 per group). *<italic>P</italic> < 0.05 vs. SN group; <sup>#</sup><italic>P</italic> < 0.05 vs TR group. SN, sham operation + normal saline; TN, TBI + normal saline; TR, TBI + rosuvastatin.</p></caption><graphic xlink:href=\"fneur-11-00816-g0001\"/></fig></sec><sec><title>NF-κB DNA-Binding Activity in the Jejunal Tissues</title><p>The results of EMSAs of NF-κB DNA-binding activity in the jejunal tissues are shown in <xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>. Low NF-κB DNA-binding activity (i.e., weak EMSA autoradiography) was found in the SN group. In contrast, NF-κB DNA-binding activity was significantly up-regulated in the TN group compared to the SN group (<italic>P</italic> < 0.01), while it was significantly suppressed in the TR group compared to the TN group (<italic>P</italic> < 0.05).</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Electrophoretic mobility shift assays (EMSAs) of NF-κB DNA-binding activity. <bold>(A)</bold> EMSA results showing NF-κB DNA-binding activities in the SN, TN, and TR groups. <bold>(B)</bold> Quantitative analysis of the NF-κB DNA-binding activity. NF-κB DNA-binding activity increased following traumatic brain injury (TBI; TN vs. SN), but the increased activity was suppressed by rosuvastatin (TR vs. TN). Bars represent mean ± SD (<italic>n</italic> = 6 per group). *<italic>P</italic> < 0.05 vs. SN group; <sup>#</sup><italic>P</italic> < 0.05 vs TR group. SN, sham operation + normal saline; TN, TBI + normal saline; TR, TBI + rosuvastatin.</p></caption><graphic xlink:href=\"fneur-11-00816-g0002\"/></fig></sec><sec><title>Concentrations of IL-1β and TNF-α in the Jejunal Tissues</title><p>ELISAs were performed to assess the concentrations of IL-1β and TNF-α in the jejunal tissues. The results showed that the concentrations of both IL-1β and TNF-α were extremely low in the SN group, and they were greatly enhanced in the TN group (<italic>P</italic> < 0.01). Rosuvastatin significantly suppressed the post-TBI concentrations of IL-1β and TNF-α in the jejunal tissues (<italic>P</italic> < 0.05) (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>).</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Changes in inflammatory mediators in jejunal tissues as determined by enzyme-linked immunosorbent assays (ELISAs). Traumatic brain injury (TBI) significantly increased the concentrations of IL-1β and TNF-α in rat jejunal tissues. In the TR group, the jejunal concentrations of IL-1β <bold>(A)</bold> and TNF-α <bold>(B)</bold> were markedly suppressed compared to those in the TN group. Bars represent mean ± SD (<italic>n</italic> = 6 per group). *<italic>P</italic> < 0.05 vs. SN group; <sup>#</sup><italic>P</italic> < 0.05 vs TR group. SN, sham operation + normal saline; TN, TBI + normal saline; TR, TBI + rosuvastatin.</p></caption><graphic xlink:href=\"fneur-11-00816-g0003\"/></fig></sec><sec><title>Histopathological Evaluation</title><p>Villus height, crypt depth, and villous surface area were determined as specific evaluation indices of mucosal damage. Histopathological assessment showed that the morphology of the jejunal mucosa was approximately normal in the SN group. TBI caused considerable damage to the mucosal structures. However, this damage was ameliorated by rosuvastatin administration (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>). Quantitative analyses demonstrated that the villus height, crypt depth, and villous surface area significantly decreased in the TN group compared to the SN group (<italic>P</italic> < 0.01). In the TR group, these parameters were significantly increased compared to those in the TN group (<italic>P</italic> < 0.05) (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>Hematoxylin and eosin (HE) staining of the mucosal structures of the jejunum. <bold>(A)</bold> Rats in the SN group exhibited normal mucosal architecture with intact villi. <bold>(B)</bold> Traumatic brain injury (TBI) resulted in shedding of epithelial cells, broken villi, focal ulcers, fusion between adjacent villi, dilation of central chyle duct, mucosal atrophy, and edema in the villus interstitium and lamina propria. <bold>(C)</bold> Rosuvastatin significantly suppressed the TBI-induced morphologic alterations of the jejunal mucosa. Scale bar = 200 μm. <bold>(D)</bold> Quantitative analysis of morphology of the jejunal mucosa with Kruskal Wallis tests. *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01 vs. SN group; <sup>#</sup><italic>P</italic> < 0.05 vs TN group. SN, sham operation + normal saline; TN, TBI + normal saline; TR, TBI + rosuvastatin.</p></caption><graphic xlink:href=\"fneur-11-00816-g0004\"/></fig><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Changes in villous height, diameter, crypt depth, and surface area of mucosa.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Groups</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Villous height (μm)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Villous diameter (μm)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Crypt depth (μm)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Surface area (mm<sup><bold>2</bold></sup>)</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SN</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">241.4 ± 28.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">47.4 ± 7.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">81.6 ± 12.6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0362 ± 0.0041</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TN</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">191.2 ± 17.6<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>#</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38.7 ± 3.2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69.3 ± 9.5<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>#</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0235 ± 0.0022<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>#</sup></xref></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">211.2 ± 22.3<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>*</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38.5 ± 5.5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">75.2 ± 7.2<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>*</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0257 ± 0.0036<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>*</sup></xref></td></tr></tbody></table><table-wrap-foot><p>Values were expressed as mean ± SD.</p><fn id=\"TN1\"><label>#</label><p>P < 0.0.05 vs. SN group, and</p></fn><fn id=\"TN2\"><label>*</label><p><italic>P < 0.05 vs. TN group</italic>.</p></fn></table-wrap-foot></table-wrap></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>In the present study, we found increases in the CD40 protein level, NF-κB DNA-binding activity, and concentrations of IL-1β and TNF-α in the jejunal tissues of rats at 24 h after TBI. However, the administration of rosuvastatin partially inhibited CD40 expression, decreased the NF-κB activation, and reduced the concentrations of IL-1β and TNF-α. Moreover, histopathological evaluation confirmed that the TBI-induced damage to the jejunal structures was ameliorated by rosuvastatin.</p><p>Previous studies demonstrated that the CD40/CD40L pathway plays a key role in intestinal inflammation by increasing the secretion of multiple pro-inflammatory cytokines and chemokines (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). Although the specific mechanisms underlying how statins inhibit CD40 expression remain poorly understood, several studies have supported the hypothesis that these effects are mediated by nitric oxide synthase (NOS)- or peroxisome proliferator-activated receptor (PPAR)-dependent pathways (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B22\" ref-type=\"bibr\">22</xref>, <xref rid=\"B23\" ref-type=\"bibr\">23</xref>). We hypothesize that the inhibitory effect of rosuvastatin on post-TBI CD40 expression in the jejunum was mediated by the same pathway.</p><p>It has been reported that NF-κB, a pivotal cytokine downstream of CD40, plays a fundamental role in regulating cytokine-mediated inflammatory processes (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B24\" ref-type=\"bibr\">24</xref>). The functional importance of NF-κB in acute inflammation is related to its ability to regulate the transcription of numerous genes, such as IL-1β, TNF-α, IL-6, intercellular adhesion molecule (ICAM)-1, and acute phase proteins, which have been shown to be critical in inflammatory processes (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B26\" ref-type=\"bibr\">26</xref>). Increasing evidence has convincingly indicated that corticosteroid hormones, antioxidants, protease inhibitors, and other compounds may treat pathological inflammatory conditions by inhibiting NF-κB activation (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). In our previous research, we demonstrated that progesterone suppressed TBI-induced NF-κB activation in the gut, decreased the intestinal production of pro-inflammatory cytokines, and protected the structures of the ileal mucosa (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). In the present study, we found that rosuvastatin blocked NF-κB activation, and subsequently down-regulated IL-1β and TNF-α levels. The inhibition of NF-κB activation might be attributable to the reduced phosphorylation and degradation of the NF-κB inhibitor protein IκB, as well as the absence of mevalonate caused by inhibiting HMG-CoA reductase (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>).</p><p>Our study had a notable limitation concerning the focus on rosuvastatin rather than multiple statins. Although researchers have reported that other statins beside rosuvastatin (such as simvastatin and atorvastatin) might exhibit anti-neuroinflammatory effects in animal models of TBI (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>, <xref rid=\"B30\" ref-type=\"bibr\">30</xref>), we did not compare these other statins with rosuvastatin. Xu et al. found that acute atorvastatin administration effectively modulated post-TBI neuroinflammation, probably by altering peripheral leukocyte invasion and the alternative polarization of microglia/macrophages (<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Additionally, Chong et al. found that the neuroprotective effect of simvastatin in TBI might be due to its anti-neuroinflammatory effects rather than its cholesterol-lowering effects (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>). However, there are few studies on the regulation of post-TBI intestinal inflammation by simvastatin and atorvastatin and, as the aim of our study concerned post-TBI extracranial complications, we did not assess the effects of rosuvastatin on neuroinflammation. We believe that rosuvastatin might share anti-inflammatory effects with the abovementioned statins, as activated neuroinflammation predominantly relies on the activation of peripheral immunocytes, and 70–80% immunocytes are located in the gut associated lymphoid tissue (GALT) (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). However, mechanistic research should now be conducted to determine whether different statins have different effects regarding inflammation.</p><p>In conclusion, we found that rosuvastatin has an anti-inflammatory effect, in addition to its ability to reduce cholesterol levels (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). This anti-inflammatory effect might involve both HMG-CoA reductase-dependent and -independent mechanisms (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>). Our results suggest that rosuvastatin can partially prevent acute TBI-induced injury to the rat jejunum, probably due to its blockade of the CD40/NF-κB pathway. The specific protection mechanisms require further exploration.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>All datasets generated for this study are included in the article/<xref ref-type=\"sec\" rid=\"s9\">Supplementary Material</xref>.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>The animal study was reviewed and approved by the Ethics Committee of Anhui Medical University.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>All authors contributed extensively to the work presented in this paper. All authors collected data. YH wrote the manuscript draft. XW and LY reviewed and edited the manuscript. CL and WC conducted the experiments. LY and HC revised it critically for important intellectual content.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><sec sec-type=\"supplementary-material\" id=\"s9\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fneur.2020.00816/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fneur.2020.00816/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><label>Supplementary Figure 1</label><caption><p>Western blots replicates of CD40 levels in the jejunal tissues for another 5 groups. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"methods-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurol.</journal-id><journal-title-group><journal-title>Frontiers in Neurology</journal-title></journal-title-group><issn pub-type=\"epub\">1664-2295</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32849254</article-id><article-id pub-id-type=\"pmc\">PMC7431907</article-id><article-id pub-id-type=\"doi\">10.3389/fneur.2020.00847</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neurology</subject><subj-group><subject>Methods</subject></subj-group></subj-group></article-categories><title-group><article-title>Ontario Neurodegenerative Disease Research Initiative (ONDRI): Structural MRI Methods and Outcome Measures</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Ramirez</surname><given-names>Joel</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/166928/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Holmes</surname><given-names>Melissa F.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Scott</surname><given-names>Christopher J. M.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Ozzoude</surname><given-names>Miracle</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/960402/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Adamo</surname><given-names>Sabrina</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/883226/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Szilagyi</surname><given-names>Gregory M.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Goubran</surname><given-names>Maged</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Gao</surname><given-names>Fuqiang</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Arnott</surname><given-names>Stephen R.</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/16004/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Lawrence-Dewar</surname><given-names>Jane M.</given-names></name><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/83795/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Beaton</surname><given-names>Derek</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/918485/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Strother</surname><given-names>Stephen C.</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/772/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Munoz</surname><given-names>Douglas P.</given-names></name><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Masellis</surname><given-names>Mario</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/913539/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Swartz</surname><given-names>Richard H.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Bartha</surname><given-names>Robert</given-names></name><xref ref-type=\"aff\" rid=\"aff7\"><sup>7</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Symons</surname><given-names>Sean</given-names></name><xref ref-type=\"aff\" rid=\"aff8\"><sup>8</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Black</surname><given-names>Sandra E.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/153283/overview\"/></contrib><contrib contrib-type=\"author\"><collab>The ONDRI Investigators</collab></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto</institution>, <addr-line>Toronto, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Medical Biophysics, University of Toronto</institution>, <addr-line>Toronto, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Rotman Research Institute, Baycrest</institution>, <addr-line>Toronto, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Thunder Bay Regional Health Research Institute</institution>, <addr-line>Thunder Bay, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Centre for Neuroscience Studies, Queen's University</institution>, <addr-line>Kingston, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto</institution>, <addr-line>Toronto, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff7\"><sup>7</sup><institution>Department of Medical Biophysics, Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario</institution>, <addr-line>London, ON</addr-line>, <country>Canada</country></aff><aff id=\"aff8\"><sup>8</sup><institution>Department of Medical Imaging, University of Toronto, Sunnybrook Health Sciences Centre</institution>, <addr-line>Toronto, ON</addr-line>, <country>Canada</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Christian Gaser, Friedrich Schiller University Jena, Germany</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Jeffrey L. Gunter, Mayo Clinic, United States; Shannon Risacher, Indiana University Bloomington, United States</p></fn><corresp id=\"c001\">*Correspondence: Joel Ramirez <email>joel.ramirez1@sunnybrook.ca</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Applied Neuroimaging, a section of the journal Frontiers in Neurology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>847</elocation-id><history><date date-type=\"received\"><day>06</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>07</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Ramirez, Holmes, Scott, Ozzoude, Adamo, Szilagyi, Goubran, Gao, Arnott, Lawrence-Dewar, Beaton, Strother, Munoz, Masellis, Swartz, Bartha, Symons, Black and the ONDRI Investigators.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Ramirez, Holmes, Scott, Ozzoude, Adamo, Szilagyi, Goubran, Gao, Arnott, Lawrence-Dewar, Beaton, Strother, Munoz, Masellis, Swartz, Bartha, Symons, Black and the ONDRI Investigators</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The Ontario Neurodegenerative Research Initiative (ONDRI) is a 3 years multi-site prospective cohort study that has acquired comprehensive multiple assessment platform data, including 3T structural MRI, from neurodegenerative patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease, amyotrophic lateral sclerosis, frontotemporal dementia, and cerebrovascular disease. This heterogeneous cross-section of patients with complex neurodegenerative and neurovascular pathologies pose significant challenges for standard neuroimaging tools. To effectively quantify regional measures of normal and pathological brain tissue volumes, the ONDRI neuroimaging platform implemented a semi-automated MRI processing pipeline that was able to address many of the challenges resulting from this heterogeneity. The purpose of this paper is to serve as a reference and conceptual overview of the comprehensive neuroimaging pipeline used to generate regional brain tissue volumes and neurovascular marker data that will be made publicly available online.</p></abstract><kwd-group><kwd>MRI</kwd><kwd>Alzheimer</kwd><kwd>Parkinson</kwd><kwd>amyotrophic lateral sclerosis</kwd><kwd>frontotemporal dementia</kwd><kwd>cerebrovascular disease</kwd><kwd>stroke</kwd><kwd>cerebral small vessel disease</kwd></kwd-group><counts><fig-count count=\"13\"/><table-count count=\"2\"/><equation-count count=\"0\"/><ref-count count=\"115\"/><page-count count=\"17\"/><word-count count=\"10516\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>The Ontario Neurodegenerative Research Initiative (ONDRI) is a multi-site prospective cohort study following patients with neurodegenerative diseases including Alzheimer's disease (AD), mild cognitive impairment (MCI), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and cerebrovascular disease (CVD). Over the course of 3 years, multiple assessment platforms acquired comprehensive data from the 520 patients including: neuroimaging (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>–<xref rid=\"B3\" ref-type=\"bibr\">3</xref>), clinical and demographic assessments (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>), neuropsychology (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>), genetic variations (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>), eye tracking and pupillometry, retinal layer analyses using spectral-domain optical coherence tomography (<xref rid=\"B8\" ref-type=\"bibr\">8</xref>), gait and balance performance (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>), and neuropathology. The multi-modal data collected from ONDRI will be used to explore earlier detection, guide development of novel therapy, and improve patient care. ONDRI's mission is to bring new diagnostic biomarkers and prognostic tools into clinical practice in order track disease progression and potential response to future symptomatic and disease-modifying therapies targeting dementia/cognitive impairment.</p><p>This paper describes the methods implemented to extract normal and pathological brain tissue volumetric information from the structural Magnetic Resonance Imaging (MRI) provided by the ONDRI neuroimaging platform. It includes a comprehensive methodological overview of the structural neuroimaging pipeline's previously published and validated components, with numerous figures to provide a visual description of how the measures were obtained from the MRI, some recommendations for reporting and data analysis, and a brief section providing some basic descriptive statistics to illustrate the whole brain volumetrics that can be obtained from the ONDRI patient cohorts.</p><p>Structural MRI processing for volumetrics was performed by the neuroimaging group in the L.C. Campbell Cognitive Neurology Research Unit, within the Hurvitz Brain Sciences Research Program, at the Sunnybrook Research Institute, in Toronto, Canada. The image processing pipeline (<xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>) has been optimized for an aging population, with a particular emphasis on accounting for chronic stroke and post stroke cortical and subcortical lesions, numerous imaging markers of cerebral small vessel disease, as well as, the focal and global brain atrophy observed in neurodegenerative patient populations such as AD and FTD.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>ONDRI MRI processing pipeline overview. General workflow moves from left to right for final volumetric output resulting in a comprehensive spreadsheet in the form of a .csv file. Hippocampal volumes are segmented using the SBHV method (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>) which was fully integrated into the pipeline and are included in the final volumetric spreadsheet. Microbleed Rating, Resting State fMRI Analysis, and Diffusion Tensor Imaging (DTI) analyses are processed separately, however, the DTI and Cortical Thickness pipelines are dependent on some components of the primary pipeline, thus, results from these processes are provided in separate spreadsheets.</p></caption><graphic xlink:href=\"fneur-11-00847-g0001\"/></fig><p>The main goal of this paper is to highlight the overall features of the neuroimaging pipeline that would be of interest to a neurologist, clinician, or non-imaging researcher seeking to utilize the ONDRI data that will be made publicly available through an application process on October, 2020. For more information on the ONDRI project, please visit: <ext-link ext-link-type=\"uri\" xlink:href=\"http://ondri.ca/\">http://ondri.ca/</ext-link>.</p></sec><sec sec-type=\"methods\" id=\"s2\"><title>Methods</title><sec><title>Study Participants</title><p>Ethics approval was obtained from all participating institutions. Participants were recruited at 14 health centers across six cities in Ontario, Canada: Hamilton General Hospital and McMaster Medical Centre in Hamilton; Hotel Dieu Hospital and Providence Care Hospital in Kingston; London Health Science Centre and Parkwood Institute in London; Elizabeth Bruyère Hospital and The Ottawa Hospital in Ottawa; Thunder Bay Regional Health Sciences Centre in Thunder Bay; and Baycrest Health Sciences (Baycrest), Centre for Addiction and Mental Health (CAMH), St. Michael's Hospital (SMH), Sunnybrook Health Sciences Centre (Sunnybrook), and Toronto Western Hospital—University Health Network (UHN) in Toronto.</p><p>Full study participant details are previously described (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). Briefly, AD/MCI patients met National Institute on Aging Alzheimer's Association criteria for probable or possible AD, or MCI (<xref rid=\"B11\" ref-type=\"bibr\">11</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>); PD patients met criteria for idiopathic PD defined by the United Kingdom's Parkinson's Disease Society Brain Bank clinical diagnostic criteria (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>); ALS patients met El Escorial World Federation of Neurology diagnostic criteria for possible, probable, or definite familial or sporadic ALS (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>); FTD patients included possible or probable behavioral variants of frontotemporal degeneration (<xref rid=\"B15\" ref-type=\"bibr\">15</xref>), agrammatic/non-fluent and semantic variants of primary progressive aphasia (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>), and possible or probable progressive supranuclear palsy (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>); CVD patients experienced a mild to moderate ischemic stroke event, verified on neuroimaging, 3 or more months prior to enrollment in compliance with the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network vascular cognitive impairment harmonization standards (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>).</p><p>For illustrative purposes of the neuroimaging pipeline outputs, baseline MRI data are included for the following ONDRI patient cohorts: 126 AD/MCI, 140 PD, 40 ALS, 53 FTD, and 161 CVD.</p></sec><sec><title>MRI Acquisition</title><p>Neuroimaging was acquired at the following sites using each site's respective 3T MRI system: a General Electric (GE, Milwaukee, WI) Discovery 750 was used at Sunnybrook, McMaster University/Hamilton General Hospital, and CAMH; a GE Signa HDxt at UHN; a Philips Medical Systems (Philips, Best, Netherlands) Achieva system at Thunder Bay Regional Health Sciences Centre; a Siemens Health Care (Siemens, Erlangen, Germany) Prisma at Sunnybrook and London Health Sciences Centre/Parkwood Hospital; a Siemens TrioTim at Ottawa Hospital/Élisabeth Bruyère Hospital, Hotel Dieu Hospital/Providence Care Hospital and Baycrest; and a Siemens Skyra at SMH.</p><p>Harmonized with the Canadian Dementia Imaging Protocol (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>), the National Institute of Neurological Disorders and Stroke–Canadian Stroke Network Vascular Cognitive Impairment Harmonization Standards (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>), full MRI acquisition protocol details for each imaging site are provided on <xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 1</xref>. In brief, the following structural MRI sequences were obtained for each study participant: 3D T1-weighted (T1), T2-weighted fluid attenuated inversion recovery (FLAIR), interleaved T2-weighted and proton density (T2/PD), and T2<sup>*</sup>gradient recalled echo (GRE). It should be noted that additional imaging protocol included a 30/32 direction diffusion tensor imaging (DTI), resting state functional MRI, and arterial spin labeling (acquired only at one site), but are beyond the scope of this paper and will be presented elsewhere (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Prior to image processing for volumetric quantification, MRI were fully evaluated by a neuroradiologist (SS) for incidental findings and for imaging quality by a medical biophysics scientist (RB).</p></sec><sec><title>Structural Image Processing Methods: Overview</title><p>The structural neuroimaging pipeline used in ONDRI is a component based algorithm commonly referred to as SABRE-Lesion Explorer (SABRE-LE) (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>–<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). This is a semi-automated personalized approach to imaging-based quantification, as it can provide a comprehensive volumetric profile at the individual patient level. While it may take longer to process each individual relative to fully automatic methods, this careful patient-focused approach is more robust to the large variability in stroke and neurodegenerative patient population. This method has been previously validated (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>–<xref rid=\"B25\" ref-type=\"bibr\">25</xref>) and implemented in other Canadian studies (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>–<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). The following sections describe the SABRE-LE comprehensive pipeline method and the volumetric data that is extracted in greater detail. Data visualization was performed using RStudio version 1.2.1335 (RStudio, Inc., Boston, MA) and ITKSnap (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>).</p></sec><sec><title>Brain Regions of Interest: SABRE</title><p>The neuroimaging pipeline integrates a brain region parcellation process called Semi-Automatic Brain Region Extraction (SABRE) (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>). This method separates the brain into 26 regions of interest (ROIs: 13 per hemisphere) derived from anatomical landmarks manually identified per hemisphere on each individual patient (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref> and <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Each imaging analyst was required to achieve an intraclass correlation coefficient (ICC) > 0.90 in order to work on ONDRI patient imaging analysis. The automatic SunnyBrook Hippocampal Volumetry (SBHV) tool (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>) was subsequently integrated into the SABRE pipeline (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>), resulting in a total of 28 ROIs (left + right hippocampus) (see following section). The SABRE brain maps are personalized maps that are unique to each individual patient and was developed from the Talairach grid system (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). Relative to many brain mapping methods that implement non-linear (i.e., “warping”) techniques to register an individual patient's MRI to a standardized template, such as the Montreal Neurological Institute brain (MNI152) (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>), the SABRE approach is essentially reversed, by mapping a brain template onto the individual patient's MRI. This method accounts for natural individual differences in anatomy but more importantly, it is a method that can compensate for significant focal and global brain atrophy that is found in stroke, dementia, and neurodegenerative patients.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>A 3-D surface volume rendering of T1-weighted MRI showing right hemisphere SABRE regions in different colours. Left hemisphere regions were made translucent for illustrative purposes, however, SABRE regions are separately parcellated for each hemisphere and delineated using individualized anatomical landmarks for both left and right sides.</p></caption><graphic xlink:href=\"fneur-11-00847-g0002\"/></fig><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>SABRE-LE neuroimaging pipeline brain tissue and lesion codes (top), and detailed SABRE brain region codes (bottom).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Imaging descripton</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Code</bold></th><th rowspan=\"1\" colspan=\"1\"/></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Supratentorial total intracranial volume</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ST_TIV</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Normal appearing gray matter</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NAGM</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Normal appearing white matter</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NAWM</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sulcal cerebrospinal fluid</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSF</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ventricular cerebropsinal fluid</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CSF</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Periventricular white matter hyperintensities</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pwmh</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Deep white matter hyperintensities</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">dWMH</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Periventricular lacunes</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">pLACN</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Deep lacunes</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">dLACN</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Enlarged perivascular spaces</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PVS</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Chronic stroke lesions</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stroke</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>SABRE brain region name</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Code</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Lobe</bold></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Superior frontal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Middle frontal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inferior frontal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Medial inferior frontal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MIF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Medial superior frontal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MSF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Medial middle frontal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MMF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Frontal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Superior parietal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Parietal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Inferior parietal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Parietal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">O</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Occipital</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Anterior temporal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">AT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Temporal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Posterior temporal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Temporal</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Anterior basal ganglia/thalamus</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ABGT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Basal ganglia/thalamus</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Posterior basal ganglia/thalamus</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PBGT</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Basal ganglia/thalamus</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hippocampus</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Medial temporal</td></tr></tbody></table><table-wrap-foot><p><italic>Note that each regional code will be preceded by an “L” or “R” indicating the left or right hemisphere</italic>.</p></table-wrap-foot></table-wrap><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>The SunnyBrook Hippocampal Volumetric (SBHV) segmentation showing left (BLUE) and right (GREEN) hippocampi overlayed on an axial T1 MRI and extracted as 3-D surface volume renderings. Note images are in radiological convention.</p></caption><graphic xlink:href=\"fneur-11-00847-g0003\"/></fig><sec><title>Hippocampus</title><p>The hippocampus is an important part of the limbic system that has been studied extensively in dementia, given its significant role in memory functions (<xref rid=\"B33\" ref-type=\"bibr\">33</xref>, <xref rid=\"B34\" ref-type=\"bibr\">34</xref>). The ONDRI pipeline incorporates the multi-atlas based Sunnybrook Hippocampal Volumetric (SBHV) segmentation tool (<xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>) that was developed and validated using the Sunnybrook Dementia Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI1) (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>).</p><p>For ONDRI, the SBHV segmentation has been fully integrated into the SABRE-LE pipeline, and includes left and right hippocampal sub-classifications for parenchyma, hypointensities, and stroke volumes (when present). Currently, there is some controversy over the pathophysiological origin and relevance of small cavities commonly observed in the hippocampus (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>–<xref rid=\"B38\" ref-type=\"bibr\">38</xref>), which are particularly relevant in the ONDRI CVD patients. Additionally, large cortico-subcortical strokes can extend from the cortex into the hippocampus. Given these vascular issues potentially affecting the overall hippocampal volume, ONDRI provides sub-classifications for parenchyma, hypointensities, and stroke volumes based on the neuroimaging characteristics (i.e., intensity) using the voxel segmentation classifications and takes a neutral stance on the pathophysiological origin of small cavities observed in this region.</p></sec></sec><sec><title>Total Intracranial Volume</title><p>The supratentorial total intracranial volume (ST-TIV) is a measure of all brain matter that is located below the dura mater. It is referred to as <italic>supratentorial</italic> because the SABRE-LE method removes all tissue below the tentorium, including the cerebellum and portions of the brain stem (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B22\" ref-type=\"bibr\">22</xref>). Although the removal of infratentorial structures was necessary for technical segmentation reasons, researchers particularly interested in the cerebellum, and brainstem can apply additional imaging tools [e.g., (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>)] to obtain these structures from the original acquisitions upon special request.</p><p>In addition to sex-related differences, there are also normal variations in head size. In order to account for these differences, most neuroimaging studies implement some form of head-size correction. This is also particularly important when assessing brain atrophy in cross-sectional studies, as a true measure of the total intracranial capacity will provide an indication of where “there used to be brain and now there is cerebrospinal fluid (CSF).” The presence of focal atrophy due to stroke and neurodegenerative processes tends to result in over and under erosion errors with many fully automated T1-based skull stripping techniques, due to the similarity in intensity between background and sulcal CSF. The SABRE-LE method accounts for the presence of focal atrophy since it includes a measure of everything below the dura mater, including sub-arachnoid CSF, thus, providing a more accurate measure of head-size in neurodegenerative patient populations (<xref ref-type=\"fig\" rid=\"F4\">Figure 4</xref>).</p><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>Axial views of T1-weighted MRI from an ONDRI FTD patient. Red arrows point to regions with significant focal brain atrophy. The SABRE-LE processing pipeline accounts for this focal atrophy since it includes a measure of everything below the dura mater, including sub-arachnoid and sulcal cerebrospinal fluid (CSF), shown in purple.</p></caption><graphic xlink:href=\"fneur-11-00847-g0004\"/></fig><p>It is important to note that there are numerous acceptable head-size correction methods reported in the literature (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). A simple method involves dividing each volume of interest by the total head size to obtain a proportional volume (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). ONDRI provides raw volumes and head size volumes (i.e., ST-TIV) for each individual patient.</p></sec><sec><title>Brain Tissue Segmentation</title><p>A robust T1 intensity-based brain tissue segmentation, optimized for aging and dementia, is performed after skull stripping and removal of non-brain tissue (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). This automatic segmentation method deals with scanner inhomogeneities by fitting localized histograms to Gaussians to allocate voxels into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) tissue classes. After manual ventricular CSF (vCSF) relabelling, there are four brain tissue types that are segmented for volumetrics using SABRE-LE (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>):</p><list list-type=\"bullet\"><list-item><p>Normal appearing gray matter (NAGM)</p></list-item><list-item><p>Normal appearing white matter (NAWM)</p></list-item><list-item><p>Sulcal cerebrospinal Fluid (sCSF)</p></list-item><list-item><p>Ventricular CSF (vCSF).</p></list-item></list><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Data is shown as mean (standard deviation) unless otherwise specified. Raw values are presented for transparency purposes.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Demographics</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>AD/MCI</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>ALS</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>FTD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>PD</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>CVD</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Number of participants</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">126</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">140</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">155</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Age, years</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">71.0 (8.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">62.0 (8.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">67.8 (7.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">67.9 (6.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">69.3 (7.4)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sex, <italic>n</italic> (%) female</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">57 (45.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">16 (40.0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19 (36.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">31 (22.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">48 (31.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ST-TIV, cc</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1235.6 (144.6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1203.6 (162.8)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1245.8 (129.6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1316.6 (127.0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1224.5 (133.2)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NAWM, cc</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">395.4 (344.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">425.0 (78.8)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">295.1 (59.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">446.1 (61.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">387.4 (54.4)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NAGM, cc</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">533.3 (51.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">556.2 (65.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">252.5 (56.0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">574.7 (47.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">535.7 (52.3)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">sCSF, cc</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">256.3 (62.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">195.9 (52.9)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">277.0 (57.8)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">252.3 (53.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">242.6 (59.3)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">vCSF, cc</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45.7 (28.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">23.8 (11.1)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">43.7 (16.6)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">38.2 (19.4)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">41.3 (23.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">pWMH<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>*</sup></xref>, mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2564.5 (2811.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1040.0 (1252.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2736.0 (1623.8)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2563.5 (2708.0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4054.0 (7468.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">dWMH<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>*</sup></xref>, mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">289.5 (424.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">208.0 (386.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">138.5 (379.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">259.5 (225.7)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">555.0 (584.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LACN<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>*</sup></xref>, mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">15.5 (66.0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">14.5 (12.2)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.5 (55.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.5 (70.0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">92.0 (291.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PVS<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>*</sup></xref>, mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">45.5 (35.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">17.5 (9.5)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">32.5 (36.3)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">34.0 (30.0)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">44.0 (33.0)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Stroke<xref ref-type=\"table-fn\" rid=\"TN1\"><sup>*</sup></xref>, mm<sup>3</sup></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">90.0<xref ref-type=\"table-fn\" rid=\"TN2\"><sup>a</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">393.0 (294.0)<xref ref-type=\"table-fn\" rid=\"TN3\"><sup>b</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">531.5 (1269.0)<xref ref-type=\"table-fn\" rid=\"TN4\"><sup>c</sup></xref></td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4644.5 (12963.0)<xref ref-type=\"table-fn\" rid=\"TN5\"><sup>d</sup></xref></td></tr></tbody></table><table-wrap-foot><fn id=\"TN1\"><label>*</label><p><italic>Data is shown as median (interquartile range)</italic>.</p></fn><fn id=\"TN2\"><label>a</label><p><italic>Available in 1/40 participants</italic>.</p></fn><fn id=\"TN3\"><label>b</label><p><italic>Available in 6/52 participants</italic>.</p></fn><fn id=\"TN4\"><label>c</label><p><italic>Available in 4/140 participants</italic>.</p></fn><fn id=\"TN5\"><label>d</label><p><italic>Available in 88/155 participants AD/MCI, Alzheimer's Disease and Mild Cognitive Impairment; ALS, Amyotrophic Lateral Sclerosis; FTD, Frontotemporal Dementia; PD, Parkinson's Disease; VCI, Vascular Cognitive Impairment; ST-TIV, supratentorial total intracranial volume; NAWM, normal appearing white matter; NAGM, normal appearing gray matter; sCSF, sulcal cerebrospinal fluid; vCSF, ventricular cerebrospinal fluid; pWMH, periventricular white matter hyperintensities; dWMH, deep white matter hyperintensities; LACN, lacunes; PVS, perivascular spaces</italic>.</p></fn></table-wrap-foot></table-wrap><p>The T1-based tissue segmentation is further corrected for misclassified volumes using a PD-T2/FLAIR-based lesion segmentation algorithm to account for the voxels appearing as GM or CSF on T1 (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>) due to WM changes from stroke and cerebral small vessel disease. For this reason, the GM and WM volumes are denoted as “normal appearing” (NAGM, NAWM) to signify that these volumes have been re-labeled as normal appearing after having been corrected with an additional multi-modal MRI segmentation approach (<xref ref-type=\"fig\" rid=\"F5\">Figure 5</xref>). Additional brain tissue volumes for stroke lesions and cerebral small vessel disease markers are discussed in the following sections.</p><fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p>Due to relative intensities on different MRI sequences, WMH (red arrows) on T2 FLAIR are not hyperintense (bright) on T1-weighted images and tend to appear as GM (gray) or CSF (blue) intensity on T1. Thus, T1-based segmentations tend to inflate the GM and CSF volumes in patients with stroke and cerebral small vessel disease. To account for this, ONDRI's imaging pipeline integrates an additional T2/FLAIR-based WMH segmentation to correct for this misclassification error (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>) to produce a normal appearing WM/GM (NAWM/NAGM) volumes.</p></caption><graphic xlink:href=\"fneur-11-00847-g0005\"/></fig><p>The NAGM and NAWM volumes can be summed to obtain a measure of parenchymal volume or reported individually for head-size corrected measures to assess potential atrophy. Additionally, a segmentation mask is generated which is used for diffusion tensor imaging (DTI) analyses, where diffusion metrics of the “normal appearing” WM tracts can be separately analyzed from the diffusion within the various types of white matter lesions including WMH, lacunar infarcts, and cortical-subcortical stroke lesions. Details of ONDRI DTI analysis pipeline are discussed elsewhere (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>).</p><p>The SABRE-LE method segments sCSF and vCSF into separate compartments. The initial T1-based segmentation automatically labels hypointense voxels into a CSF class, and then the ventricles are manually relabelled to a vCSF class by neuroimaging analysts following a standardized procedure. Note that although some vCSF segmentation tools based on standardized templates use smoothing algorithms that reclassify all voxels within the ventricular compartment as ventricles, the SABRE-LE method does not. With the SABRE-LE method, choroid plexus are not arbitrarily removed or re-classified as CSF and thus remain as part of the overall tissue segmentation. Ventricular volumes are often used as a simple indicator of overall brain atrophy, and have the potential for use as a differential indicator of disease and dementia severity (<xref ref-type=\"fig\" rid=\"F6\">Figure 6</xref>) (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>–<xref rid=\"B45\" ref-type=\"bibr\">45</xref>).</p><fig id=\"F6\" position=\"float\"><label>Figure 6</label><caption><p>Top row shows axial view of vCSF segmentation overlayed on T1 MRI for patients with AD (left) and FTD (right). Bottom row shows 3D surface volume renderings of the vCSF segmentation. Note the differences in ventricle size and the hemispheric differences between the two neurodegenerative diseases.</p></caption><graphic xlink:href=\"fneur-11-00847-g0006\"/></fig></sec><sec><title>White Matter Hyperintensities of Presumed Vascular Origin (WMH)</title><p>Also referred to as leukoaraiosis, white matter lesions, subcortical hyperintensities, and even, unidentified bright objects, WMH are radiological anomalies commonly associated with cerebral small vessel disease. Recently, the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE) (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>) have established a set of criteria that recommends the use of the term <italic>white matter hyperintensities of presumed vascular origin</italic> (WMH), as the standard terminology to refer to these regions of hyperintense (bright) signal found on particular MRI. It is important to note that as previously mentioned, WMH do not appear hyperintense on all types of MRIs and often appear isointense to GM on T1 (<xref ref-type=\"fig\" rid=\"F7\">Figure 7</xref>). Additionally, despite the naming convention, it is important to note that WMH are not limited to the white matter regions of the brain, as they are also commonly observed in subcortical GM structures such as the basal ganglia and thalamus. However, to avoid confusion between studies, ONDRI recommends the use of the more popular term “white matter hyperintensities.”</p><fig id=\"F7\" position=\"float\"><label>Figure 7</label><caption><p>Axial view of various coregistered structural MRI sequences showing the relative intensity differences of WMH. Note that white matter hyperintensities are not hyperintense (i.e., bright) on T1-weighted MRI.</p></caption><graphic xlink:href=\"fneur-11-00847-g0007\"/></fig><sec><title>Periventricular (pWMH) and Deep White (dWMH) Hyperintensities</title><p>Although WMH can be subdivided using SABRE ROIs, the most common regional delineation of WMH is the separation between periventricular (pWMH) and deep white (dWMH). Historically controversial (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>, <xref rid=\"B48\" ref-type=\"bibr\">48</xref>), this concept is based on several theories and research findings which suggest that WMH in close proximity to the ventricles (hence the term “peri-ventricular”) have a different pathological etiology (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>) and are differentially correlated with cognitive/behavior deficits in comparison to the more distal dWMH (despite the confusing fact that pWMH are technically found in deeper white matter than dWMH). Additionally, recent imaging-pathology correlations suggest that a common substrate of pWMH relates to vasogenic edema due to leakage and increased vascular resistance caused by venous collagenosis, a small vessel venular disease of the deep medullary venules (as opposed to the arterial side of the cerebral vasculature) (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>–<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). It is also interesting to note that there is no standard consensus in the literature on how to define pWMH vs. dWMH, with some papers using a proportional distance to the dura mater (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>), some using an arbitrary cut-off (typically 13 mm from the ventricles) (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>), and others using a 3D connectivity algorithm (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>, <xref rid=\"B56\" ref-type=\"bibr\">56</xref>)—the method that is currently supported by ONDRI (see <xref ref-type=\"fig\" rid=\"F8\">Figure 8</xref>).</p><fig id=\"F8\" position=\"float\"><label>Figure 8</label><caption><p>Shows different methods for segmenting periventricular and deep WMH. Left image shows a proportional distance from the ventricular lining to the dura mater; middle image shows an arbitrary distance of 13 mm from ventricles, right image shows 3D connectivity algorithm supported by ONDRI, displayed as 3D volume renderings of pWMH (red) and dWMH (blue) shown in sagittal and slightly tilted anterior views.</p></caption><graphic xlink:href=\"fneur-11-00847-g0008\"/></fig></sec></sec><sec><title>Lacunes</title><p>Lacunes of presumed vascular origin are cystic fluid-filled cavities in the subcortical brain regions (<xref rid=\"B57\" ref-type=\"bibr\">57</xref>, <xref rid=\"B58\" ref-type=\"bibr\">58</xref>). They appear hypointense (dark) on T1, hyperintense (bright) on PD and T2, and can appear as a lesion with a hypointense central core surrounded by a hyperintense rim/halo on FLAIR MRI (<xref ref-type=\"fig\" rid=\"F9\">Figure 9</xref>, bottom row). The recent STRIVE criteria (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>) provides some consensus-based guidelines regarding their definition, however, previous studies have used various terms (e.g., “<italic>white matter lesions</italic>,” “<italic>lacunar infarcts</italic>,” “<italic>covert strokes</italic>”) and radiological descriptions to classify these lesions (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>). Often difficult to differentiate from MRI-visible perivascular spaces (PVS) (next section), lacunes tend to be larger and less linear than PVS. They are associated with increased risk of stroke, dementia, and gait disturbances (<xref rid=\"B60\" ref-type=\"bibr\">60</xref>). It is important to note that due to the poor sensitivity of FLAIR in thalamic regions (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>) (<xref ref-type=\"fig\" rid=\"F9\">Figure 9</xref>, top row), the ONDRI imaging pipeline integrates an additional T2-based segmentation in order to capture any potential lesions in this subcortical region that may not appear on FLAIR.</p><fig id=\"F9\" position=\"float\"><label>Figure 9</label><caption><p>Top row shows a thalamic lacune as it appears on different coregistered MRI, hypointense (dark) on T1, hyperintense on PD-T2, and difficult to detect on FLAIR. In contrast, the bottom row shows a subcortical lacunar infarct that presents with the classic central CSF-like hypointensity with a surrounding hyperintense halo/rim on FLAIR.</p></caption><graphic xlink:href=\"fneur-11-00847-g0009\"/></fig></sec><sec><title>MRI-Visible (Enlarged) Perivascular Spaces (PVS)</title><p>Recent studies suggest that the brain utilizes the glymphatic system (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>, <xref rid=\"B63\" ref-type=\"bibr\">63</xref>) to clear fluid and metabolic waste, using a complex series of perivascular channels surrounding the brain's veins and arteries. It has been suggested that when the perivascular channels are compromised due to aging, disease, or trauma, the perivascular space becomes enlarged and consequently, visible on structural MRI (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>–<xref rid=\"B67\" ref-type=\"bibr\">67</xref>). MRI-visible (enlarged) perivascular spaces (PVS) on T2 appear as small (<3 mm diameter), linear, hyperintensities following the course of the vasculature (<xref ref-type=\"fig\" rid=\"F10\">Figure 10</xref>). Additionally, PVS appear hypointense (dark) on T1, isointense to GM on PD (vs. lacunes which are bright on PD), and are very difficult to visualize on 2D FLAIR, particularly in the basal ganglia region. Current research suggests that PVS found in the white matter regions may indicate Cerebral Amyloid Angiopathy (CAA), while PVS in the basal ganglia may be more indicative of hypertensive arteriopathy (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>–<xref rid=\"B71\" ref-type=\"bibr\">71</xref>). Moreover, recent basic science research and limited clinical evidence supports the theory that clearance of amyloid and other metabolites occurs primarily during deep sleep (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>, <xref rid=\"B73\" ref-type=\"bibr\">73</xref>).</p><fig id=\"F10\" position=\"float\"><label>Figure 10</label><caption><p>Examples of the PVS segmentation (red and yellow) over-layed onto structural MRI in axial (top 2 rows) and coronal views (bottom row).</p></caption><graphic xlink:href=\"fneur-11-00847-g0010\"/></fig><p>Previously referred to as dilated Virchow-Robin spaces, measurement of PVS burden is typically accomplished using visual rating scales under this old naming convention (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>, <xref rid=\"B75\" ref-type=\"bibr\">75</xref>). However, the novel quantitative method supported by ONDRI provides a volumetric measure of PVS. This method has been previously validated with common PVS visual scales and has been used to study AD, normal elderly, and stroke and cerebrovascular disease patients being assessed with sleep polysomnography (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>, <xref rid=\"B76\" ref-type=\"bibr\">76</xref>). Although both lacunes and PVS volumes are segmented automatically using the SABRE-LE pipeline, false positive minimization procedures are manually performed to remove incorrect segmentations and to reallocate PVS to lacunes or vice versa depending on strict intensity and shape-based criteria. Only highly trained neuroimaging analysts achieving ICCs and DICE Similarity Indices (SI) > 0.90 are allowed to perform this procedure. Moreover, a research neuroradiologist (FG) was consulted when faced with complex radiological anomalies that were commonly observed in the CVD patient cohort.</p></sec><sec><title>Cerebral Microbleeds</title><p>Although the SABRE-LE structural pipeline method used by ONDRI does not support a cerebral microbleed (CMB) segmentation algorithm, this brief section has been included to describe this important measure of cerebral small vessel disease burden. In ONDRI, CMB, and superficial siderosis burden are being assessed visually by a highly qualified neuroradiologist (SS). Cerebral microbleeds (CMB) have been shown to reflect perivascular leakage of red blood cells that can be visualized as low signal intensities (hypointense/dark spots) on T2<sup>*</sup>-weighted gradient-recalled echo (GRE) (<xref ref-type=\"fig\" rid=\"F11\">Figure 11</xref>) and susceptibility weighted imaging (SWI) (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>). There are two commonly used methods of assessing CMB burden, the Microbleed Anatomical Rating Scale (MARS) (<xref rid=\"B78\" ref-type=\"bibr\">78</xref>) and the Brain Observer MicroBleed Scale (BOMBS) (<xref rid=\"B79\" ref-type=\"bibr\">79</xref>) visual rating scales. Previous studies have shown that CMB are associated with an increased risk of stroke, intracerebral hemorrhage, cognitive decline, and dementia (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>–<xref rid=\"B84\" ref-type=\"bibr\">84</xref>). Differences in anatomical distribution suggest that CMB found in deep centrencephalic brain regions (basal ganglia, thalamus, and brain stem) are more closely related to hypertensive arteriopathy (<xref rid=\"B85\" ref-type=\"bibr\">85</xref>), while lobar CMB are more closely associated with CAA and AD pathology (<xref rid=\"B86\" ref-type=\"bibr\">86</xref>–<xref rid=\"B89\" ref-type=\"bibr\">89</xref>), leading to the development of the Boston criteria for the diagnosis of possible/probable CAA (<xref rid=\"B90\" ref-type=\"bibr\">90</xref>, <xref rid=\"B91\" ref-type=\"bibr\">91</xref>).</p><fig id=\"F11\" position=\"float\"><label>Figure 11</label><caption><p>Axial view of iron-sensitive T2* gradient echo (GRE) with red arrow pointing to cerebral microbleeds visualized as hypointensities (dark).</p></caption><graphic xlink:href=\"fneur-11-00847-g0011\"/></fig></sec><sec><title>Chronic Stroke</title><p>According to recent estimates, stroke is the 2nd most common cause of death worldwide (<xref rid=\"B92\" ref-type=\"bibr\">92</xref>) and the second leading cause of dementia (<xref rid=\"B93\" ref-type=\"bibr\">93</xref>). In a 2013 global report, there were ~25.7 million stroke survivors, and 7.5 million deaths from ischemic and hemorrhagic stroke (<xref rid=\"B94\" ref-type=\"bibr\">94</xref>). In Canada, ~62,000 people are treated for stroke and transient ischemic attack. In a series of publications, the Heart and Stroke Foundation Canadian Best Practice Committees have been developing various evidence-based recommendations to address issues regarding: telestroke technologies (<xref rid=\"B95\" ref-type=\"bibr\">95</xref>); managing transitions of care following stroke (<xref rid=\"B96\" ref-type=\"bibr\">96</xref>); mood, cognition, and fatigue following stroke (<xref rid=\"B97\" ref-type=\"bibr\">97</xref>); hyperacute stroke care (<xref rid=\"B98\" ref-type=\"bibr\">98</xref>); secondary prevention of stroke (<xref rid=\"B99\" ref-type=\"bibr\">99</xref>); and stroke during pregnancy (<xref rid=\"B100\" ref-type=\"bibr\">100</xref>, <xref rid=\"B101\" ref-type=\"bibr\">101</xref>).</p><p>Although the term “stroke” may encompass a wide range of clinical criteria (<xref rid=\"B102\" ref-type=\"bibr\">102</xref>), the Vascular Cognitive Impairment (VCI) (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>) inclusion-exclusion criteria for ONDRI CVD patients was limited to mild-moderate ischemic stroke patients, defined by a Modified Rankin Scale (MRS) (<xref rid=\"B103\" ref-type=\"bibr\">103</xref>) score of 0–3. It is important to note that although there are a number of imaging techniques used to measure acute stroke in the early stages (within a couple of hours of stroke), the MRI methods applied to ONDRI CVD patients are measures of post-stroke lesions, often referred to as <italic>chronic stroke</italic>, with structural MRI acquired > 3 months post ischemic stroke event.</p><p>As there are currently no reliable automatic ways to quantify the range of cortico-subcortical stroke lesions, ONDRI neuroimaging analysts manually delineate the stroke under the direct supervision of a highly experienced research neuroradiologist (FG). This manual delineation is strictly limited to cortical strokes appearing as hyperintense (bright) on FLAIR and hypointense (dark) on T1, although the entire stroke volume often extended into the subcortical regions of the brain (<xref ref-type=\"fig\" rid=\"F12\">Figure 12</xref>). Although this total volume does not separate the hypointense necrotic stroke core from the surrounding partially infarcted hyperintense region indicating varying degrees of gliosis and encephalomalacia, future automatic segmentation techniques are currently being tested in ONDRI to include this sub-segmentation.</p><fig id=\"F12\" position=\"float\"><label>Figure 12</label><caption><p>Axial view of coregistered structural MRI sequences (left to right): T1, PD, T2, and FLAIR. Images illustrate relative intensity differences of a large cortico-subcortical stroke lesion across various types of MRI. The last pane shows ONDRI's manual segmentation of the entire stroke core and surrounding hyperintense partially infarcted tissue volume in green.</p></caption><graphic xlink:href=\"fneur-11-00847-g0012\"/></fig></sec></sec><sec id=\"s3\"><title>Recommendations for Reporting and Analysis</title><p>Here we provide some general guidelines for reporting and analysis that can be useful for researchers wishing to use ONDRI data.</p><p>First and foremost, when reporting data for characterization of the sample being analyzed, we recommend that the original raw volumes are reported in tables for transparency and between-study comparisons; however, statistical analyses should generally be performed on head-size corrected volumes. Head size correction accounts for individual variations in intracranial capacity and sex-related differences in head size (<xref rid=\"B104\" ref-type=\"bibr\">104</xref>). Additionally, depending on the research question, the volume of interest (i.e., NAWM, NAGM, CSF, or WMH) could also be reported as a proportion of the total volume within each SABRE region, or they can be reported as a proportion of the total head-size (ST-TIV) for age-independent normalization/correction. The version or date of the data release should also be reported.</p><p>There are several ways that WMH can be analyzed and it depends on the research question in mind. The simplest approach is to sum the dWMH and pWMH, which results in a whole brain measure of small vessel disease burden. Regional analyses of WMH can also be performed to assess WMH burden within a SABRE ROI. Additionally, WMH within different ROIs can be combined by simply summing the volumes from different SABRE regions to generate a larger ROI (e.g., sum all pWMH and dWMH volumes within all frontal SABRE brain region parcellations using the Frontal Lobe Codes shown on <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><p>It is important to note that many measures of cerebral small vessel disease, such as pWMH and dWMH, are typically non-normally distributed (<xref rid=\"B105\" ref-type=\"bibr\">105</xref>), often inter-correlated (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>), are known to be age-related (<xref rid=\"B106\" ref-type=\"bibr\">106</xref>), and commonly associated with vascular risk factors such as hypertension (<xref rid=\"B107\" ref-type=\"bibr\">107</xref>). Thus, careful attention to these factors and previous research findings highlight ONDRI's recommendation to consider these additional factors when analyzing imaging-based markers of cerebral small vessel disease. Given the skewed, non-normal distribution of WMH (even after head-size correction), WMH volumes are typically transformed (e.g., log) prior to standard parametric analyses. For this reason, approaches designed to deal with complex distributions should be considered (<xref rid=\"B108\" ref-type=\"bibr\">108</xref>).</p><p>Since the pipeline automatically segments lesions in the periventricular region from the deep white regions, the lacunar volumes are also provided in this manner. While some future studies may argue a pathophysiological difference between these two locations of lacunar presentation, there are currently limited studies to suggest this anatomical delineation. Given this, we recommend that the two volumes be summed together prior to analysis. Interestingly, lacunes and PVS volumes are not typically head-size corrected in the clinical/scientific literature, however, age, sex, WMH, and a measure of brain atrophy (e.g., BPF or vCSF), and proper accounting of vascular risk are recommended covariates when analyzing lacunes and PVS (<xref rid=\"B109\" ref-type=\"bibr\">109</xref>, <xref rid=\"B110\" ref-type=\"bibr\">110</xref>). Note that in many publications, lacunes, and PVS are reported as counts (i.e., number of), because they are often measured using visual rating methods that require the user to count the number of lacunes or PVS observed on an MRI—often leading to wide variations in definitions and conflicting findings in the literature (<xref rid=\"B59\" ref-type=\"bibr\">59</xref>, <xref rid=\"B111\" ref-type=\"bibr\">111</xref>). Since the lacunes and PVS in ONDRI are quantified using segmentation based imaging analyses, PVS and lacunar volumes are provided rather than counts.</p><p>Finally, any analyses using ONDRI's CVD cohort should consider the common comorbidities of depression, obstructive sleep apnea, and cognitive impairment (<xref rid=\"B112\" ref-type=\"bibr\">112</xref>), as well the subcortical silent brain infarcts/lacunes, WMH, and potentially, CMB, which have recently been acknowledged as playing an important role in primary stroke prevention (<xref rid=\"B113\" ref-type=\"bibr\">113</xref>).</p></sec><sec id=\"s4\"><title>Results and Conclusion</title><p>Of the <italic>n</italic> = 520 patients with MRI acquired, the ONDRI neuroimaging pipeline was unable to process <italic>n</italic> = 1 FTD patient due to extreme motion artifact (despite 2 baseline attempts on 2 separate occasions), and <italic>n</italic> = 6 CVD patients due to poor imaging quality (<italic>n</italic> = 3 FLAIR not usable, <italic>n</italic> = 2 T1 not usable, <italic>n</italic> = 1 PD/T2 not acquired). To illustrate whole brain data extraction volumetric results from this pipeline, neuroimaging summary statistics for each ONDRI disease cohort are summarized on <xref rid=\"T2\" ref-type=\"table\">Table 2</xref>, and descriptive violin plots showing median and interquartile ranges are provided for whole brain ST-TIV, NAGM, NAWM, sCSF, vCSF, pWMH, and dWMH PVS, and LACN are displayed on <xref ref-type=\"fig\" rid=\"F13\">Figure 13</xref>. Stroke volumes were not graphed due to the limited number of ONDRI patients with cortico-subcortical stroke lesions.</p><fig id=\"F13\" position=\"float\"><label>Figure 13</label><caption><p>Descriptive violin plots of the ONDRI disease cohorts showing median and interquartile range volumetrics for whole brain supratentorial total intracranial volume (ST-TIV), normal appearing gray matter (NAGM), normal appearing white matter (NAWM), sulcal cerebrospinal fluid (sCSF), ventricular CSF (vCSF), MRI-visible enlarged perivascular space (PVS) volumes, periventricular white matter hyperintensities (pWMH), deep WMH (dWMH), and lacunes (LACN).</p></caption><graphic xlink:href=\"fneur-11-00847-g0013\"/></fig><p>It is important to note that the details in this manuscript focus on ONDRI's baseline data that will be released in October 2020, the longitudinal follow-up data will be forthcoming.</p><p>Additionally, a cohort of cognitively normal older adults recruited from the Brain-Eye Amyloid Memory (BEAM) study (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.clinicaltrials.gov\">clinicaltrials.gov</ext-link>—NCT02524405) with harmonized neuroimaging, neuropsychology, and data acquisition protocol, will be included in ONDRI for comparative analyses. Participants in BEAM were recruited from five sites (Sunnybrook, Baycrest, CAMH, SMH, and UHN) that also participated in ONDRI.</p><p>ONDRI is the first multi-site, multiple assessment platform study examining several neurodegenerative and neurovascular diseases using a harmonized protocol that includes standardized structural neuroimaging. The wide range of complex, and often overlapping, brain pathologies represented in this cohort of neurodegenerative patients included a number of comorbid cerebral small vessel disease markers, cortico-subcortical stroke lesions, combined with focal and global atrophy, posing significant challenges to common imaging analysis tools. In this paper, we presented the neuroimaging pipeline methods implemented in ONDRI that were used to overcome many of these challenges.</p><p>To further ensure a high level of data quality, the volumetric data generated by the ONDRI structural neuroimaging team were further subjected to comprehensive quality control analysis pipelines including a novel multivariate outlier detection algorithm developed by the ONDRI neuroinformatics group for identification of anomalous observations (<xref rid=\"B114\" ref-type=\"bibr\">114</xref>, <xref rid=\"B115\" ref-type=\"bibr\">115</xref>). Future work will include generating longitudinal measures that will also be made publicly available. As the neuroimaging data are combined with releases from ONDRI's clinical, neuropsychology, genomics, eye tracking, gait and balance, ocular, and neuropathology platforms, it becomes evident that ONDRI is a gold mine of data opening the door to an unprecedented broad range of cross-platform analyses resulting in numerous opportunities for discovery and advances in diagnosis, prognosis, outcomes, and care of neurodegenerative diseases.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>The datasets generated for this study are available on request to <email>info@ondri.ca</email>.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Ethics approval was obtained from all participating institutions. Participants were recruited at 14 health centers across six cities in Ontario, Canada: Hamilton General Hospital and McMaster Medical Centre in Hamilton; Hotel Dieu Hospital and Providence Care Hospital in Kingston; London Health Science Centre and Parkwood Institute in London; Elizabeth Bruyère Hospital and The Ottawa Hospital in Ottawa; Thunder Bay Regional Health Sciences Centre in Thunder Bay; and Baycrest Health Sciences, Centre for Addiction and Mental Health, St. Michael's Hospital, Sunnybrook Health Sciences Centre, and Toronto Western Hospital (University Health Network) in Toronto. The patients/participants provided their written informed consent to participate in this study.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>JR: conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, writing (draft, review, and editing), and supervision. MH: data curation, formal analysis, investigation, methodology, software, validation, visualization, and writing (draft, review, and editing). CS: conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, writing (review and editing), and supervision. MO: data curation, methodology, software, visualization, and writing (review and editing). SA: data curation, software, validation, visualization, and writing (review and editing). GS: conceptualization, data curation, methodology, and software. MG: data curation, investigation, methodology, software, validation, and writing (draft, review, and editing). FG: conceptualization, data curation, investigation, validation, visualization, writing (review and editing), and supervision. SA and DB: data curation, formal analysis, investigation, validation, and writing (review and editing). JL-D: investigation, resources, validation, writing (draft, review, and editing), and funding acquisition. SCS: conceptualization, investigation, writing (draft, review, and editing), supervision, and funding acquisition. DM, MM, and RS: conceptualization, resources, investigation, writing (review and editing), supervision, and funding acquisition. RB: conceptualization, data curation, resources, investigation, writing (review and editing), and supervision. SS: data curation, resources, investigation, writing (review and editing), and supervision. SB: conceptualization, resources, investigation, methodology, visualization, writing (review and editing), supervision, and funding acquisition. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><ack><p>We would like to thank the ONDRI participants for the time, consent, and participation in our study. Thank you to the L.C. Campbell Foundation, and the analysts and software developers in the LC Campbell Cognitive Neurology research team who have contributed to the ONDRI imaging analysis, including Edward Ntiri, Hassan Akhavein, Parisa Mojiri, Kirstin Walker, Rita Meena, Pugaliya Puveendrakumaran, Courtney Berezuk, and Alicia McNeely. This paper is available in preprint version online: <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.1101/2019.12.13.875823\">https://doi.org/10.1101/2019.12.13.875823</ext-link>.</p></ack><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was funded by the Ontario Neurodegenerative Disease Research Initiative, through the Ontario Brain Institute, an independent non-profit corporation, funded partially by the government of Ontario. The opinions, results, and conclusions are those of the authors and no endorsement by the Ontario Brain Institute is intended or should be inferred. Matching funds were provided by participant hospital and research foundations, including the Baycrest Foundation, Bruyere Research Institute, Centre for Addiction and Mental Health Foundation, London Health Sciences Foundation, McMaster University Faculty of Health Sciences, Ottawa Brain and Mind Research Institute, Queen's University Faculty of Health Sciences, the Thunder Bay Regional Health Sciences Centre, the University of Ottawa Faculty of Medicine, and the Windsor/Essex County ALS Association. The Temerty Family Foundation provided the major infrastructure matching funds.</p></fn></fn-group><sec id=\"s9\"><title>ONDRI Investigators</title><p>Michael Strong, Peter Kleinstiver, Natalie Rashkovan, Susan Bronskill, Sandra E. Black, Michael Borrie, Elizabeth Finger, Corinne Fischer, Andrew Frank, Morris Freedman, Sanjeev Kumar, Stephen Pasternak, Bruce Pollock, Tarek Rajji, Dallas Seitz, David Tang-Wai, Carmela Tartaglia, Brenda Varriano, Agessandro Abrahao, Marvin Chum, Christen Shoesmith, John Turnbull, Lorne Zinman, Jane Lawrence-Dewar, Donna Kwan, Brian Tan, Julia Fraser, Bill McIlroy, Ben Cornish, Karen Van Ooteghem, Frederico Faria, Manuel Montero-Odasso, Yanina Sarquis-Adamson, Alanna Black, Barry Greenberg, Wendy Hatch, Chris Hudson, Elena Leontieva, Ed Margolin, Efrem Mandelcorn, Faryan Tayyari, Sherif Defrawy, Don Brien, Ying Chen, Brian Coe, Doug Munoz, Alisia Bonnick, Leanne Casaubon, Dar Dowlatshahi, Ayman Hassan, Jennifer Mandzia, Demetrios Sahlas, Gustavo Saposnik, Richard H. Swartz, David Breen, David Grimes, Mandar Jog, Anthony Lang, Connie Marras, Mario Masellis, Tom Steeves, Dennis Bulman, Allison Ann Dilliott, Mahdi Ghani, Rob Hegele, John Robinson, Ekaterina Rogaeva, Sali Farhan, Rob Bartha, Hassan Haddad, Nuwan Nanayakkara, Joel Ramirez, Christopher Scott, Sean Symons, Courtney Berezuk, Melissa Holmes, Sabrina Adamo, Miracle Ozzoude, Mojdeh Zamyadi, Stephen Arnott, Derek Beaton, Malcolm Binns, Wendy Lou, Pradeep Raamana, Stephen Strother, Kelly Sunderland, Athena Theyers, Abiramy Uthirakumaran, Guangyong (GY) Zou, Sujeevini Sujanthan, Mojdeh Zamyadi, David Munoz, Roger A. Dixon, John Woulfe, Brian Levine, Paula McLaughlin, JB Orange, Alicia Peltsch, Angela Roberts, Angela Troyer.</p></sec><sec sec-type=\"supplementary-material\" id=\"s10\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fneur.2020.00847/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fneur.2020.00847/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Data_Sheet_1.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Haddad</surname><given-names>SMH</given-names></name><name><surname>Scott</surname><given-names>CJM</given-names></name><name><surname>Ozzoude</surname><given-names>M</given-names></name><name><surname>Holmes</surname><given-names>M</given-names></name><name><surname>Arnott</surname><given-names>SR</given-names></name><name><surname>Nanayakkara</surname><given-names>ND</given-names></name><etal/></person-group>. <article-title>Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines</article-title>. <source>PLoS ONE.</source> (<year>2020</year>) <volume>14</volume>:<fpage>e0226715</fpage>. <pub-id pub-id-type=\"doi\">10.1371/journal.pone.0226715</pub-id><pub-id pub-id-type=\"pmid\">31860686</pub-id></mixed-citation></ref><ref id=\"B2\"><label>2.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Kapoor</surname><given-names>A</given-names></name><name><surname>Bartha</surname><given-names>R</given-names></name><name><surname>Black</surname><given-names>SE</given-names></name><name><surname>Borrie</surname><given-names>M</given-names></name><name><surname>Freedman</surname><given-names>M</given-names></name><name><surname>Gao</surname><given-names>F</given-names></name><etal/></person-group>. <article-title>Structural brain magnetic resonance imaging to rule out comorbid pathology in the assessment of Alzheimer's disease dementia: findings from the Ontario Neurodegenerative Disease Research Initiative (ONDRI) study and clinical trials over the past 10 years</article-title>. <source>J Alzheimer's Dis</source>. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807820</article-id><article-id pub-id-type=\"pmc\">PMC7431908</article-id><article-id pub-id-type=\"publisher-id\">70796</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70796-3</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>DNA-damage and cell cycle arrest initiated anti-cancer potency of super tiny carbon dots on MCF7 cell line</article-title></title-group><contrib-group><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Şimşek</surname><given-names>Sinem</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\" equal-contrib=\"yes\"><name><surname>Şüküroğlu</surname><given-names>Ayça Aktaş</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Yetkin</surname><given-names>Derya</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Özbek</surname><given-names>Belma</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Battal</surname><given-names>Dilek</given-names></name><address><email>dilekbattal@mersin.edu.tr</email></address><xref ref-type=\"aff\" rid=\"Aff5\">5</xref><xref ref-type=\"aff\" rid=\"Aff6\">6</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Genç</surname><given-names>Rükan</given-names></name><address><email>rgenc@mersin.edu.tr</email></address><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.38575.3c</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2337 3561</institution-id><institution>Department of Chemical Engineering, </institution><institution>Yıldız Technical University, </institution></institution-wrap>34210 Esenler, Istanbul, Turkey </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.25769.3f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2169 7132</institution-id><institution>Department of Pharmaceutical Toxicology, Faculty of Pharmacy, </institution><institution>Gazi University, </institution></institution-wrap>06330 Ankara, Turkey </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411691.a</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0694 8546</institution-id><institution>Advanced Technology Research and Application Center, </institution><institution>Mersin University, </institution></institution-wrap>33343 Mersin, Turkey </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411691.a</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0694 8546</institution-id><institution>Department of Chemical Engineering, Faculty of Engineering, </institution><institution>Mersin University, </institution></institution-wrap>33343 Yenişehir, Mersin, Turkey </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411691.a</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0694 8546</institution-id><institution>Department of Pharmaceutical Toxicology, Faculty of Pharmacy, </institution><institution>Mersin University, </institution></institution-wrap>33169 Yenişehir, Mersin, Turkey </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412132.7</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0596 0713</institution-id><institution>Department of Pharmaceutical Toxicology, Faculty of Pharmacy, </institution><institution>Near East University, </institution></institution-wrap>99138 Nicosia, Cyprus </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13880</elocation-id><history><date date-type=\"received\"><day>21</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>13</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold>This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">While carbon-based materials have spearheaded numerous breakthroughs in biomedicine, they also have procreated many logical concerns on their overall toxicity. Carbon dots (CDs) as a respectively new member have been extensively explored in nucleus directed delivery and bioimaging due to their intrinsic fluorescence properties coupled with their small size and surface properties. Although various in vitro/in vivo studies have shown that CDs are mostly biocompatible, sufficient information is lacking regarding genotoxicity of them and underlying mechanisms. This study aims to analyze the real-time cytotoxicity of super tiny CDs (2.05 ± 0.22 nm) on human breast cancer cells (MCF7) and human primary dermal fibroblast cell cultures (HDFa) by xCELLigence analysis system for further evaluating their genotoxicity and clastogenicity to evaluate the anti-tumor potential of CDs on breast adenocarcinoma. As combined with flow cytometry studies, comet assay and cytokinesis-block micronucleus assay suggest that the CDs can penetrate to the cell nuclei, interact with the genetic material, and explode DNA damage and G0/G1 phase arrest in cancer cells even at very low concentrations (0.025 ppm) which provide a strong foundation for the design of potentially promising CD-based functional nanomaterials for DNA-damage induced treatment in cancer therapy.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Medical research</kwd><kwd>Materials science</kwd><kwd>Nanoscience and technology</kwd></kwd-group><funding-group><award-group><funding-source><institution>The Scientific and Technological Research Council of Turkey</institution></funding-source><award-id>Graduate Scholarship</award-id><principal-award-recipient><name><surname>Şimşek</surname><given-names>Sinem</given-names></name></principal-award-recipient></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Functional nanomaterials that can better target cancer cells that will improve prevention and therapy could be accomplished by the combined efforts of nanotoxicologists, cancer biologists and nanobiomaterial scientists focused on toxicology and related cancer therapy. Engineering carbon-based nanomaterials and their applications are among the most dynamic fields in modern advanced materials science and engineering<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>–<xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. To date, various carbon nanomaterials such as carbon nanotubes<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref>,<xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>, fullerenes<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>, graphene<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>, graphene oxides<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>, carbon diamonds<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref>,<xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>, and carbon dots<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup> have been synthesized and reported by various researchers. Among these, CDs have become particularly interesting because of their unique physical and chemical properties such as thermal and electrical conductivity, high mechanical strength together with their unique optic and fluorescence features<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref>–<xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>. They have been used in bio-imaging, drug delivery, nucleus targeting, and labeling, photodynamic therapy, optoelectronics, solar cells, photocatalyst design, photodetectors, and many other biological and engineering fields<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>–<xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>.</p><p id=\"Par3\">Several methods are available for the synthesis of CDs, namely, laser ablation<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>, electro-oxidation<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup> or oxidative acid treatment<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>; while nowadays, less laborsome and cheaper methods such as hydrothermal<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup> and thermal synthesis<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref>,<xref ref-type=\"bibr\" rid=\"CR29\">29</xref>,<xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup>, microwave-assisted hydrothermal synthesis<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>, ultrasound synthesis<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>, etc. are popularly exercised. CDs can be synthesized either from inorganic materials<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup> such as graphene<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>, carbon black<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>, and candle soot<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup> or biological carbon sources like food wastes, fruits, seeds and shells, plant extracts have been moving toward their application to biological sciences<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref>–<xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>.</p><p id=\"Par4\">Toxicity studies by various research groups have shown that CDs exhibit very low toxicity compared to heavy metal-based quantum dots when internalized by the cells<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref>–<xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. In almost all of the cytotoxicity studies performed to date, CDs have been demonstrated to cause a negligible effect on cell viability at concentrations sufficient for drug delivery and bio-imaging. In the last 2 years, we have been focused on new types of CDs by tailoring the surface properties of them with the target of evaluating their potential in biological applications<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR44\">44</xref>,<xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>. Very recently, utilizing different polymers as surface passivating agent and surface doping agent, we were able to show the immunostimulant and adjuvant-like effect of such CDs where no cytotoxic effect was observed<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref>,<xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup>. CDs have been widely researched for nucleus-targeted delivery, nucleus labeling, photodynamic therapy, and optical monitoring of anticancer drugs<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref>–<xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>, however, so far there are few scientific reports in existence for addressing the genotoxic activities of CDs when they administrated on their own<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref>–<xref ref-type=\"bibr\" rid=\"CR56\">56</xref></sup>. In a recent study, researchers showed the genotoxic responses of rat alveolar macrophages (NR8383) to amine-grafted graphene QDs which resulted in significant alterations in the expression of 2,898 genes after exposure for 24 h in which most of the down-regulated genes were reported the as they were responsive to “cell cycle”<sup><xref ref-type=\"bibr\" rid=\"CR57\">57</xref></sup>. In a study on the use of graphene quantum dots (GQDs) as nucleus labeling of several cell lines (L929, HT-1080, MIA PaCa-2, HeLa, and MG-63 cells), researchers showed that internalization mechanism of the GQDs by healthy cells differ from tumor cells while GQDs showed to be entering into the nucleus regardless of the cell type. Moreover, the authors showed an altered number of L929 cells in the S phase as an indication of promoted cell proliferation in the presence of GQDs<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>. Yue et al.<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup> reported that ruthenium incorporated CDs with no apparent cytotoxicity have the concentration-dependent DNA photocleavage ability on HeLa cells upon the light irradiation (6.5 mW/cm<sup>2</sup>) showing the potential of CDs in imaging and photodynamic therapy (seeTable <xref rid=\"Tab2\" ref-type=\"table\">2</xref>).</p><p id=\"Par5\"><italic>Nerium oleander</italic> (Oleander) is one of the most poisonous dwarf evergreen shrubs in the world<sup><xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>. Extracts from various parts of the plant show also anti-cancer<sup><xref ref-type=\"bibr\" rid=\"CR59\">59</xref>,<xref ref-type=\"bibr\" rid=\"CR60\">60</xref></sup>, anti-microbial<sup><xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup>, anti-inflammatory<sup><xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>, anti-diabetic<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>, and neuroprotective activities<sup><xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>. Common ingredients of Oleander extract include polysaccharides containing rhamnose, galactose, arabinose, mannose, glucose, and galacturonic acid<sup><xref ref-type=\"bibr\" rid=\"CR64\">64</xref>–<xref ref-type=\"bibr\" rid=\"CR67\">67</xref></sup>. Other components and their concentrations may vary depending on the extraction method<sup><xref ref-type=\"bibr\" rid=\"CR68\">68</xref></sup>. For instance, extracts of <italic>N. oleander</italic> leaves contain steroids, flavonoids, and terpenoids, etc.<sup><xref ref-type=\"bibr\" rid=\"CR67\">67</xref></sup>. Our group lately presented the synthesis routes for Oleander based CDs using both thermal and microwave-based synthesis methodologies<sup><xref ref-type=\"bibr\" rid=\"CR69\">69</xref>,<xref ref-type=\"bibr\" rid=\"CR70\">70</xref></sup> while we have shown that extract type (water or ethanol extraction) is one of the important parameters where the highest fluorescence and the lowest size was observed using water-based Oleander extract as a carbon source for CD synthesis.</p><p id=\"Par6\">The present study covers the effects of super tiny CDs on the cell viability of MCF7 tumor cells and normal HDFa cells together with the CD-induced differentiation in cell-cycle progression, genotoxicity, and clastogenicity on MCF7 cells. Our results suggest that CDs, alone or in combination with chemotherapeutics, may be exploited for the development of potentially promising functional nanomaterials for DNA-damage induced treatment in cancer therapy. They have the potential that could be extended to be used as new generation biolabeling and imaging agents as well. However, the possible influence of the cell cycle on cellular uptake of CDs and the mechanism of its effect on MCF7 cells needs further investigation.</p></sec><sec id=\"Sec2\"><title>Materials and methods</title><sec id=\"Sec3\"><title>Plant material</title><p id=\"Par7\"><italic>Nerium oleander</italic> leaves collected from Esenler Region, Istanbul (41°01′37.70\"N, 28°53′32.1\"E) at 82 m May 2016 were booked in Izmir, Ege University Faculty of Pharmacy Herbarium (IZEF) with number 6056.</p></sec><sec id=\"Sec4\"><title>Chemicals</title><p id=\"Par8\">Ethanol and Polyethylene Glycol (PEG 10000N), Dulbecco's Modified Eagle's medium (DMEM), trypan blue solution, Dulbecco’s Phosphate Buffered Saline (DPBS), agarose with normal melting point and low melting point, dimethyl sulphoxide, ethidium bromide, Triton X-100, phosphate-buffered saline tablets, potassium chloride (KCl), Giemsa, ethylenediaminetetraacetic acid disodium salt dihydrate (Na2-EDTA) and cytochalasin B (Cyt-B), the positive control for the genotoxicity assays, ethyl methanesulphonate (EMS) (CAS no. 62-50-0, lot 1338043) were obtained from Sigma-Aldrich (Steinheim, Germany). Trypsin Buffer, Tyrosine Inhibitor Buffer, RNase Buffer, Propidium Iodide Stain Solutions were purchased from Becton Dickinson (BD). Sodium chloride and sodium hydroxide were purchased from Merck Chemicals (Darmstadt, Germany), whereas Chromosome medium B was purchased from Biochrom AG (Berlin, Germany). Frosted microscope slides were obtained from Menzel GmbH (Braunschweig, Germany. Human breast adenocarcinoma cell line (MCF7) and the human primary dermal fibroblast cell cultures (HDFa) were obtained from ATCC with number HTB-22 and PCS-201-12, respectively. Slides were visualized for Comet Assay by fluorescence microscopy using an Olympus BX51 System equipped with a video camera CCD-4230.