Search is not available for this dataset
pmcid
large_stringlengths 8
11
| pmid
large_stringlengths 0
9
| authors
large_stringlengths 0
33.4k
| title
large_stringlengths 0
2.49k
| publication_date
large_stringlengths 10
10
| keywords
large_stringlengths 0
17k
| abstract
large_stringlengths 0
122k
| text
large_stringlengths 9
8.39M
|
|---|---|---|---|---|---|---|---|
PMC10000013 | Sayan Chatterjee,Lakshmi Vineela Nalla,Monika Sharma,Nishant Sharma,Aditya A. Singh,Fehmina Mushtaque Malim,Manasi Ghatage,Mohd Mukarram,Abhijeet Pawar,Nidhi Parihar,Neha Arya,Amit Khairnar | Association of COVID-19 with Comorbidities: An Update | 27-02-2023 | COVID-19,comorbidity,diabetes,cancer,Parkinson’s disease,cardiovascular disease | Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) which was identified in Wuhan, China in December 2019 and jeopardized human lives. It spreads at an unprecedented rate worldwide, with serious and still-unfolding health conditions and economic ramifications. Based on the clinical investigations, the severity of COVID-19 appears to be highly variable, ranging from mild to severe infections including the death of an infected individual. To add to this, patients with comorbid conditions such as age or concomitant illnesses are significant predictors of the disease’s severity and progression. SARS-CoV-2 enters inside the host cells through ACE2 (angiotensin converting enzyme2) receptor expression; therefore, comorbidities associated with higher ACE2 expression may enhance the virus entry and the severity of COVID-19 infection. It has already been recognized that age-related comorbidities such as Parkinson’s disease, cancer, diabetes, and cardiovascular diseases may lead to life-threatening illnesses in COVID-19-infected patients. COVID-19 infection results in the excessive release of cytokines, called “cytokine storm”, which causes the worsening of comorbid disease conditions. Different mechanisms of COVID-19 infections leading to intensive care unit (ICU) admissions or deaths have been hypothesized. This review provides insights into the relationship between various comorbidities and COVID-19 infection. We further discuss the potential pathophysiological correlation between COVID-19 disease and comorbidities with the medical interventions for comorbid patients. Toward the end, different therapeutic options have been discussed for COVID-19-infected comorbid patients. | Association of COVID-19 with Comorbidities: An Update
Coronavirus disease (COVID-19) is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) which was identified in Wuhan, China in December 2019 and jeopardized human lives. It spreads at an unprecedented rate worldwide, with serious and still-unfolding health conditions and economic ramifications. Based on the clinical investigations, the severity of COVID-19 appears to be highly variable, ranging from mild to severe infections including the death of an infected individual. To add to this, patients with comorbid conditions such as age or concomitant illnesses are significant predictors of the disease’s severity and progression. SARS-CoV-2 enters inside the host cells through ACE2 (angiotensin converting enzyme2) receptor expression; therefore, comorbidities associated with higher ACE2 expression may enhance the virus entry and the severity of COVID-19 infection. It has already been recognized that age-related comorbidities such as Parkinson’s disease, cancer, diabetes, and cardiovascular diseases may lead to life-threatening illnesses in COVID-19-infected patients. COVID-19 infection results in the excessive release of cytokines, called “cytokine storm”, which causes the worsening of comorbid disease conditions. Different mechanisms of COVID-19 infections leading to intensive care unit (ICU) admissions or deaths have been hypothesized. This review provides insights into the relationship between various comorbidities and COVID-19 infection. We further discuss the potential pathophysiological correlation between COVID-19 disease and comorbidities with the medical interventions for comorbid patients. Toward the end, different therapeutic options have been discussed for COVID-19-infected comorbid patients.
Coronavirus disease (COVID-19) is a communicable disease associated with the dysfunction of the upper respiratory tract caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The city of Wuhan, China was the first to document pneumonia cases of unknown etiology at the end of December 2019. It was spread to around 188 nations, resulting in many confirmed cases and severe health and socioeconomic consequences. The total number of confirmed cases worldwide reached 574,818,625 with 6,395,451 fatalities by the fourth week of July 2022; this data was presented by the Coronavirus Resource Center at Johns Hopkins University. Ejaz and colleagues reported that SARS-CoV-2 infections could cause various symptoms, from mild diseases that go away independently to dangerous ones that affect many organs. There are mainly four types of coronavirus (CoV), classified as α-CoV, β-CoV, γ-CoV, and δ-CoV. Of these, only α-CoV and β-CoV have been shown to cause animal sickness. Further, β-CoV was responsible for SARS in 2003 and the Middle East respiratory syndrome (MERS) in 2012. According to several genomic studies, SARS-CoV-2 is an encapsulated virus with a positive sense-RNA genome. The genome of SARS-CoV-2 is approximately 96% similar to that of bat CoV RaTG13. Furthermore, the genomic sequence and evolution of the analysis of SARS-CoV-2 have a 79.5% genomic similarity to the severe acute respiratory syndrome-coronavirus (SARS-CoV). SARS-CoV-2 enters human cells by attaching to the angiotensin convertase enzyme2 (ACE2) receptor of the upper respiratory tract, which acts as an entry point for this virus. Although the virus travels by intranasal and oral pathways, it affects olfactory sensory neurons. Eventually, it infects the central nervous system (CNS), causing hyposmia (loss of sensation of smell) and hypogeusia (loss of taste) as well as other sensory symptoms. Although SARS-CoV-2 infects people of all ages and genders, research shows that individuals with comorbidities are more susceptible to COVID-19 infection. Further evidence suggests that male patients 50 years of age with or without comorbidities show a significantly increased risk of death. According to the Centers for Disease Control and Prevention (CDC), USA, individuals aged 65 and above accounted for around 30% of COVID-19 infections, 45% of hospitalizations, 53% of intensive care unit (ICU) admissions, and 80% of deaths. In addition, after COVID-19 infection, those with a compromised immune system due to cancer treatments or steroids requiring hospitalization are prone to mortality. Given those the events of ICU admissions or mortality following COVID-19 infection increase. It is vital to comprehend the mechanisms and treatment alternatives that are most suited for marginalized populations. Based on clinical data on COVID-19, comorbidities like cardiovascular disease (CVD) including hypertension and diabetes have been the most prevalent. In this review, we focused on the link between COVID-19 and comorbidities such as Parkinson’s disease (PD), cancer, diabetes, and CVD. We also looked at epidemiological data, pathological relationships, and potential treatment options for COVID-19-infected people with comorbidities.
As was highlighted, this review illustrates the relationship between several comorbidities and COVID-19. A literature search was conducted online using several databases and search engines like PubMed to exploit this. The keywords such as COVID-19, SARS-CoV-2, and comorbidity in COVID-19 were used to get the most relevant articles that support this study. On the other hand, the relationship between PD, cancer, diabetes mellitus, CVD, and hypertension with COVID-19 articles were also used to accomplish this study. Besides, the rest of the articles with mismatched or irrelevant keywords were not considered for this study. All the publications were examined and referenced based on their relevancy and compatibility with the current topic of discussion. We used PubMed’s “Boolean Operators” (AND, NOT, and OR) search criterion to acquire relevant search results for this. Figure 1 depicts the strategy for obtaining the data and subsequently filtering the articles, and Table 1 provides the keywords used for searching through Pubmed. The significant comorbidities of COVID-19, such as PD, cancer, diabetes, and CVD, have been included after a literature search and screening of published research articles, meta-analyses, and systemic review studies. In this study, 198 articles were discussed in depth to show how these significant comorbidities were to blame for hospitalization, ICU admission, and mortality in most cases. In addition, we looked at the molecular and cellular mechanisms of COVID-19 and how they relate to its pathophysiology, linked with substantial comorbidities. For instance, cytokine storm is typical of all the main comorbidities described above. We looked for several inflammatory cytokines connected to COVID-19-related comorbidities in this context. Then, we checked the further information on clinical trials web site to learn more about the clinical studies that used the repurposed drugs for the appropriate comorbidities. Furthermore, we have also included future projections as well as several treatment strategies for each comorbidity. Patients with COVID-19 have received treatment using a wide range of therapeutic modalities globally. In the absence of a vaccine or a SARS-CoV neutralizing antibody, convalescent plasma therapy and pharmaceutical repurposing lead the charge.
During genomic replication, a virus’s genetic code changes (gene mutation), a phenomenon also prevalent with the SARS-CoV-2 virus, wherein constant gene mutations have led to many variants (lineage) of the same virus over time. A lineage is a set of genetically related viral variants that share a common ancestor. SARS-CoV-2 has several lineages, all of which produce COVID-19 infection. Some lineage changes propagate more rapidly and easily than others, perhaps contributing to make COVID-19 cases more common. A rise in the number of cases have imposed a higher burden on healthcare resources, resulting in additional hospitalizations and, perhaps, fatalities. In the USA, epidemiological investigations into viral genetic sequence-based monitoring and laboratory research are routinely conducted to track SARS-CoV-2 genetic lineages. The SARS-CoV-2 Interagency Group (SIG) of the US government categorized Omicron as a Variant of Concern on November 30, 2021 (Control and Prevention, 2021). According to SIG, there are four types of SARS-CoV-2 variants: 1. Variant Being Monitored (VBM): Alpha (B.1.1.7 and Q lineages), Beta (B.1.351 and descendent lineages), Gamma (P.1 and descendent lineages), Epsilon (B.1.427 and B.1.429), Eta (B.1.525), Iota (B.1.526), Kappa (B.1.617.1), 1.617.3, Mu (B.1.621, B.1.621.1), Zeta (P.2); 2. Variant of Interest (VOI): None of the variant(s) yet identified; 3. A variant of Concern (VOC): Delta (B.1.617.2 and AY lineages), Omicron (B.1.1.529 and BA lineages); 4. A variants of High Consequence (VOHC): This sort of variation is yet to be detected internationally. Omicron is constantly evolving mutations even after 3 years of the pandemic and still giving rise to several new subvariants, such as BA.2.75 and BA.4.6. Importantly, several of these new variations, including BA.2.3.20, BA.2.75.2, CA.1, BR.2, BN.1, BM.1.1.1, BU.1, BQ.1.1, and XBB, exhibit notable growth advantages over BA.5. Recently reported in the second week of October 2022 is the name of the latest lineages variant of Omicron is XBB and sublineages XBB.1 (S:252 V) found in major regions such as China, United Kindom (UK), Europe, and North America, resulting in all these countries announcing that they are now going on nationwide lockdown-like restriction once again due to the sudden surge of the new COVID variant. XBB and XBB.1 (S:252 V) are mainly found in Bangladesh, Singapore, and India. The prevalence of Delta and Omicron (BA.1) coinfections and Omicron lineages BA.1 and BA.2 coinfections were estimated at 0.18% and 0.26%, respectively. Among 6,242 hospitalized patients, ICU admission rates were 1.64%, 4.81, and 15.38% in Omicron, Delta, and Delta/Omicron patients, respectively. Among patients admitted to the ICU, there were no reports of BA.1/BA.2 coinfections. A total of 21 patients (39.6%) of the 53 coinfected patients missed vaccinations. Even though SARS-CoV-2 coinfections were rare in their clinical study, it is still essential to accurately identify them so that they can figure out how they affect patients and how likely they will make recombinants. One clinical study has been conducted in France to detect the prevalence of SARS-CoV-2 coinfection during spread of the Delta, Omicron, Delta/Omicron variant. This study was held from December 2021 to February 2022. They tested the effectiveness of four sets of whole-genome sequencing primers using 11 blends of Delta/Omicron isolates at multiple ratios, and they developed a bioinformatics technique that is impartial for identifying coinfections involving various genetic SARS-CoV-2 lineages. Applied to 21,387 samples collected from 6 December 2021 to 27 February 2022, random genomic surveillance in France, they detected 53 coinfections between different lineages. The Delta variation was the most susceptible and transmissible of all of these variants, that resulted in an increase in the percentage of fatalities as well as comorbidities such as hospitalization of older persons. The Omicron variant may spread more quickly than other variants, such as Delta; however, Omicron was less lethal compared to the Delta variant. These differences in response could be attributed to many factors such as the less efficient cleaving of the S protein fraction of omicron and more α helix stabilization than the delta variant.
COVID-19 infection is associated with aging, which is a major risk factor for severe illness and mortality, especially for those who are in long-term care facilities. In addition, people at any age with serious underlying medical conditions are more at risk of getting COVID-19 infection. The elderly, SARS-CoV-2 infected persons with comorbidities, including PD, diabetes, cancer, and hypertension (HTN) and CVD, are at higher risk of death. Early evidence from several epidemiological data sets shows that the COVID-19 case fatality ratio (CFR) increases with age. Table 2 reflects the number of CFRs in various countries; overall, the CFR of China and USA is 2.3% and 2.7%, respectively, while the global CFR was at 2.8%. Italy was the first nation to be affected by the pandemic after China. The total CFR of Italy was higher (7.2%) compared to China (2.3%). This is attributed to a more significant proportion of older adults (22.8% and 11.9%, respectively) in Italy. In addition, an 82-year-old man in Brooklyn was the first COVID-19 fatality reported in New York City. A significant case series of 5,700 COVID-19 patients admitted to hospitals in New York City revealed a similar pattern of COVID-19 fatalities with age. However, the mechanism of SARS-CoV-2 infection is still unknown. However, the primary mechanism of SARS-CoV-2 underlies the ACE2 enzyme’s expression and utilization of the ACE2 receptor, which helps to enter the SARS-CoV-2 inside the cell. Lymphopenia, abnormal respiration, and a high level of pro-inflammatory cytokines in plasma are the main manifestations ascribed to individuals with COVID-19 infection along with very high body temperature and respiratory issues. COVID-19 is caused by several metabolic and viral disorders, all of which have a part in the developing of the more complicated symptoms. The CFR for India seems to be lower than in several European nations. This might attributed to low percentage of population (6.38%) to be above the age of 65 as per 2019 statistics. According to reports, COVID-19 puts elderly persons (those over 60) at a greater risk of mortality. The relationship between CFR and several other health and socioeconomic factors, variations in the virulence of SARS-CoV-2 across geographical areas, and COVID-19 response indicators unique to certain countries has to be further studied. The projected CFRs (July 2020) based on the random- and fixed-effect models were 1.42% (95% Cl 1.19–1.70%) and 2.97% (95% CI 2.94–3.00%), respectively. Estimates made using the random-effects model were more likely to accurately reflect the real CFR for India because of the high level of variability. In earlier research, the COVID-19 CFR was estimated using random-effect models, or the CFR was provided using both random- and fixed-effect models. We made sure that states with a lot of cases and fatalities got more weightage than those with fewer cases and deaths by using a random effects model.Table 3 shows that COVID-19-infected patients with comorbidities had a higher death risk. As a consequence of aging, the body experiences progressive biological alterations in immune function, concurrently causing an increased susceptibility to age-related inflammation (inflammaging) and other associated inflammatory conditions, which makes the elderly population vulnerable to enhanced risk of infection following exposure to the virus. Inflammaging is a chronic low-stage inflammation mediated by dysfunction in the basal responses of the pattern recognition receptors (PRRs) and pathogen-associated molecular patterns (PAMPs). A progressive impairment in autophagic signals affect the PRR signals in the aging population and consequently cause the exorbitant release of reactive oxygen species (ROS). This condition may further be worsened with the binding of the virus to immune cells as they also work in synchronization with PAMPs and PRRs, thereby causing oxidative damage to cells in older individuals. Another hallmark of inflammaging is increased production of interleukins (ILs), especially IL-1β and IL-18, due to activation of the NLR family pyrin domain containing 3 (NLRP3) inflammasome, in COVID-19 infections. NLRP3 activation is strongly correlated to the aging population. This consequently causes increased pyroptosis, which is central to cell death post-infection; increased release of IL-1β, IL-18 as well as damage-associated molecular patterns (DAMPs) further cumulates inflammatory responses in the elderly. This mechanism has also been reported to be the root cause of inflammatory conditions such as cancer, diabetes, PD, and acute myocardial injury. Furthermore, with age, there is a progressive hardening of the endothelial cells, causing the increased formation of plaque, and further leading to a hypercoagulative state. Following COVID-19 infection, reports have demonstrated that a hypercoagulative state is associated with an increased risk of comorbid conditions such as ischemic stroke and myocardial infarction. Therefore, it can be concluded that aging and comorbid conditions are crucial factors in eliciting the response mediated by COVID-19 infection alone. So, in the subsequent sections, we discussed the latest reports of age-related comorbidities, such as PD, cancer, diabetes, and CVD, and how they relate to the severity and pathology of COVID-19.
The population of patients needing hospital admission is disproportionately composed of older adults, men, and people with comorbid conditions, including diabetes and CVDs. After COVID-19 expanded to multiethnic communities in Western Europe and North America, multiple studies claimed that Black, Asian, or other minority ethnic groups were more likely to be affected by the illness. According to the statistical report from USA, in several cities or cumulative analyses across large states, Black and Hispanic people had higher per capita mortality rates than White people, but the underlying reasons were unknown. A large cohort study has been conducted in the United Kingdom (UK), wherein they reported higher overall death rates for Black and South Asian people compared to White people. However, the study ignores the wide variances in the ethnic makeup of local populations across various geographic locations. In this regard, a case-control and cohort study was conducted at King’s College Hospital Foundation Trust, UK to investigate if ethnic origin influences the probability of hospital admission with severe COVID-19 and/or in-hospital mortality. Inner city adult patients with confirmed COVID-19 admitted to the hospital (n = 872 cases) were compared with 3,488 matched controls randomly drawn from a primary healthcare database consisting of 344,083 people dwelling in the same area. For the cohort study, the authors examined 1,827 people continuously hospitalized with COVID-19. Self-defined ethnicity served as the primary exposure factor and analyses were adjusted for socio-demographic and clinical characteristics. Based on the results obtained by conditional logistic regression analysis, it was demonstrated that the Black and Mixed/Other ethnicity were linked with greater admission risk than the White. Further, the Black and Mixed ethinicity could be linked to disease severity but not to in-hospital mortality. This was majorly attributed to ethinicity and partly to comorbities and socioeconomic factors. In addition, the study elucidated the association of increased in-hospital mortality with ICU admission for Asian ethinicity. Therefore,it could be concluded that COVID-19 disease outcome is influenced by the ethnic background.