</p></sec><sec id=\"Sec5\"><title>Equipment</title><p id=\"Par9\">ELMA TI-H 5 model ultrasonic bath was used during the extraction process. The thermal synthesis was conducted using a Neuve muffle furnace. Characterization studies of CDs were performed on a Shimadzu UV-1800 UV–Vis spectrophotometer, Agilent Cary Eclipse fluorescence spectrophotometer, Malvern Zeta Sizer Nano ZS, and Perkin Elmer frontier FT-IR. X-ray photoelectron spectroscopy (XPS) screening was performed using the Specs-Flex XPS spectrometer (Al Kα 1,486.7 eV). Morphology of CDs was monitored by a JEOL JEM-1400 series 120 kV Transmission Electron Microscope (TEM) and the FEI Tecnai G2 F30 HR-TEM at 300 kV. Particle core radius was calculated by measuring at least 100 individual particles using Image J program. Cell-seeding calculations were carried out with the Cedex XS analyzer (Innovatis Inc.). xCELLigence system (ACEA Biosciences Inc.) was used as a real-time cell sorter. BD FACSAria III flow cytometer (BD Biosciences, US) and BD CELLQuest Pro software (BD Biosciences, US) were used for cell-cycle analysis. Fluorescence imaging was performed by a fluorescence microscope (Olympus BX51) equipped with a CCD-4230 video camera.</p></sec><sec id=\"Sec6\"><title>Preparing plant extracts</title><p id=\"Par10\">The fresh leaves of <italic>N. oleander</italic> were washed twice with distilled water and dried at incubator at 70 °C for 2 days. Dried leaves were then grinded to powders. The water extraction procedure was performed using ultra-pure water with the final concentration 12.5 g dry leaf/100 mL in ddH<sub>2</sub>O in an ultrasonic bath for 5 h at room temperature (RT). After extraction, clear extracts were obtained by centrifugation at 5,000 rpm for 15 min and stored at + 4 °C.</p></sec><sec id=\"Sec7\"><title>Synthesis of CD using <italic>N. oleander</italic> leaf extract</title><p id=\"Par11\">1 mL aqueous extract was dispersed 1 g PEG solution pre-prepared in 2 mL of ddH<sub>2</sub>O/ethanol solution (1:1 v/v). The mixture then placed in a muffle furnace and left for the caramelization process for 45 min at 300 °C. Resulting brown solid was cooled down to RT and dissolved in 6 mL ddH<sub>2</sub>O. CDs were separated by three cycles of centrifugation at 13,500 rpm for 20 min. The supernatants were collected and dried in a vacuum oven at 60 °C for 1–2 days.</p></sec><sec id=\"Sec8\"><title>Quantum yield of CDs</title><p id=\"Par12\">The quantum yield of the CD (diluted samples to obtain an absorbance value of less than 0.10) was determined using quinine sulfate in 0.1 M H<sub>2</sub>SO<sub>4</sub> (quantum yield: 54%) as the standard sample following the procedure published previously<sup><xref ref-type=\"bibr\" rid=\"CR71\">71</xref></sup>.</p></sec><sec id=\"Sec9\"><title>In vitro cytotoxicity of CDs on MCF7 and the HDFa cell lines by xCELLigence</title><p id=\"Par13\">The xCELLigence system was used according to the manufacturer's instructions. The cell index (CI) was obtained by measuring the change in the electrical impedance in the presence and absence of cells in the wells. MCF7 and HDFa cell lines were inoculated keeping the cell number as 1 × 10<sup>4</sup> cells/well and 3 × 10<sup>4</sup> cells/well, respectively, on 16-well plates of the xCELLigence system. Afterward, cell growth in each well of e-plates was monitored every 15 min and analyzed with RTCA Software 1.2. After ~ 18–24 h of cell transplantation, cells in the ‘logarithmic growth phase’ were exposed to varying concentrations of CDs dispersed in DMSO (0.0025 ppm, 0.025 ppm, 0.25 ppm, 2.5 ppm, and 50 ppm), and monitored real-time for 72 h. The cells growing in the growth media were used as control while 50 ppm aqueous of <italic>N. oleander</italic> extract was used to compare with CDs. Experiments were carried out at least quadruplicate. All experimental steps were conducted under dark conditions to prevent additional light-induced cellular damages.</p></sec><sec id=\"Sec10\"><title>In vitro genotoxicity of CDs on MCF7 cells by comet assay (SCGE)</title><p id=\"Par14\">Using the information obtained from the cytotoxicity studies, CD concentrations of 0.25, 2.5, and 50 ppm were selected for performing the alkaline comet assays. MCF7 cells in log-phase were plated in 96-well plates and incubated for 2 days. The negative control (Untreated cells that were grown in growth media, NC), and the positive control (Cells treated with 20 mM hydrogen peroxide) were inoculated in series. The comet assay was performed in alkaline conditions (pH > 13) as described previously<sup><xref ref-type=\"bibr\" rid=\"CR72\">72</xref></sup>. Briefly, after cells were exposed to the CDs for 48 h and 72 h, the cells were collected and trypsinized. Centrifuged cells at 1,100 rpm for 5 min were counted and 1–3 × 10<sup>4</sup> cells were resuspended in 75 μL molten 0.5% low-melting-point agarose at 37 °C. The resuspended cells in agarose were put onto dry microscope slides pre-coated with 1% normal-melting agarose, and the agar solidified by keeping at RT for 10 min. The slides were thereafter immersed in cold lysing solution (2.5 M NaCl, 100 mM EDTA, 10 mM Tris, 1% Triton X-100) for at least 1 h at 4 °C. Afterward, they were transferred to an electrophoresis tank containing freshly-made electrophoresis buffer (1 mM EDTA, 300 mM NaOH; pH > 13), where they were kept for 20 min at room temperature to allow DNA unwinding. Electrophoresis was performed in the same buffer at RT for 15 min at 24 V and 300 mA (0.8 V/cm). The slides were then neutralized thrice with 0.4 M Tris buffer (pH 7.5), air-dried, and fixed in ethanol. All preparative steps were conducted under dark conditions to prevent additional DNA damages. Slides stained with ethidium bromide (0.1 mg/mL, 1:4) were analyzed under a fluorescence microscope equipped with a CCD-4230 video camera.</p></sec><sec id=\"Sec11\"><title>Slide scoring in comet assay</title><p id=\"Par15\">Comet images were analyzed following the method reported by Collins et al.<sup><xref ref-type=\"bibr\" rid=\"CR73\">73</xref></sup>. The percentage of DNA in the comet tail from 100 cells per sample (duplicate, each with 50 cells/slide) was used as a measure of the amount of DNA damage. An intensity score from class 0 (undamaged) to class 4 (ultra-high damage) was assigned to each cell<sup><xref ref-type=\"bibr\" rid=\"CR74\">74</xref></sup>. Observational blindness was employed, with the identity of the samples being withheld from the observer. Fifty cells per slide and two slides for each sample were examined to evaluate the DNA damage for each culture treated with CDs at different concentrations. The cells were classified by eye in the five categories based on the extent of DNA migration, undamaged (class 0), very little damage (class 1), moderate damage (class 2), high damage (class 3) ultrahigh damage (class 4). The ‘‘Arbitrary Unit (AU)’’ were used to express the extent of DNA damage and calculated using the following formula:<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$AU = \\mathop \\sum \\limits_{i = 0}^{4} i x N_{i}$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>U</mml:mi><mml:mo>=</mml:mo><mml:munderover><mml:mo movablelimits=\"false\">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow><mml:mn>4</mml:mn></mml:munderover><mml:mi>i</mml:mi><mml:mi>x</mml:mi><mml:msub><mml:mi>N</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mrow></mml:math><graphic xlink:href=\"41598_2020_70796_Article_Equa.gif\" position=\"anchor\"/></alternatives></disp-formula>\nN<sub><italic>i</italic></sub> = the number of scored cells in level <italic>i</italic>, <italic>i</italic> = the level of DNA damage (0, 1, 2, 3, 4).</p></sec><sec id=\"Sec12\"><title>In vitro clastogenecity of CDs on MCF7 cells by cytokinesis-block micronucleus assay (CBMN)</title><p id=\"Par17\">The in vitro cytokinesis-block micronucleus assay (CBMN) was performed based on previously published procedures with minor modifications<sup><xref ref-type=\"bibr\" rid=\"CR75\">75</xref></sup>. Cells were seeded at a density of 5 × 10<sup>4</sup> cells per well in a T25 flask and treated with CDs in dispersion. After the exposure period of 48 h and 72 h, cells were washed twice with PBS and re-incubated for 38 h in fresh medium containing 3 μg/mL Cytochalasin B. Further, cells were harvested and suspended in ice-cold KCl for 50 s at room temperature. The cells were then fixed in Carnoy’s solution (1:3 mixtures of acetic acid and methanol), and several drops of formaldehyde were added to preserve the cytoplasm. Immediately after centrifugation at 1,500 rpm (1,000<italic>g</italic>) for 10 min, cells were fixed again in Carnoy’s solution. Finally, the cells were dropped onto clean microscopic slides, air-dried, and stained with Giemsa. A total of 1,000 binucleated cells for each sample were examined microscopically for micronuclei as previously described<sup><xref ref-type=\"bibr\" rid=\"CR76\">76</xref></sup>. The levels of chromosomal damage were reported as the fold induction of micronuclei compared with the untreated control. CBMN results were accepted only when (1) they were separated from the main nuclei, but included within the corresponding cytoplasm, (2) they had a chromatin material similar to that of the main nuclei, (3) they were coplanar to the main nuclei, (iv)they were 1/16th to 1/3rd of the mean diameter of the main nuclei. In the CBMN study, toxicity was evaluated by classifying cells according to the number of nuclei<sup><xref ref-type=\"bibr\" rid=\"CR77\">77</xref></sup>.</p></sec><sec id=\"Sec13\"><title>Flow cytometry: cell cycle analysis of MCF7 cells treated with CDs</title><p id=\"Par18\">MCF7 breast cancer cells treated with CDs were analyzed by flow cytometry to determine the associated DNA index (DI) and to determine cell cycle phase distributions in these cells. 1 × 10<sup>6</sup> cell/mL cells were plated in 6-well cell culture plates and treated with CDs at three different concentrations (0.25, 2.5, and 50 ppm) and incubated for 48 h and 72 h at 37 °C, 5% CO<sub>2</sub>. The cells were detached with trypsin, centrifuged (400<italic>g</italic>, 5 min.) and washed with PBS. Cells were then collected by centrifuge and treated with 250 µL trypsin buffer and vortexed. The obtained solution was mixed with Tyrosine Inhibitor and RNase Buffer (200 µL) and the mixture was incubated for 10 min. Further, 200 μL cold (2–8 °C) Propidium Iodide (PI) Stain Solution was added into each tube and incubated for an additional 10 min at 4 °C in dark with rapid stirring. The data were analyzed by Flow Cytometry Analysis Software. Values were expressed as fractions of cells in cell cycle phases (the mean ± standard error). Each experiment was performed three times.</p></sec><sec id=\"Sec14\"><title>Statistical analysis</title><p id=\"Par19\">At least three independent experiments were carried out in triplicate for each evaluation. Data were expressed as the mean ± error (SE) and analyzed by repeated-measures ANOVA followed by the least significant difference post hoc test Bonferroni for the comet assay. An independent <italic>t</italic> test was used for the CBMN test. In all tests for comet assay and CBMN test differences were considered significant at <italic>p</italic> < 0.001 and <italic>p</italic> < 0.05 respectively. All the data analysis was carried out using software STATISTICA for comet assay and STATA MP/11 for the CBMN test.</p></sec></sec><sec id=\"Sec15\"><title>Results and discussion</title><sec id=\"Sec16\"><title>Characterization of physicochemical properties of CDs</title><p id=\"Par20\">Many studies have attempted to elucidate the mechanisms of nanoparticle toxicity and distinguish between their bulk counterparts<sup><xref ref-type=\"bibr\" rid=\"CR78\">78</xref>–<xref ref-type=\"bibr\" rid=\"CR80\">80</xref></sup>. It is well-known that the toxicity of nanoparticles (NPs) was highly dependent on their physicochemical characteristics<sup><xref ref-type=\"bibr\" rid=\"CR81\">81</xref></sup>. Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref> shows some physical properties of as-synthesized and purified CDs (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>). PEG was used as a surface passivation agent which mainly acts as surface functionalization precursors for developing highly tunable photoluminescence properties by stabilizing the dangling bonds and controlling the surface functional groups and surface states<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref>,<xref ref-type=\"bibr\" rid=\"CR82\">82</xref>,<xref ref-type=\"bibr\" rid=\"CR83\">83</xref></sup>. Use of N. Oleander as carbon source and PEG as passivating agent revealed super tiny CDs with a hydrodynamic size (Rh) of 2.05 ± 0.22 nm (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>a) and core radius of 1.79 ± 0.33 nm measured by DLS and TEM imaging, respectively. The HR-TEM image (inset in Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>) shows that the CDs displayed a highly crystalline structure with a 0.21 nm lattice spacing that is attributed to the graphitic (<italic>sp</italic><sup>2</sup>) carbon<sup><xref ref-type=\"bibr\" rid=\"CR84\">84</xref></sup>. Surface ζ-potential of − 23.5 ± 6.21 mV which could be the sign of a surface with the higher density of oxygen-rich groups<sup><xref ref-type=\"bibr\" rid=\"CR74\">74</xref>,<xref ref-type=\"bibr\" rid=\"CR75\">75</xref></sup>.<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Physicochemical properties of CDs.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Name</th><th align=\"left\">λ<sub>max</sub> (nm)</th><th align=\"left\">Rh (nm)</th><th align=\"left\">r (nm)</th><th align=\"left\">ζ-Pot(mV)</th><th align=\"left\">σ (mS/cm)</th><th align=\"left\">m (µm cm/Vs)</th></tr></thead><tbody><tr><td align=\"left\">CD</td><td align=\"left\">466</td><td char=\".\" align=\"char\">2.05 ± 0.22</td><td char=\".\" align=\"char\">1.79 ± 0.33</td><td align=\"left\">− 23.5 ± 6.21</td><td align=\"left\">0.05</td><td align=\"left\">− 1.84</td></tr></tbody></table><table-wrap-foot><p>λ<sub>max</sub> is emission maxima at 365 nm excitation; Rh is the hydrodynamic radius, r is the core radius of CDs measured by TEM imaging, σ is electrical conductivity and m is electrical mobility.</p></table-wrap-foot></table-wrap><fig id=\"Fig1\"><label>Figure 1</label><caption><p>Schematics showing the experimental procedure of CD preparation from <italic>N. Oleander</italic> aqueous extracts and TEM and HR-TEM (Inset) images of as-synthesized CDs displaying a highly crystalline structure with a 0.21 nm lattice spacing that is attributed to the graphitic (<italic>sp</italic><sup>2</sup>) carbon. Inset: photograph of the CDs emitting green colored fluorescence under a UV beam of 365 nm.</p></caption><graphic xlink:href=\"41598_2020_70796_Fig1_HTML\" id=\"MO1\"/></fig><fig id=\"Fig2\"><label>Figure 2</label><caption><p>(<bold>a</bold>) Particle hydrodynamic size distribution of CDs measured by DLS, (<bold>b</bold>) UV visible spectrum, (<bold>c</bold>) excitation dependent emission spectra, and (<bold>d</bold>) FT-IR spectrum of CDs.</p></caption><graphic xlink:href=\"41598_2020_70796_Fig2_HTML\" id=\"MO2\"/></fig></p><p id=\"Par21\">CDs are exceptional carbon-based materials with superior optical properties that can be used in biomedical applications as they are trackable by fluorescence imaging. UV–Vis spectrum of CDs was presented in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>b. CDs showed a broad absorption peak located at around 245 nm which corresponds to a typical absorption of <italic>sp</italic><sup>2</sup> carbon network which was also supported by the HR-TEM imaging (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>)<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>,<xref ref-type=\"bibr\" rid=\"CR85\">85</xref>,<xref ref-type=\"bibr\" rid=\"CR86\">86</xref></sup>. As can be seen in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>c, CDs showed the highest fluorescent emission at 466 nm upon excitation at 365 nm with the highest quantum yield of 7.12%. The fluorescence emission spectra of samples demonstrated an excitation-dependent feature originates from a combination of quantum confinement effects and the distribution of different emissive surface traps presented on the surface of CDs<sup><xref ref-type=\"bibr\" rid=\"CR82\">82</xref>,<xref ref-type=\"bibr\" rid=\"CR87\">87</xref></sup>.</p><p id=\"Par22\">FT-IR spectroscopy was carried out to characterize the surface properties of CDs. As depicted in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>d, CD showed a sharp peak around 3,600 cm<sup>−1</sup> corresponds to free O–H groups present on the particle surface. The as-prepared CDs showed peaks belong to C–H stretches in methyl and methylene groups (2,800–3,000 cm<sup>−1</sup>)<sup><xref ref-type=\"bibr\" rid=\"CR88\">88</xref></sup>. Peaks corresponding to C–O stretching located at 1,075–1,250 cm<sup>−1</sup> might be associated with the partial oxidation of CD surfaces. Sharp peaks at 1,380 and 1,460 cm<sup>−1</sup> were attributed to CH<sub>2</sub> vibrations (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>d). Many oxygen-rich functional groups including C–O–C (1,150 cm<sup>−1</sup>), C–OH (1,250 cm<sup>−1</sup>) and C–OH stretching peak at 1,380 cm<sup>−1</sup> could be indicative of a C–O–C asymmetric stretch or C–H bending arising from a methyl functional groups presenting on the CDs<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR49\">49</xref>,<xref ref-type=\"bibr\" rid=\"CR89\">89</xref></sup>.</p><p id=\"Par23\">Figure <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>a depicts the XPS wide scan spectrum of the synthesized CDs. Two bands of the XPS survey spectrum at around 284.5 eV and 531.5 represented O1<italic>s</italic> and C1<italic>s</italic>, respectively, which indicates the atomic ratio of O/C is 32.8/67.2 as calculated from the survey spectrum. The high-resolution C1<italic>s</italic> XPS spectrum (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>b) was deconvoluted into three contributions at 283.4, 284.7, and 285.4 eV, which are associated with carbon in the states of <italic>sp</italic><sup>2</sup> C (C=C, C–C) and C–OR, respectively<sup><xref ref-type=\"bibr\" rid=\"CR90\">90</xref>–<xref ref-type=\"bibr\" rid=\"CR92\">92</xref></sup>. The deconvoluted O1<italic>s</italic> spectrum (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>c) had three components peaking at 529.4, 530.9, and 531.8 eV, which are due to the C=O, C–OH, and C–O–C groups, respectively<sup><xref ref-type=\"bibr\" rid=\"CR91\">91</xref>,<xref ref-type=\"bibr\" rid=\"CR92\">92</xref></sup>. XPS results support the UV–Vis spectrophotometry and FT-IR results that CDs are purely composed of C=C core and have a highly oxygenated and reactive surface.<fig id=\"Fig3\"><label>Figure 3</label><caption><p>XPS spectra of the CDs. (<bold>a</bold>) Survey spectrum of the CDs with two major peaks of carbon and oxygen. XPS high-resolution survey spectra of (<bold>b</bold>) C1<italic>s</italic> and (c) O1<italic>s</italic> region of CDs.</p></caption><graphic xlink:href=\"41598_2020_70796_Fig3_HTML\" id=\"MO3\"/></fig></p></sec><sec id=\"Sec17\"><title>In vitro cytotoxicity of CDs on MCF7 and HDFa cells</title><p id=\"Par24\">Although MTT assay is one of the most used methods for the assessment of in vitro cytotoxicity of materials, there are reports on the interference of carbon-based nanomaterials with the colorimetric assays<sup><xref ref-type=\"bibr\" rid=\"CR93\">93</xref>–<xref ref-type=\"bibr\" rid=\"CR95\">95</xref></sup>. The xCELLigence is a technology that gives the possibility to measure the cellular growth in real-time by measuring the net adhesion of cells on a custom-designed gold electrode following the changes in electrical impedance. Thus, it gives more pre-sized information on the long-term screening of cell viability and prevents the material-dye interaction-based false responses<sup><xref ref-type=\"bibr\" rid=\"CR96\">96</xref></sup> The cytotoxicity of the CDs were conducted on MCF7 and HDFa cells. The normalized cell index (CI) is indicative of the level of adhesion and therefore associated with the viability of the cells.</p><p id=\"Par25\">Figure <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>a,b illustrates the normalized cell index of the MCF7 treated with varying concentrations of the CDs (0.0025–50 ppm) as compared to the PBS as the negative control. In all cases, cells were treated after 24 h following the seeding and the cell index was normalized at the point of treatment. The Oleander extract as control of the starting material resulted in cell death within a few hours after the treatment resulting in a sharp decrease in CI. After 24 h of treatment, CD at the highest concentration (50 ppm) revealed a significant decrease in cell growth as compared to the untreated controls. At lower concentrations, CDs did not show a similar growth trend as the untreated cells while no dose-dependent response was observed even after 72 h of treatment. These results agree well with experimental data taken from the literature that increased negative net charge and small size of CD increased the risk of cytotoxicity of them to tumor cells at high doses<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref>,<xref ref-type=\"bibr\" rid=\"CR97\">97</xref></sup>. On the other hand, the story in the case of HDFa cells was different. As depicted in Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>c, CDs did not show any cytotoxic effect at neither of the concentrations but also they significantly induced cell proliferation at CD concentrations of 0.25 ppm and below, while at 72 h of treatment with CDs even at the highest concentration resulted in increased cell proliferation which reveals that presence of CDs protected the cells from natural death. The CI value increased sixfold for the cells exposed to CDs at a concentration as low as 0.25 ppb as compared to the untreated cells. Oleandrin extract (50 ppm) was, as expected, showed to be cytotoxic for both of the cells regardless of the time of the exposure. Although the main reason is not clear yet, higher tolerability of non-cancerous cells, decreased or retarded cellular accumulation of CDs in healthy cells as compared to the cancerous cells have been highlighted in many recent reports to explain the enhancement of the cell proliferation of healthy cells after the CD exposure<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref>,<xref ref-type=\"bibr\" rid=\"CR81\">81</xref>,<xref ref-type=\"bibr\" rid=\"CR98\">98</xref>–<xref ref-type=\"bibr\" rid=\"CR100\">100</xref></sup>. Yao et al.<sup><xref ref-type=\"bibr\" rid=\"CR101\">101</xref></sup> showed that CDs specifically interact with some cellular proteins of tumor cells and downregulate the level of some proteins and the activity of enzymes that are related to tumor cell invasion ability.<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Time-dependent changes in cell index values and the average cell index values for (<bold>a</bold>), (<bold>b</bold>) MCF7 cells, and (<bold>c</bold>) HDFa cells at 48 h and 72 h after the treatment with varying concentrations of CDs as compared to the negative control (medium) and positive control (water extract of Oleandrin (50 ppm). Values represent mean ± SE, n = 3.</p></caption><graphic xlink:href=\"41598_2020_70796_Fig4_HTML\" id=\"MO4\"/></fig></p></sec><sec id=\"Sec18\"><title>Evaluation of genotoxicity and clastogenicity of CDs</title><p id=\"Par26\">The genotoxicity and clastogenicity of CDs on MCF7 cells were investigated by comet assay and CBMN, respectively. Figure <xref rid=\"Fig5\" ref-type=\"fig\">5</xref> represents AU values indicating CD-induced DNA damage at various levels. AU values of negative control were calculated to be 18.50 ± 4.20 and 33.75 ± 6.24 after 48 and 72 h respectively. In contrast, the level of DNA damage jumped to 337.75 ± 9.81 (48 h) and 292.5 ± 16.36 AU (72 h) in positive control where cells were treated with H<sub>2</sub>O<sub>2</sub> (20 mM). Increased levels of DNA damages induced by carbon dots occurred with respect to the negative control (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>). Even at the lowest CD concentration (0.25 ppm), AU value was elevated more than twofold as compared to the untreated cells. The difference between selected concentration groups was significant (<italic>p</italic> < 0.05). CD-based oxidative DNA damage can be correlated with several factors that strongly define the extent of CD-induced DNA-damage, such as the small size of these nanoparticles together with the surface charge and surface functional groups of them which alters the interaction of CDs with the cell<sup><xref ref-type=\"bibr\" rid=\"CR90\">90</xref>,<xref ref-type=\"bibr\" rid=\"CR102\">102</xref>–<xref ref-type=\"bibr\" rid=\"CR106\">106</xref></sup>. Comet tail formation attributed to the DNA breaks or failed DNA repair mechanisms induced by oxidative stress. As supported by FT-IR, XPS, and ʐ-potential results, CD surface bearing a high amount of oxygenated functional groups could lead to the production of ROSs, and as a consequence, the oxidative stress may lead the genomic instability<sup><xref ref-type=\"bibr\" rid=\"CR97\">97</xref>,<xref ref-type=\"bibr\" rid=\"CR107\">107</xref>,<xref ref-type=\"bibr\" rid=\"CR108\">108</xref></sup>. A recent study by Zhou et al.<sup><xref ref-type=\"bibr\" rid=\"CR90\">90</xref></sup> also indicated that modulating the oxygenated groups, most effectively the number of ketonic carboxyl groups, ROS production by carbon dots can also be controlled, the highest the oxygenated groups on the surface the highest the ROS production capability.<fig id=\"Fig5\"><label>Figure 5</label><caption><p>(<bold>a</bold>) Representation of the different comet classes in the alkaline comet assay where MCF7 cells were visually scored into four classes according to the tail length: Type 0: undamaged, with no tail, Type 1: with a tail shorter than the diameter of the head (nucleus), Type 2: with the tail as long as 1–2 × the diameter of the head, and Type3: with a tail longer than 2 × of the diameter of the head, Type 4: ultra-high damaged, with a longer tail length than the head diameter. (<bold>b</bold>) Representative images of non-treated control MCF7 cells and cells treated with 2.5 ppm CDs after 48 h and 72 h. DNA damage in MCF7 cells presented as arbitrary units (AU) measured by the alkaline Comet assay after treatment of cells with CDs for (<bold>c</bold>) 48 h and (<bold>d</bold>) 72 h. H202 (20 mM) was used as a positive control. (**) There is a statistically significant difference between positive control and CD-treated cells (<italic>p</italic> < 0.001).</p></caption><graphic xlink:href=\"41598_2020_70796_Fig5_HTML\" id=\"MO5\"/></fig></p><p id=\"Par27\">Clastogenic damage was also evaluated using CBMN for MCF7 cell lines (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6</xref>a,b) CDs contributed to a significant increase in DNA damage as compared to the negative control for all doses administered (0.25, 2.5 and 50 ppm) after their exposure for 48 h and 72 h (<italic>p</italic> < 0.01 and <italic>p</italic> < 0.05, respectively). A concentration-dependent enhancement of the level of clastogenic damage was observed at 48 h (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6</xref>a). After 72 h of exposure, the clastogenicity of CDs at 0.25 ppm was higher than CDs administrated at higher concentrations (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6</xref>b), and the micronucleus frequencies (MN%) decreased as compared to the lower concentrations after 48 h treatment. The difference between the MN value obtained for 0.25 ppm at two incubation times was significant (<italic>p</italic> < 0.05). The reduction overall in cellular damage at the 72nd hour can be explained by the induction of cellular damage repair mechanisms, which alters the protection of the tumor cell from further chemical attacks and reveal a subsequent recovery from existing ones at increased exposure time. The slight decrease in cell damage for higher CD concentrations can also be explained with the same phenomena. Decreased nanoparticle accumulation in tumor cells at increased exposure times have been also reported by many other researchers which might decrease the effect of CDs on tumor cells<sup><xref ref-type=\"bibr\" rid=\"CR100\">100</xref></sup>. Another explanation of the occurring plateau might be the DNA damage which leads cells either to a necrotic death or irreversibly committing those to apoptotic cell death which eventually reduces the proportion of the highly damaged cells<sup><xref ref-type=\"bibr\" rid=\"CR109\">109</xref></sup>.<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Changes in micronucleus frequencies in MCF7 cell lines treated with different concentrations of CD depending on the exposure time (<bold>a</bold>) 48 h and (<bold>b</bold>) 72 h. The statistically significant difference as compared to the negative control was shown as *<italic>p</italic> < 0.05 and **<italic>p</italic> < 0.01. Values represent mean ± SE, n = 3.</p></caption><graphic xlink:href=\"41598_2020_70796_Fig6_HTML\" id=\"MO6\"/></fig></p></sec><sec id=\"Sec19\"><title>Influence of CDs on the cell cycle of MCF7 cells</title><p id=\"Par28\">Considering the genotoxicity and clastogenicity induced by CDs, further analysis of the effects of CDs on the cell-cycle phases and apoptosis of MCF7 cells were performed. Cell proliferation is dependent on the cell-cycle progression, in which cells pass through the G0/G1 phase to the S phase and the G2/M phase. As depicted in Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>a, after 48 h of treatment, 57.5%, 34.5% and 8.0% of untreated control cells were in the G0/G1, S and G2/M phases, respectively. Cells treated with CDs at even 0.25 ppm resulted in a decrease in the number of cells in the S phase (<italic>p</italic> < 0.05), and consequently a slight increase in the number of cells in the G0/G1 phases as compared to the control group. At increased CD concentrations, it has also been shown that induced cell cycle arrest in the G0/G1 phase is happening in a concentration-dependent manner, and the data were statistically significant as compared to the control group (<italic>p</italic> < 0.001). The effect of CDs on the cell cycle was shown to be time-dependent. As depicted in Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6</xref>a,b, at 72 h, the changes in the cell cycle phases differentiated dramatically; 89.5%, 90.5% and 94.2% of MCF7 cells were in the G0/G1 phase respectively for cells treated with 0.25, 2.5 and 50 ppm CD (<italic>p</italic> < 0.005 for different concentrations of CDs), while only 76.6% of the control population was in the G0/G1 phase (<italic>p</italic> < 0.001). The significant differences (<italic>p</italic> < 0.001) in cell cycles for changing CD concentrations showing that prolonged delay in the G0/G1 phase is induced by CDs even at the lowest concentrations (0.25 ppm) which resulted in slower cell growth and delayed the entrance to S phase (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>a). As compared to untreated cells, CD (2.5 ppm) treated cells in S phase decreased by around 35% and 82% after incubation for 48 h and 72 h, respectively (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>b). Together with the genotoxicity and clastogenic analysis, the significant arrest of the cell population in G0/G1 phase, and the dramatic decrease in the population of cells in the S and G2/M phases support that CDs could trigger MCF7 cell apoptosis by inducing anomalies at G0/G1 phase by DNA damage and mutagenic stimulate (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>c). A comparison of the present study and previous literature is provided in Table <xref rid=\"Tab2\" ref-type=\"table\">2</xref>. Most of the listed studies showed that negatively charged CDs with a size lower than 10 nm which can freely enter or interact with the nucleus while there are a couple of examples reporting nucleus targeting by larger sized CDs<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref>,<xref ref-type=\"bibr\" rid=\"CR92\">92</xref>,<xref ref-type=\"bibr\" rid=\"CR100\">100</xref></sup>, relatively few of them addressing the CD-induced anomalies in cell-cycle<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref>,<xref ref-type=\"bibr\" rid=\"CR108\">108</xref>,<xref ref-type=\"bibr\" rid=\"CR110\">110</xref>,<xref ref-type=\"bibr\" rid=\"CR111\">111</xref></sup>. Our results suggest that CDs could have the potential as both drug carriers interacting with the cell-nucleus and therapeutic agents against tumor cells (Scheme <xref rid=\"Sch1\" ref-type=\"fig\">1</xref>). However, more studies should be done to handle this enormous therapeutic potential taking account the safety manners.<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Statistical analysis of G0/G1, S, and G2/M populations in MCF cells (NC) and cells treated with (<bold>a</bold>) varying concentrations of CDs (0.25–50 ppm) for 48 h and 72 h. (<bold>b</bold>) Comparison of cell cycle phase profile of NC and CD (0.25 ppm) treated cells in different cellular phases. (<bold>c</bold>) Schematic representation of the possible effect of CDs in the cell cycle progression.</p></caption><graphic xlink:href=\"41598_2020_70796_Fig7_HTML\" id=\"MO7\"/></fig><table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Comparison of the results of the present study with earlier literature.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"2\">References</th><th align=\"left\" colspan=\"2\">Carbon source</th><th align=\"left\" colspan=\"2\">Synthesis method</th><th align=\"left\" colspan=\"2\">Particle size</th><th align=\"left\" colspan=\"2\">Application</th><th align=\"left\">Cytotoxicity</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">Li et. al.<sup><xref ref-type=\"bibr\" rid=\"CR111\">111</xref></sup></td><td align=\"left\" colspan=\"2\">Ginger juice</td><td align=\"left\" colspan=\"2\">Hydrothermal</td><td align=\"left\" colspan=\"2\">8.2 ± 0.6 nm</td><td align=\"left\" colspan=\"2\">Treatment of liver cancer</td><td align=\"left\"><p><italic>Cell Type</italic>: HepG2, human hepatocellular carcinoma cell line; MCF-10A, normal mammary epithelial cell line; FL83B, normal liver cell line; A549, human lung cancer cell line; MDA-MB-231, human breast cancer cell line</p><p>Dose-dependent cytotoxicity, higher selectivity inhibition towards HepG2 cells</p><p>Effective on HepG2 cell cycle (increase in SubG1 phase), did not cause significant differences in cell cycles for the other four non-cancerous cell lines</p></td></tr><tr><td align=\"left\" colspan=\"2\">Kyung Yung et. al.<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup></td><td align=\"left\" colspan=\"2\">Citric Acid and β-alanine</td><td align=\"left\" colspan=\"2\">Microwave pyrolysis</td><td align=\"left\" colspan=\"2\">2–3 nm</td><td align=\"left\" colspan=\"2\">Cell nucleus Targeting</td><td align=\"left\"><p><italic>Cell Type</italic>: HeLa Cells</p><p>No significant cytotoxicity, nuclear localization, Nucleus targeting, and imaging ability shown in vivo</p><p>No genotoxicity evaluation</p></td></tr><tr><td align=\"left\" colspan=\"2\">Havrdova et al.<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref></sup></td><td align=\"left\" colspan=\"2\">Candle soot</td><td align=\"left\" colspan=\"2\">Oxidative acid treatment</td><td align=\"left\" colspan=\"2\">4–7 nm</td><td align=\"left\" colspan=\"2\">Cell nucleus Targeting and labeling</td><td align=\"left\"><p><italic>Cell Type</italic>: NIH/3T3, Standard mouse fibroblasts</p><p>CD-Pri: low cytotoxicity, stimulated proliferation, evoked oxidative stress and induced abnormalities in the cell cycle (G2/M arrest), no entrance to the nucleus</p><p>CD-PEG: low cytotoxicity, did not disrupt cellular morphology, toxic dose occurred at very high IC50 value and oxidative stress increased similarly like in the control. Did not cause any significant changes in the proportion of the cell cycle phases</p><p>CD-PEI: cytotoxic, entering into the cell nucleus and inducing the largest changes in the G0/G1 phase of the cell cycle and also induced G2/M arrest</p></td></tr><tr><td align=\"left\" colspan=\"2\">Periasamy et. al.<sup><xref ref-type=\"bibr\" rid=\"CR108\">108</xref></sup></td><td align=\"left\" colspan=\"2\">Commercial CDs</td><td align=\"left\" colspan=\"2\">-</td><td align=\"left\" colspan=\"2\"> < 50 nm</td><td align=\"left\" colspan=\"2\">Cell cytotoxicity</td><td align=\"left\"><p><italic>Cell Type</italic>: hMSCs, human mesenchymal stem cells</p><p>CNPs moderately reduce cell viability and cause chromatin condensation and DNA fragmentation, disrupt the expression of cell death genes</p><p>Cell cycle progression of hMSCs was arrested slightly, the number of cells in G0/G1 increased at low concentrations of CNP exposure, cell cycle was arrested in the sub-G0/G1 phase in a dose-dependent manner</p></td></tr><tr><td align=\"left\" colspan=\"2\">Kumawat et al.<sup><xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup></td><td align=\"left\" colspan=\"2\">Grape seed extract</td><td align=\"left\" colspan=\"2\">Microwave</td><td align=\"left\" colspan=\"2\">50–60 nm</td><td align=\"left\" colspan=\"2\">Nucleus Imaging, and Photoluminescent Sensing</td><td align=\"left\"><p><italic>Cell Type</italic>: L929, HT-1080, MIA PaCa-2, HeLa, and MG-63 cells</p><p>The tendency to self-localize themselves into cell nucleus regardless of cell-type. No cytotoxicity and act as an enhancer in cell proliferation in L929 confirmed by in vitro wound scratch assay and cell cycle analysis. Enhanced the number of L929 cells in S-phase</p></td></tr><tr><td align=\"left\" colspan=\"2\">Kalytchuk et. al.<sup><xref ref-type=\"bibr\" rid=\"CR110\">110</xref></sup></td><td align=\"left\" colspan=\"2\">Citric acid and L-cysteine</td><td align=\"left\" colspan=\"2\">Hydrothermal</td><td align=\"left\" colspan=\"2\">3–6.5 nm</td><td align=\"left\" colspan=\"2\">In vitro and in vivo luminescence lifetime thermometry</td><td align=\"left\"><p><italic>Cell Type</italic>: NIH/3T3, Standard mouse, fibroblasts; HeLa, human cervical cancer cells</p><p>Low cytotoxicity, No significant effect on the cell cycle of HeLa cells, Dose-dependent G0/G1 arrest slightly on NIH/3T3 cells</p></td></tr><tr><td align=\"left\" colspan=\"2\">Liu et al.<sup><xref ref-type=\"bibr\" rid=\"CR92\">92</xref></sup></td><td align=\"left\" colspan=\"2\">Young Bearly Leaves</td><td align=\"left\" colspan=\"2\">Hydrothermal</td><td align=\"left\" colspan=\"2\">1.9 and 2.7 nm (in EtoH)</td><td align=\"left\" colspan=\"2\">Cell nucleus Targeting and antiviral activity</td><td align=\"left\"><p><italic>Cell Type</italic>: PK-15 and HeLa cells</p><p>No significant cytotoxicity,  the neutral charged CDs (b-CDs) were localized in the cytoplasm and showed anti-viral activity, while the negatively charged ones (c-CDs) distributed through the whole cell and nuclear localization was also observed. Nucleus targeting and imaging ability of CDs have been shown in vitro</p><p>No genotoxicity evaluation</p></td></tr><tr><td align=\"left\" colspan=\"2\">Hill et al.<sup><xref ref-type=\"bibr\" rid=\"CR100\">100</xref></sup></td><td align=\"left\" colspan=\"2\">Glucosamine-HCl and m-phenylenediamine</td><td align=\"left\" colspan=\"2\">Microwave</td><td align=\"left\" colspan=\"2\">2.42 ± 0.55 nm</td><td align=\"left\" colspan=\"2\">LED-activated nucleus targeting and photothermal therapy</td><td align=\"left\"><p><italic>Cell Type</italic>: HDF and HeLa cells</p><p>Less cytotoxicity on HDF than HeLa cells, nuclear localization in HeLa cell line,</p><p>Nucleus targeting and imaging ability have been shown in vitro. CDs-based or LED induced cell death of cancer cells were not found to be associated with ROS production</p><p>No genotoxicity evaluation</p></td></tr><tr><td align=\"left\" colspan=\"2\">Zhang et al.<sup><xref ref-type=\"bibr\" rid=\"CR112\">112</xref></sup></td><td align=\"left\" colspan=\"2\">Citric acid (CA), and propylene diamine (PDA)</td><td align=\"left\" colspan=\"2\">Hydrothermal</td><td align=\"left\" colspan=\"2\">5 nm</td><td align=\"left\" colspan=\"2\">Cell nucleus labeling, cell-cycle imaging</td><td align=\"left\"><p><italic>Cell Type</italic>: HeLa-229 and HCerEPic</p><p>No significant cytotoxicity on both cell lines. Permeability of cancer cells to CDs is higher than that of normal cells. N-CQDs were located in the nucleus with no fluorescence on the cytoplasm The majority of labeled HeLa cells were observed in interphase</p></td></tr><tr><td align=\"left\" colspan=\"2\">Present study</td><td align=\"left\" colspan=\"2\"><italic>Nerium oleander</italic></td><td align=\"left\" colspan=\"2\">Thermal</td><td align=\"left\" colspan=\"2\">2.05 ± 0.22 nm</td><td align=\"left\" colspan=\"2\">Anti-cancer therapy</td><td align=\"left\"><p><italic>Cell Type</italic>: MCF-7, human breast cancer cells, HDFa, human primer dermal fibroblast cells</p><p>Dose-dependent cytotoxicity on MCF-7 cells, no cytotoxic effects on HDFa cells</p><p>Genotoxicity, Clastogenicity and G0/G1 arrest on MCF-7 cells</p></td></tr></tbody></table></table-wrap><fig id=\"Sch1\"><label>Scheme 1.</label><caption><p>Overview of CD impact on Human Breast Cancer (MCF-7) Cell Line.</p></caption><graphic xlink:href=\"41598_2020_70796_Sch1_HTML\" id=\"MO8\"/></fig></p></sec></sec><sec id=\"Sec20\"><title>Conclusions</title><p id=\"Par29\">Although benign biocompatibility is attributed to carbon, systematic, and reliable biosafety assessment of carbon-based nanomaterials still needs to be conducted. Carbon dots serve as a useful and usable platform for a wide range of biological and biomedical applications including bio-imaging and nucleus targeted delivery/imaging. Herein, a systematic toxicity analysis of CDs produced from the extract of <italic>N. oleander</italic> via thermal synthesis method was conducted. In contradiction to many of the previous studies, we concluded that CDs have concentration and time-dependent cytotoxic potentials (at 50 ppm) over MCF7 cells while they comforted the proliferation of healthy HDFa cells even at highest CD concentrations. CDs caused severe DNA damage evident by the formation of COMET tail, micronuclei in MCF7 cells even at concentrations as low as 0.25 ppm. The interference of CDs with cell-cycle progression resulted in cell arrest in G0/G1 phases which showed that they can interact with genetic material and could trigger MCF7 cell apoptosis. Inducing Oxidative Stress Responses and interference with the cell cycle machinery could be due to the structure and surface properties of CDs while other mechanisms could also be involved. Although further studies are warranted to investigate the mutagenicity or carcinogenicity potency of CD in mammalian cells, this work shows evidence that CDs with super tiny size and high amount of oxygen on the surface can specifically affect the cellular function of tumor cells, and thus they have the potential to be used alone as an anti-cancer therapeutic material that can selectively target cancer cells by inducing series of DNA damage.</p></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Sinem Şimşek and Ayça Aktaş Şüküroğlu.</p></fn></fn-group><ack><title>Acknowledgements</title><p>Sinem Şimşek gratefully acknowledges TUBITAK (The Scientific and Technological Research Council of Turkey) for the graduate scholarship.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>The manuscript was written through contributions of R.G., and D.B. and S.S. R.G. planned and designed C-Dot studies, S.S. performed the experiments on C-Dots. R.G., B.Ö and S.S. collected and analyzed the data. R.G planned and designed the studies on in vitro cytotoxicity of CDs. D.Y. and S.S carried out in vitro experiments and analyzed the data with R.G. D.B planned and design genotoxicity and clastogenicity studies. D.Y and A.A.Ş performed the genotoxicity and clastogenicity experiments, collected and analyzed the data. All authors take full responsibility for the content of the paper and approved the final version of the manuscript.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par30\">The authors declare no competing interests.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Cha</surname><given-names>C</given-names></name><name><surname>Shin</surname><given-names>SR</given-names></name><name><surname>Annabi</surname><given-names>N</given-names></name><name><surname>Dokmeci</surname><given-names>MR</given-names></name><name><surname>Khademhosseini</surname><given-names>A</given-names></name></person-group><article-title>Carbon-based nanomaterials: multifunctional materials for biomedical engineering</article-title><source>ACS Nano</source><year>2013</year><volume>7</volume><fpage>2891</fpage><lpage>2897</lpage><pub-id pub-id-type=\"pmid\">23560817</pub-id></element-citation></ref><ref id=\"CR2\"><label>2.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Chawla</surname><given-names>J</given-names></name><name><surname>Kumar</surname><given-names>A</given-names></name></person-group><article-title>Ranking carbon-based nanomaterials using cytotoxicity to minimize public health risks</article-title><source>Int J Environ Eng Manag</source><year>2013</year><volume>4</volume><fpage>301</fpage><lpage>308</lpage></element-citation></ref><ref id=\"CR3\"><label>3.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Chen</surname><given-names>C</given-names></name><name><surname>Haifang</surname><given-names>W</given-names></name></person-group><article-title>Biomedical applications and toxicology of carbon nanomaterials</article-title><source>Nanomaterials</source><year>2016</year><pub-id pub-id-type=\"doi\">10.1002/9783527692866</pub-id><pub-id pub-id-type=\"pmid\">28344308</pub-id></element-citation></ref><ref id=\"CR4\"><label>4.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Visalli</surname><given-names>G</given-names></name><etal/></person-group><article-title>Toxicological assessment of multi-walled carbon nanotubes on A549 human lung epithelial cells</article-title><source>Toxicol. 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pub-id-type=\"doi\">10.1038/s41467-020-17901-2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Vulnerability of progeroid smooth muscle cells to biomechanical forces is mediated by MMP13</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0002-3116-0723</contrib-id><name><surname>Pitrez</surname><given-names>Patricia R.</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Estronca</surname><given-names>Luís</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><contrib-id 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contrib-id-type=\"orcid\">http://orcid.org/0000-0003-3374-6274</contrib-id><name><surname>Nissan</surname><given-names>Xavier</given-names></name><xref ref-type=\"aff\" rid=\"Aff7\">7</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rosell</surname><given-names>Anna</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><contrib-id contrib-id-type=\"orcid\">http://orcid.org/0000-0001-8985-9302</contrib-id><name><surname>Ferreira</surname><given-names>Lino</given-names></name><address><email>lino@uc-biotech.pt</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.8051.c</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9511 4342</institution-id><institution>Center for Neuroscience and Cell Biology, </institution><institution>University of Coimbra, </institution></institution-wrap>Coimbra, Portugal </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.8051.c</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9511 4342</institution-id><institution>Faculty of Medicine, </institution><institution>University of Coimbra, </institution></institution-wrap>Coimbra, Portugal </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.430994.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1763 0287</institution-id><institution>Neurovascular Research Laboratory, </institution><institution>Vall d’Hebron Research Institute, Universitat Autònoma de Barcelona, </institution></institution-wrap>Passeig Vall d’Hebron 119-129, 08035 Barcelona, Spain </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.5399.6</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2176 4817</institution-id><institution>Aix Marseille Univ, INSERM, MMG, </institution></institution-wrap>Marseille, France </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.10025.36</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 8470</institution-id><institution>Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, </institution><institution>University of Liverpool, </institution></institution-wrap>Liverpool, L7 8TX UK </aff><aff id=\"Aff6\"><label>6</label>Progelife, Marseille, France </aff><aff id=\"Aff7\"><label>7</label>CECS, I-STEM, AFM, Institute for Stem Cell Therapy and Exploration of Monogenic Diseases, Evry Cedex, France </aff><aff id=\"Aff8\"><label>8</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.9983.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2181 4263</institution-id><institution>IMM, Instituto de Medicina Molecular, </institution><institution>Universidade de Lisboa, </institution></institution-wrap>Lisbon, Portugal </aff><aff id=\"Aff9\"><label>9</label>25 Cambridge Science Park, Mogrify Ltd, Milton Road, Cambridge, CB4 0FW UK </aff><aff id=\"Aff10\"><label>10</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411266.6</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0404 1115</institution-id><institution>Molecular Genetics Laboratory, </institution><institution>Department of Medical Genetics, La Timone Children’s Hospital, </institution></institution-wrap>Marseille, France </aff><aff id=\"Aff11\"><label>11</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.451388.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1795 1830</institution-id><institution>Developmental Biology Laboratory, </institution><institution>Francis Crick Institute, </institution></institution-wrap>London, NW1 1AT UK </aff><aff id=\"Aff12\"><label>12</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.418245.e</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9999 5706</institution-id><institution>Leibniz Institute on Aging - Fritz Lipmann Institute, </institution></institution-wrap>07745 Jena, Germany </aff><aff id=\"Aff13\"><label>13</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.414336.7</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0407 1584</institution-id><institution>CRB Assistance Publique des Hôpitaux de Marseille (CRB AP-HM, TAC), </institution></institution-wrap>Marseille, France </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>11</volume><elocation-id>4110</elocation-id><history><date date-type=\"received\"><day>21</day><month>11</month><year>2018</year></date><date date-type=\"accepted\"><day>14</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Hutchinson-Gilford Progeria Syndrome (HGPS) is a premature aging disease in children that leads to early death. Smooth muscle cells (SMCs) are the most affected cells in HGPS individuals, although the reason for such vulnerability remains poorly understood. In this work, we develop a microfluidic chip formed by HGPS-SMCs generated from induced pluripotent stem cells (iPSCs), to study their vulnerability to flow shear stress. HGPS-iPSC SMCs cultured under arterial flow conditions detach from the chip after a few days of culture; this process is mediated by the upregulation of metalloprotease 13 (MMP13). Importantly, double-mutant <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>−/−</italic></sup> mice or <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice treated with a MMP inhibitor show lower SMC loss in the aortic arch than controls. MMP13 upregulation appears to be mediated, at least in part, by the upregulation of glycocalyx. Our HGPS-SMCs chip represents a platform for developing treatments for HGPS individuals that may complement previous pre-clinical and clinical treatments.</p></abstract><abstract id=\"Abs2\" abstract-type=\"web-summary\"><p id=\"Par2\">Hutchinson-Gilford Progeria Syndrome (HGPS) is a premature aging disease and smooth muscle cells are the most affected cells in HGPS individuals. Here, the authors report a microfluidics platform with HGPS induced pluripotent stem cells and show that inhibition of metalloprotease 13 may reduce smooth muscle cell loss.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Stem-cell biotechnology</kwd><kwd>Cardiovascular diseases</kwd></kwd-group><funding-group><award-group><funding-source><institution-wrap><institution-id institution-id-type=\"FundRef\">https://doi.org/10.13039/501100001871</institution-id><institution>Ministry of Education and Science | Fundação para a Ciência e a Tecnologia (Portuguese Science and Technology Foundation)</institution></institution-wrap></funding-source><award-id>EXPL/BIM-MED/2267/2013</award-id><award-id>MITP-TB/ECE/0013/2013</award-id><award-id>SFRH/BD/71042/2010</award-id><principal-award-recipient><name><surname>Ferreira</surname><given-names>Lino</given-names></name></principal-award-recipient></award-group></funding-group><funding-group><award-group><funding-source><institution>European project ERAatUC - Grant Reference Number 669088 Miguel Servet research contract from Instituto de Salud Carlos III, Spain - Grant Reference Number CPII15/00003 Federal Government of Germany and the State of Thuringia</institution></funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\" sec-type=\"introduction\"><title>Introduction</title><p id=\"Par3\">Hutchinson–Gilford Progeria Syndrome (HGPS) is caused by a single mutation in the lamin A/C gene (<italic>LMNA</italic>), resulting in the generation of an abnormal lamin A precursor named progerin<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. One of the key reasons of premature death is the loss of smooth muscle cells (SMCs) in the medial layer of large arteries, followed by the appearance of collagen and extracellular matrix (ECM) and the development of a severe arteriosclerotic process that leads to increased arterial stiffness<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>–<xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. The reasons of SMC loss remain to be determined. It has been suggested that this may happen due to pathophysiological changes inherent to prelamin A/progerin accumulation, such as the acceleration of vascular calcification via the activation of the DNA damage response and senescence-associated secretory phenotypes in vascular SMCs<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup> or the downregulation of PARP1<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>. It has also been shown that the combined effect of progerin accumulation and mechanical stress in mouse SMCs overexpressing progerin promoted cell detachment and death, while the disruption of the linker between nucleoskeleton and cytoskeleton complex ameliorated the toxic effects of progerin<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Neither of these studies have fully addressed the reasons behind SMC detachment and thus which therapeutic approach could be effective to prevent SMC loss.</p><p id=\"Par4\">Induced pluripotent stem cells (iPSCs) offer an unlimited source of SMCs to study HGPS. Recent studies have generated iPSCs from fibroblasts obtained from individuals with HGPS (hereafter referred to as HGPS-iPSCs)<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref>–<xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. Strikingly, HGPS-iPSCs show low lamin A/C and progerin protein expression in the pluripotent state. However, the expression of progerin is reactivated after HGPS-iPSC differentiation into SMCs<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. The differentiated cells show nuclear dysmorphology, cell growth retardation, susceptibility to apoptosis, proliferation reduction, and DNA-repair defects; however, SMC performance under flow conditions has not been evaluated.</p><p id=\"Par5\">In this work, we develop an in vitro cell system comprising SMCs derived from HGPS-iPSCs cultured under flow conditions in a microfluidic device. We identify MMP13 as a mediator of SMC detachment using chemical and genetic assays. The generated double-mutant <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>−/−</italic></sup> mice show an increase in SMCs in the aortic arch and a decrease in progerin-positive cells. In addition, the inhibition of MMP13 in <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice by Batimastat, a drug that has been previously tested in clinical trials in cancer patients, reduces SMC loss. The results present here open perspectives for HGPS treatment.</p></sec><sec id=\"Sec2\" sec-type=\"results\"><title>Results</title><sec id=\"Sec3\"><title>SMCs derived from HGPS-iPSCs are functional and share similar features to progerin-expressing cells</title><p id=\"Par6\">iPSCs were generated from HGPS skin fibroblasts and characterized as previously described<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. iPSCs generated from non-disease cells (N-iPSCs), HGPS skin fibroblasts, and non-disease somatic human vascular smooth muscle cells (hVSMCs) were used as controls. The mutation in the <italic>LMNA</italic> gene, both in HGPS skin fibroblasts and HGPS-iPSCs, was confirmed by Sanger sequencing (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">1</xref>). As expected, undifferentiated HGPS-iPSCs expressed low levels of HGPS markers, such as <italic>progerin</italic>, as well as low levels of SMC markers, such as <italic>α-SMA</italic> and <italic>SMα-22</italic><sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup> (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2a</xref>). To induce the differentiation of HGPS-iPSCs or N-iPSCs into SMCs, CD34<sup>+</sup> cells were isolated by magnetic-activated cell sorting from embryoid bodies (EBs) cultured for 10 days in suspension (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1a</xref>)<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>. At this stage, HGPS-CD34<sup>+</sup> cells already express higher levels of progerin mRNA transcripts relative to N-iPSCs but relatively low levels of SMC mRNA transcripts compared with somatic hVSMCs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">2b</xref>). HGPS-CD34<sup>+</sup> cells were then cultured in SMC induction media (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">3</xref>) followed by SMC maturation media (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4</xref>) for an additional four passages. Matured SMCs are referred to as HGPS-iPSC SMCs or N-iPSC SMCs based on their phenotype, genotype, and functional properties (see below). Both HGPS-iPSC SMCs and N-iPSC SMCs have similar or higher expression of SMC mRNA transcripts than somatic hVSMCs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4a</xref>). Greater than 95% of both differentiated cells express α-SMA, smooth muscle myosin heavy chain (SMMHC), and calponin proteins (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1b</xref>). Moreover, HGPS-iPSC SMCs express progerin mRNA transcripts (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1c</xref>) and progerin protein (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4b, c</xref>). Similar results were obtained for SMCs derived from HGPS-iPSCs generated from a second Progeria individual; however, the differentiated cells showed higher progerin protein levels than the first Progeria individual (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5</xref>). Importantly, HGPS-iPSC SMCs and N-iPSC SMCs are functional as they respond to vasoactive agents such as histamine and angiotensin (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4d</xref>) and they contract after exposure to carbachol (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">4e</xref>).<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Vulnerability of HGPS-iPSC SMCs to arterial flow conditions.</title><p><bold>a</bold> Schematic representation of the methodology used to differentiate iPSCs into SMCs. <bold>b</bold> Expression of SMC markers on iPSC-derived SMCs. Percentage of positive cells expressing SMC markers as evaluated by immunofluorescence (at least 100 cells were counted per each marker). Results are mean ± SEM (<italic>n</italic> = 3 independent experiments). <bold>c</bold> Expression of progeria markers on iPSC-derived SMCs. Gene expression by qRT-PCR (gene expression was normalized by the housekeeping gene <italic>GAPDH</italic>). HGPS fibroblasts were used as control. Results are mean ± SEM (<italic>n</italic> = 4 technical replicates from a pool of three independent experiments). *, **, ***, **** denote statistical significance (<italic>p</italic> < 0.05, <italic>p</italic> < 0.01, <italic>p</italic> < 0.001, <italic>p</italic> < 0.0001). Statistical analyses were performed by one-way ANOVA followed by Newman–Keuls’s post test. <bold>d</bold> Schematic representation of the protocol used. Cells were cultured for 6–8 days in arterial flow conditions (20 dyne/cm<sup>2</sup>). <bold>e</bold> Light microscopy images of HGPS fibroblasts, hVSMCs, or HGPS-iPSC SMCs (10% of the cells accumulate progerin protein) at different culture days. Only HGPS-iPSC SMCs detached from the microfluidic system at day 4. Scale bar is 50 μm. <bold>f</bold> Number and area of cell clumps in HGPS-iPSC SMCs at different times (at least two images (×10) have been quantified per time). For area of cell clumps <italic>n</italic> > 2 images examined over three independent experiments; for cell clumps, <italic>n</italic> = 3 independent experiments. <bold>g</bold> Number of cells per surface area (mm<sup>2</sup>) during cell culture under arterial flow (at least three images (×10) have been quantified per time; <italic>n</italic> = 3–7 independent experiments). Cell number was normalized by the number of cells present at day 0. <bold>h</bold> Cell metabolism evaluated by the Presto Blue assay. Absorbance at 570 nm was measured and normalized to the 600-nm values for the experimental wells. <italic>n</italic> = 3 independent experiments. <bold>i</bold> Expression of nuclear proliferation marker, Ki67 (at least three images (×10) have been quantified per time). The percentage of Ki67 positive cells was evaluated by immunofluorescence. <italic>n</italic> > 3 images examined over three independent experiments. <bold>j</bold> Cell apoptosis evaluated by caspase-9 activity. Results were normalized by cell number. <italic>n</italic> = 3 independent experiments. From <bold>c</bold> to <bold>g</bold>, results are mean ± SEM. *, **, ***, **** denote statistical significance (<italic>p</italic> < 0.05, <italic>p</italic> < 0.01, <italic>p</italic> < 0.001, <italic>p</italic> < 0.0001). Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test <bold>i</bold> and <bold>j</bold>.</p></caption><graphic xlink:href=\"41467_2020_17901_Fig1_HTML\" id=\"d30e819\"/></fig></p><p id=\"Par7\">SMCs derived from HGPS-iPSCs share similar features to progerin-expressing cells. Cell lines forced to express progerin show the activation of several NOTCH signaling pathway effectors<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. Indeed, our results showed that HGPS-iPSC CD34<sup>+</sup> cells had higher expression of NOTCH signaling pathway mRNA transcripts than N-iPSC CD34<sup>+</sup> cells (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">6</xref>). Mature HGPS-iPSC SMCs also expressed higher levels of NOTCH ligand and receptors than N-iPSC SMCs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">6a</xref>). In addition, HGPS-iPSC SMCs responded to farnesyltransferase inhibitors, as has been shown in other Progeria cell models<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref>–<xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. In the current work, HGPS-iPSC SMCs treated with lonafarnib for 48 h accumulated nuclear prelamin A and showed a decrease in nuclear shape abnormalities and nuclear blebbing (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7a–c</xref>). Taken together, the cells differentiated from HGPS-iPSCs-expressed SMC and progeroid markers, are functional and exhibit physiological responses.</p></sec><sec id=\"Sec4\"><title>HGPS-iPSC SMCs are vulnerable to arterial shear stress</title><p id=\"Par8\">SMCs differentiated from N-iPSCs or HGPS-iPSCs were seeded in a microfluidics system and cultured under flow conditions for up to 7 days (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1d</xref>). Because SMCs from large arteries are the most affected in blood vessels in HGPS, we used a flow of 20 dyne/cm<sup>2</sup>, which is typically found in arterial blood vessels<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. N-iPSC SMCs (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g</xref>), hVSMCs, or HGPS fibroblasts (80% of which express progerin) (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1e, g</xref>) can be cultured in the microfluidics system for at least 7 days without a visible loss in cell number. In contrast, HGPS-iPSC SMCs cultured under flow conditions formed cell clumps overtime (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1f</xref>), and most of the cells detached from the substrate at day 4 as confirmed by cell number (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1g</xref>) and metabolic analyses (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1h</xref>). During this time period, the percentage of cells expressing progerin and displaying nuclear abnormalities increased significantly until day 4 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">8</xref>). Our results indicate that SMC detachment is mediated by progerin accumulation, as the inhibition of progerin by antisense morpholinos<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup> significantly decreased HGPS-iPSC SMC detachment (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">9</xref>). In addition, we showed that HGPS-iPSC SMCs with high progerin expression (30% of the cells express progerin at day 0) detached from the surface of the microfluidics system in a short time (<12 h) (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">5g</xref>). To confirm that progerin accumulation is responsible for SMC loss, a frameshift mutant stem cell line was generated (HGPS∆2-iPSCs) to knockout the HGPS mutant allele and generated a disease cell line, as previously described in the mouse<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup> (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2a</xref> and Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">10</xref>). Specifically, a two-base pair deletion on exon 11, upstream of the HGPS point mutation (1814C>T), was generated. Notably, HGPS∆2-iPSCs expressed little or no progerin upon differentiation into SMCs as demonstrated at the transcript and protein levels and did not detach under flow culture conditions (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>).<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Expression of progeria and SMC markers in HGPSΔ2-iPSC SMCs.</title><p><bold>a</bold> gRNA directs Cas9 nuclease against mutated exon 11 of <italic>LMNA</italic> gene, upstream the HGPS mutation, disrupting progerin, without altering lamin A and lamin C. Sanger sequencing for <italic>LMNA</italic> (NM_170707.4 transcript) exon 11 was performed for: N-iPSCs, HGPS-iPSCs and HGPSΔ2-iPSCs, confirming the deletion of two-base pairs in the HGPSΔ2-iPSCs. <bold>b</bold> Expression of lamin A, progerin, and SMC proteins monitored by immunofluorescence. Scale bar is 100 μm. <italic>n</italic> = 6 independent experiments. <bold>c</bold> Expression of <italic>progerin (LMNA G608G</italic> gene) in HGPS and HGPSΔ2 cell lines. Results are mean ± SEM (<italic>n</italic> = 4 technical replicates from a pool of three independent experiments). Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. <bold>d</bold> Quantification of lamin A, progerin, dysmorphic nuclei, and nuclei blebbing. Results are mean ± SEM (<italic>n</italic> = 6 independent experiments). **** denotes statistical significance (<italic>p</italic> < 0.0001). <bold>e</bold> Percentage of cells that have been differentiated from HGPSΔ2-iPSCs that express SMC markers at protein level. Results are mean ± SEM (<italic>n</italic> = 5–6 independent experiments). <bold>f</bold> Number of cells per surface (mm<sup>2</sup>) as quantified by high-content microscopy (at least three images (×10) have been quantified per time). The number of cells was evaluated after 6 days under arterial flow and was normalized by the number of cells present at day 0. <italic>n</italic> > 3 images examined over three independent experiments. <bold>g</bold>\n<italic>MMP13</italic> mRNA transcripts quantified by qRT-PCR analyses in HGPS-iPSC SMCs or HGPSΔ2-iPSC SMCs cultured under flow conditions. MMP13 mRNA transcripts were normalized by <italic>GAPDH</italic>. <italic>n</italic> = 4 technical replicates from a pool of three independent experiments. **, *** denote statistical significance (<italic>p</italic> < 0.01, <italic>p</italic> < 0.001). Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test.</p></caption><graphic xlink:href=\"41467_2020_17901_Fig2_HTML\" id=\"d30e991\"/></fig></p><p id=\"Par9\">HGPS-iPSC SMC detachment does not seem to be mediated by cell apoptosis. Before cell detachment, HGPS-iPSC SMCs showed: (i) poor proliferation (as monitored by Ki67 staining) confirming their contractile phenotype (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1i</xref>), (ii) similar levels of apoptosis as N-iPSC SMCs as confirmed by caspase-9 activity (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1j</xref>), (iii) an osteogenic differentiation program (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">11a, b</xref>), (iv) increased DNA damage<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup> (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">12</xref>), and (v) downregulation of NOTCH<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref>,<xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup> (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">13</xref>) signaling pathways. Because the in vivo shear stress from blood flow is not directly sensed by SMCs but by endothelial cells (ECs), we co-cultured SMCs differentiated from HGPS-iPSCs (directly attached to the microfluidics substrate) with human umbilical artery ECs (HUAECs, on top of the SMCs) under flow conditions. Initially, we screened different culture conditions and we found that endothelial growth media-2 (EGM-2) medium was a suitable medium to support both cells (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">14</xref>). Then, we co-cultured HUAECs and HGPS-iPSC SMCs at different ratios (1.6, 1, and 0.6) under flow conditions. In all the ratios tested, we had a monolayer of HUAECs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">15a</xref>) and HGPS-iPSC SMCs at time zero. After 6 days in flow conditions, a significant percentage (>40%) of HGPS-iPSC SMCs was lost (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">15b</xref>). For the highest ratio tested (1.6), the loss of HGPS-iPSC SMCs occurred without visible loss of ECs. Yet, for EC:SMC ratios below 1, part of ECs also detached from the microfluidic chamber indicating that, a low ECs density, may turn ECs vulnerable to flow conditions. Importantly, cell vulnerability to flow conditions was only observed in co-cultures of HGPS-iPSC SMCs but not N-iPSC SMCs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">15c</xref>).</p><p id=\"Par10\">It has been shown that a knock-in mouse line carrying a homozygous Lmna c.1827C>T;p.Gly609Gly mutated allele (<italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup>) recapitulates most of the described alterations associated with HGPS, including the loss of SMCs<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>. Thus, to validate the results obtained for the HGPS-iPSC SMCs, we isolated SMCs from wild-type (WT mSMC) and homozygous <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> (HOZ mSMC) mice. Both cells expressed calponin and α-SMA, while HOZ mSMCs, but not WT SMCs, showed dysmorphic nuclei and nuclear blebbing (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3a, b</xref>). WT mSMCs were cultured under flow conditions (120 dyne/cm<sup>2</sup> to mimic mice arterial flow shear stress<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref>,<xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>) for up to 26 days without visible loss of cells (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3c</xref>). In contrast, HOZ mSMCs detached from the substrate after 8–9 days. These results confirm that HOZ mSMCs are vulnerable to flow shear stress similar to HGPS-iPSC SMCs. Overall, our results indicate that HGPS-iPSC SMCs are vulnerable to flow shear stress, as in the case of SMCs isolated from mice carrying a HGPS-like mutation in the <italic>Lmna</italic> gene.<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Characterization and impact of flow shear stress in SMCs isolated from wild-type (WT) and homozygous (HOZ) <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice.</title><p><bold>a</bold> Mouse SMCs were cultured for 9–26 days in arterial flow conditions (120 dyne/cm<sup>2</sup>). Immunofluorescence analyses performed on mouse SMCs (6-week-old WT and HOZ <italic>Lmna</italic>\n<sup><italic>G609G/G609G</italic></sup> mice) at passage 4 for α-SMA and Lamin A. Nuclei were stained with DAPI. Scale bar is 20 µm. <italic>n</italic> = 3–4 images examined over three independent experiments. <bold>b</bold> Percentage of dysmorphic nuclei, nuclei blebbing, and SMC organized fibers in mSMCs (assessed in static conditions). <italic>n</italic> = 3-4 images examined over three independent experiments. <bold>c</bold> Percentage of adhered cells over time. Cells were cultured under flow conditions. <italic>n</italic> = 3–4 independent experiments. Statistical analyses were performed by one-way ANOVA followed by Newman–Keuls’s post test. <bold>d</bold> Quantification of MMP13 in HOZ mSMCs and WT mSMCs. Cells were analyzed at day 0 and day 8 under flow. Fluorescence signal was normalized by cell number. <italic>n</italic> = 3–4 independent experiments. Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. <bold>e</bold> Percentage of adhered cells over time. Cells were cultured under flow conditions. <italic>n</italic> = 5–6 independent experiments. In graphs <bold>b</bold>–<bold>e</bold>, results are mean ± SEM. *,**,***,**** denote statistical significance (<italic>p</italic> < 0.05, <italic>p</italic> < 0.01, <italic>p</italic> < 0.001, <italic>p</italic> < 0.0001).</p></caption><graphic xlink:href=\"41467_2020_17901_Fig3_HTML\" id=\"d30e1150\"/></fig></p></sec><sec id=\"Sec5\"><title>HGPS-iPSC SMCs have significant changes in extracellular matrix (ECM) secretion and MMP expression</title><p id=\"Par11\">To gain insights into the mechanism behind SMC detachment, we performed microarray analyses on HGPS-iPSC SMCs and N-iPSC SMCs at days 0 and 4 (before cell detachment). At day 0, 2084 genes were differentially expressed (Log2FC ≥ 1; <italic>p</italic> < 0.05) in HPGS-iPSC SMCs vs. N-iPSC SMCs. Of these genes, 51 genes were associated with cell senescence, as determined by the intersection of all the differentially expressed genes with the CellAge database<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup> (279 genes) (Supplementary Data <xref rid=\"MOESM4\" ref-type=\"media\">1</xref>). At the protein levels, HGPS-iPSC SMCs expressed higher levels of p21 and SA-β-galactosidase than N-iPSCs-SMCs and the level of senescence markers increased after culture of HGPS-iPSC SMCs in flow conditions (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">16a</xref> and Supplementary Data <xref rid=\"MOESM8\" ref-type=\"media\">5</xref>). We next performed pathway analysis on the differentially expressed genes from HGPS-iPSC SMCs at day 0 vs. day 4 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">17</xref> and Supplementary Data <xref rid=\"MOESM5\" ref-type=\"media\">2,</xref><xref rid=\"MOESM6\" ref-type=\"media\">3</xref>). In general, ECM activation, secretion, and cell adhesion pathways were upregulated, whereas cell cycle and DNA replication pathways were downregulated under arterial flow conditions at day 4. Among the fifty-seven genes that were at least threefold down- or upregulated compared with day 0 (<italic>p</italic> < 0.001) (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref>), five were related to ECM secretion (<italic>COL6A3, IBSP, BGN, SGCG, and EPPK1</italic>) and one to metalloproteases (<italic>MMP13</italic>). The expression of these genes, as well as others, was confirmed by qRT-PCR (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4a</xref>), and the molecular network of genes that were differentially expressed between days 0 and 4 in the HGPS-SMCs was examined by Ingenuity Pathway Analysis (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">17</xref>). Interestingly, pathway analysis suggested that <italic>MMP13</italic> is either a direct or indirect target of multiple genes upregulated at day 4. Moreover, <italic>MMP13</italic> transcript levels are elevated in HGPS-iPSC SMCs when compared with SMCs generated from the attenuated disease version of this line (HGPSΔ2-iPSC SMCs), specially post shear stress (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2g</xref>).<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>MMP13 activity in HGPS-iPSC SMCs cultured under flow shear stress.</title><p><bold>a</bold> Volcano plot representing differentially expressed genes in HGPS-iPSC-SMC cultured under flow conditions at day 0 and 4. Each point represents one of 53,617 genes. 26 and 31 genes were upregulated (red; fold change ≥ 3; <italic>p</italic> < 0.001) and downregulated (yellow; fold change ≤ 3; <italic>p</italic> < 0.001), respectively. Graph shows qRT-PCR validation for 16 genes with fold changes >3. Fold change was between days 0 and 4. Gene expression was normalized by the housekeeping gene <italic>GAPDH</italic>. Results are mean ± SEM, <italic>n</italic> = 4 technical replicates from a pool of three independent experiments. Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. <bold>b</bold> Schematic representation of the experimental protocol used. <bold>c</bold> Effect of HGPS-iPSC SMC or N-iPSC SMCs conditioned media (in both cases obtained after 4 days under flow conditions) on hVSMCs cultured under flow conditions. <italic>n</italic> = 1–5 images examined over three independent experiments. <bold>d</bold> Quantification of MMP13 activity (cell culture media) by ELISA. Cells were analyzed at days 0 and 4 under flow. Fluorescence signal was normalized by cell number. <italic>n</italic> = 3 independent experiments. Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. <bold>e</bold> Effect of MMP13 or BB94 inhibition in HGPS-iPSC SMC detachment. The number of cells was evaluated after 7 and 12 days under arterial flow and was normalized by the number of cells present at day 0. <italic>n</italic> = 3–5 images examined over three independent experiments. Statistical analyses were performed by one-way ANOVA followed by Newman–Keuls’s post test. <bold>f</bold> MMP13 knockdown by siRNA in HGPS-iPSC SMCs. <italic>MMP13</italic> mRNA transcripts were quantified by qRT-PCR and normalized by <italic>GAPDH</italic>. Mean ± SEM (<italic>n</italic> = 4 technical replicates from a pool of three independent experiments). Statistical analyses were performed by one-way ANOVA followed by Newman–Keuls’s post test. <bold>g</bold> Number of cells per microfluidic area during culture under flow shear conditions normalized by the number of cells in control experimental groups (i.e., cells transfected with control siRNA). <italic>n</italic> = 7 independent experiments for day 7 and <italic>n</italic> = 6 independent experiments for day 10. <bold>h</bold> Percentage of progerin-positive cells after 7 days under flow conditions with SmGM-2 media supplemented or not with MMP13 inhibitor. <italic>n</italic> = 1–5 images examined over three independent experiments. Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. <bold>i</bold> Activity of alkaline phosphatase in HGPS-iPSCs-SMC normalized by cell number per mm<sup>2</sup>, in cells cultured 4 days under flow conditions. Cells were treated or not with MMP13 inhibitor. <italic>n</italic> = 3 independent experiments. In graphs <bold>a</bold>–<bold>h</bold>, results are mean ± SEM. *, **, ***, **** denote statistical significance (<italic>p</italic> < 0.05, <italic>p</italic> < 0.01, <italic>p</italic> < 0.001, <italic>p</italic> < 0.0001).</p></caption><graphic xlink:href=\"41467_2020_17901_Fig4_HTML\" id=\"d30e1323\"/></fig><fig id=\"Fig5\"><label>Fig. 5</label><caption><title>MMP13 inhibition significantly increases SMC number in aortic arch of <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice.</title><p><bold>a</bold> Schematic representation of the animal protocol. WtWt, KiWt, KiHt, and KiKo mice (age: 10 weeks) were evaluated. <bold>b</bold> Quantification of MMP13 activity (plasma from WtWt, <italic>n</italic> = 9, and KiWt, <italic>n</italic> = 6, mice) by ELISA. Fluorescence signal was normalized by mice weight. Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. <bold>c</bold> Immunofluorescence analyses in the aortic arch for α-SMA, progerin, and heparan sulfate (HS). Cell nuclei were stained with DAPI. Scale bar is 100 µm for α-SMA staining and 50 µm for progerin and heparan sulfate staining. For α-SMA staining, <italic>n</italic> = 5 animals, except for KiHt (four animals). For progerin staining, <italic>n</italic> = 5 animals, except for KiHt (three animals). For heparan sulfate <italic>n</italic> = 6 WtWt, <italic>n</italic> = 6 KiWt, <italic>n</italic> = 4 KiHt, and <italic>n</italic> = 5 for KiKo. <bold>d</bold> Heart rates in mice (<italic>n</italic> = 8 WtWt, <italic>n</italic> = 6 KiWt, <italic>n</italic> = 7 KiHt, and <italic>n</italic> = 5 KiKo). Statistical analyses were performed by one-way ANOVA followed by Newman–Keuls’s post test. <bold>e</bold> Number of SMC nuclei in aortic arch per tissue area (mm<sup>2</sup>) (<italic>n</italic> = 2–3 slides examined over five animals, except for KiHt (four animals)). Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. <bold>f</bold> Percentage of progerin-positive cells in SMCs. <italic>n</italic> = 5 animals, except for KiHt (three animals). Statistical analyses were performed by one-way ANOVA followed by Newman–Keuls’s post test. <bold>g</bold> Expression of heparan sulfate as evaluated by immunofluorescence. Intensity of heparan sulfate was calculated in each picture (at least 16 pictures per condition) and normalized by cell number mice (<italic>n</italic> = 6 WtWt, <italic>n</italic> = 6 KiWt, <italic>n</italic> = 4, KiHt and <italic>n</italic> = 5 KiKo). In <bold>b</bold>, <bold>d</bold>–<bold>g</bold>, results are mean ± SEM. *, **, ***, **** denote statistical significance (<italic>p</italic> < 0.05, <italic>p</italic> < 0.01, <italic>p</italic> < 0.001, <italic>p</italic> < 0.0001).</p></caption><graphic xlink:href=\"41467_2020_17901_Fig5_HTML\" id=\"d30e1449\"/></fig></p><p id=\"Par12\">To further explore the gene array results, we evaluated whether the presence of ECM secreted by hVSMCs could prevent the detachment of HGPS-iPSC SMCs under arterial flow conditions. Thus, we cultured HGPS-iPSC SMCs on decellularized ECM deposited by hVSMCs or directly on top of mitotically inactivated hVSMCs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">18</xref>). Both conditions were unable to prevent HGPS-iPSC SMC detachment. Next, we tested whether conditioned media collected from HGPS-iPSC SMCs in flow conditions for 4 days could induce the detachment of flow shear stress-insensitive hVSMCs (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4b</xref>). Surprisingly, hVSMCs detach after perfusion with HGPS-iPSC SMC-conditioned media but not with N-iPSC SMC-conditioned media (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4c</xref>). Following these results and given that <italic>MMP13</italic> appears to be the downstream effector for the genes misregulated at day 4 (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">17b</xref>) we decided to quantify the concentration of MMP13 in HGPS-iPSC SMC and N-iPSC SMC culture media after flow shear stress. Remarkably, MMP13 levels increased 30-fold in the HGPS-iPSC SMC culture media, but not in the control cell culture media (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4d</xref>). Similarly, higher MMP13 levels were observed in media collected from HOZ mSMCs under flow shear stress, when compared with media from WT mSMCs (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3d</xref>). Because MMP13 is produced by cells as an inactive form (proMMP13), which is then activated by cell membrane MMPs, namely MMP14 (also called MT1-MMP) and MMP2 (also called gelatinase A)<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref></sup>, the catalytic activity of MMP13 secreted by HGPS-iPSC SMCs was analyzed (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">19</xref>). The concentration of proMMP13 and active MMP13 increased approximately eight- and five-fold, respectively, in culture media of HGPS-iPSC SMCs cultured in flow conditions from day 0 to day 4. Moreover, the concentration of proMMP13 and active MMP13 in cell culture media collected from N-iPSC SMCs cultured in flow conditions for 4 days was more than fourfold lower than the one observed with HGPS-iPSC SMCs. Altogether, our results indicate that HGPS-iPSC SMCs cultured under flow conditions showed increased cell senescence, ECM activation, secretion, and cell adhesion pathways upregulation and dysregulation in the expression of MMP13.</p></sec><sec id=\"Sec6\"><title>MMP13 mediates HGPS-iPSC SMC loss under flow conditions</title><p id=\"Par13\">Next, we tested whether the chemical inhibition of MMPs could prevent HGPS-iPSC SMC detachment. For this purpose, we used Batimastat (BB-94)<sup><xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup>, a broad spectrum matrix metalloprotease inhibitor (IC50 = 33 nM for MMP13<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>), and a specific MMP13 inhibitor pyrimidine-4,6-dicarboxylic acid, bis-(4-fluoro-3-methyl-benzylamide) (IC50 = 8 nM)<sup><xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. Remarkably, both inhibitors significantly decreased the detachment of HGPS-iPSC SMCs cultured under arterial flow conditions (at least until day 12) (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4e</xref>), and this effect was much superior to that of lonafarnib (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">7d</xref>) or inhibition through the pyrophosphate calcification process<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref></sup> (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">11c</xref>). To confirm these results, HGPS-iPSC SMCs were subjected to siRNA knockdown of MMP13 and cultured under arterial flow conditions for 10 days (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4f, g</xref>). Our results show that the knockdown of MMP13 in SMCs increased the stability of HGPS-iPSC SMCs in flow culture conditions compared with non-treated cells. We also analyzed the effects of MMP13 and BB94 inhibition in HOZ mSMCs (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3e</xref>). Similar to what was observed with HGPS-iPSC SMCs, the detachment was significantly delayed when one of the inhibitors was used. To further demonstrate the importance of MMP13 in HGPS-iPSC SMC detachment, we enforced the expression of <italic>MMP13</italic> in somatic SMCs (hVSMCs) and cultured the modified cells in flow culture conditions (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">19</xref>). Notably, the number of cells observed at day 7 is lower than the one observed in WT cells indicating that some of the modified cells were lost during the flow culture conditions.</p><p id=\"Par14\">We then asked whether the modulation of MMP13 activity could affect progerin expression associated with the vulnerability of HGPS-iPSC SMCs to flow shear stress. Interestingly, chemical inhibition of MMP13 in HGPS-iPSC SMCs cultured for 7 days in flow conditions reduced the percentage of progerin-positive cells (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4h</xref>); however, it did not decrease progerin expression in cells with high levels of progerin, such as HGPS fibroblasts. In addition, the chemical inhibition of MMP13 did not reduce the activity of alkaline phosphatase in HGPS-iPSC SMCs cultured for 7 days in flow conditions (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4i</xref>). Overall, the results obtained after chemical and genetic inhibition, the increase of MMP13 after flow shear stress and the effect of HGPS-iPSC SMC-conditioned media on cell detachment, indicate that MMP13 mediates SMC loss.</p></sec><sec id=\"Sec7\"><title>Inhibition of MMP13 in <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice significantly increased the number of SMCs in aortic arch</title><p id=\"Par15\">To confirm the importance of MMP13 dysregulation in progeroid animal models, we quantified MMP13 in the plasma of <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> and WT mice (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>). The results showed that the levels of MMP13 were higher in mutant mice (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5b</xref>). Then, we asked whether the inhibition of MMP13 in <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice could decrease SMC loss. For this purpose, we generated double-mutant lines, <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>−/−</italic></sup> and <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/−</italic></sup> (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">20</xref>), and evaluated the heart rate and SMC loss in the aortic arch<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup> of these mice at week 10 (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5a</xref>). Heart rate was chosen as a measure of the overall health status of the HGPS model and the derived double-mutant lines, given that bradycardia was a clinical abnormality evidenced in both <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mouse as well as Zmpste 24<sup><italic>−</italic>/<italic>−</italic></sup> progeria mouse models<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref>,<xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. Both double-mutant mice showed higher heart rates (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5d</xref>) and numbers of SMCs (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5c, e</xref>) in the aortic arch than <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup>\n<italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice. Interestingly, <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>−/−</italic></sup> and <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/−</italic></sup> mice showed a lower number of progerin-positive cells in the aortic arch than non-mutated mice (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5c, f</xref>). In addition, <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/−</italic></sup> mice (but not <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>−/−</italic></sup> mice) showed an increase of the aortic media thickness being similar to the non-mutated mice (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6a</xref>), as confirmed by orcein staining. We performed proteomic analyses of aortic arches from mutated and non-mutated mice (<italic>n</italic> ≥ 5 mice per strain) using data independent acquisition mass spectrometry<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref>,<xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>. Principal component analysis based on 2260 proteins detected showed that the proteome profiles of aortic arches from <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/−</italic></sup> mice were more closely related to the profile of WT mice to that of <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup>\n<italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6c</xref>). From the 161 proteins differentially expressed between the mutant and WT mice aortic arches (<italic>q</italic> < 0.05 and abs(log<sub>2</sub> fold change) > 0.58), ~25% of the proteins had similar expression in <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/−</italic></sup> mice and WT mice (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6c</xref> and Supplementary Data <xref rid=\"MOESM7\" ref-type=\"media\">4</xref>).<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Proteins differentially expressed in the aortic arch at week 10 on wild-type and mutant (<italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>−/−</italic></sup> and <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/−</italic></sup>) mice.</title><p><bold>a</bold> Orcein-stained ascending aorta (elastic fibers stain in dark brown/black). Black arrow defines the internal elastic lamina while the white arrow defines the adventitial border. Images illustrate morphological changes rather than aortic media thickness differences. KiWT mice show less compact elastic lamellae and higher irregular profiles of the elastic lamellae (labeled with *) than the other mice. Scale bar is 50 µm. In graph, aortic media thickness was measured from the internal elastic lamina to the adventitial border. Black arrow defines the internal elastic lamina while the white arrow defines the adventitial border. Results are mean ± SEM, <italic>n</italic> = 3 animals, except for KiHt (four animals). * denotes statistical significance (<italic>p</italic> < 0.05). Statistical analyses were performed by one-way ANOVA followed by Newman–Keuls’s post test. <bold>b</bold> Principal component analysis (PCA) of proteome profiles obtained from aortic arches of wild-type (WtWt) and mutant (KiWt, KiHt, KiKo) mice. <bold>c</bold> Heatmap based on 161 protein groups differentially expressed between KiWt and WtWt mice, in aortic arch, at week 10 (<italic>q</italic> < 0.05 and abs(log<sub>2</sub> fold change) > 0.58). Progerin is a mutated protein and thus not identified by the mass spectrometry. MMP13 is a secreted protein and the levels in cells were not detectable by mass spectrometry. For comparison purposes, the protein fold changes of WtWt vs. KiHt and WtWt vs. KIKo were included in the heatmap. Blue color indicates proteins downregulated in KiWt, KiHt, or KiKo as compared with WtWt, whereas red color corresponds to proteins upregulated in KiWt, KiHt, or KiKo as compared with WtWt. <italic>n</italic> = 6 for KiWt and <italic>n</italic> = 5 for WtWt, KiHt, and KiKo; age: 10 weeks.</p></caption><graphic xlink:href=\"41467_2020_17901_Fig6_HTML\" id=\"d30e1807\"/></fig></p><p id=\"Par16\">Motivated by these results, we then tested a therapeutic approach to reduce SMC loss in <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup>\n<italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice. For this purpose, we used Batimastat because human safety has been previously demonstrated in clinical trials<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup>\n<italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice at week 5 were intraperitoneal (IP) injected five times a week (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7a</xref>). At week 10, Batimastat-treated <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup>\n<italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice had similar heart rates to non-treated animals (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7c</xref>); however, they showed higher SMCs in the aortic arch than non-treated mice, as confirmed by cell nuclei counts and verified by the increase levels of SMC markers determined by qRT-PCR analyses (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7b, d, e</xref>). No differences were observed between non-treated and Batimastat-treated mice regarding progerin accumulation in the aortic arch (Supplementary Figs. <xref rid=\"MOESM1\" ref-type=\"media\">20c</xref>). Overall, our data shows that the in vivo inhibition of MMP13 by genetic or chemical interventions yielded mice having significantly higher numbers of SMCs in the aortic arch.<fig id=\"Fig7\"><label>Fig. 7</label><caption><title>MMP treatment using BB94 significantly increases SMC number in aortic arch of <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice.</title><p><bold>a</bold> Schematic representation of the animal protocol. <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice (<italic>n</italic> = 8 for treatment group and control group; age: 5 weeks) were IP injected five times a week (30 mg/kg/day; 3 mg/mL in PBS). <bold>b</bold> Immunofluorescence analyses performed on mouse SMC for α-SMA showing higher number of SMCs in treated aortic arch. Cell nuclei were stained with DAPI. SMCs were stained for α-SMA. Scale bar is 100 µm. For BB94 treatment <italic>n</italic> = 5 animals. For placebo treatment <italic>n</italic> = 7 animals. <bold>c</bold> Heart rates in mice. Wild-type mice were not exposed to BB94. <italic>n</italic> = 3 for wild-type mice, <italic>n</italic> = 8 for placebo treatment group and <italic>n</italic> = 7 for BB94 treatment group. <bold>d</bold> Number of SMC nuclei in aortic arch per tissue area (mm<sup>2</sup>) in mice treated or not with BB94. For BB94 treatment, <italic>n</italic> > 6 images examined over five animals. For placebo treatment, <italic>n</italic> > 9 images examined over seven animals. <bold>e</bold> Expression of SMC genes in aortic arches of mice treated or not with BB94. Gene expression was normalized by the housekeeping gene <italic>GAPDH</italic>. <italic>n</italic> > 3 technical replicates over six animals. **, ***, **** denote statistical significance (<italic>p</italic> < 0.01, <italic>p</italic> < 0.001, <italic>p</italic> < 0.0001). Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test <bold>d</bold> and <bold>e</bold>.</p></caption><graphic xlink:href=\"41467_2020_17901_Fig7_HTML\" id=\"d30e1954\"/></fig></p></sec><sec id=\"Sec8\"><title>Activation of MMP13 is mediated by the activation of the glycocalyx</title><p id=\"Par17\">The glycocalyx is a surface layer of proteoglycans and glycosaminoglycans that are immobilized in the cell membrane. Glycocalyx components have been shown to be involved in flow shear stress sensing by SMCs<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. To identify the mechanism underlying the upregulation of MMP13 in HGPS-iPSC SMCs cultured under arterial flow, we analyzed glycocalyx gene mRNA transcripts (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8b</xref>). Interestingly, glycocalyx transcripts were upregulated in HGPS-iPSC SMCs cultured under flow conditions for 4 days (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8b</xref>). From these upregulated genes, syndecan 2 gene (<italic>SDC2</italic>), which encodes the transmembrane (type I) heparan sulfate proteoglycan, was also upregulated in hVSMCs or N-iPSC SMCs cultured for 4 days in flow conditions (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">21</xref>). Because not all the glycocalyx mRNA transcripts were upregulated in hVSMCs and N-iPSC SMCs, the results suggest that the composition of glycocalyx is likely different in these cells when compared with HGPS-iPSC SMCs. Next, we analyzed the expression of heparan sulfate at the protein level. In contrast to control cells, the expression of heparan sulfate increased when HGPS-iPSC SMCs were cultured under flow conditions (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8a</xref>). Importantly, the enzymatic cleavage of heparan sulfate by heparinase III (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">22</xref>) decreased MMP13 concentration in the cell culture media (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8c</xref>) and significantly decreased the detachment of HGPS-iPSC SMCs cultured under flow conditions (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8d</xref>). Moreover, the enzymatic cleavage of heparan sulfate slightly decreased alkaline phosphatase activity (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8e</xref>).