Although aging is a prominent factor for comorbidities, it may not be as labeled as the only confounding factor for the disease severity. Reports have suggested that hospitalized males had the highest mortality rate as compared to females and this association was more prominent with patients with predisposing conditions such as hypertension, diabetes and obesity in an age-dependent manner. One study has shown male patients’ as a predictor of ICU admissions. Contrastingly, in context of the long term COVID-19 manifestations, women were more likely to report uneasiness, breathlessness, and fatigue following recovery. Outcomes in severity also resulted from biochemical differences in males and females; compared to male patients, females had higher lymphocyte counts, higher levels of high-density lipoprotein, as well as lower levels of highly sensitive C-reactive protein. Another hypothesis for gender disparity in protection is that the females have biallelic Toll-like receptor (TLR)7 expression, thereby leading to a better interferon (IFN)-mediated response after early infection. Interestingly, studies have reported that estrogen confers some protection against the severity of COVID-19. This has been validated in a preclinical setting, where mice infected with the SARS-CoV virus had a higher mortality rate following ovariectomy or estrogen receptor antagonist administration. Further, pregnant women with mild infection demonstrate same outcome as uninfected pregnant women. However, those with severe infection demonstrate a higher risk of perinatal infection as well as mortality, usually having a tendency to feel unwell and this further exacerbates during COVID-19 infection and may result in worsening of conditions of the patients. Children are disportionately infected with COVID-19 compared to older population but with low infection severity and could be attributed to lower concentration of ACE2 receptors in children as well as trained/acquired immunity as a result of vaccination, indigenous virus competition, as well as maternal immunity. Thus, it can be concluded that sex differences play a major role in the outcomes and severity of COVID-19 infection. Due to the pivotal role of female hormones, they may be less susceptible to long-term manifestations which are prevalent in male patients.
SARS-CoV-2 is a neurotropic virus, that can enter the CNS either by hematogenous or neuronal retrograde dissemination. In an autopsy study, viral RNA in the brains of several COVID-19 patients was detected. Patients having neurological diseases are generally more vulnerable to the respiratory system infections.and could be attributed to the involvement of central respiratory centers in the case of COVID-19 infections. There are shreds of evidence that suggest the potential of SARS-CoV-2 to enter the brain through the olfactory epithelium and cause neuronal death in mice. Furthermore, there have been cases of COVID-19 individuals developing symptomatic parkinsonism after 2–5 weeks of viral infection. SARS-CoV was discovered in the cerebral fluid of individuals suffering from acute SARS-CoV disease, as was also reported in the COVID-19 cases. Recent data from a study performed in three designated COVID-19 care hospitals of the Union Hospital of Huazhong University of Science and Technology in Wuhan, China suggests that 78 patients out of 214 COVID-19 patients demonstrated neurologic manifestations. This involved the CNS, peripheral nervous system, and skeletal muscles, subsequently indicating the neurotropic potential of this virus. Hitherto, there is not enough evidence regarding the susceptibility of PD patients to COVID-19. According to a study from the Parkinson’s and Movement Disorders Unit in Padua, Italy and the Parkinson’s Foundation Centre of Excellence at King’s College Hospital in London, UK, PD patients with an average age of more than 78.3 years and with a disease period of greater than 12.7 years are more prone to COVID-19. They also have a significantly high mortality rate of 40%. The patients in extreme conditions of PD with respiratory muscle rigidity, dyspnoea, and on deep brain stimulation or levodopa infusion therapy showed high vulnerability and 50% mortality. Moreover, SARS-CoV and H1N1 viruses (structurally and functionally similar to COVID-19) can aggravate the mechanisms involved in PD pathophysiology as supported by previous studies. A few studies also suggest the role of the CNS in COVID-19 infection indicating that PD patients might be more prone to COVID-19 infection.
There are various theories about how SARS-CoV-2 enters the CNS. Peripheral SARS-CoV-2 infection can lead to a cytokine storm, which might disrupt the blood–brain barrier integrity and might be a mechanism for SARS-CoV-2 to infiltrate into the CNS. Besides this, ACE2 receptors are highly expressed in the substantia nigra and striatum (the regions which are potentially affected in PD) making dopaminergic neurons present in these brain regions more susceptible to SARS-CoV-2 infection. Furthermore, the accumulation of alpha-synuclein (α-Syn) is the major hallmark of PD. According to previous findings, the entrance of SARS-CoV-2 into the CNS may upregulate this protein, causing aggregation. On the contrary, few studies have indicated α-Syn’s protective effect in blocking viral entry and propagation into the CNS. Moreover, its expression in the neurons can act as a barrier to viral RNA replication. In a retrospective cohort study conducted in Japan, PD patients suffering from pneumonia showed a lower mortality rate. Furthermore, the main pathophysiological pathway involved in PD development, includes autophagy disruption, ER stress, and mitochondrial dysfunction. As a result, COVID-19 may trigger frequent modulations in these pathways, as seen in SARS-CoV and influenza A virus. Proteostasis plays a vital role in protein translation, folding, and subsequent clearance with the help of heat shock proteins (HSPs). Viral infection hijacks the host cellular machinery for its replication and disrupts the proteostasis pathways by interacting with Hsp40. The viral-Hsp40 interaction results in the binding of Hsp40 with two subunits of viral RNA polymerase, which further assists the viral genome to get translocated into the nucleus via interaction with the viral nucleoprotein and inhibition of protein kinase R (PKR) activation, thereby restricting the host from producing an antiviral response. Hsp90 also modulates the activity of viral RNA polymerase after it enters the nucleus. Under normal conditions, Hsp90 and Hsp70 restrict apoptosis initiation pathways thereby reducing apoptosis. However, infection with SARS-CoV-2 suppresses Hsp90 and Hsp70 function, leading to activation of caspase cascade followed by apoptosis and subsequent propogation of infection. Autophagy lysosomal pathways and ubiquitin-proteasome pathways are two important components of proteostasis and are responsible for the degradation of impaired proteins. It has been reported that H1N1 infection obstructs autophagic flux at the initial stages resulting in the reduced number of autophagosomes and hindering autophagosome-lysosome fusion at later stages of autophagy. Both these activities result in autophagy disruption in human dopaminergic neurons and mouse brain and subsequently lead to α-Syn aggregation. Furthermore, when H1N1 was instilled intranasally in Rag knockout mice, α-Syn aggregates were found in the cells near olfactory bulbs, which may further spread in a prion-like manner to other regions of the brain and originate downstream events of PD pathogenesis. Furthermore, the ubiquitin-proteasome system can destroy viral proteins by ubiquitination; however, the H1N1 virus hijacks this mechanism and inhibits the host cell opponents of viral reproduction. This disrupts proteostasis and toxic protein aggregation. Further, SARS-CoV-2 has also been shown to act similarly to H1N1 virus and might be involved in α-Syn accumulation. Besides this, ER has also been reported as a target of various viruses. SARS-CoV-2 utilizes ER for the synthesis and processing of viral proteins. It has been shown previously that the Spike (S) protein gets collected in the ER and induces unfolded protein response (UPR) by transcriptional activation of several UPR effectors, including glucose-regulated protein 78 (GRP78), GRP94, and CCAAT/Enhancer-binding protein (C/EBP) homologous protein to aid viral replication. UPR may result in ER stress, which further activates cellular signals triggering neuronal death, are implicated in PD. Another study discovered that SARS-CoV open reading frames (ORF) 6 and 7a produce ER stress via GRP94 activation. Mitochondrial dysfunction is another pathway that connects PD with COVID-19. ORF-9b of SARS-CoV-2 degrades dynamin-like protein 1 (Drp1), involved in mitochondrial fission, thus causing mitochondrial elongation. Moreover, it suppresses antiviral cellular signaling by targeting the mitochondrial-associated adaptor molecule signalosome. ORF3b is located partially in mitochondria and is involved in apoptosis along with other accessory proteins (ORF3a, ORF6, and ORF 7a of SARS-CoV). The virus utilizes the mitochondria for caspase activation for apoptosis, thereby causing viral dissemination to other cells. The aforementioned cellular malfunctions result in increased ROS, redox imbalance, and mitochondrial and lysosomal dysfunction making the cells more susceptible to infection. According to current research, neuroinflammation is a defining factor in COVID-19 infection. Proinflammatory cytokine levels are higher in the periphery and cerebrospinal fluid in PD patients. Strikingly, studies have reported that viral infection can also induce neuroinflammation. Due to the compromised anti-inflammatory mechanisms in old age, the older population is more susceptible to develop neurodegenerative diseases as well as severe COVID-19 infection. TLRs may play a role in the immunological response to coronavirus infections indicated by the presence of PAMPs (lipopolysaccharides, dsDNA/RNA, ssRNA) in the host cells recognized by specific TLRs derived from viruses following infection. TLR 3 is known to be activated in the case of HSV-I and influenza A infection. Whenever TLRs are triggered, pro-inflammatory cytokines are released (IL-1, IL-6, and tumor necrosis factor-alpha (TNF-alpha)) and type I IFN-α/β via MyD88-dependent and MyD88-independent pathways, which further translocate nuclear factor kappa-light-chain-enhancer of activated B cell (NF-κB), interferon regulatory factor (IRF), IRF-3 and IRF-7, inside the nucleus. Moreover, NF-κB has been reported to contribute to the pathogenesis of PD by triggering the release of pro-inflammatory mediators and subsequent neuroinflammation. Therefore, it can be concluded that NF-κB plays a common role in inflammation in both PD and COVID-19 pathogenesis. Neuroinflammation can also trigger misfolding and aggregation of α-Syn. Aggregated α-Syn leads to the activation of microglia which further favors the production of pro-inflammatory cytokines, ultimately causing neurodegeneration (Figure 2).
None of the anti-Parkinsonian drugs render PD patients at risk for COVID-19; therefore, PD patients should not alter or stop any medicine without a clinician’s consultation. However, in order to avoid possible interactions, PD patients with COVID-19 infection should not use cough suppressants containing dextromethorphan and pseudoephedrine with Selegiline. Previous data suggest that the therapies used for COVID-19 such as angiotensin converting enzyme inhibitors (ACEi), might be safe for PD patients as well. Neuroprotective effects of ACEi like captopril and perindopril have been observed in PD animal models, which act by preventing dopaminergic cell loss and increasing striatal dopamine content, respectively. ACEi also work as antioxidants by reducing oxidative stress, which has been linked to PD and has been found to decrease the number of falls in a cross-sectional study involving 91 PD patients. Furthermore, hydroxychloroquine exhibits anti-Parkinsonian effects by raising Nurr1 expression, inhibiting glycogen synthase kinase-3 beta (GSK-3β) and functioning as an anti-inflammatory drug, making it a viable treatment option for PD patients infected with COVID-19. In another study, a COVID-19 patient with PD was cured with remdesivir in a clinical trial. In addition, the antiviral drug amantadine used in PD treatment has also been used for years in the treatment of influenza. Amantadine inhibits viral replication by blocking the influenza M2 ion channel, thereby preventing the delivery of viral ribonucleoprotein into the cytoplasm of the host and might have a disruptive effect on the lysosomal pathway. As a result, amantadine might be advantageous for treatment in COVID-19-positive PD patients as a potential treatment approach to lower viral load in these individuals. Oseltamivir phosphate, an anti-influenza drug, was found to be useful in PD as it inhibits H1N1-induced α-Syn aggregation. Many studies have highlighted that older people are typically deficient in vitamin D; vitamin D might have antiviral properties. As a result, vitamin D3 supplementation (2000–5000 IU/day) has been recommended in older PD patients, which might protect them against COVID-19. Moreover, it has been suggested that vitamin D3 can slow down the progression of PD. Above all, Fenoldopam, a dopamine D1 receptor agonist, was shown to be protective against inflammation as well as lung permeability and pulmonary edema in an endotoxin-induced acute lung injury mouse model. Taken together, it can be concluded that therapies used in PD and COVID-19 infection do not have any detrimental drug interaction. However, diagnosis presents a challenge in PD patients especially in the older population as they might neglect the COVID-19 symptoms because of other chronic diseases. Furthermore, early symptoms of COVID-19, for example dyssomnia, might be neglected by PD patients as they commonly suffer from olfactory dysfunction. Still, people with COVID-19 pneumonia who take medications for PD should have their doses changed because motor symptoms can impair breathing thereby worsening the condition.
There is a scarcity of data linking PD and COVID-19 outcomes. However, evidence from molecular processes of SARS coronaviruses changing proteostasis, mitochondrial and ER malfunction, and α-Syn aggregation suggests that it is necessary to identify medications acting on these pathways as a treatment for PD patients suffering from COVID-19. Detailed investigations on these mechanisms are required to determine PD patients’ sensitivity to COVID-19 as well as the safety of antiviral medicines, vaccinations, ACEi, and other antiviral drugs used for COVID-19. The indirect effect of this pandemic on PD patients including no direct patient–doctor visits, depression due to social distancing, reduced physical activity, and battery failure in patients on deep brain stimulation therapy also need attention and should be taken care of with the help of video conferencing and the availability of sufficient stock of medications.
Reports suggest patients with lung cancer, hematological cancer, or any metastatic cancer are potentially at high risk of COVID-19 infection either due to treatment or disease susceptibility. Toward this, COVID-19-infected cancer patients were studied retrospectively; analysis showed a higher incidence of severe events following infection of COVID-19 infection, especially in the patients who received the anticancer treatment for 14 days. In one study, a total of 15 (53.6%) patients developed severe clinical event, and 28.6% were found to be morbid. Similar studies suggested being vigilant toward cancer patients who are on anticancer treatments as they are prone to COVID-19 infection as a result of reduced immunity. As per a nationwide study in China, roughly 39% (7 out of 18) of cancer patients infected with COVID-19 experienced severe symptoms, compared to only 8% (124 out of 1572) of patients not suffering with cancer. Another collective report has stated that there were 52 different studies worldwide involving 18,650 cancer patients infected with COVID-19 with 4,243 deaths. The data further implied that the risk of mortality of cancer patients is about 25.6 % COVID-19 as compared to 2.3% in the normal population. COVID-19 puts lung cancer patients at significant risk of developing severe episodes. In this regard, a study reported that out of 102 lung cancer patients infected with COVID-19 infection, 62% of lung cancer patients were hospitalized, and the mortality rate was approximately 25%. In another study, 55% (6 out of 11) mortality was observed in lung cancer patients infected with COVID-19 which was very high compared to other cancers.
SARS-CoV-2 infects the host cell via the ACE2, which is a cell surface receptor. It is highly expressed on the lung epithelial cells and subsequently gets cleaved with the help of the host transmembrane serine protease2 (TMPRSS2). The viral internalization evokes the host immune response through the activation of alveolar macrophages and the complement cascade. This activation leads to a massive release of pro-inflammatory cytokines such as IL-1, IL-6, IL-8, and IFN-γ. This phenomenon, termed the cytokine storm, further causes alveolar endothelial tissue damage. Among these cytokines, IL-6 is involved in the pathophysiology of cancer, especially, lung cancer and other chronic diseases. It has been reported that IL-6 promotes tumorigenesis and anti-apoptotic signaling and is an important biomarker for cancer diagnosis and prognosis. The involvement of IL-6 in abnormally immune-activated conditions like cancer inflammation and immunosuppression have also been reported. Apart from IL-6, the activation of serine/threonine p21 activated kinase1 (PAK1), an essential component of malaria and some viral infections, is also a critical mediator of cytokine storm and gets overexpressed in SARS-CoV-2 infected lungs, resulting in mortality of COVID-19 patients. One study has reported that the human PAK, is an important component of host–pathogen interactions. PAK paralogues (Group I PAKs, include PAK1, PAK2, and PAK3; Group II PAKs, including PAK4, PAK5, and PAK6) are found in nearly all mammalian tissues, wherein they play important roles in a variety of processes including cell survival and proliferation, cell cycle progression, and cytoskeletal organization and are involved in different types of cancer. In drug development, the role of PAKs in cell survival along with proliferation, as well as participation in a variety of malignancies, is of significant interest. PAK1 activation can lead to the development of lung fibrosis by stimulation of the chemokine (C–C motif) ligand 2 (CCL2) production, thereby aggravating the patient’s condition. PAK1 blockers can help in restoring the immune response thereby combating virus-induced lung fibrosis. In this regard, the PAK1 inhibitor (propolis) was tested as a therapeutic approach for treating COVID-19 patients. Its extract components were shown to have inhibitory effects against other targets like ACE2 and TMPRSS2 (Figure 3). Besides, underlying conditions and altered immune responses increase the risk of developing venous thromboembolism, microvascular COVID-19 lung and vessels obstructive thrombo-inflammatory syndrome in cancer patients. The progressive endothelial thrombo-inflammatory syndrome may also cover the brain’s microvascular bed and other vital organs, resulting in multiple organ failures and death. Additionally, Bhotla et al. and colleagues hypothesized that platelets are getting infected due to COVID-19 infection which exacerbate the SARS-CoV-2 infection and ultimately lead to the bronchopneumonia or death. In conclusion, the biochemical and immunological characteristics outlined above demonstrate that cancer patients, are more susceptible to COVID-19 infections than the general population. However, non-biological variables such as increased contact with the healthcare system for cancer treatment may potentially contribute to the increase in COVID-19 prevalence in cancer patients.
Because of the current pandemic situation, as well as the associated risk factors, cancer patients must take additional precautions. As a result, in order to minimize the adverse consequences of the COVID-19 pandemic on highly susceptible cancer patients, hospitals should have, more robust management procedures in place. To this end, chemotherapy or surgery should be postponed, intense treatment should be provided, greater personal protection should be provided, telemedicine should be used, and a separate treatment approach for COVID-19 cancer patients should be implemented. As discussed in the previous section, IL-6 has been recognized as a crucial component of the immune response to SARS-CoV-2. Many clinical trials are currently underway to investigate treatment strategies targeting IL-6 by repurposing anti-IL-6 therapeutics for COVID-19 in cancer patients. Tocilizumab, a monoclonal antibody against the IL-6 receptor, has shown promising results in a double-blind, placebo-controlled phase-III study called EMPACTA (NCT04372186). Similarly, siltuximab, an IL-6 receptor chimeric mouse–human monoclonal antibody, has already exhibited its antitumor efficacy and is under diverse randomized control trials for further assessing its efficacy for COVID-19 patients (NCT04486521, NCT04330638, and NCT04329650). In addition, tumor reversion therapy might be the near future therapy for the treatment of cancer. The molecular biology behind the tumor reversal process is not only fascinating but alluring. Some chemical compounds used for tumor reversion include LY294002, metformin, sertraline, and ellipticine. Apart from this, T cell therapy such as immune checkpoint inhibitors (ICIs) and chimeric antigen receptor T cell (CAR-T) therapy may increase the risk of cytokine release resulting in increased severity of COVID-19 infection. Cytokine storm is directly linked to COVID-19-associated diseases such as acute respiratory distress syndrome (ARDS) and multiorgan failure. Moreover, an immunocompromised cancer patient with immunocompromised therapy and also those at risk for immune-related side effects in response to immuno-oncology treatments should be monitored closely. Another treatment approach is mesenchymal stem cell therapy; this is approved and is now used for cancer treatment alone or in combination with other drugs. It was even applied on 7 patients with COVID-19 infection, resulting in a negative effect on the expression of ACE2 along with TMPRSS2. On the sixth day, cytokine-secreting cells (CXCR3+ CD4+ T, CXCR3+ CD8+ T, and NK CXCR3+ cells) disappeared as peripheral lymphocyte counts increased. While the TNF-alpha levels were reduced, dendritic cell populations and IL10 levels were increased. Along with IL-6, other cytokines associated with the pathogenesis of COVID-19 infection in cancer patients include type I IFN, IL-1, IL-7, IL-17, and TNF-alpha. Even a clinical study named Bee-COVID was conducted with the Brazilian Green Propolis Extract for the treatment of the COVID-19 condition (NCT04480593). In conclusion, targeting these inflammatory markers may serve as a good approach to treating COVID-19 infection in cancer patients.
The COVID-19 outbreak is a global threat to the health system. Despite the substantial study, there is currently no recognized treatment for COVID-19. Still, it is unclear why some people respond abruptly to SARS-CoV-2 infection and others are asymptomatic. Further, why people with coexisting comorbidities are more susceptible to severe clinical events of COVID-19 is unclear. Being at high risk of infection and deteriorating outcomes, cancer patients are suggested to be more cautious and follow the guidelines issued by World Health Organization and the European Society for Medical Oncology.
Diabetes mellitus (DM) is a common metabolic disorder with multiple etiologies primarily associated with a deficiency of insulin secretion and/or its action. Besides the clinical complication of the disease, an individual with diabetes is more susceptible to a broad range of infections (such as foot infection, rhinocerebral mucormycosis, malignant external otitis, and gangrenous cholecystitis) as well as predisposed to certain conditions that primarily affect lungs like influenza, tuberculosis, and legionella pneumonia. Moreover, diabetes and its complications such as disrupted glycemic control and ketoacidosis were found to be a potential risk factor for mortality in the influenza A (H1N1) pandemic in 2009, SARS-COV, and MERS coronavirus infection. Several studies have reported high mortality in COVID-19 patients with diabetes. More specifically, diabetes was the most common underlying comorbidity in approximately 22% of the 32 nonsurvivors from a cohort of 52 COVID-19 patients in intensive care. Detailed clinical research on 140 hospitalized COVID-19 patients in Wuhan indicated the second highest prevalence was diabetes (12.1%) after hypertension. Another study on a subset of 1099 patients discovered that out of 177 severe cases, 16.2% of patients with seriously infected conditions had diabetes. Eighteen of these patients had composite outcomes, including death, use of mechanical ventilation, and admission to an ICU. Recent epidemiological research of 72,314 COVID-19 patients at the Chinese Center for Disease Control and Prevention found that diabetics mortality in diabetic patients was three times greater than non-diabetic patients (7.3% mortality rate as compared to the overall 2.3% mortality rate). Besides, a recent retrospective multicenter cohort study on 191 laboratory-confirmed COVID-19 cases observed a statistically significant association between diabetes and increased mortality. In yet another study, Fadini and his colleagues at the University Hospital of Padova discovered that the mortality rate of diabetic SARS-CoV-2 patients were 1.75 times greater than the general population. This research, coupled with earlier research, suggested that diabetes may exacerbate the outcome of a new coronavirus illness. The CDC data further suggested that COVID-19-infected diabetic patients have a higher risk of the developing severe symptoms. Toward this, in a retrospective research conducted in Wuhan, China, 32% of cases patients had comorbidities, 20% of which were diabetics. In addition, hyperglycemia or dysregulated glycemic management is associated with a high risk of complications. Diabetes as a major risk factor for the course and prognosis of COVID-19 was further mentioned according to a study of 174 COVID-19 patients admitted to Wuhan Union Hospital.
COVID-19 infection in diabetic patients might increase stress hormone levels such as glucocorticoids and catecholamines, leading to high glucose levels. Hyperglycemia in diabetes induces glucose allowing non-enzymatic glycosylation of lung collagen and elastin by advanced glycation end products thereby resulting in reduced elasticity of the lungs COVID-19 infection. This causes thickening of the alveolar epithelial basal lamina and microvascular alterations in the pulmonary capillary beds, further leading to a reduction in pulmonary capillary blood volume and diffusing capacity, which influence the patient’s overall survival. Even a brief period of hyperglycemia has the potential to alter immune cell function. Diabetes increases pro-inflammatory cytokines, including IL-1, IL-6, and TNF-alpha. IL-6, C-reactive protein, serum ferritin, and coagulation index, D-dimer are significantly higher in diabetic individuals than in those without diabetes. Current research suggests that diabetic patients are more prone to cytokine storms. This may be further exaggerated in response to a stimulus as seen in patients with COVID-19 infection. In a study, patients infected with COVID-19 have developed a fatal hyperinflammatory syndrome characterized by a fulminant and fatal hypertyrosinemia, and was thought to be associated with disease severity as demonstrated increased IL-2, IL-7, IL-12, TNF-alpha, and IFN-γ inducible protein 10, macrophage inflammatory protein-1α (MIP-1α/CCL3). A prolonged hyperglycemic state causes an elevated immune response, which further leads to inflammation. Interestingly, increased glucose levels were discovered to directly promote SARS-CoV-2 replication in human monocytes and to sustain SARS-CoV-2 replication through the formation of ROS and activation of hypoxia-inducible factor-1α. This massive influx of inflammatory cells has the potential to disrupt the activities of the primary insulin-responsive organs, i.e., the skeletal muscles and liver, which are primarily responsible for insulin-mediated glucose absorption. High glucose concentration in the plasma leads to cytokine production, glucotoxicity, and viral-induced oxidative stress; these factors promoted a greater risk of thromboembolic problems as well as damage to important organs in diabetic patients (Figure 4). Moreover, one enzyme, dipeptidyl peptidase-4 (CD26 or adenosine deaminase complexing protein 2), tends to bind with the virus and promote the ACE2 expression which is involved to initiate the infectious disease. Furthermore, studies have shown severe lung pathology with dysregulated immune response in mice (with existing Type2 DM (T2DM)) infected with MERS-CoV. In addition, COVID-19 patients with diabetes have an easier progression to acute respiratory distress syndrome and septic shock resulting in multiple organ failures. Another direct metabolic link that exists between coronavirus infection and diabetes is based on the expression of ACE2 present in specific tissue. ACE2 is a transmembrane glycoprotein that converts angiotensin II (Ang II) to angiotensin (Ang). ACE2 is also expressed in blood vessels, macrophages, and monocytes. The expression of ACE2 is of prime importance in the etiology of SARS-CoV-2. It has been observed that inflammatory signals produced by macrophages, such as type I IFN, increase ACE2 receptor expression. The infection of pancreatic macrophages may have triggered these inflammatory signals. As a result, more immune cells particularly pro-inflammatory monocyte/macrophages are recruited, causing further damage to islets of Langerhans and β-cells in the pancreas. Subsequently, it reduces insulin release, subsequently resulting in acute hyperglycemia and transitory diabetes in healthy people. Additionally, macrophages and monocytes are mobile, and once infected with the SARS-CoV-2 virus, they can infiltrate the cells of the pancreatic islets and spread the virus throughout the pancreas. Furthermore, SARS-CoV-2 infection of the β-cells can directly damage them, causing apoptosis, and further worsening the glycemic control of diabetic patients. Furthermore, anti-diabetic medicines, such as glucagon-like peptide 1 agonists, antihypertensive drugs, and statins, also increase ACE2 expression. COVID-19-infected patients were treated with different medications, such as systemic corticosteroids and antiviral medicines, which may potentially cause hyperglycemia. Glucocorticoid-induced DM (GIDM) is a frequent and potentially critical issue to address in clinical practice, although it may contribute to worsening hyperglycemia and ultimately aggravate the diabetic condition associated with COVID-19 infection. The detailed mechanism causing glucocorticoid-induced hyperglycemia is the promotion of weight gain, decrease in peripheral insulin sensitivity, increase in the production of glucose with the promotion of gluconeogenesis, β cell injury due to the destruction of pancreatic cells, increase in the levels of fatty acids, and impairment of insulin release. However, in the case of SARS-CoV-2 infection, the effect of diabetes on ACE2 expression still needs to be studied in detail. In a study, an increased expression of ACE2 was observed in the kidney of diabetic patients in the early stage followed by a decreased expression in later stages that relatively overlaps with the occurrence of diabetic nephropathy. Each of these pathways working synergistically may worsen the situation for diabetic patients making them frailer and further increasing the severity of COVID-19 disease.
The glucose level in diabetic patients is mainly maintained with the administration of insulin and is mainly recommended for critically ill patients infected with SARS-CoV-2. It has also been observed that insulin infusion significantly reduces the inflammatory cytokines and helps in lowering the severity of COVID-19. Metformin has been proven to have anti-inflammatory properties in preclinical investigations, and it has also been demonstrated to lower circulating levels of inflammatory biomarkers in persons with T2DM. Besides the anti-inflammatory action of metformin, it is potentially used against the SARS-CoV-2 virus. Metformin acts through inhibiting the virus–host-cell association as well as prevents the expression of ACE2 through the activation of adenosine monophosphate-activated protein kinase. Another promising molecule, DPP-4 antagonist (Linagliptin), an anti-diabetic agent with potential anti-aging properties, was repurposed to combat COVID-19 infection also demonstrates antiaging properties. Further, plant-based natural compounds such as resveratrol, catechin, curcumin, procyanidin, and theaflavin have been tested for the treatment of COVID-19 disease through in silico, in vitro, and in vivo studies. More recently, convalescent plasma therapy has also been applied for COVID-19 associated comorbidities wherein it was used to downregulate the inflammatory cytokines and the viral load in COVID-19 patients. Sulfonylurea must be avoided in COVID-19 patients comorbid with T2DM, as they can cause hypoglycemia. Thiazolidinediones can be used in mild diseases with caution as they have protective effects on the cardiovascular system. However, due to weight gain, edema, and heart failure, thiazolidinediones were not used in moderate and severe conditions.
COVID-19 pathology majorly involves inflammation that eventually leads to multiple organ failure and even death. Comorbid diabetic patients are highly susceptible to hospitalization-based COVID-19 infection. Diabetes is characterized by a persistent state of hyperglycemia that can aggravate SARS-CoV-2-induced inflammation. Therefore, diabetic patients’ medication should be monitored as some drugs can increase the expression of viral entry receptors leading to a poor prognosis of COVID-19 in these patients. Further, insulin resistance has also been reported in comorbid, especially, T2DM patients. Hence, management of diabetes is of utmost importance in infected patients. Drugs that do not interact with ACE2 receptors could be given, though insulin treatment is unanimously given for COVID-19 patients with comorbid status of diabetes.
CVD is a broad classification for a range of conditions that involve dysregulation of the heart and vascular systems. Since COVID-19 infection is a multiplexed pathophysiological condition, it is obvious that the cardiovascular systems are at the crux of exposure. Toward this, in a multicenter study conducted in Wuhan involving 191 patients, 24% of the patients that died had coronary heart disease. In addition, in a systemic review of 199 patients with COVID-19, 40% of them were diagnosed with myocarditis. Further, a meta-analysis study reported that 25% of the patients developed acute cardiac injury and the mortality rate was 20 times higher than those pre-existing comorbidities in comparison to those with no pre-existing comorbidities. As discussed in the previous section, systemic inflammation due to viral infection is prevalent and is a consequence of the cytokine storm. This inflammation is of particular concern for cardiac tissues and has been shown to cause myocarditis. The mechanism proposed for this pathology demonstrates the involvement of hepatocyte growth factor release, followed by priming of the immune cells such as T cells and subsequent release of IL-6 As a result, COVID-19 patients have been found to have pericardial effusion, which can lead to inflammation of the membranes around the heart and pericarditis. Conversely, the safety of mRNA vaccines is of particular concern as they have been reported to cause myocarditis. In the earlier section we discussed PD, its association with COVID-19 and the importance of olfactory nerves and the brain. Although nerves express ACE2 receptors, which are of utmost importance for infectivity, one cannot rule out the importance of the possible involvement of the heart–brain axis. This bidirectional communication is important as it is mediated by the control of endothelial cells and the regulation of cerebral blood flow by somatosensory signaling mechanisms. Thus, infection with COVID-19 disrupts this normal physiological function and, as a consequence, vascular–neuronal communication is disturbed thereby causing headaches and increased incidences of stroke in infected patients. Along with this, serious implications such as the neural spread of viruses and consequent, central disturbances such as anxiety are predisposed due to neuroinflammation and lower brain-derived neurotropic factor levels. Elevated levels of inflammatory biomarkers, CRP and D-dimer are the most common markers for COVID-19-associated coagulopathy. Particularly, elevated levels of D-dimer in hypertensive patients indicate the severity of the disease since COVID-19 patients’ death rates are more likely to rise with D-dimers upon admission or throughout time. Alternatively, in patients with diabetic complications, depletion of the ACE2 receptor causes activation of the renin-angiotensin-aldosterone system (RAAS), leading to β-cell destruction and an increased risk of cardiomyopathy in an age-dependent manner. However, these concepts are still premature and need a discussion on hypertension, which has been studied to a great extent with COVID-19 and is a predisposing factor for other cardiovascular complications as well. HTN is the most common disorder in people aged 50 years or more. Epidemiological studies have reported a high risk of hospitalization for patients with CVD such as HTN after COVID-19 infection. One study has shown hypertension (30%) and coronary heart disease (20%) as the most prevalent comorbidities (8%) in COVID-19-infected patients. Another investigation has shown that HTN (27%) and cardiovascular complications were the most prevalent comorbidities among COVID-19 patients with acute respiratory distress syndrome (6%). The high prevalence of hypertension in COVID-19 patients is not surprising, nor does it necessarily imply a causal relationship between hypertension and COVID-19 or its severity. Further, hypertension is extremely common in the elderly and older people appear to be at a higher risk of contracting SARS-CoV-2 and developing severe forms and complications of COVID-19. Hypertensive patients are frequently treated with ACEi or angiotensin receptor blockers (ARBs) to lower the volume of blood and ultimately blood pressure. However, the application of ACEi or ARBs in hypertensive patients is questionable as ACEi or ARBs are reported to increase the levels of ACE2 in hypertensive patients, and importantly, SARS-CoV-2, is reported to enter inside the lung cells through ACE2 receptors. Therefore, it is crucial to understand how RAAS reacts to COVID-19 infection and whether it is feasible to use ACEi or ARBs.
ACE2 is a monocarboxypeptidase that is homologous to ACE and has an an extracellular active site. Angiotensin I (Ang I) is cleaved by ACE to produce Ang II, which constricts blood vessels and increases salt and fluid retention by binding to and activating the angiotensin receptor 1 (AT1 receptor), causing HTN. However, the membrane-bound ACE2 inactivates Ang II to Angiotensin I–VII (Ang I–VII) which then binds to the Mas1 oncogene (Mas receptor), which has further shown to possess a vasodilator effect. Furthermore, ACE2 converts Ang II into Angiotensin I–IX (Ang I–IX) which is then transformed into Ang I–VII by ACE. In addition, ACE2 converts Ang I with less binding affinity as compared to Ang II. ACE2 acts as a negative regulator of the RAAS, modulating vasoconstriction, fibrosis, and hypertrophy. Hypertensive individuals have lower gene expression and/or ACE2 activity than normotensive patients. Ang II, on the other hand, inhibits ACE2. It has been reported in preclinical studies that ACE2 deficiency induces HTN in rats when Ang II exceeds. In several studies, SARS-CoV infection lowered ACE2 expression in cells, causing severe organ damage by disturbing the physiological homeostasis between ACE/ACE2 and Ang II/Ang I–VII. The ACE2 transmembrane domain is internalized along with the virus during SARS-CoV-2 infection, which reduces ACE2 expression. For some transmembrane proteinases and proteins, disintegrin is one of the proteins that may be involved in the binding and membrane fusion processes and ADAM metallopeptidases domain 17 (ADAM17), TMPRSS2, and TNF-converting enzyme. TMPRSS2 cleaves ACE2 to increase viral uptake, and ADAM17 can cleave ACE2 to produce ectodomain shedding. Importantly, lower levels of ACE2 in COVID-19-infected hypertensive patients resulted in a lower degradation rate of Ang II, overexpression of the AT1 receptor with reduced activity of Ang I–IX and I–VII and promotion of hypertension, ARDS, hypertrophy, and myocardial injury. The ACE2/Ang I–VII/Mas axis has been shown to play a beneficial role in the heart. It can enhance post-ischemic heart functions by inducing coronary vessel vasorelaxation, inhibiting oxidative stress, attenuating abnormal cardiac remodeling, and inhibiting oxidative stress. ACE2 expression rises early in the course of a heart attack but declines as the disease progresses. ACE2 knockout mice develop myocardial hypertrophy and interstitial fibrosis, which accelerates heart failure. Furthermore, ACE2 deletion in mice exacerbates diabetes-related heart failure. ACE2 expression in cardiac cells has been shown to be significantly reduced in both SARS-CoV-infected humans and mice. Due to the significant downregulation of ACE2 and overexpression of Ang II in COVID-19 infection, the lack of protective actions of Ang I–VII may exacerbate and perpetuate cardiac damage. According to current research and several clinical studies, HTN is a comorbidity in a significant proportion of individuals with severe illness. RAAS overactivation may have already occurred in these people before infection. The absence of the protective effects of Ang I–VII may accelerate and perpetuate cardiac damage due to considerable downregulation of ACE2 and overexpression of Ang II in COVID-19 infection. ACE2/Ang I–VII/Mas receptor axis counteracts excessively activated ACE/Ang-II/AT1 receptor axis as seen in HTN (Figure 5). Notably, soluble ACE2 molecules have been demonstrated to limit SARS-CoV infection and suggested that a soluble recombinant form of ACE2 molecules can act as competitive interceptor of SARS-CoV-2 virus and prevent it from latching onto cellular membrane-bound ACE2. In ARDS, recombinant human soluble ACE2 was planned to be tested clinically for its efficacy against COVID-19 infection so that a larger phase IIB trial can be performed which may potentially benefit the hypertensive COVID-19-infected patients (NCT04287686). Cardiac damage induced by SARS-CoV and SARS-CoV-2 is a major cause of mortality and morbidity, affecting up to a third of those who have the disease in its most severe form. SARS-CoV was found in one-third of human autopsy hearts, accompanied by a substantial drop in cellular ACE2. Also, the involvement of ACE2 has been studied in critically ill patients, wherein continued treatment with ACE2i (ACE2 inhibitor) was deemed to demonstrate less load on the heart as the alveolar spaces are critically dismantled in these patients. Even though the powerful immune response seen in these people might affect cardiac dysfunction similar to the lungs, Ang II is expected to contribute to the negative effects of SARS-CoV on the heart and SARS-associated cardiomyopathy. Inflammatory signals are thought to lower ACE2 cell-surface expression and transcription. Some contrasting studies report that ACE2 may also be involved in vasodilation, anti-inflammatory and antioxidant roles due to the generation of fragment Ang (I–VII) from Ang II. However, this is scantly reported and as a result, the classical hypothesis of disease worsening is most prevalent. In conclusion, a decrease in cellular ACE2 may render the cells less sensitive to SARS-CoV-2, whereas overexpression of the AT1 receptor causes more severe tissue damage. On the other hand, as AT1 receptor activity is decreased, the cell membrane becomes more sensitive to viral particles with increase in ACE2 levels.