<fig id=\"Fig8\"><label>Fig. 8</label><caption><title>MMP13 expression in SMCs is triggered by an increase in heparan sulfate.</title><p><bold>a</bold> Cells were cultured under flow conditions for 4 days and the expression of heparan sulfate was evaluated by immunofluorescence. Intensity of heparan sulfate was calculated in each picture and normalized by cell number. The normalized fluorescence intensity at day 4 was divided with the one at day 0. Scale bar is 10 μm. <italic>n</italic> > 4 images examined over six independent experiments. Statistical analyses were performed by one-way ANOVA followed by Newman–Keuls’s post test. <bold>b</bold> Gene expression of glycocalyx markers (<italic>SDC1</italic>: syndecan 1, <italic>SDC2</italic>: syndecan 2, <italic>SDC4</italic>: syndecan 4, <italic>GPC</italic>: glypican, <italic>PLC</italic>: perlecan), as evaluated by qRT-PCR, in HGPS-iPSC SMCs cultured under flow conditions. Gene expression was normalized by the housekeeping gene <italic>GAPDH</italic>, and the normalized gene expression at day 4 divided by day 0. <italic>n</italic> = 3 technical replicates from a pool of three independent experiments. <bold>c</bold> HGPS-iPSCs-SMC cultured under flow condition were treated or not with heparinase III and the number of cells per microfluidic area during culture was calculated and normalized by the number of cells present at day 2. <italic>n</italic> = 3 independent experiments. Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. <bold>d</bold> Quantification of MMP13 activity (cell culture media) by ELISA. Cells were analyzed at day 4 under flow. Fluorescence signal was normalized by cell number and then by control experimental group. <italic>n</italic> > 9 images examined over six independent experiments. Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. <bold>e</bold> Expression of alkaline phosphatase in HGPS-iPSCs SMC, normalized by cell number per mm<sup>2</sup>, in cells cultured 4 days under flow conditions. Cells were treated or not with heparinase III. <italic>n</italic> = 2 technical replicates over three independent experiments. Statistical analyses were performed by a two-tailed unpaired Student’s <italic>t</italic> test. In <bold>a</bold>–<bold>e</bold>, results are mean ± SEM. *, **, ***, **** denote statistical significance (<italic>p</italic> < 0.05, <italic>p</italic> < 0.01, <italic>p</italic> < 0.001, <italic>p</italic> < 0.0001). <bold>f</bold> Summary of the results.</p></caption><graphic xlink:href=\"41467_2020_17901_Fig8_HTML\" id=\"d30e2089\"/></fig></p><p id=\"Par18\">To further investigate a potential ECM target of MMP13 in SMCs, we monitored the expression of ECM components in hVSMCs, HUAECs, N-iPSC SMCs, and HGPS-iPSC SMCs. Our results indicate that hVSMCs express higher levels of mRNA that encode collagen 1A1, collagen 3A1, collagen 4A2, and collagen 6A3 than HUAECs (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">21c</xref>). It has been shown that MMP13 degrades very efficiently the native helix of all fibrillary collagens, including collagen type I<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>. Our proteomic results indicate that indeed collagen 1A1 is upregulated in HGPS-iPSC SMCs exposed to flow conditions (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">16b</xref>) and thus it may be a potential target for MMP13. Overall, our results indicate that activation of MMP13 is mediated, at least in part, by glycocalyx activation.</p></sec></sec><sec id=\"Sec9\" sec-type=\"discussion\"><title>Discussion</title><p id=\"Par19\">In this study, we developed a microfluidic chip formed by a monoculture or a co-culture of HGPS-SMCs (generated from iPSCs) with ECs to study the reason underlying HGPS-SMC vulnerability to flow shear stress. To generate the chip, we (i) developed a protocol to differentiate HGPS-iPSCs into functional HGPS-SMCs, (ii) demonstrated that HGPS-iPSC SMCs shared similar properties with other known progerin-expressing cells, (iii) confirmed that HGPS-iPSC SMCs were vulnerable to arterial flow shear stress, and (iv) validated the results in ex vivo SMCs isolated from <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice. Using the chip, we have identified MMP13 upregulation as an important mediator of HGPS-SMC vulnerability to flow shear stress and we confirmed MMP13’s role in vivo in <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8f</xref>). MMP13 is upregulated in a number of pathological states including atherosclerosis and rheumatoid arthritis<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref></sup>. The upregulation of MMP13 in HGPS-SMCs cultured under arterial flow conditions is in line with examples in the literature showing that enzymatic ECM remodeling is significantly altered in HGPS cells<sup><xref ref-type=\"bibr\" rid=\"CR39\">39</xref>–<xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>.</p><p id=\"Par20\">Multiple protocols have been described in the literature for the differentiation of iPSCs into SMCs, either via an intermediate progenitor stage or directed differentiation<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref>,<xref ref-type=\"bibr\" rid=\"CR42\">42</xref>–<xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>. These protocols are highly variable in terms of SMC differentiation efficiency, timescale, and functionality (nondividing contractile phenotype vs. proliferative phenotype, secretory profile), likely due to the choice of precursor population to derive the SMC subtypes, the chemical composition of the differentiation medium, as well as the choice of inductive SMC factors (e.g., PDGF-BB, TGF-β1, retinoic acid). Three previous studies have reported the differentiation of HGPS-iPSCs into SMCs<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref>,<xref ref-type=\"bibr\" rid=\"CR9\">9</xref>,<xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup> by direct differentiation<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup> or by using an intermediate progenitor (i.e., mesenchymal stem cells<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup> or CD34<sup>+</sup> cells<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>). In some cases, SMCs were not terminally differentiated (as confirmed by the expression of SMMHC)<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>, in others the percentage of SMCs was relatively low (i.e., only 50–60% of the differentiated cells showed specific SMC markers including α-SMA, calponin, and SMMHC)<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref></sup> and no indication of SMC functionality<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup> (e.g., contractility, intracellular accumulation of calcium after exposure to vasoactive agents) was reported. In the present study, we showed that the differentiation of HGPS-iPSCs induces the activation of the NOTCH signaling pathway, a hallmark of progerin-expressing cells<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. This is observed in the CD34<sup>+</sup> progenitor cells and after their differentiation into SMCs. The CD34<sup>+</sup> cells have been reported to express KDR and CD31<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup> and, thus, are likely of lateral plate mesoderm origin<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref>,<xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>. Importantly, the differentiated cells express high levels of all the SMC markers analyzed (α-SMA, calponin and SMMHC), are contractile in response to the muscarinic receptor agonist, carbachol, as observed in typical human aortic SMCs, and, when matured in culture for ~30 days, they express progerin. Therefore, our differentiation protocol compares favorably to other protocols in term of SMC yield and functionality. Interestingly, HGPS-iPSC SMCs express lower levels of calponin than in N-iPSC SMCs but the reason and possible implications behind this phenotypic difference remain to be determined. Nevertheless, most of the HGPS-iPSC SMCs expressed calponin at the protein level, both at the induction and maturation steps (Supplementary Figs. <xref rid=\"MOESM1\" ref-type=\"media\">3</xref>, <xref rid=\"MOESM1\" ref-type=\"media\">4</xref>). A previous study has reported heterogeneous sized calponin 1-staining inclusion bodies in the cytoplasm of HGPS-SMCs<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>; however, such structures were not observed in the current study.</p><p id=\"Par21\">It has been reported that in WT animals the aorta was one of the tissues with the highest expression of lamin A, while in progeroid animals the aorta was the first place where progerin was detected<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. This explains the highest susceptibility of HGPS-SMCs located in the aorta to biomechanical forces. It has been reported that mouse SMCs overexpressing progerin exposed to biomechanical forces detach from the culture vessel after substrate stretching and die<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Yet, the mechanism of SMC detachment is still poorly understood. Our study indicates that MMP13 mediates SMC detachment as chemical or genetic inhibition of MMP13 reduces significantly SMC loss. In addition, we found that the accumulation of progerin is a mediator and not the cause of SMC detachment because HGPS fibroblasts accumulate high levels of progerin and do not detach in flow conditions. Yet, both inhibition of progerin by morpholinos and the knockout of the HGPS mutant allele in HGPS-SMCs decreased or prevented SMC detachment in flow culture conditions.</p><p id=\"Par22\">Although <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/−</italic></sup> and <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>−/−</italic></sup> mice showed similar amelioration of SMCs loss in the aortic arch, our proteomic analyses in the same tissue showed that <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/−</italic></sup> mice had a closer protein profile to WT than <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>−/−</italic></sup> mice. This was consistent with the media thickness size, which was more similar between WT mice and <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup>\n<italic>Mmp13</italic><sup><italic>+/−</italic></sup> than to <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup>\n<italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice. Previous studies have shown that <italic>Mmp13</italic><sup><italic>−/−</italic></sup> mice had defects in vascularization<sup><xref ref-type=\"bibr\" rid=\"CR46\">46</xref></sup> and thus the full deficiency of MMP13 in the aortic arch might not be desirable to establish a phenotype closer to the normality.</p><p id=\"Par23\">The accumulation of proteoglycans in Progeria mouse models<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref></sup> as well as in atherosclerotic lesions in HGPS individuals<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup> has been demonstrated. According to our results, the upregulation of MMP13 in HGPS-SMCs under flow conditions is mediated by the upregulation of glycocalyx components, which have been previously implicated as flow shear stress sensors<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>. The inhibition of components of glycocalyx by enzymatic treatment decreases significantly the MMP13 levels, the osteogenic program of SMCs and SMCs detachment. Although the connection between MMP13 and glycocalyx has been shown previously for non-disease SMCs, we show here that the accumulation of glycocalyx is responsible for the MMP13 expression under shear stress conditions, which subsequently leads to the loss of HGPS-SMCs. It is possible that the activation of MMP13 expression triggered by an upregulation of glycocalyx is mediated by the phosphorylation of ERK and FAK and the activation of c-Jun signaling pathway<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup> or mediated via NOTCH signaling pathway<sup><xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. Our in vivo results indicated that the expression of heparan sulfate proteoglycans in the aortic arches at week 10 on <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice was not statistically different from the expression profile found in WT mice. It is possible that further time is needed to see this upregulation as seen in other progeroid animal models<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup> or in HGPS individuals<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Since the upregulation of heparan sulfate was not observed in <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice, it is not surprising that we could not observe a statistical decrease in heparan sulfate in <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>−/−</italic></sup> mice.</p><p id=\"Par24\">The in vivo treatment results presented here using the MMP inhibitor Batimastat open possibilities for the treatment of HGPS and vascular aging<sup><xref ref-type=\"bibr\" rid=\"CR49\">49</xref>,<xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>. Batimastat acts as an inhibitor of metalloproteinase activity by binding the zinc ion in the active site of MMPs. Batimastat has been used previously for the treatment of human cancer (e.g., malignant ascites<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup> and malignant pleural effusions<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>) with demonstrated results and few side-effects in phase I/II clinical trials. Therefore, the current study proposes Batimastat as a drug to be considered for future Progeria trials. It should be noted that most of the compounds identified so far in preclinical tests to treat Progeria have been focused: (i) in the reduction of progerin quantities, by either reducing its production or increasing its degradation; (ii) in the reduction of progerin toxicity by targeting its aberrant prenylation: or (iii) in the identification of compounds capable of restoring pathological phenotypes downstream of progerin accumulation. Although these treatments showed encouraging results in preclinical studies and, in some cases in clinical trials, they do not address SMC loss over time. The administration of a drug that prevents SMC loss in early stages of disease combined with drugs that further reduce accumulation of progerin and progerin toxicity could be of added value to extend the lives of HGPS individuals.</p><p id=\"Par25\">Future studies should address the effect of SMC preservation in large vessels in the lifespan of the animals. It is possible that the prevention of SMC loss from the large arteries might be insufficient to lead to a significant increase in animal lifespan. Evidence collected at week 12<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup> (before the <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> died of progeria disease) in a therapy that ameliorated SMC loss showed no significant alterations in terms of body weight (which is correlated with lifespan<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>). Our study performed for 10 weeks showed also no significant changes in body weight (Supplementary Fig. <xref rid=\"MOESM1\" ref-type=\"media\">20d</xref>) between <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/−</italic></sup> mice and <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup><italic>Mmp13</italic><sup><italic>+/+</italic></sup> mice. Therefore, it is possible that therapies which ameliorate SMC loss should be combined with therapies that further reduce the level of progerin in cells of the major organs, in particular the heart, which seems to present electrical defects<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. Another issue that deserves further investigation is the relationship between MMP13 and progerin. Both in vitro and in vivo results indicate that the silencing of MMP13 leads to a significant reduction of progerin in SMCs and the reason for this pattern is presently not known. Overall, our study demonstrates that the control of MMP13 expression decreases the vulnerability of SMCs in large vessels and this strategy may be of potential value to reduce the impact of the disease in Progeria individuals.</p></sec><sec id=\"Sec10\"><title>Methods</title><sec id=\"Sec11\"><title>iPSCs culture and differentiation</title><p id=\"Par26\">iPSCs were generated from HGPS skin fibroblasts provided by Coriell Institute and characterized according to Nissan et al.<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. iPSCs were derived using the Yamanaka’s original method with OCT4, KLF4, SOX2, c-MYC, transferred using retroviral vectors. All HGPS cells were obtained from Coriell Institute for Medical Research, which in turn were collected under Institutional Review Board approval and individual informed consent (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.coriell.org/0/Sections/Support/NIA/Model.aspx?PgId=351\">https://www.coriell.org/0/Sections/Support/NIA/Model.aspx?PgId=351</ext-link>). HGPS-iPSCs clone 1 (passages 43-51); HGPS-iPSCs clone 2 (passages 35-42), and N-iPSCs (passages 30-35) were maintained on mitotically inactivated mouse embryonic fibroblast (MEF) feeder layer, according to Ferreira et al.<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. Culture medium for the present work consisted of 80% KO-DMEM (Life Technologies), 0.5% L-glutamine (Life Technologies), 0.2% β-mercaptoethanol (Sigma), 1% nonessential amino acids (Invitrogen), and penicillin-streptomycin (50 U/mL:50 mg/mL) (Lonza), supplemented with 20% KnockOut™ Serum Replacement (Gibco<sup>®</sup>) and 10 ng/mL of b-FGF (Peprotech). Colonies were expanded by routine passage every 3/4 days with 1-mg/ml collagenase type IV (Life Technologies). To induce EBs formation, the iPSCs were treated with collagenase IV (1 mg/mL, Gibco) for 1 h and then transferred (2:1) to low attachment plates (Corning) containing 10 mL of differentiation medium (80% KO-DMEM (Life Technologies), 20% fetal bovine serum (FBS, Invitrogen), 0.5% L-glutamine (Life Technologies), 0.2% β-mercaptoethanol (Sigma), 1% nonessential amino acids (Invitrogen), and penicillin-streptomycin (50 U/mL:50 mg/mL) (Lonza)). EBs were cultured for 10 days at 37 °C, 5% CO<sub>2</sub> in a humidified atmosphere, with media changes every 2 days. CD34<sup>+</sup> cells were isolated from EBs at day 10 using MACS (Miltenyi Biotec). The percentage of CD34<sup>+</sup> cells in EBs was between 0.4 and 1.5%. Isolated cells were grown on 24-well plates (~3 × 10<sup>4</sup> cells/cm<sup>2</sup>) coated with 0.1% gelatin in the presence of EGM-2 (Lonza) supplemented with PDGF<sub>BB</sub> (50 ng/mL, Prepotech). After four passages, the medium was replaced by Smooth Muscle Growth Medium-2 (SmGM-2) (Lonza CC-3182) (maturation medium), for additional four passages. hVSMCs (Lonza) were used as controls for the differentiation studies. Cell cultures were maintained at 37 °C, 5% CO<sub>2</sub> in a humidified atmosphere, with media changed every 2 days. A step-by-step protocol can be found at Protocol Exchange<sup><xref ref-type=\"bibr\" rid=\"CR52\">52</xref></sup>.</p></sec><sec id=\"Sec12\"><title>Cell culture under arterial flow conditions</title><p id=\"Par27\">A suspension of HGPS-iPSC SMCs (clone 1), HGPS-iPSC SMCs (clone 2), N-iPSC SMCs, hVSMCs, or HGPS fibroblasts between 5 × 10<sup>4</sup> and 1.3 × 10<sup>5</sup> cells/cm<sup>2</sup> was applied to the entry port of an IBIDI channel (µ-Slide I <sup>0,4</sup> Luer, or µ-Slide VI <sup>0,4</sup> Luer, IBIDI) and allowed to flow inside by capillary force. After 4 h, a confluent cell layer was formed, which was then perfused with SmGM-2 medium or fibroblasts medium (DMEM supplemented with FBS (20%, v/v, Gibco), sodium pyruvate (Sigma, 1 mM) and penicillin-streptomycin (50 U/mL:50 mg/mL)) at physiological flow rate (20 dyne/cm<sup>2</sup>). Unless specified, all tests were performed at days 0 and 4 on flow culture conditions. Cell number and cell clumps were determined on slides stained with DAPI (20×) and normalized by image area (0.3524 mm<sup>2</sup>). Cell clumps areas were evaluated by ImageJ software.</p></sec><sec id=\"Sec13\"><title>MMP activity</title><p id=\"Par28\">MMP activity was quantified on cell extracts by a fluorometric red assay kit (Abcam). Cell extracts were obtained by incubating the cells with Triton X-100 (0.5%, v/v, in PBS, Sigma) for ~15 min, the cells were centrifuged and the supernatant collected. Part of cell extract (25 µL) was added to 4-aminophenylmercuric acetate (25 µL, 2 mM) and incubated for 40 min at 37 °C. Then, a MMP red substrate (50 µL) was added to the mixture and the fluorescence intensity measured in a fluorimeter (Ex/Em = 540/590 nm) after 1 h, at room temperature. An ELISA kit was used to quantify the expression of MMP13 protein. Cell culture media collected from different experiments and plasma from WtWt and KiWt mice was used for MMP13 quantification (MMP13 human ELISA kit from Abcam and Mmp13 mouse ELISA kit from USCN) according to manufacture recommendation. Briefly, standard or sample (100 µL) was added to each well and incubated for 1 h at 37 °C. Then, solutions were aspirated and detection reagent A (100 µL) was added and incubated for 1 h at 37 °C. After washing three times, detection reagent B (100 µL) was added, incubated 30 min at 37 °C and washed five times. Substrate solution (90 µL) was then added and left to incubate for 10–20 min at 37 °C. Finally, stop solution was added to the wells (50 µL) and the absorbance of the solution monitored at 450 nm.</p></sec><sec id=\"Sec14\"><title>Glycocalyx analyses</title><p id=\"Par29\">To quantify the intensity of heparan sulfate, cells were stained with heparan sulfate (1:50 for staining, 10E4 Epitope, USBiological) as described in supplementary information. ImageJ software was used to quantify the overall intensity of each image, which was then normalized for cell number. Heparinase III from Flavobacterium heparinum (Sigma), was used for the enzymatical degradation of heparan sulfate. Briefly, HGPS-iPSC-SMCs cultured under flow condition during 4 days were subjected to heparinase III treatment (0.5 U/ml for 30 min at 37 °C), and the number of cells per microfluidic area during culture was calculated.</p></sec><sec id=\"Sec15\"><title>Treatment of <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice with Batimastat</title><p id=\"Par30\">Sixteen <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice (male and female) were used. After sex and body weight randomization, animals were allocated in different groups and treated with vehicle (eight <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> control mice) or BB94 inhibitor (eight <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice treated with Batimastat in vehicle solution). IP injections were used to administrate 30 mg/kg/day of BB94 at 3 mg/mL in PBS containing 0.01% Tween 80. The treatment was administered five times per week during 6 weeks (from week 5 to week 10). The treatment duration was reduced from 10 to 6 weeks due to intra-abdominal accumulation of BB94 (precipitate). At the end of week 10 the mice were sacrificed, and the selected parameters were evaluated.</p></sec><sec id=\"Sec16\"><title>Double mutant generation and heart rate monitoring</title><p id=\"Par31\"><italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup> mice present infertility as described by Osorio and colleagues, therefore the <italic>Lmna</italic><sup><italic>G609G/G609G</italic></sup>\n<italic>Mmp13</italic><sup><italic>−/−</italic></sup> mice (KiKO) were generated from <italic>Lmna</italic><sup><italic>G609G/+</italic></sup> and <italic>Mmp13</italic><sup><italic>−/+</italic></sup> heterozygous (in a C57BL/6 background) as our colony founders (F0). The offspring presenting the <italic>Lmna</italic><sup><italic>G609G/+</italic></sup>\n<italic>Mmp13</italic><sup><italic>−/+</italic></sup> (F1) were used for further backcrossing to generate the Progeria double mutants (KiKO) and Progeria control (KiWT) genotypes used in the present study. All mice were bred in-house in ventilated cages in a temperature and humidity-controlled room with a 12-h light/dark cycle. The founder <italic>Lmna</italic><sup><italic>G609G/+</italic></sup> mice were a kind gift from Dr Lopez-Otin<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>.</p><p id=\"Par32\">Genotyping analyses were performed to select those mice carrying the <italic>Lmna</italic><sup><italic>G609G</italic></sup> mutation in homozygosis and the MMP13 deficiency or WT genes. Briefly, DNA was obtained from tails using the PureLink<sup>®</sup> Genomic DNA Mini Kit (Invitrogen) and DNA yields used for the PCR reaction using the Platinum<sup>®</sup>Taq DNA Polymerase (Invitrogen) and a combination of custom-designed oligonucleotides for the amplification of the <italic>Lmna</italic> and <italic>Mmp13</italic> genes. PCR products were run in agarose gels with RedSafe Nucleic Acid Staining Solution (Labotaq) for detecting the amplified Lmna DNA fragments (G609G allele at 240 bp and WT at 100 bp) and Mmp13 fragments (KO at 1485 bp and WT at 1300 bp).</p><p id=\"Par33\">For heart rate monitoring mice were anesthetized with isoflurane (5% induction and 2% maintenance in oxygen) and a mouse paw pulse sensor (Kent Scientific Corporation) placed in the hindlimb paws until stable heart beats were detected and recorded by the PhysioSuiteTM noninvasive monitoring system (Kent Scientific Corporation). During the procedure and until mice recovered from anesthesia body temperature was controlled with a heating pad.</p><p id=\"Par34\">All procedures were approved by the Ethics Committee of Animal Experimentation (CCEA 57/16) of the Vall d'Hebron Research Institute and were conducted in compliance with Spanish legislation and in accordance with the Directives of the European Union.</p></sec><sec id=\"Sec17\"><title>Proteomic analysis of aortic arches from WT and mutant mice</title><p id=\"Par35\">Formalin-fixed and paraffin-embedded slices of aortic arch (4 um) were processed for mass spectrometry analysis as described in the Supplementary Material Information and according to Heinze et al.<sup><xref ref-type=\"bibr\" rid=\"CR33\">33</xref></sup>. The obtained peptides were analyzed using Data Independent Acquisition<sup><xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup> on an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher) connected online with a Waters nanoAcquity UPLC system (details regarding instrument settings and data acquisition parameters can be found in the Supplementary Information). Spectral library generation, data processing, and differential expression analysis were performed in Spectronaut 11 (Biognosys AG) using default settings. PCA analysis based on the protein report table exported from Spectronaut was performed using R version 3.5.0. Data are available via ProteomeXchange with identifier PXD011652.</p></sec><sec id=\"Sec18\"><title>Statistical analysis</title><p id=\"Par36\">Statistical analyses were performed with GraphPad Prism software. Statistical significance was analyzed using two-tailed unpaired Student’s <italic>t</italic> test between two different groups. For multiple comparisons, a one-way ANOVA analysis followed by Newman–Keuls post test was performed. Results were considered significant when <italic>p</italic> < 0.05. Data are shown as mean ± SEM unless other specification.</p></sec><sec id=\"Sec19\"><title>Reporting summary</title><p id=\"Par37\">Further information on research design is available in the <xref rid=\"MOESM9\" ref-type=\"media\">Nature Research Reporting Summary</xref> linked to this article.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec20\"><supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41467_2020_17901_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41467_2020_17901_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"41467_2020_17901_MOESM3_ESM.pdf\"><caption><p>Description of Additional Supplementary Files</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM4\"><media xlink:href=\"41467_2020_17901_MOESM4_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM5\"><media xlink:href=\"41467_2020_17901_MOESM5_ESM.xlsx\"><caption><p>Supplementary Data 2</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM6\"><media xlink:href=\"41467_2020_17901_MOESM6_ESM.xlsx\"><caption><p>Supplementary Data 3</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM7\"><media xlink:href=\"41467_2020_17901_MOESM7_ESM.xlsx\"><caption><p>Supplementary Data 4</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM8\"><media xlink:href=\"41467_2020_17901_MOESM8_ESM.xlsx\"><caption><p>Supplementary Data 5</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM9\"><media xlink:href=\"41467_2020_17901_MOESM9_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media></supplementary-material></sec></sec></body><back><app-group><app id=\"App1\"><sec id=\"Sec21\"><title>Source data</title><p id=\"Par40\"><media position=\"anchor\" xlink:href=\"41467_2020_17901_MOESM10_ESM.xlsx\" id=\"MOESM10\"><caption><p>Source Data</p></caption></media></p></sec></app></app-group><fn-group><fn><p><bold>Peer review information</bold>\n<italic>Nature Communications</italic> thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p><bold>Supplementary information</bold> is available for this paper at 10.1038/s41467-020-17901-2.</p></sec><ack><title>Acknowledgements</title><p>This work was funded by FEDER through the Program COMPETE and by Portuguese fund through FCT in context of the projects EXPL/BIM-MED/2267/2013 and POCI-01-0145-FEDER-029229, as well as the European project ERAatUC (ref. 669088). PRP wishes to thank FCT for a BD fellowship (SFRH/BD/71042/2010). AR is supported by the Miguel Servet research contract CPII15/00003 from Instituto de Salud Carlos III, Spain. The FLI is a member of the Leibniz Association and is financially supported by the Federal Government of Germany and the State of Thuringia. The authors gratefully acknowledge support from the FLI proteomics core facility. The authors would like to thank Dr Carlos Lopez-Otín for providing the <italic>Lmna</italic><sup><italic>G609G/+</italic></sup> mice.</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>P.R.P. and L.F. designed the study, did the literature search and wrote the manuscript. P.R.P. conducted the study. P.R.P., L.E., and H.V. collected the in vitro data. P.R.P., G.C., A.R., A.S.-G., K.H., C.N., and N.L. conducted and analyzed in vivo data. D.T. and J.P.M. processed and analyzed raw genomic data. A.-L.E. and X.N. generated iPSCs from Progeria fibroblasts, provided expertise in the Progeria biology and in the interpretation of the results. L.M., A.B., R.L.S., P.R.P., L.F. and J.C.S. generated the isogenic cell line. T.C. performed pathological evaluation of the tissues. D.S. performed proteomics experiments and analyzed the data with the support of A.O.</p></notes><notes notes-type=\"data-availability\"><title>Data availability</title><p>The microarray datasets generated during and/or analyzed during the current study are available in the GEO/NCBI (GEO accession: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108368\">GSE108368</ext-link>). The mass spectrometry proteomics data that support the findings of this study have been deposited in the ProteomeXchange Consortium via the PRIDE<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup> partner repository with the dataset identifier <ext-link ext-link-type=\"uri\" xlink:href=\"http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD011652\">PXD011652</ext-link>. The mass spectrometry proteomics (Tandem Mass Tags—TMT) data have been deposited to the ProteomeXchange Consortium via the PRIDE<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup> partner repository with the dataset identifier <ext-link ext-link-type=\"uri\" xlink:href=\"http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD019316\">PXD019316</ext-link>. Databases used: Uniprot database (Swissprot entry only, release 2016_01, 16,747 entries); CellAge database (<ext-link ext-link-type=\"uri\" xlink:href=\"http://genomics.senescence.info/cells/\">http://genomics.senescence.info/cells/</ext-link>). The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. All the figures have associated source data. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807803</article-id><article-id pub-id-type=\"pmc\">PMC7431910</article-id><article-id pub-id-type=\"publisher-id\">69786</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-69786-2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Actinobacteria from Antarctica as a source for anticancer discovery</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Silva</surname><given-names>Leonardo Jose</given-names></name><xref ref-type=\"aff\" rid=\"Aff1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Crevelin</surname><given-names>Eduardo José</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Souza</surname><given-names>Danilo Tosta</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\"><name><surname>Lacerda-Júnior</surname><given-names>Gileno Vieira</given-names></name><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>de Oliveira</surname><given-names>Valeria Maia</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ruiz</surname><given-names>Ana Lucia Tasca Gois</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Rosa</surname><given-names>Luiz Henrique</given-names></name><xref ref-type=\"aff\" rid=\"Aff6\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Moraes</surname><given-names>Luiz Alberto Beraldo</given-names></name><xref ref-type=\"aff\" rid=\"Aff2\">2</xref></contrib><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Melo</surname><given-names>Itamar Soares</given-names></name><address><email>itamar.melo@embrapa.br</email></address><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.11899.38</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1937 0722</institution-id><institution>College of Agriculture “Luiz de Queiroz”, </institution><institution>University of São Paulo (USP), </institution></institution-wrap>Piracicaba, SP Brazil </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.11899.38</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1937 0722</institution-id><institution>Laboratory of Mass Spectrometry Applied To Natural Products Chemistry, Department of Chemistry, Faculty of Philosophy, Sciences and Letters of Ribeirão Preto (FFCLRP), </institution><institution>University of São Paulo (USP), </institution></institution-wrap>Ribeirão Preto, SP Brazil </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.420953.9</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0144 2976</institution-id><institution>Laboratory of Environmental Microbiology, </institution><institution>Brazilian Agricultural Research Corporation (EMBRAPA) – Embrapa Environment, </institution></institution-wrap>Jaguariúna, SP Brazil </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411087.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0723 2494</institution-id><institution>Microbial Resourses Division, Research Center for Chemistry, Biology and Agriculture (CPQBA), </institution><institution>University of Campinas (UNICAMP), </institution></institution-wrap>Campinas, SP Brazil </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411087.b</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0723 2494</institution-id><institution>Faculty of Pharmaceutical Sciences, </institution><institution>University of Campinas (UNICAMP), </institution></institution-wrap>Campinas, SP Brazil </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.8430.f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2181 4888</institution-id><institution>Department of Microbiology, </institution><institution>Biological Sciences Institute – Federal University of Minas Gerais (UFMG), </institution></institution-wrap>Belo Horizonte, MG Brazil </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13870</elocation-id><history><date date-type=\"received\"><day>25</day><month>1</month><year>2020</year></date><date date-type=\"accepted\"><day>3</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Although many advances have been achieved to treat aggressive tumours, cancer remains a leading cause of death and a public health problem worldwide. Among the main approaches for the discovery of new bioactive agents, the prospect of microbial secondary metabolites represents an effective source for the development of drug leads. In this study, we investigated the actinobacterial diversity associated with an endemic Antarctic species, <italic>Deschampsia antarctica</italic>, by integrated culture-dependent and culture-independent methods and acknowledged this niche as a reservoir of bioactive strains for the production of antitumour compounds. The 16S rRNA-based analysis showed the predominance of the Actinomycetales order, a well-known group of bioactive metabolite producers belonging to the Actinobacteria phylum. Cultivation techniques were applied, and 72 psychrotolerant Actinobacteria strains belonging to the genera <italic>Actinoplanes</italic>, <italic>Arthrobacter</italic>, <italic>Kribbella</italic>, <italic>Mycobacterium</italic>, <italic>Nocardia</italic>, <italic>Pilimelia</italic>, <italic>Pseudarthrobacter</italic>, <italic>Rhodococcus</italic>, <italic>Streptacidiphilus</italic>, <italic>Streptomyces</italic> and <italic>Tsukamurella</italic> were identified. The secondary metabolites were screened, and 17 isolates were identified as promising antitumour compound producers. However, the bio-guided assay showed a pronounced antiproliferative activity for the crude extracts of <italic>Streptomyces</italic> sp. CMAA 1527 and <italic>Streptomyces</italic> sp. CMAA 1653. The TGI and LC<sub>50</sub> values revealed the potential of these natural products to control the proliferation of breast (MCF-7), glioblastoma (U251), lung/non-small (NCI-H460) and kidney (786-0) human cancer cell lines. Cinerubin B and actinomycin V were the predominant compounds identified in <italic>Streptomyces</italic> sp. CMAA 1527 and <italic>Streptomyces</italic> sp. CMAA 1653, respectively. Our results suggest that the rhizosphere of <italic>D. antarctica</italic> represents a prominent reservoir of bioactive actinobacteria strains and reveals it as an important environment for potential antitumour agents.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Biotechnology</kwd><kwd>Sequencing</kwd><kwd>High-throughput screening</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">In general, microbial natural products have higher selectivity and bioactivity indices when compared to combinatorial chemistry libraries<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. The search for bioactive molecules from microorganisms has received growing attention in recent decades<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>–<xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Part of this is due to the specific action of microbial metabolites as substrates for many transport systems, which allows the release of compounds into intracellular sites<sup><xref ref-type=\"bibr\" rid=\"CR6\">6</xref>,<xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup>. However, despite the extensive search for microbial metabolites to address a great deal of clinical threat, the discovery rate of new and effective drug compounds has been declining every year, which is due, in part, to the use of traditional techniques of chemical isolation and the investigation of microorganisms in extensively studied environments<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR8\">8</xref>,<xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>.\n</p><p id=\"Par3\">Thus, bioprospecting studies have advanced into auspicious ecological niches, which tend to favour the prevalence of exotic metabolisms and endemic species<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref>,<xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. In this context, the Antarctic continent has been considered one of the most promising bioprospecting ecosystems and a valuable source to isolate new and diverse microorganisms due to its environmental peculiarities, such as extremely low temperatures and precipitation, high levels of UV radiation, ocean flooding, high salinity rates, and large unexplored areas<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>–<xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup>.</p><p id=\"Par4\">Although many organisms are adapted to the harshest environmental conditions of the Antarctic, the species <italic>Deschampsia antarctica</italic> Desv. (Poaceae) and <italic>Colobanthus quitensis</italic> (Kunth) Bartl. (Caryophyllaceae) have received more attention because they represent unique vascular plants in the whole Antarctic continent and play an important ecological role as a shelter for a plethora of microbes with wide metabolic capacities<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>,<xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>. However, the reduced dispersion of <italic>D. antarctica</italic> Desv. in few Antarctic sites has attracted special interest in ecological and bioprospecting investigations<sup><xref ref-type=\"bibr\" rid=\"CR17\">17</xref>,<xref ref-type=\"bibr\" rid=\"CR19\">19</xref>,<xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>.</p><p id=\"Par5\">In this way, several studies have been carried out to characterize the microbiome associated with Antarctic hairgrass (<italic>D. antarctica</italic>), as well as to understand the adaptive mechanisms of this species to survive the harsh environmental conditions of Antarctica<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref>–<xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>. Even so, studies exploring the potential of this microbiome to access novel cultivable strains and bioactive compounds are still scarce.</p><p id=\"Par6\">Recently, Sivalingam and colleagues (2019)<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup> reported the prominence of Actinobacteria, especially species of <italic>Streptomyces,</italic> derived from extreme sources as an extraordinary reservoir of novel biosynthetic gene clusters with potential for developing anticancer drugs. Indeed, actinomycetes have been recognized as a prolific source of natural products with a myriad of bioactivities, including phytotoxic, antimicrobial, insecticidal and mainly antiproliferative and antitumour activities<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref>–<xref ref-type=\"bibr\" rid=\"CR28\">28</xref></sup>. Compound classes belonging to the peptides, polyketides, macrolides, quinolones and others represent these bioactivities<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref>–<xref ref-type=\"bibr\" rid=\"CR32\">32</xref></sup>. Accordingly, the isolation and identification of actinomycetes have become a fruitful area of research in recent years that has subsequently led to the identification of novel Actinomycete species that should be exploited to unveil possible biosynthetic pathways and discover new bioactive natural products<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref>,<xref ref-type=\"bibr\" rid=\"CR33\">33</xref>–<xref ref-type=\"bibr\" rid=\"CR35\">35</xref></sup>. Thus, the rhizosphere bacterial composition of Antarctic hairgrass (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>) was investigated by the 16S rRNA gene sequencing and the cultivable associated actinobacteria were evaluated as potential sources of antitumour compounds.<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Satellite images (Google Earth Pro, 2019) and study sites. (<bold>A</bold>) Antarctic Continent; (<bold>B</bold>) Antarctic Peninsula; (<bold>C</bold>) South Shetland Islands; D-E–F) King George Island [sampling sites: Torre Meteoro Direito—TMD, Torre Meteoro Esquerdo—TME and Morro da Cruz—MDC (green, red and yellow location icon, respectively)]; (<bold>G</bold>) <italic>Deschampsia antarctica</italic> Desv.</p></caption><graphic xlink:href=\"41598_2020_69786_Fig1_HTML\" id=\"MO1\"/></fig></p></sec><sec id=\"Sec2\"><title>Results</title><sec id=\"Sec3\"><title>Bacterial community associated with <italic>Deschampsia antarctica</italic> rhizosphere</title><p id=\"Par7\">In this study, a total of 249.176 high-quality 16S rRNA reads were recovered after the QC filter step (Table <xref rid=\"MOESM1\" ref-type=\"media\">S1</xref>—Supplementary Material). Sequences were assigned to 229.631 OTUs, and the rarefaction curves reached the plateau phases, confirming the adequate sequencing depth of the samples. The bacterial community structure revealed by the higher-ranking taxa classification was congruent among the different rhizosphere samples (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>).<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Taxonomic composition of <italic>Deschampsia antarctica</italic> rhizosphere collected across three sites revealed by 16S rRNA marker sequencing. (<bold>A</bold>) Bacteria phylum; (<bold>B</bold>) Order-level taxonomic affiliation of OTUs filtered from Actinobacteria phylum (Caporaso et al., 2011; Caporaso et al., 2010)<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref>,<xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup>.</p></caption><graphic xlink:href=\"41598_2020_69786_Fig2_HTML\" id=\"MO2\"/></fig></p><p id=\"Par8\">The average taxonomic signatures of all samples from sites Morro da Cruz, Torre Meteoro Direito e Torre Meteoro Esquerdo (MDC, TMD and TME, respectively) showed that Actinobacteria (34%) was the most abundant phylum, followed by Chloroflexi (21%) and Proteobacteria (20%) (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>a). Members of the phyla Acidobacteria and Verrucomicrobia were detected at lower frequencies (< 5%). The ‘unclassified’ sequences contributed to 7% of the dataset, showing that the understanding of the plant-microbiome interaction is still limited and that the rhizosphere of <italic>D. antarctica</italic> may harbour unknown taxonomic groups to be further explored. A more detailed taxonomic analysis showed that the phylum Actinobacteria was mainly represented by members of the order Actinomycetales (86%), followed by Acidimicrobiales (8%) and Solirubrobacteriales (3%) (Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>b).</p></sec><sec id=\"Sec4\"><title>Functional prediction of the bacterial communities</title><p id=\"Par9\">The predictive functional profile of the entire rhizosphere-associated bacterial community resulted in more than 6,500 protein-coding genes, which could be assigned to 329 KEGG Orthology functional categories (KOs). To determine a specific functional contribution, Actinobacteria-affiliated 16S rRNA reads were extracted and compared with the predictive functional traits of the total bacterial community. Principal component analysis (PCA) showed a clear separation between the predicted functional profiles of the total bacterial community and Actinobacteria groups (Figure <xref rid=\"MOESM1\" ref-type=\"media\">S1</xref>—Supplementary Material). Nineteen KOs were significantly over-represented (p-value < 0.05) between the two datasets. The pathways carbohydrate metabolism, amino acid metabolism, metabolism of terpenoids and polyketides, xenobiotic biodegradation and metabolism, DNA replication and repair, membrane transporter, and biosynthesis of other secondary metabolites were significantly enriched (p < 0.05) in the Actinobacteria dataset when compared to the total bacterial community (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>).<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Extended error bar plot showing the predicted KO-level 2 categories significantly different between total community and Actinobacteria members from <italic>Deschampsia antarctica</italic> Desv. rhizosphere (n = 9). Only P < 0.05 is displayed. (Kanehisa; Goto, 2000; Kanehisa et al., 2019; Kanehisa, 2019; Langille et al., 2013)<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref>–<xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup>.</p></caption><graphic xlink:href=\"41598_2020_69786_Fig3_HTML\" id=\"MO3\"/></fig></p></sec><sec id=\"Sec5\"><title>Isolating, culturing and identifying actinobacteria</title><p id=\"Par10\">A total of 72 actinomycetes were isolated with the media and procedures employed in this study. The isolates belonged to the genera <italic>Actinoplanes</italic>, <italic>Arthrobacter</italic>, <italic>Kribbella</italic>, <italic>Mycobacterium</italic>, <italic>Nocardia</italic>, <italic>Pilimelia</italic>, <italic>Pseudarthrobacter</italic>, <italic>Rhodococcus</italic>, <italic>Streptacidiphilus</italic>, <italic>Streptomyces</italic> and <italic>Tsukamurella</italic> from 6 families in the phylum Actinobacteria. Genomic analysis based on 16S rRNA sequences was used to select 30 isolates as representative strains of the accessed actinomycetes diversity. The nearest type strains, the percentage of identity and the GenBank access number are presented in Table <xref rid=\"MOESM1\" ref-type=\"media\">S2</xref>—Supplementary Material. The strains are deposited in the Collection of Microorganisms of Agricultural and Environment Importance (CMAA)—Embrapa, Brazil (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>).<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Morphological diversity of Actinobacteria strains isolated from the rhizosphere of <italic>Deschampsia antarctica</italic> Desv.</p></caption><graphic xlink:href=\"41598_2020_69786_Fig4_HTML\" id=\"MO4\"/></fig></p></sec><sec id=\"Sec6\"><title>Screening for bioactive strains</title><p id=\"Par11\">First, all thirty isolated strains were evaluated against <italic>Pythium aphanidermatum</italic> CMAA 243<sup>T</sup>. Seventeen strains showed growth inhibition effects, as determined by the antagonism test (Figure <xref rid=\"MOESM1\" ref-type=\"media\">S2</xref> and Table <xref rid=\"MOESM1\" ref-type=\"media\">S3</xref>—Supplementary Material), and these strains were selected for the antiproliferative assay against human tumour cell lines; glioma (U251), breast 174 (MCF-7) and lung/non-small cells (NCI-H460). Two strains, CMAA 1527 and CMAA 1653, exhibited potent activity, considering that Total Growth Inhibition (TGI) values lower than 6.25 µg/mL. Crude extracts of both CMAA 1527 and CMAA 1653 completely inhibited the growth of breast (MCF-7, TGI < 0.25 µg/mL, both extracts), glioblastoma (U251, TGI = 3.05 and < 0.25 µg/mL, respectively) and non-small lung (NCI-H460, TGI = 0.57 and 5.8 µg/mL, respectively) tumour cells (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>).<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Antiproliferative activity in tumor cell lines. (<bold>A</bold>) <italic>Streptomyces</italic> sp. CMAA 1527 crude extract; (<bold>B</bold>) <italic>Streptomyces</italic> sp. CMAA 1653 crude extract; (<bold>C</bold>) Fraction FR-3 (produced by <italic>Streptomyces</italic> sp. CMAA 1527) and (<bold>D</bold>) Fraction FR-7 (produced by <italic>Streptomyces</italic> sp. 1653).</p></caption><graphic xlink:href=\"41598_2020_69786_Fig5_HTML\" id=\"MO5\"/></fig></p></sec><sec id=\"Sec7\"><title>Phylogenetic analysis</title><p id=\"Par12\">According to the phylogenetic reconstruction based on the 16S rRNA gene similarity the bioactive actinobacterial strains were affiliated with the genus <italic>Streptomyces</italic>. The CMAA 1527 strain was closely related to <italic>Streptomyces aurantiacus</italic> NBRC 13017<sup>T</sup>, while CMAA 1653 was related to <italic>Streptomyces fildesensis</italic> DSM 41987<sup>T</sup> (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6</xref>), although they form a distinct phyletic branch towards the phylogenetic tree.<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Phylogenetic tree based on the 16S rRNA gene sequences, obtained by Neighbor–Joining method analysis for the bioactive strains CMAA 1527 and CMAA 1653 and their closely related type strains—MEGA 7.0<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. Numbers at nodes indicate the level of bootstrap support based on 1,000 replications (only values > 50% are shown). <italic>Streptacidiphilus oryzae</italic> was used as outgroup for this study.</p></caption><graphic xlink:href=\"41598_2020_69786_Fig6_HTML\" id=\"MO6\"/></fig></p></sec><sec id=\"Sec8\"><title>Bioassay-guided fractionation and structural identification of compounds</title><p id=\"Par13\">The crude extracts obtained from <italic>Streptomyces</italic> sp. CMAA 1527 and <italic>Streptomyces</italic> sp. CMAA 1653 showed high antiproliferative activity. Both extracts were analysed by LC–MS and presented different chemical profiles (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>).<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Chromatogram TIC obtained by LC–MS of crude extracts: (<bold>A</bold>) <italic>Streptomyces</italic> sp. CMAA 1527 and (<bold>B</bold>) <italic>Streptomyces</italic> sp. CMAA 1653.</p></caption><graphic xlink:href=\"41598_2020_69786_Fig7_HTML\" id=\"MO7\"/></fig></p><p id=\"Par14\">The major peak in <italic>Streptomyces</italic> sp. CMAA 1527 crude extract was analysed by LC–MS at the retention time of 13.15 min and had mass spectra with <italic>m/z</italic> 826 (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>a). The extract was fractionated by semi-preparative HPLC, resulting in 4 grouped fractions (FR-1, FR-2, FR-3 and FR-4) that were subjected to a biological assay. Figure <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>c and Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref> shows the antiproliferative potential of fraction FR-3, the most bioactive fraction according to human cancer cell line panel. FR-3 was submitted to structural identification experiments, although other fractions showed lower biological activities (data not shown).<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>In vitro antiproliferative activity of crude extracts and bioactive fractions against human tumor and non-tumor cell lines.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\" colspan=\"9\">Cell lines</th></tr><tr><th align=\"left\">Crude extracts and bioactive fractions</th><th align=\"left\">1</th><th align=\"left\">2</th><th align=\"left\">3</th><th align=\"left\">4</th><th align=\"left\">5</th><th align=\"left\">6</th><th align=\"left\">7</th><th align=\"left\">8</th><th align=\"left\">9</th></tr></thead><tbody><tr><td align=\"left\"/><td align=\"left\" colspan=\"9\"><bold>TGI (μg/ml)</bold><sup><bold>b</bold></sup></td></tr><tr><td align=\"left\"><bold>Doxorubicin</bold><sup><bold>a</bold></sup></td><td align=\"left\">0.56 ± 0.42 (P)</td><td align=\"left\">0.42 ± 0.11 (P)</td><td align=\"left\">2.3 ± 0.9 (P)</td><td align=\"left\">4.2 ± 4.3 (P)</td><td align=\"left\">* *(P)</td><td align=\"left\">1.3 ± 0.4 (P)</td><td align=\"left\">3.6 ± 1.8 (P)</td><td align=\"left\">2.4 ± 0.8 (P)</td><td align=\"left\">0.16 ± 0.04 (P)</td></tr><tr><td align=\"left\">CMAA 1527</td><td align=\"left\">3.05 ± 0.09 (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">0.57 ± 0.67 (P)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">* * (P)</td></tr><tr><td align=\"left\">CMAA 1653</td><td align=\"left\">* * (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">5.8 ± 3.3 (P)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">0.31 ± 0.03 (P)</td></tr><tr><td align=\"left\">Fraction FR-3</td><td align=\"left\">0.65 ± 0.43 (P)</td><td align=\"left\">5.7 ± 5.7 (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">3.2 ± 4.2 (P)</td><td align=\"left\">1.5 ± 2.6 (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">* * (P)</td></tr><tr><td align=\"left\">Fraction FR-7</td><td align=\"left\">0.31 ± 0.11 (P)</td><td align=\"left\">1.4 ± 2.3 (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">1.5 ± 1.3 (P)</td><td align=\"left\">0.51 ± 0.27 (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">* * (P)</td><td align=\"left\">* *(P)</td></tr><tr><td align=\"left\"/><td align=\"left\" colspan=\"9\"><bold>LC</bold><sub><bold>50</bold></sub><bold> (μg/ml)</bold><sup><bold>c</bold></sup></td></tr><tr><td align=\"left\"><bold>Doxorubicin</bold><sup><bold>a</bold></sup></td><td align=\"left\"> > 25</td><td align=\"left\"> > 25</td><td align=\"left\"> > 25</td><td align=\"left\"> > 25</td><td align=\"left\"> > 25</td><td align=\"left\"> > 25</td><td align=\"left\"> > 25</td><td align=\"left\">25</td><td align=\"left\">1.7 ± 1.6</td></tr><tr><td align=\"left\">Fraction FR-3</td><td align=\"left\">38.8 ± 31.6</td><td align=\"left\">*</td><td align=\"left\">*</td><td align=\"left\">*</td><td align=\"left\">*</td><td align=\"left\">*</td><td align=\"left\">54.1 ± 33.4</td><td align=\"left\">11.2 ± 6.7</td><td align=\"left\">0.51 ± 0.54</td></tr><tr><td align=\"left\">Fraction FR-7</td><td align=\"left\">12.2 ± 4.9</td><td align=\"left\">162.6 ± 23.7</td><td align=\"left\">6.7 ± 3.5<sup>#</sup></td><td align=\"left\">*</td><td align=\"left\">38.1 ± 20.2<sup>#</sup></td><td align=\"left\">28.8 ± 8.2<sup>#</sup></td><td align=\"left\">13.7 ± 9.6</td><td align=\"left\">2.6 ± 1.5</td><td align=\"left\">* *</td></tr></tbody></table><table-wrap-foot><p>(a) Doxorubicin: chemotherapeutic drug; (b) TGI: Total Growth Inhibition (sample concentration required for total cell growth inhibition) and (c) LC<sub>50</sub>: Lethal Concentration 50 (sample concentration required to elicit 50% of cell death), expressed in µg/ml followed by standard error, calculated by sigmoidal regression using Origin 8.0 software. Results classified according to NCI’s criteria based on TGI values (I, inactive sample: TGI > 50 μg/ml; W, weak activity: 15 μg/ml < TGI < 50 μg/ml; M, moderate activity: 6.25 μg/ml < TGI < 15 μg/ml; P, potent activity: TGI < 6.25 μg/ml)<sup><xref ref-type=\"bibr\" rid=\"CR43\">43</xref></sup>. LC<sub>50</sub> results were compared by Test t-student [#: statistically significant difference (p ≤ 0.05) between fractions FR-3 and FR-7 in the same cell line]. Human tumor cell lines: 1 = U251 (glioblastoma); 2 = MCF-7 (breast, adenocarcinoma); 3 = NCI/ADR-RES (ovary, multi-drug resistant adenocarcinoma), 4 = 786–0 (kidney, adenocarcinoma), 5 = NCI-H460 (lung, large cell carcinoma); 6 = OVCAR-3 (ovary, adenocarcinoma), 7 = HT-29 (colon, adenocarcinoma), 8 = K-562 (chronic myeloid leukemia); Human non tumor cell line: 9 = HaCaT (immortalized keratinocyte); (*) effective concentration (TGI or GI<sub>50</sub>) > 250 μg/mL; (* *) effective concentration (TGI or GI<sub>50</sub>) < 0.25 μg/mL; (ND) not determined.</p></table-wrap-foot></table-wrap></p><p id=\"Par15\">The chemical structure of FR-3 was confirmed by the analysis of the HRESIMS spectrum, and the molecular formula was determined to be C<sub>42</sub>H<sub>51</sub>NO<sub>16</sub> as [M + H]<sup>+</sup> was observed at <italic>m/z</italic> 826.3298 u, calculated for C<sub>42</sub>H<sub>52</sub>NO<sub>16</sub><sup>+</sup> (Figure <xref rid=\"MOESM1\" ref-type=\"media\">S3</xref>—Supplementary Material). Cinerubin B was identified based on spectral data such as HRESIMS, <sup>1</sup>H NMR, COSY, gHMBC, and gHMQC. The compound was isolated as a red amorphous powder that was soluble in methanol (MeOH) and trichloromethane (CHCl<sub>3</sub>) with UV absorption maxima at 490 nm. The <sup>1</sup>H NMR spectrum of cinerubin B (Figure S4—Supplementary Material) showed signs of characteristic chemical shifts of anthracycline class; the signals of phenolic hydrogen were at δ 12.99 (s, 1H), δ 12.83 (s, 1H), and δ 12.28 (s, 1H). There were also signs of aromatic hydrogen at δ 7.75 (s, 1H) and δ 7.33 (d, <italic>J</italic> = 4.41 Hz, 2 H), as well as a broad doublet for a carbinolic hydrogen at δ 5.27 (dl, <italic>J</italic> = 2.50 Hz, 1 H). Other observed signals that are characteristic of this compound refer to three methoxyl group hydrogens at δ 3.71 (s, 3H), the hydrogen signals from an ethyl group at δ 1.76 (q, <italic>J</italic> = 7.0 Hz, 2H) and δ 1.09 (t, <italic>J</italic> = 7.0 Hz, 3H), and the methyl hydrogen signals belonging to an <italic>N</italic>-dimethyl <underline>g</underline>roup at δ 2.16 (s, 6H). In addition, the presence of three anomeric hydrogens at δ 5.49 (sl, 1H), δ 5.12 (dl, <italic>J</italic> = 2.9 Hz, 1H), and δ 5.21 (dl, <italic>J</italic> = 2.9 Hz, 1H) indicated the presence of a trisaccharide moiety in the chemical structure of the compound. The 2D correlations from the HMBC and COSY spectra (Table S4—Supplementary Material) were used to assign all signals present in the cinerubin B structure (Fig. <xref rid=\"Fig8\" ref-type=\"fig\">8</xref> and Figure S5—Supplementary Material).<fig id=\"Fig8\"><label>Figure 8</label><caption><p>The main HMBC and COSY correlations observed for cinerubin B.</p></caption><graphic xlink:href=\"41598_2020_69786_Fig8_HTML\" id=\"MO8\"/></fig></p><p id=\"Par16\">In order to identify other anthracyclines the LC–MS/MS analysis using neutral loss methodology was performed. The chemical structure of the predominant compound produced by <italic>Streptomyces</italic> sp. CMAA 1527 (cinerubin B; <italic>m/z</italic> 826.3298 u) showed a neutral loss of 240 u, which refers to a charge-remote hydrogen rearrangement with consequent loss of disaccharide cinerulosyl-2-deoxyfucosyl to form the product ion <italic>m/z</italic> 586 as a base peak, which it was attributed to pyrromycin. This methodology allowed us to show the presence of other bioactive compounds in the crude extract (Figure S6—Supplementary Material) but with lower concentrations than cinerubin B since it was not possible to perform the isolation process. These compounds were identified as 1 or 11-hydroxysulfurmycin B (<italic>m/z</italic> 854), auramycin B (<italic>m/z</italic> 812) or <italic>N</italic>-desmethylcinerubine B (<italic>m/z</italic> 812) and 1-deoxycinerubin B (<italic>m/z</italic> 810) which have been attributed to the lower bioactivities detected at the others fractions (Figure S7, S8 and S9, respectively—Supplementary Material). Furthermore, it is noteworthy that these metabolites have isomeric structures; therefore, for unambiguous confirmation, it is necessary to perform NMR spectroscopic characterization for each of the isolated metabolites.</p><p id=\"Par17\">The analysis of the <italic>Streptomyces</italic> sp. CMAA 1653 crude extract revealed a predominant peak at 25.30 min corresponding to mass spectra <italic>m/z</italic> 1,269 (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>b and Figure S10—Supplementary Material). The HRESIMS/MS spectrum showed the main product ions of <italic>m/z</italic> 956, <italic>m/z</italic> 857, <italic>m/z</italic> 657, <italic>m/z</italic> 558, <italic>m/z</italic> 459, <italic>m/z</italic> 399, and <italic>m/z</italic> 300 (Figure S11—Supplementary Material). These ions provided abundant structural information, and their identification was performed. HRESIMS/MS data were identical to those previously reported in the literature<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>. As a result of the fractionation procedures, FR-5, FR-6 and FR-7 were recovered, within the FR-7 portion as the main bioactive fraction (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>d and Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>). Thus, the compound present in the fraction FR-7 was identified as actinomycin V (C<sub>62</sub>H<sub>84</sub>N<sub>12</sub>O<sub>17</sub>) (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9</xref>). The other fractions obtained by semi-preparative HPLC did not show bioactivity in the experiments carried out in this study.<fig id=\"Fig9\"><label>Figure 9</label><caption><p>Chemical structure of actinomycin V.</p></caption><graphic xlink:href=\"41598_2020_69786_Fig9_HTML\" id=\"MO9\"/></fig></p></sec></sec><sec id=\"Sec9\"><title>Discussion</title><p id=\"Par18\">The analysis of the rhizosphere microbiota in cold extreme habitats is crucial to understand the ecological roles and unlocking the biotechnological potential of these environments. Actinobacteria was also the dominant phylum in the rhizosphere of other <italic>D. antarctica</italic> retrieved from the maritime Antarctica region, as well as soils from cold and desert environments<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref>–<xref ref-type=\"bibr\" rid=\"CR48\">48</xref></sup>. This prevalence may be explained by their spore-forming ability, UV radiation tolerance and other environmental adaptations that ensure survival in harsh conditions. Actinobacterial genera are known for their great potential to produce several bioactive substances<sup><xref ref-type=\"bibr\" rid=\"CR47\">47</xref>,<xref ref-type=\"bibr\" rid=\"CR49\">49</xref>,<xref ref-type=\"bibr\" rid=\"CR50\">50</xref></sup>, although they also have the ability to degrade and use complex organic compounds and for their bioremediation of contaminated soils<sup><xref ref-type=\"bibr\" rid=\"CR51\">51</xref></sup>. In fact, the predictive functional analysis using the Actinobacteria-derived 16S rRNA gene sequences showed enrichment of some functional categories related to carbohydrate metabolism, xenobiotic biodegradation and bioactive compound biosynthesis when compared with the total bacterial community. Vikram et al. (2016)<sup><xref ref-type=\"bibr\" rid=\"CR52\">52</xref></sup> also showed the genetic potential of Actinobacteria for C metabolism and membrane transport systems, which supports our PICRUSt functional predictions. The results obtained by 16S rRNA gene sequencing coupled to predictive functional analysis were useful to reveal diversified Actinobacteria pathways to produce biologically active metabolites. In doing so, we reported the presence of many pathways related to the biosynthesis of antibiotics such as streptomycin, novobiocin, phenylpropanoids, pyridine alkaloids, stilbenoids, neomycin, vancomycin, and tetracyclines, indicating a great bioactive potential of the bacterial community associated with <italic>D. antarctica</italic> (Table S5—Supplementary Material).</p><p id=\"Par19\">Despite advances in environmental sequencing (metagenomics) and single-cell genomics, the screening procedures of novel molecules remain largely related to the microorganisms culturing<sup><xref ref-type=\"bibr\" rid=\"CR53\">53</xref></sup>. Thus, efforts to isolate new psychrotolerant strains were performed, resulting in a diverse collection of Actinobacteria revealed by the 16S rRNA gene sequencing. However, recent studies have found that discrepancies in terms of chemical and morphological features were possible in microorganisms belonging to same taxonomy, as determined for identical 16S rRNA gene sequences<sup><xref ref-type=\"bibr\" rid=\"CR54\">54</xref></sup>. This finding was especially observed when studying bacteria from geographically distant origins or associated with different host species, in accordance to the ecovar concept<sup><xref ref-type=\"bibr\" rid=\"CR55\">55</xref>–<xref ref-type=\"bibr\" rid=\"CR57\">57</xref></sup>. Therefore, although the limitations of 16S rRNA gene have been widely recognized for screening of bioactive strains, we have assumed that the rhizosphere of <italic>D. antarctica</italic> could lead to high grade of specialism, with a lot of clonal populations. This fact was also observed in the 16S rRNA sequences of all isolated strains (data not shown), and supported by previous reports of Antarctic microbiota, which demonstrated that the type of habitat dramatically constrained the bacterial community composition<sup><xref ref-type=\"bibr\" rid=\"CR45\">45</xref>,<xref ref-type=\"bibr\" rid=\"CR58\">58</xref></sup>.</p><p id=\"Par20\">Although the Actinobacteria diversity has been underestimated through cultivation-dependent techniques, the isolation was able to access undiscovered species with the potential to produce novel bioactive compounds<sup><xref ref-type=\"bibr\" rid=\"CR59\">59</xref>–<xref ref-type=\"bibr\" rid=\"CR61\">61</xref></sup>. Some of them were not detected in microbiome analysis, possibly belonging to the rare community. Indeed, previous studies reported that cultivation techniques are still unable to access the real microbial diversity found in natural environments. The cultivation of recalcitrant microorganisms from extreme environments is difficult, especially the rhizosphere of Antarctic plants. However, the use of different culture media and growing conditions may be tested to improve the recovery of a major diversity of Actinobacteria members<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref>,<xref ref-type=\"bibr\" rid=\"CR62\">62</xref></sup>. The low sequence identity registered in this study indicates the potential to identify novel species that require a polyphasic taxonomic approach for proper characterization and description. Based on phylogenetic, phenotypic and chemotaxonomic data, a new actinobacterial species isolated from <italic>D. antarctica</italic> was recently identified, named <italic>Rhodococcus psychrotolerans</italic> sp. nov<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>.</p><p id=\"Par21\">For current purposes, antiproliferative screening using <italic>P. aphanidermatum</italic> CMAA 243<sup>T</sup> as a model organism resulted in 17 bioactive strains. The bioassay was followed the protocol successfully applied to the primary screening against the Oomycota <italic>Pythium,</italic> which contain in its hyphal walls and cell membrane, proteins, lipids and sterols such as cholesterol, compounds which resemble cancer cells. The advantage of this method is the simplicity, low cost and practicality<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>. Among these strains, <italic>Streptomyces</italic> sp. CMAA 1527 and <italic>Streptomyces</italic> sp. CMAA 1653 stood out, showing a high antiproliferative index for human tumour cells. According to the phylogenetic reconstruction and 16S rRNA gene similarity, <italic>Streptomyces</italic> sp. CMAA 1527 was closely related to <italic>Streptomyces aurantiacus</italic> NBRC 13017<sup>T</sup> (99.37%), while <italic>Streptomyces</italic> sp. CMAA 1653 was related to <italic>Streptomyces fildesensis</italic> DSM 41987<sup>T</sup> (99.72%). Interestingly, <italic>Streptomyces fildesensis</italic> DSM 41987<sup>T</sup> was isolated from Antarctic soil and was recently presented as a prominent producer of antibiotic compounds<sup><xref ref-type=\"bibr\" rid=\"CR64\">64</xref></sup>. Although both strains have been presented in the literature, they have not been listed as potential producers of antitumour compounds.</p><p id=\"Par22\">Cinerubin B is an important compound belonging to the anthracycline class. Anthracyclines are a special class of antibiotics widely used as antitumour agents, and the compounds daunomycin, doxorubicin, adriamycin, and aranciamycin anhydride have received great attention since their discovery<sup><xref ref-type=\"bibr\" rid=\"CR65\">65</xref></sup>. The pronounced activity presented in the secondary metabolites of <italic>Streptomyces</italic> sp. CMAA 1653 can be clearly justified by the presence of actinomycin V (<italic>m/z</italic> 1,269.6170 u). The product ions at <italic>m/z</italic> 956, <italic>m/z</italic> 857, <italic>m/z</italic> 657, <italic>m/z</italic> 399 and <italic>m/z</italic> 300 corresponded to the loss of the Val-Pro-Sar-MeVal chain and were similar to those observed for actinomycin D<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref></sup>. Moreover, the product ion at <italic>m/z</italic> 558 indicated the presence of another Pro-Sar-MeVal residue in structure, as well as the product ion at <italic>m/z</italic> 459, which was characteristic of the core nucleus structure of actinomycins<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref>,<xref ref-type=\"bibr\" rid=\"CR66\">66</xref></sup>. Actinomycins have been used as chemotherapeutic agents for the treatment of a variety of cancers and are produced by many <italic>Streptomyce</italic>s strains<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. They are a family of bicyclic chromopeptide lactones that attach two pentapeptide lactones of nonribosomal origin. Its use dates approximately 70 years ago, acting under several types of malignant human tumours, including nephroblastoma (Wilms’ tumour) and childhood rhabdomyosarcoma<sup><xref ref-type=\"bibr\" rid=\"CR67\">67</xref>–<xref ref-type=\"bibr\" rid=\"CR69\">69</xref></sup>, and has attracted attention for its potential to assist in the development growth of multi-drug resistance strains and inhibition of HIV-1 reverse transcriptase<sup><xref ref-type=\"bibr\" rid=\"CR44\">44</xref>,<xref ref-type=\"bibr\" rid=\"CR70\">70</xref>,<xref ref-type=\"bibr\" rid=\"CR71\">71</xref></sup>.</p></sec><sec id=\"Sec10\"><title>Conclusions</title><p id=\"Par23\">This study reveals that the root-associated bacteria of <italic>Deschampsia antarctica</italic> Desv. from Antarctic ecosystems are a rich source of molecules with antitumour properties. The isolation methods employed in this study were able to retrieve Actinobacteria taxa that were not detected in microbiome analysis, showing that the combination of both strategies may be useful for recovering both rare and abundant members of the Actinobacteria communities for biotechnological exploration. The secondary metabolites of <italic>Streptomyces</italic> sp. CMAA 1527 and <italic>Streptomyces</italic> sp. CMAA 1653 showed valuable antiproliferative activities against human cancer cells and therefore can contribute significantly to the development of drugs of technological importance. Cinerubin B and actinomycin V were identified in the bioactive fractions of <italic>Streptomyces</italic> sp. CMAA 1527 and <italic>Streptomyces</italic> sp. CMAA 1653, respectively, but many other potential bioactive compounds can still be explored from Antarctic Actinobacteria. Based on these results, one of the two native Antarctic plants represents an attractive and unique model for the study of symbiosis and the discovery of antitumour lead compounds by the associated microbiome.</p></sec><sec id=\"Sec11\"><title>Methods</title><sec id=\"Sec12\"><title>Sampling sites</title><p id=\"Par24\">Rhizospheric soil samples were collected (early summer Nov. 2014) at three different points (Morro da Cruz—62°05′04.3″ S/58°23′41.7″ W; Torre Meteoro Direito—62°05′10.4″ S/58°23′34.6″ W and Torre Meteoro Esquerdo—62°05′08.1″ S/58°23′36.6″ W) in Admiralty Bay, King George Island, South Shetland Islands, Antarctica. <italic>Deschampsia antarctica</italic> rhizospheric soil was obtained by removing plants from the soil (three plants for each collection point) and scraping 1–2 mm of soil adhering to the roots. Samples intended to actinobacteria isolation were kept at 4 °C and samples selected to genomic approach was stored at -20 °C until processed.</p></sec><sec id=\"Sec13\"><title>Bacterial 16S rRNA metabarcoding sequencing</title><p id=\"Par25\">DNA extractions of the rhizosphere samples were performed using the PowerSoil DNA Isolation Kit (MoBio Laboratories—Carlsbad, CA, USA), according to the manufacturer's instructions. DNA yield and purity was evaluated by Qubit Fluorometric Quantification (Life Technologies—San Diego, CA, USA) and NanoDrop spectrophotometer (ThermoFischer Scientific—Waltham, MA, USA) (checking by the A260nm/280 nm and A260nm/230 nm ratios). The total DNA extracted from rhizosphere samples was ordered to the massive sequencing of the 16S rDNA gene for bacterial diversity analysis. The amplicons were obtained by the amplification of V6 16S rRNA hypervariable region<sup><xref ref-type=\"bibr\" rid=\"CR72\">72</xref></sup>, using the primers 967F (5′-CAACGCGAAGAACCTTACC-3′) e 1193R (5′-CGTCRTCCCCRCCTTCC-3′)<sup><xref ref-type=\"bibr\" rid=\"CR73\">73</xref></sup>, with an additional tag of five nucleotides added for each sample (<ext-link ext-link-type=\"uri\" xlink:href=\"https://vamps.mbl.edu/\">https://vamps.mbl.edu/</ext-link>), according to Souza et al. (2017)<sup><xref ref-type=\"bibr\" rid=\"CR74\">74</xref></sup>. The PCR products were pooled in equimolar ratio and purified by the SizeSelect EX E-Gel electrophoresis system (Life Technologies Corporation) for DNA size selection. The recovered fragments were further purified using the Agencourt AMPure XP kit (Beckman Coulter—Brea, CA, USA). Purified library product was quantified using the Qubit Fluorometric Quantification. The emulsion PCR procedure and sample enrichment were performed on the Ion OneTouch 2 system using the Ion Template PGM OT2 400 kit (Life Technologies Corporation). The V2 316 chip was used for sequencing as instructed in the Ion Torrent platform manual (Personal Genome Machine—PGM, Life Technologies Corporation).</p></sec><sec id=\"Sec14\"><title>Statistical and bioinformatic analyses</title><p id=\"Par26\">Raw data obtained from Ion Torrent sequencing were converted to FASTA file and submitted to quality control (QC) using the Galaxy platform<sup><xref ref-type=\"bibr\" rid=\"CR75\">75</xref></sup>, with the following QC parameters: quality score = 25; barcode size = 5; quality window = 50, and maximum number of homopolymers = 6. Sequences with low quality and lengths < 180 bp were removed. After QC, the remaining high-quality reads were analyzed by the Quantitative Insights Into Microbial Ecology (QIIME) version 1.9 software package<sup><xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup>. Open reference operational taxonomic unit (OTU) were grouped at 97% sequence similarity OTU using UCLUST method and representative sequences of each OTU were taxonomically classified using the PyNAST alignment against GREENGENES (version gg_13_8) 16S reference database<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref>,<xref ref-type=\"bibr\" rid=\"CR76\">76</xref></sup>. Chimeric sequences were detected and removed by the UCHIME algorithm<sup><xref ref-type=\"bibr\" rid=\"CR76\">76</xref></sup> before the construction of the OTU table. Afterward, all OTUs consisting of one single sequence (singletons), chloroplasts and non-bacterial sequences were removed before taxonomic classification.</p><p id=\"Par27\">The accurate predictive analysis was performed to infer the functional contribution of rhizosphere-associated Actinobacteria in comparison with the total community. Then, the PICRUSt tool<sup><xref ref-type=\"bibr\" rid=\"CR41\">41</xref></sup> was used to predict KEEG Orthology (KO) functional profiles of the bacterial community using the 16S rRNA dataset based on OTUs and reference genomes database<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref>–<xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup>. For this, 16S reads affiliated to the Actinobacteria phylum were filtered after Greengenes database annotation. The final output of this workflow was quantified in terms of predicted function abundances per sample per OTU. Subsequently, the data were analyzed by the STAMP (Statistical Analysis of Metagenomic Profiles) software package v. 2.1.3<sup><xref ref-type=\"bibr\" rid=\"CR77\">77</xref></sup> to evaluate biologically meaningful differences between rhizosphere-associated Actinobacteria and the total bacterial community. Statistical inferences were performed by G-test (w/Yates) + Fisher's, DP method: Asymptotic-CC 0.95 and p-value > 0.05 filter.</p></sec><sec id=\"Sec15\"><title>Isolation, culturing and identification of actinobacteria from rhizosphere</title><p id=\"Par28\">A pool of rhizosphere samples (0.25 g of each sample for three collected points; n = 9) was serially diluted (10<sup>–4</sup>, 10<sup>–5</sup> and 10<sup>–6</sup>) in saline solution (NaCl 0.85%), and aliquots of 100 μL were plated on Starch Casein Agar (SCA)<sup><xref ref-type=\"bibr\" rid=\"CR78\">78</xref></sup>, Humic Acid-Vitamin Agar (HVA)<sup><xref ref-type=\"bibr\" rid=\"CR79\">79</xref></sup> and Yeast Extract Malt Extract Agar (YEME)<sup><xref ref-type=\"bibr\" rid=\"CR80\">80</xref></sup>. All media were supplemented with cycloheximide and nystatin (25 μg/mL) and incubated at 16 °C for 6 weeks. Isolates from the <italic>Deschampsia antarctica</italic> rhizosphere with the typical morphology of Actinobacteria colonies were taken from the selective isolation plates. The pure cultures obtained by repeated streaking were maintained on Glucose Yeast Extract Agar (GYEA) at 4 °C and as mixtures of mycelial fragments and spores in 20% glycerol (v/v) at − 80 °C.</p><p id=\"Par29\">Genomic DNA extraction, PCR amplification and sequencing methods were performed according to procedures adopted in Souza et al. (2017)<sup><xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. The 16S rRNA consensus contigs were obtained using PhredPhrap/Consed<sup><xref ref-type=\"bibr\" rid=\"CR81\">81</xref></sup> and were aligned by ClustalW against nearest corresponding sequences using EzBioCloud<sup><xref ref-type=\"bibr\" rid=\"CR82\">82</xref></sup> and MEGA 7.0<sup><xref ref-type=\"bibr\" rid=\"CR42\">42</xref></sup>. Phylogenetic trees were inferred using the neighbour-joining<sup><xref ref-type=\"bibr\" rid=\"CR83\">83</xref></sup> tree-making algorithm drawn from the MEGA 7.0 package. Topologies of the resultant trees were evaluated by bootstrap analysis<sup><xref ref-type=\"bibr\" rid=\"CR84\">84</xref></sup> based upon 1,000 replicates.</p></sec><sec id=\"Sec16\"><title>Cultivation, extraction, and isolation of bioactive compounds</title><p id=\"Par30\">The metabolites of <italic>Streptomyces</italic> sp. CMAA 1527 and <italic>Streptomyces</italic> sp. CMAA 1653 were obtained by growing cultures in Potato Dextrose Broth on a rotating shaker (180 rpm, 16 °C) for 10 days. After growth, the mycelium was separated by centrifugation (7,000 rpm, 15 min, room temperature) followed by filtration (0.22 μm filter membrane). Each liquid culture medium was extracted with ethyl acetate (1:3 v/v), and the organic phase was evaporated to dryness under vacuum (Büchi Waterbath B-480). From each crude extract, at least three aliquots were separated, and two of them were subjected to antifungal (oomycete <italic>Phytium aphanidermatum</italic> CMAA 243<sup>T</sup>) and antiproliferative (human tumour and non-tumour cell lines) evaluations. Aliquots of each crude extract (650 mg) were fractionated by semipreparative HPLC on a Shimadzu LC system (Shimadzu, Kyoto, Japan) equipped with a CBM-20A controller, an LC-6AD pump, a DGU-20A5 degasser, and an SPD-20A UV–vis detector. The chromatographic separation was carried out using a C<sub>18</sub> Zorbax Eclipse XDB column (250 × 9.4 mm, 5 μm; Agilent), and the mobile phase used was composed of 0.1% formic acid as system A and acetonitrile as system B. The gradient elution programme was 0—40 min from 30 to 100% of system B at a flow rate of 4 mL/min. The chromatograms were monitored using wavelengths at 320 and 500 nm.</p></sec><sec id=\"Sec17\"><title>Screening of bioactive strains</title><sec id=\"Sec18\"><title>Antifungal activity</title><p id=\"Par31\">Bioactive strains were screened as described by Santos and Melo (2016)<sup><xref ref-type=\"bibr\" rid=\"CR63\">63</xref></sup>, using <italic>Pythium. aphanidermatum</italic> CMAA 243<sup>T</sup> as a model organism. This bioassay was carried out as a primary screening for antitumour compounds from microorganisms. The antagonism assay was performed using sterile disc paper (0.5 cm diameter), crude extract solubilized in ethyl acetate (1 mg/mL) and potato dextrose agar medium. The plates were incubated at 28 °C for 24 h and evaluated according to the size of the inhibition zone, such as pronounced (+++), moderate (++), reduced (+) or absent (−).</p></sec><sec id=\"Sec19\"><title>Antiproliferative activity</title><p id=\"Par32\">The antiproliferative activity was investigated against a panel of human tumours [glioblastoma (U251), mamarian adenocarcinoma (MCF-7), ovarian multi-drug resistant adenocarcinoma (NCI/ADR-RES), large cell carcinoma of lung (NCI-H460), adenocarcinoma of kidney (786-0), ovarian adenocarcinoma (OVACAR-03), colon adenocarcinoma (HT-29) and chronic myeloid leukaemia (K-562)] and non-tumour (HaCat, immortalized keratinocytes) cell lines growing in complete medium [RPMI 1640 supplemented with 5% foetal bovine serum and 1% penicillin/streptomycin mixture (1,000 UI:1,000 μg/mL)] at 37 °C and 5% CO<sub>2</sub> in a humidified atmosphere. Human tumour and the human non-tumoural cell lines were provided by the Frederick Cancer Research & Development Center (National Cancer Institute, Frederick, MD, USA), and Dr. Ricardo Della Colleta (University of Campinas).</p><p id=\"Par33\">The in vitro assay was performed as described by Monks et al. (1991)<sup><xref ref-type=\"bibr\" rid=\"CR85\">85</xref></sup>. First, all extracts were screened against three tumour cell lines (U251, MCF-7 and NCI-H460). Then, the most promising extracts were evaluated against the complete panel. For both experiments, each extract was solubilized in dimethyl sulfoxide (100 mg/mL) followed by serial dilution in complete medium affording the final concentrations (0.25, 2.5, 25 and 250 μg/mL). Doxorubicin was used as a positive control at final concentrations of 0.025, 0.25, 2.5 and 25 μg/mL and was diluted following the same protocol.</p><p id=\"Par34\">Each cell line was grown in 96-well plates (100 μL cells/well) for 24 h and exposed for to the extracts, doxorubicin (positive control) or complete medium (negative control) for 48 h. Before (T<sub>0</sub> plate) and after (T<sub>1</sub> plates) sample addition, cells were fixed with 50% trichloroacetic acid dyed with sulforhodamine B 0.4% in acetic acid 0.1%, and colorimetric evaluation was recorded at 540 nm. The difference in absorbance between the untreated cells before (T<sub>0</sub> plate) and after (T<sub>1</sub> plates) sample addition represented 100% cell proliferation. Negative values represented a reduction in the cell population related to the T<sub>0</sub> plate. The cell proliferation data were analysed using Origin 8.0 software (OriginLab Corporation) using sigmoidal regression to calculate the TGI and GI<sub>50</sub> values (sample concentrations required total cell growth inhibition or to elicit 50% of cell death, respectively).</p></sec></sec><sec id=\"Sec20\"><title>LC–MS analysis</title><p id=\"Par35\">The compounds present in the bioactive fractions were analysed using a Waters ACQUITY UPLC <italic>H-Class</italic> system coupled to the Xevo TQ-S tandem quadrupole (Waters Corporation, Milford, MA, USA) mass spectrometer with a Z-spray source operating in the positive mode and to the diode arrangement detector (DAD) from 220 to 600 nm, according to Crevelin et al. (2014)<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. The samples were solubilized in MeOH and injected (5 µL) into a Zorbax Eclipse XDB-C18 column (150 × 4.6 mm, 3.5 µm particle size—Agilent, Santa Clara, CA, USA); the mobile phase used for gradient elution consisted of 0.1% formic acid as system A and acetonitrile with 0.1% formic acid as system B. The flow rate was 0.5 mL/min, and the gradient elution programme started with 30% B, increased to 90% B in the following 25 min, remained at 90% B for 5 min, and returned to the initial condition within the following 5 min. The source and operating parameters were optimized as follows: capillary voltage, 3.2 kV; cone voltage, 40 V; source offset, 60 V; Z-spray source temperature, 150 °C; desolvation temperature (N<sub>2</sub>), 350 °C; desolvation gas flow, 800 L/h (mass range from <italic>m/z</italic> 150 − 1,200).</p></sec><sec id=\"Sec21\"><title>High-resolution mass spectrometry analysis</title><p id=\"Par36\">The exact mass of the compounds present in the bioactive fractions was determined by high-resolution mass spectrometry (HRESIMS)<sup><xref ref-type=\"bibr\" rid=\"CR31\">31</xref>,<xref ref-type=\"bibr\" rid=\"CR86\">86</xref></sup> on a mass spectrometer microTOFII‑ESI‑Q‑TOF (Bruker Daltonics, Billerica, MA, USA) employing an infusion pump (Kd Scientific, Holliston, MA, USA) at a flow rate of 100 µL/min. The voltage of the capillary was 3,500 V, and the voltage of the end plate was − 400 V in positive ionization mode. The source and operating parameters were optimized as follows: drying gas temperature (N<sub>2</sub>), 250 °C; a flow rate of 4 mL/min and a pressure of 0.4 bar. The tandem mass spectrometry experiments (MS/MS) with collision-induced dissociation were carried out using N<sub>2</sub> as the collision gas on the selected precursor ion at collision energy values ranging from 10 to 50 eV. For internal calibration, a solution of sodium trifluoroacetic acid (Na-TFA) at a concentration of 1 mg/mL was used. Data acquisition and analysis were performed using Compass Data Analysis 4.1 software (Bruker Daltonics Corporation).</p></sec><sec id=\"Sec22\"><title>NMR analysis</title><p id=\"Par37\">Nuclear magnetic resonance (NMR) measurements were carried out on a Bruker DRX 500 spectrometer (Bruker Daltonics Corporation) operating at 500 MHz for <sup>1</sup>H and 125 MHz for <sup>13</sup>C (δ in parts per million relative to Me4Si, <italic>J</italic> in Hertz). The isolated sample (5 mg) was dissolved in deuterated chloroform (CDCl<sub>3</sub>, Sigma-Aldrich) and analysed by 1D and 2D NMR using a Shigemi 5 mm NMR microtube, as described in Crevelin et al. (2013)<sup><xref ref-type=\"bibr\" rid=\"CR86\">86</xref></sup>.</p></sec></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec23\"><p>\n<supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41598_2020_69786_MOESM1_ESM.doc\"><caption><p>Supplementary information.</p></caption></media></supplementary-material></p></sec></sec></body><back><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p> is available for this paper at 10.1038/s41598-020-69786-2.</p></sec><ack><title>Acknowledgements</title><p>This study was made possible thanks to the laboratorial support of EMBRAPA Environment and financial support provided by CNPq (National Council for Scientific and Technological), FAPESP (São Paulo Research Foundation) and ProAntar (Brazilian Antarctic Program).</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>L.J.S. carried out the rhizosphere collection. L.J.S., E.J.C., D.T.S. and A.L.T.G.R. performed the experiments. E.J.C. and G.L.V-J. tabulation, statistical analysis of data. L.J. S. E.J.C. and G.L.V-J. creation of tables and figures. The manuscript was written by L.J.S., E.J.C., D.T.S., G.L.V-J., V.M.O., A.L.T.G.R., L.H.R., L.A.B.M and I.S.M.</p></notes><notes id=\"FPar1\" notes-type=\"COI-statement\"><title>Competing interests</title><p id=\"Par38\">The authors declare no competing interests.</p></notes><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><label>1.</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Stratton</surname><given-names>CF</given-names></name><name><surname>Newman</surname><given-names>DJ</given-names></name><name><surname>Tan</surname><given-names>DS</given-names></name></person-group><article-title>Cheminformatic comparison of approved drugs natural product versus synthetic origins</article-title><source>Bioorg. Med. Chem. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Sci Rep</journal-id><journal-id journal-id-type=\"iso-abbrev\">Sci Rep</journal-id><journal-title-group><journal-title>Scientific Reports</journal-title></journal-title-group><issn pub-type=\"epub\">2045-2322</issn><publisher><publisher-name>Nature Publishing Group UK</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32807856</article-id><article-id pub-id-type=\"pmc\">PMC7431911</article-id><article-id pub-id-type=\"publisher-id\">70840</article-id><article-id pub-id-type=\"doi\">10.1038/s41598-020-70840-2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>The burden of kidney cancer and its attributable risk factors in 195 countries and territories, 1990–2017</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Safiri</surname><given-names>Saeid</given-names></name><address><email>safiris@tbzmed.ac.ir</email></address><xref ref-type=\"aff\" rid=\"Aff1\">1</xref><xref ref-type=\"aff\" rid=\"Aff2\">2</xref><xref ref-type=\"aff\" rid=\"Aff3\">3</xref></contrib><contrib contrib-type=\"author\"><name><surname>Kolahi</surname><given-names>Ali-Asghar</given-names></name><xref ref-type=\"aff\" rid=\"Aff4\">4</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mansournia</surname><given-names>Mohammad Ali</given-names></name><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Almasi-Hashiani</surname><given-names>Amir</given-names></name><xref ref-type=\"aff\" rid=\"Aff6\">6</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ashrafi-Asgarabad</surname><given-names>Ahad</given-names></name><xref ref-type=\"aff\" rid=\"Aff7\">7</xref></contrib><contrib contrib-type=\"author\"><name><surname>Sullman</surname><given-names>Mark J. M.</given-names></name><xref ref-type=\"aff\" rid=\"Aff8\">8</xref><xref ref-type=\"aff\" rid=\"Aff9\">9</xref></contrib><contrib contrib-type=\"author\"><name><surname>Bettampadi</surname><given-names>Deepti</given-names></name><xref ref-type=\"aff\" rid=\"Aff10\">10</xref><xref ref-type=\"aff\" rid=\"Aff11\">11</xref></contrib><contrib contrib-type=\"author\"><name><surname>Qorbani</surname><given-names>Mostafa</given-names></name><xref ref-type=\"aff\" rid=\"Aff12\">12</xref></contrib><contrib contrib-type=\"author\"><name><surname>Moradi-Lakeh</surname><given-names>Maziar</given-names></name><xref ref-type=\"aff\" rid=\"Aff13\">13</xref></contrib><contrib contrib-type=\"author\"><name><surname>Ardalan</surname><given-names>Mohammadreza</given-names></name><xref ref-type=\"aff\" rid=\"Aff14\">14</xref></contrib><contrib contrib-type=\"author\"><name><surname>Mokdad</surname><given-names>Ali</given-names></name><xref ref-type=\"aff\" rid=\"Aff15\">15</xref></contrib><contrib contrib-type=\"author\"><name><surname>Fitzmaurice</surname><given-names>Christina</given-names></name><xref ref-type=\"aff\" rid=\"Aff15\">15</xref><xref ref-type=\"aff\" rid=\"Aff16\">16</xref></contrib><aff id=\"Aff1\"><label>1</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412888.f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2174 8913</institution-id><institution>Social Determinants of Health Research Center, Department of Community Medicine, School of Medicine, </institution><institution>Tabriz University of Medical Sciences, </institution></institution-wrap>Tabriz, Iran </aff><aff id=\"Aff2\"><label>2</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411705.6</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0166 0922</institution-id><institution>Sports Medicine Research Center, Neuroscience Institute, </institution><institution>Tehran University of Medical Sciences, </institution></institution-wrap>Tehran, Iran </aff><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412888.f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2174 8913</institution-id><institution>Physical Medicine and Rehabilitation Research Center, Aging Research Institute, </institution><institution>Tabriz University of Medical Sciences, </institution></institution-wrap>Tabriz, Iran </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411600.2</institution-id><institution>Social Determinants of Health Research Center, </institution><institution>Shahid Beheshti University of Medical Sciences, </institution></institution-wrap>Tehran, Iran </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411705.6</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0166 0922</institution-id><institution>Department of Epidemiology and Biostatistics, School of Public Health, </institution><institution>Tehran University of Medical Sciences, </institution></institution-wrap>Tehran, Iran </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.468130.8</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 1218 604X</institution-id><institution>Department of Epidemiology, School of Health, </institution><institution>Arak University of Medical Sciences, </institution></institution-wrap>Arak, Iran </aff><aff id=\"Aff7\"><label>7</label>Department of Epidemiology, School of Health, Bam University of Medical Sciences, Bam, Iran </aff><aff id=\"Aff8\"><label>8</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.413056.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0383 4764</institution-id><institution>Department of Social Sciences, </institution><institution>University of Nicosia, </institution></institution-wrap>Nicosia, Cyprus </aff><aff id=\"Aff9\"><label>9</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.413056.