Both ACEi and ARB have been demonstrated to upregulate ACE2, and some researchers have speculated that ARB and ACEi treatments may have a deleterious effect on SARS-CoV-2 infection. Given how commonly these compounds are used to treat HTN and heart failure, this might be a major source of concern. In animal studies, ACEi treatment increased plasma Ang I–VII levels, decreased plasma Ang II levels, and increased ACE2 expression in the heart. In contrast, Ang II receptor blockers (ARBs) increase plasma levels of both Ang II and Ang I–VII, as well as ACE2 expression and activity in the heart. ACEi/ARBs, renin inhibitors, and Ang I–VII analogs may minimize organ damage by blocking the RAAS pathway and/or increasing Ang I–VII levels. In population-based research, the use of ACEi and ARBs significantly lowered the 30-day mortality in pneumonia patients requiring hospitalization. Treatment with ACEi/ARBs has also prompted concerns that increasing the expression of ACE2 in target organs might facilitate the infection-induced development in severe COVID-19 infection. According to two large cohort studies, administration of ACEi/ARBs were connected to hospitalized patients’ having a lower risk of all-cause mortality rather than an increased chance of SARS-CoV-2 infection. However, further research is warranted to examine the protective effects of ACEi/ARBs in COVID-19. Despite the various probable confounders, a decrease in membrane ACE2 expression might explain many of the anomalies seen in SARS-CoV-2 infection. There is not enough clinical evidence to suggest that there is a higher chance of acquiring a severe COVID-19 infection; further, it is unclear if continuation or discontinuation of ARB/ACEi is a wise decision. In addition, we do not know if switching to another treatment approach could worsen the patient’s situation, notably in individuals with heart failure and a poor ejection fraction. Further, whether RAAS inhibitor medication is useful or detrimental for virally induced lesions and switching to another medicine could make the patient’s situation even worse. Clinical trials to detect the effect of losartan as a potential treatment approach for COVID-19, are currently starting (NCT04311177 and NCT04312009). Further, trial to detect if stopping or continuing ACEi/ARB treatment has any consequences are underway (NCT04338009). ACE inhibitors and angiotensin-converting enzyme inhibitors (ARBs) are not only used to treat HTN and heart failure, but they also have a minor impact on ACE2. Although β-blockers are unlikely to interact with ACE or ACE2, they do lower plasma Ang II levels by preventing the conversion of pro-renin to renin. Calcium channel blockers appear to reduce Ang II-induced ACE2 downregulation. However, the data is limited to a single research article that studies nifedipine’s effect on fractionated cell extracts. Thiazides and mineralocorticoid receptor antagonists did not improve the hypertensive rats’ naturally low ACE2 activity, although mineralocorticoid receptor antagonists did reduce ACE expression. In heart failure patients, mineralocorticoid receptor antagonists, on the other hand, increase membrane ACE2 activity. Newer therapies such as DNA aptamers, short oligonucleotide sequences that bind to specific proteins, are being investigated for masking the ACE2 binding domain. In this regard, a group reported the synthesis of novel DNA aptamers that were shown to specifically bind to the ACE2-K353 domain and blocked the entry of the virus through ACE2 receptors. It is still controversial that non-ACEi/BRA drugs (β-blockers, calcium channel blockers, diuretics) are more likely to increase the risk of adverse outcomes than ACEi/BRA drugs that increase ACE2 and provide theoretical protection if the reduction in membranous ACE2 seen in HTN and obesity is important in the pathophysiology of severe COVID-19.
It has been confirmed that SARS-CoV-2 enters the lung through the ACE2 receptor followed by other tissues like the liver, bile duct, gastrointestinal system (small intestine, duodenum), esophagus, and kidney. SARS-CoV-2 can damage these organs associated with the heart and transmit it from human to human rapidly, resulting in serious illness and life-threatening diseases such as heart attack or cancer. Although higher levels of ACE2 enzyme may be expressed in hypertensive patients, this can be a marker for the severity of COVID-19. Effective treatment and prevention of coronavirus infection should begin immediately. However, developing vaccines or drugs for humans in a shorter period is difficult. Nevertheless, only Covaxin and Covishield are currently available in India. In the current scenario, if any person is infected with SARS-CoV-2, then he/she should be isolated and should be controlling the origin of the infection. Scientists are trying to develop vaccines or repurposing drugs with the help of different in vivo or in vitro studies resulting in some positive evidence for the treatment of COVID-19, but these pieces of evidence are not sufficient to cure the infection. As COVID-19 treatment options are evaluated, it will be important to understand the possible side effects of CVD, with a focus on drug–drug interactions. Further, in order to learn more about how COVID-19 affects the heart, we need comprehensive molecular tests that may look at how things work and prospective and retrospective studies with good clinical methodology.
The COVID-19 pandemic is still globally stressful. SARS-CoV-2 has infected the entire globe and has caused pneumonia-like symptoms in severely infected populations. Conversely, some new variants have been reported which have less serious effects with some patients being asymptomatic post-infection. However, the population afflicted with comorbid conditions is found to be more vulnerable to death or ICU admissions comparable to people without comorbid conditions. Age is found to be the highest risk factor in COVID-19 infection, as aged people show enhanced sensitivity to immune responses. Similarly, SARS-CoV-2 causes the cytokine storm, which makes aged people more sensitive to severe infection. There was a significant increase in mortality observed with COVID-19-infected people having age-related disease conditions such as PD, cancer, diabetes, and CVD. COVID-19 infection in comorbid patients causes mitochondrial and ER malfunction that induces α-Syn aggregation, activates TLRs, and ultimately causes nuclear translocation of NFκB through JAK/STAT3 pathway in cancer patients. SARS-CoV-2 infection in diabetic individuals leads to depletion of beta cells of islets of Langerhans creating imbalance in glucose levels. Hypertensive patients infected with COVID-19 demonstrate enhanced Ang-II levels which are responsible for vasoconstriction ultimately worsening hypertensive condition. Therefore, the population infected with SARS-CoV-2 and with comorbid conditions needs to be monitored carefully at the onset of the infection, as the symptoms may worsen with time which may lead to severe life-threatening conditions. Toward this, more clinical studies are warranted for an improved mechanistic understanding of the susceptibility of comorbid patients to COVID-19 infection. Further, the comorbid population should be vaccinated on a priority basis as compared to people without comorbid conditions. Though various studies indicate the importance of comorbid disorders as the possible determinants for COVID-19 infected individuals, a comprehensive evaluation of an clarification regarding the vulnerability in COVID-19 patients is required. This article has limits on clinical-epidemiological conclusions and lacks critical and statistical analysis of COVID-19 and comorbidities and the reader should be aware of the aforementioned restrictions in our review. |
|
PMC10000025 | Qi Min,Lu Yang,Yu Wang,Yili Liu,Mingfeng Jiang | Transcriptome-Based Evaluation of Optimal Reference Genes for Quantitative Real-Time PCR in Yak Stomach throughout the Growth Cycle | 03-03-2023 | yak,stomach,transcriptome-wide,reference gene,RT-qPCR | Simple Summary The stomach is one of the primary sites for the digestion and absorption of nutrients. Quantifying related gene expression patterns using quantitative real-time PCR (RT-qPCR) is conducive to further understanding the molecular mechanisms underlying nutrition metabolism in the yak stomach. The authenticity of RT-qPCR data is affected by the selection of reference genes. Unfortunately, no studies have demonstrated suitable reference genes for the normalization of RT-qPCR data in the yak stomach. In this study, 15 candidate reference genes (CRGs) were identified according to transcriptome sequencing (RNA-seq) results and the previous literature. Five algorithms were used to evaluate the stability of the CRGs across the entire developmental stage in the yak stomach. RPS15, MRPL39, and RPS23 were found to be the most stable genes in the yak stomach from birth to adulthood. This study indicates the appropriate reference genes for gene expression analysis via RT-qPCR in the yak stomach. Abstract Efficient nutritional assimilation and energy metabolism in the stomachs of yaks contribute to their adaption to harsh environments. Accurate gene expression profile analysis will help further reveal the molecular mechanism of nutrient and energy metabolism in the yak stomach. RT-qPCR is regarded as an accurate and dependable method for analyzing gene expression. The selection of reference genes is essential to obtain meaningful RT-qPCR results, especially in longitudinal gene expression studies of tissues and organs. Our objective was to select and validate optimal reference genes from across the transcriptome as internal controls for longitudinal gene expression studies in the yak stomach. In this study, 15 candidate reference genes (CRGs) were determined according to transcriptome sequencing (RNA-seq) results and the previous literature. The expression levels of these 15 CRGs were quantified using RT-qPCR in the yak stomach, including the rumen, reticulum, omasum and abomasum at five stages: 0 days, 20 days, 60 days, 15 months and three years old (adult). Subsequently, the expression stabilities of these 15 CRGs were evaluated via four algorithms: geNorm, NormFinder, BestKeeper and the comparative CT method. Furthermore, RefFinder was employed to obtain a comprehensive ranking of the stability of CRGs. The analysis results indicate that RPS15, MRPL39 and RPS23 are the most stable genes in the yak stomach throughout the growth cycle. In addition, to verify the reliability of the selected CRGs, the relative expression levels of HMGCS2 were quantified via RT-qPCR using the three most stable or the three least stable CRGs. Overall, we recommend combining RPS15, MRPL39 and RPS23 as reference genes for the normalization of RT-qPCR data in the yak stomach throughout the growth cycle. | Transcriptome-Based Evaluation of Optimal Reference Genes for Quantitative Real-Time PCR in Yak Stomach throughout the Growth Cycle
The stomach is one of the primary sites for the digestion and absorption of nutrients. Quantifying related gene expression patterns using quantitative real-time PCR (RT-qPCR) is conducive to further understanding the molecular mechanisms underlying nutrition metabolism in the yak stomach. The authenticity of RT-qPCR data is affected by the selection of reference genes. Unfortunately, no studies have demonstrated suitable reference genes for the normalization of RT-qPCR data in the yak stomach. In this study, 15 candidate reference genes (CRGs) were identified according to transcriptome sequencing (RNA-seq) results and the previous literature. Five algorithms were used to evaluate the stability of the CRGs across the entire developmental stage in the yak stomach. RPS15, MRPL39, and RPS23 were found to be the most stable genes in the yak stomach from birth to adulthood. This study indicates the appropriate reference genes for gene expression analysis via RT-qPCR in the yak stomach.
Efficient nutritional assimilation and energy metabolism in the stomachs of yaks contribute to their adaption to harsh environments. Accurate gene expression profile analysis will help further reveal the molecular mechanism of nutrient and energy metabolism in the yak stomach. RT-qPCR is regarded as an accurate and dependable method for analyzing gene expression. The selection of reference genes is essential to obtain meaningful RT-qPCR results, especially in longitudinal gene expression studies of tissues and organs. Our objective was to select and validate optimal reference genes from across the transcriptome as internal controls for longitudinal gene expression studies in the yak stomach. In this study, 15 candidate reference genes (CRGs) were determined according to transcriptome sequencing (RNA-seq) results and the previous literature. The expression levels of these 15 CRGs were quantified using RT-qPCR in the yak stomach, including the rumen, reticulum, omasum and abomasum at five stages: 0 days, 20 days, 60 days, 15 months and three years old (adult). Subsequently, the expression stabilities of these 15 CRGs were evaluated via four algorithms: geNorm, NormFinder, BestKeeper and the comparative CT method. Furthermore, RefFinder was employed to obtain a comprehensive ranking of the stability of CRGs. The analysis results indicate that RPS15, MRPL39 and RPS23 are the most stable genes in the yak stomach throughout the growth cycle. In addition, to verify the reliability of the selected CRGs, the relative expression levels of HMGCS2 were quantified via RT-qPCR using the three most stable or the three least stable CRGs. Overall, we recommend combining RPS15, MRPL39 and RPS23 as reference genes for the normalization of RT-qPCR data in the yak stomach throughout the growth cycle.
The yak (Bos grunniens), a precious domesticated ruminant, also known as the “boat on the plateau”, is mostly found on the Qinghai-Tibetan Plateau and nearby areas at an altitude above 3000 m. As the most significant livestock in this region, yaks are capable of surviving and providing milk, meat, hair and cheese for local herders in a hostile environment [1]. A previous study found that efficient nutritional assimilation and energy metabolism in the yak stomach contributes to their adaption to a harsh environment [2]. In ruminants, the stomach and small intestine are mostly where nutrients are digested and absorbed [3]. Additionally, the development of the yak stomach at different stages plays a vital role in digestive ability and nutrient supply [4]. Thus, accurate analysis of gene expression profiles in the yak stomach are of major priority to further reveal the molecular mechanisms of nutrient and energy metabolism. As a typical ruminant, a remarkable feature of the yak is that it has a complex stomach consisting of four gastric compartments: rumen, reticulum, omasum and abomasum [5]. The first three compartments of the compound stomach (i.e., rumen, reticulum and omasum) are commonly referred to as the “forestomach” and perform cooperative functions [6]. They serve as fermentative chambers where bacteria break down the ingested cellulose, producing enormous amounts of gas [7]. By contrast, only the abomasum can generate digestive juices and gastric enzymes [7]. Hence, the abomasum is also called the true stomach. In newborn ruminants, dietary requirements are fulfilled by the uptake of colostrum, which is digested in the abomasum to provide energy and essential nutrients, as well as immunity molecules [8]. In comparison, the rumen acts as the primary location of digestion and absorption in grown ruminants, and microorganisms decompose ingested feed in the rumen to produce volatile fatty acids (VFA) that serve as the main source of energy [9]. Understanding which genes in the stomach are crucial for nutrient absorption and digestion and how they might be regulated to contribute to growth and maintenance is a major concern in the field of yak research. RT-qPCR is extensively used for the analysis of gene expression patterns due to its sensitivity, accuracy and specificity, as well as practical simplicity [10,11]. However, several drawbacks such as nucleic acid quality, poor choice of primers or probes and inappropriate data and statistical analyses encumber the authenticity of RT-qPCR results [12]. Therefore, various strategies have been applied to normalize RT-qPCR results. The use of reference genes that are not affected by study conditions is a generally accepted strategy for normalizing RT-qPCR data [13]. Despite the fact that several genes such as ACTB and GAPDH are commonly employed as reference genes in a wide range of studies, it is unlikely that any genes have enough overall expression stability to be appropriate for any kind of experiment [14]. Therefore, it is essential to select reliable reference genes for the specific experimental context under study. To date, no studies have shown suitable reference genes for the normalization of RT-qPCR data in the yak stomach. Many studies have concentrated on verifying subsets of frequently used reference genes for specific experimental contexts [14]. However, it is biased to select the CRGs from a minority of genes and assume that at least a few of those genes are appropriate for the certain experimental context. The emergence of high-throughput RNA-seq technology provides a novel strategy for identifying reference genes [15]. Based on the RNA-seq dataset, CRGs with stable expression and high abundance were preliminarily selected. Subsequently, the expression levels of CRGs were quantified using RT-qPCR and their stabilities were evaluated using geNorm [16], NormFinder [17], BestKeeper [18] and the comparative CT method [19]. This strategy has been successful for identifying reference genes for fish [20], Holstein cows [21], goats [22], and so on. The purpose of this study was to select and validate reliable reference genes from across the transcriptome that can serve as internal controls for longitudinal gene expression studies in the yak stomach throughout the growth cycle.
All the experimental protocols were approved by the Institutional Animal Care and Use Committee of Southwest Minzu University (permit number: 2020-07-02-11). All Maiwa yaks were raised in Hongyuan County of Sichuan Province and fed with natural lactation and pasture. A total of 15 Maiwa yaks (7 males and 8 females) were selected from the same herd at 5 different growth stages: 0 days (lactating stage), 20 days (lactating stage and starting to graze), 60 days (lactating stage and graze stage), 15 months (graze stage but still lactating) and 3 years old (natural graze stage). For sample collection, three separate yaks of each age were slaughtered. The stomach tissues of the yaks, including rumen, reticulum, omasum and abomasum, were rinsed immediately in 0.1% DEPC water after slaughter and frozen in liquid nitrogen until processing for total RNA extraction.
The total RNA of the rumen, reticulum, omasum and abomasum tissues were extracted using the mirVana miRNA Isolation Kit (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol. The purity and concentration of total RNA were confirmed using the NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The integrity of total RNA was assessed using 1% agarose gel electrophoresis. The cDNA was generated from 1000 ng total RNA using the PrimeScript RT reagent Kit with gDNA Eraser (TaKaRa, Dalian, China) in a reaction mixture of 20 µL. The cDNA was stored at −80 °C until required.
Based on our previous RNA-seq results of the compound stomach at five stages in fifteen yaks (unpublished data), 7 CRGs, ribosomal protein S15 (RPS15), ribosomal protein S23 (RPS23), 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ), ribosomal protein L13a (RPL13A), β-actin (ACTB), ribosomal protein S9 (RPS9) and glyceraldehyde-3-phos-phate dehydrogenase (GAPDH), were selected according to the fragments per kilobase of exon model per million mapped reads (FPKM) and the coefficient of variation (CV, %). The value of FPKM was higher than 100 and the CV value was less than 20%. FPKM = cDNA fragments/[mapped fragments (millions) × transcript length (kb)] and CV = standard deviation (SD) FPKM/MeanFPKM × 100%. Subsequently, based on the previous literature, eight genes were selected as CRGs: ubiquitously expressed prefoldin-like chaperone (UXT), dystrobrevin binding protein (DBNDD2), DEAD box polypeptide 54 (DDX54), hydroxymethylbilane synthase (HMBS), protein phosphatase 1 regulatory inhibitor subunit 11 (PPP1R11), mitochondrial ribosomal protein S15 (MRPS15), mitochondrial ribosomal protein L39 (MRPL39) and TATA box binding protein (TBP).
Primers for RT-qPCR were designed using Primer-BlAST with a length of 20 ± 3 bases and amplicon sizes ranging from 100 to 150 bp. The sequences of the CRGs were obtained from NCBI (https://www.ncbi.nlm.nih.gov/ accessed on 25 June 2022). The primer specificity of each CRG was verified using 2% agarose gel electrophoresis and melting curve analysis. To validate the specificity of each primer pair, the products of PCR were purified and sequenced using a 3730 DNA analyzer (ABI, Carlsbad, CA, USA), and the sequencing results were compared with all potential transcript sequences in NCBI using BLAST.
All RT-qPCR assays were carried out in triplicate for each sample using the LightCycler 96 System (Roche Diagnostics, Indianapolis, IN, USA). The total volume of each reaction mixture was 20 µL, including 10 µL of TB Green Premix Ex Taq II (TaKaRa, Dalian, China), 2 µL of diluted cDNA, 0.5 µL of each of 10 μM forward and reverse primers and 7 µL of RNase Free dH2O. The PCR program was conducted as follows: 95 °C for 30 s (pre-denaturation), 40 cycles of 95 °C for 5 s and 60 °C for 30 s (quantitative analysis), 95 °C for 5 s and 60 °C for 1 min (melting curves analysis). To determine the correlation coefficient (R2) and amplification efficiency (E) for each primer pair, a five-point standard curve was generated using a five-fold dilution of cDNA. The correlation coefficient (R2) and amplification efficiency (E) of each primer pair were calculated using the LightCycler 96 System. A modified Pfaffl equation was used to determine the relative quantity (RQ) of each gene [23]: Cq (calibrator) = Cq for the arithmetic mean of all samples at 5 stages, Cq (sample) = Cq for the sample. The formula for calculating the relative expression level of a target gene is as follows: RQGOI: the RQ value of the target gene, Geomean[RQREFS]: the geometric mean of the RQ value of selected reference genes. The normalization factor (NF) was calculated using the geometric mean of the RQ value of the selected reference genes [23].
The expression stability of 15 CRGs was evaluated using 4 algorithms: geNorm, NormFinder, BestKeeper and the comparative CT method. In addition, RefFinder (http://blooge.cn/RefFinder/ accessed on 10 October 2022) was used to synthesize the evaluation results of the above four algorithms to give an overall ranking.
HMGCS2 is the key rate-limiting enzyme in the ketogenic pathway and plays an important role in the digestion and absorption of nutrients in the stomach. The expression levels of HMGCS2 were quantified using RT-qPCR to validate the selected reference genes. The expression levels of HMGCS2 in the stomach at 5 stages were normalized using the three most stable gene combinations and the three most unstable gene combinations identified from this study. The relative mRNA expression of HMGCS2 was calculated using the 2−∆∆Ct method. In addition, statistical significance was analyzed using one-way analysis of variance via SPSS 25.0 software (IBM, Armonk, NY, USA). A p value below 0.05 was regarded as statistically significant.
The 260/280 ratio of total RNA for each sample ranged from 1.8 to 2.2, and the purity and concentration were qualified for subsequent experiments (Table S1). The RNA of all samples clearly displayed two prospective bands at 18 s and 28 s without any signs that the products were degraded (Figure S1). The above results indicate that the RNAs of all samples were equipped for cDNA synthesis.
The criteria for preliminary selection of reference genes were relatively high transcriptome abundance and low expression variation [15]. As a result, preliminary selection comprised genes with relatively high transcriptome abundance (FPKM > 100) as identified by the mean FPKM value and low variability as identified by the coefficient of variation (CV < 20%). A total of 80 CRGs were preliminarily selected using our previous RNA-seq results of the stomach at five stages in fifteen yaks (Table S2). Furthermore, 7 genes (RPS15, RPS23, YWHAZ, RPL13A, ACTB, GAPDH, and RPS9) were considered as CRGs due to their lower CV values, higher FPKM values, and easier primer designs. In addition, eight CRGs were selected based on previous studies. Among these CRGs: UXT, HMBS, MRPS15, PPP1R11, MRPL39 and TBP were validated to be appropriate reference genes for RT-qPCR in yak [13,24,25,26]. Additionally, DBNDD2 and DDX54 were verified as suitable reference genes for RT-qPCR in the rumen epithelium of cows [27]. In conclusion, 15 genes were chosen as CRGs for further evaluation.
The details of primer pairs of 15 CRGs are displayed in Table 1. Primer pairs of amplification efficiency (%) ranged from 91 to 109%, the amplicon’s size lay in 100–286 bp and the R2 of each primer pair was not less than 0.99. The specificity of primer pairs for each gene was verified via 2% agarose gel electrophoresis (Figure S2) and melting curve analysis (Figure S3). To further validate the specificity of each primer pair, the products of PCR were purified and sequenced. Then, the sequencing results were compared with all potential transcript sequences in NCBI using BLAST (Table S3).
The mean Cq values of all tested samples calculated to determine the expression levels of the 15 CRGs are illustrated in Figure 1. Cq value had a negative correlation with gene expression level. In other words, higher gene expression levels are associated with lower Cq values and vice versa. The Cq values of all CRGs ranged from 18.49 to 32.67. For each CRG, the mean and median Cq values were relatively close. Among all the CRGs, RPS23 demonstrated the highest expression level, with Cq = 20.18 ± 0.76, while PPP1R11 had the lowest expression level, with Cq = 30.59 ± 0.89.
In this study, four algorithms: geNorm, NormFinder, BestKeeper and the comparative CT method were used to evaluate CRGs for stability ranking. The stability rankings obtained from the four algorithms were different. Thus, RefFinder was employed to obtain a total score that was used to rank the stability of the 15 CRGs (Table 2). The M-value was calculated via geNorm analysis to identify gene expression stability. Then, the M-value was used to rank the stability of expression for the 15 CRGs, and the M-value was negatively correlated with the stability of gene expression. According to the geNorm method, the results show that RPS15 and DBNDD2 were the most stable CRGs with the lowest M-value of 0.48, while RPL13A was the least stable gene with the highest M-value of 0.74 in the yak stomach throughout the growth cycle. The NormFinder algorithm was used to calculate the stability value (SV) to identify the ranking of the CRGs, with the most stable gene showing the lowest SV. For the yak stomach throughout the growth cycle, the most stable gene was RPS15 with the lowest SV of 0.35, and RPL13A was the most unstable gene with the highest SV of 0.62. The BestKeeper and the comparative CT method regard standard deviation (SD) as one of the criteria to evaluate the stability of gene expression.. The lower the SD value, the more stable the gene expression. Based on BestKeeper analysis, RPS15 was the most stable gene, whereas YWHAZ was the least stable gene with the highest SD value. By contrast, according to the comparative CT method, MRPL39 had the highest stability, and the YWHAZ was the most unstable gene. Based on the results obtained using these four algorithms, RefFinder was used for comprehensive ranking. As a result, the comprehensive rankings according to stability from the highest to the lowest are RPS15 > MRPL39 > RPS23 > DDX54 > DBNDD2 > GAPDH > TBP > RPL13A > MRPS15 > PPP1R11 > ACTB > HMBS > RPS9 > UXT > YWHAZ.
The pairwise variation values (V) were calculated using geNorm software, which is a valid tool to identify the optimal number of reference genes for RT-qPCR. Vandesompele et al. [16] proposed taking 0.15 as a cut-off value below which the inclusion of additional reference genes is not necessary. Thus, according to the cut-off value (V = 0.15), the results indicate that the combination of three genes was the optimal number for normalization of RT-qPCR data in the yak stomach throughout the growth cycle (Figure 2A). Furthermore, low pairwise variation values correspond to a high correlation coefficient [16]. Clearly, there is no need to include an additional gene when using the three most stable reference genes for calculating the NF (Figure 2C). In contrast, it is essential to have more than an additional gene when using the two most stable reference genes for calculation of NF (Figure 2B). Thus, we recommend the combination of the three most stable genes (RPS15, MRPL39, and RPS23) to normalize RT-qPCR data in the yak stomach throughout the growth cycle.
To verify the effect of the combination of RPS15, MRPL39 and RPS23 for the normalization of RT-qPCR data, the expression of HMGCS2 was quantified via RT-qPCR in yak stomach at 5 stages (0 d, 20 d, 60 d, 15 m and adult). Moreover, the expression patterns of HMGCS2 in yak stomach at 5 stages were also identified using the FPKM of RNA-seq results. The results show a correspondence between the RT-qPCR and RNA-seq, indicating the RT-qPCR data of HMGCS2 using the RPS15, MRPL39 and RPS23 for normalization were reliable (Figure 3). To further validate the selection of CRGs, the three most stable CRGs (RPS15, MRPL39, and RPS23) and the three least stable CRGs (RPS9, UXT and YWHAZ) were used to normalize the expression of HMGCS2. As shown in Figure 4A,B, the expression patterns of HMGCS2 in the rumen, reticulum, omasum and abomasum at five stages (0 d, 20 d, 60 d, 15 m and adult) were similarly obtained using FPKM based on RNA-seq results and the combination of three most stable CRGs (RPS15, MRPL39, and RPS23) for normalization. Furthermore, the expression of HMGCS2 in the rumen, reticulum and omasum were the lowest at 0 d and the highest at adulthood, while the opposite was true in the abomasum. However, compared with the expression of HMGCS2 based on RNA-seq results (Figure 4A), normalization of HMGCS2 expression using the three least stable CRGs (RPS9, UXT and YWHAZ) demonstrated significant differences (Figure 4C). Hence, it is essential to select suitable reference gene combinations to normalize the expression of target genes.
Gene expression analysis via RT-qPCR is a dependable and extensively used method to reveal the molecular mechanism of digestion and absorption of nutrients in the stomach. The use of reference genes is the most credible strategy for taking into account the initial concentration of RNA, sample loss during experimentation, the efficiency of cDNA synthesis, and so on [28]. However, the selection of inappropriate reference genes also affects the authenticity of RT-qPCR data [29]. Therefore, selecting suitable reference genes is essential to obtain meaningful RT-qPCR results. Until now, strategies for identifying reference genes from the transcriptome have been widely used. Reference genes selected from the transcriptome increase the reproducibility and sensitivity of results, give a stronger correlation between protein expression levels, and have better detection and coverage [30]. Although RNA-seq screening has many merits in predicting reference genes, this strategy is not absolutely trustworthy and needs further validation via RT-qPCR [15,31]. In this study, 15 CRGs were determined via RNA-seq and the previous literature, and further verified using RT-qPCR. In this study, geNorm, NormFinder, BestKeeper and the comparative CT method were employed to assess the stability of 15 CRGs. Ribosomal protein S15 (RPS15) is a component of the 40S ribosomal subunit and functions as a nuclear export factor [32]. Although different algorithms were used for the stability ranking of 15CRGs, RPS15 had the best stability in all the algorithms except the comparative CT method (geNorm, NormFinder and BestKeeper) (Table 2). Furthermore, RPS15 was also the most stable gene in the comprehensive ranking of the results of the four algorithms using RefFinder. This is consistent with Bionaz et al. [28] finding that RPS15 is one of the best reference genes used for the normalization of gene expression data in the bovine mammary gland during the lactation cycle. Tyrosine 3 monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ), belonging to the 14-3-3 protein family, participates in various cell activities including cell growth, cell cycle, apoptosis, and so on [33,34]. Some studies have demonstrated the consideration of YWHAZ as an appropriate reference gene due to its high stability in cattle [35], buffaloes [36] and yak [25]. By comparison, it seems that YWHAZ was the least stable gene in our study (Table 2). Even so, the M value of YWHAZ (0.73) derived from the geNorm analysis is well below the threshold (M = 1.5) proposed by Vandesompele et al. [16], suggesting that it is also a relatively stable gene in the yak stomach. These results indicate that the stability of reference genes is highly specific and should be evaluated for a given experimental context. Although RPS15 had the highest stability in our evaluation, we still do not recommend using it alone as the reference gene for the normalization of RT-qPCR data in the yak stomach. Many studies show that using a single gene as the reference gene should be avoided [16,18,28]. It has been reported that using a single reference gene results in significant bias [37]. To date, no specific theory prescribes a certain number of reference genes to be used. Use of geNorm can provide the optimal number of reference genes needed to eliminate the majority of technical variation [11]. Accuracy and practicality are trade-offs when determining the optimal number of reference genes. It is an unnecessary waste of resources to use more reference genes if the inclusion of additional genes has no significant effect on NF [16]. In our study, there was no significant change between the NF calculated with the three most stable CRGs and that calculated with the four most stable CRGs, indicating that it was superfluous to add a gene for normalization (Figure 2C). In addition, the digestive tract of the yak has three developmental stages: pre-rumination (0–20 days), transition from pre-rumination to rumination (20–60 days), and rumination (after 60 days). The diets of yaks are different at different developmental stages. Therefore, we evaluated the stability of these genes in yak stomach tissue over five developmental stages. Our results support the use of these reference genes in the normalization of RT-qPCR data under different dietary conditions. As a result, we recommend using the combination of three most stable genes (RPS15, MRPL39, and RPS23) to calculate the NF for normalization of RT-qPCR data in the yak stomach throughout the growth cycle. The expression profiles of HMGCS2 were quantified via RT-qPCR in the yak stomach at five developmental stages, and its expression levels were normalized by the selected combination of reference genes. HMGCS2 is the key rate-limiting enzyme in the ketogenic pathway and induces the biosynthesis of HMG-CoA, which is the central metabolite of rumen epithelial cells [38,39]. The ketogenic capacity of ruminal epithelium in ruminants increases with age, and newborn ruminants have no ketogenic capacity [40]. Thus, we hypothesized that the expression level of HMGCS2 in the rumen should increase in terms of age, as well as those in the reticulum and omasum because they serve analogous functions to the rumen. In this paper, the expression level of HMGCS2 did increase with age, as determined using RNA-seq and RT-qPCR in the rumen, reticulum and omasum (Figure 3A–C). For newborn ruminants, the rumen was not fully developed and ingested colostrum is instead digested in the abomasum [8]. Consequently, the expression level of HMGCS2 in the abomasum should be highest at birth and lowest in adulthood (Figure 3D). In addition, although target genes with significant expression changes can be identified using less stable reference genes, target genes with imperceptible expression changes can only be detected using the best reference genes [20,37]. Our results confirm that significant changes in the expression of HMGCS2 between birth and adulthood could be identified using either the three most stable CRGs (RPS15, MRPL39, and RPS23) or the three least stable CRGs (RPS9, UXT and YWHAZ). However, when the change in HMGCS2 expression is slight, errors may occur when using RPS9, UXT and YWHAZ for normalization. For example, in the rumen, there were no significant differences in HMGCS2 expression levels between 60 days and 15 months either based on the results of RNA-seq or using RPS15, MRPL39, and RPS23 for normalization, whereas its expression levels normalized using RPS9, UXT and YWHAZ had significant differences (p < 0.05) between 60 days and 15 months (Figure 4). These results imply that using suitable reference genes is essential for accurate normalization of target gene expression.
In this study, 15 CRGs were selected using transcriptome sequencing results and the previous literature, and their expression stability was evaluated using five algorithms. Therefore, we recommend the combination of the three most stable genes RPS15, MRPL39, and RPS23 as reference genes for the normalization of RT-qPCR data in the yak stomach throughout the growth cycle. |
|
PMC10000032 | Maria Brockmann,Christoph Leineweber,Tom Hellebuyck,An Martel,Frank Pasmans,Michaela Gentil,Elisabeth Müller,Rachel E. Marschang | Establishment of a Real-Time PCR Assay for the Detection of Devriesea agamarum in Lizards | 28-02-2023 | Devriesea agamarum,polymerase chain reaction,Uromastyx sp.,Pogona vitticeps,bearded dragon,lizard,reptile,cheilitis,dermatitis | Simple Summary Bacterial infections can play an important role in dermatitis in lizards. The bacterial species Devriesea (D.) agamarum is a known cause of dermatitis, cheilitis and even fatal disease in lizards. Disease has most often been reported in Uromastyx species, but other lizards may also be affected. However, some are asymptomatic carriers, increasing the risk of spreading D. agamarum. Usually, D. agamarum is detected with culture-based methods. It was the aim of this study to establish a real-time PCR assay to expand diagnostic options in routine diagnostics. The presented assay is able to detect D. agamarum in clinical samples, decreasing laboratory turn-around time in comparison to conventional culture-based detection methods. This enables a fast therapeutic approach for affected animals and decreases the risk of spread. Abstract (1) Background: Devriesea (D.) agamarum is a potential cause of dermatitis and cheilitis in lizards. The aim of this study was to establish a real-time PCR assay for the detection of D. agamarum. (2) Methods: Primers and probe were selected targeting the 16S rRNA gene, using sequences of 16S rRNA genes of D. agamarum as well as of other bacterial species derived from GenBank. The PCR assay was tested with 14 positive controls of different D. agamarum cultures as well as with 34 negative controls of various non-D. agamarum bacterial cultures. Additionally, samples of 38 lizards, mostly Uromastyx spp. and Pogona spp., submitted to a commercial veterinary laboratory were tested for the presence of D. agamarum using the established protocol. (3) Results: Concentrations of as low as 2 × 104 colonies per mL were detectable using dilutions of bacterial cell culture (corresponding to approximately 200 CFU per PCR). The assay resulted in an intraassay percent of coefficient of variation (CV) of 1.31% and an interassay CV of 1.80%. (4) Conclusions: The presented assay is able to detect D. agamarum in clinical samples, decreasing laboratory turn-around time in comparison to conventional culture-based detection methods. | Establishment of a Real-Time PCR Assay for the Detection of Devriesea agamarum in Lizards
Bacterial infections can play an important role in dermatitis in lizards. The bacterial species Devriesea (D.) agamarum is a known cause of dermatitis, cheilitis and even fatal disease in lizards. Disease has most often been reported in Uromastyx species, but other lizards may also be affected. However, some are asymptomatic carriers, increasing the risk of spreading D. agamarum. Usually, D. agamarum is detected with culture-based methods. It was the aim of this study to establish a real-time PCR assay to expand diagnostic options in routine diagnostics. The presented assay is able to detect D. agamarum in clinical samples, decreasing laboratory turn-around time in comparison to conventional culture-based detection methods. This enables a fast therapeutic approach for affected animals and decreases the risk of spread.