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 0383 4764</institution-id><institution>Department of Life and Health Sciences, </institution><institution>University of Nicosia, </institution></institution-wrap>Nicosia, Cyprus </aff><aff id=\"Aff10\"><label>10</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.468198.a</institution-id><institution-id institution-id-type=\"ISNI\">0000 0000 9891 5233</institution-id><institution>Center for Immunization and Infection Research in Cancer (CIIRC), </institution><institution>H. Lee Moffitt Cancer Center and Research Institute, </institution></institution-wrap>Tampa, FL USA </aff><aff id=\"Aff11\"><label>11</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.214458.e</institution-id><institution-id institution-id-type=\"ISNI\">0000000086837370</institution-id><institution>Department of Epidemiology, School of Public Health, </institution><institution>University of Michigan, </institution></institution-wrap>Ann Arbor, MI USA </aff><aff id=\"Aff12\"><label>12</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411705.6</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 0166 0922</institution-id><institution>Non-Communicable Diseases Research Center, </institution><institution>Alborz University of Medical Sciences, </institution></institution-wrap>Karaj, Iran </aff><aff id=\"Aff13\"><label>13</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.411746.1</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 4911 7066</institution-id><institution>Preventive Medicine and Public Health Research Center, </institution><institution>Iran University of Medical Sciences, </institution></institution-wrap>Tehran, Iran </aff><aff id=\"Aff14\"><label>14</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.412888.f</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2174 8913</institution-id><institution>Kidney Research Center, </institution><institution>Tabriz University of Medical Sciences, </institution></institution-wrap>Tabriz, Iran </aff><aff id=\"Aff15\"><label>15</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.34477.33</institution-id><institution-id institution-id-type=\"ISNI\">0000000122986657</institution-id><institution>Institute for Health Metrics and Evaluation, </institution><institution>University of Washington, </institution></institution-wrap>Seattle, WA USA </aff><aff id=\"Aff16\"><label>16</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.34477.33</institution-id><institution-id institution-id-type=\"ISNI\">0000000122986657</institution-id><institution>Department of Medicine, Division of Hematology, </institution><institution>University of Washington, </institution></institution-wrap>Seattle, WA USA </aff></contrib-group><pub-date pub-type=\"epub\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>17</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>13862</elocation-id><history><date date-type=\"received\"><day>12</day><month>2</month><year>2020</year></date><date date-type=\"accepted\"><day>3</day><month>8</month><year>2020</year></date></history><permissions><copyright-statement>© The Author(s) 2020</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">Kidney cancer globally accounts for more than 131,000 deaths each year and has been found to place a large economic burden on society. However, there are no recent articles on the burden of kidney cancer across the world. The aim of this study was to present a status report on the incidence, mortality and disability-adjusted life years (DALYs) associated with kidney cancer in 195 countries, from 1990 to 2017. Vital registration and cancer registry data (total of 23,660 site-years) were used to generate the estimates. Mortality was estimated first and the incidence and DALYs were calculated based on the estimated mortality values. All estimates were presented as counts and age-standardised rates per 100,000 population. The estimated rates were calculated by age, sex and according to the Socio-Demographic Index (SDI). In 2017, kidney cancer accounted for 393.0 thousand (95% UI: 371.0–404.6) incident cases, 138.5 thousand (95% UI: 128.7–142.5) deaths and 3.3 million (95% UI: 3.1–3.4) DALYs globally. The global age-standardised rates for the incidence, deaths and DALY were 4.9 (95% UI: 4.7–5.1), 1.7 (95% UI: 1.6–1.8) and 41.1 (95% UI: 38.7–42.5), respectively. Uruguay [15.8 (95% UI: 13.6–19.0)] and Bangladesh [1.5 (95% UI: 1.0–1.8)] had highest and lowest age-standardised incidence rates, respectively. The age-standardised death rates varied substantially from 0.47 (95% UI: 0.34–0.58) in Bangladesh to 5.6 (95% UI: 4.6–6.1) in the Czech Republic. Incidence and mortality rates were higher among males, than females, across all age groups, with the highest rates for both sexes being observed in the 95+ age group. Generally, positive associations were found between each country’s age-standardised DALY rate and their corresponding SDI. The considerable burden of kidney cancer was attributable to high body mass index (18.5%) and smoking (16.6%) in both sexes. There are large inter-country differences in the burden of kidney cancer and it is generally higher in countries with a high SDI. The findings from this study provide much needed information for those in each country that are making health-related decisions about priority areas, resource allocation, and the effectiveness of prevention programmes. The results of our study also highlight the need for renewed efforts to reduce exposure to the kidney cancer risk factors and to improve the prevention and the early detection of this disease.</p></abstract><kwd-group kwd-group-type=\"npg-subject\"><title>Subject terms</title><kwd>Cancer epidemiology</kwd><kwd>Renal cancer</kwd></kwd-group><funding-group><award-group><funding-source><institution>Shahid Beheshti University of Medical Sciences</institution></funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2020</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Introduction</title><p id=\"Par2\">Globally, the number of cancer cases increased from 18.3 million in 2007 to 24.5 million in 2017<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. Cancers also accounted for 9.6 million deaths in 2017, or 17% of all deaths<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. There are large inter-country variations in the incidence, mortality, years lived with disability (YLD), years of life lost (YLL) and disability adjusted life years (DALYs) for all types of cancers<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. Therefore, up-to-date statistics on the incidence, mortality and DALYs for the different types of cancer is essential information for those making health-related decisions about priority areas, resource allocation, the effectiveness of prevention programmes and the need for additional research.</p><p id=\"Par3\">Kidney cancers are one of the most important cancers, due in part to the large economic burden of metastatic kidney cancer, which has been estimated to be $1.6 billion (2006 USD) in selected countries<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup> and to globally account for more than 131,000 deaths and 342,000 incident cases each year<sup><xref ref-type=\"bibr\" rid=\"CR4\">4</xref></sup>. The etiology of kidney cancer is mainly unknown, but appears to be multifactorial in nature<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. A number of different risk factors have been studied, some of which are modifiable, thus creating an opportunity for primary prevention. The risk factors for kidney cancer have been categorized as: (a) life style risk factors—tobacco smoking, excess body weight, alcohol consumption, physical activity and diet; (b) medical history—hypertension, chronic kidney diseases, kidney stones, and diabetes mellitus; (c) environmental and occupational exposures—trichloroethylene and aristolochic acid; (d) genetic risk factors and others<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. The attributable burden of these risk factors have not been reported in previous research, although this information is very helpful in the development and prioritization of prevention programs.</p><p id=\"Par4\">Although, a number of studies have examined the burden of kidney cancer, to the best of our knowledge there have been no comprehensive articles published recently. In fact, previous studies have reported the incidence and mortality of kidney cancer only at a global<sup><xref ref-type=\"bibr\" rid=\"CR7\">7</xref></sup> or regional level<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>, and have not reported data from all the individual countries. This information is important, as countries may have completely different epidemiological patterns for kidney cancer and thus using global or regional-level data may be inappropriate. In addition, the most recent review paper on the global epidemiology of kidney cancer used older data<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Furthermore, there have been no articles about kidney cancer from the latest release (September 2018) from GLOBOCAN (Global Cancer Incidence, Mortality and Prevalence), which is another valuable source of information about the burden of disease<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. Therefore, this article reports the incidence, mortality and DALYs for kidney cancer and its attributable risk factors from 1990 to 2017 in 195 countries; by age, sex and socio-demographic Index (SDI).</p></sec><sec id=\"Sec2\"><title>Methods</title><sec id=\"Sec3\"><title>Overview</title><p id=\"Par5\">The Global Burden of Disease (GBD) study is a comprehensive research programme that studies the burden of disease across the world. GBD 2017 included estimates for 195 countries, grouped into 7 super-regions and 21 regions, from 1990 to 2017. In total, 355 diseases and injuries, 282 causes of death and 84 risk factors were analyzed in this iteration of the GBD programme. Methods, including changes from previous updates, have been described in detail in the GBD 2017 capstone manuscripts<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref>,<xref ref-type=\"bibr\" rid=\"CR10\">10</xref>,<xref ref-type=\"bibr\" rid=\"CR11\">11</xref></sup>. Furthermore, additional information on the data sources used, results, and analytical code can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"https://vizhub.healthdata.org/gbd-compare/\">https://vizhub.healthdata.org/gbd-compare/</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://ghdx.healthdata.org/gbd-results-tool\">https://ghdx.healthdata.org/gbd-results-tool</ext-link>. All rates are reported per 100,000 person-years, with 95% uncertainty intervals (UIs). The UIs take into account uncertainty due to measurement error, potential biases, and the modelling process. The estimates were computed using the mean estimate across 1,000 draws, and the 95% UIs were specified on the basis of the 25th and 975th ranked values across all 1,000 draws. The GBD world population standard was used for the calculation of age-standardized rates<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>.</p></sec><sec id=\"Sec4\"><title>Estimation framework</title><p id=\"Par6\">In accordance with version 10 of the International Classification of Diseases (ICD)<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>, all tumors coded as C64-C65.9, D30.0-D30.1, D41.0-D41.1 were considered to be kidney cancer. The estimates of kidney cancer mortality were calculated using vital registration system data (n = 18,557 site-years), vital registration-sample data (n = 761 site-years) and cancer registry data (n = 4,342 site-years)<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. These data were provided by collaborators, or accessed via publicly available sources. Due to the sparsity of the mortality data, and the lack of vital registration systems in some locations with incidence data, GBD transformed incidence data into mortality estimates by multiplying the incidence data by independently modeled mortality-to-incidence ratios (MIR). The MIRs were modeled using data from locations where both mortality and incidence data for kidney cancer were available for the same year. The initial MIR model used a linear-step mixed-effects model with a logit link function, with the Healthcare Access and Quality Index (HAQ) as a predictive covariate<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref>,<xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. The estimates from this model were then smoothed over space and time and adjusted for time, space and age using Spatio-Temporal Gaussian process regression<sup><xref ref-type=\"bibr\" rid=\"CR1\">1</xref></sup>. Mortality data from vital registration systems and the mortality estimates from cancer registry incidence data were combined and used as an input for a cause of death ensemble model (CODEm)<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. The covariates in the CODEm are used to predict mortality, if data is sparse or missing. These covariates do not need to have a proven causal association, only robust evidence of an association with kidney cancer mortality is required<sup><xref ref-type=\"bibr\" rid=\"CR12\">12</xref></sup>. The covariates used in CODEm have been presented elsewhere<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>. The CoDCorrect algorithm was used to adjust the sum of predicted single cause mortalities in an age–sex–state–year group, in order to be consistent with the results from the all-cause mortality estimations<sup><xref ref-type=\"bibr\" rid=\"CR2\">2</xref></sup>.</p><p id=\"Par7\">The incidence of kidney cancer was estimated by dividing the final mortality estimates by the MIR. The 10-year prevalence of kidney cancer was computed by modelling survival of each incidence cohort using the MIR as a scalar to identify which countries fell between a theoretical best and a theoretical worst-case survival rate. Those cohort members who had survived more than 10-years were considered to be cured and these cases were assigned to one of two sequelae: <italic>diagnosis and primary therapy</italic> or the <italic>controlled</italic> phase of kidney cancer. Those in the cohort that had died within the last 10 years were divided into four sequelae, as presented in Table <xref rid=\"MOESM5\" ref-type=\"media\">1</xref> of the Online Appendix. The following durations of 5.3, 5.38, and 1 month were used for the <italic>diagnosis and primary therapy</italic>, <italic>metastatic</italic> and <italic>terminal</italic> phases, respectively. The remaining time was assigned to the <italic>controlled</italic> phase. Finally, each sequela-specific prevalence rate was multiplied with specific disability weights to estimate the sequela-specific YLDs.</p><p id=\"Par8\">The Socio-Demographic Index (SDI) was used to examine the association between country development and the burden of kidney cancer. The shape of the association between kidney cancer burden, measured as DALYs, with the SDIs of 21 regions and 195 countries and territories were determined using smoothing splines models<sup><xref ref-type=\"bibr\" rid=\"CR13\">13</xref></sup>. The observed burden of kidney cancer was compared with the level expected, based on the SDI values of the countries and regions.</p><p id=\"Par9\">The SDI ranges from 0–1, with 0 reflecting the lowest level of development and 1 the highest. SDI is comprised of the total fertility rate in women under 25 years old, mean education for those aged 15 and above, and lag distributed income per capita. The global maps were generated using R, software version 3.5.2.</p></sec><sec id=\"Sec5\"><title>Risk factors</title><p id=\"Par10\">The proportion of kidney cancer DALYs attributable to high BMI<sup><xref ref-type=\"bibr\" rid=\"CR14\">14</xref></sup>, smoking<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup> and occupational exposure to Trichloroethylene<sup><xref ref-type=\"bibr\" rid=\"CR16\">16</xref></sup> were also calculated, as robust evidence exists of their association with kidney cancer<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. Using all available evidence, the relative risk and prevalence of exposure were estimated separately for each risk factor and their corresponding population attributable fractions (PAFs) were estimated using the theoretical minimum exposure approach. The DALYs that were attributable to each risk factor were computed by multiplying the total DALYs for kidney cancer by the PAFs for each risk factor–outcome pair for each age group, sex, location, and year. The definition of these risk factors and their relative risks for kidney cancer are fully described elsewhere<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>.</p></sec><sec id=\"Sec6\"><title>Ethics approval and consent to participate</title><p id=\"Par11\">Publicly available data were used in this report. The ethical committee of Shahid Beheshti University of Medical Sciences approved the project (IR.SBMU.RETECH.REC.1397.1368). This study was based upon publicly available data and solely reflects the opinion of its authors and not that of the Institute for Health Metrics and Evaluation.</p></sec><sec id=\"Sec7\"><title>Consent for publication</title><p id=\"Par12\">Not applicable.</p></sec></sec><sec id=\"Sec8\"><title>Results</title><sec id=\"Sec9\"><title>Global level</title><p id=\"Par13\">In 2017, there were 393.0 thousand (95% UI: 371.0–404.6) incident cases of kidney cancer, with an age-standardised rate of 4.9 (95% UI: 4.7–5.1). This increased by 4.7% (95% UI: − 1.1 to 11.7) between 1990 and 2017, but this increase was not statistically significant. About 138.5 thousand (95% UI: 128.7–142.5) deaths occurred due to kidney cancer, with an age-standardised death rate of 1.7 (95% UI: 1.6–1.8). This death rate increased by 4.4% (95% UI: − 0.3 to 10.5) across the measurement period, but was not statistically significant. Kidney cancer accounted for 3.3 million (95% UI: 3.1–3.4) DALYs in 2017, with an age-standardised rate of 41.1 (95% UI: 38.7–42.5). There was a decrease of 3.6% (95% UI: − 9.2 to 3.2) across the reporting period, but again this was not statistically significant (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>).<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Incidents, Deaths, and DALYs for kidney cancer in 2017 and the percentage change in age-standardized rates by location.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" colspan=\"3\">Incidence (95% UI)</th><th align=\"left\" colspan=\"3\">Deaths (95% UI)</th><th align=\"left\" colspan=\"3\">DALYs (95% UI)</th></tr><tr><th align=\"left\">2017<break/>Counts</th><th align=\"left\">2017<break/>ASRs</th><th align=\"left\">PCs in ASRs<break/>1990–2017</th><th align=\"left\">2017<break/>Counts</th><th align=\"left\">2017<break/>ASRs</th><th align=\"left\">PCs in ASRs<break/>1990–2017</th><th align=\"left\">2017<break/>Counts</th><th align=\"left\">2017<break/>ASRs</th><th align=\"left\">PCs in ASRs<break/>1990–2017</th></tr></thead><tbody><tr><td align=\"left\">Global</td><td align=\"left\">393,043 (371,162, 404,595)</td><td align=\"left\">4.9 (4.7, 5.1)</td><td align=\"left\">4.7 (− 1.1, 11.7)</td><td align=\"left\">138,526 (128,656, 142,522)</td><td align=\"left\">1.8 1.6, 1.8)</td><td align=\"left\">4.4 (− 0.3, 10.5)</td><td align=\"left\">3,284,321 (3,085,565, 3,393,164)</td><td align=\"left\">41.1 (38.7, 42.5)</td><td align=\"left\">− 3.6 (− 9.2, 3.2)</td></tr><tr><td align=\"left\">High‐income North America</td><td align=\"left\">68,843 (65,664, 74,202)</td><td align=\"left\">12.2 (11.6, 13.2)</td><td align=\"left\">3.5 (− 3.6, 21.5)</td><td align=\"left\">19,048 (18,298, 20,091)</td><td align=\"left\">3.1 (3, 3.3)</td><td align=\"left\">− 1 (− 6.3, 11)</td><td align=\"left\">407,780 (387,523, 435,782)</td><td align=\"left\">72.6 (68.9, 78)</td><td align=\"left\">− 9.1 (− 14.9, 4)</td></tr><tr><td align=\"left\">Canada</td><td align=\"left\">4,364 (3,851, 4,843)</td><td align=\"left\">7.2 (6.4, 8)</td><td align=\"left\">35.2 (18.4, 52.2)</td><td align=\"left\">1988 (1751, 2,168)</td><td align=\"left\">2.9 (2.6, 3.2)</td><td align=\"left\">20.1 (7.7, 31)</td><td align=\"left\">39,609 (35,572, 43,294)</td><td align=\"left\">65.4 (58.9, 71.2)</td><td align=\"left\">11.3 (− 1, 21.8)</td></tr><tr><td align=\"left\">Greenland</td><td align=\"left\">9 (7, 10)</td><td align=\"left\">12.6 (10.7, 14.3)</td><td align=\"left\">9.2 (− 8.9, 31.8)</td><td align=\"left\">3 (3, 4)</td><td align=\"left\">5.2 (4.2, 5.8)</td><td align=\"left\">− 2.9 (− 18, 15.5)</td><td align=\"left\">81 (69, 91)</td><td align=\"left\">113.1 (96.2, 127.6)</td><td align=\"left\">− 9.3 (− 23.7, 8.4)</td></tr><tr><td align=\"left\">USA</td><td align=\"left\">64,469 (61,274, 70,161)</td><td align=\"left\">12.7 (12.1, 14)</td><td align=\"left\">2.6 (− 4.8, 21.7)</td><td align=\"left\">17,057 (16,377, 18,210)</td><td align=\"left\">3.1 (3, 3.3)</td><td align=\"left\">− 2.7 (− 8.3, 10.4)</td><td align=\"left\">368,083 (349,156, 397,599)</td><td align=\"left\">73.4 (69.3, 79.6)</td><td align=\"left\">− 10.6 (− 16.6, 3.4)</td></tr><tr><td align=\"left\">Australasia</td><td align=\"left\">3,873 (3,494, 4,320)</td><td align=\"left\">8.8 (7.9, 9.8)</td><td align=\"left\">28.1 (14.5, 45.3)</td><td align=\"left\">1588 (1,436, 1739)</td><td align=\"left\">3.3 (3, 3.6)</td><td align=\"left\">0.1 (− 9.3, 14.3)</td><td align=\"left\">31,608 (28,621, 34,962)</td><td align=\"left\">73.1 (66.3, 80.9)</td><td align=\"left\">− 3.5 (− 12.8, 10.5)</td></tr><tr><td align=\"left\">Australia</td><td align=\"left\">3,249 (2,856, 3,689)</td><td align=\"left\">8.8 (7.7, 9.9)</td><td align=\"left\">31.9 (15.6, 52)</td><td align=\"left\">1,342 (1,196, 1,497)</td><td align=\"left\">3.3 (2.9, 3.7)</td><td align=\"left\">− 1 (− 12.4, 14.2)</td><td align=\"left\">26,566 (23,590, 29,815)</td><td align=\"left\">73 (65.1, 82.2)</td><td align=\"left\">− 4.2 (− 15, 10.9)</td></tr><tr><td align=\"left\">New Zealand</td><td align=\"left\">624 (556, 692)</td><td align=\"left\">9 (8, 10)</td><td align=\"left\">12.1 (− 1.8, 27.8)</td><td align=\"left\">246 (223, 269)</td><td align=\"left\">3.2 (3, 3.5)</td><td align=\"left\">6.2 (− 4.2, 19.2)</td><td align=\"left\">5,042 (4,579, 5,503)</td><td align=\"left\">73.6 (67.1, 80.1)</td><td align=\"left\">0 (− 9.3, 12.2)</td></tr><tr><td align=\"left\">High‐income Asia‐Pacific</td><td align=\"left\">16,893 (14,907, 18,246)</td><td align=\"left\">4.4 (3.9, 4.8)</td><td align=\"left\">35.1 (13.1, 50.4)</td><td align=\"left\">8,974 (7,762, 9,523)</td><td align=\"left\">1.9 (1.7, 2)</td><td align=\"left\">24.9 (1.5, 33.9)</td><td align=\"left\">149,031 (134,762, 159,397)</td><td align=\"left\">39.6 (35.9, 42.7)</td><td align=\"left\">11.8 (− 5.3, 21.2)</td></tr><tr><td align=\"left\">Brunei</td><td align=\"left\">29 (25, 34)</td><td align=\"left\">8.1 (7.1, 9.4)</td><td align=\"left\">58.2 (28.5, 95.6)</td><td align=\"left\">9 (8, 10)</td><td align=\"left\">2.9 (2.6, 3.5)</td><td align=\"left\">28.7 (3.2, 58)</td><td align=\"left\">255 (225, 299)</td><td align=\"left\">71.8 (63.6, 83.9)</td><td align=\"left\">30 (5.1, 57.2)</td></tr><tr><td align=\"left\">Japan</td><td align=\"left\">13,140 (11,485, 14,232)</td><td align=\"left\">4.4 (4, 4.9)</td><td align=\"left\">28.3 (11.8, 42.2)</td><td align=\"left\">7,387 (6,321, 7,812)</td><td align=\"left\">1.9 (1.7, 2)</td><td align=\"left\">18.6 (− 1.4, 26.3)</td><td align=\"left\">114,712 (103,112, 122,136)</td><td align=\"left\">39.6 (36.3, 42.4)</td><td align=\"left\">5.5 (− 7.4, 13.1)</td></tr><tr><td align=\"left\">Singapore</td><td align=\"left\">226 (195, 259)</td><td align=\"left\">3.3 (2.8, 3.7)</td><td align=\"left\">25.7 (0, 48.5)</td><td align=\"left\">84 (75, 95)</td><td align=\"left\">1.2 (1.1, 1.4)</td><td align=\"left\">7 (− 13, 23.1)</td><td align=\"left\">2025 (1768, 2,301)</td><td align=\"left\">29.3 (25.7, 33.2)</td><td align=\"left\">3.7 (− 15.6, 19.4)</td></tr><tr><td align=\"left\">South Korea</td><td align=\"left\">3,498 (3,014, 3,995)</td><td align=\"left\">4.3 (3.7, 5)</td><td align=\"left\">102.6 (36.1, 145.7)</td><td align=\"left\">1,494 (1,311, 1669)</td><td align=\"left\">1.8 (1.6, 2)</td><td align=\"left\">104 (31, 140.5)</td><td align=\"left\">32,039 (27,744, 36,254)</td><td align=\"left\">39.3 (34.1, 44.6)</td><td align=\"left\">70.1 (19, 100.8)</td></tr><tr><td align=\"left\">Western Europe</td><td align=\"left\">72,675 (65,478, 76,756)</td><td align=\"left\">9.2 (8.3, 9.7)</td><td align=\"left\">12.6 (5.6, 23.5)</td><td align=\"left\">30,325 (27,097, 31,837)</td><td align=\"left\">3.3 (3, 3.4)</td><td align=\"left\">3.4 (− 2.1, 11)</td><td align=\"left\">553,194 (510,179, 582,255)</td><td align=\"left\">70.7 (65.6, 74.5)</td><td align=\"left\">− 6.2 (− 11.1, 2)</td></tr><tr><td align=\"left\">Andorra</td><td align=\"left\">8 (6, 11)</td><td align=\"left\">6.7 (4.5, 8.5)</td><td align=\"left\">16.3 (− 13, 51.2)</td><td align=\"left\">4 (2, 5)</td><td align=\"left\">2.7 (1.8, 3.5)</td><td align=\"left\">1 (− 22, 24.8)</td><td align=\"left\">76 (51, 98)</td><td align=\"left\">60.6 (40.5, 77.4)</td><td align=\"left\">− 4.7 (− 28.4, 22.1)</td></tr><tr><td align=\"left\">Austria</td><td align=\"left\">1,129 (1,018, 1,308)</td><td align=\"left\">7 (6.3, 8.4)</td><td align=\"left\">− 25.7 (− 34.8, − 0.2)</td><td align=\"left\">572 (526, 626)</td><td align=\"left\">3.1 (2.8, 3.5)</td><td align=\"left\">− 32.7 (− 39.3, − 13.4)</td><td align=\"left\">10,070 (9,207, 11,596)</td><td align=\"left\">63 (57.4, 74.2)</td><td align=\"left\">− 39.3 (− 45.8, − 20)</td></tr><tr><td align=\"left\">Belgium</td><td align=\"left\">1,494 (1,341, 1663)</td><td align=\"left\">7.4 (6.7, 8.4)</td><td align=\"left\">5.7 (− 6.2, 24)</td><td align=\"left\">757 (685, 833)</td><td align=\"left\">3.2 (2.9, 3.5)</td><td align=\"left\">− 6 (− 14.8, 6.9)</td><td align=\"left\">13,435 (12,190, 14,971)</td><td align=\"left\">66.8 (60.4, 75.1)</td><td align=\"left\">− 13.6 (− 21.6, − 0.1)</td></tr><tr><td align=\"left\">Cyprus</td><td align=\"left\">74 (62, 87)</td><td align=\"left\">4.2 (3.5, 4.9)</td><td align=\"left\">93.5 (23.3, 146.1)</td><td align=\"left\">31 (26, 36)</td><td align=\"left\">1.6 (1.4, 1.9)</td><td align=\"left\">64.9 (0.9, 106.9)</td><td align=\"left\">672 (565, 782)</td><td align=\"left\">37.7 (31.6, 44)</td><td align=\"left\">58.8 (− 0.9, 99.4)</td></tr><tr><td align=\"left\">Denmark</td><td align=\"left\">880 (780, 975)</td><td align=\"left\">8.5 (7.5, 9.4)</td><td align=\"left\">58.1 (38.4, 77.8)</td><td align=\"left\">417 (370, 457)</td><td align=\"left\">3.7 (3.3, 4)</td><td align=\"left\">15.2 (2.9, 27.8)</td><td align=\"left\">8,165 (7,297, 8,970)</td><td align=\"left\">80 (71.7, 87.8)</td><td align=\"left\">8.5 (− 3.6, 21.5)</td></tr><tr><td align=\"left\">Finland</td><td align=\"left\">1,021 (909, 1,116)</td><td align=\"left\">9.4 (8.4, 10.3)</td><td align=\"left\">13.7 (1.6, 27.4)</td><td align=\"left\">443 (394, 482)</td><td align=\"left\">3.6 (3.3, 3.9)</td><td align=\"left\">− 2.7 (− 12.3, 7.6)</td><td align=\"left\">8,185 (7,433, 8,939)</td><td align=\"left\">77.5 (70.6, 84.5)</td><td align=\"left\">− 9.7 (− 18.1, 0.1)</td></tr><tr><td align=\"left\">France</td><td align=\"left\">9,048 (7,620, 10,090)</td><td align=\"left\">7.8 (6.6, 8.8)</td><td align=\"left\">9.2 (− 2.6, 23.8)</td><td align=\"left\">4,583 (3,805, 5,058)</td><td align=\"left\">3.2 (2.8, 3.6)</td><td align=\"left\">− 4.4 (− 13.3, 5.2)</td><td align=\"left\">81,076 (69,457, 89,681)</td><td align=\"left\">70.6 (61.2, 78)</td><td align=\"left\">− 10.9 (− 19, − 1.2)</td></tr><tr><td align=\"left\">Germany</td><td align=\"left\">17,693 (14,910, 20,045)</td><td align=\"left\">10.5 (9, 11.9)</td><td align=\"left\">0.8 (− 13.6, 19)</td><td align=\"left\">7,333 (6,149, 8,276)</td><td align=\"left\">3.8 (3.2, 4.2)</td><td align=\"left\">3.6 (− 9.2, 20.9)</td><td align=\"left\">131,513 (113,699, 147,761)</td><td align=\"left\">79.2 (69.2, 89)</td><td align=\"left\">− 8.9 (− 20.5, 6.4)</td></tr><tr><td align=\"left\">Greece</td><td align=\"left\">1,343 (1,211, 1,485)</td><td align=\"left\">6.8 (6.1, 7.6)</td><td align=\"left\">49.6 (31.7, 68.9)</td><td align=\"left\">682 (624, 738)</td><td align=\"left\">2.8 (2.5, 3)</td><td align=\"left\">26.9 (14.6, 39.5)</td><td align=\"left\">12,106 (11,051, 13,185)</td><td align=\"left\">61.2 (56, 66.7)</td><td align=\"left\">22.7 (11.2, 35.1)</td></tr><tr><td align=\"left\">Iceland</td><td align=\"left\">61 (55, 68)</td><td align=\"left\">12.5 (11.3, 14)</td><td align=\"left\">26.2 (10.7, 43.8)</td><td align=\"left\">28 (26, 31)</td><td align=\"left\">5.2 (4.8, 5.7)</td><td align=\"left\">9.3 (− 2.1, 22.7)</td><td align=\"left\">555 (506, 613)</td><td align=\"left\">113.6 (103.8, 125.6)</td><td align=\"left\">4.2 (− 6.8, 16.7)</td></tr><tr><td align=\"left\">Ireland</td><td align=\"left\">555 (483, 630)</td><td align=\"left\">8.2 (7.1, 9.3)</td><td align=\"left\">33.9 (14.2, 56)</td><td align=\"left\">243 (214, 271)</td><td align=\"left\">3.4 (2.9, 3.7)</td><td align=\"left\">19.2 (3.5, 36.2)</td><td align=\"left\">5,019 (4,367, 5,657)</td><td align=\"left\">73.6 (64, 83.3)</td><td align=\"left\">9.2 (− 6.1, 24.8)</td></tr><tr><td align=\"left\">Israel</td><td align=\"left\">673 (598, 755)</td><td align=\"left\">6.3 (5.6, 7.1)</td><td align=\"left\">17.3 (2.4, 33.3)</td><td align=\"left\">312 (278, 344)</td><td align=\"left\">2.7 (2.4, 3)</td><td align=\"left\">3.1 (− 7.9, 14.1)</td><td align=\"left\">6,000 (5,419, 6,666)</td><td align=\"left\">56.6 (51, 63)</td><td align=\"left\">− 2.6 (− 13.3, 9)</td></tr><tr><td align=\"left\">Italy</td><td align=\"left\">10,742 (9,244, 12,082)</td><td align=\"left\">8.9 (7.6, 10)</td><td align=\"left\">3.6 (− 10.1, 18.8)</td><td align=\"left\">4,311 (3,659, 4,755)</td><td align=\"left\">2.9 (2.5, 3.2)</td><td align=\"left\">− 1.4 (− 11.2, 8.1)</td><td align=\"left\">75,306 (65,578, 83,159)</td><td align=\"left\">62.8 (54.7, 69.6)</td><td align=\"left\">− 13.5 (− 22.4, − 4.2)</td></tr><tr><td align=\"left\">Luxembourg</td><td align=\"left\">40 (33, 48)</td><td align=\"left\">4.4 (3.7, 5.3)</td><td align=\"left\">1.6 (− 16.8, 25.1)</td><td align=\"left\">18 (15, 21)</td><td align=\"left\">1.9 (1.6, 2.2)</td><td align=\"left\">− 9.6 (− 24.9, 8.7)</td><td align=\"left\">357 (301, 424)</td><td align=\"left\">39.9 (33.6, 47.6)</td><td align=\"left\">− 17.1 (− 31.5, 0.8)</td></tr><tr><td align=\"left\">Malta</td><td align=\"left\">56 (50, 62)</td><td align=\"left\">7.1 (6.4, 7.8)</td><td align=\"left\">28.1 (12.1, 47.8)</td><td align=\"left\">26 (24, 29)</td><td align=\"left\">2.9 (2.7, 3.2)</td><td align=\"left\">6.8 (− 5, 20.7)</td><td align=\"left\">508 (462, 559)</td><td align=\"left\">63.9 (58.2, 70.3)</td><td align=\"left\">5.3 (− 6.4, 19.4)</td></tr><tr><td align=\"left\">Netherlands</td><td align=\"left\">2,802 (2,422, 3,083)</td><td align=\"left\">9.1 (7.9, 10)</td><td align=\"left\">25.5 (9.9, 42.9)</td><td align=\"left\">1,340 (1,143, 1,457)</td><td align=\"left\">3.9 (3.4, 4.3)</td><td align=\"left\">12.7 (3.8, 25.1)</td><td align=\"left\">25,485 (22,401, 27,802)</td><td align=\"left\">82.8 (73.2, 90.1)</td><td align=\"left\">2 (− 6.9, 14.5)</td></tr><tr><td align=\"left\">Norway</td><td align=\"left\">981 (907, 1,057)</td><td align=\"left\">11.3 (10.5, 12.3)</td><td align=\"left\">52 (34.9, 67.7)</td><td align=\"left\">379 (353, 402)</td><td align=\"left\">4 (3.7, 4.2)</td><td align=\"left\">18.6 (6.9, 26.9)</td><td align=\"left\">7,293 (6,810, 7,769)</td><td align=\"left\">85.2 (79.5, 91)</td><td align=\"left\">10.5 (1, 18.8)</td></tr><tr><td align=\"left\">Portugal</td><td align=\"left\">1,269 (1,121, 1,447)</td><td align=\"left\">6.6 (5.8, 7.5)</td><td align=\"left\">9.6 (− 6.8, 29.5)</td><td align=\"left\">468 (422, 526)</td><td align=\"left\">2 (1.8, 2.2)</td><td align=\"left\">− 0.4 (− 11.5, 11.2)</td><td align=\"left\">8,933 (8,043, 10,093)</td><td align=\"left\">46.3 (41.3, 52.5)</td><td align=\"left\">− 13.3 (− 23.2, 1.5)</td></tr><tr><td align=\"left\">Spain</td><td align=\"left\">9,810 (8,449, 10,990)</td><td align=\"left\">11.6 (10, 13.1)</td><td align=\"left\">33.5 (14, 53.2)</td><td align=\"left\">2,554 (2,217, 2,789)</td><td align=\"left\">2.6 (2.3, 2.9)</td><td align=\"left\">30.9 (16.6, 43.4)</td><td align=\"left\">49,418 (42,877, 54,345)</td><td align=\"left\">61 (52.9, 67.3)</td><td align=\"left\">19.7 (6.3, 31.8)</td></tr><tr><td align=\"left\">Sweden</td><td align=\"left\">1,390 (1,276, 1,497)</td><td align=\"left\">7.6 (6.9, 8.2)</td><td align=\"left\">4.7 (− 4.9, 23.8)</td><td align=\"left\">795 (726, 846)</td><td align=\"left\">3.7 (3.4, 4)</td><td align=\"left\">− 15.5 (− 21.7, − 1.5)</td><td align=\"left\">14,056 (12,851, 15,046)</td><td align=\"left\">77.4 (70.8, 82.8)</td><td align=\"left\">− 21.2 (− 27, − 8)</td></tr><tr><td align=\"left\">Switzerland</td><td align=\"left\">932 (827, 1,056)</td><td align=\"left\">6 (5.3, 6.8)</td><td align=\"left\">16.8 (− 1.1, 38.3)</td><td align=\"left\">419 (372, 465)</td><td align=\"left\">2.4 (2.1, 2.7)</td><td align=\"left\">0.2 (− 13.4, 14.5)</td><td align=\"left\">7,497 (6,652, 8,411)</td><td align=\"left\">49.3 (43.9, 55.5)</td><td align=\"left\">− 9.3 (− 21.4, 5.7)</td></tr><tr><td align=\"left\">United Kingdom</td><td align=\"left\">10,597 (10,149, 11,084)</td><td align=\"left\">9.5 (9.1, 9.9)</td><td align=\"left\">33.1 (23.8, 41)</td><td align=\"left\">4,576 (4,372, 4,762)</td><td align=\"left\">3.6 (3.5, 3.8)</td><td align=\"left\">15.4 (5.3, 21.8)</td><td align=\"left\">86,894 (83,373, 90,837)</td><td align=\"left\">79.3 (76, 82.8)</td><td align=\"left\">3.8 (− 2.6, 9.6)</td></tr><tr><td align=\"left\">Southern Latin America</td><td align=\"left\">9,096 (8,140, 10,168)</td><td align=\"left\">11.6 (10.4, 13)</td><td align=\"left\">− 4.2 (− 17.8, 44.4)</td><td align=\"left\">3,495 (3,186, 3,857)</td><td align=\"left\">4.3 (3.9, 4.7)</td><td align=\"left\">− 7.2 (− 18, 29.5)</td><td align=\"left\">80,858 (73,155, 90,634)</td><td align=\"left\">103.1 (93.2, 115.7)</td><td align=\"left\">− 18.5 (− 28.6, 18.1)</td></tr><tr><td align=\"left\">Argentina</td><td align=\"left\">6,094 (5,320, 6,975)</td><td align=\"left\">12 (10.5, 13.8)</td><td align=\"left\">− 7.6 (− 23.5, 51)</td><td align=\"left\">2,292 (2025, 2,574)</td><td align=\"left\">4.3 (3.8, 4.9)</td><td align=\"left\">− 9.5 (− 23, 35.6)</td><td align=\"left\">54,163 (47,680, 61,399)</td><td align=\"left\">106.6 (93.8, 121.2)</td><td align=\"left\">− 20.7 (− 33, 23.7)</td></tr><tr><td align=\"left\">Chile</td><td align=\"left\">2,269 (1967, 2,613)</td><td align=\"left\">10 (8.7, 11.5)</td><td align=\"left\">10.1 (− 6.4, 33.2)</td><td align=\"left\">911 (806, 1,020)</td><td align=\"left\">3.9 (3.5, 4.4)</td><td align=\"left\">4.6 (− 8.3, 21.5)</td><td align=\"left\">20,213 (17,775, 23,120)</td><td align=\"left\">89 (78.2, 102.1)</td><td align=\"left\">− 8 (− 19.8, 9.4)</td></tr><tr><td align=\"left\">Uruguay</td><td align=\"left\">731 (632, 882)</td><td align=\"left\">15.8 (13.6, 19)</td><td align=\"left\">11.3 (− 8.4, 64.3)</td><td align=\"left\">291 (254, 339)</td><td align=\"left\">5.5 (4.8, 6.5)</td><td align=\"left\">0.2 (− 14.7, 38.4)</td><td align=\"left\">6,478 (5,633, 7,630)</td><td align=\"left\">138.9 (120.5, 163.3)</td><td align=\"left\">− 7.1 (− 21.2, 30.8)</td></tr><tr><td align=\"left\">Eastern Europe</td><td align=\"left\">32,267 (30,307, 33,740)</td><td align=\"left\">10 (9.5, 10.5)</td><td align=\"left\">17.3 (4.1, 33.8)</td><td align=\"left\">12,953 (12,178, 13,374)</td><td align=\"left\">3.8 (3.6, 3.9)</td><td align=\"left\">23.1 (10.6, 36.7)</td><td align=\"left\">318,581 (304,822, 330,166)</td><td align=\"left\">98.9 (94.7, 102.7)</td><td align=\"left\">13 (1.1, 27.2)</td></tr><tr><td align=\"left\">Belarus</td><td align=\"left\">1,470 (1,231, 1689)</td><td align=\"left\">9.8 (8.2, 11.3)</td><td align=\"left\">241 (88.1, 324.9)</td><td align=\"left\">619 (530, 703)</td><td align=\"left\">3.9 (3.3, 4.4)</td><td align=\"left\">277.5 (114.9, 359.9)</td><td align=\"left\">14,933 (12,890, 17,140)</td><td align=\"left\">99.1 (85.5, 113.8)</td><td align=\"left\">241.8 (109, 319.1)</td></tr><tr><td align=\"left\">Estonia</td><td align=\"left\">296 (215, 354)</td><td align=\"left\">12.1 (9, 14.4)</td><td align=\"left\">191.1 (93.8, 261.6)</td><td align=\"left\">121 (84, 141)</td><td align=\"left\">4.5 (3.2, 5.2)</td><td align=\"left\">247.4 (124, 321.7)</td><td align=\"left\">2,366 (1747, 2,776)</td><td align=\"left\">99.4 (75.3, 116.9)</td><td align=\"left\">192.9 (99.1, 255)</td></tr><tr><td align=\"left\">Latvia</td><td align=\"left\">377 (280, 440)</td><td align=\"left\">10.3 (7.9, 12.1)</td><td align=\"left\">216.3 (78.8, 293.8)</td><td align=\"left\">182 (135, 209)</td><td align=\"left\">4.5 (3.4, 5.1)</td><td align=\"left\">256.3 (113.4, 336.1)</td><td align=\"left\">3,689 (2,895, 4,275)</td><td align=\"left\">103.4 (83.6, 120)</td><td align=\"left\">212 (99.4, 283.9)</td></tr><tr><td align=\"left\">Lithuania</td><td align=\"left\">571 (452, 636)</td><td align=\"left\">11.1 (8.9, 12.5)</td><td align=\"left\">186.7 (97.2, 241.2)</td><td align=\"left\">274 (214, 301)</td><td align=\"left\">4.8 (3.8, 5.2)</td><td align=\"left\">219.3 (130.8, 270.1)</td><td align=\"left\">5,832 (4,762, 6,422)</td><td align=\"left\">115.3 (95.2, 127.3)</td><td align=\"left\">188.5 (115.4, 233.9)</td></tr><tr><td align=\"left\">Moldova</td><td align=\"left\">353 (316, 391)</td><td align=\"left\">6.8 (6.1, 7.6)</td><td align=\"left\">17.3 (0.5, 37.5)</td><td align=\"left\">129 (118, 141)</td><td align=\"left\">2.3 (2.1, 2.5)</td><td align=\"left\">31.5 (16.4, 49.6)</td><td align=\"left\">3,611 (3,298, 3,916)</td><td align=\"left\">70.1 (63.8, 76)</td><td align=\"left\">19.3 (6, 34.4)</td></tr><tr><td align=\"left\">Russia</td><td align=\"left\">22,657 (21,404, 23,939)</td><td align=\"left\">10.2 (9.7, 10.8)</td><td align=\"left\">4.8 (− 7, 20.5)</td><td align=\"left\">8,670 (8,269, 8,945)</td><td align=\"left\">3.7 (3.6, 3.8)</td><td align=\"left\">9.7 (2.3, 17.9)</td><td align=\"left\">210,097 (202,511, 217,950)</td><td align=\"left\">94.5 (91.2, 98)</td><td align=\"left\">− 1.9 (− 9.6, 10)</td></tr><tr><td align=\"left\">Ukraine</td><td align=\"left\">6,542 (5,855, 7,208)</td><td align=\"left\">9.5 (8.5, 10.6)</td><td align=\"left\">31.8 (3.8, 70.3)</td><td align=\"left\">2,958 (2,709, 3,210)</td><td align=\"left\">4 (3.6, 4.3)</td><td align=\"left\">33.6 (4.1, 73.2)</td><td align=\"left\">78,052 (71,093, 85,143)</td><td align=\"left\">114 (103.6, 124.7)</td><td align=\"left\">35.3 (6.5, 74.7)</td></tr><tr><td align=\"left\">Central Europe</td><td align=\"left\">17,167 (14,768, 18,046)</td><td align=\"left\">8.6 (7.5, 9.1)</td><td align=\"left\">26.3 (9.6, 34)</td><td align=\"left\">8,099 (7,044, 8,503)</td><td align=\"left\">3.8 (3.3, 4)</td><td align=\"left\">36.9 (20.2, 45.3)</td><td align=\"left\">175,474 (155,504, 184,347)</td><td align=\"left\">90.3 (80.1, 94.7)</td><td align=\"left\">20.8 (9.4, 27.4)</td></tr><tr><td align=\"left\">Albania</td><td align=\"left\">192 (155, 242)</td><td align=\"left\">4.9 (4, 6.1)</td><td align=\"left\">36.1 (6.5, 73.2)</td><td align=\"left\">83 (67, 103)</td><td align=\"left\">2 (1.6, 2.5)</td><td align=\"left\">52.2 (20.2, 91.6)</td><td align=\"left\">1914 (1548, 2,402)</td><td align=\"left\">50.1 (41, 61.5)</td><td align=\"left\">38.3 (10.1, 72.4)</td></tr><tr><td align=\"left\">Bosnia and Herzegovina</td><td align=\"left\">436 (375, 499)</td><td align=\"left\">7.6 (6.5, 8.7)</td><td align=\"left\">39.7 (6.1, 67.1)</td><td align=\"left\">186 (162, 212)</td><td align=\"left\">3.1 (2.8, 3.6)</td><td align=\"left\">68.3 (21.7, 96.4)</td><td align=\"left\">4,288 (3,739, 4,867)</td><td align=\"left\">76.5 (66.8, 86.8)</td><td align=\"left\">55.7 (14.8, 82.3)</td></tr><tr><td align=\"left\">Bulgaria</td><td align=\"left\">703 (613, 781)</td><td align=\"left\">5.6 (4.8, 6.2)</td><td align=\"left\">98.2 (60.2, 130.4)</td><td align=\"left\">325 (289, 356)</td><td align=\"left\">2.3 (2.1, 2.5)</td><td align=\"left\">124.5 (84.4, 156.6)</td><td align=\"left\">7,743 (6,862, 8,491)</td><td align=\"left\">63.8 (56.4, 70)</td><td align=\"left\">114.5 (81, 143.7)</td></tr><tr><td align=\"left\">Croatia</td><td align=\"left\">1,000 (790, 1,119)</td><td align=\"left\">12.3 (9.9, 13.8)</td><td align=\"left\">152.3 (60.5, 202.6)</td><td align=\"left\">336 (274, 371)</td><td align=\"left\">3.8 (3.1, 4.1)</td><td align=\"left\">186.8 (94.7, 232.3)</td><td align=\"left\">6,753 (5,754, 7,460)</td><td align=\"left\">84.5 (72.6, 93)</td><td align=\"left\">150.1 (78.8, 188.3)</td></tr><tr><td align=\"left\">Czech Republic</td><td align=\"left\">2,608 (2097, 2,895)</td><td align=\"left\">13.1 (10.7, 14.5)</td><td align=\"left\">20.8 (− 1.2, 36.5)</td><td align=\"left\">1,173 (954, 1,285)</td><td align=\"left\">5.6 (4.6, 6.1)</td><td align=\"left\">35 (10.4, 48.8)</td><td align=\"left\">23,383 (19,693, 25,528)</td><td align=\"left\">120 (102.1, 130.6)</td><td align=\"left\">15.5 (− 1.9, 27.3)</td></tr><tr><td align=\"left\">Hungary</td><td align=\"left\">1738 (1566, 1926)</td><td align=\"left\">9.8 (8.8, 10.9)</td><td align=\"left\">− 23.5 (− 33.3, − 12.5)</td><td align=\"left\">788 (723, 853)</td><td align=\"left\">4.1 (3.8, 4.4)</td><td align=\"left\">− 11.9 (− 20.1, − 3)</td><td align=\"left\">16,557 (15,177, 18,020)</td><td align=\"left\">96 (88.3, 104.6)</td><td align=\"left\">− 18.7 (− 26.6, − 9.6)</td></tr><tr><td align=\"left\">Macedonia</td><td align=\"left\">138 (116, 158)</td><td align=\"left\">4.5 (3.6, 5.2)</td><td align=\"left\">108.9 (14.2, 157.4)</td><td align=\"left\">53 (46, 61)</td><td align=\"left\">1.6 (1.4, 1.9)</td><td align=\"left\">120.4 (24.9, 169)</td><td align=\"left\">1,377 (1,154, 1574)</td><td align=\"left\">45.7 (37.5, 51.9)</td><td align=\"left\">119.1 (21.8, 165.6)</td></tr><tr><td align=\"left\">Montenegro</td><td align=\"left\">54 (46, 65)</td><td align=\"left\">5.7 (4.9, 6.7)</td><td align=\"left\">− 0.2 (− 16.4, 21.8)</td><td align=\"left\">23 (20, 27)</td><td align=\"left\">2.3 (2, 2.7)</td><td align=\"left\">13.4 (− 4.1, 38.3)</td><td align=\"left\">528 (458, 624)</td><td align=\"left\">55.7 (48.5, 64.9)</td><td align=\"left\">4 (− 11.6, 24.9)</td></tr><tr><td align=\"left\">Poland</td><td align=\"left\">5,290 (4,596, 5,803)</td><td align=\"left\">8.1 (7.1, 8.9)</td><td align=\"left\">20.6 (9.1, 33.3)</td><td align=\"left\">3,171 (2,710, 3,466)</td><td align=\"left\">4.6 (3.9, 5)</td><td align=\"left\">25.6 (15, 36.9)</td><td align=\"left\">68,059 (60,165, 74,216)</td><td align=\"left\">106.3 (94.4, 115.8)</td><td align=\"left\">6.2 (− 2.7, 17.4)</td></tr><tr><td align=\"left\">Romania</td><td align=\"left\">2,290 (2053, 2,536)</td><td align=\"left\">7.1 (6.4, 7.8)</td><td align=\"left\">26.7 (11.6, 44.1)</td><td align=\"left\">941 (870, 1,048)</td><td align=\"left\">2.7 (2.5, 2.9)</td><td align=\"left\">53.6 (39.1, 67.1)</td><td align=\"left\">22,473 (20,734, 24,368)</td><td align=\"left\">72 (66.3, 78)</td><td align=\"left\">35 (22, 49.1)</td></tr><tr><td align=\"left\">Serbia</td><td align=\"left\">1,165 (933, 1,318)</td><td align=\"left\">7.9 (6.3, 9)</td><td align=\"left\">13.3 (− 5.2, 42.1)</td><td align=\"left\">524 (429, 586)</td><td align=\"left\">3.3 (2.7, 3.7)</td><td align=\"left\">29.3 (7.1, 60.9)</td><td align=\"left\">11,597 (9,294, 12,985)</td><td align=\"left\">79.6 (62.5, 89.8)</td><td align=\"left\">15.8 (− 2.4, 42.8)</td></tr><tr><td align=\"left\">Slovakia</td><td align=\"left\">1,234 (652, 1,440)</td><td align=\"left\">14.1 (7.7, 16.5)</td><td align=\"left\">156.2 (10.7, 216.5)</td><td align=\"left\">349 (182, 402)</td><td align=\"left\">3.8 (2, 4.4)</td><td align=\"left\">148.4 (5.4, 203.5)</td><td align=\"left\">7,928 (4,385, 9,198)</td><td align=\"left\">90.9 (51.2, 105.1)</td><td align=\"left\">124.8 (0.2, 175.8)</td></tr><tr><td align=\"left\">Slovenia</td><td align=\"left\">318 (277, 359)</td><td align=\"left\">8.1 (7, 9.1)</td><td align=\"left\">44.8 (20.6, 67.4)</td><td align=\"left\">146 (125, 163)</td><td align=\"left\">3.4 (2.9, 3.8)</td><td align=\"left\">67.5 (40, 91)</td><td align=\"left\">2,873 (2,514, 3,208)</td><td align=\"left\">74.5 (65.5, 83)</td><td align=\"left\">48.2 (27.3, 68.3)</td></tr><tr><td align=\"left\">Central Asia</td><td align=\"left\">5,185 (4,844, 5,520)</td><td align=\"left\">6.3 (5.9, 6.7)</td><td align=\"left\">12.3 (− 7, 37.4)</td><td align=\"left\">1704 (1611, 1791)</td><td align=\"left\">2.2 (2.1, 2.4)</td><td align=\"left\">25.6 (2.2, 55.7)</td><td align=\"left\">52,337 (49,287, 55,105)</td><td align=\"left\">62.4 (58.9, 65.6)</td><td align=\"left\">16.8 (− 3, 41)</td></tr><tr><td align=\"left\">Armenia</td><td align=\"left\">246 (201, 273)</td><td align=\"left\">6.1 (5, 6.7)</td><td align=\"left\">284.2 (115, 390.1)</td><td align=\"left\">107 (85, 117)</td><td align=\"left\">2.6 (2, 2.8)</td><td align=\"left\">396.6 (187.3, 526.2)</td><td align=\"left\">2,382 (2026, 2,612)</td><td align=\"left\">59.1 (50.4, 64.5)</td><td align=\"left\">301.1 (144.6, 391)</td></tr><tr><td align=\"left\">Azerbaijan</td><td align=\"left\">869 (723, 1,038)</td><td align=\"left\">8.3 (7, 9.8)</td><td align=\"left\">− 1.6 (− 25.6, 34.7)</td><td align=\"left\">286 (238, 339)</td><td align=\"left\">2.9 (2.5, 3.5)</td><td align=\"left\">11.8 (− 16.7, 58.9)</td><td align=\"left\">8,954 (7,515, 10,555)</td><td align=\"left\">84.1 (71.1, 99.2)</td><td align=\"left\">2.5 (− 21.1, 39)</td></tr><tr><td align=\"left\">Georgia</td><td align=\"left\">371 (323, 420)</td><td align=\"left\">6.9 (6, 7.8)</td><td align=\"left\">36.7 (15.3, 62.2)</td><td align=\"left\">144 (128, 159)</td><td align=\"left\">2.5 (2.2, 2.8)</td><td align=\"left\">54.8 (34.4, 77.6)</td><td align=\"left\">3,783 (3,336, 4,235)</td><td align=\"left\">71.2 (63.2, 79.4)</td><td align=\"left\">45 (25.6, 66.4)</td></tr><tr><td align=\"left\">Kazakhstan</td><td align=\"left\">1,403 (1,254, 1566)</td><td align=\"left\">7.7 (6.9, 8.6)</td><td align=\"left\">− 10.7 (− 34, 25.8)</td><td align=\"left\">490 (448, 536)</td><td align=\"left\">2.8 (2.6, 3.1)</td><td align=\"left\">− 1.4 (− 27, 37.6)</td><td align=\"left\">13,958 (12,704, 15,385)</td><td align=\"left\">76.3 (69.7, 83.7)</td><td align=\"left\">− 6.6 (− 31.1, 29.3)</td></tr><tr><td align=\"left\">Kyrgyzstan</td><td align=\"left\">232 (195, 270)</td><td align=\"left\">4.6 (3.9, 5.2)</td><td align=\"left\">57.3 (9.8, 91.2)</td><td align=\"left\">71 (63, 81)</td><td align=\"left\">1.6 (1.4, 1.8)</td><td align=\"left\">68 (31.8, 94.4)</td><td align=\"left\">2,298 (1998, 2,646)</td><td align=\"left\">44.2 (38.8, 50.4)</td><td align=\"left\">64.2 (21.9, 93.5)</td></tr><tr><td align=\"left\">Mongolia</td><td align=\"left\">124 (106, 146)</td><td align=\"left\">5 (4.3, 5.8)</td><td align=\"left\">44.2 (− 5.6, 92.8)</td><td align=\"left\">39 (33, 45)</td><td align=\"left\">1.9 (1.5, 2.2)</td><td align=\"left\">61.2 (0.6, 109.1)</td><td align=\"left\">1,189 (1,035, 1,377)</td><td align=\"left\">46.8 (40.3, 53.7)</td><td align=\"left\">56.3 (− 1.5, 102.8)</td></tr><tr><td align=\"left\">Tajikistan</td><td align=\"left\">389 (327, 453)</td><td align=\"left\">5.6 (4.8, 6.5)</td><td align=\"left\">13.2 (− 8.9, 43.9)</td><td align=\"left\">107 (93, 123)</td><td align=\"left\">1.8 (1.6, 2.2)</td><td align=\"left\">22.1 (− 1.7, 50.9)</td><td align=\"left\">3,989 (3,445, 4,558)</td><td align=\"left\">56.1 (48.8, 64.4)</td><td align=\"left\">16.6 (− 6.4, 44)</td></tr><tr><td align=\"left\">Turkmenistan</td><td align=\"left\">364 (314, 416)</td><td align=\"left\">8.1 (7.1, 9.2)</td><td align=\"left\">27.8 (3.8, 57.4)</td><td align=\"left\">113 (99, 127)</td><td align=\"left\">2.7 (2.4, 3.1)</td><td align=\"left\">40.2 (13.7, 69.4)</td><td align=\"left\">3,739 (3,294, 4,185)</td><td align=\"left\">82.1 (72.3, 91.7)</td><td align=\"left\">33.3 (9.1, 60)</td></tr><tr><td align=\"left\">Uzbekistan</td><td align=\"left\">1,187 (1,022, 1,364)</td><td align=\"left\">4.4 (3.8, 5)</td><td align=\"left\">45.9 (6.5, 112.6)</td><td align=\"left\">348 (305, 397)</td><td align=\"left\">1.5 (1.3, 1.7)</td><td align=\"left\">63.8 (16.1, 147.1)</td><td align=\"left\">12,043 (10,632, 13,581)</td><td align=\"left\">43.4 (38.3, 49)</td><td align=\"left\">48.5 (11.7, 109)</td></tr><tr><td align=\"left\">Central Latin America</td><td align=\"left\">13,764 (13,075, 14,577)</td><td align=\"left\">5.7 (5.4, 6)</td><td align=\"left\">28.5 (20.1, 44.5)</td><td align=\"left\">4,542 (4,308, 4,801)</td><td align=\"left\">2 (1.9, 2.1)</td><td align=\"left\">22.5 (14.9, 33.6)</td><td align=\"left\">126,948 (120,716, 133,945)</td><td align=\"left\">52.4 (49.9, 55.4)</td><td align=\"left\">15.9 (9.3, 29.6)</td></tr><tr><td align=\"left\">Colombia</td><td align=\"left\">1879 (1611, 2,187)</td><td align=\"left\">3.6 (3.1, 4.2)</td><td align=\"left\">29.4 (8.6, 51.3)</td><td align=\"left\">637 (549, 720)</td><td align=\"left\">1.2 (1, 1.3)</td><td align=\"left\">21.1 (4.7, 37.6)</td><td align=\"left\">17,749 (15,141, 20,137)</td><td align=\"left\">34 (28.9, 38.5)</td><td align=\"left\">17.6 (1.9, 34.1)</td></tr><tr><td align=\"left\">Costa Rica</td><td align=\"left\">246 (212, 280)</td><td align=\"left\">5 (4.3, 5.6)</td><td align=\"left\">73 (43.8, 110.2)</td><td align=\"left\">89 (78, 100)</td><td align=\"left\">1.8 (1.6, 2)</td><td align=\"left\">60.3 (36.7, 91.1)</td><td align=\"left\">2,285 (1999, 2,589)</td><td align=\"left\">46.2 (40.4, 52.1)</td><td align=\"left\">59.4 (35.8, 87.3)</td></tr><tr><td align=\"left\">El Salvador</td><td align=\"left\">209 (167, 270)</td><td align=\"left\">3.6 (2.9, 4.7)</td><td align=\"left\">38 (7.7, 76.3)</td><td align=\"left\">69 (57, 86)</td><td align=\"left\">1.2 (1, 1.5)</td><td align=\"left\">44.3 (16.6, 80.8)</td><td align=\"left\">1847 (1508, 2,307)</td><td align=\"left\">32.2 (26.2, 40.5)</td><td align=\"left\">25.1 (1.7, 54.7)</td></tr><tr><td align=\"left\">Guatemala</td><td align=\"left\">478 (413, 571)</td><td align=\"left\">3.7 (3.2, 4.3)</td><td align=\"left\">14.5 (− 5.1, 46)</td><td align=\"left\">132 (118, 152)</td><td align=\"left\">1.2 (1, 1.3)</td><td align=\"left\">13 (− 1.7, 34.9)</td><td align=\"left\">4,362 (3,891, 5,080)</td><td align=\"left\">32.9 (29.3, 37.9)</td><td align=\"left\">5 (− 8.5, 29.3)</td></tr><tr><td align=\"left\">Honduras</td><td align=\"left\">238 (175, 322)</td><td align=\"left\">3.4 (2.6, 4.6)</td><td align=\"left\">50.5 (10.5, 94.9)</td><td align=\"left\">71 (55, 93)</td><td align=\"left\">1.2 (0.9, 1.5)</td><td align=\"left\">53.4 (19.6, 96.1)</td><td align=\"left\">2,102 (1549, 2,842)</td><td align=\"left\">30 (22.8, 40.5)</td><td align=\"left\">23.7 (− 6.5, 56.7)</td></tr><tr><td align=\"left\">Mexico</td><td align=\"left\">8,279 (7,859, 8,694)</td><td align=\"left\">6.9 (6.5, 7.2)</td><td align=\"left\">39.1 (31.1, 47.1)</td><td align=\"left\">2,783 (2,658, 2,911)</td><td align=\"left\">2.4 (2.3, 2.5)</td><td align=\"left\">31.9 (24.4, 38.8)</td><td align=\"left\">76,149 (72,453, 79,896)</td><td align=\"left\">63.4 (60.4, 66.5)</td><td align=\"left\">24 (17.3, 30.3)</td></tr><tr><td align=\"left\">Nicaragua</td><td align=\"left\">166 (138, 226)</td><td align=\"left\">3.2 (2.7, 4.4)</td><td align=\"left\">− 1 (− 21.3, 40)</td><td align=\"left\">50 (43, 69)</td><td align=\"left\">1.1 (0.9, 1.5)</td><td align=\"left\">2.4 (− 14.7, 39.3)</td><td align=\"left\">1542 (1,324, 2065)</td><td align=\"left\">29.8 (25.5, 40.6)</td><td align=\"left\">− 11.9 (− 26.6, 23)</td></tr><tr><td align=\"left\">Panama</td><td align=\"left\">172 (151, 193)</td><td align=\"left\">4.4 (3.8, 4.9)</td><td align=\"left\">87.5 (59.2, 117.2)</td><td align=\"left\">59 (53, 65)</td><td align=\"left\">1.5 (1.3, 1.6)</td><td align=\"left\">69.6 (49.3, 88.8)</td><td align=\"left\">1582 (1,421, 1752)</td><td align=\"left\">40.2 (36.1, 44.6)</td><td align=\"left\">71.2 (51, 91.8)</td></tr><tr><td align=\"left\">Venezuela</td><td align=\"left\">2096 (1741, 2,523)</td><td align=\"left\">7 (5.8, 8.4)</td><td align=\"left\">− 3.8 (− 25, 61.6)</td><td align=\"left\">653 (548, 784)</td><td align=\"left\">2.3 (1.9, 2.8)</td><td align=\"left\">− 6.3 (− 24.6, 42.6)</td><td align=\"left\">19,329 (16,215, 23,065)</td><td align=\"left\">64.5 (54.4, 77.1)</td><td align=\"left\">− 9.9 (− 26.9, 41.1)</td></tr><tr><td align=\"left\">Andean Latin America</td><td align=\"left\">2,830 (2,458, 3,162)</td><td align=\"left\">5 (4.4, 5.6)</td><td align=\"left\">6.4 (− 8, 24.6)</td><td align=\"left\">982 (844, 1,093)</td><td align=\"left\">1.8 (1.6, 2)</td><td align=\"left\">14 (0.8, 30.8)</td><td align=\"left\">25,816 (22,594, 28,915)</td><td align=\"left\">46 (40.2, 51.3)</td><td align=\"left\">− 3.1 (− 15.9, 11.9)</td></tr><tr><td align=\"left\">Bolivia</td><td align=\"left\">530 (415, 664)</td><td align=\"left\">5.6 (4.5, 7)</td><td align=\"left\">25 (− 10.3, 76.1)</td><td align=\"left\">176 (142, 217)</td><td align=\"left\">2.1 (1.7, 2.5)</td><td align=\"left\">32.6 (1.9, 74.7)</td><td align=\"left\">4,838 (3,824, 5,947)</td><td align=\"left\">50.9 (40.3, 62.7)</td><td align=\"left\">12.6 (− 18.6, 57)</td></tr><tr><td align=\"left\">Ecuador</td><td align=\"left\">757 (640, 872)</td><td align=\"left\">4.9 (4.2, 5.6)</td><td align=\"left\">28 (7, 66.9)</td><td align=\"left\">247 (218, 277)</td><td align=\"left\">1.7 (1.5, 1.9)</td><td align=\"left\">28.1 (12.1, 63.1)</td><td align=\"left\">6,865 (6,003, 7,734)</td><td align=\"left\">44.5 (39.1, 50.1)</td><td align=\"left\">16.9 (1.4, 48.8)</td></tr><tr><td align=\"left\">Peru</td><td align=\"left\">1542 (1,262, 1,840)</td><td align=\"left\">4.9 (4.1, 5.9)</td><td align=\"left\">− 5 (− 23.7, 17.2)</td><td align=\"left\">558 (455, 652)</td><td align=\"left\">1.8 (1.5, 2.1)</td><td align=\"left\">5.1 (− 14.3, 26.9)</td><td align=\"left\">14,114 (11,645, 16,629)</td><td align=\"left\">45.4 (37.4, 53.5)</td><td align=\"left\">− 13.3 (− 29.6, 4.9)</td></tr><tr><td align=\"left\">Caribbean</td><td align=\"left\">2,310 (2052, 2,813)</td><td align=\"left\">4.7 (4.1, 5.7)</td><td align=\"left\">− 23.6 (− 34.4, 21.4)</td><td align=\"left\">762 (696, 896)</td><td align=\"left\">1.5 (1.4, 1.8)</td><td align=\"left\">− 21.9 (− 30.2, 16.4)</td><td align=\"left\">20,946 (18,796, 25,112)</td><td align=\"left\">42.6 (38.1, 51.1)</td><td align=\"left\">− 29 (− 37.9, 8)</td></tr><tr><td align=\"left\">Antigua and Barbuda</td><td align=\"left\">5 (4, 6)</td><td align=\"left\">4.9 (4.2, 6)</td><td align=\"left\">− 16.2 (− 31.8, 32.8)</td><td align=\"left\">2 (1, 2)</td><td align=\"left\">1.6 (1.4, 1.9)</td><td align=\"left\">− 19.5 (− 31.6, 18.9)</td><td align=\"left\">44 (39, 52)</td><td align=\"left\">45.1 (40.1, 53.6)</td><td align=\"left\">− 21.1 (− 33.1, 17.5)</td></tr><tr><td align=\"left\">The Bahamas</td><td align=\"left\">25 (22, 30)</td><td align=\"left\">6.2 (5.4, 7.4)</td><td align=\"left\">− 14.2 (− 29.2, 26.6)</td><td align=\"left\">7 (6, 8)</td><td align=\"left\">1.9 (1.7, 2.2)</td><td align=\"left\">− 20 (− 31.1, 11.1)</td><td align=\"left\">223 (198, 259)</td><td align=\"left\">56.2 (49.9, 65.4)</td><td align=\"left\">− 21.4 (− 32.5, 9.7)</td></tr><tr><td align=\"left\">Barbados</td><td align=\"left\">29 (25, 39)</td><td align=\"left\">7 (6, 9.5)</td><td align=\"left\">− 24.1 (− 38.4, 26)</td><td align=\"left\">11 (9, 14)</td><td align=\"left\">2.3 (2, 3)</td><td align=\"left\">− 27.8 (− 38.7, 12.8)</td><td align=\"left\">263 (230, 341)</td><td align=\"left\">63.4 (55.5, 81.9)</td><td align=\"left\">− 29.7 (− 40.5, 9)</td></tr><tr><td align=\"left\">Belize</td><td align=\"left\">17 (15, 25)</td><td align=\"left\">5.4 (4.7, 7.8)</td><td align=\"left\">22.7 (− 2.1, 111.8)</td><td align=\"left\">4 (4, 6)</td><td align=\"left\">1.6 (1.4, 2.2)</td><td align=\"left\">13.6 (− 4.5, 80.8)</td><td align=\"left\">154 (137, 211)</td><td align=\"left\">48.2 (42.6, 66)</td><td align=\"left\">10.2 (− 7.7, 78.8)</td></tr><tr><td align=\"left\">Bermuda</td><td align=\"left\">7 (6, 8)</td><td align=\"left\">6.2 (5.4, 7.9)</td><td align=\"left\">− 43.9 (− 54.2, − 11.8)</td><td align=\"left\">3 (2, 3)</td><td align=\"left\">2.3 (2, 2.8)</td><td align=\"left\">− 43.5 (− 51.5, − 15.4)</td><td align=\"left\">62 (55, 77)</td><td align=\"left\">58.9 (52.4, 72.9)</td><td align=\"left\">− 47.6 (− 54.9, − 21.2)</td></tr><tr><td align=\"left\">Cuba</td><td align=\"left\">843 (720, 1,045)</td><td align=\"left\">5.1 (4.4, 6.4)</td><td align=\"left\">− 23.3 (− 38.1, 35.2)</td><td align=\"left\">315 (274, 375)</td><td align=\"left\">1.8 (1.5, 2.1)</td><td align=\"left\">− 20.8 (− 33.4, 28.6)</td><td align=\"left\">7,741 (6,721, 9,408)</td><td align=\"left\">47.5 (41.5, 57.6)</td><td align=\"left\">− 27.8 (− 39.3, 22.7)</td></tr><tr><td align=\"left\">Dominica</td><td align=\"left\">6 (5, 7)</td><td align=\"left\">7 (6.1, 9.1)</td><td align=\"left\">9.2 (− 11.2, 76.2)</td><td align=\"left\">2 (2, 2)</td><td align=\"left\">2.1 (1.9, 2.7)</td><td align=\"left\">− 1.1 (− 15.2, 50.2)</td><td align=\"left\">51 (46, 64)</td><td align=\"left\">65.5 (57.8, 81.3)</td><td align=\"left\">4.3 (− 10.8, 58.5)</td></tr><tr><td align=\"left\">Dominican Republic</td><td align=\"left\">286 (226, 451)</td><td align=\"left\">2.9 (2.3, 4.6)</td><td align=\"left\">− 24.4 (− 41.9, 45.9)</td><td align=\"left\">86 (68, 127)</td><td align=\"left\">0.9 (0.7, 1.4)</td><td align=\"left\">− 17.3 (− 35.6, 49.4)</td><td align=\"left\">2,636 (2,126, 4,142)</td><td align=\"left\">27.2 (21.9, 42.6)</td><td align=\"left\">− 29.7 (− 44.5, 32)</td></tr><tr><td align=\"left\">Grenada</td><td align=\"left\">6 (5, 7)</td><td align=\"left\">4.3 (3.7, 5.4)</td><td align=\"left\">− 15.7 (− 32.4, 53.6)</td><td align=\"left\">2 (2, 2)</td><td align=\"left\">1.4 (1.2, 1.7)</td><td align=\"left\">− 18.3 (− 31.5, 39.2)</td><td align=\"left\">51 (45, 63)</td><td align=\"left\">39.5 (34.9, 48.6)</td><td align=\"left\">− 18.9 (− 32.7, 38.2)</td></tr><tr><td align=\"left\">Guyana</td><td align=\"left\">36 (30, 44)</td><td align=\"left\">5.1 (4.3, 6.3)</td><td align=\"left\">− 6.3 (− 25.3, 54.6)</td><td align=\"left\">9 (8, 11)</td><td align=\"left\">1.5 (1.3, 1.8)</td><td align=\"left\">− 12.8 (− 27.7, 35.9)</td><td align=\"left\">317 (276, 377)</td><td align=\"left\">45.5 (39.6, 54)</td><td align=\"left\">− 13.9 (− 28.6, 35)</td></tr><tr><td align=\"left\">Haiti</td><td align=\"left\">455 (329, 723)</td><td align=\"left\">5 (3.7, 7.7)</td><td align=\"left\">− 25.2 (− 51.3, 20.2)</td><td align=\"left\">107 (79, 160)</td><td align=\"left\">1.5 (1.1, 2.1)</td><td align=\"left\">− 18.4 (− 43.1, 15.5)</td><td align=\"left\">4,061 (3,086, 6,168)</td><td align=\"left\">43.9 (33.6, 65.9)</td><td align=\"left\">− 31.5 (− 53.6, 7.2)</td></tr><tr><td align=\"left\">Jamaica</td><td align=\"left\">110 (82, 175)</td><td align=\"left\">3.8 (2.9, 6.1)</td><td align=\"left\">− 16.9 (− 42, 69.2)</td><td align=\"left\">33 (25, 49)</td><td align=\"left\">1.1 (0.9, 1.7)</td><td align=\"left\">− 25.6 (− 45.4, 37.9)</td><td align=\"left\">938 (717, 1,399)</td><td align=\"left\">33 (25.4, 49.3)</td><td align=\"left\">− 24.1 (− 45.2, 42.8)</td></tr><tr><td align=\"left\">Puerto Rico</td><td align=\"left\">248 (222, 280)</td><td align=\"left\">4.3 (3.8, 4.9)</td><td align=\"left\">− 9 (− 23.7, 39)</td><td align=\"left\">105 (95, 115)</td><td align=\"left\">1.5 (1.4, 1.7)</td><td align=\"left\">− 12.8 (− 24.5, 26.4)</td><td align=\"left\">2,252 (2024, 2,496)</td><td align=\"left\">39.4 (35.4, 44.2)</td><td align=\"left\">− 15.4 (− 26.3, 22)</td></tr><tr><td align=\"left\">Saint Lucia</td><td align=\"left\">9 (8, 12)</td><td align=\"left\">4.5 (3.9, 5.9)</td><td align=\"left\">− 25.3 (− 40.8, 29.1)</td><td align=\"left\">3 (3, 4)</td><td align=\"left\">1.4 (1.3, 1.8)</td><td align=\"left\">− 28.8 (− 40.2, 17.4)</td><td align=\"left\">84 (73, 107)</td><td align=\"left\">41.3 (36.2, 52.5)</td><td align=\"left\">− 30.2 (− 41.3, 15)</td></tr><tr><td align=\"left\">Saint Vincent and the Grenadines</td><td align=\"left\">7 (6, 9)</td><td align=\"left\">5.3 (4.6, 7)</td><td align=\"left\">− 14.8 (− 34.1, 67)</td><td align=\"left\">2 (2, 3)</td><td align=\"left\">1.5 (1.3, 2)</td><td align=\"left\">− 23.8 (− 37.6, 41.1)</td><td align=\"left\">59 (53, 77)</td><td align=\"left\">46.1 (41.1, 59.6)</td><td align=\"left\">− 20.9 (− 35.2, 46.3)</td></tr><tr><td align=\"left\">Suriname</td><td align=\"left\">30 (25, 41)</td><td align=\"left\">5 (4.2, 6.9)</td><td align=\"left\">− 12.6 (− 30.8, 52.9)</td><td align=\"left\">9 (8, 12)</td><td align=\"left\">1.5 (1.3, 2.1)</td><td align=\"left\">− 14.4 (− 28.8, 42.2)</td><td align=\"left\">272 (236, 363)</td><td align=\"left\">45.7 (39.7, 60.7)</td><td align=\"left\">− 20 (− 33.3, 33)</td></tr><tr><td align=\"left\">Trinidad and Tobago</td><td align=\"left\">93 (72, 145)</td><td align=\"left\">5.6 (4.3, 8.6)</td><td align=\"left\">− 36.9 (− 53.1, 30.7)</td><td align=\"left\">29 (23, 43)</td><td align=\"left\">1.7 (1.3, 2.4)</td><td align=\"left\">− 41.4 (− 54.7, 10.2)</td><td align=\"left\">833 (663, 1,219)</td><td align=\"left\">50.7 (40.4, 73.5)</td><td align=\"left\">− 40.8 (− 53.7, 12.8)</td></tr><tr><td align=\"left\">Virgin Islands</td><td align=\"left\">16 (13, 19)</td><td align=\"left\">9.6 (8, 11.5)</td><td align=\"left\">19.6 (− 4.2, 56.6)</td><td align=\"left\">6 (5, 7)</td><td align=\"left\">3.3 (2.8, 3.9)</td><td align=\"left\">11.7 (− 12, 43.1)</td><td align=\"left\">152 (124, 180)</td><td align=\"left\">90 (75.5, 105.3)</td><td align=\"left\">9.5 (− 12.5, 38.1)</td></tr><tr><td align=\"left\">Tropical Latin America</td><td align=\"left\">11,698 (11,105, 12,216)</td><td align=\"left\">5 (4.8, 5.3)</td><td align=\"left\">36.1 (27.7, 44.9)</td><td align=\"left\">4,029 (3,809, 4,211)</td><td align=\"left\">1.8 (1.7, 1.8)</td><td align=\"left\">36.2 (28.8, 43.9)</td><td align=\"left\">107,712 (102,442, 112,126)</td><td align=\"left\">46.7 (44.3, 48.6)</td><td align=\"left\">21.1 (14, 28.5)</td></tr><tr><td align=\"left\">Brazil</td><td align=\"left\">11,441 (10,833, 11,963)</td><td align=\"left\">5.1 (4.8, 5.3)</td><td align=\"left\">37 (28.3, 46.1)</td><td align=\"left\">3,945 (3,727, 4,124)</td><td align=\"left\">1.8 (1.7, 1.8)</td><td align=\"left\">37.1 (29.7, 44.5)</td><td align=\"left\">105,372 (100,014, 109,676)</td><td align=\"left\">46.9 (44.4, 48.8)</td><td align=\"left\">21.8 (14.7, 29.4)</td></tr><tr><td align=\"left\">Paraguay</td><td align=\"left\">257 (208, 326)</td><td align=\"left\">4.5 (3.6, 5.7)</td><td align=\"left\">7.7 (− 16.6, 52.8)</td><td align=\"left\">84 (68, 108)</td><td align=\"left\">1.6 (1.3, 2)</td><td align=\"left\">6.9 (− 15.9, 51.5)</td><td align=\"left\">2,341 (1906, 2,934)</td><td align=\"left\">40.6 (33.1, 51.1)</td><td align=\"left\">− 0.4 (− 21.8, 37.5)</td></tr><tr><td align=\"left\">East Asia</td><td align=\"left\">52,291 (46,830, 56,228)</td><td align=\"left\">2.8 (2.5, 3)</td><td align=\"left\">20.7 (− 9.5, 39.7)</td><td align=\"left\">18,634 (16,488, 19,987)</td><td align=\"left\">1 (0.9, 1)</td><td align=\"left\">49.1 (4.5, 75.5)</td><td align=\"left\">472,461 (417,968, 507,412)</td><td align=\"left\">25.1 (22.5, 26.9)</td><td align=\"left\">20.7 (− 12.7, 38.7)</td></tr><tr><td align=\"left\">China</td><td align=\"left\">48,211 (43,259, 52,182)</td><td align=\"left\">2.7 (2.4, 2.9)</td><td align=\"left\">18.4 (− 11.1, 37.9)</td><td align=\"left\">17,168 (15,361, 18,575)</td><td align=\"left\">0.9 (0.8, 1)</td><td align=\"left\">45.9 (2.5, 72.5)</td><td align=\"left\">438,138 (389,970, 473,581)</td><td align=\"left\">24.6 (21.9, 26.5)</td><td align=\"left\">18.4 (− 14.6, 37)</td></tr><tr><td align=\"left\">North Korea</td><td align=\"left\">1,131 (748, 1757)</td><td align=\"left\">3.7 (2.5, 5.7)</td><td align=\"left\">15.8 (− 15.7, 51.4)</td><td align=\"left\">313 (208, 486)</td><td align=\"left\">1 (0.7, 1.6)</td><td align=\"left\">20.6 (− 9.8, 52.4)</td><td align=\"left\">9,321 (6,158, 14,465)</td><td align=\"left\">31.2 (21.3, 47.1)</td><td align=\"left\">24 (− 8.2, 58.4)</td></tr><tr><td align=\"left\">Taiwan (Province of China)</td><td align=\"left\">2,107 (1633, 2,332)</td><td align=\"left\">5.9 (4.5, 6.5)</td><td align=\"left\">136.9 (51.7, 173.8)</td><td align=\"left\">853 (646, 933)</td><td align=\"left\">2.3 (1.7, 2.5)</td><td align=\"left\">188.4 (84.1, 224.7)</td><td align=\"left\">17,392 (13,993, 18,898)</td><td align=\"left\">48.7 (38.8, 52.7)</td><td align=\"left\">147.2 (70.1, 175.6)</td></tr><tr><td align=\"left\">Southeast Asia</td><td align=\"left\">20,831 (17,706, 22,722)</td><td align=\"left\">3.3 (2.8, 3.6)</td><td align=\"left\">23 (5.9, 41.9)</td><td align=\"left\">5,975 (5,097, 6,474)</td><td align=\"left\">1 (0.9, 1.1)</td><td align=\"left\">35 (18.6, 52.8)</td><td align=\"left\">176,890 (149,465, 192,463)</td><td align=\"left\">28.1 (23.8, 30.5)</td><td align=\"left\">28.2 (9.4, 47.6)</td></tr><tr><td align=\"left\">Cambodia</td><td align=\"left\">401 (308, 528)</td><td align=\"left\">3.1 (2.4, 4.1)</td><td align=\"left\">13.6 (− 21.7, 67.9)</td><td align=\"left\">111 (87, 144)</td><td align=\"left\">1 (0.8, 1.3)</td><td align=\"left\">29 (− 8.1, 78.5)</td><td align=\"left\">3,354 (2,615, 4,303)</td><td align=\"left\">26 (20.3, 33.4)</td><td align=\"left\">17.9 (− 19.5, 70.9)</td></tr><tr><td align=\"left\">Indonesia</td><td align=\"left\">7,406 (6,250, 8,437)</td><td align=\"left\">3.2 (2.7, 3.6)</td><td align=\"left\">37.9 (10.9, 68)</td><td align=\"left\">2088 (1747, 2,391)</td><td align=\"left\">1 (0.9, 1.2)</td><td align=\"left\">64.4 (33.7, 92)</td><td align=\"left\">62,168 (51,970, 70,541)</td><td align=\"left\">26.6 (22.4, 30.1)</td><td align=\"left\">42.1 (12.3, 76)</td></tr><tr><td align=\"left\">Laos</td><td align=\"left\">192 (139, 262)</td><td align=\"left\">3.7 (2.7, 5)</td><td align=\"left\">15.8 (− 27.2, 89.1)</td><td align=\"left\">47 (36, 62)</td><td align=\"left\">1.1 (0.8, 1.4)</td><td align=\"left\">33.7 (− 11.2, 96)</td><td align=\"left\">1654 (1,226, 2,162)</td><td align=\"left\">30.9 (23.4, 40.3)</td><td align=\"left\">20.4 (− 21.4, 97.7)</td></tr><tr><td align=\"left\">Malaysia</td><td align=\"left\">872 (718, 1,177)</td><td align=\"left\">3.3 (2.7, 4.3)</td><td align=\"left\">35.2 (5.2, 82.8)</td><td align=\"left\">295 (245, 389)</td><td align=\"left\">1.2 (1, 1.6)</td><td align=\"left\">47 (14.6, 99.9)</td><td align=\"left\">7,582 (6,238, 10,172)</td><td align=\"left\">28 (23.2, 37.2)</td><td align=\"left\">40.2 (9.1, 89.7)</td></tr><tr><td align=\"left\">Maldives</td><td align=\"left\">8 (7, 9)</td><td align=\"left\">2.5 (2.1, 2.9)</td><td align=\"left\">− 22.2 (− 48.3, 35.3)</td><td align=\"left\">2 (2, 3)</td><td align=\"left\">0.9 (0.7, 1)</td><td align=\"left\">− 6.2 (− 33.8, 36.1)</td><td align=\"left\">71 (62, 82)</td><td align=\"left\">21.7 (18.8, 24.9)</td><td align=\"left\">− 16.1 (− 44.8, 42.8)</td></tr><tr><td align=\"left\">Mauritius</td><td align=\"left\">56 (50, 64)</td><td align=\"left\">3.6 (3.2, 4)</td><td align=\"left\">36 (15.8, 56.5)</td><td align=\"left\">18 (16, 20)</td><td align=\"left\">1.1 (1, 1.2)</td><td align=\"left\">43.5 (27.7, 60.3)</td><td align=\"left\">480 (435, 531)</td><td align=\"left\">30.6 (27.7, 33.5)</td><td align=\"left\">40.4 (24.9, 56.8)</td></tr><tr><td align=\"left\">Myanmar</td><td align=\"left\">2,128 (1661, 2,701)</td><td align=\"left\">4.4 (3.5, 5.6)</td><td align=\"left\">20.3 (− 21.2, 80.2)</td><td align=\"left\">589 (463, 743)</td><td align=\"left\">1.3 (1.1, 1.7)</td><td align=\"left\">38.2 (− 4.6, 90.4)</td><td align=\"left\">17,704 (14,062, 22,120)</td><td align=\"left\">36.5 (29.2, 45.5)</td><td align=\"left\">25.3 (− 16.9, 85)</td></tr><tr><td align=\"left\">Philippines</td><td align=\"left\">3,849 (3,044, 4,572)</td><td align=\"left\">4.4 (3.6, 5.1)</td><td align=\"left\">47.8 (19.8, 80.1)</td><td align=\"left\">918 (769, 1,051)</td><td align=\"left\">1.2 (1, 1.4)</td><td align=\"left\">54.1 (31.3, 80.3)</td><td align=\"left\">33,067 (27,040, 37,956)</td><td align=\"left\">37 (30.8, 42.4)</td><td align=\"left\">50.9 (25.6, 75.8)</td></tr><tr><td align=\"left\">Sri Lanka</td><td align=\"left\">1592 (1,154, 1986)</td><td align=\"left\">6.3 (4.6, 7.8)</td><td align=\"left\">− 24.9 (− 43.8, 0.1)</td><td align=\"left\">541 (384, 663)</td><td align=\"left\">2.2 (1.6, 2.7)</td><td align=\"left\">− 16.9 (− 35.5, 7.5)</td><td align=\"left\">13,677 (9,934, 16,943)</td><td align=\"left\">53.6 (39.7, 65.9)</td><td align=\"left\">− 19.5 (− 38.6, 4.1)</td></tr><tr><td align=\"left\">Seychelles</td><td align=\"left\">6 (5, 7)</td><td align=\"left\">5.4 (4.4, 6.1)</td><td align=\"left\">79.6 (17.6, 117.9)</td><td align=\"left\">2 (2, 2)</td><td align=\"left\">1.8 (1.5, 2)</td><td align=\"left\">95.8 (26.6, 132.3)</td><td align=\"left\">52 (42, 59)</td><td align=\"left\">46.8 (38.1, 52.6)</td><td align=\"left\">90.8 (23.6, 125.7)</td></tr><tr><td align=\"left\">Thailand</td><td align=\"left\">2,368 (1807, 2,710)</td><td align=\"left\">2.6 (1.9, 3)</td><td align=\"left\">17.4 (− 2.4, 40.1)</td><td align=\"left\">763 (594, 867)</td><td align=\"left\">0.8 (0.6, 0.9)</td><td align=\"left\">21.7 (4.2, 41.4)</td><td align=\"left\">20,556 (15,156, 23,609)</td><td align=\"left\">22.7 (16.6, 25.9)</td><td align=\"left\">23.3 (5.1, 43.8)</td></tr><tr><td align=\"left\">East Timor</td><td align=\"left\">28 (17, 40)</td><td align=\"left\">3 (1.8, 4.2)</td><td align=\"left\">26.6 (− 28.4, 104.7)</td><td align=\"left\">8 (5, 11)</td><td align=\"left\">0.9 (0.6, 1.3)</td><td align=\"left\">54.2 (− 5.6, 120.4)</td><td align=\"left\">243 (151, 350)</td><td align=\"left\">25 (15.7, 36.1)</td><td align=\"left\">31.9 (− 25.5, 114.2)</td></tr><tr><td align=\"left\">Vietnam</td><td align=\"left\">1898 (1529, 2,344)</td><td align=\"left\">2 (1.6, 2.4)</td><td align=\"left\">12.6 (− 11.7, 46.5)</td><td align=\"left\">585 (488, 708)</td><td align=\"left\">0.7 (0.6, 0.8)</td><td align=\"left\">20.8 (− 2, 56.4)</td><td align=\"left\">16,048 (13,078, 19,619)</td><td align=\"left\">16.7 (13.7, 20.3)</td><td align=\"left\">18.2 (− 5.1, 51)</td></tr><tr><td align=\"left\">Oceania</td><td align=\"left\">266 (205, 354)</td><td align=\"left\">2.9 (2.3, 3.7)</td><td align=\"left\">9.4 (− 8, 28.9)</td><td align=\"left\">59 (46, 74)</td><td align=\"left\">0.9 (0.7, 1.1)</td><td align=\"left\">14.3 (0.5, 30.8)</td><td align=\"left\">2,285 (1784, 2,917)</td><td align=\"left\">24.4 (19.2, 30.9)</td><td align=\"left\">12.5 (− 3.1, 30.9)</td></tr><tr><td align=\"left\">American Samoa</td><td align=\"left\">1 (1, 2)</td><td align=\"left\">2.8 (2.4, 3.4)</td><td align=\"left\">3.1 (− 20.1, 31.9)</td><td align=\"left\">0 (0, 0)</td><td align=\"left\">0.9 (0.8, 1.1)</td><td align=\"left\">3.7 (− 19.4, 35.6)</td><td align=\"left\">12 (10, 14)</td><td align=\"left\">23.6 (20.3, 27.8)</td><td align=\"left\">3.9 (− 18.7, 33.9)</td></tr><tr><td align=\"left\">Federated States of Micronesia</td><td align=\"left\">3 (2, 4)</td><td align=\"left\">3.6 (2.5, 4.7)</td><td align=\"left\">12 (− 18.4, 45.6)</td><td align=\"left\">1 (1, 1)</td><td align=\"left\">1.1 (0.8, 1.4)</td><td align=\"left\">16.7 (− 9.2, 46.2)</td><td align=\"left\">25 (18, 33)</td><td align=\"left\">30 (21.1, 39.1)</td><td align=\"left\">15.3 (− 14, 47.5)</td></tr><tr><td align=\"left\">Fiji</td><td align=\"left\">21 (17, 24)</td><td align=\"left\">2.5 (2.1, 3)</td><td align=\"left\">16.1 (− 12, 50.7)</td><td align=\"left\">5 (4, 6)</td><td align=\"left\">0.8 (0.7, 0.9)</td><td align=\"left\">16.3 (− 10.7, 50.6)</td><td align=\"left\">175 (146, 205)</td><td align=\"left\">21.1 (17.7, 24.6)</td><td align=\"left\">18.5 (− 8.8, 52.7)</td></tr><tr><td align=\"left\">Guam</td><td align=\"left\">10 (8, 11)</td><td align=\"left\">5.3 (4.4, 6.2)</td><td align=\"left\">8.5 (− 18.4, 48.7)</td><td align=\"left\">3 (3, 3)</td><td align=\"left\">1.7 (1.4, 1.9)</td><td align=\"left\">5.6 (− 20.4, 46.3)</td><td align=\"left\">87 (73, 101)</td><td align=\"left\">47 (39.4, 54.1)</td><td align=\"left\">11.3 (− 16.8, 51.7)</td></tr><tr><td align=\"left\">Kiribati</td><td align=\"left\">4 (3, 5)</td><td align=\"left\">4.3 (3.4, 5.2)</td><td align=\"left\">33.8 (1, 71.3)</td><td align=\"left\">1 (1, 1)</td><td align=\"left\">1.3 (1, 1.5)</td><td align=\"left\">39.7 (9.3, 71.2)</td><td align=\"left\">32 (25, 39)</td><td align=\"left\">36.5 (28.8, 43.7)</td><td align=\"left\">39.2 (6.7, 74.8)</td></tr><tr><td align=\"left\">Marshall Islands</td><td align=\"left\">2 (1, 3)</td><td align=\"left\">4.7 (3.5, 6.6)</td><td align=\"left\">28.8 (1.8, 65)</td><td align=\"left\">0 (0, 1)</td><td align=\"left\">1.5 (1.1, 2)</td><td align=\"left\">32.6 (8.3, 62.6)</td><td align=\"left\">17 (12, 23)</td><td align=\"left\">39.9 (29.4, 55.4)</td><td align=\"left\">32.7 (7.4, 64.8)</td></tr><tr><td align=\"left\">Northern Mariana Islands</td><td align=\"left\">2 (2, 3)</td><td align=\"left\">4.3 (3.4, 5.2)</td><td align=\"left\">− 0.2 (− 25.4, 30.9)</td><td align=\"left\">1 (1, 1)</td><td align=\"left\">1.5 (1.2, 1.8)</td><td align=\"left\">6 (− 19.7, 35.9)</td><td align=\"left\">22 (17, 27)</td><td align=\"left\">38.1 (30.4, 45.9)</td><td align=\"left\">3.6 (− 22.7, 35.1)</td></tr><tr><td align=\"left\">Papua New Guinea</td><td align=\"left\">182 (130, 260)</td><td align=\"left\">2.7 (2, 3.8)</td><td align=\"left\">6.3 (− 16.6, 34.2)</td><td align=\"left\">37 (27, 50)</td><td align=\"left\">0.7 (0.5, 1)</td><td align=\"left\">13.7 (− 6.7, 37.3)</td><td align=\"left\">1552 (1,144, 2,125)</td><td align=\"left\">22.8 (16.7, 30.9)</td><td align=\"left\">9.7 (− 11.4, 34.8)</td></tr><tr><td align=\"left\">Samoa</td><td align=\"left\">3 (3, 4)</td><td align=\"left\">2.1 (1.7, 2.6)</td><td align=\"left\">2.6 (− 19.9, 31.4)</td><td align=\"left\">1 (1, 1)</td><td align=\"left\">0.7 (0.6, 0.9)</td><td align=\"left\">14.2 (− 8.9, 43.3)</td><td align=\"left\">27 (22, 33)</td><td align=\"left\">17.5 (14.2, 21.3)</td><td align=\"left\">3.9 (− 16.2, 28.9)</td></tr><tr><td align=\"left\">Solomon Islands</td><td align=\"left\">13 (8, 17)</td><td align=\"left\">2.9 (1.9, 3.9)</td><td align=\"left\">11.8 (− 13.3, 44.1)</td><td align=\"left\">3 (2, 4)</td><td align=\"left\">0.8 (0.6, 1.2)</td><td align=\"left\">15.4 (− 7.8, 44.9)</td><td align=\"left\">106 (70, 147)</td><td align=\"left\">23.9 (15.9, 32.8)</td><td align=\"left\">14.5 (− 9.6, 45.5)</td></tr><tr><td align=\"left\">Tonga</td><td align=\"left\">4 (3, 5)</td><td align=\"left\">4.1 (2.9, 5.7)</td><td align=\"left\">35.5 (3.3, 76.5)</td><td align=\"left\">1 (1, 1)</td><td align=\"left\">1.4 (1, 1.8)</td><td align=\"left\">35.8 (5, 74.9)</td><td align=\"left\">31 (22, 43)</td><td align=\"left\">35.5 (25.4, 48.6)</td><td align=\"left\">43.8 (10.6, 87.3)</td></tr><tr><td align=\"left\">Vanuatu</td><td align=\"left\">8 (4, 14)</td><td align=\"left\">4 (2, 6.8)</td><td align=\"left\">26 (− 6.3, 72.4)</td><td align=\"left\">2 (1, 4)</td><td align=\"left\">1.2 (0.6, 2.1)</td><td align=\"left\">32.4 (1.9, 73.7)</td><td align=\"left\">72 (36, 124)</td><td align=\"left\">34.9 (17.1, 60.1)</td><td align=\"left\">30.8 (− 2.4, 76.9)</td></tr><tr><td align=\"left\">North Africa and Middle East</td><td align=\"left\">15,083 (12,976, 16,227)</td><td align=\"left\">3.1 (2.7, 3.3)</td><td align=\"left\">25.9 (3.5, 56.6)</td><td align=\"left\">4,497 (3,911, 4,820)</td><td align=\"left\">1.1 (0.9, 1.1)</td><td align=\"left\">26.3 (7.8, 59)</td><td align=\"left\">140,564 (120,365, 150,183)</td><td align=\"left\">28.7 (24.8, 30.7)</td><td align=\"left\">13.3 (− 7, 38.7)</td></tr><tr><td align=\"left\">Afghanistan</td><td align=\"left\">691 (386, 1,261)</td><td align=\"left\">3.7 (2.1, 6.1)</td><td align=\"left\">10.3 (− 27.2, 154.3)</td><td align=\"left\">142 (81, 231)</td><td align=\"left\">1.1 (0.6, 1.6)</td><td align=\"left\">4 (− 26.3, 91.8)</td><td align=\"left\">6,284 (3,636, 10,835)</td><td align=\"left\">31.8 (18.2, 51.3)</td><td align=\"left\">− 3 (− 35.1, 113.6)</td></tr><tr><td align=\"left\">Algeria</td><td align=\"left\">690 (557, 802)</td><td align=\"left\">1.9 (1.5, 2.2)</td><td align=\"left\">44.6 (20, 76.1)</td><td align=\"left\">216 (174, 248)</td><td align=\"left\">0.7 (0.5, 0.8)</td><td align=\"left\">29.7 (7.8, 59.6)</td><td align=\"left\">6,251 (5,132, 7,132)</td><td align=\"left\">17.3 (14.1, 19.8)</td><td align=\"left\">30.3 (8.8, 58.3)</td></tr><tr><td align=\"left\">Bahrain</td><td align=\"left\">39 (32, 46)</td><td align=\"left\">3.2 (2.7, 3.8)</td><td align=\"left\">− 23.3 (− 36.6, − 3.1)</td><td align=\"left\">11 (10, 13)</td><td align=\"left\">1.3 (1.1, 1.4)</td><td align=\"left\">− 31.8 (− 43.1, − 14)</td><td align=\"left\">367 (313, 426)</td><td align=\"left\">30.3 (26.1, 34.5)</td><td align=\"left\">− 31 (− 42.2, − 13.4)</td></tr><tr><td align=\"left\">Egypt</td><td align=\"left\">2,121 (1698, 2,576)</td><td align=\"left\">2.7 (2.2, 3.3)</td><td align=\"left\">42.9 (13.8, 79.2)</td><td align=\"left\">523 (435, 621)</td><td align=\"left\">0.8 (0.7, 1)</td><td align=\"left\">40.9 (15.1, 73.7)</td><td align=\"left\">19,791 (16,429, 23,176)</td><td align=\"left\">25 (21.3, 29.3)</td><td align=\"left\">26.9 (4, 52)</td></tr><tr><td align=\"left\">Iran</td><td align=\"left\">2,376 (1965, 2,632)</td><td align=\"left\">3.2 (2.6, 3.5)</td><td align=\"left\">35.7 (7.1, 70.6)</td><td align=\"left\">775 (640, 821)</td><td align=\"left\">1.2 (0.9, 1.2)</td><td align=\"left\">44.5 (24.6, 76.6)</td><td align=\"left\">21,874 (18,404, 23,460)</td><td align=\"left\">29.5 (24.7, 31.6)</td><td align=\"left\">19.8 (− 0.9, 46.7)</td></tr><tr><td align=\"left\">Iraq</td><td align=\"left\">903 (753, 1,066)</td><td align=\"left\">2.8 (2.4, 3.3)</td><td align=\"left\">− 3.4 (− 35.3, 35.2)</td><td align=\"left\">229 (196, 253)</td><td align=\"left\">0.9 (0.8, 1)</td><td align=\"left\">− 16.2 (− 42.3, 10.9)</td><td align=\"left\">8,697 (7,402, 9,922)</td><td align=\"left\">26.4 (22.5, 29.4)</td><td align=\"left\">− 12.9 (− 40.5, 18.6)</td></tr><tr><td align=\"left\">Jordan</td><td align=\"left\">190 (155, 229)</td><td align=\"left\">2.6 (2.1, 3.1)</td><td align=\"left\">49 (11.5, 97.8)</td><td align=\"left\">57 (47, 68)</td><td align=\"left\">1 (0.8, 1.2)</td><td align=\"left\">62.1 (20.4, 106.7)</td><td align=\"left\">1797 (1,483, 2098)</td><td align=\"left\">24.5 (20.1, 28.8)</td><td align=\"left\">35.6 (2.8, 75.2)</td></tr><tr><td align=\"left\">Kuwait</td><td align=\"left\">83 (69, 101)</td><td align=\"left\">2.6 (2.2, 3.1)</td><td align=\"left\">5.6 (− 12.7, 27.6)</td><td align=\"left\">24 (21, 28)</td><td align=\"left\">1 (0.8, 1.1)</td><td align=\"left\">4.2 (− 11.1, 20.9)</td><td align=\"left\">777 (672, 918)</td><td align=\"left\">24.8 (21.5, 29.1)</td><td align=\"left\">− 3.8 (− 18.8, 13.6)</td></tr><tr><td align=\"left\">Lebanon</td><td align=\"left\">217 (180, 253)</td><td align=\"left\">3.4 (2.8, 3.9)</td><td align=\"left\">55.2 (19, 108.2)</td><td align=\"left\">78 (60, 91)</td><td align=\"left\">1.3 (1, 1.5)</td><td align=\"left\">51.3 (19.2, 99.2)</td><td align=\"left\">2044 (1675, 2,390)</td><td align=\"left\">31.8 (25.4, 37.2)</td><td align=\"left\">41.4 (11, 87.1)</td></tr><tr><td align=\"left\">Libya</td><td align=\"left\">238 (191, 293)</td><td align=\"left\">4.5 (3.6, 5.5)</td><td align=\"left\">53.3 (9.2, 120.9)</td><td align=\"left\">74 (59, 91)</td><td align=\"left\">1.7 (1.3, 2.1)</td><td align=\"left\">42.9 (4.2, 108.7)</td><td align=\"left\">2,230 (1781, 2,734)</td><td align=\"left\">42.3 (33.9, 51.6)</td><td align=\"left\">37.3 (0.6, 97)</td></tr><tr><td align=\"left\">Morocco</td><td align=\"left\">622 (491, 763)</td><td align=\"left\">1.8 (1.5, 2.3)</td><td align=\"left\">49.9 (12.1, 108.1)</td><td align=\"left\">201 (162, 244)</td><td align=\"left\">0.6 (0.5, 0.8)</td><td align=\"left\">42.3 (7.8, 95.6)</td><td align=\"left\">5,630 (4,531, 6,841)</td><td align=\"left\">16.7 (13.5, 20.2)</td><td align=\"left\">33.7 (1.7, 80.5)</td></tr><tr><td align=\"left\">Palestine</td><td align=\"left\">139 (101, 166)</td><td align=\"left\">4.2 (3.1, 4.9)</td><td align=\"left\">20.6 (− 14.6, 69.5)</td><td align=\"left\">35 (28, 40)</td><td align=\"left\">1.4 (1.1, 1.6)</td><td align=\"left\">21.6 (− 9.8, 61.1)</td><td align=\"left\">1,247 (898, 1,426)</td><td align=\"left\">37 (28.5, 42.1)</td><td align=\"left\">7.8 (− 21.7, 46.3)</td></tr><tr><td align=\"left\">Oman</td><td align=\"left\">70 (55, 88)</td><td align=\"left\">2.6 (2, 3.3)</td><td align=\"left\">82 (33.4, 150.4)</td><td align=\"left\">20 (15, 24)</td><td align=\"left\">1 (0.8, 1.2)</td><td align=\"left\">82.1 (32.4, 151.9)</td><td align=\"left\">660 (525, 823)</td><td align=\"left\">25.1 (19.5, 31.1)</td><td align=\"left\">66.2 (23.7, 126.4)</td></tr><tr><td align=\"left\">Qatar</td><td align=\"left\">42 (31, 54)</td><td align=\"left\">3.2 (2.4, 4.1)</td><td align=\"left\">− 44.3 (− 65.1, − 3.4)</td><td align=\"left\">12 (9, 15)</td><td align=\"left\">1.4 (1, 1.7)</td><td align=\"left\">− 49.4 (− 67.5, − 10.8)</td><td align=\"left\">404 (298, 525)</td><td align=\"left\">30.9 (23.1, 38.6)</td><td align=\"left\">− 48.8 (− 68, − 11.2)</td></tr><tr><td align=\"left\">Saudi Arabia</td><td align=\"left\">688 (544, 888)</td><td align=\"left\">3.2 (2.6, 4)</td><td align=\"left\">133.5 (65.8, 277)</td><td align=\"left\">200 (162, 253)</td><td align=\"left\">1.3 (1.1, 1.6)</td><td align=\"left\">138.7 (65.6, 279.1)</td><td align=\"left\">6,484 (5,204, 8,169)</td><td align=\"left\">30.5 (24.7, 37.7)</td><td align=\"left\">111 (48.8, 227)</td></tr><tr><td align=\"left\">Sudan</td><td align=\"left\">702 (491, 975)</td><td align=\"left\">2.6 (1.8, 3.5)</td><td align=\"left\">30 (− 17.1, 95.2)</td><td align=\"left\">171 (127, 230)</td><td align=\"left\">0.8 (0.6, 1.1)</td><td align=\"left\">40.7 (5.6, 99.7)</td><td align=\"left\">6,776 (4,822, 9,406)</td><td align=\"left\">23.6 (17.4, 31.7)</td><td align=\"left\">20.8 (− 18.7, 75.4)</td></tr><tr><td align=\"left\">Syria</td><td align=\"left\">311 (247, 387)</td><td align=\"left\">2.1 (1.7, 2.6)</td><td align=\"left\">47.1 (7.2, 103.6)</td><td align=\"left\">96 (77, 120)</td><td align=\"left\">0.7 (0.6, 0.9)</td><td align=\"left\">40.2 (3.6, 93.8)</td><td align=\"left\">2,903 (2,324, 3,566)</td><td align=\"left\">19.6 (15.8, 24)</td><td align=\"left\">33 (− 3.9, 81.1)</td></tr><tr><td align=\"left\">Tunisia</td><td align=\"left\">271 (213, 339)</td><td align=\"left\">2.2 (1.8, 2.8)</td><td align=\"left\">25.1 (− 9.8, 84.8)</td><td align=\"left\">97 (76, 121)</td><td align=\"left\">0.8 (0.7, 1)</td><td align=\"left\">24.7 (− 9.4, 89.4)</td><td align=\"left\">2,336 (1844, 2,914)</td><td align=\"left\">19.3 (15.3, 24)</td><td align=\"left\">11.5 (− 18.9, 60.7)</td></tr><tr><td align=\"left\">Turkey</td><td align=\"left\">3,928 (3,318, 4,480)</td><td align=\"left\">4.5 (3.8, 5.2)</td><td align=\"left\">0.6 (− 25.1, 38.3)</td><td align=\"left\">1,351 (1,155, 1534)</td><td align=\"left\">1.6 (1.3, 1.8)</td><td align=\"left\">3.1 (− 19.8, 35.7)</td><td align=\"left\">36,891 (30,965, 41,441)</td><td align=\"left\">42.9 (36.1, 48.2)</td><td align=\"left\">− 9.1 (− 33, 24.2)</td></tr><tr><td align=\"left\">United Arab Emirates</td><td align=\"left\">352 (204, 603)</td><td align=\"left\">5.4 (3, 9.4)</td><td align=\"left\">74 (14.1, 172.9)</td><td align=\"left\">83 (46, 145)</td><td align=\"left\">2 (1, 3.5)</td><td align=\"left\">63.3 (6.2, 152.8)</td><td align=\"left\">3,294 (1917, 5,569)</td><td align=\"left\">52.4 (29.2, 89.3)</td><td align=\"left\">58.2 (3.2, 146.3)</td></tr><tr><td align=\"left\">Yemen</td><td align=\"left\">397 (257, 632)</td><td align=\"left\">2.1 (1.4, 3.2)</td><td align=\"left\">42.8 (− 17.5, 163.9)</td><td align=\"left\">97 (65, 148)</td><td align=\"left\">0.7 (0.4, 1)</td><td align=\"left\">43.9 (− 10.1, 141)</td><td align=\"left\">3,697 (2,462, 5,891)</td><td align=\"left\">18.7 (12.4, 28.7)</td><td align=\"left\">26.5 (− 26.7, 129.5)</td></tr><tr><td align=\"left\">South Asia</td><td align=\"left\">27,980 (25,445, 29,678)</td><td align=\"left\">1.9 (1.7, 2)</td><td align=\"left\">48.3 (21.9, 78.6)</td><td align=\"left\">8,316 (7,511, 8,799)</td><td align=\"left\">0.6 (0.6, 0.7)</td><td align=\"left\">39.1 (17, 69)</td><td align=\"left\">251,842 (225,690, 265,848)</td><td align=\"left\">16.8 (15, 17.7)</td><td align=\"left\">33.2 (8.3, 59.3)</td></tr><tr><td align=\"left\">Bangladesh</td><td align=\"left\">1990 (1,390, 2,502)</td><td align=\"left\">1.5 (1, 1.8)</td><td align=\"left\">− 2.3 (− 25.3, 35)</td><td align=\"left\">578 (412, 714)</td><td align=\"left\">0.5 (0.3, 0.6)</td><td align=\"left\">− 5.4 (− 23.3, 18.1)</td><td align=\"left\">18,172 (12,854, 22,378)</td><td align=\"left\">13.4 (9.5, 16.4)</td><td align=\"left\">− 10.3 (− 30.4, 21.1)</td></tr><tr><td align=\"left\">Bhutan</td><td align=\"left\">14 (10, 19)</td><td align=\"left\">2.1 (1.5, 2.7)</td><td align=\"left\">22 (− 17, 88.4)</td><td align=\"left\">4 (3, 6)</td><td align=\"left\">0.7 (0.5, 1)</td><td align=\"left\">24.7 (− 10.7, 77.4)</td><td align=\"left\">130 (95, 171)</td><td align=\"left\">18.7 (13.6, 24.6)</td><td align=\"left\">11.6 (− 23.2, 72.1)</td></tr><tr><td align=\"left\">India</td><td align=\"left\">22,225 (20,048, 23,856)</td><td align=\"left\">1.9 (1.7, 2)</td><td align=\"left\">51.6 (22.7, 83)</td><td align=\"left\">6,757 (6,142, 7,167)</td><td align=\"left\">0.6 (0.6, 0.7)</td><td align=\"left\">42.1 (16.5, 73.8)</td><td align=\"left\">199,234 (179,984, 212,661)</td><td align=\"left\">16.7 (15.1, 17.8)</td><td align=\"left\">35.7 (8.5, 63.2)</td></tr><tr><td align=\"left\">Nepal</td><td align=\"left\">416 (278, 630)</td><td align=\"left\">1.8 (1.2, 2.7)</td><td align=\"left\">49.9 (7.1, 100.6)</td><td align=\"left\">132 (90, 198)</td><td align=\"left\">0.6 (0.4, 0.9)</td><td align=\"left\">49.8 (8, 98.5)</td><td align=\"left\">3,765 (2,545, 5,691)</td><td align=\"left\">15.8 (10.7, 23.8)</td><td align=\"left\">35.2 (− 0.3, 76.9)</td></tr><tr><td align=\"left\">Pakistan</td><td align=\"left\">3,335 (2,562, 4,236)</td><td align=\"left\">2.2 (1.8, 2.8)</td><td align=\"left\">74.5 (31.3, 125.5)</td><td align=\"left\">844 (672, 1,044)</td><td align=\"left\">0.7 (0.6, 0.9)</td><td align=\"left\">62.7 (28.7, 111.1)</td><td align=\"left\">30,541 (23,806, 38,242)</td><td align=\"left\">20.1 (15.9, 24.9)</td><td align=\"left\">55.2 (19.6, 98)</td></tr><tr><td align=\"left\">Southern sub‐Saharan Africa</td><td align=\"left\">2057 (1832, 2,285)</td><td align=\"left\">3.3 (2.9, 3.6)</td><td align=\"left\">25.2 (13, 37)</td><td align=\"left\">627 (559, 685)</td><td align=\"left\">1.1 (1, 1.2)</td><td align=\"left\">24.9 (13.4, 39.6)</td><td align=\"left\">19,099 (16,966, 21,292)</td><td align=\"left\">30.3 (26.9, 33.5)</td><td align=\"left\">14.7 (3.7, 24.8)</td></tr><tr><td align=\"left\">Botswana</td><td align=\"left\">47 (35, 61)</td><td align=\"left\">2.8 (2.2, 3.6)</td><td align=\"left\">46.2 (10.4, 92.3)</td><td align=\"left\">14 (10, 17)</td><td align=\"left\">1 (0.8, 1.3)</td><td align=\"left\">34 (2.5, 74.2)</td><td align=\"left\">428 (318, 550)</td><td align=\"left\">25.8 (19.6, 32.7)</td><td align=\"left\">27 (− 2, 65.8)</td></tr><tr><td align=\"left\">Lesotho</td><td align=\"left\">51 (38, 64)</td><td align=\"left\">3.6 (2.7, 4.4)</td><td align=\"left\">94.2 (40.8, 169.2)</td><td align=\"left\">14 (11, 17)</td><td align=\"left\">1.2 (0.9, 1.4)</td><td align=\"left\">64.4 (23.9, 125)</td><td align=\"left\">469 (361, 578)</td><td align=\"left\">32.5 (25, 40)</td><td align=\"left\">68.8 (22.8, 135.6)</td></tr><tr><td align=\"left\">Namibia</td><td align=\"left\">40 (33, 49)</td><td align=\"left\">2.4 (2, 2.8)</td><td align=\"left\">26 (− 4.1, 62)</td><td align=\"left\">12 (10, 14)</td><td align=\"left\">0.8 (0.7, 1)</td><td align=\"left\">15.2 (− 9.4, 42.4)</td><td align=\"left\">382 (317, 455)</td><td align=\"left\">22.1 (18.6, 25.9)</td><td align=\"left\">14.5 (− 11.9, 46)</td></tr><tr><td align=\"left\">South Africa</td><td align=\"left\">1679 (1,478, 1903)</td><td align=\"left\">3.5 (3.1, 3.9)</td><td align=\"left\">18 (5.5, 30.3)</td><td align=\"left\">527 (465, 585)</td><td align=\"left\">1.2 (1.1, 1.3)</td><td align=\"left\">22.2 (10.5, 37.1)</td><td align=\"left\">15,551 (13,664, 17,632)</td><td align=\"left\">32.3 (28.3, 36.4)</td><td align=\"left\">8.6 (− 3.1, 19.5)</td></tr><tr><td align=\"left\">Swaziland</td><td align=\"left\">31 (23, 42)</td><td align=\"left\">4.3 (3.1, 5.7)</td><td align=\"left\">51.4 (11.9, 107.8)</td><td align=\"left\">9 (6, 12)</td><td align=\"left\">1.4 (1.1, 1.9)</td><td align=\"left\">32.9 (1.6, 81.3)</td><td align=\"left\">302 (226, 404)</td><td align=\"left\">40.4 (30, 54.6)</td><td align=\"left\">38.8 (5.8, 90.4)</td></tr><tr><td align=\"left\">Zimbabwe</td><td align=\"left\">209 (168, 255)</td><td align=\"left\">2.2 (1.8, 2.6)</td><td align=\"left\">41.2 (10.4, 80.5)</td><td align=\"left\">52 (44, 62)</td><td align=\"left\">0.7 (0.6, 0.9)</td><td align=\"left\">17 (− 7.6, 46.3)</td><td align=\"left\">1967 (1668, 2,356)</td><td align=\"left\">19.8 (16.8, 23.6)</td><td align=\"left\">24.9 (− 1.4, 57.2)</td></tr><tr><td align=\"left\">Western sub‐Saharan Africa</td><td align=\"left\">8,838 (7,399, 10,332)</td><td align=\"left\">2.9 (2.5, 3.4)</td><td align=\"left\">33.3 (5.3, 67.6)</td><td align=\"left\">1956 (1696, 2,265)</td><td align=\"left\">0.9 (0.8, 1.1)</td><td align=\"left\">25.3 (1.7, 52.1)</td><td align=\"left\">83,548 (70,625, 98,563)</td><td align=\"left\">26.4 (22.8, 30.7)</td><td align=\"left\">19.8 (− 5.4, 49.1)</td></tr><tr><td align=\"left\">Benin</td><td align=\"left\">285 (205, 387)</td><td align=\"left\">3.8 (2.8, 4.9)</td><td align=\"left\">81 (36.4, 137.6)</td><td align=\"left\">65 (49, 84)</td><td align=\"left\">1.2 (0.9, 1.5)</td><td align=\"left\">58.8 (22.9, 110.2)</td><td align=\"left\">2,709 (1986, 3,695)</td><td align=\"left\">34.1 (25.8, 44.8)</td><td align=\"left\">61.8 (23.1, 111.2)</td></tr><tr><td align=\"left\">Burkina Faso</td><td align=\"left\">487 (335, 702)</td><td align=\"left\">3 (2.2, 4)</td><td align=\"left\">61.3 (20, 121.8)</td><td align=\"left\">97 (73, 128)</td><td align=\"left\">0.9 (0.7, 1.1)</td><td align=\"left\">36.8 (3, 79.5)</td><td align=\"left\">4,535 (3,183, 6,465)</td><td align=\"left\">26.8 (20.1, 35.4)</td><td align=\"left\">46.4 (9.8, 94.6)</td></tr><tr><td align=\"left\">Cameroon</td><td align=\"left\">794 (582, 1,105)</td><td align=\"left\">4.6 (3.4, 6.1)</td><td align=\"left\">53.5 (16.8, 99.7)</td><td align=\"left\">191 (142, 252)</td><td align=\"left\">1.5 (1.1, 1.9)</td><td align=\"left\">36.4 (5.7, 74)</td><td align=\"left\">7,482 (5,494, 10,131)</td><td align=\"left\">42 (30.9, 55.8)</td><td align=\"left\">39.6 (5.6, 79.2)</td></tr><tr><td align=\"left\">Cape Verde</td><td align=\"left\">26 (22, 32)</td><td align=\"left\">5.6 (4.6, 6.8)</td><td align=\"left\">85.1 (38, 153.5)</td><td align=\"left\">7 (6, 9)</td><td align=\"left\">1.6 (1.4, 2)</td><td align=\"left\">76.1 (38.4, 149)</td><td align=\"left\">252 (213, 301)</td><td align=\"left\">53.7 (45.6, 64)</td><td align=\"left\">63.9 (24.1, 120.3)</td></tr><tr><td align=\"left\">Chad</td><td align=\"left\">302 (209, 433)</td><td align=\"left\">2.7 (2, 3.4)</td><td align=\"left\">77.1 (36.2, 129.1)</td><td align=\"left\">59 (43, 76)</td><td align=\"left\">0.8 (0.6, 1)</td><td align=\"left\">51 (20.7, 87)</td><td align=\"left\">2,888 (2010, 4,018)</td><td align=\"left\">24.3 (18.1, 30.7)</td><td align=\"left\">60.6 (23.3, 102.9)</td></tr><tr><td align=\"left\">Cote d'Ivoire</td><td align=\"left\">322 (248, 410)</td><td align=\"left\">2.3 (1.8, 2.8)</td><td align=\"left\">56.5 (21.3, 98.1)</td><td align=\"left\">82 (65, 101)</td><td align=\"left\">0.8 (0.6, 0.9)</td><td align=\"left\">40.4 (10.5, 76.2)</td><td align=\"left\">2,948 (2,301, 3,739)</td><td align=\"left\">20.2 (16.1, 24.9)</td><td align=\"left\">41.5 (10.9, 78.2)</td></tr><tr><td align=\"left\">The Gambia</td><td align=\"left\">31 (23, 40)</td><td align=\"left\">2.5 (1.9, 3.2)</td><td align=\"left\">54.3 (14.1, 99.2)</td><td align=\"left\">9 (7, 11)</td><td align=\"left\">0.9 (0.7, 1.1)</td><td align=\"left\">41.3 (6.8, 80.4)</td><td align=\"left\">284 (209, 366)</td><td align=\"left\">22.9 (17.5, 28.6)</td><td align=\"left\">37.5 (3.5, 76.2)</td></tr><tr><td align=\"left\">Ghana</td><td align=\"left\">702 (525, 946)</td><td align=\"left\">3.1 (2.5, 4)</td><td align=\"left\">− 4.6 (− 31.1, 60.1)</td><td align=\"left\">160 (129, 198)</td><td align=\"left\">0.9 (0.8, 1.2)</td><td align=\"left\">6.1 (− 16.8, 53.9)</td><td align=\"left\">6,442 (4,864, 8,547)</td><td align=\"left\">28 (22.3, 34.8)</td><td align=\"left\">− 13.5 (− 36.4, 43.8)</td></tr><tr><td align=\"left\">Guinea</td><td align=\"left\">202 (149, 269)</td><td align=\"left\">2.7 (2.1, 3.7)</td><td align=\"left\">37.5 (1.3, 91.6)</td><td align=\"left\">53 (42, 72)</td><td align=\"left\">0.9 (0.7, 1.3)</td><td align=\"left\">32.3 (1, 87.5)</td><td align=\"left\">1882 (1,407, 2,459)</td><td align=\"left\">24.3 (19, 32.9)</td><td align=\"left\">21.9 (− 8.1, 68.1)</td></tr><tr><td align=\"left\">Guinea‐Bissau</td><td align=\"left\">50 (35, 70)</td><td align=\"left\">4.3 (3.1, 6)</td><td align=\"left\">61.7 (24.7, 109.6)</td><td align=\"left\">11 (8, 15)</td><td align=\"left\">1.3 (0.9, 1.8)</td><td align=\"left\">42.8 (13.1, 76)</td><td align=\"left\">455 (326, 644)</td><td align=\"left\">38.1 (27.1, 53.2)</td><td align=\"left\">45 (12.4, 85.7)</td></tr><tr><td align=\"left\">Liberia</td><td align=\"left\">95 (63, 140)</td><td align=\"left\">3.2 (2.3, 4.7)</td><td align=\"left\">28.2 (− 7.6, 71.4)</td><td align=\"left\">23 (16, 32)</td><td align=\"left\">1.1 (0.8, 1.5)</td><td align=\"left\">24.4 (− 1.9, 57)</td><td align=\"left\">896 (604, 1,316)</td><td align=\"left\">29.4 (20.9, 41.5)</td><td align=\"left\">14.2 (− 18, 52.7)</td></tr><tr><td align=\"left\">Mali</td><td align=\"left\">373 (267, 524)</td><td align=\"left\">2.5 (1.9, 3.1)</td><td align=\"left\">32.6 (− 2.3, 78.5)</td><td align=\"left\">80 (62, 100)</td><td align=\"left\">0.8 (0.6, 1)</td><td align=\"left\">24.7 (− 2, 56.9)</td><td align=\"left\">3,581 (2,604, 4,984)</td><td align=\"left\">22.6 (17.6, 28.3)</td><td align=\"left\">21.1 (− 9.4, 59)</td></tr><tr><td align=\"left\">Mauritania</td><td align=\"left\">100 (74, 133)</td><td align=\"left\">3.8 (2.9, 4.9)</td><td align=\"left\">54.9 (12.3, 108.1)</td><td align=\"left\">26 (21, 34)</td><td align=\"left\">1.2 (1, 1.6)</td><td align=\"left\">39.7 (4.6, 82.9)</td><td align=\"left\">909 (681, 1,189)</td><td align=\"left\">33.6 (25.8, 43)</td><td align=\"left\">40.5 (1.9, 86)</td></tr><tr><td align=\"left\">Niger</td><td align=\"left\">369 (228, 555)</td><td align=\"left\">2.3 (1.5, 3.1)</td><td align=\"left\">18 (− 16.8, 62.4)</td><td align=\"left\">70 (46, 93)</td><td align=\"left\">0.7 (0.5, 0.9)</td><td align=\"left\">11.9 (− 11.4, 40.6)</td><td align=\"left\">3,510 (2,253, 5,091)</td><td align=\"left\">20.8 (13.5, 27.5)</td><td align=\"left\">5.2 (− 27.2, 42.1)</td></tr><tr><td align=\"left\">Nigeria</td><td align=\"left\">4,062 (2,932, 5,453)</td><td align=\"left\">2.7 (2, 3.7)</td><td align=\"left\">24.3 (− 19.4, 94.6)</td><td align=\"left\">865 (652, 1,147)</td><td align=\"left\">0.8 (0.6, 1.1)</td><td align=\"left\">16.7 (− 22.2, 69.2)</td><td align=\"left\">38,796 (28,645, 51,849)</td><td align=\"left\">24.7 (18.6, 33)</td><td align=\"left\">11.3 (− 27.8, 69.7)</td></tr><tr><td align=\"left\">Sao Tome and Principe</td><td align=\"left\">5 (3, 9)</td><td align=\"left\">3.4 (2.2, 4.8)</td><td align=\"left\">36.5 (− 18.3, 113.3)</td><td align=\"left\">1 (1, 2)</td><td align=\"left\">1 (0.7, 1.3)</td><td align=\"left\">50.2 (8.6, 104.2)</td><td align=\"left\">56 (31, 95)</td><td align=\"left\">33.3 (20.6, 47.9)</td><td align=\"left\">19.7 (− 27.9, 88.5)</td></tr><tr><td align=\"left\">Senegal</td><td align=\"left\">331 (241, 448)</td><td align=\"left\">3.3 (2.5, 4.3)</td><td align=\"left\">54.1 (9.6, 105.4)</td><td align=\"left\">85 (65, 109)</td><td align=\"left\">1.1 (0.8, 1.4)</td><td align=\"left\">39.5 (3, 79.7)</td><td align=\"left\">3,085 (2,249, 4,181)</td><td align=\"left\">30 (22.6, 38.7)</td><td align=\"left\">37.3 (− 3.1, 83)</td></tr><tr><td align=\"left\">Sierra Leone</td><td align=\"left\">164 (122, 215)</td><td align=\"left\">3 (2.4, 3.7)</td><td align=\"left\">55.9 (12.8, 109.7)</td><td align=\"left\">37 (30, 45)</td><td align=\"left\">0.9 (0.8, 1.1)</td><td align=\"left\">44.2 (9.3, 94.8)</td><td align=\"left\">1566 (1,172, 2017)</td><td align=\"left\">27.4 (21.7, 33.6)</td><td align=\"left\">38.1 (− 0.8, 82.3)</td></tr><tr><td align=\"left\">Togo</td><td align=\"left\">138 (103, 177)</td><td align=\"left\">2.8 (2.1, 3.5)</td><td align=\"left\">49.7 (14.8, 94.3)</td><td align=\"left\">33 (25, 41)</td><td align=\"left\">0.9 (0.7, 1.1)</td><td align=\"left\">34.7 (5.6, 66.9)</td><td align=\"left\">1,272 (949, 1635)</td><td align=\"left\">24.9 (18.9, 31.3)</td><td align=\"left\">34.5 (4.3, 71.6)</td></tr><tr><td align=\"left\">Eastern sub‐Saharan Africa</td><td align=\"left\">6,912 (5,656, 8,386)</td><td align=\"left\">2.5 (2.1, 2.9)</td><td align=\"left\">7.7 (− 29.3, 63.9)</td><td align=\"left\">1,467 (1,259, 1696)</td><td align=\"left\">0.8 (0.7, 0.9)</td><td align=\"left\">5.2 (− 25.8, 38.7)</td><td align=\"left\">66,589 (54,910, 79,913)</td><td align=\"left\">22.8 (19.5, 26.4)</td><td align=\"left\">− 2.4 (− 37.5, 50.4)</td></tr><tr><td align=\"left\">Burundi</td><td align=\"left\">165 (120, 224)</td><td align=\"left\">2.1 (1.6, 2.6)</td><td align=\"left\">− 10.4 (− 37.3, 35.2)</td><td align=\"left\">33 (27, 41)</td><td align=\"left\">0.6 (0.5, 0.8)</td><td align=\"left\">− 12.9 (− 35, 21.1)</td><td align=\"left\">1613 (1,195, 2082)</td><td align=\"left\">19.4 (15.5, 23.4)</td><td align=\"left\">− 17.6 (− 40.9, 23.5)</td></tr><tr><td align=\"left\">Comoros</td><td align=\"left\">14 (9, 19)</td><td align=\"left\">2.4 (1.7, 3.2)</td><td align=\"left\">10.8 (− 28.9, 55.9)</td><td align=\"left\">4 (3, 5)</td><td align=\"left\">0.8 (0.6, 1)</td><td align=\"left\">6.4 (− 29.5, 42.8)</td><td align=\"left\">128 (85, 176)</td><td align=\"left\">22.4 (15.6, 29.4)</td><td align=\"left\">− 1.4 (− 36.4, 36.6)</td></tr><tr><td align=\"left\">Djibouti</td><td align=\"left\">25 (16, 38)</td><td align=\"left\">3.2 (2.1, 4.6)</td><td align=\"left\">42.7 (− 9.3, 122.8)</td><td align=\"left\">6 (4, 9)</td><td align=\"left\">1 (0.7, 1.4)</td><td align=\"left\">39.2 (− 4.6, 98.1)</td><td align=\"left\">237 (153, 357)</td><td align=\"left\">29.3 (19.3, 42.2)</td><td align=\"left\">28 (− 16.2, 94.2)</td></tr><tr><td align=\"left\">Eritrea</td><td align=\"left\">132 (69, 207)</td><td align=\"left\">3.3 (1.8, 4.6)</td><td align=\"left\">36.4 (− 22.2, 123)</td><td align=\"left\">27 (15, 38)</td><td align=\"left\">1 (0.5, 1.3)</td><td align=\"left\">25 (− 23.7, 78)</td><td align=\"left\">1,256 (646, 1944)</td><td align=\"left\">30.4 (16.6, 43.1)</td><td align=\"left\">22.9 (− 31.2, 100.3)</td></tr><tr><td align=\"left\">Ethiopia</td><td align=\"left\">1859 (1,299, 2,448)</td><td align=\"left\">2.7 (1.9, 3.3)</td><td align=\"left\">− 16.6 (− 52.4, 69.6)</td><td align=\"left\">406 (296, 488)</td><td align=\"left\">0.9 (0.6, 1.1)</td><td align=\"left\">− 11.9 (− 47.5, 52.2)</td><td align=\"left\">17,477 (12,846, 21,960)</td><td align=\"left\">24.5 (17.7, 29.4)</td><td align=\"left\">− 24.3 (− 57.5, 54.9)</td></tr><tr><td align=\"left\">Kenya</td><td align=\"left\">447 (321, 565)</td><td align=\"left\">1.5 (1.1, 1.9)</td><td align=\"left\">29.5 (2.5, 52.2)</td><td align=\"left\">115 (86, 146)</td><td align=\"left\">0.5 (0.4, 0.7)</td><td align=\"left\">17.8 (− 4, 38)</td><td align=\"left\">4,152 (2,997, 5,301)</td><td align=\"left\">13.9 (10.3, 17.7)</td><td align=\"left\">17.2 (− 7.6, 36.9)</td></tr><tr><td align=\"left\">Madagascar</td><td align=\"left\">349 (229, 496)</td><td align=\"left\">1.9 (1.3, 2.4)</td><td align=\"left\">4.7 (− 24.5, 45.9)</td><td align=\"left\">73 (52, 95)</td><td align=\"left\">0.6 (0.4, 0.7)</td><td align=\"left\">3.3 (− 21.5, 33.2)</td><td align=\"left\">3,362 (2,261, 4,622)</td><td align=\"left\">17.3 (12.2, 22.3)</td><td align=\"left\">− 6.4 (− 30.1, 28.4)</td></tr><tr><td align=\"left\">Malawi</td><td align=\"left\">380 (246, 597)</td><td align=\"left\">2.8 (2.1, 3.7)</td><td align=\"left\">7.3 (− 41.6, 130.8)</td><td align=\"left\">80 (58, 102)</td><td align=\"left\">0.8 (0.7, 1)</td><td align=\"left\">6.8 (− 28.9, 74.4)</td><td align=\"left\">3,713 (2,417, 5,566)</td><td align=\"left\">26.6 (19.5, 34.2)</td><td align=\"left\">− 2.4 (− 46.6, 105)</td></tr><tr><td align=\"left\">Mozambique</td><td align=\"left\">447 (287, 662)</td><td align=\"left\">2.4 (1.8, 3.2)</td><td align=\"left\">27.3 (− 12.3, 79.5)</td><td align=\"left\">103 (74, 135)</td><td align=\"left\">0.8 (0.6, 1.1)</td><td align=\"left\">22.6 (− 7.8, 77)</td><td align=\"left\">4,680 (2,995, 6,772)</td><td align=\"left\">22.8 (16.6, 29.9)</td><td align=\"left\">11.9 (− 24.4, 55.1)</td></tr><tr><td align=\"left\">Rwanda</td><td align=\"left\">215 (138, 324)</td><td align=\"left\">2.3 (1.6, 3.1)</td><td align=\"left\">− 2.1 (− 40.2, 67.2)</td><td align=\"left\">48 (34, 64)</td><td align=\"left\">0.7 (0.5, 0.9)</td><td align=\"left\">− 5.6 (− 37.9, 38.5)</td><td align=\"left\">2080 (1,374, 3,104)</td><td align=\"left\">21.2 (15, 28.6)</td><td align=\"left\">− 9.3 (− 44.8, 50.3)</td></tr><tr><td align=\"left\">Somalia</td><td align=\"left\">324 (182, 510)</td><td align=\"left\">2.7 (1.7, 3.8)</td><td align=\"left\">18.5 (− 32.1, 139)</td><td align=\"left\">66 (40, 96)</td><td align=\"left\">0.8 (0.5, 1.1)</td><td align=\"left\">11.3 (− 28, 82.7)</td><td align=\"left\">3,160 (1721, 5,024)</td><td align=\"left\">24.8 (15.2, 35.4)</td><td align=\"left\">6.1 (− 38.9, 120.8)</td></tr><tr><td align=\"left\">South Sudan</td><td align=\"left\">236 (145, 366)</td><td align=\"left\">3.1 (2, 4.7)</td><td align=\"left\">32.4 (− 12.2, 114.5)</td><td align=\"left\">46 (30, 69)</td><td align=\"left\">0.9 (0.6, 1.4)</td><td align=\"left\">14.5 (− 19, 68.3)</td><td align=\"left\">2,303 (1,461, 3,554)</td><td align=\"left\">28.9 (18.9, 43.6)</td><td align=\"left\">19.2 (− 17.9, 88.6)</td></tr><tr><td align=\"left\">Tanzania</td><td align=\"left\">1,302 (927, 1824)</td><td align=\"left\">2.9 (2.2, 3.8)</td><td align=\"left\">39.8 (− 4.6, 120.1)</td><td align=\"left\">260 (202, 331)</td><td align=\"left\">0.8 (0.7, 1)</td><td align=\"left\">22.3 (− 10.4, 69.9)</td><td align=\"left\">12,521 (9,275, 16,959)</td><td align=\"left\">26.8 (20.6, 34)</td><td align=\"left\">26 (− 13.7, 95.2)</td></tr><tr><td align=\"left\">Uganda</td><td align=\"left\">647 (440, 944)</td><td align=\"left\">2.2 (1.7, 2.9)</td><td align=\"left\">51.9 (8.7, 106.5)</td><td align=\"left\">126 (96, 163)</td><td align=\"left\">0.7 (0.5, 0.8)</td><td align=\"left\">39.1 (7.6, 79.3)</td><td align=\"left\">6,378 (4,398, 9,152)</td><td align=\"left\">20.9 (15.9, 26.4)</td><td align=\"left\">39.3 (− 1.1, 90.3)</td></tr><tr><td align=\"left\">Zambia</td><td align=\"left\">365 (259, 510)</td><td align=\"left\">3 (2.2, 3.8)</td><td align=\"left\">0.8 (− 30.5, 50.6)</td><td align=\"left\">74 (56, 93)</td><td align=\"left\">0.9 (0.7, 1.2)</td><td align=\"left\">− 3.5 (− 27.2, 23.9)</td><td align=\"left\">3,486 (2,588, 4,683)</td><td align=\"left\">27.4 (21.1, 34.6)</td><td align=\"left\">− 9 (− 36.3, 34.5)</td></tr><tr><td align=\"left\">Central sub‐Saharan Africa</td><td align=\"left\">2,186 (1756, 2,726)</td><td align=\"left\">2.7 (2.2, 3.3)</td><td align=\"left\">12.8 (− 18.4, 49.9)</td><td align=\"left\">494 (414, 595)</td><td align=\"left\">0.8 (0.7, 1.1)</td><td align=\"left\">7.2 (− 17.9, 31.6)</td><td align=\"left\">20,758 (16,716, 25,241)</td><td align=\"left\">24.5 (20.5, 29.5)</td><td align=\"left\">1.6 (− 25.7, 36)</td></tr><tr><td align=\"left\">Angola</td><td align=\"left\">630 (467, 877)</td><td align=\"left\">3.5 (2.7, 4.4)</td><td align=\"left\">18.7 (− 20.6, 89.1)</td><td align=\"left\">140 (112, 177)</td><td align=\"left\">1.1 (0.9, 1.4)</td><td align=\"left\">23 (− 10.5, 68.8)</td><td align=\"left\">5,998 (4,426, 8,323)</td><td align=\"left\">31.8 (25.2, 39.8)</td><td align=\"left\">6.6 (− 28, 65.5)</td></tr><tr><td align=\"left\">Central African Republic</td><td align=\"left\">97 (61, 143)</td><td align=\"left\">2.9 (2.1, 4)</td><td align=\"left\">19.2 (− 15.4, 70.8)</td><td align=\"left\">21 (15, 28)</td><td align=\"left\">0.9 (0.7, 1.1)</td><td align=\"left\">3.3 (− 23.9, 37.4)</td><td align=\"left\">930 (601, 1,330)</td><td align=\"left\">27.2 (19.6, 36.2)</td><td align=\"left\">7.3 (− 22.1, 50.8)</td></tr><tr><td align=\"left\">Congo</td><td align=\"left\">133 (93, 188)</td><td align=\"left\">3.9 (2.8, 5.3)</td><td align=\"left\">26.2 (− 21.2, 78.5)</td><td align=\"left\">33 (24, 43)</td><td align=\"left\">1.3 (0.9, 1.6)</td><td align=\"left\">21.6 (− 19.3, 62.2)</td><td align=\"left\">1,203 (865, 1637)</td><td align=\"left\">35.5 (25.5, 46.4)</td><td align=\"left\">12.2 (− 28.7, 58)</td></tr><tr><td align=\"left\">DR Congo</td><td align=\"left\">1,243 (902, 1,720)</td><td align=\"left\">2.2 (1.6, 3.1)</td><td align=\"left\">4.5 (− 31.8, 48)</td><td align=\"left\">277 (203, 373)</td><td align=\"left\">0.7 (0.5, 1)</td><td align=\"left\">− 3.4 (− 33.4, 27)</td><td align=\"left\">11,836 (8,923, 15,474)</td><td align=\"left\">20.5 (15, 27.7)</td><td align=\"left\">− 5.5 (− 37, 34.7)</td></tr><tr><td align=\"left\">Equatorial Guinea</td><td align=\"left\">31 (18, 51)</td><td align=\"left\">4.5 (2.8, 6.9)</td><td align=\"left\">51.9 (− 8.2, 156.1)</td><td align=\"left\">8 (5, 12)</td><td align=\"left\">1.6 (1, 2.3)</td><td align=\"left\">61.9 (7.3, 156.9)</td><td align=\"left\">296 (167, 502)</td><td align=\"left\">41.4 (25.1, 64.6)</td><td align=\"left\">37.2 (− 12.7, 128.5)</td></tr><tr><td align=\"left\">Gabon</td><td align=\"left\">52 (40, 70)</td><td align=\"left\">4.2 (3.3, 5.5)</td><td align=\"left\">46.2 (1.4, 94.2)</td><td align=\"left\">15 (12, 20)</td><td align=\"left\">1.4 (1.1, 1.8)</td><td align=\"left\">36.7 (0.5, 81.4)</td><td align=\"left\">496 (376, 668)</td><td align=\"left\">39.3 (30.5, 51.8)</td><td align=\"left\">31.8 (− 7.4, 75.9)</td></tr></tbody></table><table-wrap-foot><p><italic>PCs</italic> percentage changes, <italic>ASRs</italic> age-standardised rates, <italic>DALYs</italic> disability adjusted life years, <italic>UI </italic>uncertainty interval.</p></table-wrap-foot></table-wrap></p></sec><sec id=\"Sec10\"><title>Regional level</title><p id=\"Par14\">At the regional-level, we found that High-income North-America [12.1 (95% UI: 11.6–13.2)], Southern Latin America [11.6 (95% UI: 10.4–13.0)] and Eastern Europe [10.0 (95% UI: 9.5–10.5)] had the highest age-standardised incidence rates. In contrast, South Asia [1.9 (95% UI: 1.7–2.0)], Eastern Sub-Saharan Africa [2.5 (95% UI: 2.1–2.9)] and Central Sub-Saharan Africa [2.7 (95% UI: 2.2–3.3)] had the lowest age-standardised incidence rates. The age-standardised death rates were highest in Southern Latin America [4.3 (95% UI: 3.9–4.7)], Central Europe [3.8 (95% UI: 3.3–4.0)] and Eastern Europe [3.8 (95% UI: 3.6–3.9)]. In contrast, South Asia [0.62 (95% UI: 0.56–0.66)], Eastern Sub-Saharan Africa [0.77 (95% UI: 0.67–0.88)] and Central Sub-Saharan Africa [0.85 (95% UI: 0.68–1.1)] had the lowest age-standardised death rates. The age-standardised incidence and death rates were higher for males in all of the GBD regions, although this difference was not statistically significant in all regions (Fig. <xref rid=\"Fig1\" ref-type=\"fig\">1</xref>a, b).<fig id=\"Fig1\"><label>Figure 1</label><caption><p>The age-standardised incidence (<bold>a</bold>) and death (<bold>b</bold>) rates of kidney cancer in 2017 for the 21 GBD regions by sex.</p></caption><graphic xlink:href=\"41598_2020_70840_Fig1_HTML\" id=\"MO1\"/></fig></p><p id=\"Par15\">Most regions experienced an increase in age-standardised incidence rates, with South Asia [48% (95% UI: 22–80)], Tropical Latin America [36% (95% UI: 28–45)] and High-income Asia Pacific [35% (95% UI: 13–50)] showing the largest increases. In contrast, the Caribbean [− 24% (95% UI: − 34 to 21)] and Southern Latin America [− 4% (95% UI: − 18 to 44)] showed non-significant decreases in their age-standardised incidence rates. The age-standardised death rates increased the most in East Asia [49% (95% UI: 5–75)], South Asia [39% (95% UI: 17–69)] and Central Europe [37% (95% UI: 20–45)]. The opposite was true for the Caribbean [− 22% (95% UI: − 30 to 16)], Southern Latin America [− 7% (95% UI: − 18 to 30)] and High-income North America [− 1% (95% UI: − 6 to 11)], which all showed non-significant decreases in age-standardised death rates (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>). The percentage change in age-standardised incidence and death rates, by GBD region and by sex, are presented in Fig. <xref rid=\"Fig2\" ref-type=\"fig\">2</xref>a, b.<fig id=\"Fig2\"><label>Figure 2</label><caption><p>The percentage change in the age-standardised incidence (<bold>a</bold>) and death (<bold>b</bold>) rates of kidney cancer from 1990 to 2017 for the 21 GBD regions by sex.</p></caption><graphic xlink:href=\"41598_2020_70840_Fig2_HTML\" id=\"MO2\"/></fig></p><p id=\"Par16\">In 1990 the highest number of incident cases were found in Western Europe [44,006 (95% UI: 38,633–45,652)], High-Income North America [39,473 (95% UI: 35,501–40,625)] and East Asia [25,170 (95% UI: 22,570–30,076)]. In 2017, the highest numbers were found in Western Europe 72,675 (95% UI: 65,477–76,756)], High-Income North America [68,842 (95% UI: 65,663–74,202)] and East Asia [52,290 (95% UI: 46,830–56,228)] (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>a). In 1990, the number of deaths were found to be highest in Western Europe [18,583 (95% UI: 16,369–19,093)], High-Income North America [11,117 (95% UI: 10,077–11,359)] and Eastern Europe [8,812 (95% UI: 7,751–9,794)]. In 2017, the highest number of deaths were found in Western Europe [30,325 (95% UI: 27,097–31,837)], High-Income North America [19,048 (95% UI: 18,297–20,091)] and East Asia [18,634 (95% UI: 16,488–19,986)] (Fig. <xref rid=\"Fig3\" ref-type=\"fig\">3</xref>b).<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Number of incident cases (<bold>a</bold>) and deaths (<bold>b</bold>) of kidney cancer from 1990 to 2017 for the 21 GBD regions.</p></caption><graphic xlink:href=\"41598_2020_70840_Fig3_HTML\" id=\"MO3\"/></fig></p></sec><sec id=\"Sec11\"><title>National level</title><p id=\"Par17\">In 2017, the age-standardised incidence rates ranged from 1.5 to 15.8 per 100,000 people for the 195 countries studied. Uruguay [15.8 (95% UI: 13.6–19.0)], Slovakia [14.1 (95% UI: 7.7–16.5)] and the Czech Republic [13.1 (95% UI: 10.7–14.5)] had the highest age-standardised incidence rates. In contrast, Bangladesh [1.5 (95% UI: 1.0–1.8)], Kenya [1.5 (95% UI: 1.1–1.9)] and Nepal [1.8 (95% UI: 1.1–2.6)] had the lowest age-standardised incidence rates (Fig. <xref rid=\"Fig4\" ref-type=\"fig\">4</xref>). The age-standardised death rate also varied substantially by country, ranging from 0.47 to 5.6 per 100,000 people. The Czech Republic [5.6 (95% UI: 4.6–6.1)], Uruguay [5.5 (95% UI: 4.8–6.5)] and Iceland [5.2 (95% UI: 4.8–5.7)] had the highest age-standardised death rates, while Bangladesh [0.47 (95% UI: 0.34–0.58)], Kenya [0.52 (95% UI: 0.39–0.66)] and Madagascar [0.58 (95% UI: 0.41–0.73)] had the lowest rates (Fig. <xref rid=\"Fig5\" ref-type=\"fig\">5</xref>).<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Age-standardised incidence rate (per 100,000 population), by country, for 2017. <italic>ATG</italic> Antigua and Barbuda, <italic>VCT</italic> Saint Vincent and the Grenadines, <italic>BRB</italic> Barbados, <italic>COM</italic> Comoros, <italic>DMA</italic> Dominica, <italic>GRD</italic> Grenada, <italic>MDV</italic> Maldives, <italic>MUS</italic> Mauritius, <italic>LCA</italic> Saint Lucia, <italic>TTO</italic> Trinidad and Tobago, <italic>TLS</italic> Timor-Leste, <italic>SYC</italic> Seychelles, <italic>MLT</italic> Malta, <italic>SGP</italic> Singapore, <italic>MHL</italic> Marshall Islands, <italic>KIR</italic> Kiribati, <italic>SLB</italic> Solomon Islands, <italic>FSM</italic> Federated States of Micronesia, <italic>VUT</italic> Vanuatu, <italic>WSM</italic> Samoa, <italic>FJI</italic> = Fiji, <italic>TON</italic> Tonga. Maps were generated using R software version 3.5.2. (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.R-project.org/\">https://www.R-project.org/</ext-link>).</p></caption><graphic xlink:href=\"41598_2020_70840_Fig4_HTML\" id=\"MO4\"/></fig><fig id=\"Fig5\"><label>Figure 5</label><caption><p>Age-standardised death rate (per 100,000), by country, for 2017. <italic>ATG</italic> Antigua and Barbuda, <italic>VCT</italic> Saint Vincent and the Grenadines, <italic>BRB</italic> Barbados, <italic>COM</italic> Comoros, <italic>DMA</italic> Dominica, <italic>GRD</italic> Grenada, <italic>MDV</italic> Maldives, <italic>MUS</italic> Mauritius, <italic>LCA</italic> Saint Lucia, <italic>TTO</italic> Trinidad and Tobago, <italic>TLS</italic> Timor-Leste, <italic>SYC</italic> Seychelles, <italic>MLT</italic> Malta, <italic>SGP</italic> Singapore, <italic>MHL</italic> Marshall Islands, <italic>KIR</italic> Kiribati, <italic>SLB</italic> Solomon Islands, <italic>FSM</italic> Federated States of Micronesia, <italic>VUT</italic> Vanuatu, <italic>WSM</italic> Samoa, <italic>FJI</italic> Fiji, <italic>TON</italic> Tonga. Maps were generated using R software version 3.5.2. (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.R-project.org/\">https://www.R-project.org/</ext-link>).</p></caption><graphic xlink:href=\"41598_2020_70840_Fig5_HTML\" id=\"MO5\"/></fig></p><p id=\"Par18\">The percentage change in age-standardised incidence rates, from 1990 to 2017, differed substantially between countries, with Armenia [284.2% (95% UI: 115.0–390.1)], Belarus [241.0% (95% UI: 88.1–324.9)] and Latvia [216.3% (95% UI: 78.8–293.8)] showing the largest increases. In contrast, Qatar [− 44.3% (95% UI: − 65.1 to − 3.4)] Bermuda [− 43.9% (95% UI: − 54.2 to − 11.8)] and Trinidad and Tobago [− 36.9% (95% UI: − 53.1 to 30.7)] showed decreasing trends, although not all of these were statistically significant. The percentage change in age-standardised death rates (from 1990 to 2017) also differed between countries. The largest increases were seen in Armenia [396.6% (95% UI: 187.3–526.2)], Belarus [277.5% (95% UI: 114.9–359.9)] and Latvia [256.3% (95% UI: 113.4–336.1)]. In contrast, the largest decreases during this period were found in Qatar [− 49.4% (95% UI: − 67.5 to − 10.8)], Bermuda [− 43.5% (95% UI: − 51.5 to − 15.4)] and Trinidad and Tobago [− 41.4% (95% UI: − 54.7 to 10.2)] (Table <xref rid=\"Tab1\" ref-type=\"table\">1</xref>). However, it is important to again note that some of these increases or reductions were not statistically significant.</p></sec><sec id=\"Sec12\"><title>Age and sex patterns</title><p id=\"Par19\">Sex differences in the incident rates first appeared in the 35–39 age group and increased up to the oldest age group (95+). The number of incidents was also higher in males, from the 30–34 age group up to the 85–89 age group, with a peak being seen in the 65–69 age group (Fig. <xref rid=\"Fig6\" ref-type=\"fig\">6</xref>). The death rate was also higher in males, than in females, in all age groups. The number of deaths was also higher in males in all age groups, except the 1–4 and 95+ age groups (Online Appendix Fig. <xref rid=\"MOESM2\" ref-type=\"media\">1</xref>). However, the pattern for DALY rates was slightly different, such that the trend started declining after 80–84 for males and 85–89 for females. The number of DALYs was also higher in males, in most of the age groups, except the 5–9, 10–14, 90–94 and 95+ age groups. The number of DALYs peaked in the 60–64 age group (Online Appendix Fig. <xref rid=\"MOESM3\" ref-type=\"media\">2</xref>). The YLL rate peaked in the 80–84 age group, which comprised a large proportion of the DALYs (Online Appendix Fig. <xref rid=\"MOESM4\" ref-type=\"media\">3</xref>).<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Global number of incidents and age-standardised incidence rate of kidney cancer per 100,000 population by age and sex, 2017; Dotted and dashed lines indicate 95% upper and lower uncertainty intervals, respectively.</p></caption><graphic xlink:href=\"41598_2020_70840_Fig6_HTML\" id=\"MO6\"/></fig></p></sec><sec id=\"Sec13\"><title>Burden of kidney cancer by SDI</title><p id=\"Par20\">At the regional-level, the age-standardised DALY rate increased up to an SDI of approximately 0.74 and then decreased with increasing SDI values (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>). The global age-standardised DALY rate was initially higher than expected, but then the rate fell below the expected level for the last 14 years. High-income Asia–Pacific was the only region in the GBD high-income super regions that had a lower than expected DALY rate across the entire measurement period. For the GBD super-regions of Central Europe, Eastern Europe and Central Asia, only Eastern Europe had a higher than expected DALY rate for the entire measurement period, while Central Europe had a higher than expected level for the last 13 years. From the Latin America and Caribbean super-region, only Southern and Central Latin America had higher than expected DALY rates across the entire measurement period. In the Sub-Saharan Africa super-region, only Southern Sub-Saharan Africa was found to have a lower than expected DALY rate for the entire measurement period. In the Southeast Asia, East Asia and Oceania super-regions, only Southeast Asia and East Asia had a lower than expected DALY rate from 1990 to 2017, but Oceania was lower than expected during the last 3 years of the measurement period. The South Asia region had a lower than expected DALY rate for the entire measurement period, while the North Africa and Middle East region was lower for most of the measurement period (from 1992 to 2017) (Fig. <xref rid=\"Fig7\" ref-type=\"fig\">7</xref>). Figure <xref rid=\"Fig8\" ref-type=\"fig\">8</xref> presents the country-level age-standardised DALY rates and its expected relationship with SDI. The expected patterns were non-linear in nature, peaking at an SDI of 0.84. However, there were large national differences in age-standardised DALY rates. Uruguay, the Czech Republic, Lithuania, Ukraine, Iceland, Greenland and many other countries showed higher than their expected DALY rates, whereas Singapore, Kuwait, China, Algeria, Morocco and many other countries had much lower than expected DALY rates, based only on their SDI.<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Age-standardised DALY rates for kidney cancer for the 21 Global Burden of Disease regions by Socio-demographic Index, 1990–2017; Expected values based on Socio-demographic Index and disease rates in all locations are shown as the black line. For each region, points from left to right depict estimates from each year from 1990 to 2017. <italic>DALY</italic> disability-adjusted life-year.</p></caption><graphic xlink:href=\"41598_2020_70840_Fig7_HTML\" id=\"MO7\"/></fig><fig id=\"Fig8\"><label>Figure 8</label><caption><p>Age-standardised DALY rates of kidney cancer in 195 countries and Socio-demographic Index, 2017; Expected values are shown as the black line. <italic>DALY</italic> disability-adjusted life-year.</p></caption><graphic xlink:href=\"41598_2020_70840_Fig8_HTML\" id=\"MO8\"/></fig></p></sec><sec id=\"Sec14\"><title>Risk factors</title><p id=\"Par21\">Globally, 18% of kidney cancer DALYs was attributable to high BMI in both sexes (Male: 16.5%; Female: 22.1%). The proportion of kidney cancer DALYs there were attributable to high BMI ranged from 7.1% in Eastern Sub-Saharan Africa to 29.2% in High-income North America. Furthermore, 16.6% of kidney cancer DALYs was attributable to smoking in both sexes, but this burden was higher in males (21.6%) than females (7.3%). The smoking-attributable burden also differed across GBD regions, ranging from 3.9% in Western Sub-Saharan Africa to 22.9% in Eastern Europe. Finally, the burden of kidney cancer attributable to occupational exposure to trichloroethylene was negligible (Fig. <xref rid=\"Fig9\" ref-type=\"fig\">9</xref>).<fig id=\"Fig9\"><label>Figure 9</label><caption><p>Percent of kidney cancer DALYs attributable to risk factors for the 21 Global Burden of Disease regions in 2017. <italic>DALY</italic> disability-adjusted life-year.</p></caption><graphic xlink:href=\"41598_2020_70840_Fig9_HTML\" id=\"MO9\"/></fig></p></sec></sec><sec id=\"Sec15\"><title>Discussion</title><p id=\"Par22\">This study reported the incidence, mortality, and DALYs for kidney cancer in 195 countries from 1990 to 2017. Globally, the age-standardised incidence and death rates have increased while the DALY rates have declined, although neither of these changes were statistically significant. Our results show that there has been little or no progress in reducing the burden of Kidney cancer over the past 28 years and we call for renewed efforts to reduce the burden of this disease. GLOBOCAN estimated that there were 403,262 (95% UI: 387,315–419,865) incident cases of kidney cancer globally in 2018, which is close to our estimate of 393,043 (95% UI: 371,162–404,595) in 2017. However, GLOBOCAN estimated that there were 175,098 deaths (95% UI: 166,193–184,480), which is much higher than the 138,526 (95% UI: 128,656–142,522) estimated in the present research<sup><xref ref-type=\"bibr\" rid=\"CR9\">9</xref></sup>. There are several reasons for these differences, which may related to the data sources and/or different methodological approaches. The GBD methodology considers all causes of deaths in each run, whereas GLOBOCAN only provides cancer mortality.</p><p id=\"Par23\">Previous research has found that more developed countries have a higher incidence of kidney cancer than less developed countries<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>, which was also confirmed in the present study. The association that development level has with kidney cancer incidence and mortality has only been investigated in a small number of studies and these studies only used data from selected countries, meaning that their results must be interpreted with caution<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref>,<xref ref-type=\"bibr\" rid=\"CR17\">17</xref></sup>. There are also a number of other problems with the previous research on this topic. For example, previous research found that the incidence rate of kidney cancer was twice as high in developed countries as in developing countries<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. However, this research only compared two states of development (developed vs. developing) and they did not measure each country’s level of development using the SDI. In fact, using only two categories of development leads to information loss, which means that accurate and precise patterns may not be produced. Secondly, previous research determined development status using the Human Development Index (HDI), which is problematic as one of the HDI’s components (life expectancy) is associated with health. Therefore, the association that development status has with kidney cancer may have previously been over-estimated<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. In order to address this issue, in GBD 2017 we used the SDI, which does not include any health-related components. Thirdly, considering the association between the variables to be linear may be inaccurate<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Hence, we examined the non-linear association between SDI and kidney cancer burden, in order to determine the shape of the association. Finally, previous research examined the association between the HDI of a specific year (e.g., 2000) with the incidence and mortality rates of a different year (e.g., 2012)<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. In this study, we examined the same years.</p><p id=\"Par24\">There are a number of possible reasons for the higher burden of kidney cancer in developed countries. Firstly, the prevalence of risk factors, such as smoking, high BMI and low physical activity and hypertension may be higher in developed countries than in developing regions<sup><xref ref-type=\"bibr\" rid=\"CR8\">8</xref></sup>. Secondly, the increases in the incidence of kidney cancer could also be partly due to improvements in the early detection of cancer using imaging procedures, such as ultrasonography, computed tomography, and magnetic resonance imaging in high income countries<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref></sup>. Perhaps the increases in the incidences of kidney cancer may be due to exposure to occupational and environmental risk factors, such as trichloroethylene, cadmium, arsenic, radon and nitrate. Although we know exposure to these risk factors have declined in the developed world, there is no evidence to suggest this same pattern has been replicated in the developing world<sup><xref ref-type=\"bibr\" rid=\"CR3\">3</xref>,<xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR18\">18</xref></sup>.</p><p id=\"Par25\">Although a number of risk factors have found to be associated with kidney cancer, the attributable burden was only calculated for those risk factors that had robust evidence of their relationship with kidney cancer<sup><xref ref-type=\"bibr\" rid=\"CR10\">10</xref></sup>. Therefore, the attributable burden was calculated for two life style risk factors (smoking and high body mass index) and one environmental and occupational risk factor (occupational exposure to trichloroethylene).</p><p id=\"Par26\">High BMI (overweight/obesity) is one of the important risk factors, contributing 18.5% to the burden of kidney cancer in the population. Previous research has found that the prevalence of obesity has continuously increased in most countries during the period 1990–2015 and has doubled in more than 70 countries<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. Moreover, in many countries the rate of increase in childhood obesity has been greater than the rate of increase among adults<sup><xref ref-type=\"bibr\" rid=\"CR19\">19</xref></sup>. There are a number of approaches that can be taken to reduce the prevalence of being overweight and obese and to thereby reduce the burden of this disease. These measures should include a ban on advertising unhealthy foods, improving school meals, taxation, subsidies, and incentives to increase the production of healthy foods<sup><xref ref-type=\"bibr\" rid=\"CR20\">20</xref></sup>.</p><p id=\"Par27\">Our study also found that smoking contributes 16.6% to the burden of kidney cancer (both sexes) and reducing exposure to this risk factor could play an important role in decreasing the burden of this disease. A study of the global progress in reducing the prevalence of smoking has reported heterogeneous findings, according to country, development status, and sex. Globally, the age-standardised prevalence rate of daily smoking declined by 28.4% and 34.4%, respectively, among men and women from 1990 to 2015<sup><xref ref-type=\"bibr\" rid=\"CR21\">21</xref></sup>. There is a need to achieve greater success in the control of tobacco smoking through the use of effective, comprehensive, and adequately implemented and enforced policies.</p><p id=\"Par28\">The third risk factor assessed in our study was exposure to Trichloroethylene, which is usually used as a metal cleaner and degreaser<sup><xref ref-type=\"bibr\" rid=\"CR15\">15</xref></sup>. The burden of kidney cancer that was attributable to occupational exposure to trichloroethylene was found to be 0.1%. The attributable burden of this risk factor is negligible, as the populations’ exposure is very low. A meta-analysis found that occupational exposure to trichloroethylene increased the risk of kidney cancer by 32%<sup><xref ref-type=\"bibr\" rid=\"CR22\">22</xref></sup>.</p><p id=\"Par29\">Physical activity and alcohol consumption have also been considered to be lifestyle risk factors. A meta-analysis found a negative association between physical activity and kidney cancer<sup><xref ref-type=\"bibr\" rid=\"CR23\">23</xref></sup>, which was also confirmed in a pooled analysis of cohort studies<sup><xref ref-type=\"bibr\" rid=\"CR24\">24</xref></sup>. However, research has also shown that prolonged sitting does not increase kidney cancer among men and women<sup><xref ref-type=\"bibr\" rid=\"CR25\">25</xref></sup>. Therefore, it is not entirely clear how physical activity changes the risk of kidney cancer and its association with kidney cancer has not been examined independently from high body mass index and hypertension<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>.</p><p id=\"Par30\">Alcohol consumption is another potential risk factor that has been extensively studied. Two meta-analyses<sup><xref ref-type=\"bibr\" rid=\"CR26\">26</xref>,<xref ref-type=\"bibr\" rid=\"CR27\">27</xref></sup> have found there to be an inverse relationship between alcohol consumption and the risk of kidney cancer, and these findings have been confirmed by large scale prospective studies<sup><xref ref-type=\"bibr\" rid=\"CR28\">28</xref>,<xref ref-type=\"bibr\" rid=\"CR29\">29</xref></sup>. All of the aforementioned studies reported a lower risk for drinkers, compared to non-drinkers or light drinkers. GBD 2017 did not calculate the burden of kidney cancer attributable to alcohol consumption, but this addition has been suggested for future GBD cycles, as there is sufficient evidence of the association between these two variables.</p><p id=\"Par31\">Although the association between diet and kidney cancer has been examined in previous research, the evidence is not robust<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. Most studies have reported no association between fruit and vegetable intake and the risk of kidney cancer, and nutrient specific associations have not been reported<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>.</p><p id=\"Par32\">Medical history, including hypertension, chronic kidney disease, kidney stones and diabetes mellitus have also been found to be associated with kidney cancer<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref>,<xref ref-type=\"bibr\" rid=\"CR6\">6</xref></sup>. Several studies have reported that hypertension increases the risk of kidney cancer and have reported dose–response relationships between blood pressure and kidney cancer risk<sup><xref ref-type=\"bibr\" rid=\"CR30\">30</xref>,<xref ref-type=\"bibr\" rid=\"CR31\">31</xref></sup>. This risk factor has also been suggested for inclusion in the next cycle of the GBD study. Chronic kidney disease has also been found to be a risk factor for kidney cancer<sup><xref ref-type=\"bibr\" rid=\"CR32\">32</xref>–<xref ref-type=\"bibr\" rid=\"CR34\">34</xref></sup>. Furthermore, previous large scale studies<sup><xref ref-type=\"bibr\" rid=\"CR35\">35</xref>,<xref ref-type=\"bibr\" rid=\"CR36\">36</xref></sup> and one meta-analysis<sup><xref ref-type=\"bibr\" rid=\"CR37\">37</xref></sup> have found a higher risk of kidney cancer to be associated with kidney stones. Finally, previous large scale studies<sup><xref ref-type=\"bibr\" rid=\"CR38\">38</xref>,<xref ref-type=\"bibr\" rid=\"CR39\">39</xref></sup> and a meta-analysis<sup><xref ref-type=\"bibr\" rid=\"CR40\">40</xref></sup> have found diabetes mellitus to be associated with a higher risk of kidney cancer.</p><p id=\"Par33\">Considering the evidence reported above, there is sufficient evidence to suggest that the risk of kidney cancer is associated with: alcohol consumption, hypertension, chronic kidney diseases, kidney stones and diabetes mellitus. These risk factors should all be included into the next cycle of the GBD project to inform public policy and health policy makers how much of the kidney cancer burden could be attributed to each of these risk factors. However, the evidence for physical activity, diet and several other risk factors are not compelling and further research is needed.</p><p id=\"Par34\">The present study provides important information on the proportion of the kidney cancer burden that is attributable to modifiable risk factors, such as smoking, high body mass index and occupational exposure to trichloroethylene, which can be used for primary prevention purposes. However, the role of other risk factors, such as hypertension and diabetes mellitus, should be calculated in future iterations of the GBD project. In addition, improvements in diagnostic measures are needed, with the identification of blood- and urine-based markers being one approach with considerable merit<sup><xref ref-type=\"bibr\" rid=\"CR5\">5</xref></sup>. These improvements are needed to allow earlier detection of kidney cancer and thereby a better prognosis for patients.</p></sec><sec id=\"Sec16\"><title>Strengths and limitations</title><p id=\"Par35\">The present research had a number of limitations. Firstly, it is possible that in some countries the rate of cancer detection is low and hence the incidence is lower than reported here. Secondly, some countries do not have the vital statistics to capture the causes of death. GBD methodology adjusts for these biases and provides uncertainty intervals for all estimates.</p></sec><sec id=\"Sec17\"><title>Conclusions</title><p id=\"Par36\">There has been little or no improvement in the burden of kidney cancer over the last 28 years. Our study provides much needed information about the burden of kidney cancer in each country, to enable countries to better plan to address their burden and to allocate their limited resources more appropriately. Our results highlight the need for renewed efforts to reduce exposure to risk factors and to improve the prevention and early detection of this disease.</p></sec><sec sec-type=\"supplementary-material\"><title>Supplementary information</title><sec id=\"Sec18\"><p>\n<supplementary-material content-type=\"local-data\" id=\"MOESM1\"><media xlink:href=\"41598_2020_70840_MOESM1_ESM.docx\"><caption><p>Supplementary Legends.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM2\"><media xlink:href=\"41598_2020_70840_MOESM2_ESM.pdf\"><caption><p>Supplementary Figure 1.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM3\"><media xlink:href=\"41598_2020_70840_MOESM3_ESM.pdf\"><caption><p>Supplementary Figure 2.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM4\"><media xlink:href=\"41598_2020_70840_MOESM4_ESM.pdf\"><caption><p>Supplementary Figure 3.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"MOESM5\"><media xlink:href=\"41598_2020_70840_MOESM5_ESM.doc\"><caption><p>Supplementary Table 1.</p></caption></media></supplementary-material></p></sec></sec></body><back><glossary><title>Abbreviations</title><def-list><def-item><term>GBD</term><def><p>Global burden of disease</p></def></def-item><def-item><term>DALY</term><def><p>Disability adjusted life-year</p></def></def-item><def-item><term>YLDs</term><def><p>Years lived with disability</p></def></def-item><def-item><term>YLLs</term><def><p>Years of life lost</p></def></def-item><def-item><term>SDI</term><def><p>Socio-demographic index</p></def></def-item><def-item><term>HAQ</term><def><p>Healthcare access and quality</p></def></def-item><def-item><term>BMI</term><def><p>Body mass index</p></def></def-item><def-item><term>UI</term><def><p>Uncertainty interval</p></def></def-item></def-list></glossary><fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group><sec><title>Supplementary information</title><p> is available for this paper at 10.1038/s41598-020-70840-2.</p></sec><ack><title>Acknowledgements</title><p>We would like to thank the Institute for Health Metrics and Evaluation staff and its collaborators who collected and made these data publicly available. In addition, we would like to acknowledge the financial support from the Social Determinants of Health Research Center at the Shahid Beheshti University of Medical Sciences. GBD 2017 was funded by the Bill and Melinda Gates Foundation. However, the foundation was not involved in any way in the preparation of this manuscript. The present report was also supported by the Social Determinants of Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Grant Number: 17548).</p></ack><notes notes-type=\"author-contribution\"><title>Author contributions</title><p>S.S., A.A.K., A.M. and C.F. designed the study. S.S., A.A.-H., M.M.L., A.A.-A. and M.Q. tabulated data and performed the statistical analyses. S.S., A.A.K., M.A.M., M.J.S., M.A. and D.B. drafted the initial manuscript. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Oncol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Oncol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Oncol.</journal-id><journal-title-group><journal-title>Frontiers in Oncology</journal-title></journal-title-group><issn pub-type=\"epub\">2234-943X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850410</article-id><article-id pub-id-type=\"pmc\">PMC7431913</article-id><article-id pub-id-type=\"doi\">10.3389/fonc.2020.01307</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Oncology</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>MNX1 Promotes Malignant Progression of Cervical Cancer via Repressing the Transcription of p21<sup>cip1</sup></article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Zhu</surname><given-names>Biqing</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Wu</surname><given-names>Yaqin</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Luo</surname><given-names>Jing</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Quanli</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Huang</surname><given-names>Jian</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Qian</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Xu</surname><given-names>Lin</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/953088/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Lu</surname><given-names>Emei</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c002\"><sup>*</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Ren</surname><given-names>Binhui</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"corresp\" rid=\"c003\"><sup>*</sup></xref></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Radiation Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Department of Cardiothoracic Surgery, Jinling Hospital, Medical School of Nanjing University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff><aff id=\"aff3\"><sup>3</sup><institution>Jiangsu Key Laboratory of Molecular and Translational Cancer Research</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Ihab Younis, Carnegie Mellon University in Qatar, Qatar</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Weifeng Ding, Nantong University, China; Massimo Broggini, Mario Negri Pharmacological Research Institute (IRCCS), Italy</p></fn><corresp id=\"c001\">*Correspondence: Lin Xu <email>xulin83@njmu.edu.cn</email></corresp><corresp id=\"c002\">Emei Lu <email>lem13705179888@sina.cn</email></corresp><corresp id=\"c003\">Binhui Ren <email>robbishren@163.com</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Cancer Genetics, a section of the journal Frontiers in Oncology</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†These authors have contributed equally to this work</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>1307</elocation-id><history><date date-type=\"received\"><day>14</day><month>4</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Zhu, Wu, Luo, Zhang, Huang, Li, Xu, Lu and Ren.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Zhu, Wu, Luo, Zhang, Huang, Li, Xu, Lu and Ren</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Motor neuron and pancreas homeobox 1 (MNX1) is a development-related genes and has been found to be highly expressed in several cancers. However, its biological function in cervical cancer remains largely unexplored. QRT-PCR, western blot, and IHC showed that MNX1 was abnormally overexpressed in cervical cancer tissues and cell lines. The high expression level of MNX1 correlated with poorer clinicopathologic characteristics in cervical cancer patients. Evaluated by RTCA (Real Time Cellular Analysis) proliferation assay, colony formation assay, EdU assay, transwell assay, and matrigel assay, we found that knockdown of MNX1 inhibited proliferation, migration and invasion of cervical cancer <italic>in vitro</italic>, while overexpression of MNX1 promoted malignant phenotype of cervical cancer. And subcutaneous xenograft model confirmed the malignant phenotype of MNX1 <italic>in vivo</italic>. Furthermore, flow cytometry, chromatin immunoprecipitation, and luciferase reporter assay indicated that MNX1 accelerated cell cycle transition by transcriptionally downregulating cyclin-dependent kinases p21<sup>cip1</sup>. In summary, our study revealed that MNX1 exerted an oncogenic role in cervical cancer via repressing the transcription of p21<sup>cip1</sup> and thus accelerating cell cycle progression. Our results suggested that MNX1 was a potential diagnostic marker and therapeutic target for cervical cancer patients.</p></abstract><kwd-group><kwd>cervical cancer</kwd><kwd>MNX1</kwd><kwd>cell cycle</kwd><kwd>transcription</kwd><kwd>p21<sup>cip1</sup></kwd></kwd-group><counts><fig-count count=\"7\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"52\"/><page-count count=\"14\"/><word-count count=\"7605\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>As one of the most common gynecological malignant tumors, cervical cancer is the fourth leading cause of cancer-related death among women worldwide (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Although efforts (including periodic cancer screening, prompt surgical treatment, chemotherapy, and radiotherapy) have been made to decrease the mortality of cervical cancer, the prognosis of patients is still poor and cervical cancer remains an important public health issue (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>). The pathogenesis of cervical cancer has not been clearly illustrated, but it is confirmed that the activation of tumor-promoting genes and the inactivation of tumor suppressor genes participate in the progression of cervical cancer (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>). To screen for novel abnormally expressed genes functioning in cervical cancer may provide potential prognostic markers and therapeutic targets for treatment.</p><p>MNX1 (Motor neuron and pancreas homeobox 1, also known as HB9, HLXB9) is a member of homeobox gene family and encodes a nuclear protein (<xref rid=\"B4\" ref-type=\"bibr\">4</xref>). The homeobox genes are a group of genes containing homeobox (a 180 base pairs long DNA sequence) and encode homeodomain proteins that act as transcription factors (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). Many homeobox genes have been proved to be implicated in various human cancers, acting as oncogenes, or tumor suppressors (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). MNX1 was firstly found to be expressed in lymphoid and pancreatic tissues and defined as a novel human homeobox gene in 1994 (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>). Early studies showed that MNX1 was involved invertebrate and pancreatic development (<xref rid=\"B10\" ref-type=\"bibr\">10</xref>, <xref rid=\"B11\" ref-type=\"bibr\">11</xref>) and motor neuronal differentiation (<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Defects in this gene result in Currarino syndrome, an autosomic dominant congenital malformation (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). In follow-up study, MNX1 was found to be abnormally expressed in several cancer types, including prostate cancer, hepatocellular carcinoma and acute myeloid leukemia (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>–<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). Furthermore, recent studies confirmed that MNX1 played oncogenic roles in colorectal cancer, breast cancer, and bladder cancer (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>–<xref rid=\"B19\" ref-type=\"bibr\">19</xref>).</p><p>The aim of this study is to identify the expression and function of MNX1 in cervical cancer. Our results revealed that MNX1 was significantly upregulated in cervical cancer and correlated with poorer prognosis. The knockdown or overexpressed MNX1 inhibited or promoted aggressiveness of cervical cancer, including proliferation, migration, and invasion capacities, by enhancing or repressing the transcription of p21<sup>cip1</sup> thus regulating the G2/M cell cycle transition. These findings suggested that MNX1 might be a potential diagnostic marker and therapeutic target for cervical cancer.</p></sec><sec sec-type=\"materials and methods\" id=\"s2\"><title>Materials and Methods</title><sec><title>Bioinformatics</title><p>The TCGA dataset termed TCGA_CESC_exp_HiS-eqV2-2015-02-24 was downloaded from the UCSC cancer browser (<ext-link ext-link-type=\"uri\" xlink:href=\"https://genome-cancer.ucsc.edu/\">https://genome-cancer.ucsc.edu/</ext-link>) (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>) to evaluate the expression of MNX1 in cervical cancer and adjacent normal tissues. GEPIA (Gene Expression Profiling Interactive Analysis) (<ext-link ext-link-type=\"uri\" xlink:href=\"http://gepia.cancer-pku\">http://gepia.cancer-pku</ext-link>. cn/index.html) was used to analyze the expression of MNX1 with Disease Free Survival (DFS) of cervical cancer patients. The cBioPortal website (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www\">http://www</ext-link>. cbioportal.org/) (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>) was utilized to obtain highly co-expressed genes with MNX1. Totally 208 genes highly correlated with MNX1 (Pearson score > 0.4; <xref ref-type=\"supplementary-material\" rid=\"SM1\">Table S1</xref>) were submitted to DAVID Bioinformatics Resources 6.8 (<ext-link ext-link-type=\"uri\" xlink:href=\"http://david.abcc.ncifcrf.gov/\">http://david.abcc.ncifcrf.gov/</ext-link>) (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>) for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome Pathway analysis. And we analyzed the binding site of MNX1 and p21<sup>cip</sup> promoters through the Jaspar Database (<ext-link ext-link-type=\"uri\" xlink:href=\"http://jaspardev.genereg.net/\">http://jaspardev.genereg.net/</ext-link>) (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>).</p></sec><sec><title>Human Cervical Cancer Cell Lines</title><p>The human normal cervical cell lines (Hacat) and cervical cancer cell lines (HeLa, Siha, Caski, and C33a) were purchased from American Type Culture Collection (ATCC, USA). HeLa, Siha, C33a, and Hacat cells were incubated in DMEM medium (KeyGEN, Nanjing, China), and Caski cells were cultured in RPMI1640 (KeyGEN, Nanjing, China) medium containing 10% fetal bovine serum (GIBCO-BRL, Invitrogen, Carlsbad, CA, USA) and cultured at 37°C in a humidified incubator containing 5% CO<sub>2</sub>.</p></sec><sec><title>Human Cervical Cancer Tissues</title><p>The 40 pairs of cervical cancer tissues and adjacent tissues were selected from the Affiliated Cancer Hospital of Nanjing Medical University and informed consent was obtained from all subjects. All tumors and paired non-tumor tissues were confirmed by experienced pathologists and no patients received chemotherapy or radiotherapy before surgery. The mRNA expression of MNX1 and p21<sup>cip1</sup> in cervical cancer tissues was detected by qRT-PCR. Collection of human tissue samples was conducted in accordance with the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS). This study was approved by the Ethics Committee of the Nanjing Medical University Affiliated Cancer Hospital.</p></sec><sec><title>Tissue Microarrays</title><p>Paired cervical cancer tissue microarrays were obtained from Shanghai Outdo Biotech Co., Ltd. (Cat. No. OD-CT-RpUtr03-004 and OD-CT-RpUtr03-006). Totally 62 pairs of paraffin-embedded human cervical cancer sections were analyzed for MNX1 expression. All tissues were examined by two experienced pathologists and the TNM stage was confirmed in each patient with blinded methods. The sections were incubated with an anti-MNX1 primary antibody (1:100, Abcam, ab79541). The IHC scores were calculated according to intensity and percentage of positive cells. The staining intensity was evaluated as the basis of four grades: 0 (negative staining), 1(weak staining), 2 (moderate staining), or 3 (strong staining). The product (percentage of positive cells and respective intensity scores) was used as the final staining scores (a minimum value of 0 and a maximum value of 300).</p></sec><sec><title>RNA Preparation, Reverse Transcription, and qRT-PCR</title><p>TRIzol reagent (Invitrogen, Carlsbad, CA, USA) was used to extract total RNA from tissue samples or cultured cells according to the manufacturer's protocol. A Reverse Transcription Kit (Takara, Cat: RR036A, KeyGEN) was utilized to generate cDNA. QRT-PCR was performed with SYBR Select Master Mix (Applied Biosystems,Cat: 4472908. KeyGEN, Nanjing, China) and primers are shown in <xref ref-type=\"supplementary-material\" rid=\"SM2\">Table S2</xref>.</p></sec><sec><title>Western Blotting</title><p>Lysis buffer (RIPA, KeyGEN) containing protease inhibitors (PMSF, KeyGEN) was used to extract protein of cells and tissues, and protein concentration was detected with a BCA Kit (KeyGEN). Protein samples (40 μg) were loaded into 10% sodium dodecyl sulfate polyacrylamide electrophoresis (SDSPAGE) gels and transferred onto a PVDF membrane after electrophoresis. The membrane was blocked with non-fat milk for 2 h, and incubated overnight with antibodies against respective antibodies: MNX1 (Abcam, ab79541, 1:1,000); p21<sup>cip1</sup> (Cell Signaling Technology; 2947, 1:1,000); p<sup>Thr161</sup>-CDK1 (Cell Signaling Technology, 9114, 1:1,000); CDK1 (Cell Signaling Technology, 9116, 1:1,000); p27<sup>kip1</sup> (Cell Signaling Technology, 3686; 1:1,000); CyclinB1 (Abcam, ab72, 1:1,000); CyclinE1 (Abcam, ab3927, 1:1,000); CyclinE1 (Abcam, ab3927, 1:1,000); CyclinD1(Santa Cruz Biotechnology, sc-246, 1:1,000); β-actin (Abcam, ab15265, 1:1,000).</p></sec><sec><title>siRNA and Plasmid Transfection</title><p>The siRNAs targeting MNX1 and p21<sup>cip1</sup> were conducted and purchased from RiboBio, Guangzhou, China. All siRNA sequences are shown in <xref ref-type=\"supplementary-material\" rid=\"SM3\">Table S3</xref>. The full-length cDNA of human MNX1 were PCR-amplified and cloned into the expression vector Pgpu6/gfp/neo (Vigene Biosciences, Shandong, China). The siRNAs and overexpression plasmids were transfected into cervical cancer cells according to the Lipofectamine 3000 Reagent (Invitrogen, Carlsbad, CA, USA) protocol. Non-sense RNAi (si-NC) and empty plasmids (oe-NC) was used as negative controls.</p></sec><sec><title>Cell Proliferation Assay</title><p>The cell proliferation assays were performed 24 h after transfection. For Real TimexCELLigence analysis system (RTCA), 8,000 cells/100 μL were seeded in E-plates, and the plates were locked into the RTCA DP device in the incubator to calculate the “cell index” value. In colony formation assay, a total of 200 cells were placed in afresh 6-well-plate and the cells were stained with 0.1% crystal violet solution after 10–14 days. For 5-ethynyl-2′-deoxyuridine (EdU) assay (keyFluor488 Click-IT EDU Kit, RiboBio, Guangzhou, China), the transfected cells were placed in 96-well-plates (8,000 cells/well) overnight in a CO<sub>2</sub> incubator. Then, cells were incubated with 100 μL/well of 10 μM EdU for 2 h at 37°C and fixed with 50 μL 4% paraformaldehyde-containing PBS for 30 min at room temperature. Subsequently, the cells were cultured for 5 min with 50 μL of 2 mg/mL glycine and then washed with 100 μL 3% BSA in PBS. After permeabilization with 0.5% Triton X-100 for 20 min, the cells were cultured with 1 × Click-iT reaction solution for 30 min at room temperature in dark conditions. After that, cells were incubated with 100 μL/well of 1 × Hoechst 33,342 solutions for 30 min at room temperature in the dark after washing with 100 μL of PBS. The cells were then imaged using fluorescence microscopy and proliferation cell ratios were counted from five random fields in every well. Each experiment was repeated three times. A total of 400 cells in a fresh six-well-plates were maintained in medium containing 10% FBS; the medium was replaced every 3 or 4 days. After 2 weeks, the cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Each experiment was repeated three times.</p></sec><sec><title>Migration and Invasion Assay</title><p>For wound healing assay, cells were growing on the 6-well-plate, then artificial scratch on a confluent monolayer of cells was created with a 200 μL pipette tip. The medium was replaced with the serum-free and cells imaged 48 h later. For transwell and matrigel assay, totally 40,000 transfected cells were added to the upper chamber of Transwell assay inserts (8 μM PET, 24-well Millicell) or a Matrigel coated membrane (BD Biosciences) containing 200 μL serum-free DMEM media. The lower chambers were filled with 800 μl DMEM media containing 10% FBS. After a 24-h (migration assay) or 48-h (invasion assay) incubation, the cells were fixed with 4% polyformaldehyde, stained with crystal violet, and imaged. Migration and invasion were assessed by counting cell nuclei from five random fields on every filter. Each experiment was repeated three times. RTCA was also used to evaluated the ability of migration and invasion. CIM-plates installation and baseline measurement was carried out according to the instructions. Add 100 μl of mixed, serum-free cell suspension (4 × 10<sup>4</sup> cells) to the upper chamber in CIM-plates, and the plates were locked into the RTCA DP device in the incubator to calculate the “cell index” value.</p></sec><sec><title>Cell Cycle Analysis</title><p>Cells were digested with 0.25% trypsin-EDTA and fixed with 70% ethanol for 12 h at 4°C. The ethanol-suspended cells were centrifuged and stained with PI staining solution for 10 min in the dark at 37°C. A FACSCalibur flow cytometer was used to detect cell cycle distribution. The percentage of the cells in G0–G1, S, and G2–M were counted and compared.</p></sec><sec><title>Chromatin Immunoprecipitation (ChIP)</title><p>Cells were cross-linked in 4% paraformaldehyde and the reaction was quenched with glycine. After two washes with cold PBS, cells were added with pre-cooling PBS containing cocktail (Halt™ Protease Inhibitor Cocktail, Thermo Scientific, #78430) and scraped into a centrifuge tube. The cells were centrifuged for 10 min at 800 g at 4°C, then added with 500 μL cell lysis buffer (containing 2.5 μL cocktail) and incubated on ice for 15 min. Cells were then centrifuged for 5 min at 800 × g, 4°C and cell precipitates were resuspended in 500 μL nucleus lysis buffer (containing 2.5 μL cocktail). The cells were sonicated (amplitude 30%) on ice for 10 min and soluble chromatin was obtained by centrifuging for 10 min at 12,000 g at 4°C. Five micrograms of anti-MNX1 antibody (Sigma-Aldrich; SAB2101494) coupled to magnetic beads (Resin M2, Sigma, Shanghai, China) was used to immunoprecipitate the DNA-protein complex, and the IgG antibody was used as a negative control. The immunoprecipitation products were washed with 500 μL low salt buffer, high salt buffer, LiCi buffer, and TE buffer successively, all for 5 min at 4°C. The ChIP elution buffer (containing proteinase K) was used for DNA purification. The beads were wiped out on a magnetic frame and the DNA was eluted with elution buffer C from adsorption column. ChIP DNA samples were subjected to PCR amplification with primers specific to p21<sup>cip1</sup> promoter region. PCR products were then used for agarose gel electrophoresis. The sequence of primers used are shown in <xref ref-type=\"supplementary-material\" rid=\"SM4\">Table S4</xref> and GAPDH was used as a control.</p></sec><sec><title>Luciferase Reporter Assay</title><p>The p21<sup>cip1</sup> (CDKN1A) promoter region (−2,000 bp) was amplified and cloned into luciferase reporter plasmid (pGL3-basic). The p21<sup>cip1</sup> promoter wild-type plasmids or mutant-type plasmids were co-transfected with CMV-MNX1 expression plasmids in HEK293T cells, and CMV-empty vectors were used as a negative control. Relative luciferase activity was corrected for Renilla luciferase activity of pGL3-basic, and normalized to the activity of the control.</p></sec><sec><title>Xenograft Model</title><p>All animal studies were conducted in accordance with NIH animal use guidelines and protocols were approved by Nanjing Medical University Animal Care Committee. Sixteen female nude mice (4–6 weeks old) were purchased from Nanjing Medical University School of Medicine's accredited animal facility. The mice were randomly divided into two groups using random number generator. In each group, 1.0 × 10<sup>6</sup> exponentially growing cervical cancer cells were injected in axilla subcutaneously. Before tumor transplantation, cells were transfected with shRNAs or overexpression plasmids. The transfection was performed by transient transfection according to the specification of Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA). The sh-NC and empty vector pcDNA3.1 were used as controls and totally 5 μg plasmid vectors were transfected into cells for each group. The sequences of shRNAs are shown in <xref ref-type=\"supplementary-material\" rid=\"SM5\">Table S5</xref>. Tumors were harvested at 6 weeks after injection. The weight of tumor was measured on the scale and tumor volume was estimated using calipers ([length × width<sup>2</sup>]/2). And tissues were immunohistochemically stained with MNX1 (Abcam, ab79541, 1:100), Ki67 (Abcam, ab79541, 1:100), and p21<sup>cip1</sup> (Abcam, ab109520, 1:100). Western blotting was performed as previously described. No blinding was done in the animal studies.</p></sec><sec><title>Statistical Analysis</title><p>Results are presented as the mean ± standard deviation (SD). Statistical analyses were performed using SPSS Statistics (version 20.0, Chicago, Ill) and GraphPad Prism 6 software (GraphPad Software, Inc., La Jolla, CA, USA). <italic>P</italic> < 0.05 was considered statistically significant.</p></sec></sec><sec sec-type=\"results\" id=\"s3\"><title>Results</title><sec><title>Overexpression of MNX1 Correlates With Poorer Prognosis and More Aggressive Clinical Features</title><p>Analysis of TCGA dataset revealed that the mRNA expression of MNX1 was remarkably upregulated in cervical cancer tissues compared with para-tumor tissues (<italic>p</italic> = 0.0003, <xref ref-type=\"fig\" rid=\"F1\">Figure 1A</xref>). In GEPIA (Gene Expression Profiling Interactive Analysis) website, patients with higher expression of MNX1 bore a worse disease free survival (<italic>n</italic><sub>high</sub> = 73, <italic>n</italic><sub>low</sub> = 72, <italic>p</italic> = 0.019, <xref ref-type=\"fig\" rid=\"F1\">Figure 1B</xref>). The expression of MNX1 in cervical cancer tissues were significantly higher than adjacent tissues in 85% (34/40) of 40 cervical cancer patients (<italic>p</italic> < 0.001, <xref ref-type=\"fig\" rid=\"F1\">Figures 1C,D</xref>). IHC assays based on tissue microarrays (TMAs) were performed to detect the protein expression of MNX1 in 62 paired human cervical cancer tissues and para-tumor tissues, and results showed that staining scores of MNX1 were higher in cancer tissues (<italic>p</italic> < 0.0001, <xref ref-type=\"fig\" rid=\"F1\">Figure 1E</xref>). Combined with the patients' clinical information, the expression of MNX1 was higher in patients with more advanced TNM stage (stage I–II vs. III–IV, <italic>p</italic> < 0.0001, <xref ref-type=\"fig\" rid=\"F1\">Figure 1F</xref>), T stage (T1 vs. T2–T3, <italic>p</italic> = 0.041, <xref ref-type=\"fig\" rid=\"F1\">Figure 1G</xref>), and N stage (N0 vs. N1, <italic>p</italic> < 0.0001, <xref ref-type=\"fig\" rid=\"F1\">Figure 1H</xref>). Moreover, MNX1 staining scores were linked to higher pathological grade (level II vs. III, <italic>p</italic> = 0.02, <xref ref-type=\"fig\" rid=\"F1\">Figure 1I</xref>) and larger tumor maximum diameter (<italic>d</italic> < 3 vs. ≥3 cm, <italic>p</italic> < 0.0001, <xref ref-type=\"fig\" rid=\"F1\">Figure 1J</xref>). And IHC images of two patients with different clinical stages were presented (<xref ref-type=\"fig\" rid=\"F1\">Figure 1K</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>MNX1 is upregulated in CC tissues and positively correlates with aggressive clinical characteristics. <bold>(A)</bold> MNX1 is up-regulated in CC tissues compared with adjacent normal tissues in TCGA dataset (<italic>P</italic> = 0.0003). <bold>(B)</bold> Patients with high expression of MNX1 have poor Disease Free Survival (DFS) in CC (<italic>p</italic> = 0.023). <bold>(C,D)</bold> The mRNA expression of MNX1 in cervical cancer tissues was significantly higher than that in adjacent normal tissues in 85% (34/40) patients (<italic>p</italic> < 0.001). <bold>(E)</bold> The MNX1 staining score was up-regulated compared with that in adjacent normal tissues (<italic>p</italic> < 0.0001). <bold>(F)</bold> The MNX1 staining score was positively correlated with TNM stage (<italic>p</italic> < 0.0001), <bold>(G)</bold> T stage (<italic>p</italic> = 0.041), <bold>(H)</bold> lymph node metastasis (<italic>p</italic> < 0.0001), <bold>(I)</bold> tumor differentiation (<italic>p</italic> = 0.02), and <bold>(J)</bold> local primary tumor diameter (<italic>p</italic> < 0.0001) in CC patients. <bold>(K)</bold> Representative IHC staining images in TMAs were shown. Error bars represent the mean ± SD values. NS, No significance. *** represents <italic>P</italic> < 0.001.</p></caption><graphic xlink:href=\"fonc-10-01307-g0001\"/></fig></sec><sec><title>Knockdown of MNX1 Inhibited Progression of Cervical Cancer <italic>in vitro</italic></title><p>To evaluate the expression of MNX1 in cell lines, qRT-PCR and western blotting were performed and results showed that MNX1 was generally upregulated in cervical cancer cell lines compared with normal human cervical cell lines (Hacat) (<xref ref-type=\"fig\" rid=\"F2\">Figures 2A,B</xref>). To further investigate the biological function of MNX1 in cervical cancer, two specific siRNAs targeting MNX1 were transfected into HeLa and Siha cells. Both two siRNAs showed favorable suppression efficiency in HeLa (<xref ref-type=\"fig\" rid=\"F2\">Figures 2C,D</xref>) and Siha cells (<xref ref-type=\"fig\" rid=\"F2\">Figures 2E,F</xref>). The RTCA proliferation assay (<xref ref-type=\"fig\" rid=\"F2\">Figure 2G</xref>), EDU assay (<xref ref-type=\"fig\" rid=\"F2\">Figure 2H</xref>), and colony formation assay (<xref ref-type=\"fig\" rid=\"F2\">Figure 2I</xref>) showed that knockdown of MNX1 inhibited the proliferation ability of cervical cancer in HeLa and Siha cells. Moreover, RTCA migration assay (<xref ref-type=\"fig\" rid=\"F2\">Figure 2J</xref>), transwell assay, and matrigel assay (<xref ref-type=\"fig\" rid=\"F2\">Figure 2K</xref>), and wound healing assay (<xref ref-type=\"fig\" rid=\"F2\">Figure 2L</xref>) revealed that silencing MNX1 inhibited the ability of cervical cancer cells to migrate and invade. These results suggest that MNX1 plays a vital role in the malignant phenotype of cervical cancer.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Knockdown of MNX1 suppressed the proliferation, migration, and invasion in CC cells. <bold>(A,B)</bold> MNX1 mRNA and protein level are upregulated in CC cell lines. <bold>(C–F)</bold> Two specific siRNA (si1 and si2) of MNX1 were designed and the transfection efficiencies of siRNAs in HeLa and Siha cells were analyzed by qRT-PCR and western blot. <bold>(G–I)</bold> The proliferation abilities were evaluated by xCELLigence system assay, EdU incorporation assay, and colony formation assay were inhibited after knockdown of MNX1 in HeLa and Siha cells. <bold>(J)</bold> The xCELLigence system assay, <bold>(K)</bold> Transwell and Matrigel assay, and <bold>(L)</bold> wound healing assay indicated that migration and invasion capacities were suppressed after si-MNX1 in HeLa and Siha cells. Error bars represent the mean ± SD values of three independent experiments. *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001, NS, No significance.</p></caption><graphic xlink:href=\"fonc-10-01307-g0002\"/></fig></sec><sec><title>Ectopic Expression of MNX1 Enhanced Aggressiveness of Cervical Cancer <italic>in vitro</italic></title><p>To further verify the biological role of MNX1 in cervical cancer, a pcDNA3.1 plasmid to overexpress MNX1 was constructed and transfected into C33a and HeLa cells. The plasmid effectively upregulated the expression of MNX1, confirmed by qRT-PCR and western blotting (<xref ref-type=\"fig\" rid=\"F3\">Figures 3A,B</xref>). Consistently, our results showed that ectopic expression of MNX1 promotes proliferation, migration, and invasion (<xref ref-type=\"fig\" rid=\"F3\">Figures 3C–G</xref>) of cervical cancer cells.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Ectopic expression of MNX1 enhanced aggressive abilities in C33a and HeLa cells. <bold>(A,B)</bold> The pcDNA3.1-MNX1 was synthesize and the transfection efficiencies were analyzed by qRT-PCR and western blot. The proliferation functions were measured by <bold>(C)</bold> the xCELLigence system assay, <bold>(D)</bold> colony formation assays, and <bold>(E)</bold> EdU incorporation assays were elevated in oe-MNX1 C33a and HeLa cells. <bold>(F)</bold> The Transwell assay and Matrigel invasion assay, <bold>(G)</bold> wound healing assay also showed that oe-MNX1 strengthened migration and invasion capacities. Error bars represent the mean ± SD values of three independent experiments. *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001, NS, No significance.</p></caption><graphic xlink:href=\"fonc-10-01307-g0003\"/></fig></sec><sec><title>si-MNX1 Induced G2/M Cell Cycle Arrest and Upregulated the Expression of p21<sup>cip1</sup></title><p>Two hundred and eight genes highly correlated with MNX1 were used for GO, KEGG, and Reactome pathway analysis. Results showed that MNX1 may participate in “transcription” and “metabolism” pathway (<xref ref-type=\"fig\" rid=\"F4\">Figure 4A</xref>). Cell cycle detection showed that knockdown of MNX1 induced G2/M cell cycle arrest in HeLa and Siha cells (<xref ref-type=\"fig\" rid=\"F4\">Figure 4B</xref>). Furthermore, we examined the effect of MNX1 on the expression of cell cycle key-related genes, including p15<sup>ink4b</sup>, p16<sup>ink4a</sup>, p21<sup>cip1</sup>, p27<sup>kip1</sup>, CDK1, CDK2, CDK4, cyclinB1, cyclinD1, and cyclinE1. Both in HeLa and Siha cells, knockdown of MNX1 upregulated the expression of p21<sup>cip1</sup>, which has been confirmed as a tumor suppressor gene in multiple cancers (<xref ref-type=\"fig\" rid=\"F4\">Figure 4C</xref>). And western blotting results suggested that knockdown of MNX1 increased the expression of p21<sup>cip1</sup> while decreased the expression of phosphorylated CDK1 (p<sup>Thr161</sup>-CDK1), a downstream effector of p21<sup>cip1</sup> (<xref ref-type=\"fig\" rid=\"F4\">Figure 4D</xref>). Consistently with these results, ectopic expression of MNX1 decreased the expression of p21<sup>cip1</sup> while increased the expression of p<sup>Thr161</sup>-CDK1 in C33a and HeLa cells (<xref ref-type=\"fig\" rid=\"F4\">Figures 4E,F</xref>). It suggested that MNX1 might exerted its biological function via modulating the expression of p21<sup>cip1</sup>.</p><fig id=\"F4\" position=\"float\"><label>Figure 4</label><caption><p>Knockdown of MNX1 expression induced G2/M phage arrest by regulating the p21<sup>cip1</sup> expression. <bold>(A)</bold> Many genes were enriched in regulation of transcription by GO analysis. Most of the genes were enriched in the metabolic pathways by KEGG and Reactome pathway analysis. <bold>(B)</bold> Knockdown of MNX1 generated G2/M stage arrest in HeLa and Siha cells were measured by flow cytometry. <bold>(C,F)</bold> The p21<sup>cip1</sup> mRNA levels were upregulated or downregulated after si- or oe-MNX1 in CC cell lines. <bold>(D,E)</bold> The protein level of p21<sup>cip1</sup> was upregulated or downregulated while the expression of p<sup>Thr161</sup>-CDK1 was decreased or increased after knockdown or ectopic MNX1 of CC cells. The expression of CDK1, CCNE1, CCND1, and CCNB1 had no obvious changes. Error bars represent the mean ± SD values of three independent experiments. *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001, NS, No significance.</p></caption><graphic xlink:href=\"fonc-10-01307-g0004\"/></fig></sec><sec><title>MNX1 Suppressed the Expression of p21<sup>cip1</sup> via Binding to Its Promoter Region</title><p>Our previous results showed that knockdown or ectopic expression of MNX1 altered the expression of p21<sup>cip1</sup>. To further verify the mechanism, we analyzed the correlation between MNX1 and p21<sup>cip1</sup> in 40 cases of CC samples, and the results were shown that MNX1 and p21<sup>cip1</sup> had a negative correlation (<italic>n</italic> = 40, <italic>p</italic> < 0.001) (<xref ref-type=\"fig\" rid=\"F5\">Figure 5A</xref>). As transcription factors usually bind to sequence-specific DNA to regulate transcription, we utilized Jaspar Database to predict the binding site between MNX1 and the promoter region (upstream 2,000 bp of coding region) of CDKN1A (the gene symbol of p21<sup>cip1</sup>). It turned out that MNX1 was predicted to have four binding sites with the promoter region of CDKN1A, of which 1,371–1,380 bp (AACAATAAAT) and 226–235 bp (GCCCATTAAT) showed higher combination scores (<xref ref-type=\"fig\" rid=\"F5\">Figure 5B</xref>). Accordingly, the wild CDKN1A promoter region and mutant types (226-MT and 1371-MT) were generated and cloned into luciferase reporter vector (pGL3-basic; <xref ref-type=\"fig\" rid=\"F5\">Figure 5C</xref>). And in luciferase reporter assay, overexpression of MNX1 inhibited the transcriptional activity of the wild CDKN1A promoter but not mutant type (<xref ref-type=\"fig\" rid=\"F5\">Figure 5D</xref>). Moreover, ChIP assay also revealed that MNX1 bound to the p21<sup>cip1</sup> promoter region in HeLa and Siha cells (<xref ref-type=\"fig\" rid=\"F5\">Figures 5E,F</xref>).</p><fig id=\"F5\" position=\"float\"><label>Figure 5</label><caption><p>MNX1 bounds to the p21<sup>cip1</sup> promoter region and suppresses p21<sup>cip1</sup> transcription. <bold>(A)</bold> The expression of MNX1 and p21<sup>cip1</sup> is negatively correlated in 40 cervical cancer tissues (<italic>P</italic> < 0.001). <bold>(B)</bold> The JARSPAR database indicates that MNX1 has several binding sites with the promoter region of p21<sup>cip1</sup>. <bold>(C)</bold> Schematic diagram shows that the two sites with the highest score of MNX1 on p21<sup>cip1</sup> promoter and the mutant p21<sup>cip1</sup> promoter were selected. <bold>(D)</bold> Overexpression of MNX1 remarkably decreased wild type but not mutant p21<sup>cip1</sup> promoter luciferase activity (p21<sup>cip1</sup>-226, <italic>p</italic> < 0.01; p21<sup>cip1</sup>-1371, <italic>p</italic> < 0.01). <bold>(E)</bold> Chromatin immunoprecipitation (ChIP) assays using normal IgG or anti-MNX1 demonstrated that MNX1 directly binding to p21<sup>cip1</sup> promoter region. <bold>(F)</bold> The results of ChIP-PCR product electrophoresis were showed that a clear band was observed in the anti-MNX1 group, while almost no band was detected in the IgG control group. **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001.</p></caption><graphic xlink:href=\"fonc-10-01307-g0005\"/></fig></sec><sec><title>Silencing p21<sup>cip1</sup> Rescued the Function of si-MNX1</title><p>To determine whether the function of MNX1 was relied on p21<sup>cip1</sup>, we designed three siRNAs (<xref ref-type=\"supplementary-material\" rid=\"SM3\">Table S3</xref>) to knockdown the expression of p21<sup>cip1</sup> in HeLa cells. The si1-p21<sup>cip1</sup> showed the best transfection efficiency (<xref ref-type=\"fig\" rid=\"F6\">Figure 6A</xref>) and it was used for the following experiment. RTCA proliferation assay, colony formation assay, EDU assay, transwell assay, matrigel assay, and would healing assay revealed that silencing p21<sup>cip1</sup> partially rescued the decreased proliferation, migration, and invasion ability of HeLa cells caused by knockdown of MNX1 (<xref ref-type=\"fig\" rid=\"F6\">Figures 6B–F</xref>). And western blotting showed that the protein level of p21<sup>cip1</sup> and p<sup>Thr161</sup>-CDK1 were partially reversed by silencing p21<sup>cip1</sup> (<xref ref-type=\"fig\" rid=\"F6\">Figure 6G</xref>).</p><fig id=\"F6\" position=\"float\"><label>Figure 6</label><caption><p>Downregulation of p21<sup>cip1</sup> partially recovered the malignant phenotypes of si-MNX1 cells. <bold>(A)</bold> The transfection efficiency of p21<sup>cip1</sup> was determined by qRT-PCR and si1-p21<sup>cip1</sup> was chosen to further experiments. <bold>(B–D)</bold> The proliferative abilities were partially rescued after knockdown p21<sup>cip1</sup> in si-MNX1 HeLa cells were measured by the xCELLigence system assay, colony formation assay, and EdU incorporation assay. <bold>(E,F)</bold> The invasion and migration capacities have also been significantly improved after knockdown p21<sup>cip1</sup> in si-MNX1 cells compared with si-MNX1 alone cells. <bold>(G)</bold> The protein level of p21<sup>cip1</sup> and p<sup>Thr161</sup>-CDK1 were partially reversed when knockdown of p21<sup>cip1</sup> in si-MNX1 compared with si-MNX1 alone. Error bars represent the mean ± SD values of three independent experiments. *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001, NS, No significance.</p></caption><graphic xlink:href=\"fonc-10-01307-g0006\"/></fig></sec><sec><title>MNX1 Promoted Tumor Growth of Cervical Cancer <italic>in vivo</italic></title><p>The xenograft models were used to explore the function of MNX1 <italic>in vivo</italic>. The shRNA-MNX1 (shRNA-NC as control) was transfected into HeLa cells and the knockdown efficiency was confirmed by qRT-PCR and western blotting (<xref ref-type=\"fig\" rid=\"F7\">Figures 7A,B</xref>). Results showed that knockdown of MNX1 inhibited tumor growth (measured by tumor weight and volume) <italic>in vivo</italic> (<xref ref-type=\"fig\" rid=\"F7\">Figures 7C–E</xref>). IHC staining and western blotting of harvested tumors revealed that knockdown of MNX1 upregulated the protein level of p21<sup>cip1</sup> and downregulated ki-67 and p<sup>Thr161</sup>-CDK1 <italic>in vivo</italic> (<xref ref-type=\"fig\" rid=\"F7\">Figures 7F,G</xref>). Moreover, ectopic expression of MNX1 promoted tumor growth and altered the expression of p21<sup>cip1</sup> and ki-67 <italic>in vivo</italic> (<xref ref-type=\"fig\" rid=\"F7\">Figures 7H–K</xref>).</p><fig id=\"F7\" position=\"float\"><label>Figure 7</label><caption><p>Knockdown or overexpression of MNX1 inhibited or promoted tumor growth <italic>in vivo</italic>. <bold>(A,B)</bold> The transfection efficiency of sh-MNX1 was measured by qRT-PCR and Western blot. <bold>(C)</bold> A total of eight nude female mice were sacrificed and xenograft tumors were collected after injection with sh-MNX1 cells 6 weeks. <bold>(D,E)</bold> Tumor volume and weight were reduced in the sh-MNX1 group compared with those in the sh-NC group. <bold>(F)</bold> The expression of MNX1 and Ki-67 was downregulated and p21<sup>cip1</sup> was upregulated in sh-MNX1 xenograft tumors analyzing by IHC staining. <bold>(G)</bold> The protein level of MNX1, p<sup>Thr161</sup>-CDK1 were downregulated and p21<sup>cip1</sup> was upregulated in sh-MNX1 mouse xenograft tumors analyzed by western blot. <bold>(H)</bold> A total of eight nude female mice were sacrificed and xenograft tumors were collected after injection with oe-MNX1 cells 6 weeks. <bold>(J,K)</bold> Tumor volume and weight was increased in the oe-MNX1 group compared with those in the oe-NC group. <bold>(I)</bold> The expression of MNX1 and Ki-67 was upregulated and p21<sup>cip1</sup> was downregulated in oe-MNX1 xenograft tumors analyzing by IHC staining. Error bars represent the mean ± SD values. *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01, ***<italic>P</italic> < 0.001, NS, No significance.</p></caption><graphic xlink:href=\"fonc-10-01307-g0007\"/></fig></sec></sec><sec sec-type=\"discussion\" id=\"s4\"><title>Discussion</title><p>In this study, we identified MNX1, a transcription factor of homeobox family, was significantly upregulated and involved in the progression of cervical cancer. The overexpression of MNX1 correlated with advanced clinical stages and poorer prognosis of cervical cancer patients. Furthermore, MNX1 exerted its oncogenic role via modulating the expression of p21<sup>cip1</sup>, especially by targeting the promoter region of p21<sup>cip1</sup> thus to repress its transcription. In accordance with our findings, a recent article showed that MNX1 had a role in the progression of cervical cancer, partially through upregulating cell cycle regulators CCNE1 and CCNE2 (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). And MNX-AS1, the antisense lncRNA of MNX1, was also reported to promote the invasion and metastasis of gastric cancer through repression of CDKN1A (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>). All this results indicated that MNX1 played a critical role in cancer growth and cell cycle progression, and MNX1 might serve as a useful diagnostic and treatment target for cervical cancer.</p><p>MNX1is a member of homeobox gene family, which all contain a homeobox (a DNA sequence, around 180 base pairs long) and encode homeodomain protein products as transcription factors (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). This cluster of genes has been identified to participate in the regulation of development and morphogenesis in animals, fungi, and plants (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>). For example, CDX1, which is stably expressed in the human intestine, plays an important role in embryonic epicardial development (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). And the protagonist of our study, MNX1, participates in motor neuron development and neurodegenerative processes of zebrafish (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>) and moreover controls cell fate choice in the developing endocrine pancreas (<xref rid=\"B31\" ref-type=\"bibr\">31</xref>). In recent years, more and more researches uncovered the role of development-related homeobox genes in carcinogenesis and these genes show great application prospect in tumor diagnosis and prevention, as the role of carcino-embryonic antigen (CEA) in gastroenteric tumors and alpha fetal protein (AFP) in liver cancer (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>–<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). For instance, PDX1 is a key regulator in pancreatic development and β-cell function (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>) and meanwhile dynamically regulates pancreatic ductal adenocarcinoma initiation and maintenance (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). HOXC13, a highly conserved transcription factor involved in morphogenesis of all multicellular organisms, is aberrantly expressed and associated with cancer progression in esophageal cancer (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>), lung adenocarcinoma (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>), and liposarcomas (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>). Likewise, MNX1 has been reported to promote sustained proliferation in bladder cancer by upregulating CCNE1/2 (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>) and to act as a novel oncogene in prostate cancer (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). And in our study, MNX1 was also confirmed to be upregulated in cervical cancer and enhance the progression of cervical cancer.</p><p>In terms of mechanism, we found that MNX1 promoted tumor growth of cervical cancer via accelerating the progression of the cell cycle, especially by modulating the expression of p21<sup>cip1</sup>. Cell cycle is a vital process by which a cell leads to duplication and disorders of the cell cycle regulation may lead to tumor formation (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). The cell cycle progress is determined by two types of regulatory factors, cyclins and cyclin-dependent kinases (CDKs) (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). Active cyclin-CDK complexes phosphorylate proteins to elevate the expression levels of cyclins and enzymes required for DNA replication (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). Conversely, the cell cycle progression can be prevented by inhibitors by binding to and thus inactivating cyclin-CDK complexes, such as p21<sup>cip1</sup>, p27<sup>kip1</sup>, p16<sup>ink4a</sup>, and so on (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>). The p21<sup>cip1</sup>, also known as cyclin-dependent kinase inhibitor 1 (CDKN1A), has been identified as a regulator of cell cycle and a tumor suppressor in multiple kinds of cancers (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). Our results proved that MNX1 repressed the transcription of p21<sup>cip1</sup> by directly targeting its promoter region and furthermore promoted the phosphorylation of downstream CDK1. The MNX1-p21<sup>cip1</sup>-p<sup>Thr161</sup>CDK1 axis played crucial roles in the progression of cervical cancer and meanwhile provided new evidence for the pathogenesis of cervical cancer. Moreover, the association between cervical cancer and HPV has long been identified. As a sexually transmitted agent, HPV are involved in transformation and maintaining of transformed status (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). Many studies have reported that HPV can also alter the expression of p21 (<xref rid=\"B48\" ref-type=\"bibr\">48</xref>–<xref rid=\"B50\" ref-type=\"bibr\">50</xref>). Thus, we searched the GEO dataset to seek for information about the relationship between MNX1 and HPV viral infection. We analyzed the GSE dataset 103,546 and found that there were no significant changes in the expression of MNX1 (NM_005515) in HaCat cells infected with HPV11E6 or HPV18E6. In GSE3292 (GDS1667), HPV positive or negative head and neck squamous cell carcinoma (HNSCC) showed no expression differences of MNX1 (<xref ref-type=\"supplementary-material\" rid=\"SM6\">Figure S1</xref>). This information suggests that MNX1 might not be directly involved in HPV carcinogenesis and further investigations are needed in the future. In addition, cervical cancer is almost invariably associated with p53 loss (either mutation of HPV infection) and p53 is a very well know activator of p21. In this study, we proved that MNX1 exerted an oncogenic role in cervical cancer via suppressing the expression of p21 with binding to its promoter region. And we performed qRT-PCR and western blot in cervical cancer cells and results showed that knockdown of MNX1 did not affect the expression of p53. Transcription factor could directly regulate the expression of target genes by binding to the gene promoter. We speculated that MNX1 mediated downregulation of p21 was independent on p53. And in this study, three cervical cancer cells (HeLA, Siha, and C33A) were used to study the function of MNX1. We noted that p53 was lowly expressed in Hela cells, positively expressed in Siha cells and mutant of codon 273 in C33A cells. It indicated that MNX1 could function in cervical cancer cells with different p53 status. Moreover, it has been reported that in p53 mutation acute myeloid leukemia, MNX1 was identified as one of the hub genes from the protein–protein interaction network (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>). And in hematopoietic stem and progenitor cells, the oncogenic potential of MNX1 was mediated via p53-p21 signaling pathway (<xref rid=\"B52\" ref-type=\"bibr\">52</xref>). In breast cancer, enrichment analysis suggested that MNX1 is probably involved in biological processes and pathways related to nuclear division, cell cycle, and p53 signaling (<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). And a recent study showed that MNX1 had a role in the progression of cervical cancer, partially through upregulating cell cycle regulators CCNE1 and CCNE2 (<xref rid=\"B24\" ref-type=\"bibr\">24</xref>). These studies suggested that the function of MNX1 might be translatable to other tumor types.</p><p>In summary, we identified the homeobox member MNX1 as a tumor-promoting gene in cervical cancer. The upregulated MNX1 correlated with more advanced clinical pathological characteristics and poorer prognosis of cervical cancer patients. And MNX1 exerted its oncogenic role by repressing the transcription of p21<sup>cip1</sup> thus to promote the progression of cell cycle. We believe that there are other genes beside p21 regulated by MNX1 in cervical cancer. RNA-seq and ChIP-seq experiments may need to confirm this cluster of genes. And as the downregulation of MNX1 inhibited tumor growth of cervical cancer, MNX1 may represent promising targets for the diagnosis and anti-tumor therapy in cervical cancer patients. Through knockdown of MNX1, it might combine the function of MNX1 with chemotherapy.</p></sec><sec sec-type=\"data-availability\" id=\"s5\"><title>Data Availability Statement</title><p>The original contributions presented in the study are included in the article/supplementary files, further inquiries can be directed to the corresponding author/s.</p></sec><sec id=\"s6\"><title>Ethics Statement</title><p>The studies involving human participants were reviewed and approved by Ethics Committee of the Nanjing Medical University Affiliated Cancer Hospital. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Nanjing Medical University Animal Care Committee.</p></sec><sec id=\"s7\"><title>Author Contributions</title><p>LX, EL, and BR designed and supervised the study. BZ, YW, and JL were responsible for acquisition of data, interpretation of data, and article drafting. QZ contributed to experiments <italic>in vitro</italic>. JH and QL helped to analyze the data and revise the article. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s8\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This research was supported by Research Project of Nanjing Medical University Affiliated Cancer Hospital (ZM201706), National Natural Science Foundation of China (No. 81802598), the National Natural Science Foundation of China (No. 81672869), Jiangsu Provincial Medical Outstanding Talent (LX) and Jiangsu Provincial Medical Youth Talent (BR, QNRC2016657), The talents program of Jiangsu cancer hospital YC201814, The 333 talent project: BRA2019325.</p></fn></fn-group><sec sec-type=\"supplementary-material\" id=\"s9\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fonc.2020.01307/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fonc.2020.01307/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Table_1.xls\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM2\"><media xlink:href=\"Table_2.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM3\"><media xlink:href=\"Table_3.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM4\"><media xlink:href=\"Table_4.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM5\"><media xlink:href=\"Table_5.xlsx\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM6\"><media xlink:href=\"Image_1.pdf\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" article-type=\"chapter-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"publisher-id\">978-3-030-41480-1</journal-id><journal-id journal-id-type=\"doi\">10.1007/978-3-030-41480-1</journal-id><journal-id journal-id-type=\"nlm-ta\">Human Challenge Studies in Endemic Settings </journal-id><journal-title-group><journal-title>Human Challenge Studies in Endemic Settings </journal-title><journal-subtitle>Ethical and Regulatory Issues</journal-subtitle></journal-title-group><isbn publication-format=\"print\">978-3-030-41479-5</isbn><isbn publication-format=\"electronic\">978-3-030-41480-1</isbn></journal-meta><article-meta><article-id pub-id-type=\"pmc\">PMC7431914</article-id><article-id pub-id-type=\"publisher-id\">2</article-id><article-id pub-id-type=\"doi\">10.1007/978-3-030-41480-1_2</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>History of Human Challenge Studies</article-title></title-group><contrib-group><contrib contrib-type=\"author\" corresp=\"yes\"><name><surname>Jamrozik</surname><given-names>Euzebiusz</given-names></name><address><email>zeb.jamrozik@monash.edu</email></address><xref ref-type=\"aff\" rid=\"Aff3\">3</xref><xref ref-type=\"aff\" rid=\"Aff4\">4</xref><xref ref-type=\"aff\" rid=\"Aff5\">5</xref></contrib><contrib contrib-type=\"author\"><name><surname>Selgelid</surname><given-names>Michael J.</given-names></name><address><email>michael.selgelid@monash.edu</email></address><xref ref-type=\"aff\" rid=\"Aff6\">6</xref></contrib><aff id=\"Aff3\"><label>3</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.1002.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 7857</institution-id><institution>Monash Bioethics Centre, </institution><institution>Monash University, </institution></institution-wrap>Melbourne, VIC Australia </aff><aff id=\"Aff4\"><label>4</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.1008.9</institution-id><institution-id institution-id-type=\"ISNI\">0000 0001 2179 088X</institution-id><institution>Department of Medicine, Royal Melbourne Hospital, </institution><institution>University of Melbourne, </institution></institution-wrap>Melbourne, VIC Australia </aff><aff id=\"Aff5\"><label>5</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.4991.5</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 8948</institution-id><institution>Nuffield Department of Population Health, Wellcome Centre for Ethics and the Humanities and The Ethox Centre, </institution><institution>University of Oxford, </institution></institution-wrap>Oxford, UK </aff><aff id=\"Aff6\"><label>6</label><institution-wrap><institution-id institution-id-type=\"GRID\">grid.1002.3</institution-id><institution-id institution-id-type=\"ISNI\">0000 0004 1936 7857</institution-id><institution>Monash Bioethics Centre, </institution><institution>Monash University, </institution></institution-wrap>Clayton, VIC Australia </aff></contrib-group><pub-date pub-type=\"epub\"><day>19</day><month>08</month><year>2020</year></pub-date><pub-date pub-type=\"pmc-release\"><day>19</day><month>08</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2021</year></pub-date><fpage>9</fpage><lpage>23</lpage><permissions><copyright-statement>© The Author(s) 2021</copyright-statement><license license-type=\"OpenAccess\"><license-p><bold>Open Access</bold> This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.</license-p><license-p>The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</license-p></license></permissions><abstract id=\"Abs1\"><p id=\"Par1\">The intentional infection of human beings with pathogens with the aim of achieving benefits (chiefly, the prevention of more severe disease) has occurred for centuries; the (semi-)systematic testing and recording of such methods dates to the 18th Century in England.</p></abstract><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Editor(s) (if applicable) and The Author(s) 2021</meta-value></custom-meta><custom-meta><meta-name>issue license</meta-name><meta-value><bold>Open Access</bold> This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.\nThe images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id=\"Sec1\"><title>Experimental Infection in the 18th–19th Century</title><p id=\"Par2\">The intentional infection of human beings with pathogens with the aim of achieving benefits (chiefly, the prevention of more severe disease) has occurred for centuries; the (semi-)systematic testing and recording of such methods dates to the 18th Century in England (Halsband <xref ref-type=\"bibr\" rid=\"CR33\">1953</xref>; Weiss and Esparza <xref ref-type=\"bibr\" rid=\"CR75\">2015</xref>). Although the credit for initiating a modern science of vaccination is usually accorded to Edward Jenner (1749–1823), who pioneered the use of cowpox (cow giving rise to the <italic>vache</italic> in vaccine, a term coined by Jenner) to prevent smallpox, variolation (sometimes referred to as ‘inoculation’, i.e., the prevention of smallpox by injection or insufflation of material believed to produce a mild infection and thus convey an attenuated risk of the disease) began much earlier, in Asia and the Eastern Mediterranean, and was introduced to England and North America in the early 18th Century (Timonius and Woodward <xref ref-type=\"bibr\" rid=\"CR68\">1714</xref>; Halsband <xref ref-type=\"bibr\" rid=\"CR33\">1953</xref>, Gross and Sepkowitz <xref ref-type=\"bibr\" rid=\"CR31\">1998</xref>; Weiss and Esparza <xref ref-type=\"bibr\" rid=\"CR75\">2015</xref>). Furthermore, that prior infection with cowpox protected humans against infection with smallpox was widely believed in cattle farming communities in England (and elsewhere) long before Jenner’s experiments; at least one English farmer, Benjamin Jesty, is known to have intentionally infected members of his family with cowpox as a means of preventing smallpox in 1774 (i.e., 25 years before Jenner’s experiments) (Gross and Sepkowitz <xref ref-type=\"bibr\" rid=\"CR31\">1998</xref>). Yet, although there were some earlier ‘trials’ of smallpox variolation and cowpox vaccination, Jenner’s testing of the cowpox vaccine was more systematic, and involved intentional exposure to smallpox after vaccination (with cowpox) to test efficacy in 1796. However, unlike modern human challenge studies, Jenner’s investigations in the late 18th Century did not involve (i) systematic study of the methods required to induce disease safely and reliably in humans or (ii) the testing of preventive/therapeutic interventions against a reliable model of infection.</p><p id=\"Par3\">One of Jenner’s teachers had been the prominent Scottish surgeon John Hunter, a local pioneer of smallpox variolation (which carried higher risks than the later practice of vaccination) (Turk and Allen <xref ref-type=\"bibr\" rid=\"CR70\">1990</xref>). Although Hunter is credited with many positive achievements, he has also become infamous for his attempt to prove his (later falsified) theory that gonorrhoea and syphilis were in fact the same disease. In 1767, Hunter used an experimental (challenge) technique: the injection of “venereal matter” from a patient with gonorrhoea into the penis of a single research subject (Dempster <xref ref-type=\"bibr\" rid=\"CR19\">1978</xref>). Though it is sometimes claimed, even in recent times (Gladstein <xref ref-type=\"bibr\" rid=\"CR27\">2005</xref>), that Hunter himself was the subject, there is no contemporaneous evidence to support this theory, and it appears more likely that Hunter experimented on another individual—especially since it was known that he had attempted to transmit gonorrhoea to others via inoculation of the skin (Dempster <xref ref-type=\"bibr\" rid=\"CR19\">1978</xref>). Importantly, the research subject developed evidence of syphilis, which Hunter took (erroneously) as evidence in favour of his theory (that gonorrhoea and syphilis were the same disease); it now appears more likely that the patient with gonorrhoea from whom the sample was obtained was also infected with syphilis. Thus, the experiment was scientifically flawed and (although mercury-based treatment was provided for the experimental syphilis infection (Wright <xref ref-type=\"bibr\" rid=\"CR77\">1981</xref>)) arguably carried significant risks that many would consider unacceptably high—especially on the assumption that this was not a case of self-experimentation (self-experimentation is discussed further below).</p><p id=\"Par4\">During the 19th Century there were significant developments in microbiological understanding of infectious disease. Towards the end of the century in particular, challenge experiments generally became more systematic (discussed below in ‘Early challenge studies with vector-borne diseases’). However, from 1800<bold>–</bold>1880, several experiments that would now be judged highly unethical took place in Europe (Macneill <xref ref-type=\"bibr\" rid=\"CR40\">2010</xref>). Irish, German, and Russian physician-investigators injected infectious material from patients with gonorrhoea and syphilis into children and babies, and at least one baby died as a result. Although these investigations did appear to confirm the transmission of such infections, the studies were poorly controlled (due to the rudimentary knowledge of microbiology and lack of available treatments at the time, and perhaps also to the callousness of the investigators). The use of, and harm to, minors (even though some were teenagers who were said to have agreed to participate), furthermore, struck some physicians of the time as immoral and likely unnecessary (Macneill <xref ref-type=\"bibr\" rid=\"CR40\">2010</xref>).</p><p id=\"Par5\">One reason it was not necessary to experiment on others (including minors) is that challenge studies can involve self-experimentation, which—especially under such uncontrolled and uncertain conditions—might be considered ethically preferable to the recruitment of others. For example, two scientists deliberately infected themselves with cholera bacteria in 1892. One developed clinical cholera, and this was taken as significant evidence linking the microbe with the disease (Benyajati <xref ref-type=\"bibr\" rid=\"CR9\">1966</xref>). Another early challenge study, testing a typhoid vaccine in two ‘Officers of the Indian Medical Service’ took place in 1896. Though few details are supplied, if these individuals were medically trained and aware of the details of the study then they may have been able to provide (what would now be considered) proper informed consent to the risks to which they were exposed (Wright <xref ref-type=\"bibr\" rid=\"CR76\">1896</xref>).</p></sec><sec id=\"Sec2\"><title>Early Challenge Studies with Vector-Borne Diseases</title><p id=\"Par6\">In the late 19th and early 20th Century additional early challenge experiments began to occur on a larger scale and (generally) with increasing scientific rigor. Challenge studies investigating what would now be referred to as vector-borne diseases (e.g., yellow fever, malaria, and dengue) were particularly prominent at the time, and often conducted in endemic countries (in contrast to later challenge studies which have been predominantly conducted in non-endemic countries). Famous early examples of such experiments include (i) the failed attempts by Carlos Finlay to transmit yellow fever from symptomatic patients to healthy individuals in Cuba from 1881<bold>–</bold>1893<xref ref-type=\"fn\" rid=\"Fn1\">1</xref> (Finlay <xref ref-type=\"bibr\" rid=\"CR21\">1886</xref>, <xref ref-type=\"bibr\" rid=\"CR22\">1937</xref>; Clements and Harbach <xref ref-type=\"bibr\" rid=\"CR16\">2017</xref>) and (ii) the successful transmission of malaria via infected mosquitoes in Italy in 1898 by Battista Grassi (Grassi et al. <xref ref-type=\"bibr\" rid=\"CR30\">1898</xref>; Capanna <xref ref-type=\"bibr\" rid=\"CR11\">2006</xref>). The latter study provided the first experimental evidence that malaria was transmitted to humans by mosquitoes.<xref ref-type=\"fn\" rid=\"Fn2\">2</xref> Since malaria was, at that time, endemic to much of Italy (potentially casting doubt on Grassi’s findings, because individuals exposed to mosquitoes during the trial could have contracted the disease elsewhere), a similar experiment was repeated in London (with infected mosquitoes transported from Italy) by Patrick Manson in 1900 (Manson <xref ref-type=\"bibr\" rid=\"CR42\">1900</xref>). Manson infected two volunteers (thought to include his son), and successfully cured the induced infection by administration of quinine (Cox <xref ref-type=\"bibr\" rid=\"CR17\">2010</xref>).</p><p id=\"Par9\">Elsewhere during the same period, other early research on yellow fever employed challenge study techniques, though such efforts were sometimes unsuccessful and/or harmful. In 1897, Giuseppe Sanarelli, an Italian physician in Uruguay, claimed to have isolated a bacterial cause of yellow fever (now known to be caused by a virus) and injected a culture of these bacteria into 5 hospital patients, perhaps without their knowledge or consent, of whom 3 died (Lederer <xref ref-type=\"bibr\" rid=\"CR39\">2008</xref>). The famous Canadian physician William Osler condemned these experiments in the following terms:<disp-quote><p id=\"Par10\">To deliberately inject a poison of known high degree of virulency into a human being, unless you obtain that man’s sanction, is not ridiculous, it is criminal. (Sternberg <xref ref-type=\"bibr\" rid=\"CR63\">1898</xref>)</p></disp-quote>\n</p><p id=\"Par11\">Beyond Osler’s sentiment regarding the importance of obtaining a person’s sanction (i.e., consent) to be challenged, many contemporary readers will additionally object to Sanarelli’s apparent callous disregard for the safety of his ‘research subjects’. While modern HCS standardly involve healthy volunteers, Sanarelli’s subjects were neither healthy (being hospital patients) nor volunteers. The virulence of the organism is a more complex issue: the ‘poison’ in questions was, if not <italic>known</italic> to be highly virulent (since this was being ‘tested’), then at least <italic>expected</italic> to be highly virulent (although in retrospect Sanarelli was clearly ill-informed about the infectious agent he thought he was administering). It is noteworthy, however, that other researchers at the time were also using potentially highly virulent forms of the (presumed) agent of yellow fever, albeit with greater care, less harm, and ultimately greater scientific success.