(1) Background: Devriesea (D.) agamarum is a potential cause of dermatitis and cheilitis in lizards. The aim of this study was to establish a real-time PCR assay for the detection of D. agamarum. (2) Methods: Primers and probe were selected targeting the 16S rRNA gene, using sequences of 16S rRNA genes of D. agamarum as well as of other bacterial species derived from GenBank. The PCR assay was tested with 14 positive controls of different D. agamarum cultures as well as with 34 negative controls of various non-D. agamarum bacterial cultures. Additionally, samples of 38 lizards, mostly Uromastyx spp. and Pogona spp., submitted to a commercial veterinary laboratory were tested for the presence of D. agamarum using the established protocol. (3) Results: Concentrations of as low as 2 × 104 colonies per mL were detectable using dilutions of bacterial cell culture (corresponding to approximately 200 CFU per PCR). The assay resulted in an intraassay percent of coefficient of variation (CV) of 1.31% and an interassay CV of 1.80%. (4) Conclusions: The presented assay is able to detect D. agamarum in clinical samples, decreasing laboratory turn-around time in comparison to conventional culture-based detection methods.
Devriesea (D.) agamarum is a bacterial species known to cause dermatitis and cheilitis in lizards. Disease has most often been described in Uromastyx spp. [1,2,3,4,5]. However, D. agamarum can also infect other lizards [6,7]. It has been reported in captive [8,9] as well as in free-ranging lizards [7]. Clinical signs of disease generally include dermatitis or cheilitis, often described with a yellow crusty appearance [10]. Disease outbreaks with extensive mortality have also been reported, especially if lizards developed septicaemia. [7,11]. Bearded dragons have been described to asymptomatically carry D. agamarum in their oral cavities [2,3]. Treatment of affected animals usually includes debridement of dermal lesions and systemic use of antibiotics—especially cephalosporines are considered effective [4,12]—and may also require disinfection of the enclosure [13]. Autovaccines have also been discussed as a treatment method [14]. Therefore, a fast and reliable diagnostic approach is an important consideration, both in clinical disease with suspected D. agamarum infection and in entry controls. D. agamarum is relatively easily cultured at 37 °C but also grows at temperatures of 25−42 °C on Columbia agar with 5% sheep blood [11]. Diagnosis can, however, be complicated in laboratories with limited experience with this pathogen. Matrix-Assisted Laser Desorption/Ionisation Time-of-Flight Mass Spectrometry (MALDI-TOF MS), one of the most frequently used standard techniques for the identification of bacteria in routine laboratory diagnostics, may not (yet) be able to identify D. agamarum when working with standardized databases [15]. However, this issue is likely to be overcome as databases expand. Currently, laboratories can improve D. agamarum identification by implementing and expanding their own MALDI-TOF MS databases or using 16S rRNA gene sequencing to identify cultured but unidentified isolates. Another option would be a specific PCR assay for D. agamarum, which might prove especially valuable if other infectious agents, such as viral or fungal pathogens, are also suspected, allowing concurrent testing from the same sample. The aim of this study was, therefore, to develop a PCR assay for the detection of D. agamarum.
In total, 14 D. agamarum isolates were used in this study as positive controls. Three D. agamarum isolates (GenBank accession numbers: MT664091-93) were obtained from routine diagnostic submissions at Laboklin GmbH & Co. KG (Bad Kissingen Germany) in 2019 [15], while 11 were isolated between 2005 and 2009 at the Faculty of Veterinary Medicine, Ghent University (Table 1). Non-D. agamarum isolates (n = 34) were obtained from the German Collection of Microorganisms and Cell Cultures (DSMZ, Braunschweig, Germany). Some were included in order to determine the ability of the assay to exclude a broad spectrum of different bacterial species. Others, like Brachybacterium sp. or Dermabacter sp., were included as their sequences were described to be highly similar to D. agamarum [11] (Table 2). These 34 isolates served to determine the specificity of the PCR.
Pure cultures of each strain were incubated in 750 μL lysis buffer (MagNA Pure DNA Tissue Lysis Buffer, Roche, Mannheim, Germany) and 75 μL proteinase K (proteinase K, lyophilisiert, ≥30 U/mg, Carl Roth GmbH & Co KG, Karlsruhe, Germany) for one hour at 65 °C. From this, 200 μL were utilized for automated nucleic acid (NA) extraction using the MagNA Pure 96 DNA and Viral NA Small Volume Kit (Roche, Mannheim, Germany) according to the manufacturer’s instructions. The resulting NAs were eluted in a volume of 100 μL. The isolated NAs were kept at −18 °C until the PCR tests were performed. The DNA used for the dilution series was extracted manually using the QIAamp® DNA MicroKit (50) (Qiagen, Hilden, Germany), and the DNA concentration was measured with a spectrophotometer (NanoDrop 2000, Thermo Fisher Scientific, Inc., Wilmington, NC, USA).
The 16S rRNA gene was selected as the target region based on the availability of sequence data from a variety of isolates. Sequences of D. agamarum (NZ_LN849456.1, LN849456.1, NR_044368.1, EU009865.1, KF647330.1) were retrieved from GenBank, and multiple sequence alignment was performed with other sequences of different bacterial species (e.g., Agromyces species, Arthobacter species, Brachybacterium species, Dermabacter species, Pseudomonas species) using MUSCLE (https://www.ebi.ac.uk/Tools/msa/muscle/ last accessed on 20 February 2023). The primers and probe were designed using primer3 (https://primer3.ut.ee/ last accessed on 20 February 2023). Reactions included 1.0 µL of each primer (10 µM), 1.0 µL of the probe (2 µM), 4.0 µL DNA Process Control Detection Kit qPCR Reaction Mix and 5.0 µL template DNA in a total volume of 20.0 µL. Amplification was performed with a LightCycler 96 (Roche, Mannheim, Germany) in a 96-well format. The following protocol was used: Preincubation at 95 °C for 30 s followed by 40 cycles of two-step-amplification (95 °C for 5 s and 60 °C for 30 s). PCR-grade water (Roche, Mannheim, Germany) served as a negative control. While all other bacterial samples were negative, Dermabacter hominis produced positive PCR results at an annealing temperature of 60 °C (Figure 1). Therefore, DNA of Dermabacter hominis (DSM 30958) from the DSMZ as well as DNA of D. agamarum (GenBank Accession number: MT664092.1/0919Ur) [15], was tested in duplicate with different annealing temperatures (protocol 1: 65.0 °C, protocol 2: 66.0 °C, protocol 3: 67.0 °C) using a LightCycler 96 (Roche, Mannheim, Germany).
Various non-D. agamarum bacterial species (Table 2) were tested in duplicate for positive reactions at 66 °C. The following protocol was used: Preincubation at 95 °C for 30 s followed by 40 cycles of two-step-amplification (95 °C for 5 s and 66 °C for 30 s). PCR-grade water (Roche, Mannheim, Germany) served as a negative control. DNA of 14 D. agamarum isolates (Table 1) was also tested. To assess the intraassay repeatability of the PCR, standard deviations were calculated for 10-fold serial dilutions in triplicate on a single plate. For the interassay reproducibility, standard deviations were calculated for a 10-fold serial dilution series which was amplified three times daily for two days. The standard deviations of the CT values were used to calculate the percent of coefficient of variation (CV%). Detection limit for bacterial cell dilutions: In order to determine the sensitivity of the assay, a culture of D. agamarum (isolate MT664091.1/0219Bf) was used, starting with a dilution (D0) of 0.5 McFarland (1.5 × 108 per mL). This suspension (D0) was 10-fold serially diluted (D1–D10) in duplicate, and 1 mL of each dilution was inoculated onto Columbia agar with defibrinated sheep blood (Becton Dickinson GmbH, Heidelberg, Germany/Oxoid GmbH, Wesel, Germany), incubated at 36 °C and checked for growth after 30 h. Colony forming units (CFU) were counted if the result was expected to be between 0 and 300 CFU. These results were then used to determine the limit of detection of the assay. DNA was extracted from 200 µL of each dilution (D1–D10) as described above, and PCR was carried out in duplicate. The detection of Dermabacter hominis (DSM 30958) was quantified in the same way. Detection limit for DNA from pure culture: To evaluate the assay’s sensitivity, PCR was carried out in triplicate using serial 10-fold dilutions of DNA prepared from colonies of D. agamarum (isolate MT664091.1/0219Bf). Spiked-in matrix: The assay was also evaluated using a spiked-in matrix (D. agamarum-negative-tested lizard skin with a known concentration of target DNA). For these assays, 10 µL of the above-described dilutions D0 to D6 were inoculated onto D. agamarum-negative-tested lizard skin. The skin was incubated in 500 µL lysis buffer, and 50 µL proteinase K and NA were extracted from 200 µL of this suspension as described above and eluted in a total volume of 100 µL NA. The PCR was carried out in duplicate as described above.
Clinical samples from lizards for which appropriate material (skin, crusts, dry swab) was submitted to a commercial veterinary laboratory between March 2022 and December 2022 and for which the submitting veterinarian indicated an interest in D. agamarum diagnostics were tested using the established protocol to evaluate the PCR for use with clinical samples. Some of the samples were derived from animals showing clinical signs, others from asymptomatic animals tested in the context of a health check—often when D. agamarum had been isolated from animals in the group previously. If suitable material was available (skin or swab in a transport medium), bacteriology was performed (as previously described [15]). Identification of isolates was based on growth characteristics on agar plates (Columbia Agar with defibrinated sheep blood and Endo Agar, Becton Dickinson GmbH, Heidelberg, Germany), biochemical parameters and MALDI-TOF MS. In doubt, colonies were also re-checked via PCR. If the results of the bacteriological culture were available, the results of the PCR and culture would be compared. Samples were considered PCR positive if the cycle threshold (CT) was <35.0 and equivocal if the CT was ≥35.0, but a signal was obtained. If swabs of different origins regarding the localisation (e.g., dermal and oral) of the same animal were received, all samples were tested separately. Amplicons from positive samples were sequenced (ABI PRISM 3130 XL Genetic Analyser, Applied Biosystems, Foster City, CA, USA) and sequences were analysed by BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi, last accessed on 20 February 2023).
The selected primer and probe sequences (Eurofins MWG Operon, Ebersberg, Germany) are shown in Table 3. The product size was expected to be 246 base pairs. Optimisation of annealing temperature was performed using three protocols with different annealing temperatures. In protocol 1 (65 °C), D. agamarum DNA was detected with CT values of 14.58 and 16.49 and Dermabacter hominis with CT values of 29.43 and 29.62. In protocol 2 (66 °C), D. agamarum DNA was detected with CT values of 16.22 and 16.14, and Dermabacter hominis showed values of 34.28 and 35.19. In protocol 3 (67 °C), D. agamarum was detected with CT values of 15.57 and 16.57, while Dermabacter hominis was not detected. Therefore, an annealing temperature of 66 °C was chosen for all further analyses as it was considered sufficient to discriminate between pure cultures of D. agamarum and Dermabacter hominis DNA without losing sensitivity for the detection of D. agamarum.
Specificity of the assay using the protocol with 66 °C as an annealing temperature was determined using 14 D. agamarum isolates (Table 1) and 34 non-D. agamarum bacterial isolates (Table 2) in duplicate. No signal was obtained from any bacterial DNA from isolates other than D. agamarum except for Dermabacter hominis. The CT value obtained using the DNA from pure cultures of Dermabacter hominis were high (34.55 and 34.48) in comparison to those reached using DNA from D. agamarum (12.08–18.14) but still below the threshold set for clinical samples. As these are the results for DNA extracted from pure culture, 66 °C was considered sufficient for further testing of samples without prior cultivation as samples without prior cultivation are expected to yield a lower pathogen level. The intraassay CV was calculated to be 1.31%, while the interassay CV was 1.80%. Detection limit for bacterial cell dilutions: A positive PCR signal was detected for D. agamarum dilutions D0–D4. An equivocal signal was detected for D5 and D6. In culture, D4 corresponded to 2 × 104 colonies per mL. Therefore, the assay sensitivity was 2 × 104 colonies per mL with dilutions of bacterial cell culture serving as a template. This corresponds to approximately 200 CFU per PCR. For the serially diluted culture of Dermabacter hominis, no positive PCR signals were observed. Two dilutions (D0 and D1) resulted in equivocal CT values, with D0 being set to 0.5 McFarland (1.5 × 108 per mL). Detection limit for DNA from pure culture: The DNA concentration was determined to be 42.5 ng/µL (A260/A280: 1.94). D. agamarum DNA was detectable in dilutions up to 1:105. Therefore, DNA concentrations of as low as 425 fg/µL were detectable in the PCR. Spiked-in matrix: Spiked-in matrixes produced clearly positive results up to skin spiked with 10 µL of D2 with D2 corresponding to 2 × 106 colonies per mL (approximately 360 CFU per PCR considering dilution during sample preparation).
In order to test the use of the developed PCR for clinical samples, a total of 48 samples from 38 lizards were tested for the presence of D. agamarum (Table 4). The samples were derived from several species, mostly agamids (Pogona spp. and Uromastyx spp.), and were of different origins (zoological collection, animal rescue centre, private owner) from Germany and the Netherlands. Some of these animals were asymptomatic and were tested in the context of a health check. Others showed clinical signs such as skin lesions, hyperkeratosis or stomatitis (Table 4). Samples were mostly derived from the oral cavity or skin/crusts. Of the 38 animals tested, D. agamarum was detected by PCR in 16 animals (42.10%). A further five animals (13.16%) were considered to have equivocal results, and 17 animals (44.74%) were negative for D. agamarum. One animal (animal 3) that tested negative proved to be infected with a fungus of the family Onygenaceae. A bacteriological examination was performed for 33 of the 38 animals, but D. agamarum was not cultured. However, in most of these cases, various other bacterial species (Table 4) were cultured when bacteriology was performed.
D. agamarum is an important pathogen causing skin lesions and, in some cases, systemic disease in lizards. Depending on the species, some animals can be inapparent carriers, while others may develop severe diseases. Diagnosis of the causative agent is therefore important in order to facilitate treatment as well as to prevent the spread of disease. Since animals may suffer when untreated and the risks of spreading increase with time, a fast diagnostic approach is important. The detection of D. agamarum is commonly achieved via culture, followed in some cases by 16S rRNA gene sequencing [11,16]. The PCR developed in this study provides a time-saving tool compared to culture and bacterial identification. Detection of D. agamarum and concurrent bacteriological examination was performed in 33 of the 38 animals, resulting in 13 of 33 clearly PCR-positive animals but no culture-positives. D. agamarum is expected to be abundantly present in symptomatic animals. Culturing of D. agamarum is not considered difficult and has been successfully performed in this laboratory before [15]. However, a successful culture depends on the quality of the submitted samples. Appropriate samples include affected tissue below hyperkeratotic crusts or inside of the crusts as well as organs in septicaemic lizards and subcutaneous granulomas. In asymptomatic animals as well as in symptomatic animals, isolation from the oral cavity, gastrointestinal tract or healthy skin may be challenging. D. agamarum was cultured in the laboratory in which the study was performed during the study period, but these samples were excluded from the study as no suitable material for concurrent PCR testing (e.g., dry swab, skin) was available. In this study, six animals (1, 2, 21 and 22–24) were known to have been symptomatic and had positive PCR results. Bacteriological culture was performed in three of these animals (22–24). In animal 24, a positive PCR result was only obtained from the skin sample, which was not tested by bacteriological culture. Animal 6 might have been symptomatic (no information was received, but due to the reported previous treatment, it seemed likely). It was treated with antibiotics prior to sampling, which might have influenced the bacteriology results. Possible reasons for the failure of culturing D. agamarum out of positive clinical samples in this study include previous antibiotic treatment, incorrect sampling techniques, contamination with (oral) microbiota, increased transport time, inappropriate transport conditions or overgrowth by other bacteria. The latter is especially important as in the presented cases, no selective media for gram-positive bacteria were used, and various different bacterial species are expected to be present on the skin [17,18]. PCR analysis is useful if the performance of bacterial culture is difficult, e.g., due to previous treatment with antibiotics, inadequate preanalytical conditions (such as increased or decreased temperature, increased transport time, inadequate transport medium), or in cases in which overgrowth by other bacteria make detection challenging or impossible. The detection of D. agamarum via PCR can also simplify concurrent PCR testing for other known pathogens, e.g., viral or fungal pathogens known to cause dermatitis [19,20], since the same extracted nucleic acids can be used. In general, PCR is advantageous when culturable samples are unavailable, for example, when older samples are tested or stored DNA is examined. However, bacterial DNA can persist in the environment [21], and D. agamarum has been shown to survive for several months in the environment, depending on the conditions [13]. A PCR could therefore detect bacteria even in cases in which these were not responsible for clinical signs or in which no replication-competent bacteria were present. The PCR developed here was not 100% specific. Pure cultures of Dermabacter hominis did result in a weak positive signal. However, if diluted, only equivocal results were observed. Dermabacter hominis is genetically closely related to D. agamarum [2,11,22]. Dermabacter hominis is associated with the human microbiome [23]. It is occasionally described in human clinical samples such as abscesses or blood cultures [24,25,26] but is usually found to be of minor clinical significance [27]. So far, its clinical importance in reptiles is very unclear. Contamination during sampling or sample preparation should be considered a possible option leading to false equivocal results. However, clinical samples are expected to yield less bacterial DNA, making false equivocal results less likely. The 16S rRNA gene is known to be highly conserved between bacterial species, which makes it a useful target if the aim is to identify different bacterial species. It is a commonly used target for bacterial detection, and therefore a large amount of sequence data is available for a wide range of bacterial species. However, it may not be ideal for differentiating closely related bacteria. Currently, the availability of sequence data for D. agamarum other than the 16S rRNA gene is limited, but in the future, other targets may prove to be better options. In the meantime, especially equivocal CT values should be evaluated with caution in the face of clinical signs, sampling and sample preparation, and ideally, retesting is recommended. Possibly, skin samples might prove more useful than swabs as they yielded lower CT values in two of the three animals for which both sample types were available, but this might be highly dependent on the sampling method. The PCR protocol developed in this study proved helpful for the detection of D. agamarum in clinical samples. D. agamarum was detected in oral swabs from clinically healthy Pogona species and serrated casquehead iguana (Laemanctus serratus), while the results in which equivocal results were obtained were also from clinically healthy Pogona species as well as from clinically healthy black hardun (Laudakia stellio picea). Pogona species have previously been shown to be possible inapparent carriers of D. agamarum and a possible source of infection for more sensitive species [2,3]. Therefore, this PCR protocol may not only be useful for clinical cases but also as a screening tool. However, the number of tested samples is still small, and testing of larger sample numbers is necessary in order to confirm the usefulness of this method for clinical practice.