</p><p id=\"Par12\">In the same year (1897), in Mexico, a Dr. Ruis injected the blood of yellow fever patients into 3 individuals (whether they consented is unknown) without producing symptoms of disease. Ruis’ unsuccessful experiments pre-dated the successful and larger scale studies led by Walter Reed (Reed <xref ref-type=\"bibr\" rid=\"CR50\">1902</xref>). In 1900 experiments by Reed and other members of the Yellow Fever Commission in Cuba demonstrated the transmission of yellow fever to healthy volunteers (i) by injection of blood from confirmed cases and (ii) by mosquitoes fed on confirmed cases. Reed’s research ultimately led to the development of methods to prevent infection by avoiding mosquitoes (Reed et al. <xref ref-type=\"bibr\" rid=\"CR51\">1900</xref>; Lederer <xref ref-type=\"bibr\" rid=\"CR39\">2008</xref>; Clements and Harbach <xref ref-type=\"bibr\" rid=\"CR16\">2017</xref>).</p><p id=\"Par13\">Reed is frequently credited for establishing a prototype of informed consent for research because subjects in these (what would now be described as) yellow fever challenge studies were asked to sign a contract that outlined the expected risks of the research. The contract also entailed payment of $100 (USD)—the equivalent of more than $3000 in 2019—for two months’ participation in the research, which was doubled ($200, equivalent to ~$6000) for those who contracted yellow fever (Lederer <xref ref-type=\"bibr\" rid=\"CR39\">2008</xref>; Clements and Harbach <xref ref-type=\"bibr\" rid=\"CR16\">2017</xref>). The contract also made note of the high background risk of contracting yellow fever in Cuba at the time, and the relative benefits of high quality medical care for those infected in the course of the research (versus being infected in an uncontrolled fashion with less medical oversight) (Lederer <xref ref-type=\"bibr\" rid=\"CR39\">2008</xref>). Although no research subjects in these initial experiments died, the majority contracted yellow fever (in some cases with severe symptoms). One member of the research team (Jesse Lazear) died from yellow fever despite the best available care (Lederer <xref ref-type=\"bibr\" rid=\"CR39\">2008</xref>). It has been suggested that Reed’s relatively scrupulous proto-consent procedures were motivated by his awareness of earlier criticisms of Sanarelli (Lederer <xref ref-type=\"bibr\" rid=\"CR39\">2008</xref>). It has nevertheless been argued that Reed’s consent form did not sufficiently emphasise the risk of death due to experimental infection with yellow fever (Chaves-Carballo <xref ref-type=\"bibr\" rid=\"CR12\">2013</xref>). Subsequent attempts to develop a yellow fever vaccine in Cuba using a challenge study based on the work of Reed’s team (and using a similar consent form) led to three deaths among research participants, public outcry, and the termination of such experiments in Cuba (Chaves-Carballo <xref ref-type=\"bibr\" rid=\"CR12\">2013</xref>).</p><p id=\"Par14\">Elsewhere, from 1902 onwards, researchers in Lebanon, Syria, the Philippines, and Australia conducted early challenge studies with dengue virus (which was, at the time, endemic in parts of all four countries, though it has since been eliminated in Australia), which were followed by later studies (from the 1930s onwards) in the USA and Japan (Cleland et al. <xref ref-type=\"bibr\" rid=\"CR15\">1918</xref>; Cleland and Bradley <xref ref-type=\"bibr\" rid=\"CR14\">1919</xref>; Simmons et al. <xref ref-type=\"bibr\" rid=\"CR60\">1930</xref>; Larsen et al. <xref ref-type=\"bibr\" rid=\"CR38\">2015</xref>). Early dengue challenge studies sometimes recruited participants who were military personnel and/or medical researchers (including cases of self-experimentation), although Australian researchers also recruited patients from a local asylum (few details regarding recruitment procedures were published), perhaps because of a reported “unexpected difficulty of obtaining volunteers, even with a considerable monetary inducement” (amount not specified) (Cleland and Bradley <xref ref-type=\"bibr\" rid=\"CR14\">1919</xref>).</p><p id=\"Par15\">Similarly, early challenge studies of leishmaniasis were conducted in endemic regions of North Africa, the Eastern Mediterranean, and India.<xref ref-type=\"fn\" rid=\"Fn3\">3</xref> In 1910 investigators published results demonstrating that the inoculation of the skin of research subjects with parasites presumed to cause cutaneous leishmaniasis caused local eruption of the disease (though few details regarding the participants were published) (Nicolle and Manceux <xref ref-type=\"bibr\" rid=\"CR48\">1910</xref>; Row <xref ref-type=\"bibr\" rid=\"CR56\">1912</xref>). In 1921, the transmission of cutaneous leishmaniasis by sand flies was demonstrated in a human challenge study involving self-experimentation (Théodoridès <xref ref-type=\"bibr\" rid=\"CR67\">1997</xref>). Two decades later, after multiple failed attempts (Killick-Kendrick <xref ref-type=\"bibr\" rid=\"CR37\">2013</xref>), researchers in India demonstrated the transmission of visceral leishmaniasis (kala-azar) to 5 out of 5 healthy volunteers by infected sand fly bite (Swaminath et al. <xref ref-type=\"bibr\" rid=\"CR65\">1942</xref>). The researchers were particularly alert to potential challenges of conducting such research in endemic areas, including (i) the potential role of prior immunity among participants from endemic regions (as a result of previous exposure) and (ii) the possibility that participants might be bitten by other insects and/or infected with leishmania during the study. As a result, they recruited volunteers from a nearby non-endemic area, transported them to a research facility in an endemic area where the experimental infection took place under (by the standards of the time) strict isolation from contact with other insects, and returned them to a non-endemic area for longer term observation. Volunteers were also ‘generously compensated’ with payment of 400 rupees per month (at a time when the usual wage for unskilled labour was less than 200 rupees per month (Palekar <xref ref-type=\"bibr\" rid=\"CR49\">1957</xref>)) and provided with curative treatment (Killick-Kendrick <xref ref-type=\"bibr\" rid=\"CR37\">2013</xref>). Of note, these human experiments were considered controversial at the time, and previous requests to use prisoners as research subjects were denied (although this may have been in part because local authorities would not permit a reduction in prison sentences as an inducement for inmates to participate (Killick-Kendrick <xref ref-type=\"bibr\" rid=\"CR37\">2013</xref>); such inducements had been used in early smallpox vaccine research in the 18th century during which six British prisoners were freed as a reward for participation (Halsband <xref ref-type=\"bibr\" rid=\"CR33\">1953</xref>)). Senior British Army officials also refused to approve the use of human participants (multiple animal studies, including challenge studies, were also being conducted) although it has been suggested that the practice was unofficially tolerated, in part because of the significant expected scientific value of the research (Killick-Kendrick <xref ref-type=\"bibr\" rid=\"CR37\">2013</xref>).</p></sec><sec id=\"Sec3\"><title>Malariotherapy</title><p id=\"Par17\">The 1927 Nobel Prize in Medicine was awarded to the Austrian psychiatrist Julius Wagner-Jauregg for the discovery of malariotherapy (intentional infection with malaria as treatment) for neurosyphilis,<xref ref-type=\"fn\" rid=\"Fn4\">4</xref> which became a routine treatment in many psychiatric hospitals, administered either by mosquito challenge or by direct injection of human blood infected with malaria (Chopra et al. <xref ref-type=\"bibr\" rid=\"CR13\">1941</xref>; Snounou and Pérignon <xref ref-type=\"bibr\" rid=\"CR61\">2013</xref>). The use of this ‘therapeutic’ malaria infection was widespread in Europe, North and South America, and India (Chopra et al. <xref ref-type=\"bibr\" rid=\"CR13\">1941</xref>)<xref ref-type=\"fn\" rid=\"Fn5\">5</xref> until the 1940s when penicillin was discovered as an effective treatment for syphilis (Frith <xref ref-type=\"bibr\" rid=\"CR25\">2012</xref>; Snounou and Pérignon <xref ref-type=\"bibr\" rid=\"CR61\">2013</xref>). The methods used to ‘prove’ that malariotherapy was effective for neurosyphillis appear quite rudimentary in comparison with 21st Century science (e.g., because the many case series published at the time lacked control subjects), and any attempt to undertake a modern, retrospective review of malariotherapy would inevitably be subject to possible biases, making it difficult to draw firm conclusions (Austin et al. <xref ref-type=\"bibr\" rid=\"CR4\">1992</xref>). Some patients died after receiving malariotherapy but, again, it is difficult to know how many of these cases were due to malaria infection itself, as opposed to other factors, including complications of neurosyphilis.<xref ref-type=\"fn\" rid=\"Fn6\">6</xref> In any case, (neuro)psychiatric patients undergoing malariotherapy were effectively used as research subjects by malariologists in de facto human challenge studies that improved scientific understanding of malaria with regards to (i) confirmation that malaria was caused by several different species of <italic>Plasmodium</italic> parasites, (ii) the natural history of disease, (iii) acquired immunity, (iv) transmission dynamics, and (v) the dormant liver stage of vivax malaria<xref ref-type=\"fn\" rid=\"Fn7\">7</xref> (Shortt et al. <xref ref-type=\"bibr\" rid=\"CR58\">1948</xref>; Snounou and Pérignon <xref ref-type=\"bibr\" rid=\"CR61\">2013</xref>). At least one investigation reportedly involved consent from the patient and his spouse for a study including liver biopsies (Shortt et al. <xref ref-type=\"bibr\" rid=\"CR58\">1948</xref>). These malaria challenge studies undertaken as part of malariotherapy (with or without what would now be considered valid consent to research participation) are still cited by HCS researchers today, including in some of the endemic-region malaria HCS reviewed in detail below (Shekalaghe et al. <xref ref-type=\"bibr\" rid=\"CR57\">2014</xref>; Vallejo et al. <xref ref-type=\"bibr\" rid=\"CR72\">2016</xref>). Psychiatric malariotherapy patients were, furthermore, the source of parasites used in other studies, including the Stateville Penitentiary program discussed below (Alving et al. <xref ref-type=\"bibr\" rid=\"CR2\">1948</xref>; Miller <xref ref-type=\"bibr\" rid=\"CR44\">2013</xref>).</p><p id=\"Par22\">While it was not necessary to use malariotherapy patients to study malaria (since at least one similar study with liver biopsy was done contemporaneously in a particularly altruistic healthy volunteer (Shortt et al. <xref ref-type=\"bibr\" rid=\"CR59\">1949</xref>)), researchers may have reasoned that malariotherapy patients were ideal candidates for such studies in light of expectations (based on what was known at the time) that they would directly benefit from infection. In retrospect, however, it is questionable whether (i) all patients with neurosyphilis were able to understand and consent to such research (even in cases where consent was sought), and (ii) the persistent use of malariotherapy in the era of penicillin (as a treatment for neurosyphilis) could have been ethically justified.</p></sec><sec id=\"Sec4\"><title>Infamous 20th Century Cases and the Rise of Modern Research Ethics</title><p id=\"Par23\">The genesis of modern research ethics (including the development of relevant codes, declarations, guidelines, principles, etc.) is frequently traced to responses to egregious cases of unethical research in the 20th century (Hope and McMillan <xref ref-type=\"bibr\" rid=\"CR34\">2004</xref>; Meltzer and Childress <xref ref-type=\"bibr\" rid=\"CR43\">2008</xref>). Several of these infamous cases involved intentional infection of research subjects. For example, some of the atrocities committed in the wartime research programs of Germany and Japan during World War II involved intentional infection with pathogens including anthrax, chlamydia, cholera, dysentery, glanders, hantavirus, malaria, paratyphoid, plague, tetanus, tuberculosis, typhoid, and typhus (Tsuchiya <xref ref-type=\"bibr\" rid=\"CR69\">2008</xref>; Weindling <xref ref-type=\"bibr\" rid=\"CR74\">2008</xref>; Bambery et al. <xref ref-type=\"bibr\" rid=\"CR7\">2015</xref>). These programs collectively involved thousands of victims, many of whom died as a result. Prisoners were violently forced to ‘participate’ (with no option to refuse nor effort to seek consent); participation often involved uncontrolled infection with pathogens known to cause severe disease and sometimes involved the torture and murder of those infected (e.g., by vivisection) (Tsuchiya <xref ref-type=\"bibr\" rid=\"CR69\">2008</xref>; Weindling <xref ref-type=\"bibr\" rid=\"CR74\">2008</xref>). Despite claims that such research aimed to improve measures to protect military personnel from infectious diseases, much of the ‘research’ and/or the procedures involved therein did not have a sound scientific rationale and thus would not have been able to inform the development of such measures, even if it had been conducted in a less violent manner (Tsuchiya <xref ref-type=\"bibr\" rid=\"CR69\">2008</xref>; Weindling <xref ref-type=\"bibr\" rid=\"CR74\">2008</xref>).</p><p id=\"Par24\">In the USA, contemporaneous war-related research also included recruitment of prisoners for infection challenge studies. Although these were conducted under more humane conditions, the voluntariness of consent, and the legitimacy of recruiting prisoners for research more generally, has since been called into question (Bonham and Moreno <xref ref-type=\"bibr\" rid=\"CR10\">2008</xref>; Miller <xref ref-type=\"bibr\" rid=\"CR44\">2013</xref>). Of particular relevance to current malaria challenge research, American military research during (and after) WWII included the Stateville Penitentiary experiments (discussed in more detail below), which involved infection of prisoners with malaria (including, in later studies, resistant strains of malaria) (Arnold et al. <xref ref-type=\"bibr\" rid=\"CR3\">1961</xref>; Miller <xref ref-type=\"bibr\" rid=\"CR44\">2013</xref>).</p><p id=\"Par25\">Later (in 1946<bold>–</bold>48), studies of sexually transmitted infections performed by American researchers in Guatemala involved intentional infection of vulnerable groups (e.g., sex workers, prisoners, soldiers, mentally disabled and institutionalised patients) with pathogens (e.g., bacteria causing syphilis, gonorrhoea, and chancroid) without their knowledge or consent, and also involved deliberately withholding treatment for these infections (Frieden and Collins <xref ref-type=\"bibr\" rid=\"CR23\">2010</xref>; Gutmann and Wagner <xref ref-type=\"bibr\" rid=\"CR32\">2012</xref>). In the United States, the Willowbrook School study of infectious hepatitis (1950s to 1970s) involved the intentional infection of mentally disabled, institutionalised children with viral hepatitis (which was, at that time, endemic at the school with very high rates of background infection in both ‘patients’ and staff), with the aim of better describing its natural history, and testing preventive and/or therapeutic interventions (Ward et al. <xref ref-type=\"bibr\" rid=\"CR73\">1958</xref>; Rothman <xref ref-type=\"bibr\" rid=\"CR55\">1982</xref>; Robinson and Unruh <xref ref-type=\"bibr\" rid=\"CR52\">2008</xref>).</p><p id=\"Par26\">Although these studies were eventually met with widespread condemnation, there is a consensus in the (limited) academic research ethics literature (discussed below in more detail) that it was not intentional infection per se that made these studies unethical, but rather other issues, particularly those related to (i) lack of or inadequate informed consent, and/or (ii) exploitation and/or brutal treatment of vulnerable populations (Miller and Grady <xref ref-type=\"bibr\" rid=\"CR45\">2001</xref>; Hope and McMillan <xref ref-type=\"bibr\" rid=\"CR34\">2004</xref>; Miller and Rosenstein <xref ref-type=\"bibr\" rid=\"CR46\">2008</xref>; Bambery et al. <xref ref-type=\"bibr\" rid=\"CR7\">2015</xref>).</p><p id=\"Par27\">Nevertheless, in high-income countries, studies did continue among populations sometimes described as ‘vulnerable’, e.g., prisoners (Glew et al. <xref ref-type=\"bibr\" rid=\"CR28\">1974</xref>) and military personnel (among whom, similarly, it may be more difficult to assure truly voluntary informed consent) (Bonham and Moreno <xref ref-type=\"bibr\" rid=\"CR10\">2008</xref>). In a retrospective analysis of the Stateville penitentiary malaria challenge studies conducted by the US military, Franklin Miller contrasts this research program with the (other) abusive wartime research discussed above, noting that (although imprisoned) subjects were invited (not forced) to volunteer, carefully screened for health conditions, and monitored closely during the studies—meaning that such research practices would be largely in accordance with many (subsequently developed) codes of research ethics (Miller <xref ref-type=\"bibr\" rid=\"CR44\">2013</xref>). Miller does note, however, that during the research severe adverse reactions and one death occurred (the latter reportedly due neither to infection challenge nor to the antimalarial drugs being trialled), raising plausible but unverifiable concerns that researchers may have been more willing to expose prisoners to higher risks because they were incarcerated (and/or because of the perceived urgency of army research that could save the lives of deployed soldiers) (Miller <xref ref-type=\"bibr\" rid=\"CR44\">2013</xref>).</p><p id=\"Par28\">By the 1970s, a consensus was building (although it has perhaps never become unanimous) among research ethicists in developed countries that research among such ‘captive’ groups could be ethically problematic, ultimately resulting in more careful review of research involving military personnel (although this has not necessarily resolved the underlying ethical tensions) (Bonham and Moreno <xref ref-type=\"bibr\" rid=\"CR10\">2008</xref>; Miller <xref ref-type=\"bibr\" rid=\"CR44\">2013</xref>), and strict regulations regarding research in prisons that eventually curtailed the recruitment of prisoners (Mishkin <xref ref-type=\"bibr\" rid=\"CR47\">2000</xref>; Rosenbaum and Sepkowitz <xref ref-type=\"bibr\" rid=\"CR54\">2002</xref>).<xref ref-type=\"fn\" rid=\"Fn8\">8</xref> Some have noted that adult students as a group may sometimes share characteristics with other ‘captive’ groups that might lead to concerns about their ability to consent (although perhaps to a lesser degree)—especially where they are financially or professionally dependent on their academic superiors and/or required to enroll as research participants as part of their studies (Bonham and Moreno <xref ref-type=\"bibr\" rid=\"CR10\">2008</xref>). Such considerations may be important for more recent challenge studies, which frequently recruit from student populations.</p></sec><sec id=\"Sec5\"><title>Late 20th Century</title><p id=\"Par30\">Later, some post-WWII challenge studies, such as those in the UK Common Cold Unit, involved volunteers from the general population. Although they predated modern ethics regulations, these studies reportedly involved a careful explanation of risks, voluntary consent, and isolation to prevent third party transmission; they did, however, involve risks that were not well characterised at the time, such as the potential for transmission of other pathogens (e.g., in bodily secretions used to administer the infection challenge) for which there were no testing methods available (Tyrrell <xref ref-type=\"bibr\" rid=\"CR71\">1992</xref>).</p><p id=\"Par31\">Elsewhere, at least one early (post-WWII) malaria challenge study took place took place in East Africa (an endemic-region), investigating the degree to which sickle cell trait (a genetic condition affecting red blood cells) protects against malaria. In the 1954 publication of this study (Allison <xref ref-type=\"bibr\" rid=\"CR1\">1954</xref>), the 30 research subjects are described as adult male volunteers from the Luo people, and it is mentioned that risks were controlled by giving infected subjects “a prolonged course of antimalarial chemotherapy”. Few other details apart from the infection rate in sickle cell trait versus non-sickle trait participants are noted—e.g., the publication records neither the presence nor severity of symptoms among participants, nor any consent process. In 1956 there was also a case of self-experimentation by a single investigator in Nigeria who infected himself with Zika virus and attempted to transmit the virus from himself to laboratory mice (Bearcroft <xref ref-type=\"bibr\" rid=\"CR8\">1956</xref>).</p><p id=\"Par32\">While it is possible (perhaps likely) that other endemic region/low-resource country challenge studies were conducted between World War II and 1992 (the date of the first case study reviewed later in this report), our review found that there was a very sparse literature regarding endemic region challenge research during this period, especially as compared to the significant and relatively numerous studies published from the late 19th Century to World War II. This may in part be due to the significant social changes that occurred in endemic regions (many of which were previously controlled by European imperial powers) at the end of the colonial period.</p><p id=\"Par33\">Ethical concerns (and reactions to the egregious cases discussed above) have perhaps contributed to a reluctance to undertake more HCS in LMICs (in addition to any technical difficulties regarding the availability of necessary laboratory infrastructure etc.) because (i) impoverished individuals and communities may be (perceived to be) particularly vulnerable (e.g., to various kinds of harm, exploitation, inducement by monetary payment, etc.), and (ii) valid informed consent may be (perceived to be) more difficult to assure in some populations within LMICs (e.g., because of language barriers, limited educational background, etc.).</p><p id=\"Par34\">In any case, HCS research has been largely concentrated in HICs, even where such research addresses pathogens that are primarily endemic in LMICs. For example, in the latter half of the 20th Century, North American and European researchers developed malaria challenge models, ultimately leading to several parallel research programs (Spring et al. <xref ref-type=\"bibr\" rid=\"CR62\">2014</xref>; Friedman-Klabanoff et al. <xref ref-type=\"bibr\" rid=\"CR24\">2019</xref>). At the outset, such studies were subject to few regulatory requirements and/or ethical oversight mechanisms. Parasites were obtained from infected human ‘donors’; challenges involved multiple ‘wild-type’ malaria pathogens (rather than, in the case of falciparum malaria, the few well-characterised laboratory strains in widespread use today); and prisoners and/or army personnel featured prominently among early recruitment of participants (see discussion of these groups above) (Friedman-Klabanoff et al. <xref ref-type=\"bibr\" rid=\"CR24\">2019</xref>).</p><p id=\"Par35\">Since the 1980s, improvements in scientific techniques as well as rigorous regulatory and ethical oversight have supported the development of multiple HCS research programs, studying a wide range of pathogens predominantly in HICs (Miller <xref ref-type=\"bibr\" rid=\"CR44\">2013</xref>; Darton et al. <xref ref-type=\"bibr\" rid=\"CR18\">2015</xref>). Studies collectively enrolling tens of thousands of healthy volunteers (Darton et al. <xref ref-type=\"bibr\" rid=\"CR18\">2015</xref>; Evers et al. <xref ref-type=\"bibr\" rid=\"CR20\">2015</xref>) have been safely conducted with no deaths and very few serious or lasting harms reported among HCS participants (Roestenberg et al. <xref ref-type=\"bibr\" rid=\"CR53\">2012</xref>; Darton et al. <xref ref-type=\"bibr\" rid=\"CR18\">2015</xref>). Pathogens/diseases studied in such trials have included adenovirus, BCG (bacille Calmette–Guérin—an attenuated form of <italic>M. bovis</italic> used as a tuberculosis vaccine), campylobacter, <italic>Candida albicans</italic>, cholera, coronaviruses, cryptosporidium, <italic>Cyclospora cayetanensis</italic>, cytomegalovirus, dengue, <italic>E. coli</italic>, giardia, hepatitis A & B, hookworm (<italic>Ancyclostoma caninum</italic> and <italic>Necator americanus</italic>), influenza, gonorrhoea, <italic>H. ducreyi</italic>, <italic>H. pylori</italic>, listeria, malaria, norovirus, parainfluenza, parvovirus, pneumococcus, Q fever, respiratory syncytial virus, rhinovirus, rotavirus, scabies, streptococci (non-pneumococcal), <italic>Shigella spp.</italic>, <italic>Strongyloides spp.</italic>, and typhoid. Overall, not only have the vast majority of HCS been conducted in HICs, but most studies have hitherto focused on pathogens that cause disease in HICs (though they may also affect LMICs), rather than those that are primarily endemic to LMICs; for example, rhinovirus (a cause of the common cold) has been the pathogen associated with by far the greatest number of HCS (at least 55 studies enrolling, collectively, >18,000 participants, in both respects more than for any other pathogen) (Kalil et al. <xref ref-type=\"bibr\" rid=\"CR36\">2012</xref>; Darton et al. <xref ref-type=\"bibr\" rid=\"CR18\">2015</xref>; Evers et al. <xref ref-type=\"bibr\" rid=\"CR20\">2015</xref>). HCS have resulted in unique insights into host-pathogen interactions, as well as the accelerated development of beneficial interventions, including for pathogens primarily endemic in LMICs. For example, HCS have played a role in the development of recently approved and/or licensed vaccines against typhoid (Jin et al. <xref ref-type=\"bibr\" rid=\"CR35\">2017</xref>), cholera (Tacket et al. <xref ref-type=\"bibr\" rid=\"CR66\">1999</xref>), and malaria (Ballou <xref ref-type=\"bibr\" rid=\"CR6\">2009</xref>).</p></sec><sec id=\"Sec6\"><title>Capacity Building in Low- and Middle-Income Countries</title><p id=\"Par36\">More recently, there have been calls for more HCS in endemic settings (particularly for pathogens that are primarily endemic in LMICs) in order to accelerate vaccine development and test new interventions in the populations at highest risk of relevant diseases (Gibani et al. <xref ref-type=\"bibr\" rid=\"CR26\">2015</xref>; Gordon et al. <xref ref-type=\"bibr\" rid=\"CR29\">2017</xref>; Baay et al. <xref ref-type=\"bibr\" rid=\"CR5\">2018</xref>). Our review identified no HCS conducted in LMICs from 1956 until 1992 when what appears to be the first LMIC HCS in nearly 40 years took place in Thailand (Suntharasamai et al. <xref ref-type=\"bibr\" rid=\"CR64\">1992</xref>). Researchers from LMICs have, however, participated in international HCS projects (where the challenge infection takes place in a HIC). For example, Thai researchers furnished mosquitoes infected with malaria parasites for HCS conducted in the USA (Malaria Vaccine Initiative <xref ref-type=\"bibr\" rid=\"CR41\">2016</xref>). However, it has been reported that Thai institutions were initially reluctant to conduct vivax malaria HCS in Thailand (partly because Thai authorities were awaiting further evidence of the safety and utility of the challenge model in question) (Malaria Vaccine Initiative <xref ref-type=\"bibr\" rid=\"CR41\">2016</xref>).</p><p id=\"Par37\">From 1992 onwards, Thai researchers successfully conducted challenge studies with cholera and <italic>Shigella</italic> in populations of Thai volunteers. Elsewhere, in the last two decades, well-established research centres in Colombia, Tanzania, Kenya, and Gabon have successfully conducted malaria HCS, often in collaboration with HIC HCS researchers (discussed in detail in Chap. 10.1007/978-3-030-41480-1_5). Researchers in more LMICs—including Equatorial Guinea, India, Indonesia, Malawi, Mali, Uganda, and Vietnam—are understood to be considering and/or conducting HCS at present.</p><p id=\"Par38\">Later, we discuss the ethical and scientific case for conducting (more) appropriately designed HCS in endemic LMICs in greater detail (Section “10.1007/978-3-030-41480-1_3”). However, even if there is an especially strong case for conducting such studies, certain (other) ethical issues related to their design and conduct warrant particularly careful attention. This is because (i) HCS may sometimes involve, or at least be perceived to involve, particularly high levels of risks (for participants and third parties) and other burdens for participants (and such studies must therefore be carefully designed and conducted to ensure that expected benefits outweigh risks and burdens), and (ii) local and/or international community acceptance of HCS being conducted in endemic LMICs may be contingent on such studies being designed and conducted to especially high ethical (and scientific) standards, and (iii) certain ethical considerations, though familiar in research ethics discourse, may have particular (underexplored) implications in the context of endemic LMIC HCS. The evaluation of these latter implications may both improve the design and conduct of LMIC HCS and/or provide novel case studies relevant to ongoing debates in research ethics. The remainder of this report summarises ethical and regulatory issues relevant to such studies, including insights from stakeholders interviewed for the current project, followed by a comparative review of LMIC HCS published in 1992<bold>–</bold>2018.</p></sec></body><back><fn-group><fn id=\"Fn1\"><label>1</label><p id=\"Par7\">Although Finlay’s overall hypothesis was correct, these experiments failed to demonstrate transmission because the interval between biting infected patients and biting healthy individuals (now known as the ‘extrinsic incubation period’) was too short.</p></fn><fn id=\"Fn2\"><label>2</label><p id=\"Par8\">Ronald Ross, the English contemporary of Grassi who was awarded the 1902 Nobel prize for identifying mosquito transmission of malaria, had (in 1897) shown that parasites were transmitted from human malaria patients to mosquitoes but used challenge studies in birds (with avian malaria), rather than human challenge, to show the transmission <italic>from</italic> mosquitoes.</p></fn><fn id=\"Fn3\"><label>3</label><p id=\"Par16\">We are grateful to Dr. Kate Emary for pointing us in the direction of early leishmania challenge studies.</p></fn><fn id=\"Fn4\"><label>4</label><p id=\"Par18\">The debilitating end-stage of syphilis that was relatively common at the time and had no effective treatment.</p></fn><fn id=\"Fn5\"><label>5</label><p id=\"Par19\">Interestingly (in the context of this review of endemic-region HCS research) while most malariotherapy programs in Europe and North America reportedly used <italic>P. vivax</italic>, at least one Indian centre used <italic>P. falciparum</italic> (which usually causes a more severe form of malaria) because it was believed that the local population had significant immunity to <italic>P. vivax</italic> that would attenuate the benefits of malariotherapy (see Chopra et al. <xref ref-type=\"bibr\" rid=\"CR13\">1941</xref>).</p></fn><fn id=\"Fn6\"><label>6</label><p id=\"Par20\">However, when falciparum malaria (a more severe form) was used by mistake in malaria-naïve patients for malariotherapy instead of vivax malaria (the milder form of malaria usually used), the mortality rate was much higher (Austin et al. <xref ref-type=\"bibr\" rid=\"CR4\">1992</xref>). In contrast, see the use of falciparum in India in the footnote above.</p></fn><fn id=\"Fn7\"><label>7</label><p id=\"Par21\">First identified in 1948 when a malariotherapy patient reportedly consented to a liver biopsy (see Shortt et al. <xref ref-type=\"bibr\" rid=\"CR58\">1948</xref>)—i.e., in a proto-challenge study.</p></fn><fn id=\"Fn8\"><label>8</label><p id=\"Par29\">In one countervailing consideration, Rosenbaum and Sepkowitz (<xref ref-type=\"bibr\" rid=\"CR54\">2002</xref>) cite a case of a group of prisoners at (US) Jackson State Prison filing an (unsuccessful) lawsuit arguing that prisoners should have more freedom to participate in research, though this may have been partly because of a view that participation in research would entail thorough medical examination and care, which can be difficult for prisoners to access under usual circumstances.</p></fn></fn-group><ref-list id=\"Bib1\"><title>References</title><ref id=\"CR1\"><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Allison</surname><given-names>AC</given-names></name></person-group><article-title>Protection afforded by sickle-cell trait against subtertian malarial infection</article-title><source>British Medical Journal</source><year>1954</year><volume>1</volume><issue>4857</issue><fpage>290</fpage><pub-id pub-id-type=\"pmid\">13115700</pub-id></element-citation></ref><ref id=\"CR2\"><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Alving</surname><given-names>AS</given-names></name><name><surname>Craige</surname><given-names>B</given-names></name><name><surname>Pullman</surname><given-names>TN</given-names></name><name><surname>Whorton</surname><given-names>CM</given-names></name><name><surname>Jones</surname><given-names>R</given-names></name><name><surname>Eichelberger</surname><given-names>L</given-names></name></person-group><article-title>Procedures used at Stateville penitentiary for the testing of potential antimalarial agents</article-title><source>The Journal of Clinical Investigation</source><year>1948</year><volume>27</volume><issue>3</issue><fpage>2</fpage><lpage>5</lpage></element-citation></ref><ref id=\"CR3\"><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Arnold</surname><given-names>J</given-names></name><name><surname>Alving</surname><given-names>AS</given-names></name><name><surname>Clayman</surname><given-names>CB</given-names></name><name><surname>Hochwald</surname><given-names>RS</given-names></name></person-group><article-title>Induced primaquine resistance in vivax malaria</article-title><source>Transactions of the Royal Society of Tropical Medicine and Hygiene</source><year>1961</year><volume>55</volume><issue>4</issue><fpage>345</fpage><lpage>350</lpage><pub-id pub-id-type=\"pmid\">13684386</pub-id></element-citation></ref><ref id=\"CR4\"><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Austin</surname><given-names>SC</given-names></name><name><surname>Stolley</surname><given-names>PD</given-names></name><name><surname>Lasky</surname><given-names>T</given-names></name></person-group><article-title>The history of malariotherapy for neurosyphilis: Modern parallels</article-title><source>JAMA</source><year>1992</year><volume>268</volume><issue>4</issue><fpage>516</fpage><lpage>519</lpage><pub-id pub-id-type=\"pmid\">1619744</pub-id></element-citation></ref><ref id=\"CR5\"><mixed-citation publication-type=\"other\">Baay, M.F.D., T.L. 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"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Oncol</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Oncol</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Oncol.</journal-id><journal-title-group><journal-title>Frontiers in Oncology</journal-title></journal-title-group><issn pub-type=\"epub\">2234-943X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850430</article-id><article-id pub-id-type=\"pmc\">PMC7431921</article-id><article-id pub-id-type=\"doi\">10.3389/fonc.2020.01347</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Oncology</subject><subj-group><subject>Mini Review</subject></subj-group></subj-group></article-categories><title-group><article-title>The Ways of Isolating Neoantigen-Specific T Cells</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Li</surname><given-names>Qing</given-names></name><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/867251/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Ding</surname><given-names>Zhen-Yu</given-names></name><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/764008/overview\"/></contrib></contrib-group><aff><institution>Department of Biotherapy, Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University</institution>, <addr-line>Chengdu</addr-line>, <country>China</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Cyrille J. Cohen, Bar-Ilan University, Israel</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Manel Juan, Hospital Clínic de Barcelona, Spain; Rodabe N. Amaria, University of Texas MD Anderson Cancer Center, United States</p></fn><corresp id=\"c001\">*Correspondence: Zhen-Yu Ding <email>dingzhenyu@scu.edu.cn</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Oncology</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>10</volume><elocation-id>1347</elocation-id><history><date date-type=\"received\"><day>12</day><month>12</month><year>2019</year></date><date date-type=\"accepted\"><day>26</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Li and Ding.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Li and Ding</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Immunotherapy has revolutionized the standard of care for a range of malignancies. Accumulating evidence suggests that the success of immunotherapy is likely attributable to neoantigen-specific T cells. Thus, adoptive cell therapy with these neoantigen-specific T cells is highly promising. This strategy has proven to successfully elicit tumor regression or even complete remission in metastatic cancer patients. However, a fundamental challenge is to effectively identify and isolate neoantigen-specific T cells or their T cell receptors (TCRs), from either tumor-infiltrating lymphocytes (TILs) or peripheral blood lymphocytes (PBLs), and many methods have been developed to this end. In this review, we focus on the current proposed strategies for identifying and isolating neoantigen-specific T cells.</p></abstract><kwd-group><kwd>adoptive cell therapy (ACT)</kwd><kwd>neoantigen-specific T cells</kwd><kwd>T cell receptor (TCR)</kwd><kwd>tumor infiltrating lymphocytes (TILs)</kwd><kwd>peripheral blood lymphocytes (PBLs)</kwd></kwd-group><counts><fig-count count=\"2\"/><table-count count=\"0\"/><equation-count count=\"0\"/><ref-count count=\"84\"/><page-count count=\"8\"/><word-count count=\"6393\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Several immunotherapeutic strategies that harness the exquisite specificity of the immune system to eliminate tumors have emerged during the past decade; these include cancer vaccines, immune checkpoint blockade, and adoptive cell therapy (ACT) (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>) with the potential to revolutionize the standard of care for a range of malignancies.</p><p>To a large extent, the specificity of immunotherapy is dependent on the recognition of specific tumor antigens, especially neoantigens. Neoantigens are a kind of tumor antigen derived from tumor-specific somatic mutations and are highly restricted to tumor cells with minimal established immune tolerance (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>). Neoantigen-based cancer vaccines have shown promising therapeutic effects in the clinic (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). In addition, a growing body of evidence indicates that neoantigen-specific T cells underlie the success of the recently emergent immune checkpoint inhibitor therapy (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>–<xref rid=\"B13\" ref-type=\"bibr\">13</xref>). Adoptive transfer of autologous, <italic>in vitro</italic> expanded, tumor-infiltrating lymphocytes (TILs) was reported to achieve dramatic clinical responses in some metastatic cancer patients, especially in those with melanoma and cervical cancer (<xref rid=\"B14\" ref-type=\"bibr\">14</xref>–<xref rid=\"B19\" ref-type=\"bibr\">19</xref>). In-depth studies have revealed the critical roles of neoantigen-specific T cells in maintaining durable responses following ACT (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>–<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). In support of these findings, the adoptive transfer of selected TILs targeting neoantigens led to significant tumor regression (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>–<xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Increasing research attention has been shifted to identifying and selecting neoantigen-specific T cells (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>–<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). However, such a “precise targeting” strategy poses a great challenge in terms of the identification and isolation of neoantigen-specific T cells. Methods have been proposed and developed for this purpose. Here, we attempt to summarize the known strategies for isolating neoantigen-specific T cells.</p></sec><sec id=\"s2\"><title>Identification and Isolation of Neoantigen-Specific T Cells From TILs</title><p>Researchers have long attempted to isolate neoantigen-specific subpopulations from the background of transferred TILs. In early studies, an autologous tumor cell cDNA library was constructed and used as a pool to screen for neoantigen-specific T cells (<xref rid=\"B20\" ref-type=\"bibr\">20</xref>, <xref rid=\"B21\" ref-type=\"bibr\">21</xref>). In a study of a melanoma patient who experienced a complete response going beyond 7 years following adoptive TIL transfer, one T cell clone specific for a mutated antigen PPP1R3B was identified and shown to be responsible for the antitumor effects (<xref rid=\"B22\" ref-type=\"bibr\">22</xref>).</p><p>However, the time-consuming and laborious process required to identify neoepitope-responsive T cells has hindered their extensive functional assessment (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). Advances in next-generation sequencing have enabled the rapid assessment of the mutational landscape of human cancers and made it possible to identify immunogenic mutated tumor antigens through <italic>in silico</italic> analysis. Rosenberg's group first employed predicted neo-peptides, obtained by whole-exome sequencing and human leucocyte antigen (HLA) class I–binding algorithms, for TIL screening. Using this approach, they identified 7 neoantigens recognized by 3 therapeutic bulk TIL cultures that mediated objective tumor regressions in three individuals with melanoma (<xref rid=\"B23\" ref-type=\"bibr\">23</xref>). Using a similar method, neoantigen-specific CD8+ TILs could also be identified in hematological malignancies, such as acute lymphoblastic leukemia (ALL) (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Prickett et al. (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>) and Stevanovic et al. (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>) also demonstrated that neoantigen-specific T cells could be identified from therapeutic TILs by screening tandem minigene (TMG) libraries encoding cancer mutations identified from patients' tumors by whole-exome sequencing. This finding might further facilitate the recognition of neoantigen-specific T cells because it circumvents the need for prediction of HLA–peptide binding and synthesis of a large number of peptides.</p><p>With the advent of these techniques, the field of ACT took a great leap from bulk TILs to neoantigen-specific T cells. A concise flowchart showing the steps involved in identifying and isolating neoantigen-specific T cells for ACT is summarized in <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref>. Tran et al. (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>) successfully performed neoantigen-specific T cell therapy in a 43-year-old woman with extensively metastatic and intensively treated cholangiocarcinoma. After administration of a bulk lymphocyte population containing a high percentage of neoantigen ERBB2IP-specific CD4+T cells, the patient showed a long-lasting objective clinical response without obvious toxicity. Subsequently, neoantigen-specific T cells were identified in one colon cancer patient and another breast cancer patient, and reinfusion of these specific T cells led to a partial response in one patient and a durable complete response in another (<xref rid=\"B28\" ref-type=\"bibr\">28</xref>, <xref rid=\"B29\" ref-type=\"bibr\">29</xref>). Currently, ACT with neoantigen-specific T cells is being tested in clinical trials in both solid and hematological tumors (<xref ref-type=\"supplementary-material\" rid=\"SM1\">Supplementary Table 1</xref>).</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>The general approach of identifying and isolating neoantigen-specific TILs for ACT. The tumor cells from excised tumor tissue and matched normal cells underwent whole-exome sequencing (WES) and RNA sequencing to identify non-synonymous mutations. Based on the information, either tandem minigenes (TMGs) or peptides were then synthesized. These TMGs or peptides were pulsed into autologous antigen presenting cells (APCs), such as dendritic cells (DCs) or B cells, and they were processed and presented in the context of major histocompatibility complex (MHC). On the other side, the excised tumors were minced into ~1–2 mm<sup>3</sup> fragments and placed in 24-well plates stimulated with IL-2. Then, the TILs were cocultured with these pulsed APCs. The identification of the individual neoantigen-specific T subpopulation was dependent on the IFN-γ enzyme-linked immunospot (ELISPOT) assay and the activation of the markers such as CD137(41BB) or CD134(OX40) on the T cell surfaces when recognizing their cognate target antigen. T cells with these activation surface markers would be purified by flow cytometry. Then, the sorted T cells were subject to rapid expansion <italic>in vitro</italic> and reinfusion to the tumor-bearing patient.</p></caption><graphic xlink:href=\"fonc-10-01347-g0001\"/></fig><p>However, the extensive expansion of neoantigen-specific T cells during preparation compromises their proliferation potential (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>). In addition, the method involved requires sophisticated equipment and a time period of several months. For most metastatic patients, this time frame is unacceptable. To address these issues, additional attempts have been made, using either surface markers or T cell receptor (TCR) redundancy.</p></sec><sec id=\"s3\"><title>Approaches Based on Surface Markers</title><p>CD137 belongs to the tumor necrosis factor receptor superfamily (<xref rid=\"B37\" ref-type=\"bibr\">37</xref>, <xref rid=\"B38\" ref-type=\"bibr\">38</xref>). It functions as a costimulatory molecule to promote the proliferation and survival of activated T cells (<xref rid=\"B39\" ref-type=\"bibr\">39</xref>, <xref rid=\"B40\" ref-type=\"bibr\">40</xref>). CD137 expression is highly restricted to transiently activated CD8+ T cells but almost undetectable in resting cells. Upregulated CD137 can be detected on stimulated CD8+ T cells of all phenotypes (e.g., naïve T cells as well as early and late memory effector T cells) (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). Naturally occurring tumor-reactive T cells stimulated by tumor antigens also express CD137 as proven by Ye et al. (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>). In a clinical trial (Trial registration ID: NCT02111863) among 6 patients with melanoma who underwent adoptive transfer with CD137-selected TILs, only 1 patient achieved partial response, and the remaining 5 progressed. The study was terminated.</p><p>This approach has its pitfalls: Because CD137 is an activation marker, CD137+ T cells obtained by large-scale production are generally overactivated and highly differentiated with limited proliferative potential. A potential solution is to obtain TCRs from these CD137+T cells instead. This strategy was reported by Parkhurst et al. (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>). Briefly, CD8+ T cells were stimulated overnight with immunogenic mutated TMG RNAs. Subsequently, the CD8+ T cell population with the highest CD137 expression was sorted by fluorescent-activated cell sorting (FACS), and expanded <italic>in vitro</italic>. Then, dominant TCR α and β chains were sequenced in the enriched populations. Twenty-seven TCRs from 6 patients that recognized 14 neoantigens expressed by autologous tumor cells were identified. However, this process was time-consuming (2–3 months).</p><p>A simplified protocol was proposed by Seliktar-Ofir et al. (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). Here, TILs, but not CD8+ T cells, were cocultured with autologous tumor cells; CD137+ T cells were isolated by magnetic bead separation and expanded. No further TCR sequencing was performed. The entire process took only 35 days. T cells stimulated with neoantigens or other tumor-associated antigens exhibit upregulated CD137 expression (<xref rid=\"B25\" ref-type=\"bibr\">25</xref>, <xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>, <xref rid=\"B45\" ref-type=\"bibr\">45</xref>). Therefore, a CD137-based selection protocol was advocated for its broad antigen coverage including both neoantigen and shared tumor antigens without prior knowledge of epitope specificity. However, the prerequisite of the establishment of autologous tumor cell lines poses a challenge.</p><p>Direct and indirect evidence shows that the interaction between PD-1 and PD-L1 inhibits T lymphocyte function, leading to evasion of persistent inflammatory or autoimmune reactions (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>–<xref rid=\"B48\" ref-type=\"bibr\">48</xref>). However, this protective mechanism is hijacked by tumors to escape immune surveillance, PD-1 has been characterized as an inhibitory receptor on chronically stimulated T-cells in the tumor microenvironment (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>). At the tumor site, TILs are exposed to tumor antigens; the binding of TCR and antigen upregulates either costimulatory or coinhibitory receptors to promote or inhibit T cell activation and function, respectively (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). Therefore, PD-1+ T cell populations among TILs may contain a large proportion of tumor-specific T cells. The findings of Inozume et al. (<xref rid=\"B50\" ref-type=\"bibr\">50</xref>) and Ahmadzadeh et al. (<xref rid=\"B51\" ref-type=\"bibr\">51</xref>), that tumor-responsive T cells are enriched among CD8+PD1+ lymphocytes from fresh melanoma specimens, provide direct support for this notion.</p><p>In another study, Gros et al. (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>) demonstrated that PD-1 expression on CD8+ TILs in fresh melanoma tumor specimens enabled identification of a diverse patient-specific repertoire of clonally expanded tumor-reactive cells, including mutated neoantigen-specific CD8+ lymphocytes. Although PD-1 is an inhibitory receptor expressed on T cells, studies have shown that IL-2 restored the antitumor function of T cells <italic>in vitro</italic> (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>). However, on antigen-experienced terminally differentiated effector memory (T<sub>EMRA</sub>) cells, PD-1 is either not expressed or expressed at very low levels (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B52\" ref-type=\"bibr\">52</xref>). Therefore, a PD-1-based enrichment strategy may not be suitable for these cells.</p><p>Screening strategies based on CD137 or PD-1 expression are suitable for CD8+ T cells, mainly in melanoma. Epithelial cancers, which account for more than 80% of all human malignancies, harbor fewer mutations than melanoma (<xref rid=\"B53\" ref-type=\"bibr\">53</xref>). They exhibit compromised capability to induce mutation-specific T cell responses, together with a limited number of infiltrating neoantigen-specific TILs (<xref rid=\"B32\" ref-type=\"bibr\">32</xref>). In addition, CD4+T cells have been shown to play an important role in mediating tumor regression in animal models and patients (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>, <xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B54\" ref-type=\"bibr\">54</xref>–<xref rid=\"B56\" ref-type=\"bibr\">56</xref>). However, CD137 or PD-1 is expressed on CD8+ cells as a sole marker; therefore, it may not be reliably used to enrich activated CD4+ cells (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>, <xref rid=\"B57\" ref-type=\"bibr\">57</xref>). CD134 is transiently expressed on CD4 + T cells stimulated by antigens and can be used as a marker for the classification of mutant reactive T cells (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>).</p><p>Recently, Yossef et al. (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>) reported an approach in which the TILs that expressed CD134 or CD137and/or PD-1 were isolated by FACS. Thus, both CD4+ T and T<sub>EMRA</sub> cells were rescued, which would otherwise be missed if a single marker were used. Sorted cells underwent limiting-dilution in microwell plates to avoid the overgrowth of non-specific T cells. Cultures were tested for the ability to recognize a 25-mer peptide pool encompassing possible neoantigens. Notably, the highly oligoclonal nature of these T cells makes possible the convenient application of single cell sequencing of their TCRs. In 6 patients with metastatic epithelial cancer, this high-throughput approach led to the detection of CD4+ and CD8+ T cells targeting 18 and 1 neoantigens, respectively, whereas only 6 and 2 neoantigens were identified by using the TIL fragment screening approach. In 2 patients in which no neoantigen was found by traditional screening, the novel approach identified 5 distinct neoantigen-specific TCR clones for one patient and a highly potent MHC class II–restricted KRAS<sup>G12V</sup>-reactive TCR for the other. In a metastatic tumor sample from a patient with serous ovarian cancer, 3 MHC class II–restricted TCRs targeting the TP53<sup>G245S</sup> hot-spot mutation were identified.</p></sec><sec id=\"s4\"><title>TCR Frequency</title><p>TCR sequence analysis is used as a tool to monitor T cell responses to specific antigens by measuring the abundance of T cell clonotypes (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B59\" ref-type=\"bibr\">59</xref>, <xref rid=\"B60\" ref-type=\"bibr\">60</xref>). The advent of next-generation sequencing has enabled identification of the full TCR repertoire of TILs (<xref rid=\"B61\" ref-type=\"bibr\">61</xref>, <xref rid=\"B62\" ref-type=\"bibr\">62</xref>). This valuable data for TCRs from tumor-reactive TILs could be used to modify T cells (TCR-T). However, the lengthy expansion process and excessive stimulation would result in TCR repertoire switching (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>). To avoid this problem, Pasetto et al. (<xref rid=\"B63\" ref-type=\"bibr\">63</xref>) directly performed TCR sequencing of the fresh enzymatically digested melanoma tissues prior to <italic>in vitro</italic> expansion. As described earlier, tumor-reactive clonotypes were enriched in CD8+PD-1+ TIL subsets in melanoma (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>). The authors analyzed the TCR repertoire of TILs in CD8+, CD8–, CD8+PD-1–, or CD8+PD-1+ subsets, respectively, and found that many of the most frequently occurring TCR clonotypes present in the CD8+PD-1+ TIL subset recognized the autologous tumor and tumor antigens, included neoantigens. This report provided a much more convenient approach to efficiently identify tumor-reactive T cells based solely on the frequency of TCR and PD-1 expression, without prior knowledge of the specific neoantigen. However, this strategy must be applied with caution because the isolated TCR clones may be self-reactive and result in deleterious on-target, off-tumor toxicities (<xref rid=\"B43\" ref-type=\"bibr\">43</xref>).</p></sec><sec id=\"s5\"><title>Isolation of Neoantigen-Specific T Cells From Peripheral Blood Lymphocytes (PBLs)</title><p>In some situations, neoantigen-specific T cells were undetectable in the TIL compartment, possibly owing to the following factors: presentation of neoantigens in a non-inflammatory context (<xref rid=\"B64\" ref-type=\"bibr\">64</xref>), impaired T cell infiltration because of the sparse distribution of adhesion molecules on these cells (<xref rid=\"B65\" ref-type=\"bibr\">65</xref>, <xref rid=\"B66\" ref-type=\"bibr\">66</xref>), and presence of immunosuppressive cytokines and cells (e.g., regulatory T cells) in the tumor microenvironment (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>). Furthermore, the tissue from which TILs may be obtained poses a challenge. In this regard, peripheral blood is an alternative and reliable source for neoantigen-specific T cells.</p><p>The first attempt is considered to have been made by a group led by Lennerz et al. (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>). In this study, a system of “mixed lymphocyte-tumor cells” (MLTC) was established, wherein peripheral blood mononuclear cells (PBMCs) from a patient with metastatic melanoma were cocultured with autologous tumor cells. The MTLC system could be viewed as a simplified <italic>in vitro</italic> simulation of the tumor microenvironment. Furthermore, cytotoxic T lymphocyte (CTLs) clone derived by limiting dilution from the MLTC system or MLTC were subject to autologous tumor cell cDNA library screening. T cell clones reactive to 5 mutated epitopes were obtained.</p><p>The use of MHC-peptide tetramers is a canonical method to identify and study a certain antigen-specific T cell subset (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>–<xref rid=\"B71\" ref-type=\"bibr\">71</xref>). For ACT, tetramers were used to isolate and expand tumor antigen-specific T cells (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>). Moreover, in immune checkpoint inhibitor (ICI)-treated cancer patients, MHC-peptide tetramers have been successfully used to monitor neoantigen-specific T cells (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>, <xref rid=\"B12\" ref-type=\"bibr\">12</xref>). Cohen et al. (<xref rid=\"B73\" ref-type=\"bibr\">73</xref>) used this method to sort neoantigen-specific T cells from the PBLs of patients with metastatic melanoma. In brief, a panel of MHC-peptide tetramers consisting of predicted neo-epitopes was synthesized and used to screen PBLs. Neoantigen-specific T cells targeting 8 of the 9 mutated epitopes identified from TILs could be isolated from autologous peripheral blood with frequencies ranging between 0.4 and 0.002%. In cancers with intermediate mutational loads, such as multiple myeloma, the use of MHC-peptide tetramers could also isolate neoantigen-specific T cells from the PBLs (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>). However, this method was only applied to CD8+ T cells and required HLA-binding prediction algorithms to guide the synthesis of HLA-peptide tetramers.</p><p>A previous study has shown that PD-1 expression could guide the identification of neoantigen-specific CD8+ T cells from the tumor microenvironment (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>). The same strategy could be adopted for isolation from PBLs (<xref rid=\"B75\" ref-type=\"bibr\">75</xref>). In one study, 4 patients with metastatic melanoma were enrolled. CD8+ PBLs were expanded <italic>in vitro</italic> and cocultured with autologous DCs, which were electroporated with <italic>in vitro</italic> transcribed TMG RNA for mutant epitopes. In 3 out of 4 patients, neoantigen-specific lymphocytes could be isolated from the CD8+PD-1+ lymphocyte subset, but not the CD8+PD-1– lymphocyte subset (<xref rid=\"B75\" ref-type=\"bibr\">75</xref>).</p><p>The isolation of neoantigen-specific cells from the PBLs of patients with epithelial cancer is even more challenging. Preexisting antigen-specific memory T cells may represent a potential solution. Memory T cells, including central memory T cells (T<sub>CM</sub>), effector memory T cells (T<sub>EM</sub>), and T<sub>EMRA</sub> from PBLs were cocultured with DCs loaded with candidate neoantigens in the TMG or peptide form (<xref rid=\"B76\" ref-type=\"bibr\">76</xref>). After coculturing, memory cells were restimulated with DCs loaded with all TMGs and then sorted by the expression of CD134 and CD137 to enrich for neoantigen-reactive T cells. The resulting cells were then expanded and screened against all TMGs to test for neoantigen recognition. With this highly sensitive “<italic>in vitro</italic> stimulation (IVS)” method, T cells targeting KRAS<sup><italic>G</italic>12<italic>D</italic></sup> and KRAS<sup><italic>G</italic>12<italic>V</italic></sup> were successfully isolated from 3 out of 6 epithelial cancer patients. This new method enabled identification and isolation of neoantigen-reactive T cells from the blood circulation at very low frequencies.</p><p>The identification of neoantigen-specific T cells from naïve T cells is also of interest. A previous report showed that both naïve and activated neoantigen-specific T cells could be expanded from the peripheral blood of follicular lymphoma patients by priming with peptide-pulsed DCs (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>). Using the same method, neoantigen-specific T cells were successfully expanded from the peripheral blood of HLA-matched healthy donors (<xref rid=\"B30\" ref-type=\"bibr\">30</xref>, <xref rid=\"B78\" ref-type=\"bibr\">78</xref>). These preliminary results support the use of naïve T cells as an alternative source for ACT; however, their exceptionally low frequencies in peripheral blood and requirement for repeated stimulation pose hurdles (<xref rid=\"B79\" ref-type=\"bibr\">79</xref>).</p><p>Recently, a large, library-based “mini-lines” screening approach was proposed, which aimed to identify naïve antigen-reactive T cells from small volumes of blood (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>–<xref rid=\"B82\" ref-type=\"bibr\">82</xref>). This system began with a small-scale culture in 96-well plates with 2,000 initial T cells in each well. The small-scale culture underwent a rapid 1,000- to 5,000-fold expansion (mini-line). Thousands of such well-scaled cultures were conducted simultaneously. Each T cell clone was maintained at a frequency of 1 in 2,000 but amplified to an absolute number of 1,000–5,000 cells, which is a sufficient number for routine detection. Applying this high-throughput parallel T cell culture system, neoantigen-specific T cells were identified and expanded 3–9 months prior to the first tumor recurrence in a patient with high-grade serous ovarian cancer. However, the long duration of culture possibly rendered this method more suitable as a preemptive therapeutic strategy (<xref rid=\"B83\" ref-type=\"bibr\">83</xref>).</p></sec><sec sec-type=\"discussion\" id=\"s6\"><title>Discussion</title><p>After decades of efforts, the adoptive transfer of neoantigen-specific T cells is finally close to readiness for clinical application. High efficacy of this immunotherapeutic strategy has been achieved in a number of cancer patients and the prospects are promising. However, these approaches are also quite costly and hard to apply to large numbers of patients. The current methods of identifying neoantigen-specific T cells are summarized in <xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref> and <xref ref-type=\"supplementary-material\" rid=\"SM2\">Supplementary Table 2</xref>. More convenient and effective screening methods for neoantigen-specific T cells remain necessary, some strategies to improve neoantigen-specific T cells identification are shown in <xref ref-type=\"fig\" rid=\"F2\">Figure 2A</xref>.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p><bold>(A)</bold> Strategies of identifying neoantigen-specific T cells. The limitations of current methods of identifying neoantigen-specific T cells and strategies to improve neoantigen-specific T cells identification. TILs, tumor infiltrating lymphocytes; PBLs, peripheral blood lymphocytes; PD-1, programmed cell death-1; T<sub>EMRA</sub> cells, terminally differentiated effector memory cells; TCR, T cell receptor; TMG, tandem minigene; MHC, major histocompatibility complex. <bold>(B)</bold> The “blueprint” of isolating neoantigen-specific T cells from peripheral blood after neoantigen-targeting vaccine. After several rounds of immunization with neoantigen vaccines, T cells are collected from the patient's peripheral blood and neoantigen-specific T cells are identified and isolated from these T cells. Then, the neoantigen-specific T cells undergo rapid expansion (REP), or their TCRs are exploited to modify autologous lymphocytes. The expanded neoantigen-specific T cells or modified TCR-T cells are reinfused to the patient.</p></caption><graphic xlink:href=\"fonc-10-01347-g0002\"/></fig><p>It is feasible to obtain neoantigen-targeting T cells from PBLs although their frequencies are generally much lower than TILs (<xref rid=\"B73\" ref-type=\"bibr\">73</xref>, <xref rid=\"B75\" ref-type=\"bibr\">75</xref>, <xref rid=\"B76\" ref-type=\"bibr\">76</xref>). However, increasing the frequencies of these valuable neoantigen-specific T cells in peripheral blood remains a challenge.</p><p>Vaccination with neo-peptides has been shown to prime CD4+ and CD8+ T-cell responses in mouse models (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>, <xref rid=\"B84\" ref-type=\"bibr\">84</xref>). Patients treated with vaccines generated neoantigen-specific T cells (<xref rid=\"B2\" ref-type=\"bibr\">2</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>). It could be reasonably inferred that the isolation of neoantigen-reactive T cells from the peripheral blood would be more easily achieved following neoantigen-specific vaccination. This neoantigen-based combo immunotherapy has its advantages: first, isolation and expansion of TILs <italic>in vitro</italic> is not necessary. Second, cancer vaccines not only elicit neoantigen-specific T cell responses and amplify existing tumor-specific T cells responses, but they also increase the breadth and diversity of the tumor-specific T cell response (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>, <xref rid=\"B7\" ref-type=\"bibr\">7</xref>). Multiclonal T cells may, thus, be obtained. Third, the relatively easy preparation of cancer vaccines would buy time for the isolation of neoantigen-specific T cells in maintaining the performance of patients. The “blueprint” is shown in <xref ref-type=\"fig\" rid=\"F2\">Figure 2B</xref>.</p></sec><sec sec-type=\"conclusions\" id=\"s7\"><title>Conclusion</title><p>The previous decade has witnessed the emergence of immunotherapy for cancer. Accumulating evidence suggests that neoantigen-specific T cells underlie successful immunotherapy. Therefore, the isolation of neoantigen-specific T lymphocytes represents the “holy grail” for cancer immunotherapy. However, a fundamental challenge is to effectively identify and isolate neoantigen-specific T cells. The developments summarized in this review and future breakthroughs are anticipated to translate the adoptive transfer of neoantigen-specific T cells into a powerful weapon in our armamentarium against cancer.</p></sec><sec id=\"s8\"><title>Author Contributions</title><p>QL prepared the manuscript draft. Z-YD revised it critically for important intellectual content and approved the final version. QL and Z-YD contributed to the conception and design of the review. All authors contributed to the article and approved the submitted version.</p></sec><sec id=\"s9\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This work was supported by the National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University (Z2018B18).</p></fn></fn-group><sec sec-type=\"supplementary-material\" id=\"s10\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fonc.2020.01347/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fonc.2020.01347/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SM1\"><media xlink:href=\"Table_1.DOCX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SM2\"><media xlink:href=\"Table_2.DOCX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><label>1.</label><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Hu</surname><given-names>Z</given-names></name><name><surname>Ott</surname><given-names>PA</given-names></name><name><surname>Wu</surname><given-names>CJ</given-names></name></person-group>. <article-title>Towards personalized, tumour-specific, therapeutic vaccines for cancer</article-title>. <source>Nat Rev Immunol.</source> (<year>2018</year>) <volume>18</volume>:<fpage>168</fpage>–<lpage>82</lpage>. <pub-id 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"review-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Pediatr</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Pediatr</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Pediatr.</journal-id><journal-title-group><journal-title>Frontiers in Pediatrics</journal-title></journal-title-group><issn pub-type=\"epub\">2296-2360</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32850553</article-id><article-id pub-id-type=\"pmc\">PMC7431922</article-id><article-id pub-id-type=\"doi\">10.3389/fped.2020.00440</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Pediatrics</subject><subj-group><subject>Review</subject></subj-group></subj-group></article-categories><title-group><article-title>Dietary Prevention of Atopic March in Pediatric Subjects With Cow's Milk Allergy</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Carucci</surname><given-names>Laura</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/977730/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Nocerino</surname><given-names>Rita</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"author-notes\" rid=\"fn002\"><sup>†</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/678993/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Paparo</surname><given-names>Lorella</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/583171/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Di Scala</surname><given-names>Carmen</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/585345/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Berni Canani</surname><given-names>Roberto</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/217284/overview\"/></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Translational Medical Science, University of Naples Federico II</institution>, <addr-line>Naples</addr-line>, <country>Italy</country></aff><aff id=\"aff2\"><sup>2</sup><institution>ImmunoNutritionLab at the CEINGE Advanced Biotechnologies Research Center, University of Naples Federico II</institution>, <addr-line>Naples</addr-line>, <country>Italy</country></aff><aff id=\"aff3\"><sup>3</sup><institution>European Laboratory for the Investigation of Food-Induced Diseases, University of Naples Federico II</institution>, <addr-line>Naples</addr-line>, <country>Italy</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Task Force for Microbiome Studies, University of Naples Federico II</institution>, <addr-line>Naples</addr-line>, <country>Italy</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Gianvincenzo Zuccotti, University of Milan, Italy</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Carla Mastrorilli, University of Parma, Italy; Rashmi Ranjan Das, All India Institute of Medical Sciences, India</p></fn><corresp id=\"c001\">*Correspondence: Roberto Berni Canani <email>berni@unina.it</email></corresp><fn fn-type=\"other\" id=\"fn001\"><p>This article was submitted to Pediatric Immunology, a section of the journal Frontiers in Pediatrics</p></fn><fn fn-type=\"other\" id=\"fn002\"><p>†These authors have contributed equally to this work</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>8</volume><elocation-id>440</elocation-id><history><date date-type=\"received\"><day>13</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>23</day><month>6</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Carucci, Nocerino, Paparo, Di Scala and Berni Canani.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Carucci, Nocerino, Paparo, Di Scala and Berni Canani</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>Cow's milk allergy (CMA) is one of the most prevalent food allergies and the most expensive allergic diseases in the pediatric age. There is no cure for CMA, and actual disease management is based on strict avoidance of cow milk protein-containing foods, access to rescue medication, and use of substitutive formulas. Early-life CMA could be one of the first steps of the “allergic march” (AM), leading to the occurrence of other atopic manifestations later in the life, including asthma and oculorhinitis, with subsequent further increase of costs for health care systems and families of affected children. In the last years, diet is emerged as a relevant strategy to prevent allergic diseases through, at least in part, epigenetic modulation of immune system. We provide an overview of studies that investigate the potential role of different dietary strategies in preventing the AM in pediatric patients with CMA.</p></abstract><kwd-group><kwd>allergic march</kwd><kwd>food allergy</kwd><kwd>breast milk</kwd><kwd>infant formula</kwd><kwd>gut microbiota</kwd><kwd>epigenetics</kwd></kwd-group><counts><fig-count count=\"3\"/><table-count count=\"3\"/><equation-count count=\"0\"/><ref-count count=\"84\"/><page-count count=\"9\"/><word-count count=\"6720\"/></counts></article-meta></front><body><sec sec-type=\"intro\" id=\"s1\"><title>Introduction</title><p>Affecting up to 3% of children worldwide, cow's milk allergy (CMA) is one of the earliest and most prevalent food allergies (FA) in the pediatric age. It is also responsible for the vast majority of food-induced anaphylaxis cases in the Italian pediatric population, with significant costs for the healthcare system and families, and it emerged as one of the most expensive allergic diseases (<xref rid=\"B1\" ref-type=\"bibr\">1</xref>–<xref rid=\"B8\" ref-type=\"bibr\">8</xref>).</p><p>Although most subjects with CMA naturally outgrow it over time, studies evidence a wide range of ages and rates of resolution with an increased risk of persistence in recent decades, mainly due to negative gene–environment interaction leading to the breakdown of immune tolerance mechanisms (<xref rid=\"B9\" ref-type=\"bibr\">9</xref>–<xref rid=\"B12\" ref-type=\"bibr\">12</xref>). In addition, evidence suggests that early-life CMA could be one of the first steps of the “allergic march” (AM), leading to the occurrence of other allergic disorders during childhood. Indeed, the occurrence of allergic sensitization in these children increases the risk of later developing asthma and allergic oculorhinitis (AR), in particular when sensitization occurs along with atopic dermatitis (AD) (<xref rid=\"B13\" ref-type=\"bibr\">13</xref>–<xref rid=\"B15\" ref-type=\"bibr\">15</xref>). <xref ref-type=\"fig\" rid=\"F1\">Figure 1</xref> depicts the natural history of AM in CMA children. According to data from several clinical studies, up to 45% of CMA children develop other atopic manifestations later in the life, also after the immune tolerance acquisition to cow milk proteins (<xref rid=\"B3\" ref-type=\"bibr\">3</xref>, <xref rid=\"B5\" ref-type=\"bibr\">5</xref>, <xref rid=\"B16\" ref-type=\"bibr\">16</xref>–<xref rid=\"B18\" ref-type=\"bibr\">18</xref>). The development of AM is driven by genetic predisposition, but environmental factors may play a key role in its clinical expression. Indeed, as shown by longitudinal studies, only a minority of children follow the classic pathway of AM (starting from AD and followed by sequential development of FA, asthma, and AR) (<xref rid=\"B19\" ref-type=\"bibr\">19</xref>, <xref rid=\"B20\" ref-type=\"bibr\">20</xref>). Earlier recognition of at-risk infants, regardless of CMA temporal appearance, allows fielding effective strategies to limit the occurrence of other atopic manifestations later in the life.</p><fig id=\"F1\" position=\"float\"><label>Figure 1</label><caption><p>The atopic march in pediatric patients with cow's milk allergy. Atopic dermatitis (AD) is commonly considered the first step of the atopic march (AM), however, AD and cow's milk allergy (CMA) could co-exist, particularly in those with early onset, severe, and persistent atopic eczema. CMA affects about 1/3 of patients with AD. Data from several clinical studies demonstrate that up to 45% of children affected by CMA will develop other atopic manifestations later in the life, also after the immune tolerance acquisition to cow's milk proteins.</p></caption><graphic xlink:href=\"fped-08-00440-g0001\"/></fig><p>There is no cure for CMA, and actual disease management is based on strict avoidance of cow's milk protein-containing foods, access to rescue medication, and use of substitutive formulas (<xref rid=\"B21\" ref-type=\"bibr\">21</xref>–<xref rid=\"B25\" ref-type=\"bibr\">25</xref>).</p><p>Due to the increasing prevalence, persistence, and risk for developing other atopic manifestations in children with CMA, preventive strategies are highly advocated. In the last years, diet is emerging as a relevant strategy to prevent allergic diseases through the active modulation of the immune system (<xref rid=\"B26\" ref-type=\"bibr\">26</xref>). This review is focused on the potential role of different dietary strategies in preventing the AM in pediatric patients with CMA.</p></sec><sec id=\"s2\"><title>The Potential of Breastfeeding</title><p>Breastfeeding is the best dietary strategy for newborn infants due to its optimal nutritional properties and several bioactive compounds that influence health status. Studies suggest a protective role on the onset of FA, asthma, and AD, both in low- and high-risk infants breastfed for at least 3–4 months (<xref rid=\"B27\" ref-type=\"bibr\">27</xref>–<xref rid=\"B33\" ref-type=\"bibr\">33</xref>). A WHO report suggests that allergic diseases are lower in exclusively breastfed compared to non-breastfed infants (<xref rid=\"B34\" ref-type=\"bibr\">34</xref>). A reduction of about 4% in FA risk for every additional month of exclusive breastfeeding has also been estimated (<xref rid=\"B35\" ref-type=\"bibr\">35</xref>). Unfortunately, most available data on breastfeeding and allergic diseases are based on observational, retrospective, underpowered studies, and present several confounding factors, such as the inclusion of partially breastfed infants (<xref rid=\"B36\" ref-type=\"bibr\">36</xref>, <xref rid=\"B37\" ref-type=\"bibr\">37</xref>). Another limiting aspect is that the protective mechanisms against FA and other atopic manifestations are still not completely characterized. Breast milk contains several potential protective factors against allergy. Some compounds could be able to exert an indirect effect on immune system through a modulation of infant gut microbioma (GM), whereas other components could exert a direct modulatory effect on the infant immune system toward a protection against allergic diseases (<xref rid=\"B38\" ref-type=\"bibr\">38</xref>, <xref rid=\"B39\" ref-type=\"bibr\">39</xref>) (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><table-wrap id=\"T1\" position=\"float\"><label>Table 1</label><caption><p>Main immunomodulatory factors in human milk.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Regulation of infant's immune system through a direct interaction with immune cells</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Regulation of infant's immune system through a modulation of gut microbiome</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cytokines</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lactoferrin</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Bacterial DNA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Lisozyme</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">miRNAs</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Secretory IgA</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Short chain fatty acids</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Human milk bacteria</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Human milk oligosaccharides</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Human milk oligosaccharides</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Omega-3 fatty acids</td><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap><p>The GM is emerging as a pivotal regulator of immune tolerance development (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>). Breastfeeding shapes infant GM, both by direct transition of the human milk bacteria (HMBs), and indirectly through milk compounds such as human milk oligosaccharides (HMOs), secretory IgA, and antimicrobial factors, which could impact bacterial growth and metabolism (<xref rid=\"B40\" ref-type=\"bibr\">40</xref>). Studies have suggested that breast milk owns unique microbiome, including beneficial commensal and potentially probiotic bacteria (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>). HMBs can originate from maternal skin, newborn oral cavity, or mostly from the maternal gut (the “entero-mammary pathway”) and are influenced by mode of delivery, with a lower bacteria variety and abundance in cesarean compared to vaginal delivery (<xref rid=\"B42\" ref-type=\"bibr\">42</xref>, <xref rid=\"B43\" ref-type=\"bibr\">43</xref>). Breast milk is considered the second source of microbes to infant GM, and it has been estimated that breastfed infants could receive from human milk microbiota up to 8 × 10<sup>5</sup> bacteria daily (<xref rid=\"B44\" ref-type=\"bibr\">44</xref>). Considering the pivotal role of GM in influencing the infant immune system function against CMA (<xref rid=\"B45\" ref-type=\"bibr\">45</xref>), it is possible to hypothesize that HMBs could be an innovative target of intervention. Interestingly, it has been demonstrated that in the milk of allergic mothers the bifidobacteria counts were significantly lower than in the milk of non-allergic mothers (<xref rid=\"B46\" ref-type=\"bibr\">46</xref>).</p><p>Regarding breast milk non-microbial components, human milk oligosaccharides (HMOs) are a group of non-digestible carbohydrates that are able to regulate the immune system function in a direct or indirect way. The HMO composition in breast milk is influenced by environmental (such as maternal diet) and genetic factors, and a possible role in FA has been suggested (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>). Recent studies reported an association between different genetically induced HMO composition and the development of CMA and FA (<xref rid=\"B47\" ref-type=\"bibr\">47</xref>, <xref rid=\"B48\" ref-type=\"bibr\">48</xref>). Interestingly, one recent study highlighted the ability of specific HMOs, pulled from human milk, to induce the maturation of human monocyte-derived dendritic cells (DC) (moDC). The derived HMO moDC are able to promote T reg induction from native CD4<sup>+</sup> T cells, with a final tolerogenic effect on the infant's immune system (<xref rid=\"B49\" ref-type=\"bibr\">49</xref>); but the best characterized HMO properties are related to the prebiotic modulation of early microbial gut colonization with bifidobacteria and <italic>lactobacilli</italic>, which are involved in the production of tolerogenic metabolites short-chain fatty acids (SCFA), in particular, butyrate (<xref rid=\"B41\" ref-type=\"bibr\">41</xref>, <xref rid=\"B50\" ref-type=\"bibr\">50</xref>–<xref rid=\"B54\" ref-type=\"bibr\">54</xref>). Supporting this view, it has been reported that the GM of allergic infants lacks genes encoding key enzymes for HMO metabolization with the consequent impairment of butyrate production (<xref rid=\"B55\" ref-type=\"bibr\">55</xref>).</p><p>Butyrate may prevent allergy diseases though different ways, involving a regulation of the epithelial barrier (at skin, gut, and respiratory tract level), a direct effect on Th1/Th2 cytokine expression, and the activation of regulatory T cells (Tregs) (<xref rid=\"B56\" ref-type=\"bibr\">56</xref>–<xref rid=\"B60\" ref-type=\"bibr\">60</xref>). Many effects are mediated by the epigenetic modulation of gene expression, suggesting the possibility of a long-lasting regulatory effect on immune tolerance network (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>).</p><p>The origin of butyrate in breast milk is still largely undefined. The mammalian gland is able to regulate the concentration of several macro- and micronutrients in human milk. Thus, it is possible to hypothesize that some mechanisms of regulation could modulate the butyrate content in human milk. However, recent evidence supports the hypothesis that, at least in part, human milk butyrate could be produced by the HMBs. The hypothesis of a pivotal contribution by mammalian gland/breast milk microbiota in butyrate production is supported by recent observations demonstrating the presence of potential butyrate-producer bugs (<xref rid=\"B54\" ref-type=\"bibr\">54</xref>, <xref rid=\"B61\" ref-type=\"bibr\">61</xref>–<xref rid=\"B65\" ref-type=\"bibr\">65</xref>).</p><p>An example of a potential pathway in butyrate production in breast milk could be derived by HMO metabolization by selected bacteria, as recently demonstrated by others (<xref rid=\"B62\" ref-type=\"bibr\">62</xref>, <xref rid=\"B66\" ref-type=\"bibr\">66</xref>).</p><p>Of note, increasing observations demonstrate the presence of significant butyrate concentrations in breast milk, ranging from 0.01 to >5.0 mM (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>–<xref rid=\"B70\" ref-type=\"bibr\">70</xref>) (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>).</p><table-wrap id=\"T2\" position=\"float\"><label>Table 2</label><caption><p>Available data on butyrate concentrations in human milk.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>N° samples</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>N° mothers</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Min value</bold><break/>\n<bold>(mM)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Max value</bold><break/>\n<bold>(mM)</bold></th><th valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><bold>Methods</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Maria et al. (<xref rid=\"B68\" ref-type=\"bibr\">68</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">150</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">GC-MS utilizing a lipase assisted sample preparation (deuterated butyric acid (BA-D7) as an internal standard)</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Schwab et al. (<xref rid=\"B70\" ref-type=\"bibr\">70</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">HPLC-RI with external standard</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Prentice et al. (<xref rid=\"B69\" ref-type=\"bibr\">69</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">102</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">102</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.0</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">H-NMR and GC-MS</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Dai et al. (<xref rid=\"B67\" ref-type=\"bibr\">67</xref>)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">180</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">Methyl esterification of SCFAs and GC analysis</td></tr></tbody></table><table-wrap-foot><p><italic>GC-MS, gas chromatography-mass spectrometry; HPLC-RI, high performance liquid chromatography with refractive index detection; H-NMR, nuclear magnetic resonance spectroscopy; SCFAs, short chain fatty acids</italic>.</p></table-wrap-foot></table-wrap><p>In line with these data, our preliminary observation from 109 healthy mothers show a median butyrate concentration in mature human milk of 0.75 mM (range 0.16–1.97 mM) (<xref rid=\"B58\" ref-type=\"bibr\">58</xref>). Interestingly, several preclinical data show that this butyrate concentration is able to modulate several components of immune tolerance network mainly through epigenetic mechanisms (<xref rid=\"B6\" ref-type=\"bibr\">6</xref>, <xref rid=\"B56\" ref-type=\"bibr\">56</xref>–<xref rid=\"B60\" ref-type=\"bibr\">60</xref>).</p><p>Altogether, these data strongly suggest the potential pivotal role of a modulation of breast milk composition for innovate preventive strategies against CMA and against the occurrence of AM in CMA children.</p></sec><sec id=\"s3\"><title>The Potential of Formula Choice</title><p>The first evidence on the possible role of infant formulas in preventing AM in CMA infants was provided about 10 years ago. In a prospective cohort study of 119 children with IgE-mediated CMA, a multivariate analysis of risk factors for the occurrence of AM revealed that the use of an extensively hydrolyzed casein-based formula (EHCF) represented a protective factor for other allergic diseases, compared to other hypoallergenic formulas or soy-based formulas (OR 0.76; 95% CI: 0.149–0.945, <italic>p</italic> = 0.038) (<xref rid=\"B71\" ref-type=\"bibr\">71</xref>).</p><p>To our knowledge, to date, only one randomized controlled trial was performed to test the potential of a formula-based dietary intervention on AM prevention in CMA pediatric patients (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). In this prospective trial, a total of 220 infants with IgE-mediated CMA (67% males, median age 5.0 months) were randomized into two dietary groups: 110 subjects were placed on EHCF-based diet, and 110 children were placed on EHCF + probiotic <italic>Lactobacillus rhamnosus</italic> GG (LGG)-based diet. Patients were followed up for 36 months. In the complete case analysis (CCA), the absolute risk difference (ARD) for the occurrence of at least one atopic manifestation over 36 months was −0.23 (95% CI −0.36 to −0.10, <italic>p</italic> < 0.001). Even under the worst-case scenario, a difference in favor of EHCF+LGG was still detected. Using the CCA estimate of the ARD, the number needed to treat was 4 (95% CI 3–10) (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>). These findings are consistent with those of recent studies revealing that the first-line approach with EHCF+LGG for CMA infants may slow down the AM, compared to infants treated with other formulas. A retrospective observational study on 211 subjects with CMA was conducted for new score validation for the risk of developing AM, using selected clinical and laboratory data (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>). The authors found that the type of substitutive formula for CMA treatment may influence the natural history of these children. They divided the patients into five groups, based on formula composition: vegetable-based formulas (rice or soy), high-grade extensively hydrolyzed formula (EHF) for those in which >95% of peptides were 1,000 kDa, high-grade EHF plus LGG (EHF+LGG), low-grade EHF for those with a higher proportion of peptides (>1,000 kDa), or amino acid–based formulas. Authors found that the risk of AM occurrence decreased in those treated with high-grade EHF (OR 0.42; 95% CI 0.20–0.87, <italic>p</italic> = 0.02), and these results were stronger in patients treated with high-grade EHF+LGG (OR 0.30; 95% CI 0.09–0.98, <italic>p</italic> = 0.048). The authors concluded that the first-line approach with EHF may be beneficial to prevent the occurrence of AM, and LGG implementation strengthened this trend. They supposed that the hypoallergenic composition of this high-grade EHF and the GM may have helped to positively influence the immune tolerance network, decreasing the risk of developing AM (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>). Similarly, in a recent retrospective cohort study of 940 infants with CMA, a binary logistic regression analysis showed that infants fed with extensively hydrolyzed whey formula (EHWF) had a significantly higher relative risk at 24 months of AD (OR: 3.438; 95% CI: 1.975–5.985; <italic>p</italic> < 0.001) and asthma (OR: 2.651; 95% CI: 1.242–5.660; <italic>p</italic> < 0.02) compared with those fed with EHCF+LGG. The authors concluded that the first-line therapeutic approach for newly diagnosed CMA children with EHCF+LGG, reducing the development of other allergic diseases later in life, may slow down the AM (<xref rid=\"B73\" ref-type=\"bibr\">73</xref>). Current guidelines provided by scientific societies (EAACI, DRACMA, NICE, ESPGHAN, NIAID, BSACI) strongly suggest avoiding unmodified animal milk proteins for CMA dietary treatment. In addition, there is no evidence supporting the potential role of such mammalian milks in preventing AM in FA patients (<xref rid=\"B74\" ref-type=\"bibr\">74</xref>). All available studies focused on the potential role of formulas in preventing AM are summarized in <xref rid=\"T3\" ref-type=\"table\">Table 3</xref>.</p><table-wrap id=\"T3\" position=\"float\"><label>Table 3</label><caption><p>The studies exploring the potential of formula choice in preventing atopic march in pediatric patients affected by cow's milk allergy.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>References</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Study design/population</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Age</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Sample size</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Intervention/duration</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Outcomes</bold></th><th valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Results</bold></th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Berni Canani et al. (<xref rid=\"B5\" ref-type=\"bibr\">5</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Parallel-arm RCT/IgE-mediated CMA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">1–12 months</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> = 220 <break/>\n<italic>I</italic> = 110 <break/>\n<italic>C</italic> = 110</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">I = EHCF+ LGG <break/> C = EHCF; for 36 months</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The occurrence of any atopic manifestation (eczema, urticaria, asthma, oculo-rhinitis) during the 36 months of the study.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The ARD of any atopic manifestation for EHCF+LGG vs. EHCF was:<break/> (1) −0.23 [95% CI −0.36 to −0.10, <italic>p</italic> < 0.001] at CCA;<break/> (2) −0.22 [95% CI −0.35 to −0.09, <italic>p</italic> < 0.001] at SA-EQS;<break/> (3) −0.33 [95% CI −0.45 to −0.21, <italic>p</italic> < 0.001] at SA-BCS;<break/> (4) −0.08 [95% CI −0.21 to 0.04, <italic>p</italic> = 0.5] at SA-WCS.<break/> The SA-EQS estimate was very similar to the CCA estimate. On absolute grounds, the SA-BCS was 10% higher and the SA-WCS was 15% lower than the CCA estimate.<break/> Even under the worst case scenario, a difference in favor of EHCF+LGG was still present (8%). Using the CCA estimate of the ARD, the NNT was 4 (95% CI 3 to 10).</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sánchez-Valverde et al. (<xref rid=\"B71\" ref-type=\"bibr\">71</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Observational cohort study/IgE-mediated CMA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">4 ± 2.63 months</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> = 119</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">More extensively hydrolyzed high grade hydrolysates (+EH/HGH), which are those in which >95% of peptides are of < 1,000 kDa, and less extensively hydrolyzed hydrolysates and soya milk formulas.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">To evaluate factors that could predict development of atopic march in children with IgE-mediated CMA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Multivariate analysis of risk factor, for the occurrence of AM revealed that EHCF use represented a protective factor for other allergic diseases compared to other hypoallergenic formulas or soy-based formula (OR 0.76; 95% CI: 0.149–0.945, <italic>p</italic> = 0.038).</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gil et al. (<xref rid=\"B72\" ref-type=\"bibr\">72</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective observational cohort study/only IgE-mediated CMA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mean age at diagnosis 5.07 ± 2.67 months <break/> Mean age at the end of follow up 14.41 ± 5.42 years</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> = 211</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Five groups, based on formula composition: vegetable-based formulas (rice or soy), high grade EHF in which >95% peptides were 1,000 kDa, high-grade EHF + LGG, low-grade EHF in which higher proportion of peptides > 1,000 kDa, or amino acid–based formulas.</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">To evaluate if a new scoring system could determine the risk of developing allergic march</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The risk of AM occurrence decreased in those treated with high grade EHF (OR 0.42; 95% CI 0.20–0.87, <italic>p</italic> = 0.02), and these results were stronger in patients treated with high-grade EHF + LGG (OR 0.30; 95% CI 0.09–0.98, <italic>p</italic> = 0.048).</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Guest and Fuller (<xref rid=\"B73\" ref-type=\"bibr\">73</xref>)</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Retrospective cohort study/IgE- and non IgE-mediated CMA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mean age <break/> I = 4.2 ± 2.7 months <break/> C = 5.4 ± 2.9 months</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><italic>N</italic> = 940 <break/>\n<italic>I</italic> = 470 <break/>\n<italic>C</italic> = 470</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">I = EHCF+LGG<break/> C = EHWF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">The occurrence of any allergic manifestations over a period of 24 months from the start of formula</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Binary logistic regression analysis showed that infants fed with EHWF had a significant higher relative risk at 24 months of atopic dermatitis (OR: 3.438; 95% CI: 1.975–5.985; <italic>p</italic> < 0.001) and asthma (OR: 2.651; 95% CI: 1.242–5.660; <italic>p</italic> < 0.02) compared with those fed with EHCF+LGG.</td></tr></tbody></table><table-wrap-foot><p><italic>RCT, randomized controlled trial; N, total number of subjects; I, intervention; C, control; EHF, extensively hydrolyzed formula; EHCF, extensively hydrolyzed casein formula; LGG, Lactobacillus rhamnosus GG; EHWF, extensively hydrolyzed whey formula; eHF, extensively hydrolyzed formula; IgE, immunoglobulin E; CMA, cow's milk allergy; AM, atopic march; ARD, absolute risk difference; CCA, complete case analysis; SA-EQS, sensitivity analysis—equal worst outcome scenario; SA-BCS, sensitivity analysis—best case scenario; SA-WCS, sensitivity analysis-worst case scenario; NNT, Number needed to treat; 95% CI, 95% confidence interval; OR, odds ratio</italic>.