A real-time PCR was developed that is able to detect D. agamarum in clinical samples. The assay provides a fast method for the detection of this important pathogen of lizards but should be evaluated with further samples in the clinical context. |
|
PMC10000039 | Neelam A. Topno,Veerbhan Kesarwani,Sandeep Kumar Kushwaha,Sarwar Azam,Mohammad Kadivella,Ravi Kumar Gandham,Subeer S. Majumdar | Non-Synonymous Variants in Fat QTL Genes among High- and Low-Milk-Yielding Indigenous Breeds | 28-02-2023 | milk fat,whole-genome sequencing,SNPs,genomic variation,variant calling,indigenous breeds | Highlights What are the main findings? Differentially expressed milk fat QTL genes explored with whole genome se-quencing for variant analysis. Identified non-synonymous SNPs for hub and bottleneck QTL genes associated with milk fat traits. What is the implication of the main finding? Identified differential pattern(s) of SNPs in fat QTLs between high and low milk yield breeds. Impact of the identified SNP pattern(s) on milk fat traits can be further explored. Simple Summary Milk fat is a crucial trait that varies significantly among cattle breeds and determines the milk quality and pricing value. Indigenous breeds have disparity in milk quantity and quality. Our study is one of a kind which helps to decipher the variations at the genetic level correlated with transcriptional level among high and low milk-yielding cattle breeds exploring the fat QTLs. We assessed and unveiled a few key differences between the high and low-milk-yield breeds. Abstract The effect of breed on milk components—fat, protein, lactose, and water—has been observed to be significant. As fat is one of the major price-determining factors for milk, exploring the variations in fat QTLs across breeds would shed light on the variable fat content in their milk. Here, on whole-genome sequencing, 25 differentially expressed hub or bottleneck fat QTLs were explored for variations across indigenous breeds. Out of these, 20 genes were identified as having nonsynonymous substitutions. A fixed SNP pattern in high-milk-yielding breeds in comparison to low-milk-yielding breeds was identified in the genes GHR, TLR4, LPIN1, CACNA1C, ZBTB16, ITGA1, ANK1, and NTG5E and, vice versa, in the genes MFGE8, FGF2, TLR4, LPIN1, NUP98, PTK2, ZTB16, DDIT3, and NT5E. The identified SNPs were ratified by pyrosequencing to prove that key differences exist in fat QTLs between the high- and low-milk-yielding breeds. | Non-Synonymous Variants in Fat QTL Genes among High- and Low-Milk-Yielding Indigenous Breeds
What are the main findings? Differentially expressed milk fat QTL genes explored with whole genome se-quencing for variant analysis. Identified non-synonymous SNPs for hub and bottleneck QTL genes associated with milk fat traits. What is the implication of the main finding? Identified differential pattern(s) of SNPs in fat QTLs between high and low milk yield breeds. Impact of the identified SNP pattern(s) on milk fat traits can be further explored.
Milk fat is a crucial trait that varies significantly among cattle breeds and determines the milk quality and pricing value. Indigenous breeds have disparity in milk quantity and quality. Our study is one of a kind which helps to decipher the variations at the genetic level correlated with transcriptional level among high and low milk-yielding cattle breeds exploring the fat QTLs. We assessed and unveiled a few key differences between the high and low-milk-yield breeds.
The effect of breed on milk components—fat, protein, lactose, and water—has been observed to be significant. As fat is one of the major price-determining factors for milk, exploring the variations in fat QTLs across breeds would shed light on the variable fat content in their milk. Here, on whole-genome sequencing, 25 differentially expressed hub or bottleneck fat QTLs were explored for variations across indigenous breeds. Out of these, 20 genes were identified as having nonsynonymous substitutions. A fixed SNP pattern in high-milk-yielding breeds in comparison to low-milk-yielding breeds was identified in the genes GHR, TLR4, LPIN1, CACNA1C, ZBTB16, ITGA1, ANK1, and NTG5E and, vice versa, in the genes MFGE8, FGF2, TLR4, LPIN1, NUP98, PTK2, ZTB16, DDIT3, and NT5E. The identified SNPs were ratified by pyrosequencing to prove that key differences exist in fat QTLs between the high- and low-milk-yielding breeds.
India has become the largest milk producer in the world [1]. Several schemes involving crossbreeding have been implemented to enhance milk production in the country. As a result, the number of crossbred cattle increased and contributed to around 28 percent of total milk production in India (ca. 188 million tons), surpassing the contribution of indigenous cattle [2]. However, indigenous cattle breeds are well known for their heat tolerance and disease resistance [3], and the crossbreds have been found to be susceptible to tropical diseases and harsh climatic conditions and require constant good management practices. To strike a balance between increasing demand for milk and the change in the environment due to global warming, exploring the genomic merit of indigenous cattle/breeds becomes even more important. Milk being a polygenic trait with medium heritability, the majority of animal breeding research has centered on quantitative trait loci (QTLs) with moderate to large effects on milk production traits. The DGAT1 on chromosome 14 [4,5,6], the growth hormone receptor (GHR) on chromosome 20 [7], and the ABCG2 [8] or SPP1 (Osteopontin) on chromosome 6 [9] are well-known QTL genes that have been fully characterized with a strong putative or well-confirmed causal mutation. The two QTLs DGAT1 (K232A) and ABCG2 (Y581S) in Bos taurus have been suggested to be associated with increased fat yield and fat and protein percent in milk with a decrease in milk yield [6,8,10,11,12,13]. The GHR mutation F279Y has been observed to have a significant effect on milk composition (fat and protein percentage) and milk yield [14]. The locus c.8514C > T in the intronic region of SPP1 has also been found to have a significant effect on milk production and milk composition [15]. However, the DGAT1 and ABCG2 genes have been found to be fixed among Indian breeds Sahiwal, Rathi, Deoni, Tharparkar, Red Kandhari, and Punganur [16]. Currently, the animal QTL database (QTLdb) contains 1,93,216 QTLs for different bovine traits, out of which 83,458 QTLs have been reported for milk traits [17]. Milk is the primary source of nutrition for infants, as well as adults. Besides its nutritional value, it has a major role in imparting growth and immunity through intrinsic milk components such as growth factors, chemokines, anti-inflammatory molecules, antioxidants, prebiotics, and probiotics [8,9]. Milk has four major components, fat (3.6%), protein (3.2%), lactose (4.7%), and water (87%), along with other various kinds of minerals, enzymes, vitamins, and dissolved gases. Various research studies have shown that several factors, such as lactation stage, genetics, environmental factors, and diet management, influence milk quality. The variability of milk composition among popular dairy breeds Brown Swiss, Holstein Friesian, Jersey, Simmental, Grey Alpine, and Rendena under the same dairy management practices has been explored, and Holstein Friesian had higher milk yield with lower fat content (27.45 kg/d, 4.04%) [18], whereas Jersey had lower milk yield with relatively higher fat content (17.27 kg/d, 5.65%) [13]. A low fat percentage has also been reported for Ayrshire, Brown Swiss, Guernsey, Holstein Friesian, and Jersey breeds in the United States [19]. Furthermore, the effect of breed has been found to significantly influence the water (p ≤ 0.0001), protein (p ≤ 0.05), total solids (p ≤ 0.05), fat (p ≤ 0.05), milk urea nitrogen (p ≤ 0.001), and ash (p ≤ 0.0001) content of milk [20]. India, with a huge diversity of 50 cattle breeds, forms an ideal ground to study genetic variation at the genomic level vis-à-vis milk traits [21]. The cost of the milk world-over varies with the percentage of fat present in the milk. Exploring the variations in fat QTLs across breeds would shed light on the variable fat content in their milk. No such studies have been reported in the past for indigenous cattle breeds to evaluate the variation across indigenous breeds within the fat QTLs. Therefore, the objective of the present study was to explore genomic variation(s) within the fat QTLs that were identified to be differentially expressed in lactation across indigenous breeds, which were divided into high- (Sahiwal and Gir) and low-milk-yield (Gaolao, Deoni, Pulikulam, Hallikar, Dangi, and Amritmahal) groups.
QTL genes associated with milk fat traits (milk fat content and percentage) and metabolism were extracted from the Animal QTLdb database [22], and the duplicates were removed. QTL genes (286 and 256 (total of 542)) were identified for milk fat yield and milk fat percentage, respectively, with 417 unique genes for both traits (Supplementary File 1). Functional annotation of the 125 QTL genes commonly associated with milk fat yield and milk fat percentage was performed in g:Profiler [23] and ShinyGO [24] to identify the enriched biological processes. Protein interaction network analysis of the 417 genes was performed using the Search Tool for the Retrieval of Interacting Genes Search Tool for the Retrieval of Interacting Genes 11.0 (STRING 11.0) database at a confidence score value of 0.5 against model species Bos taurus [25]. The interaction network was imported into the Cytoscape 3.8.0 software (Institute for System Biology, CA, USA) for visualization. The hub and bottleneck genes were identified in the interaction network using the Cytohubba plugin of Cytoscape [26] considering the degree of association between the genes and by taking the bottleneck approach, which takes into account the top 20% of the degree of distribution of the proteins in the network [26]. A total of 74 QTL genes were identified to be hub/ bottleneck genes. The hub genes were the genes that had the highest degree of association, and the bottleneck genes were the key connectors having a high betweenness (measure the centrality of the nodes) among different clusters in protein interactions [27].
The publicly available milk transcriptome bioproject ID (PRJNA419906) was used to analyze the expression of QTL genes associated with milk fat traits. This bioproject was considered in this study as it has data generated from Jersey (a breed with high fat content ranging from 4.10–4.86%) and Kashmiri (a breed with low fat content ranging from 3.20–3.94%) [28]. The data were generated from mammary epithelial cells (MECs) collected on Day 15 (D15), D90, and D250 from six lactating cows (three Jersey and three Kashmiri cattle). These days represent early, mid-, and late lactation, respectively [28]. The data were downloaded from the Sequence Read Archive (SRA) of the NCBI database, and the fastq-dump program of SRAtoolkit [29] was used to extract the fastq reads. Quality assessment and control of RNA-seq data were performed through Fast QC Version 0.11.5 [30], MultiQC Version 1.8 [31], and trimmomatic Version 0.39 [32]. All the high-quality reads were mapped to the Bos indicus genome (GCF_003369695.1) using STAR Version 2.5.4b with the default parameters [33]. Gene expression was estimated using RSEM [34], and differential gene expression was performed through the DESeq2-R package [35]. The differentially expressed genes among the 74 fat QTL hub and bottleneck genes were identified.
Indigenous cattle breeds for the proposed study were grouped into high- and low-milk-yield-breed groups. The high-milk-yield group (avg milk yield per day 8 kg) included Sahiwal (n = 4) and Gir (n = 4), and the low-milk-yield group (avg milk yield per day 2.5 kg) included 6 animals representing the breeds Gaolao, Deoni, Hallikar, Dangi, Pulikulam, and Amritmahal [36]. Animals of different breeds were considered in the study to have a true representation of both the high- and low-milk-yield groups. Genomic DNA (gDNA) was extracted from the blood samples of the animals from these breeds using the nucleospin blood L-kit (Macherey-Nagel), and the integrity of the genomic DNA was checked on agarose. After estimating the concentration of gDNA (Nanodrop2000, ThermoFischer Scientific), DNA libraries were prepared as per the manufacturer’s protocol (Illumina sequencing platform) for paired-end sequencing (2150 bp).
Sequencing data generated on an Illumina platform were pre-processed for quality assessment and improvement (base quality, nucleotide distribution, GC content, adaptor sequence, duplication, length distribution, etc.) by FastP [37]. All high-quality reads were mapped to the Bos indicus reference genome (Brahman-GCF_003369695.1) using BWA aligner [38]. Variant calling was performed from the aligned data using freebayes [39] and GATK [40]. For the GATK- and freebayes-generated vcf files, only SNPs were selected, leaving aside all indels and insertions. After freebayes variant calling, the low-quality variants were filtered by vcftools Version 1.10 [41] for Q > 20. In the GATK pipeline, the paired-end Illumina Hi-Seq raw reads for each individual were first converted into an unaligned bam; the illumina adapters were marked; the sam was converted back to FASTQ; the reads were mapped to the reference Brahman genome (GCF 003369695.1); the unaligned and mapped bams were combined. Finally, the duplicate marked clean bam was used to generated GVCF for each animal using GATK haplotypecaller. The GVCFs generated for all the animals were combined to call the variants using the genotypeGVCF module. The parameters for GATK VariantRecalibration to generate the VQSLOD score were QD < 2.0, MQRankSum < −8.5, ReadPosRankSum < −8.0, FS > 60.0, MQ < 40.0, SOR > 3.0, and DP 30x (depth or coverage). The final set of SNPs after recalibration by GATK included the selection of SNPs that passed. We further used the GATK-filtered set and the freebayes-filtered set to identify the common SNPs across these variant callers. From this vcf file, the SNPs in the differentially expressed hub and bottleneck genes (i.e., genes that are hub/bottleneck and are differentially expressed as identified in Section 2.2) were extracted using an in-house perl script. The non-synonymous SNPs (nsSNPs) in the coding regions of these were identified through the SnpEff tool [42].
Three nsSNPs that were found to be distinctly different between the high- and low-milk-yield groups were selected for validation and were genotyped in PyroMark Q48 (Qiagen) as per the manufacturer’s protocol. These nsSNPs were found in the differentially expressed hub and bottleneck genes GHR, LPIN1, and TLR4. GHR was one of the genes having the maximum number of interactions in the network. LPIN1 had the maximum SNP count of 10, whereas TLR4 was one of the top 10 highly upregulated genes. PCR and sequencing primers were designed using PyroMark Assay Design Software 2.0 (Qiagen). The PCR amplification was performed in a 20 µL reaction, with the thermal cycling conditions, which included an initial denaturation of 95 °C for 3 min followed by 40 cycles of 95 °C for 30 s, 65 °C for 30 s, 72 °C for 1 min, and a final extension of 72 °C for 10 min. Sequence analysis was performed by PyroMark Q48 Autoprep software Version 2.4.2 in SNP analysis assay mode for 14 animals. The schematic representation of the study is given in Supplementary File 1: Figure S1.
Animal QTLdb was used to extract fat-trait-associated QTL genes. In the QTLdb, after the removal of duplicate genes (Supplementary File 1), 286 and 256 genes were found associated with milk fat yield and percentage, respectively, whereas 125 common genes were found between milk fat yield and percentage (Figure 1A). A total of 417 unique genes were found to be associated with milk fat and other milk traits. These genes were also found to be annotated for other milk traits such as milk yield, protein yield, and percentage. Among all genes, 24 genes were found to be associated with both fat yield and fat percentage traits only (Supplementary File 1). The common genes (125) were found enriched in biosynthetic-, catabolic-, regulatory-, transportation-, and cellular-response-associated metabolic processes. Among the metabolic genes associated with milk fat traits, the genes associated with milk fatty acid metabolism were FASN, GPAT4, DGKG, ELOVL6, and LIPIN1 (Supplementary File 5: Table S1).
The publicly available RNA-seq bioproject (PRJNA419906) has library sizes ranging from 7764992200–13682843400 bp and 6842583600–12088807400 bp, for Jersey and Kashmiri, respectively (Supplementary File 5: Figure S2A). Further, gene expression counts per million (CPM), principal component analysis (PCA), and multidimensional scaling (MDS) (Supplementary File 5: Figure S2A,C,D) of the samples were assessed. The PCA and MDS plots of sequenced RNA-seq libraries showed a high level of similarity within breeds and relatively low variation between the lactation stages of breeds. A total of 70 genes were found to be upregulated and 52 genes downregulated in the Jersey breed in comparison with the Kashmiri breed. Differentially expressed transcripts (DETs) were also explored for fat QTL genes (Supplementary File 2). The volcano plots of differentially expressed genes (DEGs) and DETs depicting the distribution of upregulated and downregulated genes are shown in Figure 2. In both the Jersey and Kashmiri breeds, Beta-lactoglobulin (LOC113901792), Casein beta (CSN2), and Casein alpha s1 (CSN1S1) were identified to be among the highly expressed top 20 genes (Supplementary File 5: Table S2). The DETs are listed in Supplementary File 2. In the Jersey breed, CXCL-8, TLR4, and OLR1 were among the highly upregulated genes. CXCL-8/IL8, produced by macrophages, epithelial cells, and airway smooth muscle cells, is a neutrophil chemotactic factor that induces chemotaxis in target cells and other granulocytes to initiate movement toward infection sites, whereas OLR1, a receptor on macrophages, epithelial cells, and airway smooth muscle cells, is involved in rapid oxidization of low-density lipoprotein (LDL), which is more readily recognized by the TLR4 receptor. The list of top 10 upregulated and downregulated DEGs is given in Supplementary File 5: Table S3.
A protein interaction network analysis was performed among 417 milk fat QTL genes, and the interaction network was generated with 403 nodes and 671 edges. Based on the degree of association, 50 hub and 50 bottleneck genes were selected from the network (Supplementary File 2). A total of 74 QTL genes were found to be either hub or bottleneck genes (Figure 1B). Out of these, 25 genes (which accounted for 18 hubs/17 bottleneck genes) were differentially expressed in Jersey and Kashmiri (Figure 1C, Supplementary File 2). Out of these genes, ten genes possessed both hub and bottleneck gene characteristics. The SRC and DGAT1 genes were among the top differentially expressed hub and bottleneck genes (Table 1). SRC had the highest degree of association (30) with a log2 fold change of 1.480587, followed by DGAT1 with a degree of 25 and a log2FC of 0.921104.