</p></table-wrap-foot></table-wrap></sec><sec id=\"s4\"><title>Potential Mechanisms of Action of Infant Formulas</title><p>It has been suggested that selected milk protein hydrolysates used for CMA management may be able to not only avoid allergic symptoms in CMA infants due to the breakdown of IgE antigens but also play a role in immune system modulation, inducing tolerance and preventing allergic sensitization (<xref rid=\"B75\" ref-type=\"bibr\">75</xref>–<xref rid=\"B79\" ref-type=\"bibr\">79</xref>). These peptides are able to interact with TLR2 and TLR4, modulating cytokine release by epithelial and immune cells (<xref rid=\"B80\" ref-type=\"bibr\">80</xref>). It has also been demonstrated that specific peptides from casein hydrolysates, driving T cell switching from Th2 to Th1 or to Tregs subtype, could exert a protective effect for FA (<xref rid=\"B77\" ref-type=\"bibr\">77</xref>, <xref rid=\"B81\" ref-type=\"bibr\">81</xref>). Animal studies have demonstrated that these peptides can suppress Th2 response through an IL-10 up regulation and IL-2 down-regulation (<xref rid=\"B75\" ref-type=\"bibr\">75</xref>). Moreover, the production of the tolerogenic cytokine IL-10 was higher in Jurkat T cells that underwent a casein hydrolysate stimulus (<xref rid=\"B79\" ref-type=\"bibr\">79</xref>). Preliminary data by our group suggest that formula choice is able to induce immune system modulation through epigenetic mechanisms in CMA infants (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>, <xref rid=\"B82\" ref-type=\"bibr\">82</xref>, <xref rid=\"B83\" ref-type=\"bibr\">83</xref>); specifically, evidence suggests that EHCF+LGG is able to modulate GM, raising the abundance of selected genera (<italic>Roseburia, Coprococcus</italic>, and <italic>Blautia</italic>) with increased production of butyrate (<xref rid=\"B16\" ref-type=\"bibr\">16</xref>). A significant difference in DNA methylation of Th2 and Th1 cytokine (IL-4, IL-5, IL-10, and IFN-γ) genes and of FoxP3, the transcription factor that modulates the fate of Tregs, was observed in infants treated with EHCF+LGG who develop immune tolerance compared to children who received other formulas (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>, <xref rid=\"B83\" ref-type=\"bibr\">83</xref>). A DNA methylation status of all allergy-related genes in infants treated with EHCF+LGG was closer to that observed in healthy children. Analyzing the potential factors able to modulate DNA methylation status in tolerant children, the authors found that the variable that greatly influenced the DNA methylation status was EHCF+LGG formula use (<xref rid=\"B82\" ref-type=\"bibr\">82</xref>, <xref rid=\"B83\" ref-type=\"bibr\">83</xref>). A longitudinal study, the EPICMA trial, compared the DNA methylation of FoxP3, Th1/Th2 cytokine genes, and allergy-related microRNAs (miRNAs) profile in IgE-mediated CMA infants taking EHCF+LGG compared to soy formula. This study demonstrated that treatment with EHCF+LGG is characterized by a more pronounced effect on FoxP3 demethylation compared to soy formula and by a higher methylation status of IL-4 and IL-5 and a lower methylation status of IL-10 and IFN-γ (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). Moreover, children treated with EHCF+LGG showed a selected miRNA expression toward a Th1-oriented response, leading to the activation of immune tolerance mechanisms (<xref rid=\"B17\" ref-type=\"bibr\">17</xref>). However, the impact of diet on epigenetic mechanisms may not only be direct but also mediated by the GM (<xref rid=\"B84\" ref-type=\"bibr\">84</xref>). So, the Diet-GM-Epigenetic axis creates a coherent picture that may be useful for developing potential strategies against AM in CMA children (<xref ref-type=\"fig\" rid=\"F2\">Figure 2</xref>). Altogether, these data highlight the relevance of “immune-nutrition management” able to reduce disease duration and to protect against the occurrence of other atopic manifestations the CMA children.</p><fig id=\"F2\" position=\"float\"><label>Figure 2</label><caption><p>Active diet therapy in pediatric patients with cow's milk allergy. “Active diet therapy” means the possibility to influence the cow's milk allergy (CMA) disease course and to limit the occurrence of other atopic manifestations later in the life. Emerging evidence suggests the importance of formula choice for the management of CMA pediatric patients. It has been demonstrated that the use of extensively hydrolyzed casein formula (EHCF) containing the probiotic <italic>L. rhamnosus</italic> GG (LGG) could exert a modulation of immune tolerance network mediated by the activity of selected casein hydrolysis-derived peptides and by activity of LGG on gut microbiota structure and function leading to an increased production of the short chain fatty acid butyrate. Several non-immune (gut barrier integrity) and immune (cytokines, immune cells) tolerogenic factors are involved in such modulatory action. Many effects are mediated by epigenetic mechanisms. Altogether these mechanisms are able to stimulate a faster acquisition of immune tolerance to cow's milk peptides and to limit the occurrence of atopic march.</p></caption><graphic xlink:href=\"fped-08-00440-g0002\"/></fig></sec><sec sec-type=\"conclusions\" id=\"s5\"><title>Conclusions</title><p>During the last years, much has changed about AM knowledge. The actual strategies to halt the AM are depicted in <xref ref-type=\"fig\" rid=\"F3\">Figure 3</xref>. Despite the lack of cure, novelties about CMA dietary management are moving from “passive” elimination diet to an “active diet-therapy” able to reduce disease duration and to protect against the occurrence of AM. The latter strategy is supported by better knowledge on the role of diet, breastfeeding, gut microbiome, and tolerogenic mechanisms. Thus, an active diet-therapy able to modulate the GM composition, restoring microbial equilibrium and optimal butyrate production, is a positive example of the potential of such strategy. The best nutritional choice for CMA infants is breastfeeding, but recent evidence suggests that breast milk composition could be influenced by environmental factors including maternal diet that could represent relevant target of intervention for preventive strategy against AM in CMA infants. If breastfeeding is not possible, evidence suggests that casein hydrolysate-based infant formula with the adjunction of the probiotic LGG could be able to stimulate immune tolerance acquisition and to reduce the incidence of AM in children with CMA.</p><fig id=\"F3\" position=\"float\"><label>Figure 3</label><caption><p>Halting the Atopic March. Several strategies are available to counteract step by step the atopic march. These strategies are targeting the skin, gut, and respiratory tract barrier.</p></caption><graphic xlink:href=\"fped-08-00440-g0003\"/></fig></sec><sec id=\"s6\"><title>Author Contributions</title><p>RB designed and structured the review, wrote, and read the manuscript. LC and RN analyzed literature, wrote, and read the manuscript. LP and CD analyzed literature and read the manuscript. All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.</p></sec><sec id=\"s7\"><title>Conflict of Interest</title><p>The Department of Translational Medical Science received research grants from Danone, Kraft Heinz, Humana, Mead Johnson Nutrition, Nestlè, and United Pharmaceutical. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">Front Neurosci</journal-id><journal-id journal-id-type=\"iso-abbrev\">Front Neurosci</journal-id><journal-id journal-id-type=\"publisher-id\">Front. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Neuroscience</journal-title></journal-title-group><issn pub-type=\"ppub\">1662-4548</issn><issn pub-type=\"epub\">1662-453X</issn><publisher><publisher-name>Frontiers Media S.A.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32848554</article-id><article-id pub-id-type=\"pmc\">PMC7431923</article-id><article-id pub-id-type=\"doi\">10.3389/fnins.2020.00774</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Neuroscience</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Lasting and Sex-Dependent Impact of Maternal Immune Activation on Molecular Pathways of the Amygdala</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Keever</surname><given-names>Marissa R.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Zhang</surname><given-names>Pan</given-names></name><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><uri xlink:type=\"simple\" xlink:href=\"http://loop.frontiersin.org/people/704849/overview\"/></contrib><contrib contrib-type=\"author\"><name><surname>Bolt</surname><given-names>Courtni R.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Antonson</surname><given-names>Adrienne M.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Rymut</surname><given-names>Haley E.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Caputo</surname><given-names>Megan P.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Houser</surname><given-names>Alexandra K.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Hernandez</surname><given-names>Alvaro G.</given-names></name><xref ref-type=\"aff\" rid=\"aff3\"><sup>3</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Southey</surname><given-names>Bruce R.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Rund</surname><given-names>Laurie A.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Johnson</surname><given-names>Rodney W.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref></contrib><contrib contrib-type=\"author\"><name><surname>Rodriguez-Zas</surname><given-names>Sandra L.</given-names></name><xref ref-type=\"aff\" rid=\"aff1\"><sup>1</sup></xref><xref ref-type=\"aff\" rid=\"aff2\"><sup>2</sup></xref><xref ref-type=\"aff\" rid=\"aff4\"><sup>4</sup></xref><xref ref-type=\"aff\" rid=\"aff5\"><sup>5</sup></xref><xref ref-type=\"aff\" rid=\"aff6\"><sup>6</sup></xref><xref ref-type=\"corresp\" rid=\"c001\"><sup>*</sup></xref></contrib></contrib-group><aff id=\"aff1\"><sup>1</sup><institution>Department of Animal Sciences, University of Illinois at Urbana-Champaign</institution>, <addr-line>Urbana, IL</addr-line>, <country>United States</country></aff><aff id=\"aff2\"><sup>2</sup><institution>Illinois Informatics Institute, University of Illinois at Urbana-Champaign</institution>, <addr-line>Urbana, IL</addr-line>, <country>United States</country></aff><aff id=\"aff3\"><sup>3</sup><institution>High-throughput Sequencing and Genotyping Unit, Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign</institution>, <addr-line>Urbana, IL</addr-line>, <country>United States</country></aff><aff id=\"aff4\"><sup>4</sup><institution>Neuroscience Program, University of Illinois at Urbana-Champaign</institution>, <addr-line>Urbana, IL</addr-line>, <country>United States</country></aff><aff id=\"aff5\"><sup>5</sup><institution>Department of Statistics, University of Illinois at Urbana-Champaign</institution>, <addr-line>Urbana, IL</addr-line>, <country>United States</country></aff><aff id=\"aff6\"><sup>6</sup><institution>Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign</institution>, <addr-line>Urbana, IL</addr-line>, <country>United States</country></aff><author-notes><fn fn-type=\"edited-by\"><p>Edited by: Noèlia Fernàndez-Castillo, Centre for Biomedical Network Research (CIBER), Spain</p></fn><fn fn-type=\"edited-by\"><p>Reviewed by: Silvia Pellegrini, University of Pisa, Italy; Tewarit Sarachana, Chulalongkorn University, Thailand</p></fn><corresp id=\"c001\">*Correspondence: Sandra L. Rodriguez-Zas, <email>rodrgzzs@illinois.edu</email></corresp><fn fn-type=\"other\" id=\"fn004\"><p>This article was submitted to Neurogenomics, a section of the journal Frontiers in Neuroscience</p></fn></author-notes><pub-date pub-type=\"epub\"><day>11</day><month>8</month><year>2020</year></pub-date><pub-date pub-type=\"collection\"><year>2020</year></pub-date><volume>14</volume><elocation-id>774</elocation-id><history><date date-type=\"received\"><day>01</day><month>5</month><year>2020</year></date><date date-type=\"accepted\"><day>01</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>Copyright © 2020 Keever, Zhang, Bolt, Antonson, Rymut, Caputo, Houser, Hernandez, Southey, Rund, Johnson and Rodriguez-Zas.</copyright-statement><copyright-year>2020</copyright-year><copyright-holder>Keever, Zhang, Bolt, Antonson, Rymut, Caputo, Houser, Hernandez, Southey, Rund, Johnson and Rodriguez-Zas</copyright-holder><license xlink:href=\"http://creativecommons.org/licenses/by/4.0/\"><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p></license></permissions><abstract><p>The prolonged and sex-dependent impact of maternal immune activation (MIA) during gestation on the molecular pathways of the amygdala, a brain region that influences social, emotional, and other behaviors, is only partially understood. To address this gap, we investigated the effects of viral-elicited MIA during gestation on the amygdala transcriptome of pigs, a species of high molecular and developmental homology to humans. Gene expression levels were measured using RNA-Seq on the amygdala for 3-week-old female and male offspring from MIA and control groups. Among the 403 genes that exhibited significant MIA effect, a prevalence of differentially expressed genes annotated to the neuroactive ligand–receptor pathway, glutamatergic functions, neuropeptide systems, and cilium morphogenesis were uncovered. Genes in these categories included corticotropin-releasing hormone receptor 2, glutamate metabotropic receptor 4, glycoprotein hormones, alpha polypeptide, parathyroid hormone 1 receptor, vasointestinal peptide receptor 2, neurotensin, proenkephalin, and gastrin-releasing peptide. These categories and genes have been associated with the MIA-related human neurodevelopmental disorders, including schizophrenia and autism spectrum disorders. Gene network reconstruction highlighted differential vulnerability to MIA effects between sexes. Our results advance the understanding necessary for the development of multifactorial therapies targeting immune modulation and neurochemical dysfunction that can ameliorate the effects of MIA on offspring behavior later in life.</p></abstract><kwd-group><kwd>immune activation</kwd><kwd>pigs</kwd><kwd>RNA-seq</kwd><kwd>neuropeptides</kwd><kwd>glutamatergic pathway</kwd><kwd>GABAergic pathway</kwd></kwd-group><counts><fig-count count=\"2\"/><table-count count=\"10\"/><equation-count count=\"0\"/><ref-count count=\"124\"/><page-count count=\"19\"/><word-count count=\"0\"/></counts></article-meta></front><body><sec id=\"S1\"><title>Introduction</title><p>The maternal immune response triggered by pathogens and other environmental stressors during gestation can also elicit an indirect response by the fetal immune cells (<xref rid=\"B59\" ref-type=\"bibr\">Kroismayr et al., 2004</xref>; <xref rid=\"B84\" ref-type=\"bibr\">Odorizzi and Feeney, 2016</xref>; <xref rid=\"B95\" ref-type=\"bibr\">Prins et al., 2018</xref>). Viral infection during gestation, for example, activates a cytokine-related signaling cascade, and molecules from this process can cross the placenta and reach the fetal brain. The resulting maternal immune activation (MIA) can impact fetal developmental processes and exert long-term postnatal effects in the offspring (<xref rid=\"B102\" ref-type=\"bibr\">Rutherford et al., 2014</xref>). The relationship between MIA and neurodevelopmental disorders, including schizophrenia spectrum disorders (SSD) and autism spectrum disorders (ASD), and neurodegenerative disorders, such as Alzheimer’s disease (AD), in offspring has been established (<xref rid=\"B58\" ref-type=\"bibr\">Knuesel et al., 2014</xref>; <xref rid=\"B21\" ref-type=\"bibr\">Canetta et al., 2016</xref>; <xref rid=\"B74\" ref-type=\"bibr\">Mattei et al., 2017</xref>). These diseases share some behavior symptoms, comorbidities such as eating disorders, and genetic and environmental (i.e., MIA) agents (<xref rid=\"B22\" ref-type=\"bibr\">Canitano and Pallagrosi, 2017</xref>). The previous neurological disorders have been associated with abnormal structure and dysregulation of the amygdala (<xref rid=\"B105\" ref-type=\"bibr\">Schumann et al., 2011</xref>; <xref rid=\"B32\" ref-type=\"bibr\">Fernandez-Irigoyen et al., 2014</xref>) and share genes and molecular mechanisms including histocompatibility complex (MHC) genes (<xref rid=\"B4\" ref-type=\"bibr\">Anders and Kinney, 2015</xref>), glutamatergic and GABAergic-associated genes (<xref rid=\"B15\" ref-type=\"bibr\">Bourgeron, 2009</xref>; <xref rid=\"B70\" ref-type=\"bibr\">Marin, 2012</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Li et al., 2016</xref>), and mitochondrial activity processes (<xref rid=\"B92\" ref-type=\"bibr\">Pieczenik and Neustadt, 2007</xref>; <xref rid=\"B109\" ref-type=\"bibr\">Sragovich et al., 2017</xref>).</p><p>The fetal amygdala is susceptible to inflammatory signals, and the plasticity of this brain structure to MIA can lead to alterations of the developmental trajectory. These disruptions may have long-lasting and maladaptive consequences for the offspring, due to the significant role that the amygdala plays in many neurological pathways. Located in the forebrain, the amygdala influences social interaction, cognition, neuroendocrine, behavior, learning, memory, emotion, and autonomic systems. The amygdala also modulates the response of these processes to stressors, including pathogenic infections and those resulting from management practices, such as weaning (<xref rid=\"B116\" ref-type=\"bibr\">Tian et al., 2015</xref>). The amygdala experiences high uptake of gonadal hormones and is anatomically connected to other sexually dimorphic nuclei. Therefore, this brain region is involved in regulation of several dimorphic functions such as aggression, sexual behavior, gonadotropin secretion, and integration of olfactory information (<xref rid=\"B48\" ref-type=\"bibr\">Hines et al., 1992</xref>). Evidence supports the differential activation of the amygdala to stimuli between males and females (<xref rid=\"B56\" ref-type=\"bibr\">Killgore and Yurgelun-Todd, 2001</xref>), including differences in the sexual responses and emotional memory (<xref rid=\"B46\" ref-type=\"bibr\">Hamann, 2005</xref>), and differential vulnerability to insult (<xref rid=\"B9\" ref-type=\"bibr\">Baird et al., 2007</xref>). Due to the interconnected and multi-regulatory nature of this brain structure, insults to the amygdala can impact the individual’s social, locomotor, and feeding behavior (<xref rid=\"B91\" ref-type=\"bibr\">Petrovich and Gallagher, 2003</xref>); growth and reproductive physiology; health status; and immunological response to secondary stressors.</p><p>Recent studies lend support to the link between MIA and altered amygdala function (<xref rid=\"B24\" ref-type=\"bibr\">Carlezon et al., 2019</xref>). In mice, MIA elicited by polyinosinic:polycytidylic acid [Poly(I:C)] increased the synaptic strength of glutamatergic projections from the prefrontal cortex to the amygdala (<xref rid=\"B63\" ref-type=\"bibr\">Li et al., 2018</xref>). In open-field tests, mice exposed to MIA spent less time in the center and traveled a higher distance, indicative of a higher anxiety behavior incidence than the control counterparts. These findings suggest that the change in the balance between excitation (glutamatergic) and inhibition (feedforward GABAergic) modified the spike output of amygdala neurons, therefore affecting brain circuits that could regulate behavior in SSD and ASD. A candidate gene study of the effects of social stress during gestation reported that the expression of a corticotropin-releasing hormone receptor in the amygdala of 10-week-old pigs was higher in females than in males (<xref rid=\"B102\" ref-type=\"bibr\">Rutherford et al., 2014</xref>). This study concluded that prenatal stress substantially increased anxiety-related behaviors in female pigs. Studies of the impact of maternal stressors during gestation on specific amygdala molecular profiles and associated neurological or behavioral disorders in the offspring later in life highlight the complexity of the molecular mechanisms underlying the pathophysiology of MIA.</p><p>Research on the lasting effects of MIA in pigs complements the insights offered by rodent models (<xref rid=\"B7\" ref-type=\"bibr\">Antonson et al., 2019</xref>). The advantages of studying a pig model stem from the greater homology of humans to pigs, rather than to rodents, when considering organ physiology, size, development and, in particular, brain growth and development processes (<xref rid=\"B83\" ref-type=\"bibr\">Murphy et al., 2014</xref>). A pig model that has offered insights into MIA employs porcine reproductive and respiratory syndrome virus (PRRSV) to elicit MIA. This immune challenge activates the microglia (i.e., macrophage-like cells in the brain) and is associated with behavioral changes in neonatal pigs (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>, <xref rid=\"B6\" ref-type=\"bibr\">2018</xref>).</p><p>The study of MIA elicited by PRRSV allows for the characterization of the impact of a live viral pathogen that self-replicates in the host, evoking extended activation of immune pathways. PRRSV challenge during gestation is a well-characterized, replicable, and effective method for inducing MIA in pigs (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>, <xref rid=\"B6\" ref-type=\"bibr\">2018</xref>). In addition, PRRSV outbreaks impose a major economic burden to the livestock industry. PRRSV is an enveloped single-stranded RNA virus that infects alveolar macrophages, causing interstitial pneumonia and increased serum levels of the cytokines interleukin 1 beta, interleukin 6, and tumor necrosis factor alpha (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>). The persistent repercussions of MIA on the molecular pathways of the pig amygdala are yet to be investigated. Moreover, the potentially distinct vulnerability to the prolonged effects of MIA between sexes remains unknown.</p><p>The overarching goal of the present study is to advance the understanding of the impact of MIA on the molecular mechanisms of the amygdala. Three supporting objectives are explored: (a) characterization of prolonged transcriptome changes elicited by viral MIA in pigs, a species that has high neurodevelopmental homology with humans, and food production value; (b) identification of molecular pathways that present differential vulnerability to MIA between sexes; and (c) understanding the effect of MIA on molecular interactions assisted by gene network inference. The findings from these complementary analyses support the use of multiple therapeutic targets to ameliorate the potential detrimental effect of MIA on the offspring physiology and behavior.</p></sec><sec sec-type=\"materials|methods\" id=\"S2\"><title>Materials and Methods</title><sec id=\"S2.SS1\"><title>Animal Experiments</title><p>All experimental procedures used published protocols (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>, <xref rid=\"B6\" ref-type=\"bibr\">2018</xref>). The animal studies were approved by the Illinois Institutional Animal Care and Use Committee (IACUC) at the University of Illinois and are in compliance with the USDA Animal Welfare Act and the NIH Public Health Service Policy on the Humane Care and Use of Animals.</p><p>Camborough gilts born and raised at the University of Illinois at Urbana-Champaign herd were inseminated at 205 days of age using PIC 359 boar sperm (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>, <xref rid=\"B6\" ref-type=\"bibr\">2018</xref>). All gilts were PRRSV negative and were moved at gestation day (GD) 69 into disease-containment chambers maintained at 22°C and a 12 h light/dark cycle with lights on at 7:00 AM. The gilts were fed daily 2.3 kg of a gestational diet and had <italic>ad libitum</italic> water access. One week after acclimation, four gilts were intranasally inoculated with live PRRSV strain P129-BV (School of Veterinary Medicine at Purdue University, West Lafayette, IN, United States) using 5 mL of 1 × 10<sup>5</sup> median tissue culture infectious dose (TCID<sub>50</sub>) diluted in sterile Dulbecco’s modified Eagle medium (DMEM; 5 mL total volume). The four gilts in the Control group were intranasally inoculated with an equal volume of sterile DMEM. PRRSV inoculation corresponded to the last third of gestation in pigs and humans, during initiation of rapid fetal brain growth (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>, <xref rid=\"B6\" ref-type=\"bibr\">2018</xref>). PRRSV and Control groups were housed in separate containment chambers.</p><p>The rectal temperatures and diet consumption of the gilts were recorded daily until farrowing (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>, <xref rid=\"B6\" ref-type=\"bibr\">2018</xref>). The PRRSV-inoculated gilts were offered the maximum fed daily, and feed refusal was measured. The Control gilts were fed the same amount consumed by the PRRSV-inoculated gilts on the previous day. The daily body temperature and feed intake levels were compared using a mixed-effects model analyzed with PROC MIXED (SAS Institute Inc., Cary, NC, United States). The model included the effects of gilt treatment and replicate while accommodating for heterogeneity of variance between MIA groups.</p><p>Farrowing was induced with an intramuscular injection of 10 mg of Lutalyse (dinoprost tromethamine, Pfizer, New York, NY, United States) on GD 113 in consideration that the average gestation length is approximately 114 days (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>, <xref rid=\"B6\" ref-type=\"bibr\">2018</xref>). Gilts farrowed in individual farrowing crates of standard dimensions (1.83 × 1.83 m). After farrowing, the gilts were fed twice a day up to 5 kg of a nutritionally complete diet for the lactating period and water remained available <italic>ad libitum</italic>. Pigs received intramuscular injections of iron dextran (100 mg/pig, Butler Schein Animal Health, Dublin, OH, United States) and Excede for Swine (25 mg/pig; Zoetis, Parsippany, NJ, United States) to control for respiratory diseases. The pigs remained with their mothers until PD 22. The body weight of pigs was measured daily and analyzed using the mixed-effects model in SAS, PROC MIXED (SAS Institute Inc., Cary, NC, United States). The model included the effect of MIA and the random effect of gilt, accommodating for heteroscedasticity between pig treatment and sex groups. The impact of MIA was studied at PD 22 because this is a common age to wean pigs. The study of transcriptome profiles from older pigs could be confounded with changes in diet and environment associated with weaning, while profiles from younger pigs would hinder the assessment of the prolonged effects of MIA.</p></sec><sec id=\"S2.SS2\"><title>RNA Extraction and Sequencing</title><p>A balanced experimental design was studied, including 24 pigs evenly distributed between maternal PPRSV activated (MPA group of pigs) and Control gilts (CON group of pigs), each group encompassing males and females (denoted Ma and Fe, respectively). At PD 22, pigs were removed from the farrowing crate and anesthetized intramuscularly using a telazol:ketamine:xylazine drug cocktail (50 mg of tiletamine; 50 mg of zolazepam) reconstituted with 2.5 mL ketamine (100 g/L) and 2.5 mL xylazine (100 g/L; Fort Dodge Animal Health, Fort Dodge, IA, United States) at a dose of 0.03 mL/kg body weight, following protocols (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>).</p><p>Following anesthetization, pigs were euthanized using an intracardiac injection of sodium pentobarbital (86 mg/kg body weight, Fata Plus, Vortech Pharmaceuticals, Dearborn, MI, United States). Pig brains were extracted, the amygdalae were recognized using the stereotaxic atlas of the pig brain (<xref rid=\"B31\" ref-type=\"bibr\">Felix et al., 1999</xref>), dissected out, flash frozen on dry ice, and stored at −80°C following published protocols (<xref rid=\"B7\" ref-type=\"bibr\">Antonson et al., 2019</xref>). RNA was isolated using EZNA isolation kit following the manufacturer’s instructions (Omega Biotek, Norcross, GA, United States). The RNA integrity numbers of the samples were above 7.5, indicating low RNA degradation. The RNA-Seq libraries were prepared with TruSeq Stranded mRNAseq Sample Prep kit (Illumina Inc., San Diego, CA, United States). The libraries were quantitated by qPCR and sequenced on one lane on a NovaSeq 6000 for 151 cycles from each end of the fragments using NovaSeq S4 reagent kit. FASTQ files were generated and demultiplexed with the bcl2fastq v2.20 conversion software. Paired-end reads (150 nt long) were obtained, and the FASTQ files are available in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database (experiment accession number <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"GSE149695\">GSE149695</ext-link>).</p></sec><sec id=\"S2.SS3\"><title>RNA Sequence Mapping and Differential Expression Analysis</title><p>The average Phred quality score of the reads assessed using FastQC (<xref rid=\"B5\" ref-type=\"bibr\">Andrews, 2010</xref>) was > 35 across all read positions, and therefore, no reads were trimmed. The paired-end reads from the individual samples were aligned to the <italic>Sus scrofa</italic> genome (version Sscrofa 11.1; <xref rid=\"B96\" ref-type=\"bibr\">Pruitt et al., 2007</xref>) using kallisto v0.43.0 (<xref rid=\"B16\" ref-type=\"bibr\">Bray et al., 2016</xref>) with default settings. The normalized (trimmed mean of <italic>M</italic>-values) gene expression values were described using a generalized linear model encompassing the effects of the MIA group (MPA or CON levels), sex (Fe or Ma levels), and MIA-by-sex interaction and analyzed using edgeR (version 3.14.0) in the R v. 3.3.1 environment (<xref rid=\"B100\" ref-type=\"bibr\">Robinson et al., 2010</xref>). Genes supported by > 5 transcripts per million (TPM) by each MIA–sex combination were analyzed to ensure adequate representation across comparisons.</p><p>Orthogonal pairwise contrasts between MIA and sex groups were evaluated in addition to testing for the effects of MIA-by-sex interaction and main effects of MIA and sex. The four groups compared in the contrasts, identified by treatment followed by the sex levels, are: MPA_Fe, MPA_Ma, CON_Fe, and CON_Ma. The <italic>P</italic>-values were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate (FDR) approach (<xref rid=\"B13\" ref-type=\"bibr\">Benjamini and Hochberg, 1995</xref>).</p></sec><sec id=\"S2.SS4\"><title>Functional Enrichment and Network Inference</title><p>Two complementary approaches were used to identify over-represented functional categories among the genes exhibiting differential expression across MIA and sex groups (<xref rid=\"B19\" ref-type=\"bibr\">Caetano-Anollés et al., 2015</xref>, <xref rid=\"B20\" ref-type=\"bibr\">2016</xref>; <xref rid=\"B36\" ref-type=\"bibr\">Gonzalez-Pena et al., 2016a</xref>, <xref rid=\"B37\" ref-type=\"bibr\">b</xref>). Functional categories investigated included Gene Ontology (GO) biological processes (BPs), GO molecular functions (MF), and KEGG pathways. The Gene Set Enrichment Analysis (GSEA) approach implemented in the software package GSEA-P 2.0 (<xref rid=\"B113\" ref-type=\"bibr\">Subramanian et al., 2007</xref>) was used to identify category over-representation with gene over- and under-expressed while considering all genes analyzed. The normalized enrichment score (NES) of the categories in the Molecular Signature Database (MSigDB) was calculated using the maximum deviation of the cumulative sum based on the signed and standardized fold change. The statistical significance of the enrichment was assessed using the FDR-adjusted <italic>P</italic>-value computed from 1000 permutations.</p><p>The over-representation of functional categories was also evaluated among genes that exhibited a significant MIA-by-sex interaction or main effect using the Database for Annotation, Visualization and Integrated Discovery (DAVID 6.8) (<xref rid=\"B50\" ref-type=\"bibr\">Huang et al., 2009</xref>). The enrichment of Direct GO categories in the DAVID database was assessed. The <italic>Sus scrofa</italic> genome was used as the background for enrichment testing, and enrichment is reported using the Expression Analysis Systematic Explorer (EASE) score that was computed using a one-tailed jackknifed Fisher hypergeometric exact test. Functional categories were clustered based on gene annotation, and the statistical significance of clusters is summarized as the geometric mean of the -log<sub>10</sub> EASE scores of the categories (<xref rid=\"B28\" ref-type=\"bibr\">Delfino et al., 2011</xref>; <xref rid=\"B106\" ref-type=\"bibr\">Serao et al., 2011</xref>; <xref rid=\"B27\" ref-type=\"bibr\">Delfino and Rodriguez-Zas, 2013</xref>).</p></sec><sec id=\"S2.SS5\"><title>Weighted Gene Co-expression Network Analysis and Gene Network Visualization</title><p>An approach complementary to the identification of differentially expressed genes was used to uncover co-expression networks using Weighted Gene Co-expression Network Analysis (WGCNA) version 1.68 (<xref rid=\"B60\" ref-type=\"bibr\">Langfelder and Horvath, 2008</xref>). The input data were voom-transformed read count values generated using the limma package (version 3.40.2) (<xref rid=\"B99\" ref-type=\"bibr\">Ritchie et al., 2015</xref>) in R (version 3.6.1). Genes were filtered to remove those with low expression levels or no variation across samples per developer recommendations. The number of genes used for network analysis was 16,175 genes. Considering potential for interaction patterns, a sex-dependent soft-thresholding power was used to call for network topology analysis. The lowest power values that support a scale-free topology power used were 15 for the CON_Ma-MPA_Ma contrast and 27 for the MPA_Fe-MPA_Ma contrast. The Pearson correlation coefficient of the normalized expression values was used to identify modules of connected genes. The minimum module size was set to 30, with the deepSplit set to 2, and the mergeCutHeight set to 0.15. Module profiles were identified using the correlation between the eigengene of each module and pig group. Enrichment of functional categories among the genes in each module profile was explored with DAVID using the <italic>Sus scrofa</italic> genome as background, and testing included an FDR multiple test adjustment.</p><p>Further understanding of the impact of the MIA-by-sex interaction was gained through the reconstruction of gene networks using the BisoGenet package (<xref rid=\"B71\" ref-type=\"bibr\">Martin et al., 2010</xref>) in the Cytoscape platform (<xref rid=\"B107\" ref-type=\"bibr\">Shannon et al., 2003</xref>). Information from gene and protein interactions annotated in databases including BIOGRID, HPRD, DIP, BIND, INTACT, and MINT was used to visualize relationships between genes (<xref rid=\"B103\" ref-type=\"bibr\">Salwinski et al., 2004</xref>; <xref rid=\"B2\" ref-type=\"bibr\">Alfarano et al., 2005</xref>; <xref rid=\"B78\" ref-type=\"bibr\">Mishra et al., 2006</xref>; <xref rid=\"B110\" ref-type=\"bibr\">Stark et al., 2006</xref>; <xref rid=\"B55\" ref-type=\"bibr\">Kerrien et al., 2007</xref>; <xref rid=\"B65\" ref-type=\"bibr\">Licata et al., 2012</xref>). Networks highlighting differences in gene levels associated with MIA within sex (i.e., the contrasts MPA_Ma-CON_Ma and MPA_Fe-CON_Fe) were compared. The network framework includes genes that exhibited a significant MIA-by-sex interaction effect (FDR-adjusted <italic>P</italic> < 0.1) and are annotated to enriched functional categories. The framework genes were identified by full nodes with size reflecting the differential expression level between the MPA and CON groups. The network edges depict known molecular relationships curated in the BisoGenet databases. The framework genes were connected through correlated genes listed in the BisoGenet database of molecular interactions that did not reach significant MIA-by-sex interaction effect. The comparison of these networks offered insights into the simultaneous effect of MIA across interacting genes and enabled the detection of shared and distinct co-regulation patterns between MPA and CON pigs across sexes.</p></sec></sec><sec id=\"S3\"><title>Results</title><sec id=\"S3.SS1\"><title>Maternal Immune Activation and Sequencing Metrics</title><p>The differences between MPA and CON gilts in rectal temperatures and daily diet consumption indicated the activation of the maternal immune system in response to PRRSV. The difference in body temperature between CON and MPA gilts on GD 87 was -1.00°C (standard error 0.35°C; <italic>P</italic> < 0.005). The difference in feed refusal between CON and MPA gilts on GD 88 was -927.6 g (standard error 201.2 g; <italic>P</italic> < 0.0001). A significant increase in rectal temperatures and decrease in feed intake (<italic>P</italic> < 0.001) was observed within 48 h of inoculation and returned to baseline levels within 10 days for body temperature and within 14 days for feed intake. At 21 days of age, CON pigs were 1.20 kg heavier than MPA pigs (standard error = 0.5673; <italic>P</italic> < 0.089) while no significant sex or interaction effects were detected.</p><p>The sequencing of the 24 RNA samples produced 6.6 billion sequenced reads, and 69 million paired-end reads per sample. The number of reads was consistent across MIA and sex groups (coefficient of variation < 0.1), and the effects of MIA, sex, and MIA-by-sex interaction were tested on 16,175 genes that surpassed the minimum number of reads per MIA–sex combination.</p></sec><sec id=\"S3.SS2\"><title>Transcriptome Changes Associated With Maternal Immune Activation That Are Sex-Dependent</title><p>Overall, 328 genes exhibited a significant (FDR-adjusted <italic>P</italic> < 0.1) MIA-by-sex interaction effect, and among these, 273 genes had a significant effect at FDR-adjusted <italic>P</italic> < 0.05. The profile of these genes indicated that the effect of MIA differed between females and males. Forty-six genes that presented a MIA-by-sex interaction effect are listed in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref> together with their expression pattern and <italic>P</italic>-value. The majority of the genes in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>, including neurotensin (NTS), displayed a reversal in the expression level between CON and MPA groups across sexes (i.e., opposite Log<sub>2</sub>[fold change] sign across sexes). An extended list including 328 genes that exhibited a MIA-by-sex interaction effect at FDR-adjusted <italic>P</italic> < 0.1 is provided in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table A</xref>.</p><table-wrap id=\"T1\" position=\"float\"><label>TABLE 1</label><caption><p>Genes exhibiting significant (FDR-adjusted P-value < 0.1) maternal immune activation-by-sex interaction effect.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene symbol</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>CON Fe-CON Ma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">MPA Fe-MPA Ma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CON Fe-MPA Fe</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CON Ma-MPA Ma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CON Fe-MPA Ma</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">CON Ma-MPA Fe</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RGS16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><5E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–3.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–2.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.81</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CGA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><5E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–5.86</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.49</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.13</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">POMC</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><5E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–2.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.86</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GPX3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><5E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.63</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.82</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RELN</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><5E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.66</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.57</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">VIPR2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><5E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.14</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ANKRD34C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><5E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.87</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.71</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.93</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GBP1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><5E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.66</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GRM4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><5E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.97</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.91</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.70</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CCDC136</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.3E-09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.24</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SLC17A6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.1E-08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.54</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.23</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BTBD11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.4E-08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.62</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.45</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.67</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.22</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TTR</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.0E-08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.00</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.53</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.76</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CACNA2D3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.8E-07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.50</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.23</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CRHR2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.8E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.97</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NDNF</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.8E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.70</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.61</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CXCL12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.2E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.41</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.30</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">USP43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.4E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.64</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CCDC17</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.1E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.30</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.26</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.40</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KCNIP4</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.2E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.35</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CAMK2N2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.6E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ALDH1A2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.4E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.35</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.50</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.98</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.83</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GRP</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.5E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.61</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.28</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PENK</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.6E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SYT12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.9E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PTH1R</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.7E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.52</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.18</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HBB</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.7E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.70</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ESYT1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.5E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.49</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.20</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">EFHD1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.6E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.06</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BHLHE22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.59</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.61</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.55</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ZFP37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.2E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.60</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.19</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.41</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.78</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SLC2A2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.5E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.50</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.12</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">THRSP</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.1E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.51</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.52</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NR4A3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.3E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LOC396781</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.4E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.75</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–5.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–3.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–4.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–4.63</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">C1QTNF1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.5E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.32</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.46</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.14</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RAB27A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.97</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.77</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.20</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.72</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NTS</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.9E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.81</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.78</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.47</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.35</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GVIN1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.9E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.41</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–2.22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.42</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.81</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SSTR1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.6E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.22</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CCDC9B</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.8E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.35</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.25</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.10</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CCDC33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.4E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.49</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CCDC162P</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.4E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.48</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.23</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.12</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.25</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PTH</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.4E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.29</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.39</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.65</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SYNPO2L</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.5E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.59</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.44</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.43</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.16</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CHGB</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.58</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.16</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.21</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.96</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>Log<sub>2</sub>[fold change] between two maternal immune activation-sex groups: MPA, PRRSV-induced maternal immune activation; CON, control; Fe, females; Ma, males.</italic></attrib></table-wrap-foot></table-wrap><p>Another frequent pattern among the genes that displayed a MIA-by-sex interaction effect was characterized by a consistent expression profile between CON and MPA across sexes, albeit the magnitude differed between sexes (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). For example, glycoprotein hormones, alpha polypeptide (CGA) was over-expressed in CON relative to MPA, but the differential was higher in males than in females. Other genes presenting this pattern included guanylate-binding protein 1 (GBP1), transthyretin (TTR), aldehyde dehydrogenase 1 family member A2 (ALDH1A2), hemoglobin subunit beta (HBB), and basic helix-loop-helix family member e22 (BHLHE22).</p><p>Notable is the significant MIA-by-sex interaction effect on genes associated with neuropeptides and hormones, and genes that participate in glutamatergic processes. Genes under-expressed in MPA relative to CON males while presenting the opposite pattern in females (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) included NTS, the neuropeptide gene proenkephalin (PENK), the neuropeptide gene gastrin-releasing peptide (GRP), the neuropeptide-related gene vasoactive intestinal peptide receptor 2 (VIPR2), corticotropin releasing hormone receptor 2 (CRHR2), neuron-derived neurotrophic factor (NDNF), reelin (RELN), glutamate metabotropic receptor 4 (GRM4), solute carrier family 17 member 6 (SLC17A6), calcium voltage-gated channel auxiliary subunit alpha 2 delta 3 (CACNA2D3), EF-hand domain family member D1 (EFHD1), glutathione peroxidase 3 (GPX3), parathyroid hormone 1 receptor (PTH1R), thyroid hormone responsive (THRSP), and CGA. The CGA gene codes for the alpha subunit protein of the hormones chorionic gonadotropin (CG), luteinizing hormone (LH), follicle-stimulating hormone (FSH), and thyroid-stimulating hormone (TSH).</p></sec><sec id=\"S3.SS3\"><title>Functional and Network Analysis of Genes That Exhibit Sex-Dependent Associations With Maternal Immune Activation</title><p>The genes expressing significant MIA-by-sex interaction effects were analyzed for functional enrichment. <xref rid=\"T2\" ref-type=\"table\">Table 2</xref> presents the clusters of most enriched and informative categories from the DAVID analysis, and the complete list of categories is in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table B</xref>. The categories in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref> encompass genes presenting the most frequent interaction profile characterized by under-expression in CON females relative to males but over-expression in MPA females relative to males. These genes include KEGG Autoimmune thyroid disease (Cluster 1) and BP brain development (GO:0007420) (Cluster 4).</p><table-wrap id=\"T2\" position=\"float\"><label>TABLE 2</label><caption><p>Most enriched DAVID clusters and supporting functional categories (enrichment score ES > 1.3) among the genes presenting significant maternal immune activation-by-sex interaction effect.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>Category</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Category identifier and name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>b</sup>FDR <italic>P</italic>-value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 1</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 2.74</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc05320:Autoimmune thyroid disease</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.90E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.50E-04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:000250∼Antigen processing and presentation of peptide or polysaccharide antigen via MHC class II</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.90E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.40E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc04514:Cell-adhesion molecules (CAMs)</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.30E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.20E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc05323:Rheumatoid arthritis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.80E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.60E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc05164:Influenza A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.00E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.70E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 2</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.9</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0051050∼Positive regulation of transport</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.40E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.50E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0051049∼Regulation of transport</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.50E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.20E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0050801∼Ion homeostasis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.30E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.60E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0048878∼Chemical homeostasis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.80E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.80E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0030001∼Metal ion transport</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.50E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.70E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 3</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.75</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0048871∼Multicellular organismal homeostasis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.00E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.70E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 4</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.69</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0061564∼Axon development</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.40E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.70E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0007420∼Brain development</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.30E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.10E-01</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>BP, biological process; KEGG, KEGG pathway. <sup><italic>b</italic></sup>False discovery rate adjusted P-value.</italic></attrib></table-wrap-foot></table-wrap><p>Enrichment results from GSEA complemented the findings from DAVID. Highly enriched informative categories among genes that have a MIA-by-sex interaction effect are presented in <xref rid=\"T3\" ref-type=\"table\">Table 3</xref>, and the extended list of categories is presented in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table C</xref>. The categories in <xref rid=\"T3\" ref-type=\"table\">Table 3</xref> support pathways in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref> including ion homeostasis (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>) and regulation of voltage-gated calcium channel activity processes (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>). Notably, the enrichment of the neuroactive ligand receptor interaction pathway and the hormone and neuropeptide activity processes include genes such as CGA and VIPR2 that were identified in <xref rid=\"T1\" ref-type=\"table\">Table 1</xref>.</p><table-wrap id=\"T3\" position=\"float\"><label>TABLE 3</label><caption><p>Enriched informative categories (NES > |1.3|) using GSEA among the genes based on the overall maternal immune activation-by-sex interaction.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>Category</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Category identifier and name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>b</sup>NES</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup><italic>c</italic></sup>FDR <italic>P</italic>-value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc04080:Neuroactive ligand receptor interaction</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.84</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.3E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc04912:GnRH signaling pathway</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.9E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0005179∼Hormone activity</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.80</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.2E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0006970∼Response to osmotic stress</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.5E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0019221∼Cytokine mediated signaling pathway</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.33</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.1E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.4E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:1901385∼Regulation of voltage gated calcium channel activity</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.37</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.2E-01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.4E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc04020:Calcium signaling pathway</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.34</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.2E-01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.4E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0085029∼Extracellular matrix assembly</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">−1.31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8E-01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.5E-01</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>MF, molecular function; KEGG, KEGG pathway; BP, biological process. <sup><italic>b</italic></sup>Normalized enrichment score; negative values indicate genes under-expression in CON females relative to males but over-expression in MPA females relative to males. <sup><italic>c</italic></sup>False discovery rate adjusted P-value.</italic></attrib></table-wrap-foot></table-wrap><p>Network visualization furthered the understanding of the impact of MIA on the relationships among genes that exhibited a significant MIA-by-sex interaction effect. The networks in <xref ref-type=\"fig\" rid=\"F1\">Figures 1</xref>, <xref ref-type=\"fig\" rid=\"F2\">2</xref> depict the relationships between genes in the enriched neuroactive ligand receptor pathway that highlight the differential expression between CON and MPA in males and females (i.e., CON_Ma-MPA_Ma and CON_Fe-MPA_Fe contrasts), respectively. Red and blue rectangular nodes represent framework genes, and edges represent the known associations between genes based on curated databases of molecular interactions. Red and blue nodes denote over- or under-expression of the gene in CON relative to MPA, and the size is an inverse logarithmic function of the differential expression <italic>P</italic>-value. The simultaneous study of the differential expression pattern and connectivity among genes highlights the discrepancy in network modules elicited by MIA between the sexes.</p><fig id=\"F1\" position=\"float\"><label>FIGURE 1</label><caption><p>Network of genes differentially expressed in the amygdala of males (Ma) from control (CON) relative to maternal immune activation (MPA) (contrast between CON_Ma and MPA_Ma). Framework square node color: red and blue denote framework genes over- and under-expressed in CON relative to MPA pigs, respectively; framework node size: -Log<sub>10</sub>[<italic>P</italic>-value]; other genes connecting framework nodes were not differentially expressed in this study.</p></caption><graphic xlink:href=\"fnins-14-00774-g001\"/></fig><fig id=\"F2\" position=\"float\"><label>FIGURE 2</label><caption><p>Network of genes differentially expressed in the amygdala of females (Fe) from control (CON) relative to maternal immune activation (MPA) (contrast between CON_Fe and MPA_Fe). Framework square node color: red and blue denote framework genes over- and under-expressed in CON relative to MPA pigs, respectively; framework node size: -Log<sub>10</sub>[<italic>P</italic>-value]; other genes connecting framework nodes were not differentially expressed in this study.</p></caption><graphic xlink:href=\"fnins-14-00774-g002\"/></fig></sec><sec id=\"S3.SS4\"><title>Transcriptome Changes Associated With Maternal Immune Activation</title><p>Overall, genes exhibited differential (FDR-adjusted <italic>P</italic> < 0.1) expression between MPA and CON pigs, irrespective of sex. <xref rid=\"T4\" ref-type=\"table\">Table 4</xref> lists notable highly differentially expressed genes, and the complete list is in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table D</xref>. The majority of these genes were over-expressed in MPA relative to CON pigs. Among the genes over-expressed in MPA compared to CON pigs were islet amyloid polypeptide (IAPP), ankyrin repeat domain 24 (ANKRD24), interferon-induced transmembrane protein 1 (IFITM1) and 3 (IFITM3), cathepsin C (CTSC), mitogen-activated protein kinase kinase 7 (MAP2K7), heparan sulfate-glucosamine 3-sulfotransferase 5 (HS3ST5), secreted phosphoprotein 1 (SPP1), immunoglobulin heavy chain (IGHG), and transforming acidic coiled-coil-containing protein 1 (TACC1). Among the genes under-expressed in MPA relative to CON pigs are insulin-like growth factor 2 (IGF2), cellular retinoic acid-binding protein 2 (CRABP2), and aldehyde dehydrogenase 1 family member A1 (ALDH1A1).</p><table-wrap id=\"T4\" position=\"float\"><label>TABLE 4</label><caption><p>Representative genes differentially expressed (FDR-adjusted <italic>P</italic>-value < 0.1) between pigs from control (CON) relative to PRRSV-treated (MPA) gilts.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene symbol</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>CON-MPA</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>b</sup>FDR <italic>P</italic>-value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IGHG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IgG heavy chain</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–4.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.1E-31</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IFITM3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Interferon induced transmembrane prot 3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.6E-11</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.2E-08</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IGF2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Insulin-like growth factor 2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.28</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.1E-07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.7E-04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PAQR6</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Progestin and adipoQ receptor family member 6</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.9E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6E-04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RGS8</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Regulator of G protein signaling 8</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.88</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.6E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0E-03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NDNF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Neuron-derived neurotrophic factor</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.27</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.5E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.3E-03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HS3ST5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Heparan sulfate-glucosamine 3-sulfotransferase 5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.5E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.8E-03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">CTSC</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cathepsin C</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.1E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.7E-03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">SPP1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Secreted phosphoprotein 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.89</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.1E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.7E-03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TACC1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Transforming acidic coiled-coil-containing protein 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.55</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.5E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.7E-03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IFITM1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Interferon induced transmembrane prot 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.15</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.4E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.2E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ALDH1A1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Aldehyde dehydrogenase 1 fam mem A1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.7E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.5E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">PLEKHD1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pleckstrin homology coiled-coil domain-containing D1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.99</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.6E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.5E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HERC5</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">HECT and RLD domain-containing E3 ubiquitin ligase 5</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.7E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.6E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IAPP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Islet amyloid polypeptide</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–1.36</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.0E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.7E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ISLR</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Immunoglobulin superfamily contain leucine rich repeat</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.13</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.3E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.5E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ANKRD24</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ankyrin repeat domain 24</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.73</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.7E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.9E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MAP2K7</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mitogen-activated protein kinase kinase 7</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">–0.64</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.9E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0E-01</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>Log<sub>2</sub>[fold change] between CON and MPA pigs. <sup><italic>b</italic></sup>False discovery rate adjusted P-value.</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS5\"><title>Functional Analysis of Genes Associated With Maternal Immune Activation</title><p><xref rid=\"T5\" ref-type=\"table\">Table 5</xref> presents the top significant clusters of informative enriched categories from the DAVID analysis of genes differentially expressed between MPA and CON groups across sexes (the extended list of categories is presented in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table E</xref>). Some categories identified by the DAVID analysis are consistent with the categories detected at more significant levels among the genes presenting an MIA-by-sex interaction effect (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref>) and include the BP angiogenesis (GO:0001525) and KEGG autoimmune thyroid disease and Epstein–Barr virus infection pathways (<xref rid=\"T5\" ref-type=\"table\">Table 5</xref>). Also enriched (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table E</xref>) were the BP homeostatic (GO:0042592), MF ion binding (GO:0043167), and BP anatomical structure formation in morphogenesis (GO:0048646).</p><table-wrap id=\"T5\" position=\"float\"><label>TABLE 5</label><caption><p>Clusters of enriched functional categories (enrichment score ES > 1.3) among the genes presenting significant maternal immune activation effect, and representative categories identified using DAVID.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>Category</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Category identifier and name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>b</sup>FDR <italic>P</italic>-value</td></tr><tr><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Cluster 1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ES = 1.51</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0048646∼anatomic structure formation in morphogenesis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.7E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.7E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0001525∼angiogenesis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.4E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.8E-01</td></tr><tr><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 2</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.40</bold></td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc05330:Allograft rejection</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.7E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.0E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc05169:Epstein–Barr virus infection</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.3E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.7E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc05320:Autoimmune thyroid disease</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.0E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.1E-01</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>BP, biological process; KEGG, KEGG pathway. <sup><italic>b</italic></sup>False discovery rate adjusted P-value.</italic></attrib></table-wrap-foot></table-wrap><p>The GSEA enrichment results within the gene expression patterns of CON relative to MPA groups complemented the findings from DAVID. The most informative enriched categories are presented in <xref rid=\"T6\" ref-type=\"table\">Table 6</xref>, and the extended list of categories is presented in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table F</xref>. Enriched clusters of genes over-expressed in CON relative to MPA detected by GSEA were the BP enrichment of microtubule bundle formation (GO:0001578) and cilium morphogenesis (GO:0060271).</p><table-wrap id=\"T6\" position=\"float\"><label>TABLE 6</label><caption><p>Enriched informative categories (NES > |1.3|) among the genes differentially expressed between pigs from the control relative to the maternal immune activation group using GSEA.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>Category</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Category identifier and name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>b</sup>NES</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup><italic>c</italic></sup>FDR <italic>P</italic>-value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0001578∼Microtubule bundle formation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-08</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0060271∼Cilium morphogenesis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-08</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0044782∼Cilium organization</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.97</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-08</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0035082∼Axoneme assembly</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8E-04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0003341∼Cilium movement</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.94</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.4E-04</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>BP, biological process. <sup><italic>b</italic></sup>Normalized enrichment score, positive values refer to genes under-expressed in maternal immune activated relative to control pigs. <sup><italic>c</italic></sup>False discovery rate adjusted P-value.</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS6\"><title>Transcriptome Differences Between Sexes Independent of Maternal Immune Activation</title><p>Overall, 150 genes were differentially expressed between males and females (FDR-adjusted <italic>P</italic> < 0.05). These genes exhibited a consistent differential expression between sexes, irrespective of the MIA group. The complete list of genes differentially expressed between sexes at FDR-adjusted <italic>P</italic> < 0.1 is available in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table G</xref>, and the majority were over-expressed in males relative to females. Among the previous genes, excluding those that presented MIA-by-sex interaction effect, a selection of informative genes is listed in <xref rid=\"T7\" ref-type=\"table\">Table 7</xref>. Genes over-expressed in males relative to females included eukaryotic translation initiation factor 1A, Y-linked (EIF1AY), leptin receptor (LEPR), luteinizing hormone beta polypeptide (LHB), LIM homeobox 9 (LHX9), luteinizing hormone beta polypeptide (LHB), and immunoglobulin family member 1 (IGSF1).</p><table-wrap id=\"T7\" position=\"float\"><label>TABLE 7</label><caption><p>Informative genes presenting significant differential expression between males (Ma) and females (Fe).</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene symbol</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gene name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>Ma-Fe</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>b</sup>FDR <italic>P</italic>-value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">EIF1AY</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Eukaryotic translation initiation factor 1A, Y-linked</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">13.68</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-08</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LHX9</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LIM homeobox 9</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.38</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-08</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LHB</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Luteinizing hormone beta polypeptide</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.92</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><1.0E-08</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">TRPC3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Transient receptor potential cation channel C3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.83</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.7E-07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.4E-05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">WNT3</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Wnt family member 3</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.72</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.0E-07</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0E-04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">IGSF1</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Immunoglobulin superfamily member 1</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.85</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.4E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3E-03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">NPM2</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Nucleophosmin/nucleoplasmin 2</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.79</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.8E-05</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.8E-03</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RORA</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">RAR-related orphan receptor A</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.74</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.0E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.4E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">LEPR</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Leptin receptor</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">0.62</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.8E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.4E-02</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>Log<sub>2</sub>[fold change] between male and female offspring. <sup><italic>b</italic></sup>False discovery rate adjusted P-value.</italic></attrib></table-wrap-foot></table-wrap><p>Informative categories among the DAVID clusters of enriched categories for the genes differentially expressed between sexes are listed in <xref rid=\"T8\" ref-type=\"table\">Table 8</xref> (a complete list is available in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table H</xref>). The previous categories include BP gland development (GO:0048732), response to hormone (GO:0009725), and brain development (GO:0007420).</p><table-wrap id=\"T8\" position=\"float\"><label>TABLE 8</label><caption><p>Clusters of informative enriched functional categories (enrichment score ES > 1.3) among the genes differentially expressed between sexes identified using DAVID.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>Category</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Category identifier and name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>b</sup>FDR <italic>P</italic>-value</td></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 1</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.82</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0005201∼extracellular matrix structural constituent</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.0E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.9E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 2</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.74</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc04080:Neuroactive ligand-receptor interaction</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.7E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.4E-04</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0009725∼response to hormone</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.3E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.4E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 3</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.74</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0048732∼gland development</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.7E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.7E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 4</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.45</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0007420∼brain development</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.1E-01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.9E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0030900∼forebrain development</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.9E-01</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.7E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 5</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.41</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0020037∼heme binding</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.2E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.1E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0005506∼iron ion binding</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.3E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.3E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 6</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.35</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0009790∼embryo development</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.8E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.4E-01</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0090596∼sensory organ morphogenesis</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.9E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.1E-01</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>BP, biological process; MF, molecular function; KEGG, KEGG pathway. <sup><italic>b</italic></sup>False discovery rate adjusted P-value.</italic></attrib></table-wrap-foot></table-wrap></sec><sec id=\"S3.SS7\"><title>Weighted Gene Co-expression Network Analysis</title><p>The WGCNA study of the correlation between expression and experimental factors was based on the log-transformed TPM expression level of 16,175 genes. This study identified 62 modules of expression correlated with MIA groups among males. The number of genes in each module ranges from 32 to 1381; <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Figure A</xref> depicts the relationship between gene modules using a dendrogram. The correlation (corr) between the eigengene expression profile and maternal treatment in males is depicted in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Figure B</xref>. A fairly even distribution of positive and negative correlation estimates was observed. The genes in the modules pale violet red (corr = –0.58, <italic>P</italic> < 0.03) and gray60 (corr = –0.59, <italic>P</italic> < 0.06) presented a strong negative correlation, indicating that low expression levels were associated with MPA.</p><p>The enrichment analysis of the genes in the WGCNA modules provided additional insights into the processes impacted by MIA. The extended list of enriched categories across modules is available in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table I</xref>. <xref rid=\"T9\" ref-type=\"table\">Table 9</xref> lists representative clusters of enriched categories in the gene modules that were highly correlated with MIA differences in males. Complementary categories that were enriched in modules encompassing gene patterns negatively correlated with MPA include the KEGG AD and oxidative phosphorylation pathways in the pale violet red3 and the gray60 modules, MF NADH dehydrogenase activity (GO:0003954) in the gray60 module, and the KEGG ribosomal pathway and associated GO processes in the light yellow module. Confirming the enrichment results from the previous differential expression analysis (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table B</xref>), categories enriched in modules encompassing gene patterns negatively correlated with MPA include ion transport (also in <xref rid=\"T2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table H</xref>) in the pale violet red3 and gray60 modules, and oxidoreductase activity in the gray60 module (also in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table H</xref>).</p><table-wrap id=\"T9\" position=\"float\"><label>TABLE 9</label><caption><p>Clusters of enriched functional categories (enrichment score ES > 1.3) among the genes in modules presenting a significant correlation with maternal immune activation (MPA) relative to control within males using DAVID.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>Category</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Category identifier and name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>b</sup>FDR <italic>P</italic>-value</td></tr><tr><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MODULE</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Gray60 (low expression in MPA)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 1</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 14.5</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ssc05010:Alzheimer’s disease</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.3E-18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.1E-17</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Ssc05012:Parkinson’s disease</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.8E-18</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.7E-16</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 2</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 7.32</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0016651∼oxidoreductase activity, acting on NAD(P)H</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.6E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.5E-07</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MF</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0003954∼NADH dehydrogenase activity</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.4E-09</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">6.8E-07</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 3</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 6.41</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0042775∼mitochondrial ATP synthesis electron</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">4.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.6E-07</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0046034∼ATP metabolic process</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.0E-10</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.7E-07</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0009116∼nucleoside metabolic process</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.8E-08</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.4E-06</td></tr><tr><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>MODULE</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Light yellow (low expression in MPA)</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 1</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 4.18</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">KEGG</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">ssc03010:Ribosome</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.7E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.9E-05</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0006412∼translation</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.5E-06</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.7E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 1</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 2.49</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0007006∼mitochondrial membrane organization</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">5.1E-04</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">7.8E-02</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0007007∼inner mitochondrial organization</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.1E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.5E-01</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>BP, biological process; MF, molecular function; KEGG, KEGG pathway. <sup><italic>b</italic></sup>False discovery rate adjusted P-value.</italic></attrib></table-wrap-foot></table-wrap><p>The WGCNA study identified 61 modules of expression correlated with sex among MPA pigs. The number of genes in each module range from 34 to 2082 and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Figure C</xref> depicts the dendrogram of modules. Positive correlation estimates that indicate higher expression in male than female were identified in the ivory (corr = 0.65, <italic>P</italic> < 0.03) and antique maroon (corr = 0.62, <italic>P</italic> < 0.04) modules, whereas negative correlations denoting lower expression in male were identified in sienna3 (corr = –0.65, <italic>P</italic> < 0.03) (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Figure D</xref>). <xref rid=\"T10\" ref-type=\"table\">Table 10</xref> lists representative clusters of enriched functional categories in these modules. The extended list of categories and modules is in <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table J</xref>. Within the ivory module of positively correlated gene patterns, the BP synapse (GO:0007416) and neural development (GO:0007399) categories were enriched.</p><table-wrap id=\"T10\" position=\"float\"><label>TABLE 10</label><caption><p>Clusters of enriched functional categories (enrichment score ES > 1.3) among the genes in modules presenting a significant correlation with sex within the maternal immune activation treatment using DAVID.</p></caption><table frame=\"hsides\" rules=\"groups\" cellspacing=\"5\" cellpadding=\"5\"><thead><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><sup>a</sup>Category</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Category identifier and name</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><italic>P</italic>-value</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\"><sup>b</sup>FDR <italic>P</italic>-value</td></tr><tr><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">MODULE</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Sienna3 (low expression in males)</td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 1</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.58</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0044255∼cellular lipid metabolic process</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.1E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0E+00</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0044283∼small molecule biosynthetic process</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">3.3E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.8E-01</td></tr><tr><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>MODULE</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Ivory (high expression in males)</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"center\" colspan=\"4\" rowspan=\"1\"><hr/></td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>Cluster 1</bold></td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"><bold>ES = 1.47</bold></td><td rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"justify\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0007416∼synapse assembly</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">8.1E-03</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">1.0E+00</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">BP</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">GO:0007399∼nervous system development</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">2.7E-02</td><td valign=\"top\" align=\"center\" rowspan=\"1\" colspan=\"1\">9.1E-01</td></tr></tbody></table><table-wrap-foot><attrib><italic><sup><italic>a</italic></sup>BP, biological process; MF, molecular function; KEGG, KEGG pathway. <sup><italic>b</italic></sup>False discovery rate adjusted P-value.</italic></attrib></table-wrap-foot></table-wrap></sec></sec><sec id=\"S4\"><title>Discussion</title><p>The present study uses a validated model of MIA triggered by a live viral (i.e., PRRSV) infection during a critical neurodevelopmental stage (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>) to gain innovative insights into sex-dependent molecular changes in the amygdala. The changes in gilt body weight and temperature within 2 weeks postinfection were consistent with the mode of action of the PRRSV and previous reports (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>). These results indicate that the PRRSV-infected gilts experienced extended activation of inflammatory pathways during gestation. The present study characterized the prolonged effect of the MIA, 60 days after exposure, on 3-week-old offspring.</p><p>Our study identified patterns of differential gene expression and prevalently dysregulated gene networks and processes, some of which have been reported in clinical and preclinical studies of AD, ASD, and SSD (<xref rid=\"B69\" ref-type=\"bibr\">Malkova et al., 2012</xref>; <xref rid=\"B120\" ref-type=\"bibr\">Xuan and Hampson, 2014</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Aavani et al., 2015</xref>). These disorders have been associated with MIA and amygdala functions, yet the corresponding neurological and molecular changes have been studied mostly using pathogen mimetic challenges on rodents (<xref rid=\"B120\" ref-type=\"bibr\">Xuan and Hampson, 2014</xref>; <xref rid=\"B1\" ref-type=\"bibr\">Aavani et al., 2015</xref>). Moreover, our study identified sex-dependent molecular patterns that are consistent with the differential prevalence and symptoms of ASD, SSD, and MIA-related disorders between sexes reported in rodent and human studies (<xref rid=\"B119\" ref-type=\"bibr\">Wischhof et al., 2015</xref>). For example ASD tends to be more prevalent in young males (<xref rid=\"B57\" ref-type=\"bibr\">Kirsten et al., 2010</xref>; <xref rid=\"B45\" ref-type=\"bibr\">Haida et al., 2019</xref>), while SSD tends to be more prevalent in females (<xref rid=\"B11\" ref-type=\"bibr\">Bale et al., 2010</xref>; <xref rid=\"B10\" ref-type=\"bibr\">Bale, 2011</xref>). Similarly, lower sociability and preference for social novelty were observed in 2 weeks old pigs from gilts inoculated with PRRSV at GD 76 than from control gilts (<xref rid=\"B8\" ref-type=\"bibr\">Antonson et al., 2017</xref>). A discussion of the molecular mechanisms impacted by MIA can offer insights into therapies to ameliorate the lasting effects on physiology and behavior.</p><sec id=\"S4.SS1\"><title>Sex-Dependent Transcriptome Changes Associated With Maternal Immune Activation</title><p>The evaluation of the 328 genes presenting a significant MIA-by-sex interaction effect augmented the understanding of the differential response of transcripts to MIA between sexes (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table A</xref>). The majority of the previous genes were under-expressed in MPA relative to CON males, and the profile in females was opposite or less extreme. Many genes presenting a significant MIA-by-sex effect code for neuropeptides and hormones, or participate in glutamatergic processes.</p><p>The lower NTS levels in the amygdala of male rats associated with lower conditioned place preference (<xref rid=\"B61\" ref-type=\"bibr\">Laszlo et al., 2010</xref>) is consistent with the lower level of NTS transcripts in MPA males observed in the present study (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). The under-expression of neuropeptide gene POMC in MPA relative to CON males (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) may be associated with the changes in POMC-related peptide transmission that has been reported in the brains of patients diagnosed with SSD (<xref rid=\"B53\" ref-type=\"bibr\">Jamali and Tramu, 1997</xref>). The under-expression of the neuropeptide gene PENK in MPA relative to CON males (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) is in agreement with lower levels of PENK expression in the brains of mice models of SSD (<xref rid=\"B40\" ref-type=\"bibr\">Gottschalk et al., 2013</xref>). PENK was also differentially expressed in the amygdala of an ASD rat model using prenatal valproic acid exposure (<xref rid=\"B85\" ref-type=\"bibr\">Oguchi-Katayama et al., 2013</xref>). Valproic acid treatment during pregnancy was associated with a sevenfold increase in ASD incidence, social difficulties, and reduced attention (<xref rid=\"B101\" ref-type=\"bibr\">Roullet et al., 2013</xref>). Also, CACNA2D3 was under-expressed in the amygdala of rats exposed prenatally to valproic acid and coincided with social behavior abnormalities including heightened anxiety (<xref rid=\"B12\" ref-type=\"bibr\">Barrett et al., 2017</xref>). In the present study, the interaction pattern of CACNA2D3 was characterized by under-expression in MPA relative to CON males (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>).</p><p>The over-expression of the neuropeptide receptor VIPR2 in the amygdala of MPA relative to CON females (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) is consistent with reports that duplications in this gene confer a significant risk for SSD (<xref rid=\"B80\" ref-type=\"bibr\">Morris and Pratt, 2014</xref>). The differential expression of the VIP receptor is particularly important because GABAergic interneurons in the amygdala express the neuropeptide VIP that facilitates cell firing (<xref rid=\"B98\" ref-type=\"bibr\">Rhomberg et al., 2018</xref>) and maintains the balance of pro- and anti-inflammatory cytokines (<xref rid=\"B72\" ref-type=\"bibr\">Martinez et al., 2020</xref>). The under-expression of GPX3 in MPA relative to CON males (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) is consistent with the association between GPX3 gene expression and SSD (<xref rid=\"B124\" ref-type=\"bibr\">Zhao et al., 2018</xref>).</p><p>The sex-dependent response to MIA of hormone receptor CRHR genes detected in our study (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) has been also reported by others. The expression of CRHR1 in the amygdala of 10 weeks old female pigs from sows exposed to a social stressor during mid-gestation was higher than in pigs from control gilts, whereas no stressor effects were observed in males nor in the expression of CRHR2 (<xref rid=\"B102\" ref-type=\"bibr\">Rutherford et al., 2014</xref>). Also, the expression of CRHR was associated with SSD (<xref rid=\"B79\" ref-type=\"bibr\">Mistry et al., 2013</xref>). The alignment between the profiles of the CRHR2 and neuropeptide GRP genes observed in our study (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) is in agreement with reports of simultaneous release of CRH and GRP in the amygdala of rats elicited by the stress hormone corticosterone (<xref rid=\"B76\" ref-type=\"bibr\">Merali et al., 2008</xref>). The over-expression of the parathyroid hormone receptor PTH1R gene in response to MIA (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>) is consistent with the over-expression of this gene in SSD and AD (<xref rid=\"B51\" ref-type=\"bibr\">Ibanez et al., 2014</xref>). The over-expression of the functionally related thyroid hormone responsive gene THRSP in MPA relative to CON females further supports the PTH1R pattern (<xref rid=\"B82\" ref-type=\"bibr\">Munshi and Rosenkranz, 2018</xref>).</p><p>Several genes in the glutamatergic and GABAergic pathways displayed sex-dependent MIA effects (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). This shared pattern may stem from pro-inflammatory cytokines intensifying glutamatergic release by the amygdala in a sex-dependent manner. The glutamate receptor GRM4 was under-expressed in MPA relative to CON males, while a less extreme and opposite trend was detected in females (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table A</xref>). Supporting our finding, the expression of glutamate receptor genes was lower in SSD brains (<xref rid=\"B75\" ref-type=\"bibr\">Meador-Woodruff and Healy, 2000</xref>). The pattern differences between sexes could be connected with differences in gene expression across sexes in multiple GRM genes including GRM4 in association with behavior disorders (<xref rid=\"B41\" ref-type=\"bibr\">Gray et al., 2015</xref>). The expression pattern of SLC17A6, a gene in the glutamatergic pathway, was consistent with that of GRM4 (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Moreover, NDNF and RELN (two genes in the GABAergic pathway) displayed similar expression patterns in our study in agreement with previous reports (<xref rid=\"B49\" ref-type=\"bibr\">Hou and Capogna, 2018</xref>). NDNF interneurons evoke inhibitory postsynaptic potentials mediated by GABA receptors. Also, the GABAergic gene RELN has been associated with ASD and SSD (<xref rid=\"B22\" ref-type=\"bibr\">Canitano and Pallagrosi, 2017</xref>) and is under-expressed in the prefrontal cortex of subjects diagnosed with SSD (<xref rid=\"B29\" ref-type=\"bibr\">Fatemi et al., 2005</xref>). Consistent with our results, genes that regulate neural migration of GABAergic interneurons were under-expressed in the brain of offspring from rats exposed to lipopolysaccharide (LPS)-induced MIA (<xref rid=\"B88\" ref-type=\"bibr\">Oskvig et al., 2012</xref>). The activity of GABA and glutamate on serotonergic neurons is modulated by chemokine ligands such as CXCL12 (<xref rid=\"B112\" ref-type=\"bibr\">Stuart and Baune, 2014</xref>), and consistent with this interaction, CXCL12 and CXCL13 were under-expressed in MPA relative to CON males (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table A</xref>). Our results are also consistent with findings that LPS injection of adult mice dysregulated CXCL12, which in turn increased glutamatergic release in the amygdala, and anxiety-like behavior occurrence (<xref rid=\"B122\" ref-type=\"bibr\">Yang et al., 2016</xref>). EFHD1, a calcium binding protein associated with synaptic transmission and levels of gamma glutamyltransferase was over-expressed in MPA relative to CON females (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table A</xref>). This gene was also over-expressed in the amygdala of patients diagnosed with SSD (<xref rid=\"B25\" ref-type=\"bibr\">Chang et al., 2017</xref>).</p><p>Among the 16,175 genes tested for differential expression in the present study, the profile of several proinflammatory and neuroinflammatory genes were consistent with those from a candidate gene study of the amygdala from 9 days old mice exposed to MIA (<xref rid=\"B24\" ref-type=\"bibr\">Carlezon et al., 2019</xref>). Consistent with the patterns observed in mice exposed to viral mimetic Poly(I:C) MIA (<xref rid=\"B24\" ref-type=\"bibr\">Carlezon et al., 2019</xref>), an interaction between PRRSV-elicited MIA and sex was detected in pigs for the genes glial fibrillary acidic protein (GFAP), nitric oxide synthases 1 and 2 (NOS1 and NOS2, respectively), and translocator protein (TSPO)-associated protein (0.006 < <italic>P</italic> < 0.02). These patterns, albeit consistent, failed to surpass the FDR-adjusted <italic>P</italic> < 0.05 threshold. Also consistent with the MIA study of mice amygdala (<xref rid=\"B24\" ref-type=\"bibr\">Carlezon et al., 2019</xref>), the differences in expression between MPA and CON males for tumor necrosis factor alpha (TNF-α), and interleukin 1 beta (IL-1β), were not statistically significant. The expression levels for interleukin 6 (IL-6) and interleukin 1 beta (IL-1β) among MPA males were below the minimum threshold for testing, and therefore, the interaction effects for these genes are not reported. IL-6 and IL-1β were differentially expressed between MIA groups in female mice (<xref rid=\"B24\" ref-type=\"bibr\">Carlezon et al., 2019</xref>); however, the reported relative abundances for these genes suggests that MIA male mice, like MPA male pigs, presented the lowest levels of IL-6 and IL-1β abundance of all groups studied.</p><p>The pattern of several genes presenting a MIA-by-sex interaction effect was characterized by the same relative abundance between MIA groups, albeit sexes differed in magnitude (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Males presented a more extreme under-expression of CGA in MPA relative to CON than females (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). This difference could be associated with the participation of CGA in multiple hormone processes that regulate female reproductive performance. The lower impact of MIA on the CGA levels in females may prevent the dysregulation of multiple downstream processes associated with reproductive function. Likewise, the under-expression of TTR in MPA relative to CON was more acute in males than in females (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Over-expression of TTR was noted in the amygdala of rats treated with MK-801, a N-methyl-D-aspartate antagonist that elicits SSD-like behavior (<xref rid=\"B73\" ref-type=\"bibr\">Matsuoka et al., 2008</xref>). The differential expressions of HBB and GBP1 among MIA groups detected in our study are also observed in the amygdala of SSD cases (<xref rid=\"B25\" ref-type=\"bibr\">Chang et al., 2017</xref>).</p></sec><sec id=\"S4.SS2\"><title>Functional Analysis of Sex-Dependent Maternal Immune Activation Transcriptome</title><p>The study of over-represented functional categories among the genes presenting sex-dependent profiles between MPA and CON pigs (<xref rid=\"T2\" ref-type=\"table\">Tables 2</xref>, <xref ref-type=\"supplementary-material\" rid=\"TS1\">3</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Tables B</xref>, <xref ref-type=\"supplementary-material\" rid=\"TS1\">C</xref>) identified categories consistent with previous studies of MIA and amygdala inflammation. The enrichment of the KEGG pathways related to autoimmune disease and antigen processing and presentation via histocompatibility complex (MHC) (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table B</xref>) are in agreement with the reported association between autoimmune diseases, SSD, and variants in the MHC gene family (<xref rid=\"B4\" ref-type=\"bibr\">Anders and Kinney, 2015</xref>). Autoimmune reactions are capable of inducing psychiatric symptoms that are mediated by the amygdala such as those associated with SSD (<xref rid=\"B62\" ref-type=\"bibr\">Lennox et al., 2012</xref>). Genes annotated to MHC receptor activity are over-expressed in the amygdala of individuals that have SSD (<xref rid=\"B25\" ref-type=\"bibr\">Chang et al., 2017</xref>). The cytokine-mediated signaling pathway was also over-represented among the genes under-expressed in MPA relative to CON pigs (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>). Consistent with our results, the expression of 28 genes annotated to immune stimulus had MIA-by-sex interaction effects in the microglia of GD 97 fetuses after GD 76 PRRSV injection (<xref rid=\"B7\" ref-type=\"bibr\">Antonson et al., 2019</xref>).</p><p>Multiple BPs associated with homeostasis and extracellular matrix assembly were enriched among the genes presenting a significant MIA-by-sex effect (<xref rid=\"T2\" ref-type=\"table\">Tables 2</xref>, <xref rid=\"T3\" ref-type=\"table\">3</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Tables B</xref>, <xref ref-type=\"supplementary-material\" rid=\"TS1\">C</xref>). This finding is in agreement with the deficit in perineuronal nets in the amygdala of patients diagnosed with SSD (<xref rid=\"B90\" ref-type=\"bibr\">Paylor et al., 2016</xref>). These nets are extracellular matrix structures that support the high metabolic demand of the interneurons, and contribute to ion homeostasis around them.</p><p>The enrichment of BP axon and brain development among the genes presenting an MIA-by-sex effect (<xref rid=\"T2\" ref-type=\"table\">Table 2</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table B</xref>) is consistent with a report that LPS-elicited MIA is associated with under-expression of neurodevelopmental genes in the rat fetal brain, including genes linked to ASD (<xref rid=\"B88\" ref-type=\"bibr\">Oskvig et al., 2012</xref>). Furthermore, the amygdala of rats prenatally exposed to valproic acid, a stressor that leads to ASD, presented activation of neuron development pathways (<xref rid=\"B12\" ref-type=\"bibr\">Barrett et al., 2017</xref>). Similarly, the KEGG pathway of cell-adhesion molecules (CAMs), molecules that are fundamental for nervous system development and maintenance, was enriched among the genes presenting a sex-dependent MIA effect. This pathway was also enriched among genes under-expressed in the brain of rats exposed to LPS-triggered MIA and among genes under-expressed in the cortex of patients diagnosed with ASD (<xref rid=\"B67\" ref-type=\"bibr\">Lombardo et al., 2018</xref>).</p><p>The KEGG pathway neuroactive ligand receptor interaction was enriched among the genes presenting a significant interaction effect characterized by under-expression in the amygdala of CON relative to MPA males (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref>). This pattern is aligned with findings that Poly(I:C)-elicited MIA augmented the synaptic strength of glutamatergic projections from the frontal cortex into the amygdala of mice (<xref rid=\"B63\" ref-type=\"bibr\">Li et al., 2018</xref>). The neuroactive ligand receptor pathway encompasses neuroreceptor genes such as dopamine, serotonin, GABA, and glutamate receptors.</p><p>The impact of sex-dependent MIA effects on neuropeptide and hormone genes (e.g., CGA, POMC, and SSTR1) expressed in the amygdala is evidenced by the enrichment of GnRH signaling pathway and hormone activity among the genes under-expressed in MPA relative to CON pigs (<xref rid=\"T3\" ref-type=\"table\">Table 3</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table C</xref>). Our results suggest that the disruption of glucocorticoid hormone balance on the HPA axis initiated by MIA can have long-lasting effects because amygdala processes are regulated by glucocorticoid receptors and glucocorticoids repress GnRH secretion.</p></sec><sec id=\"S4.SS3\"><title>Impact of Maternal Immune Activation on Gene Networks Within Sex</title><p>Further understanding of sex-dependent effects of MIA on the co-expression of gene sets was gained from the identification of WGCNA modules of genes that share relative expression profiles between the CON and MPA groups in males (<xref rid=\"T9\" ref-type=\"table\">Table 9</xref>) or that share relative expression profiles between sexes in MPA pigs (<xref rid=\"T10\" ref-type=\"table\">Table 10</xref>). The WGCNA gene modules profiling changes between MIA groups in males uncovered enrichment of genes annotated to AD (<xref rid=\"T9\" ref-type=\"table\">Table 9</xref>). This result is consistent with findings of common molecular mechanisms shared between SSD and AD (<xref rid=\"B94\" ref-type=\"bibr\">Prestia, 2011</xref>; <xref rid=\"B114\" ref-type=\"bibr\">Sumitomo et al., 2018</xref>). Likewise, the enrichment of ATP metabolic processes among the module of genes associated with MIA effects in males is in agreement with evidence that mitochondrial dysfunction associated with SSD (<xref rid=\"B93\" ref-type=\"bibr\">Prabakaran et al., 2004</xref>).</p><p>Insights into the distinct vulnerability to MIA between sexes on the interplay between critical genes was gained from the study of the network of genes that had a significant MIA-by-sex effect in the enriched neuroactive ligand receptor pathway. The comparison of <xref ref-type=\"fig\" rid=\"F1\">Figures 1</xref>, <xref ref-type=\"fig\" rid=\"F2\">2</xref> highlights the distinct interaction between genes in response to PRRSV in males and females, respectively. Notably, males present a strong and consistent over-expression (i.e., red color) of genes in CON relative to MPA, with the exception of SSTR1. On the other hand, females present a weaker expression differential with a slight majority of genes under-expressed (i.e., blue color) in CON relative to MPA pigs. These results suggest lower vulnerability to MIA effects on gene expression in females than in males at 3 weeks of age. An example of this pattern is the module of the neuropeptide receptors, angiotensin II receptor type 1 (AGTR1), and PTH1R. The co-expression of these two G-coupled receptors is consistent with the shared metabolic function (<xref rid=\"B97\" ref-type=\"bibr\">Regard et al., 2008</xref>). Distinct to the opposite patterns between sexes observed in the previous network cluster, the highly connected CGA and GH1 are over-expressed in CON relative to MPA in both sexes, albeit the differences are more extreme in males than in females. Correlated under-expression of GH1 and CGA was observed in the cerebellum and prefrontal cortex of rats that presented altered depressive-like behavior (<xref rid=\"B121\" ref-type=\"bibr\">Yamamoto et al., 2015</xref>).</p></sec><sec id=\"S4.SS4\"><title>Sex-Independent Associations Between Maternal Immune Activation and Transcriptome Changes</title><p>The 161 differentially expressed genes between MPA and CON pigs (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table D</xref>) included genes supported by previous studies of MIA. Additionally, the differential expression of several genes in <xref rid=\"T4\" ref-type=\"table\">Table 4</xref> has been linked to neurological disorders such as SSD, ASD, and AD. The over-expression of ANKRD24 in MPA relative to CON pigs (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>) is supported by the over-expression of an ankyrin repeat domain family member (ANKRD32) in rat fetal brains exposed to MIA elicited by LPS (<xref rid=\"B88\" ref-type=\"bibr\">Oskvig et al., 2012</xref>). IFITM3, IFITM1, and CTSC were over-expressed in the amygdala of MPA relative to CON pigs (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>). Similarly, these genes were over-expressed in the amygdala of individuals diagnosed with SSD (<xref rid=\"B115\" ref-type=\"bibr\">Takao et al., 2012</xref>; <xref rid=\"B25\" ref-type=\"bibr\">Chang et al., 2017</xref>). Consistent with our results, IFITM3 was over-expressed in the hippocampi of neonatal mice treated with Poly(I:C) that resulted in developmental impairment of the central nervous system and lasting brain dysfunction. Conversely, <italic>Ifitm3<sup>–/–</sup></italic> mice treated with Poly(I:C) exhibited normal neural development and did not present neural deficiencies (<xref rid=\"B52\" ref-type=\"bibr\">Ibi et al., 2013</xref>). CTSC was also over-expressed in the hippocampus of Shn-2 KO mice that exhibited SSD-like behaviors (<xref rid=\"B115\" ref-type=\"bibr\">Takao et al., 2012</xref>). MAP2K7 was over-expressed in MPA relative to CON pigs (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>), and this gene has been implicated in SSD incidence. MAP2K7 exclusively activates c-Jun N-terminal kinases (JNK) (<xref rid=\"B66\" ref-type=\"bibr\">Lisnock et al., 2000</xref>; <xref rid=\"B123\" ref-type=\"bibr\">Zeke et al., 2016</xref>), a mediator of the MIA response in the developing fetus that likely contributes to the neurological abnormalities in SSD (<xref rid=\"B87\" ref-type=\"bibr\">Openshaw et al., 2019</xref>).</p><p>HS3ST5 and SPP1 were over-expressed in the amygdala of MPA compared to CON pigs (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>), and both genes have been linked to ASD. Consistent with the patterns in our study, the expression of SPP1 in the temporal cortex of humans was higher in individuals diagnosed with ASD compared to controls (<xref rid=\"B33\" ref-type=\"bibr\">Garbett et al., 2008</xref>). Indeed, SPP1 participates in multiple immuno-related pathways in neural tissues (<xref rid=\"B23\" ref-type=\"bibr\">Carecchio and Comi, 2011</xref>; <xref rid=\"B17\" ref-type=\"bibr\">Brown, 2012</xref>). Genome-wide association studies identified a genetic variation near HS3ST5 that was significantly associated with ASD while a single-nucleotide polymorphism within this gene has been associated with SSD (<xref rid=\"B117\" ref-type=\"bibr\">Wang et al., 2009</xref>, <xref rid=\"B118\" ref-type=\"bibr\">2015</xref>).</p><p>TACC1, CRABP2, and ALDH1A1 participate in the retinoid signaling and metabolic pathways and were differentially expressed in MPA compared to CON pigs (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table D</xref>). Dysregulation of retinoid pathways may disrupt neural development leading to SSD (<xref rid=\"B39\" ref-type=\"bibr\">Goodman, 1996</xref>), and retinoid toxicity and deficiency are associated with central nervous system abnormalities (<xref rid=\"B68\" ref-type=\"bibr\">Maden, 1994</xref>). The differential expression of genes in the retinoid pathways is consistent with the previously described changes in the expression of genes in the thyroid hormone cascades. Defective cross talks between the retinoid and/or thyroid hormone processes have been associated with the development of SSD (<xref rid=\"B89\" ref-type=\"bibr\">Palha and Goodman, 2006</xref>). CRABP2 was under-expressed in MPA compared to CON pigs (<xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table D</xref>), and mutations in this gene have been implicated in SSD (<xref rid=\"B38\" ref-type=\"bibr\">Goodman, 1995</xref>). Aldehyde dehydrogenase 1 family member A1 (ALDH1A1) was under-expressed in MPA compared to CON pigs (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>). ALDH1A1 was under-expressed in the amygdala of neonatal rats exposed to odor-shock conditioning, mimicking the effects of unpredictable early life trauma and resulting in amygdala dysfunction (<xref rid=\"B104\" ref-type=\"bibr\">Sarro et al., 2014</xref>).</p><p>IGF2 was under-expressed in MPA compared to CON pigs (<xref rid=\"T4\" ref-type=\"table\">Table 4</xref>), and consistent with our results, the systemic administration of IGF2 reduces ASD phenotypes; promotes normal social interaction, cognition, and executive function; and reduces repetitive behavior (<xref rid=\"B111\" ref-type=\"bibr\">Steinmetz et al., 2018</xref>). TTR was over-expressed in the amygdala of MPA compared to CON pigs. Over-expression of TTR was noted in the amygdala of rats treated with MK-801, a N-methyl-D-aspartate antagonist that elicits SSD-like behavior (<xref rid=\"B73\" ref-type=\"bibr\">Matsuoka et al., 2008</xref>).</p></sec><sec id=\"S4.SS5\"><title>Functional Analysis of Sex-Independent Maternal Immune Activation Transcriptome</title><p>The functional categories over-represented among the genes differentially expressed between MPA and CON pigs (<xref rid=\"T5\" ref-type=\"table\">Table 5</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Tables E</xref>) were supported by previous studies of MIA and associated neurodevelopmental disorders. Among these categories was the BP anatomical structure formation involved in morphogenesis and angiogenesis (<xref rid=\"T5\" ref-type=\"table\">Table 5</xref>). This result is consistent with the under-expression of early growth response 1 (EGR1, a regulator of angiogenic factors) in the GD 97 fetal microglia of MPA relative to CON pigs of both sexes after the GD 76 PRRSV challenge (<xref rid=\"B7\" ref-type=\"bibr\">Antonson et al., 2019</xref>). Angiogenesis was also enriched among differentially expressed genes in the frontal cortex of rats that are exposed to valproic acid <italic>in utero</italic> (<xref rid=\"B47\" ref-type=\"bibr\">Hill et al., 2015</xref>) to model ASD (<xref rid=\"B30\" ref-type=\"bibr\">Favre et al., 2013</xref>). The observed enrichment of the BP angiogenesis was also reported among genes dysregulated in the hippocampal microglia of mice exposed to Poly(I:C) relative to control mice (<xref rid=\"B74\" ref-type=\"bibr\">Mattei et al., 2017</xref>).</p><p>Several KEGG pathways associated with inflammation and infection, including allograft rejection, Epstein–Barr virus infection, and autoimmune thyroid disease, were enriched, among the genes differentially expressed between MPA and CON pigs (<xref rid=\"T5\" ref-type=\"table\">Table 5</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table E</xref>). This finding is consistent with the significant effect of MIA from GD 76 PRRSV injection on expression of 12 genes in the amygdala of GD 97 fetuses from both sexes (<xref rid=\"B7\" ref-type=\"bibr\">Antonson et al., 2019</xref>). At this fetal stage, most of the genes differentially expressed between MPA and CON gilts were annotated to the BP immune response, including genes annotated to the BP cytokine-mediated signaling pathway, and to the toll-like receptor pathway (<xref rid=\"B7\" ref-type=\"bibr\">Antonson et al., 2019</xref>). This enrichment is in agreement with reports of amygdala inflammation and transcriptome changes in glial cells in response to MIA elicited by LPS administration in mice that persist into adulthood (<xref rid=\"B86\" ref-type=\"bibr\">O’Loughlin et al., 2017</xref>).</p><p>Many BPs that modulate neurodevelopment were enriched among genes under-expressed in MPA compared to CON pigs, including microtubule bundle formation, cilium morphogenesis, cilium organization, cilium movement, and axoneme assembly (<xref rid=\"T6\" ref-type=\"table\">Table 6</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table F</xref>). Reduced neuronal primary cilia can reduce cellular communication during development, the number of dendrites (<xref rid=\"B34\" ref-type=\"bibr\">Goetz and Anderson, 2010</xref>; <xref rid=\"B42\" ref-type=\"bibr\">Guadiana et al., 2013</xref>), and can hinder neurogenesis in the adult brain (<xref rid=\"B3\" ref-type=\"bibr\">Amador-Arjona et al., 2011</xref>). Furthermore, primary cilia participate in the development of the circuitry of GABAergic interneurons, and disrupted cilia formation leads to dysregulated excitatory/inhibitory signaling between neurons (<xref rid=\"B44\" ref-type=\"bibr\">Guo et al., 2017</xref>). This finding is consistent with the glutamatergic and GABAergic-associated genes that presented a significant MIA-by-sex interaction effect previously discussed (<xref rid=\"T1\" ref-type=\"table\">Table 1</xref>). Disruption of this circuitry underlies the neurological disorders associated with ASD and SSD (<xref rid=\"B15\" ref-type=\"bibr\">Bourgeron, 2009</xref>; <xref rid=\"B70\" ref-type=\"bibr\">Marin, 2012</xref>; <xref rid=\"B64\" ref-type=\"bibr\">Li et al., 2016</xref>), and olfactory neuronal precursors collected from SSD patients were found to have less primary cilia growth <italic>in vitro</italic> compared to controls (<xref rid=\"B81\" ref-type=\"bibr\">Munoz-Estrada et al., 2018</xref>).</p></sec><sec id=\"S4.SS6\"><title>Main Effect of Sex on Gene Expression and Functional Enrichment</title><p>The amygdala is a sexually dimorphic area of the brain that is highly responsive to signaling from gonadal steroid hormones (<xref rid=\"B48\" ref-type=\"bibr\">Hines et al., 1992</xref>; <xref rid=\"B26\" ref-type=\"bibr\">Cooke and Woolley, 2005</xref>). Supporting this, 150 genes were differentially expressed (<xref rid=\"T7\" ref-type=\"table\">Table 7</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table G</xref>), and most were over-expressed in males compared to females. The activity of the amygdala has been related to cortisol response, immune responses, and hormonal physiology that differs between sexes (<xref rid=\"B35\" ref-type=\"bibr\">Goldstein et al., 2019</xref>). The amygdala is a brain region dense in sex steroid, glucocorticoid, and cytokine receptors that are key co-activators of the HPA axis. The amygdala of male mice has more excitatory synapses per neuron compared to female mice (<xref rid=\"B26\" ref-type=\"bibr\">Cooke and Woolley, 2005</xref>; <xref rid=\"B43\" ref-type=\"bibr\">Guneykaya et al., 2018</xref>).</p><p>The enriched KEGG pathway neuroactive ligand–receptor interaction and biological process response to hormone (<xref rid=\"T8\" ref-type=\"table\">Table 8</xref> and <xref ref-type=\"supplementary-material\" rid=\"TS1\">Supplementary File S1 Table H</xref>) are supported by the evidence indicating that the amygdala development is greatly influenced by sex hormones (<xref rid=\"B26\" ref-type=\"bibr\">Cooke and Woolley, 2005</xref>). LHX9 and LHB were strongly over-expressed, while IGSF1 was slightly over-expressed in males compared to females (<xref rid=\"T7\" ref-type=\"table\">Table 7</xref>). The importance of LHX9 in the sexual dimorphism of males and females is highlighted by evidence that <italic>Lhx9<sup>–/–</sup></italic> mice fail to produce gonads, and male <italic>Lhx9<sup>–/–</sup></italic> resemble females phenotypically (<xref rid=\"B14\" ref-type=\"bibr\">Birk et al., 2000</xref>). IGSF1-deficient males have later testosterone secretion than individuals with normal IGSF1 expression (<xref rid=\"B54\" ref-type=\"bibr\">Joustra et al., 2013</xref>).</p><p>Several developmental BP including gland, embryo, sensory organ, and brain development, along with the MF extracellular matrix structural constituent, were enriched among the genes differentially expressed between sexes (<xref rid=\"T8\" ref-type=\"table\">Table 8</xref>). Supporting these categories, EIF1AY was over-expressed in the amygdala of males compared to females (<xref rid=\"T7\" ref-type=\"table\">Table 7</xref>). Consistent with our results, EIF1AY was over-expressed in the male brain at early childhood, puberty, and adulthood and contributes to the structural sexual dimorphism of the amygdala (<xref rid=\"B108\" ref-type=\"bibr\">Shi et al., 2016</xref>).</p></sec><sec id=\"S4.SS7\"><title>Additional Considerations</title><p>The results of our study of long-lasting changes in the amygdala transcriptome profiles at approximately 60 days after exposure to PRRSV advanced the understanding of the impact of MIA on molecular pathways associated with neurodevelopmental processes, neurodegenerative diseases, and behavior disorders. Biological processes impacted at in the amygdala of PD 22 pigs were also impacted at other developmental stages such as fetal GD 83, 96, or 111 (<xref rid=\"B6\" ref-type=\"bibr\">Antonson et al., 2018</xref>, <xref rid=\"B7\" ref-type=\"bibr\">2019</xref>) and included antigen processing and presentation of peptide or polysaccharide antigen via MHC class II and immune response. Likewise, neurodevelopmental processes impacted in the present study were also reported in the amygdala of rats prenatally exposed to MIA by means of valproic acid (<xref rid=\"B12\" ref-type=\"bibr\">Barrett et al., 2017</xref>).</p><p>Notably, we identified sex-dependent vulnerability to the effects of MIA on gene expression in the amygdala of pigs at PD 22. Sex-independent effects dominated in GD 96 fetuses (<xref rid=\"B7\" ref-type=\"bibr\">Antonson et al., 2019</xref>). This comparison supports the hypothesis that sex-dependent effects of MIA in the amygdala become stronger as the animal develops. Additional understanding of MIA effects on the amygdala requires the evaluation of female and male pigs at older ages, in consideration that female pigs reach puberty at 5 months of age. Also, evaluation of the impact of MIA on other brain structures that interact with the amygdala will enable the determination of broader molecular profiles.</p></sec></sec><sec id=\"S5\"><title>Conclusion</title><p>The present study advances the understanding of the prolonged effects of MIA in the molecular pathways of the amygdala, a brain structure key to social, feeding, and other behaviors. The RNA-Seq profiling of 3-week-old female and male pigs, 2 months after viral infection during gestation, offered insights into MIA-associated neurodevelopmental diseases in humans such as ASD and SSD and potential effects in livestock health. The prevalent and sex-dependent dysregulation of genes in immune pathways was detected, supporting established immunotherapies to alleviate the pathophysiology of SSD, ASD, and AD (<xref rid=\"B18\" ref-type=\"bibr\">Busche et al., 2015</xref>; <xref rid=\"B77\" ref-type=\"bibr\">Miller and Buckley, 2016</xref>).</p><p>Our study detected lesser explored molecular processes affected by MIA including the neuroactive ligand–receptor, glutamatergic, amyloid peptides, neuropeptide, retinoid, and ciliogenesis systems. The detection of the previous processes backs the integration of therapies based on immune modulation together with therapies that target neurochemical dysfunction in MIA-associated disorders. The effectiveness of these therapies may be further advanced by our innovative identification of frequently disrupted neuropeptide systems. Our functional and network analyses solidify the promise of multifactorial therapeutic strategies combining immune and neurochemical targets to ameliorate MIA-associated neurodevelopmental and neurodegenerative disorders.</p></sec><sec sec-type=\"data-availability\" id=\"S6\"><title>Data Availability Statement</title><p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/\">https://www.ncbi.nlm.nih.gov/</ext-link>, <ext-link ext-link-type=\"DDBJ/EMBL/GenBank\" xlink:href=\"GSE149695\">GSE149695</ext-link>.</p></sec><sec id=\"S7\"><title>Ethics Statement</title><p>The animal studies were approved by the Illinois Institutional Animal Care and Use Committee (IACUC) at the University of Illinois, and are in compliance with the USDA Animal Welfare Act and the NIH Public Health Service Policy on the Humane Care and Use of Animals.</p></sec><sec id=\"S8\"><title>Author Contributions</title><p>RJ and SR-Z contributed to the conception and design of the study. CB, AA, LR, HR, MC, AKH, and AGH organized the animal experiments and collected the data. MK, PZ, and BS performed the bioinformatics analyses. MK and SR-Z interpreted the results and wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.</p></sec><sec id=\"conf1\"><title>Conflict of Interest</title><p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec></body><back><fn-group><fn fn-type=\"financial-disclosure\"><p><bold>Funding.</bold> This study was supported by the USDA NIFA AFRI (grant no. 2018-67015-27413).</p></fn></fn-group><sec id=\"S10\" sec-type=\"supplementary material\"><title>Supplementary Material</title><p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frontiersin.org/articles/10.3389/fnins.2020.00774/full#supplementary-material\">https://www.frontiersin.org/articles/10.3389/fnins.2020.00774/full#supplementary-material</ext-link></p><supplementary-material content-type=\"local-data\" id=\"TS1\"><media xlink:href=\"Table_1.XLSX\"><caption><p>Click here for additional data file.</p></caption></media></supplementary-material></sec><ref-list><title>References</title><ref id=\"B1\"><mixed-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Aavani</surname><given-names>T.</given-names></name><name><surname>Rana</surname><given-names>S. 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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><?properties manuscript?><front><journal-meta><journal-id journal-id-type=\"nlm-journal-id\">101529516</journal-id><journal-id journal-id-type=\"pubmed-jr-id\">37816</journal-id><journal-id journal-id-type=\"nlm-ta\">J Phys Conf Ser</journal-id><journal-id journal-id-type=\"iso-abbrev\">J Phys Conf Ser</journal-id><journal-title-group><journal-title>Journal of physics. Conference series</journal-title></journal-title-group><issn pub-type=\"ppub\">1742-6588</issn><issn pub-type=\"epub\">1742-6596</issn></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32831894</article-id><article-id pub-id-type=\"pmc\">PMC7431925</article-id><article-id pub-id-type=\"doi\">10.1088/1742-6596/1063/1/012068</article-id><article-id pub-id-type=\"manuscript\">NISTPA1616363</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Toward development of optimum specimen designs and modeling of in-plane uniaxial compression testing of aluminum alloy 2024 and AISI 1008 steel sheet material</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><name><surname>Banerjee</surname><given-names>D K</given-names></name><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Calhoun</surname><given-names>C A</given-names></name><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Iadicola</surname><given-names>M A</given-names></name><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Luecke</surname><given-names>W E</given-names></name><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib><contrib contrib-type=\"author\"><name><surname>Foecke</surname><given-names>T J</given-names></name><xref ref-type=\"aff\" rid=\"A1\">1</xref></contrib></contrib-group><aff id=\"A1\"><label>1</label>Material Measurement Laboratory, NIST, Gaithersburg, Maryland, USA</aff><author-notes><corresp id=\"CR1\"><email>Dilip.Banerjee@nist.gov</email></corresp></author-notes><pub-date pub-type=\"nihms-submitted\"><day>31</day><month>7</month><year>2020</year></pub-date><pub-date pub-type=\"ppub\"><year>2018</year></pub-date><pub-date pub-type=\"pmc-release\"><day>18</day><month>8</month><year>2020</year></pub-date><volume>1063</volume><elocation-id>10.1088/1742-6596/1063/1/012068</elocation-id><permissions><license license-type=\"open-access\"><license-p>Content from this work may be used under the terms of the <ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/3.0\">Creative Commons Attribution 3.0 licence</ext-link>.</license-p></license></permissions><abstract id=\"ABS1\"><p id=\"P1\">Tension-compression testing is commonly conducted to understand and predict springback during a stamping process. However, large strains are generally difficult to achieve during the in-plane compression portion of the test. Proper specimen design and control of frictional forces are necessary for obtaining large strains. This paper describes extensive finite element analyses (FEA) and optimization studies (Phase 1) that were conducted to calibrate the model test assembly for three different buckling modes obtained in uniaxial compression tests of aluminum alloy 2024 and American Iron and Steel Institute (AISI) 1008 steel specimens. In addition to obtaining these three buckling modes correctly, calibrated FEA model predicted forces matched measured forces reasonably well. Also, a good agreement between computed and measured stress-strain data was demonstrated for one compression experiment. In the Phase 2 optimization study, optimum specimen geometries will be developed by using these verified, optimum FEA model test assemblies in three types of compression buckling experiments.</p></abstract></article-meta></front><body><sec id=\"S1\"><label>1.</label><title>Introduction</title><p id=\"P2\">Springback often occurs in sheet metal forming and its prediction poses one of the more complicated modeling challenges. Springback results due to through thickness residual stresses that develop during the complex loading associated with the stamping process, and can cause significant spatial distortions in stamped parts. Tension-compression testing, in conjunction with numerical modeling, is commonly conducted to help understand and predict springback during a stamping process. Large strain is generally difficult to achieve during the in-plane compression portion of the test, since sheet material specimens can exhibit multiple buckling modes. Therefore, a careful design of the specimen geometry is needed for deformation of specimens to large strains during combined tension and compression tests, even when antibuckling guides are used. Compression testing of sheet materials is typically performed using dog bone specimens having length to width ratio of 3[<xref rid=\"R1\" ref-type=\"bibr\">1</xref>]. The efficacy of compression tests depends on the ratio of gauge length to thickness of the specimen. Researchers have obtained in-plane compression strains ranging from 0.01 to 0.15 by varying the gauge length/thickness ratios from 16 to 2 [<xref rid=\"R2\" ref-type=\"bibr\">2</xref>,<xref rid=\"R3\" ref-type=\"bibr\">3</xref>]. Specimen design is crucial since it influences the largest unsupported length that can be used to obtain the maximum plastic strain in the specimen. A hybrid approach, employing both small specimen size and side plate supports applying lateral forces, can help improve the maximum strain that can be achieved, but the approach is still vulnerable as buckling can result both in the supported and the unsupported regions. The state of stress is strongly influenced by the lateral force and the coefficient of friction at the interface between the specimen/side plates and the specimen/tab region. The study of plastic flow behavior is affected by the presence of friction. Additionally, any variation in frictional forces can influence the uniformity of the stress field. Several modes of buckling have been reported in the literature [<xref rid=\"R4\" ref-type=\"bibr\">4</xref>].</p><p id=\"P3\">An extensive finite element analysis (FEA) and optimization study are planned to identify and optimize aluminum alloy 2024 and AISI 1008 steel compressive test specimen geometries. In this work (Phase 1), the model parameters are verified using a combination of simulated and measured data obtained with the experimental set up shown in <xref rid=\"F1\" ref-type=\"fig\">figure 1</xref>. The paper is organized as follows. First an overview of mechanical tests is provided followed by a discussion on modeling of tests using FEA. Finally, an insight into Phase 1 optimization studies is provided for the three types of buckling modes (two described in [<xref rid=\"R4\" ref-type=\"bibr\">4</xref>] and one additional mode). The Phase 1 optimization goal is to obtain the three buckling modes and determine unknown modeling parameters (e.g., friction coefficients). Phase 2 is planned to use this verified model to optimize the specimen geometry.</p></sec><sec id=\"S2\"><label>2.</label><title>Overview of compression tests conducted</title><p id=\"P4\">Uniaxial compression tests were conducted using a new experimental set up (an improvement over the one described in [<xref rid=\"R8\" ref-type=\"bibr\">8</xref>]) that uses digital image correlation (DIC) for displacement and strain measurement. Solid, flat plates (anti-buckling guide, ABG) along with a lateral force were used for out-of-plane buckling restraint. An initial special specimen design was chosen based on a parametric study with the goal to minimize buckling outside of the restrained region. Applied constant lateral plate force allows for more reliable biaxial and friction corrections than in tests where the support is provided by plates fixed in lateral position in opposition to the changing thickness of the specimen. For a short column like region (e.g., the unsupported region of the specimen) failure is caused by plastic yielding as opposed to buckling. Depending on the variations in specimen geometry parameters, types of material considered, and magnitude of the lateral forces, three different buckling modes were observed: a) bottom gap or <italic>L</italic>-buckling [<xref rid=\"R4\" ref-type=\"bibr\">4</xref>], b) in-plane buckling or <italic>W</italic>-buckling [<xref rid=\"R4\" ref-type=\"bibr\">4</xref>], and top fillet buckling (not seen in [<xref rid=\"R4\" ref-type=\"bibr\">4</xref>]). Uniaxial compression tests were conducted using different specimen designs (<xref rid=\"T1\" ref-type=\"table\">table 1</xref> and <xref rid=\"F2\" ref-type=\"fig\">figure 2(b))</xref> in order to obtain the three different buckling modes using the setup shown in <xref rid=\"F1\" ref-type=\"fig\">figure 1</xref>. This employs a modified servo-hydraulic test frame, where strains were measured using a DIC system that uses two 9.1-megapixel cameras. Load cells above and below the hydraulic grips permit the measurement of the frictional forces applied to the specimen by the ABGs. Two pneumatic lateral actuators attached to the vertical supports of the load frame impart a constant lateral force to the specimen. The pressure in the cylinders was held constant throughout the tests. Ball and socket joints between the ABGs and load cells ensure that the faces of ABG plates and specimen faces remain parallel. Teflon film and petroleum jelly were used on the specimen/ABG interfaces for lubrication and minimization of friction. The DIC acquisition is conducted on the exposed side of the specimen (thickness direction) that was not obscured by the ABGs. A virtual extensometer of about 25 mm length is selected in the middle of the gauge region. Strains are computed by the DIC software by knowing the initial 3D coordinates and monitoring the displacements and extension of this virtual extensometer. Three different buckling modes were considered: bottom gap buckling or <italic>L</italic>-buckling, in-plane or <italic>W</italic>-buckling, and top fillet buckling using different specimen geometries. <xref rid=\"T1\" ref-type=\"table\">Table 1</xref> lists key parameters defining the geometry of each of these specimens. Note that two different specimen geometries were used for in-plane buckling tests. <xref rid=\"F2\" ref-type=\"fig\">Figure 2(a)</xref> shows images of buckled specimens for the three buckling modes. <xref rid=\"F2\" ref-type=\"fig\">Figure 2(b)</xref> shows a schematic of the specimen design. For the bottom gap buckling, aluminum alloy 2024 was used. AISI 1008 steel specimens were used for the two other buckling mode specimens. A combination of the specimen geometry, material properties, the axial loading, lubrication, and the lateral forces applied through the ABGs will ultimately determine the type of buckling. All the test specimens were cut from sheets by waterjet. Sheet thickness was 1.14 mm for all tests except for the Al 2024 specimens, which were 1 mm thick. All tests were conducted under displacement control. Strain rates ranged from 4x10<sup>−4</sup> s<sup>−1</sup> to 6.9x10<sup>−4</sup>s<sup>−1</sup>. The top of the specimens was fixed and compressive vertical displacement was applied to the bottom grip by the hydraulic actuator. Force-displacement data for all tests were recorded and compared with FEA simulations for calibration of the model and parameter optimization.</p><sec id=\"S3\"><label>2.1</label><title>Bottom gap buckling (L-buckling)</title><p id=\"P5\">The bottom gap buckling study was conducted using six aluminum alloy 2024 specimens summarized in <xref rid=\"T2\" ref-type=\"table\">table 2</xref>. The unsupported length at the top of the specimen was always 3.45 mm. Uniaxial compression experiments were run with different initial bottom gap spacing. <xref rid=\"T2\" ref-type=\"table\">Table 2</xref> also lists mean lateral forces. The buckling can be compared with the well-known Euler buckling [<xref rid=\"R5\" ref-type=\"bibr\">5</xref>]. The constitutive material data needed for the FEA study were generated by conducting uniaxial tensile stress on the S160609-ERR-100 specimen (see <xref rid=\"T2\" ref-type=\"table\">table 2</xref>).</p></sec><sec id=\"S4\"><label>2.2</label><title>In-plane (W-buckling)</title><p id=\"P6\">The in-plane buckling experiments were conducted using AISI 1008 steel specimens. All tests were conducted with 3.6 kN lateral force applied to specimens by the ABGs. As positioned in the test frame, 3.45 mm at the top and 19.8 mm at the bottom of the specimen length were unsupported. Five tests were conducted with the In-plane1 specimen geometry (<xref rid=\"T1\" ref-type=\"table\">table 1</xref>), with net uniaxial displacements of 7 mm (one test), 9 mm (two tests), and 18 mm (two tests). All specimens except the one with 7 mm net displacement buckled. A separate in-plane compression test to a 4.8 mm displacement was conducted with the In-plane2 specimen to demonstrate the uniformity of deformation achieved in the gauge section. Another In-plane2 specimen was tested in tension to generate the constitutive material data.</p></sec><sec id=\"S5\"><label>2.3</label><title>Top fillet buckling</title><p id=\"P7\">Three identical tests were conducted for the top fillet buckling tests. The unsupported specimen length at the top and bottom were the same as they were for the in-plane buckling tests. The mean lateral forces applied to the specimens by the ABGs were half of those that were applied for the case of bottom gap and in-plane buckling, i.e., 1.8 kN. Buckling in the top fillet buckling specimen always occurred on the top of the specimen (opposite the actuator). The interaction of dynamic and static friction may be the cause.</p></sec></sec><sec id=\"S6\"><label>3.</label><title>FEA and Phase 1 optimization described</title><p id=\"P8\">After the trial geometry was determined, FEA simulation is used to obtain an optimized specimen test configuration. <xref rid=\"F3\" ref-type=\"fig\">Figure 3</xref> shows the FEA test assembly with ABGs and a bottom plate to approximate the imperfect boundary condition at the bottom grip face. In the Phase 1 optimization, the FEA model parameter values such as the coefficient of friction between the ABGs and the specimen and that between the specimen bottom and the bottom plate and a small numerical force imperfection (explained later) were optimized by minimizing the differences between measured and FEA obtained force-displacement data in Isight software [<xref rid=\"R6\" ref-type=\"bibr\">6</xref>]. The Isight process flow includes a procedure where initial model parameters are improved by repeated runs of the FEA model in Abaqus software [<xref rid=\"R7\" ref-type=\"bibr\">7</xref>] (see <xref rid=\"F4\" ref-type=\"fig\">figure 4</xref>). The FEA model of the specimen was constructed in Abaqus using appropriate specimen dimensions and test assembly configurations for each type of buckling study. Linear hexahedral, reduced integration elements, C3D8R, were used along with mapped meshing. Material properties (except constitutive material behavior data) are given in <xref rid=\"T3\" ref-type=\"table\">table 3</xref>. Isotropic hardening was used in all FEA models as very little kinetic hardening is seen for 1008 steel specimens [<xref rid=\"R8\" ref-type=\"bibr\">8</xref>]. Isotropic hardening was also assumed for Al 2024 specimens. Both the specimen and ABGs were classified as deformable bodies. Material properties for both specimen and ABGs were defined. The bottom plate (by which the compressive displacement is applied) was defined as an analytical rigid body. Surface-to-surface contacts were created between specimen surfaces and each of the two ABGs and initial Coulomb friction (constant value) was chosen for interaction between specimen and ABGs. Additional surface-to-surface contacts were created between the specimen bottom and the bottom plate with a different value of initial Coulomb friction for interaction. The kinematic contact method with finite sliding was used as the mechanical constraint formulation in Abaqus/Explicit [<xref rid=\"R7\" ref-type=\"bibr\">7</xref>]. The Phase 1 optimization process flow is shown in <xref rid=\"F4\" ref-type=\"fig\">figure 4</xref>. The design variables are two friction coefficients at the specimen/ABG and the specimen bottom/the bottom plate interfaces, and a very small numerical force imperfection that was applied at an appropriate location (which varies for the three types of buckling studies) to introduce buckling instability (see below). The objective function is the minimization of the sum of square of differences between the measured and computed forces at the top of the specimen for the entire displacement history <inline-formula><mml:math display=\"inline\" id=\"M14\"><mml:mrow><mml:mo>(</mml:mo><mml:mstyle mathvariant=\"normal\"><mml:mi>Δ</mml:mi></mml:mstyle><mml:msubsup><mml:mi>F</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>m</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula>. The NLPQLP [<xref rid=\"R5\" ref-type=\"bibr\">5</xref>] optimization algorithm used is a special implementation of a sequential quadratic programming (SQP) method. In this method, a quadratic programming subproblem is formulated and solved by conducting a quadratic approximation of the Lagrangian function and a linearization of constraints. It is a gradient based method and is well suited for problems with continuous design spaces. For each optimization run with a given set of values for the design parameters, Abaqus is run and the force outputs at specimen top and displacements are extracted in the module “Extract RF’s and U”. A Python script is executed by this module to extract these data from Abaqus output database (ODB) file. These computed data (reaction force vs. displacement) are then compared with measured data in the “Data Matching” module. The ODB files, which are no longer needed, are then deleted in the “Delete Files” module. This sequence of operations is continued for each run. Isight determines the optimal model parameters within the explored space. These values of model parameters are then used for all runs for the study of that type of buckling. Note that U1, U2, U3 are FEA displacements in X, Y, and Z directions.</p><sec id=\"S7\"><label>3.1</label><title>Bottom buckling FEA and Phase 1 optimization study</title><p id=\"P9\">For the bottom buckling experiments, the Phase 1 optimization was conducted with specimen S160418-DJP-022 (see <xref rid=\"T2\" ref-type=\"table\">table 2</xref>). For this analysis, only two design variables were chosen, i.e., the friction coefficient at the specimen/ABG and specimen/bottom plate interfaces (which are the same). No force imperfection for inducing instability was applied. The optimum value of the friction coefficient was found to be 0.0719. Subsequently, this value was used for all the analyses for tests listed in <xref rid=\"T2\" ref-type=\"table\">table 2</xref> and shown in <xref rid=\"F6\" ref-type=\"fig\">figure 6</xref>. A comparison between measured and computed reaction forces at the top of the specimen is shown in <xref rid=\"F5\" ref-type=\"fig\">figure 5</xref> for the optimum run. Also, a comparison between measured and computed reaction forces at the specimen top as a function of bottom gap for all the tests is shown in <xref rid=\"F6\" ref-type=\"fig\">figure 6</xref>. <xref rid=\"F7\" ref-type=\"fig\">Figure 7</xref> shows a typical buckling result (not to scale).</p></sec><sec id=\"S8\"><label>3.2</label><title>In-plane and top fillet buckling FEA and Phase 1 optimization study</title><p id=\"P10\">The FEA assembly models for both of these studies were similar as the top and bottom gap (unsupported lengths) were identical (e.g., 3.45 mm and 19.8 mm respectively). The differences are in specimen geometric parameters (<xref rid=\"F2\" ref-type=\"fig\">figure 2</xref>) and the lateral forces applied through ABGs. The lateral force for the top-fillet buckling tests was 1.8 kN, half of that in in-plane test. For the in-plane buckling, a small numerical force imperfection (L<sub>instab</sub>) was applied to induce instability in the X-direction at a node located at mid-point of the gauge area (along the plane of the specimen). For the top-fillet buckling, that force imperfection was applied in the out-of-plane Z-direction at one node on the middle of the top-fillet region. For each study, the FEA constitutive model calibration strategy was similar. Stress-strain data used for the FEA model for both problems came from a uniaxial tensile test using the specimen design for the In-plane2 geometry in <xref rid=\"T1\" ref-type=\"table\">table 1</xref>. In the Phase 1 optimization study, friction coefficients (at ABG, fric1 and bottom, fric2) and the force imperfection for inducing instability (L<sub>instab</sub>) were the model parameters optimized. The objective function was minimization of <inline-formula><mml:math display=\"inline\" id=\"M15\"><mml:mrow><mml:mstyle mathvariant=\"normal\"><mml:mi>Δ</mml:mi></mml:mstyle><mml:msubsup><mml:mi>F</mml:mi><mml:mrow><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>m</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></inline-formula>. For the in-plane buckling the optimum design parameters were fric1=0.011, fric2=0.69, L<sub>instab</sub> =5.462 N. <xref rid=\"F8\" ref-type=\"fig\">Figure 8</xref> shows compares the reaction forces at specimen top for the experiment to the Phase 1 optimum FEA run. A typical plot showing the specimen deformed shape is shown in <xref rid=\"F9\" ref-type=\"fig\">figure 9</xref> (not to scale). For top-fillet buckling specimen, the match of reaction forces was similar (not shown). <xref rid=\"F10\" ref-type=\"fig\">Figure 10</xref> shows the final shape (not to scale) of the optimum FEA run with out of plane displacement contour plot. For the top fillet buckling, the Phase 1 optimum model parameters were fric1=0.010, fric2=0.494, L<sub>instab</sub> =2.26 N. <xref rid=\"F11\" ref-type=\"fig\">Figure 11</xref> shows the stress-strain data (measured vs. FEA) for the case of the In-plane2 specimen (<xref rid=\"T1\" ref-type=\"table\">table 1</xref>) using the Phase 1 optimum model parameters for the in-plane optimum run as mentioned above. For the FEA result, the axial normal stress and strain data were collected by taking average of all nodal values along a 25 mm long virtual extensometer positioned in the middle of the specimen gauge length. The FEA results show a slight over-prediction of stress.</p></sec></sec><sec id=\"S9\"><label>4.</label><title>Discussion and future study</title><p id=\"P11\">For the in-plane compression testing using the model setup described here, uncertainties in the stress measurements and nonuniformity in strain measurement are due to frictional forces at specimen/ABG interface. Hence, frictional forces must be minimized. Note that the use of two load cells allows for a measurement of the average frictional forces on the entire interface, not just the gauge area. For the bottom buckling tests, very little plastic strain was achieved except in specimen 24 and 26. This is due to final net displacement prior to buckling of less than 2 mm. <xref rid=\"F6\" ref-type=\"fig\">Figure 6</xref> shows a reasonable match of reaction forces for all six tests, except toward the end of the tests. Assumption of constant values of friction coefficient may be the reason. The same is true for top fillet and in-plane tests (<xref rid=\"F8\" ref-type=\"fig\">figure 8</xref>). The reason behind oscillations in FEA reaction forces seen for in-plane (<xref rid=\"F8\" ref-type=\"fig\">figure 8</xref>) tests is not clear. This is possibly due to how Abaqus [<xref rid=\"R7\" ref-type=\"bibr\">7</xref>] calculates friction on a discrete surface. The forces tend to jump for dissimilar meshes belonging to each of the two contacting surfaces, when nodes slide past each other. Desired buckling shapes were obtained in optimized FEA tests (<xref rid=\"F7\" ref-type=\"fig\">figure 7</xref>, <xref rid=\"F9\" ref-type=\"fig\">9</xref>, <xref rid=\"F10\" ref-type=\"fig\">10</xref>). Agreement in stress-strain data is very good (<xref rid=\"F11\" ref-type=\"fig\">figure 11</xref>). The error in estimation of initial yield strength (at zero plastic strain) in material model used in FEA may be the cause of the slight discrepancy. The assumption of isotropic hardening could also contribute to this error. Strain uniformity is desired in the gauge length of the virtual extensometer. This indeed was seen in test (<xref rid=\"F12\" ref-type=\"fig\">figure 12</xref>) and optimum FEA results (not shown). <xref rid=\"F12\" ref-type=\"fig\">Figure 12</xref> shows local <italic>ε</italic>yy at several true strain levels during compression, where horizontal lines denote the average value of the local true strain computed with gauge length of 25 mm. The optimum values of friction coefficients obtained at specimen/ABG interface for both top fillet and in-plane buckling tests were similar to those reported in ref [<xref rid=\"R8\" ref-type=\"bibr\">8</xref>]. Specimen geometry and boundary conditions (ABG friction and lateral forces) determine which buckling mode is likely to occur. Fillet geometry and lateral forces seem to have a strong influence on top fillet buckling, opposite to the moving actuator. In Phase 2 of the optimization study, optimum specimen design parameters (<xref rid=\"F2\" ref-type=\"fig\">figure 2b</xref>) will be obtained that maximize plastic strain in gauge region before the onset of buckling. Identification of this onset is mathematically difficult. But one possible approach is to identify the point at which the positional strain rate gradients (<italic>dε/dx</italic> etc.) in specimen areas of interest show an abrupt and significant change.</p></sec></body><back><fn-group><fn id=\"FN1\"><label>2</label><p id=\"P12\">Certain commercial software or materials are identified to describe a procedure or concept adequately. Such identification is not intended to imply recommendation, endorsement, or implication by NIST that the software or materials are necessarily the best available for the purpose.</p></fn></fn-group><ref-list><title>References</title><ref id=\"R1\"><label>[1]</label><element-citation publication-type=\"book\"><source>ASTM E9-89A (2000)</source>\n<year>2006</year>\n<publisher-name>ASTM International PA USA</publisher-name></element-citation></ref><ref id=\"R2\"><label>[2]</label><mixed-citation publication-type=\"journal\"><name><surname>Abel</surname><given-names>A</given-names></name> and <name><surname>Ham</surname><given-names>RK</given-names></name>\n<year>1966</year>\n<source>Acta Met</source>. <volume>14</volume>(<issue>11</issue>) pp <fpage>1489</fpage>–<lpage>1494</lpage></mixed-citation></ref><ref id=\"R3\"><label>[3]</label><mixed-citation publication-type=\"journal\"><name><surname>Bate</surname><given-names>PS</given-names></name> and <name><surname>Wilson</surname><given-names>DV</given-names></name>\n<year>1986</year>\n<source>Acta Met</source>. <volume>34</volume>(<issue>6</issue>) pp <fpage>1097</fpage>–<lpage>1105</lpage></mixed-citation></ref><ref id=\"R4\"><label>[4]</label><mixed-citation publication-type=\"journal\"><name><surname>Boger</surname><given-names>RK</given-names></name>\n<etal/>\n<year>2005</year>\n<source>Int. J. Plasticity</source>\n<volume>21</volume> pp <fpage>2319</fpage>–<lpage>2343</lpage></mixed-citation></ref><ref id=\"R5\"><label>[5]</label><mixed-citation publication-type=\"book\"><name><surname>Popov</surname><given-names>EP</given-names></name>\n<year>1998</year>\n<source>Engineering Mechanics of Solids</source>\n<edition>2<sup>nd</sup></edition> ed (<publisher-name>Prentice-Hall</publisher-name>\n<publisher-loc>NJ USA</publisher-loc>)</mixed-citation></ref><ref id=\"R6\"><label>[6]</label><element-citation publication-type=\"web\"><source>Isight 5.9 software Dassault Systemes</source>\n<comment><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.3ds.com\">http://www.3ds.com</ext-link></comment></element-citation></ref><ref id=\"R7\"><label>[7]</label><element-citation publication-type=\"web\"><source>ABAQUS14.6<xref ref-type=\"fn\" rid=\"FN1\">2</xref> software Dassault Systemes</source>\n<comment><ext-link ext-link-type=\"uri\" xlink:href=\"http://www.3ds.com\">http://www.3ds.com</ext-link></comment></element-citation></ref><ref id=\"R8\"><label>[8]</label><mixed-citation publication-type=\"journal\"><name><surname>Stoudt</surname><given-names>MR</given-names></name>, <name><surname>Levine</surname><given-names>LE</given-names></name>, and <name><surname>Ma</surname><given-names>L</given-names></name>\n<year>2017</year>\n<source>Exp Mech</source>. <volume>57</volume> (<issue>1</issue>) pp <fpage>155</fpage>–<lpage>163</lpage><pub-id pub-id-type=\"pmid\">28133391</pub-id></mixed-citation></ref></ref-list></back><floats-group><fig id=\"F1\" orientation=\"portrait\" position=\"float\"><label>Figure 1.</label><caption><p id=\"P13\">Setup with DIC, ABGs, and two load cells.</p></caption><graphic xlink:href=\"nihms-1616363-f0001\"/></fig><fig id=\"F2\" orientation=\"portrait\" position=\"float\"><label>Figure 2a.</label><caption><p id=\"P14\">Buckling modes in compression tests; 2b. Schematic specimen sketch.</p></caption><graphic xlink:href=\"nihms-1616363-f0002\"/></fig><fig id=\"F3\" orientation=\"portrait\" position=\"float\"><label>Figure 3.</label><caption><p id=\"P15\">FEA model assembly with ABGs and bottom plate.</p></caption><graphic xlink:href=\"nihms-1616363-f0003\"/></fig><fig id=\"F4\" orientation=\"portrait\" position=\"float\"><label>Figure 4.</label><caption><p id=\"P16\">Isight optimization process flow.</p></caption><graphic xlink:href=\"nihms-1616363-f0004\"/></fig><fig id=\"F5\" orientation=\"portrait\" position=\"float\"><label>Figure 5.</label><caption><p id=\"P17\">Measured and optimum FEA reaction forces at top vs. displacement.</p></caption><graphic xlink:href=\"nihms-1616363-f0005\"/></fig><fig id=\"F6\" orientation=\"portrait\" position=\"float\"><label>Figure 6.</label><caption><p id=\"P18\">Measured and FEA reaction forces (after Phase 1 optimization) at specimen top vs. bottom gap for all tests in <xref rid=\"T2\" ref-type=\"table\">table 2</xref>.</p></caption><graphic xlink:href=\"nihms-1616363-f0006\"/></fig><fig id=\"F7\" orientation=\"portrait\" position=\"float\"><label>Figure 7.</label><caption><p id=\"P19\">Optimum FEA predicted shape and U3 displacement for specimen 22 (<xref rid=\"T2\" ref-type=\"table\">table 2</xref>).</p></caption><graphic xlink:href=\"nihms-1616363-f0007\"/></fig><fig id=\"F8\" orientation=\"portrait\" position=\"float\"><label>Figure 8.</label><caption><p id=\"P20\">Measured and optimum FEA reaction forces at top vs. displacement for In-plane 1 buckling specimen.</p></caption><graphic xlink:href=\"nihms-1616363-f0008\"/></fig><fig id=\"F9\" orientation=\"portrait\" position=\"float\"><label>Figure 9.</label><caption><p id=\"P21\">FEA predicted shape and U1 for In-plane 1 specimen (optimum run).</p></caption><graphic xlink:href=\"nihms-1616363-f0009\"/></fig><fig id=\"F10\" orientation=\"portrait\" position=\"float\"><label>Figure 10.</label><caption><p id=\"P22\">FEA predicted shape and U3 for top fillet buckling specimen (optimum run).</p></caption><graphic xlink:href=\"nihms-1616363-f0010\"/></fig><fig id=\"F11\" orientation=\"portrait\" position=\"float\"><label>Figure 11.</label><caption><p id=\"P23\">Measured and optimum FEA axial normal stress vs. strain for In-plane 2 buckling specimen (<xref rid=\"T1\" ref-type=\"table\">table 1</xref>).</p></caption><graphic xlink:href=\"nihms-1616363-f0011\"/></fig><fig id=\"F12\" orientation=\"portrait\" position=\"float\"><label>Figure 12.</label><caption><p id=\"P24\">DIC <italic>ε</italic>yy along reduced parallel length of the In-plane2 specimen (<xref rid=\"T1\" ref-type=\"table\">table 1</xref>).</p></caption><graphic xlink:href=\"nihms-1616363-f0012\"/></fig><table-wrap id=\"T1\" position=\"float\" orientation=\"portrait\"><label>Table 1.</label><caption><p id=\"P25\">Specimen geometric parameters.</p></caption><table frame=\"above\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Specimen</th><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">R (mm)</th><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">A (mm)</th><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">w (mm)</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Bottom gap</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">20</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Top fillet</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">In-plane 1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">In-plane 2</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">19</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">36</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">18</td></tr></tbody></table></table-wrap><table-wrap id=\"T2\" position=\"float\" orientation=\"portrait\"><label>Table 2.</label><caption><p id=\"P26\">Bottom gap tests with lateral forces and displacements.</p></caption><table frame=\"above\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Specimen</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Bottom<break/>gap (mm)</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Displace<break/>ment (mm)</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Side Force<break/>(kN)</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Tension/<break/>compr<break/>ession</th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S160418-DJP-022</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">31.63</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.43</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.576</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S160418-DJP-023</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">33.78</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.84</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.858</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S160418-DJP-024</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">21.31</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.6</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.586</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S160418-DJP-026</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">23.8</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">4.58</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.583</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S160418-DJP-027</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">28.85</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.32</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.57</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S160418-DJP-028</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">26.24</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">1.97</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">3.56</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">C</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">S160609-ERR-100</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">12.44</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">T</td></tr></tbody></table></table-wrap><table-wrap id=\"T3\" position=\"float\" orientation=\"portrait\"><label>Table 3.</label><caption><p id=\"P27\">Material properties used.</p></caption><table frame=\"above\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Material</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Young's<break/>Modulus<break/>GPa</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Yield<break/>Strength<break/>MPa</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Poisson's<break/>ratio</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Density<break/>kg/m<sup>3</sup></th></tr></thead><tbody><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Al Alloy 2024</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">73.1</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">324</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">2780</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Steel (ABG)</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">210</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">236.4</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7890</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AISI 1008 (Steel)</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">192</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">195</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">0.3</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">7872</td></tr></tbody></table></table-wrap></floats-group></article>\n"
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[
"<!DOCTYPE article\nPUBLIC \"-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.2 20190208//EN\" \"JATS-archivearticle1-mathml3.dtd\">\n<article xmlns:xlink=\"http://www.w3.org/1999/xlink\" xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" article-type=\"research-article\"><?properties open_access?><front><journal-meta><journal-id journal-id-type=\"nlm-ta\">J Educ Eval Health Prof</journal-id><journal-id journal-id-type=\"iso-abbrev\">J Educ Eval Health Prof</journal-id><journal-id journal-id-type=\"publisher-id\">JEEHP</journal-id><journal-title-group><journal-title>Journal of Educational Evaluation for Health Professions</journal-title></journal-title-group><issn pub-type=\"epub\">1975-5937</issn><publisher><publisher-name>Korea Health Personnel Licensing Examination Institute</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type=\"pmid\">32668826</article-id><article-id pub-id-type=\"pmc\">PMC7431942</article-id><article-id pub-id-type=\"doi\">10.3352/jeehp.2020.17.21</article-id><article-id pub-id-type=\"publisher-id\">jeehp-17-21</article-id><article-categories><subj-group subj-group-type=\"heading\"><subject>Educational/Faculty Development Material</subject></subj-group></article-categories><title-group><article-title>Guidelines for the management of extravasation</article-title></title-group><contrib-group><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">http://orcid.org/0000-0003-0661-4735</contrib-id><name><surname>Kim</surname><given-names>Jung Tae</given-names></name><xref ref-type=\"aff\" rid=\"af1-jeehp-17-21\"><sup>1</sup></xref><xref ref-type=\"corresp\" rid=\"c1-jeehp-17-21\">*</xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">http://orcid.org/0000-0002-0210-8213</contrib-id><name><surname>Park</surname><given-names>Jeong Yun</given-names></name><xref ref-type=\"aff\" rid=\"af2-jeehp-17-21\"><sup>2</sup></xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">http://orcid.org/0000-0001-7352-249X</contrib-id><name><surname>Lee</surname><given-names>Hyun Jung</given-names></name><xref ref-type=\"aff\" rid=\"af1-jeehp-17-21\"><sup>1</sup></xref></contrib><contrib contrib-type=\"author\"><contrib-id contrib-id-type=\"orcid\" authenticated=\"false\">http://orcid.org/0000-0001-5791-4063</contrib-id><name><surname>Cheon</surname><given-names>Young Ju</given-names></name><xref ref-type=\"aff\" rid=\"af1-jeehp-17-21\"><sup>1</sup></xref></contrib><aff id=\"af1-jeehp-17-21\"><label>1</label>Department of Pharmacy, Kyung Hee University Hospital at Gangdong, Seoul, <country>Korea</country></aff><aff id=\"af2-jeehp-17-21\"><label>2</label>Department of Clinical Nursing, University of Ulsan, Seoul, <country>Korea</country></aff><aff id=\"edit1-jeehp-17-21\">Hallym University, Korea</aff></contrib-group><contrib-group><contrib contrib-type=\"editor\"><name><surname>Huh</surname><given-names>Sun</given-names></name><role>Editor</role><xref ref-type=\"aff\" rid=\"edit1-jeehp-17-21\"/></contrib></contrib-group><author-notes><corresp id=\"c1-jeehp-17-21\"><label>*</label>Corresponding email: <email>jtkim@khnmc.or.kr</email></corresp></author-notes><pub-date pub-type=\"collection\"><year>2020</year></pub-date><pub-date pub-type=\"epub\"><day>10</day><month>8</month><year>2020</year></pub-date><volume>17</volume><elocation-id>21</elocation-id><history><date date-type=\"received\"><day>16</day><month>7</month><year>2020</year></date><date date-type=\"accepted\"><day>16</day><month>7</month><year>2020</year></date></history><permissions><copyright-statement>© 2020, Korea Health Personnel Licensing Examination Institute</copyright-statement><copyright-year>2020</copyright-year><license><license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type=\"uri\" xlink:href=\"http://creativecommons.org/licenses/by/4.0/\">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p></license></permissions><abstract><p>The purpose of these practice guidelines is to offer and share strategies for preventing extravasation and measures for handling drugs known to cause tissue necrosis, which may occur even with the most skilled experts at intravenous (IV) injection. Herein, general knowledge about extravasation is first described, including its definition, incidence, risk factors, diagnosis, differential diagnosis, and extravasation injuries. Management of extravasation includes nursing intervention and thermal application. At the first sign of extravasation, nursing intervention with following steps is recommended: stop administration of IV fluids immediately, disconnect the IV tube from the cannula, aspirate any remaining drug from the cannula, administer drug-specific antidote, and notify the physician. Local thermal treatments are used to decrease the site reaction and absorption of the infiltrate. Local cooling (ice packs) aids in vasoconstriction, theoretically limiting the drug dispersion. Although clear benefit has not been demonstrated with thermal applications, it remains a standard supportive care. The recommended application schedule for both warm and cold applications is 15 to 20 minutes, every 4 hours, for 24 to 48 hours. For prevention of extravasation, health professionals should be familiar with the extravasation management standard guidelines. They should regularly check the extravasation kit, assess patients’ sensory changes, tingling or burning, and always pay attention to patients’ words. The medical team’s continuous education on extravasation is essential. With the practical use of these guidelines, it is expected to reduce the occurrence rate of extravasation and contribute to patient care improvement.</p></abstract><kwd-group><kwd>Extravasation</kwd><kwd>Antidotes</kwd><kwd>Intravenous injections</kwd><kwd>Patient care</kwd><kwd>Risk factors</kwd></kwd-group></article-meta></front><body><sec><title>Introduction</title><p>Extravasation refers to the leakage of injected drugs from blood vessels causing damage to the surrounding tissues. Common symptoms and signs of extravasation include pain, stinging or burning sensations, and edema around the intravenous (IV) injection site. In severe cases, extravasation may cause tissue dysfunction or physical defects, resulting in a delay of attempted treatment, patients’ distrust, and numerous other issues. To prevent extravasation, a clinical specialist should perform the venipuncture or injection, who with relevant skills and management ability understands the properties of the injected drug. The primary purpose of these guidelines is to minimize the side-effects of IV injection, by suggesting proper and prompt emergency measures for extravasation and the appropriate treatments corresponding to the properties of the injected drug. The second purpose is to raise the medical team’s awareness of extravasation in order to prevent extravasation with careful injection, recover patient trust, and increase patient satisfaction. These guidelines consist of following topics: basic knowledge about extravasation, extravasation management, and extravasation prevention. Antidotes, special drug management, drugs with high osmolarity, and drugs with pH are provided as supplement files (<xref ref-type=\"supplementary-material\" rid=\"SD1\">Supplements 1</xref>–<xref ref-type=\"supplementary-material\" rid=\"SD4\">4</xref>). These contents are derived from authors’ experiences and the references [<xref rid=\"b1-jeehp-17-21\" ref-type=\"bibr\">1</xref>-<xref rid=\"b21-jeehp-17-21\" ref-type=\"bibr\">21</xref>]. It is anticipated that these guidelines would help health professionals to prevent extravasation during IV and central vein injection and to promote patient safety should extravasation occur in any case.</p></sec><sec><title>Extravasation</title><sec><title>Definition</title><p>Extravasation is the leakage of an injected drug out of the blood vessels, damaging the surrounding tissues. In terms of cancer therapy, extravasation refers to the inadvertent infiltration of chemotherapeutic drugs in the tissues surrounding the IV site.</p><p>Extravasated drugs are classified according to their potential for causing damage as ‘vesicant,’ ‘irritant,’ and ‘nonvesicant.’ Vesicant drugs are also classified into 2 groups: DNA binding and non-DNA binding.</p></sec><sec><title>Incidence</title><p>The frequency of extravasation in adults is reported to be between 0.1% and 6%. Some data suggest that the incidence is decreasing probably due to improvements in the infusion procedure, early recognition of the drug leakage, and training in management techniques.</p></sec><sec><title>Risk factors</title><p>Risk factors can be classified under patient-related, procedure-related, and product or product-related factors.</p><sec><title>Patient-related factors</title><p>- Small and fragile veins in infants, children, or elderly patients</p><p>- Vessels that may burst easily</p><p>- Cancer patients with hardened and thickened vessels due to frequent venipuncture</p><p>- Patients with vessels that move easily during venipuncture attempts</p><p>- Patients with excised lymph nodes, limb amputation, or closed vena cava</p><p>- Obesity in which peripheral venous access is more difficult</p><p>- Patients who move around a lot</p></sec><sec><title>Procedure-related factors</title><p>- Untrained or inexperienced staff</p><p>- Multiple attempts at cannulation</p><p>- High flow pressure</p></sec><sec><title>Product or product-related factors</title><p>- Inadequate choice of equipment (peripheral catheter choice, size, or steel needle)</p><p>- Inadequate dressings</p><p>- Poor cannula fixation</p></sec></sec><sec><title>Diagnosis</title><p>Patients must be informed to report any changes in sensation, signs, or symptoms during the IV administration of any chemotherapeutic drug and to alert the healthcare professionals to early signs of extravasation. Particular information must be given when a vesicant drug is administered. Extravasation must be suspected if any of the following specific signs or symptoms are presented (<xref rid=\"t1-jeehp-17-21\" ref-type=\"table\">Table 1</xref>).</p><sec><title>In the case of peripheral IV catheter</title><p>- Possibly no initial symptoms of extravasation</p><p>- Redness, pruritus, and edema around the injection site</p><p>- Fluid injection rate slows down or stops</p><p>- Blood backflow does not work well or there is leakage of medication around the needle</p><p>- A complaint of discomfort or pain and occasional expression of searing pain or numbness</p><p>- Initial physical symptoms usually appear immediately but also might appear several days or weeks later.</p></sec><sec><title>In the case of central venous catheter</title><p>- Often causes stinging pain</p><p>- Edema around the port insertion or in the chest, or medication leakage around the catheter insertion</p><p>- Redness in the chest, collarbone, or neck where a central venous catheter is inserted</p><p>- No blood backflow</p><p>- Symptoms may appear early or late.</p></sec></sec><sec><title>Differential diagnosis</title><sec><title>Flare reaction</title><p>Spots or solid lines with blisters can be suddenly felt along the vessels injected with drugs. Pain, edema, and ulcer do not appear, and symptoms disappear within 30 to 90 minutes.</p></sec><sec><title>Vessel irritation</title><p>Pain, tightening, and skin discoloration tend to worsen. Blood backflow works well, and edema or ulcer do not occur. Pain or tightening occurs along the vein, and it is caused mainly by drugs such as vinorelbine and dacarbazine. Hot fomentations can be applied to the dilated veins to mitigate the symptoms.</p></sec><sec><title>Venous shock</title><p>Occurs due to contraction of the vessel wall and usually happens as soon as the fluid injection begins. For the most part, blood does not backflow. Discoloration and edema do not occur. Venous shock can occur when injecting very cold medication or when medication is injected at a rapid pace. Hot fomentations can dilate the veins and mitigate the symptoms.</p></sec></sec><sec><title>Extravasation injuries</title><p>While the injury is usually minor and resolves spontaneously, some cases result in serious complications, including full-thickness skin loss and muscle and tendon necrosis requiring reconstructive surgery or even amputation, leading to longer hospital stays, increased morbidity, and increased costs.</p><sec><title>Pain</title><p>Narcotic analgesics may be required to reduce severe pain around widespread extensive necrosis.</p></sec><sec><title>Physical defects</title><p>Patients may be unable to work for some time; quality of life must be compensated for if a patient’s occupation requires full physical mobility, and exposure of the disfigurement in public can cause a psychological impact.</p></sec><sec><title>Medical expense</title><p>Depending on the situation, patients will bear the cost of hospitalization and medical expenses for cosmetic surgeries, and secondary medical problems might occur if the condition worsens.</p></sec><sec><title>Disease control</title><p>Treatment suspension wastes time and other problems can occur due to delayed treatment. If bone marrow function decreases, anticancer treatments may be delayed due to infection caused by leukopenia.</p></sec><sec><title>Time</title><p>The patient’s normal activities, such as at home, work, school, etc., may be disrupted until the patient is fully recovered.</p></sec><sec><title>Psychological impact on the nurse and the patient</title><p>Therapists will always feel nervous during the medical team-patient communication because of guilt. Communication and trust between patients and nurses can be interfered due to extravasation.</p></sec></sec></sec><sec><title>Management of extravasation</title><sec><title>Nursing interventions</title><p>At the first sign of extravasation, the following steps are recommended: (1) stop administration of IV fluids immediately, (2) disconnect the IV tube from the cannula, (3) aspirate any residual drug from the cannula, (4) administer a drug-specific antidote, and (5) notify the physician (<xref rid=\"f1-jeehp-17-21\" ref-type=\"fig\">Fig. 1</xref>).</p><p>Elevation of the limb may aid in reabsorption of the infiltrate or extravasated vesicant by decreasing capillary hydrostatic pressure. Apply sterile dressing over the area of extravasation, regularly assess the extravasation site during every shift, and take medical photographs and consult the department of cosmetic surgery if necessary.</p></sec><sec><title>Thermal application</title><p>Local thermal treatments are used to decrease the site reaction and absorption of the infiltrate. Local cooling (ice packs) aids in vasoconstriction, theoretically limiting the drug dispersion. Cold application is recommended for extravasation of DNA-binding vesicants except for mechlorethamine (nitrogen mustard), contrast media, and hyperosmolar fluids. The use of local warming therapy (dry heat) is based on the theory that it enhances vasodilation, thus enhancing the dispersion of the vesicant agent and decreasing drug accumulation in the local tissue. The use of local warming is recommended for the extravasation of non–DNA-binding vesicants. Although clear benefit has not been demonstrated with thermal applications, it remains a standard supportive care, and the recommended application schedule for both warm and cold applications is 15 to 20 minutes, every 4 hours, for 24 to 48 hours.</p><sec><title>Local cooling</title><p>- It causes contraction of blood vessels, minimizing the spread of drugs to other tissues and reducing topical infections and pain.</p><p>- Directions: apply cold fomentations for 15 to 20 minutes four to 6 times per day (for 1 day or more).</p></sec><sec><title>Local warming</title><p>- It dilates the blood vessels around the extravasation site, increases dispersion and absorption of the medicinal fluid by increasing the blood flow, and helps to quickly purge medicinal fluid that has leaked from the extravasation site.</p><p>- Directions: apply hot fomentations for 20 to 30 minutes four to 6 times per day (for 1 day or more).</p></sec></sec><sec><title>Documentation</title><p>Because errors associated with IV administration can result in fatal or life-threatening outcomes, administration of IV fluids and medications can be a high-risk, with adverse outcomes potentially leading to malpractice claims.</p><p>An incident of extravasation must be correctly documented and reported. Documentation procedure may differ between treatment centers (documentation form); however, certain items are mandatory for patient safety and legal purposes: (1) patient name and number, (2) date and time of the extravasation, (3) name of the drug extravasated and the diluent used (if applicable), (4) signs and symptoms (also reported by the patient), (5) description of the IV access, (6) extravasation area (and the approximate amount of the drug extravasated), and (7) management steps with time and date.</p><p>Photographic documentation can be helpful for follow-up procedures. The patient must be informed of the scope of the problem (<xref ref-type=\"supplementary-material\" rid=\"SD5\">Supplement 5</xref>).</p></sec></sec><sec><title>Prevention of extravasation</title><sec><title>General guidelines</title><p>Most extravasations can be prevented with the systematic implementation of careful, standardized, and evidence-based administration techniques. The staff involved in the infusion and management of cytotoxic drugs must be trained to implement several preventive protocols for the minimization of the risk of extravasation. It is important to remember that the degree of damage is dependent on the type of the drug, the drug concentration, the localization of the extravasation, and the length of time for which the drug develops its potential for damage.</p><p>- Be familiar with the extravasation management standard guidelines and prepare the extravasation kit.</p><p>- Regularly check the extravasation kit and refill any used medications. Extravasation kit includes the following: 25G needle, 10-cc syringe, and 1-mL syringe; disinfection swabs, sterile gauze, and adhesive bandage; saline solution (1 ampule); sterile distilled water (1 ampule); dimethyl sulfoxide 99% solution; hyaluronidase 1,500 U/mL (refrigerated); hydrocortisone cream 1%; sodium thiosulfate 25% solution; and warm pack and an ice pack (frozen).</p><p>- Assess patient’s sensory changes, tingling or burning, and always pay attention to the words of patients.</p></sec><sec><title>Preventive strategies: peripheral venous access device extravasation</title><p>- Do not insert the cannula in the joints because it is difficult to secure, and neural damage and tendon injury can be caused if extravasation occurs due to vesicant drugs.</p><p>- Do not insert the cannula in the antecubital fossa area, where it is extremely difficult to detect extravasation.</p><p>- Veins on the back of the hand can be used, and in some cases, observation is easier. But it must be done carefully because this area can suffer a more severe injury due to extravasation.</p><p>- For observation, do not cover the cannula area with opaque gauze.</p><p>- Secure the cannula during the administration of the drug.</p><p>- Even if there is an existing IV route, secure a new route when administering vesicant drugs.</p><p>- If in doubt, re-insert the cannula and administer the drug.</p><p>- Watch for edema, inflammation, and pain around the cannula during administration.</p><p>- Check for blood backflow before/during administration, and always rinse the catheter with a saline solution in between administrations.</p><p>- Dilute stimulant drugs as much as possible and inject them at a proper rate.</p><p>- Once the needle is removed, apply pressure to the puncture site for about five minutes and elevate the limb.</p></sec><sec><title>Preventive strategies: central venous access device extravasation</title><p>- Check for blood backflow before injection to ensure that the catheter is positioned in the vein.</p><p>- Check if there is any local discomfort or swelling by running a saline solution through the catheter, and then inject the drug.</p><p>- After the injection, make sure to run a saline solution through the catheter.</p></sec></sec><sec><title>Conclusion</title><p>Extravasation is a serious complication during patient care. Although drugs can be administered by methods (e.g., micro-patch, micro-injection) other than IV injection, extravasation cannot be totally avoided because there are drugs that can only be administered through IV or central vein injection. The guidelines described herein are based on the authors’ best practice for the management and prevention of the extravasation. However, no guidelines can be perfect, and they need to be regularly updated. It will be our pleasure if these guidelines are used in the training of health professionals to promote patients’ safety.</p></sec></body><back><fn-group><fn fn-type=\"participating-researchers\"><p><bold>Authors’ contributions</bold></p><p>Conceptualization: JTK, JYP, HJL, YJC. Data curation: JTK, JYP, HJL, YJC. Project administration: JTK, JYP, HJL, YJC. Visualization: JTK, JYP, HJL, YJC. Writing–original draft: JTK, JYP, HJL, YJC. Writing–review & editing: JTK, JYP, HJL, YJC.</p></fn><fn fn-type=\"COI-statement\"><p><bold>Conflict of interest</bold></p><p>No potential conflict of interest relevant to this article was reported.</p></fn><fn fn-type=\"financial-disclosure\"><p><bold>Funding</bold></p><p>None.</p></fn><fn id=\"fn1-jeehp-17-21\"><p><bold>Data availability</bold></p><p>None.</p></fn></fn-group><ack><p>None.</p></ack><fn-group><fn><p>Editors’ note: I found this booklet, guidelines for the management of extravasation on November 15, 2019, at the 2019 Fall Conference of the Korean Society for Clinical Pharmacology and Therapeutics. It is the brilliant info like a diamond that is useful for the training of the interns, nurses, and medical laboratory technicians who routinely work for the vascular injection. I remember the case of the complication of extravasation in my patient in 1985 when I was an intern. The patient should receive the skin graft for recovering her arm skin. If I understood the guidelines for extravasation management, it might be possible to prevent the necrosis of her arm skin. This guideline will enable health professions to manage the patients’ extravasation appropriately.</p></fn></fn-group><sec sec-type=\"supplementary-material\"><title>Supplementary materials</title><p>Data files are available from Harvard Dataverse: <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.7910/DVN/NUWZML\">https://doi.org/10.7910/DVN/NUWZML</ext-link></p><supplementary-material content-type=\"local-data\" id=\"SD1\"><caption><p>Supplement 1. Antidotes for the care of the extravasation.</p></caption><media mimetype=\"application\" mime-subtype=\"docx\" xlink:href=\"jeehp-17-21-suppl1.docx\" orientation=\"portrait\" id=\"d38e460\" position=\"anchor\"/></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SD2\"><caption><p>Supplement 2. Special drug management for care of the extravasation.</p></caption><media mimetype=\"application\" mime-subtype=\"docx\" xlink:href=\"jeehp-17-21-suppl2.docx\" orientation=\"portrait\" id=\"d38e465\" position=\"anchor\"/></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SD3\"><caption><p>Supplement 3. Drugs with high osmolarity.</p></caption><media mimetype=\"application\" mime-subtype=\"xlsx\" xlink:href=\"jeehp-17-21-suppl3.xls\" orientation=\"portrait\" id=\"d38e470\" position=\"anchor\"/></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SD4\"><caption><p>Supplement 4. Drugs with pH.</p></caption><media mimetype=\"application\" mime-subtype=\"xlsx\" xlink:href=\"jeehp-17-21-suppl4.xls\" orientation=\"portrait\" id=\"d38e475\" position=\"anchor\"/></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SD5\"><caption><p>Supplement 5. Photographic documentation of extravasation case.</p></caption><media mimetype=\"application\" mime-subtype=\"docx\" xlink:href=\"jeehp-17-21-suppl5.docx\" orientation=\"portrait\" id=\"d38e480\" position=\"anchor\"/></supplementary-material><supplementary-material content-type=\"local-data\" id=\"SD6\"><caption><p>Supplement 6. The audio recording of the abstract.</p></caption><media xlink:href=\"jeehp-17-21-abstract-recording.avi\" mimetype=\"application\" mime-subtype=\"avi\" orientation=\"portrait\" id=\"d38e485\" position=\"anchor\"/></supplementary-material></sec><ref-list><title>References</title><ref id=\"b1-jeehp-17-21\"><label>1</label><element-citation publication-type=\"book\"><source>Drug information: Clinical Computerized Information System: vol. 113</source><publisher-loc>Englewood (CO)</publisher-loc><publisher-name>Micromedex Inc</publisher-name><comment>[date unknown]</comment></element-citation></ref><ref id=\"b2-jeehp-17-21\"><label>2</label><element-citation publication-type=\"book\"><article-title>BIT Druginfo website [Internet]</article-title><publisher-loc>Seoul</publisher-loc><publisher-name>BIT Druginfo</publisher-name><year>2020</year><comment>[cited 2020 Jul 10]. 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Forum</source><year>2006</year><volume>33</volume><fpage>1134</fpage><lpage>1141</lpage><pub-id pub-id-type=\"doi\">10.1188/06.ONF.1134-1141</pub-id><pub-id pub-id-type=\"pmid\">17149396</pub-id></element-citation></ref><ref id=\"b21-jeehp-17-21\"><label>21</label><element-citation publication-type=\"journal\"><person-group person-group-type=\"author\"><name><surname>Langer</surname><given-names>SW</given-names></name></person-group><article-title>Extravasation of chemotherapy</article-title><source>Curr Oncol Rep</source><year>2010</year><volume>12</volume><issue>4</issue><fpage>242</fpage><lpage>246</lpage><pub-id pub-id-type=\"doi\">10.1007/s11912-010-0110-7</pub-id><pub-id pub-id-type=\"pmid\">20480268</pub-id></element-citation></ref></ref-list></back><floats-group><fig id=\"f1-jeehp-17-21\" orientation=\"portrait\" position=\"float\"><label>Fig. 1.</label><caption><p>Steps to be taken of the extravasation.</p></caption><graphic xlink:href=\"jeehp-17-21f1\"/></fig><table-wrap id=\"t1-jeehp-17-21\" orientation=\"portrait\" position=\"float\"><label>Table 1.</label><caption><p>Extravasation assessment tool</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" valign=\"middle\" align=\"center\" colspan=\"1\"/><th colspan=\"5\" valign=\"middle\" align=\"center\" rowspan=\"1\">Grade<hr/></th></tr><tr><th valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">0</th><th valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">1</th><th valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">2</th><th valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">3</th><th valign=\"middle\" align=\"center\" rowspan=\"1\" colspan=\"1\">4</th></tr></thead><tbody><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Color</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Normal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pink</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Red</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Blanched</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Blackened</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Integrity</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Unbroken</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Blistered</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Superficial skin loss</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tissue loss exposing subcutaneous tissue</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Tissue loss exposing muscle/bone with a deep crater or necrosis</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Skin temperature</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Normal</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Warm</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Hot</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Edema</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Absent</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Non-pitting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pitting</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Mobility</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Full</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Slightly limited</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Very limited</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Immobile</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Pain</td><td colspan=\"5\" valign=\"top\" align=\"left\" rowspan=\"1\">Rate on a scale of 0–10</td></tr><tr><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Fever</td><td valign=\"top\" align=\"left\" rowspan=\"1\" colspan=\"1\">Normal</td><td colspan=\"4\" valign=\"top\" align=\"left\" rowspan=\"1\">Elevated (highest value during 24 hours)</td></tr></tbody></table></table-wrap></floats-group></article>\n"
] |