Illumina short read (Paired end) data of 14 samples from both groups of high- and-low-milk-yield breeds had 12.77 billion reads. After preprocessing, clean data included 11.02 billion reads, which is ca. 1516 Gb data. Each dataset had a minimum sequencing depth of ≥30x with an average GC content of 45.26%. The processed datasets contained on average 97.91% Q20 bases and 93.97% Q30 bases. The high-quality trimmed data aligned to the Brahman reference genome with an overall alignment rate of >95%. Initial variant calling on the aligned data provided 63,357,363 variants, which were filtered for high quality. After quality filtering on Q20, a total of 33,976,892 SNPs were identified across the genomes (Supplementary File 3). Upon GATK analysis, 39,625,917 variants were found to pass the variant calibration. A total of 25,956,231 SNPs were found to be common among the variant callers. From these, SNPs in the 25 differentially expressed hub and bottleneck milk fat QTL genes were extracted, out of which 20 genes were found to have non-synonymous substitutions in the coding regions (Table 2). The variants identified in these 20 genes were further explored for two kinds of genomic variant patterns, i.e., fixed SNP pattern in the cattle of the high-milk-yield group vs. variable SNP pattern in cattle of the low-milk-yield group, or vice versa. The fixed SNP pattern in high-milk-yield breeds in comparison to low-milk-yield breeds was observed in the genes GHR, TLR4, LPIN1, CACNA1C, ZBTB16, ITGA1, ANK1, and NTG5E (Table 3), and the opposite was observed in the genes MFGE8, FGF2, TLR4, LPIN1, NUP98, PTK2, ZTB16, DDIT3, and NT5E (Table 4). SNPs C/G, C/A, and G/A were confirmed in GHR, TLR4, and LPIN1 in the Amritmahal, Pulikulam, and Dangi breeds (low-milk-yield) as against SNPs C/C, C/C, and G/G in the Gir and Sahiwal breed (high-milk-yield), respectively (Figure 3) (Supplementary File 4). In the TLR4 gene, variant g.107083326A>C was found in the low-milk-yield group, but the same variant was fixed in the high-milk-yield group. Similarly, in the LPIN1 gene variant, g.85211528C>G was found in the low-milk-yield group, but this variant was fixed in the high-milk-yield group. In the NUP98 gene and LPIN1 variants, g.32707374G>A and g.85205642T>G, respectively, were found in the high-milk-yield group, but were found to be fixed (g.32707374G>G; g.85205642T>T) in the low-milk-yield group. Another LPIN1 variant, g.85205642T>G, was observed in the high-milk-yield group, but was fixed in the low-milk-yield group. LPIN1 and ITGA1 had the maximum SNP count of 10, and the genes PTK2, IGF1R, DDIT3, CXCL8, and LPL had the least SNP count (Table 2).
The Bos indicus genome is an interesting model to study the genomic potential of different indigenous cattle breeds such as Sahiwal, Gir, Amritmahal, Dangi, Gaolao, Deoni, Pulikulam, and Hallikar, which are highly adapted to different tropical conditions with varying milking potential. The availability of bovine QTL resources such as the Animal QTL database and the collection of QTLs for different traits have provided the opportunity to investigate genomic variation among indigenous breeds for milk-associated traits. Milk quality such as fat yield and percentage are highly variable traits among breeds. Jersey is one among the high-milk-producing breeds worldwide, whereas Kashmiri is one of the poorly performing breeds in the Kashmir region of India. Therefore, we aimed at differences in the expression of fat QTL genes between the two contrasting breeds, Jersey and Kashmiri. In this study, significantly expressed hub and bottleneck fat QTL genes were further analyzed to identify the genomic variants from the whole-genome sequence data between high- (Sahiwal and Gir) and low- (Amritmahal, Dangi, Gaolao, Deoni, Pulikulam, and Hallikar) milk-yield indigenous breeds. This understanding of low- and high-milk-yield breeds for milk fat quality may help in enhancing the quality of milk in the long run. To explore the fat QTL genes, MEC RNA-seq data were processed and analyzed. The high level of similarity within breeds and relatively low variation between lactation confirmed the selection of RNA-seq data to explore differences between breeds rather than to explore difference in lactation stages of breeds. Among the highly expressed genes identified, it was observed that the Jersey breed has allocated more resources for the immune system, whereas the Kashmiri breed for regulation of ribosomal proteins. Among the top 10 upregulated genes, CXC motif chemokine ligand 8 was the most-upregulated gene. It is reported to be involved in various biological pathways such as increased insulin resistance, uncoupling of the GH/IGF1 axis, and an increase in mammary cell proliferation to improve metabolic health and milk yield [43]. Diacylglycerol kinase gamma (DGKG), another upregulated gene, is a member of the type I diacylglycerol kinases and is highly upregulated (log2FC = 4.03) in Jersey. It plays a role in lipid metabolism by modulating the balance between diacylglycerol and phosphatidic acid. Phosphatidic acid is a lipid second messenger to activate protein kinase C isoforms, ras guanyl nucleotide-releasing proteins, and some transient receptor potential channels [44]. Most of the top-upregulated genes in Jersey have been found to have a role in adipogenesis (ETS2) [45], adipocyte differentiation (OLR1, PARM1) [46,47], glucose transport (SLC6A9) (log2FC = 4.49) [48], glucose uptake (SLC45A4) [49], thyroid hormone synthesis (TG) [50], and aldosterone secretion (KCNK9) [51]. The upregulation of these genes in Jersey indicates their involvement in lipid biosynthesis in the mammary gland during lactation. Furthermore, UDP-glucose 6-dehydrogenase (UGDH), which is involved in the biosynthesis of glycosaminoglycans, hyaluronan, chondroitin sulfate, and heparan sulfate, has been found (log2FC = 0.75) to be upregulated in the Jersey breed. UGDH’s expression pattern in liver cells has been associated with an indispensable role in the metabolism of carbohydrates, fats, and proteins in dairy cattle [52]. Moreover, UGDH has been found close to two reported QTLs for fat yield, fat percentage, and protein yield [53]. During lactation, various morphological changes happen in the mammary tissue to support cellular differentiation, tissue elasticity, and reduced fat storage capacity in the animal. Upregulated GRH13 is a transcription factor that mediates the proliferation of epithelial cells [54]. Similarly, MTMR3/3-PAP, a catalytically inactive member of the myotubularin gene family that coprecipitates the activity of lipid phosphatidylinositol 3-phosphate-3-phosphatase, is upregulated [55] in Jersey. Matrilin 2 (MATN2) and prolyl 4 hydroxylase (P4HA3), which are important to maintaining the newly synthesized collagen’s stability, are also upregulated [56,57]. P4HA3 catalyzes the formation of 4-hydroxyproline (Hyp), which ensures the proper folding of procollagens during post-translational modification. The upregulation of MATN2 and P4HA3 probably may help in increasing the elasticity of the udder gland in Jersey during lactation. Further, the downregulation of MFGE8 and ELOVL16 may be responsible for the high fat content in milk and the shift to C18 from C16 fatty acids, respectively, in Jersey. MFGE8 regulates the absorption of free fatty acids and increases intracellular triglycerides’ hydrolase activity, thereby restricting the storage of fat [58], whereas ELOVL fatty acid elongase (ELOVL16) elongates C16 saturated and monounsaturated fatty acids to C18 fatty acids [59]. Further, IDH1, which catalyzes the conversion of isocitrate to α-ketoglutarate and generates the primary source of NADPH for de novo fatty acid synthesis [60], has been to be found downregulated in Jersey (log2FC = −1.632). The dysregulation of the genes MFGE8, EVOLVL16, and IDH1 might be responsible for the variation in the fat yield and composition in Jersey and Kashmiri cows. In addition, the decreased expression of these genes may be linked to the increased expression of genes involved in metabolism, glucose transport, and other transport activities, leading to higher milk production performance in Jersey. Among the ten QTLdb milk and milk trait genes that were differentially expressed and had hub and bottleneck gene characteristics, four genes were found enriched in metabolic pathways (Supplementary File 5: Table S1). SRC was identified as the top hub gene interacting with several genes in the protein–protein interaction network. SRC, a non-receptor tyrosine kinase, performs a wide variety of cellular functions in terms of metabolism and is primarily involved in impaired glucose uptake [61]. Genes Diacylglycerol O-acyltransferase 1 (DGAT1) and ecto-5′-nucleotidase (NT5E) possess hub and bottleneck gene features. DGAT1 encodes a protein that catalyzes the conversion of diacylglycerol and fatty acyl CoA to triacylglycerol. DGAT1 is one of the highly studied genes for milk yield and fat quality [62,63]. The NT5E gene encodes a plasma membrane protein that catalyzes the conversion of extracellular nucleotides to membrane-permeable nucleosides. SRC and DGAT1 were found to be highly upregulated in Jersey with log2FC = 1.48 and log2FC = 0.92, respectively. In the network, DGAT1 was found to interact with Glycerol-3-phosphate acyltransferase 4 (GPAT4) (also known as AGPAT6). This gene has been found to be involved in triglyceride biosynthesis and is comprised of acyltransferase motifs, which are essential for binding to substrates and catalyzing acyltransferase reactions. The AGPAT6 gene, which is highly expressed in mammary gland epithelium during lactation [64], was also observed to be upregulated. NT5E was found to interact with BDNF, NTRK2, and ZNRF4. NT5E is involved in various biological process such as adenosine biosynthetic process, AMP catabolic process, leukocyte cell–cell adhesion, and negative regulation of inflammatory response [65]. DGKG, which was upregulated, was found to be a bottleneck gene in the network and had interactions with PRKCG and RNT. Phosphatidate phosphatase (LPIN1), an enzyme involved in lipid metabolism [66], was upregulated and found as a hub gene in the network having interactions with PPARA and PPARGC1A. LPIN1 is involved in various biological processes related to lipid metabolism such as the triglyceride biosynthetic process, the fatty acid catabolic process, and the regulation of transcription by RNA polymerase II [66]. The gene PTK2 is well known for its association with milk production traits [67], and CACNA1A has a role in hormone regulation of lactation [68]. Similarly, ZBTB16 is involved in bovine adipogenesis [69]. NUP98 is associated with protein percentage [70]. TLR4 is a mastitis-associated marker [71]. FGF2 expression is reported to be associated with milk production traits [72]. In this study, breed (high-milk-yield and low-milk-yield groups) differences in nsSNPs within these hub and bottleneck genes (GHR, TLR4, LPIN1, CACNA1C, MFGE8, PTK2, ZBTB16, FGF2, and NUP98) were identified. These fat QTL nsSNPs may have a role in the existing fat and milk yield differences between the breed groups. However, further studies to evaluate the impact of these SNPs on fat yield/percentage or milk yield need to be carried out.
In this study, initially, DEGs in Jersey epithelial cells were identified, and these were further explored in the QTL database as being the major hub and bottleneck genes. The transcriptome in Jersey indicated higher expression of genes involved in metabolism, glucose transport, and other transport activities, leading to higher milk production performance. The 20 differentially expressed hub and bottleneck fat QTL genes were explored for non-synonymous genomic variants in the whole-genome sequence data, which were generated from fourteen animals. The fixed SNP pattern in high-milk-yield breeds in comparison to low-milk-yield breeds was observed in the genes GHR, TLR4, LPIN1, CACNA1C, ZBTB16, ITGA1, ANK1, and NTG5E, and the opposite was observed in the genes MFGE8, FGF2, TLR4, LPIN1, NUP98, PTK2, ZTB16, DDIT3, and NT5E. The role of these SNPs needs to be further explored. |
|
PMC10000040 | Yue Sun,Yanze Yu,Jinhao Guo,Linqiang Zhong,Minghai Zhang | Alterations in Fecal Microbiota Linked to Environment and Sex in Red Deer (Cervus elaphus) | 04-03-2023 | cervidae,KEGG,fecal microbiota,16s rRNA | Simple Summary The gut microbiota forms a complex microecosystem in vertebrates and is affected by various factors. Wild and captive red deer currently live in the same region but have vastly different diets. In this study, the 16S rRNA sequencing technology was performed to evaluate variations in the fecal microbiota of wild and captive individuals of both sexes of red deer. It was found that the composition and function of fecal microbiota in wild and captive environments were significantly different. As a key intrinsic factor, sex has a persistent impact on the formation and development of gut microbiota. Overall, this study reveals differences in the in the fecal microbiota of red deer based on environment and sex. These data could guide future applications of population management in red deer conservation. Abstract Gut microbiota play an important role in impacting the host’s metabolism, immunity, speciation, and many other functions. How sex and environment affect the structure and function of fecal microbiota in red deer (Cervus elaphus) is still unclear, particularly with regard to the intake of different diets. In this study, non-invasive molecular sexing techniques were used to determine the sex of fecal samples from both wild and captive red deer during the overwintering period. Fecal microbiota composition and diversity analyses were performed using amplicons from the V4–V5 region of the 16S rRNA gene sequenced on the Illumina HiSeq platform. Based on Picrust2 prediction software, potential function distribution information was evaluated by comparing the Kyoto Encyclopedia of Genes and Genome (KEGG). The results showed that the fecal microbiota of the wild deer (WF, n = 10; WM, n = 12) was significantly enriched in Firmicutes and decreased in Bacteroidetes, while the captive deer (CF, n = 8; CM, n = 3) had a significantly higher number of Bacteroidetes. The dominant species of fecal microbiota in the wild and captive red deer were similar at the genus level. The alpha diversity index shows significant difference in fecal microbiota diversity between the males and females in wild deer (p < 0.05). Beta diversity shows significant inter-group differences between wild and captive deer (p < 0.05) but no significant differences between female and male in wild or captive deer. The metabolism was the most important pathway at the first level of KEGG pathway analysis. In the secondary pathway of metabolism, glycan biosynthesis and metabolism, energy metabolism, and the metabolism of other amino acids were significantly different. In summary, these compositional and functional variations in the fecal microbiota of red deer may be helpful for guiding conservation management and policy decision-making, providing important information for future applications of population management and conservation. | Alterations in Fecal Microbiota Linked to Environment and Sex in Red Deer (Cervus elaphus)
The gut microbiota forms a complex microecosystem in vertebrates and is affected by various factors. Wild and captive red deer currently live in the same region but have vastly different diets. In this study, the 16S rRNA sequencing technology was performed to evaluate variations in the fecal microbiota of wild and captive individuals of both sexes of red deer. It was found that the composition and function of fecal microbiota in wild and captive environments were significantly different. As a key intrinsic factor, sex has a persistent impact on the formation and development of gut microbiota. Overall, this study reveals differences in the in the fecal microbiota of red deer based on environment and sex. These data could guide future applications of population management in red deer conservation.
Gut microbiota play an important role in impacting the host’s metabolism, immunity, speciation, and many other functions. How sex and environment affect the structure and function of fecal microbiota in red deer (Cervus elaphus) is still unclear, particularly with regard to the intake of different diets. In this study, non-invasive molecular sexing techniques were used to determine the sex of fecal samples from both wild and captive red deer during the overwintering period. Fecal microbiota composition and diversity analyses were performed using amplicons from the V4–V5 region of the 16S rRNA gene sequenced on the Illumina HiSeq platform. Based on Picrust2 prediction software, potential function distribution information was evaluated by comparing the Kyoto Encyclopedia of Genes and Genome (KEGG). The results showed that the fecal microbiota of the wild deer (WF, n = 10; WM, n = 12) was significantly enriched in Firmicutes and decreased in Bacteroidetes, while the captive deer (CF, n = 8; CM, n = 3) had a significantly higher number of Bacteroidetes. The dominant species of fecal microbiota in the wild and captive red deer were similar at the genus level. The alpha diversity index shows significant difference in fecal microbiota diversity between the males and females in wild deer (p < 0.05). Beta diversity shows significant inter-group differences between wild and captive deer (p < 0.05) but no significant differences between female and male in wild or captive deer. The metabolism was the most important pathway at the first level of KEGG pathway analysis. In the secondary pathway of metabolism, glycan biosynthesis and metabolism, energy metabolism, and the metabolism of other amino acids were significantly different. In summary, these compositional and functional variations in the fecal microbiota of red deer may be helpful for guiding conservation management and policy decision-making, providing important information for future applications of population management and conservation.
Red deer (Cervus elaphus), which belong to the family Cervidae, order Artiodactyla, distributed in Asia, Europe, North America, and North Africa [1]. The red deer is a typical forest-inhabiting mammal in northeast China and has an important ecological status in the forest ecosystem [2]. Owing to habitat fragmentation, the populations of red deer in the wild are currently in sharp decline [2]. Using captive populations as reintroduction resources is an effective strategy to restore the populations of wild red deer [3]. The complex gut microbiota systems in the mammalian gut are composed of large fractions of microbes [4]. The gut microbiota are a complex product of the long-term evolution of hosts and microbes [4]. Recent studies have shown that not only are gut microbiota a part of the host, but they also have a significant impact on the health of the host, such as promoting immunity, digestion, metabolism, and intestinal endocrine hormones, among others [5,6,7]. Simultaneously, the complex and flexible gut microbiota can be affected by multiple environmental and host genotypes [8]. Many studies have shown that diet is an important factor that affects the structure and function of the fecal microbiota [9,10,11]. For example, changes in diet alter the function and diversity of fecal microbiota as well as the relative abundance of some microorganisms [12]. Moreover, diet-induced loss of microbial function and diversity will increase the risk of diversity loss and extinction through generational amplification [13]. It was necessary to investigate the gut microbiome by comparing differences between wild and captive red deer. However, to date, there has been a lack of studies comparing the gut microbiota between wild and captive red deer [11]. Because of sex differences in behavior and physiology, sex as an important intrinsic factor leads to differences in gut microbiota among individuals within species [14,15,16]. Although the results are inconsistent, animal species with significant sexual dimorphism and human studies have shown sex-related differences in gut microbiota. In mice (Mus musculus), poultry, and forest musk deer (Moschus berezovskii), the composition of the gut or fecal microbiota shows sex differences [17,18,19]. At present, few studies have analyzed the sexual dimorphism of fecal microbiota in red deer. In order to save endangered populations, artificial breeding of wild populations is carried out. The food types and nutrient intake ratios obtained in captivity and wild environments are very different, especially for endangered cervidae [20]. Therefore, monitoring the digestive system of captive animals and identifying standardized levels of nutritional requirements and fiber composition is critical for captive wild animals to determine whether they have acclimated to artificially provided food and new environments—a part of wildlife conservation’s main problem [21]. Using captive populations as reintroduction resources is an effective strategy to restore the populations of wild red deer. The composition of gut microbiota in wild populations can be a good indicator of the breeding direction of the captive population [9]. Therefore, understanding the impact of dietary differences between wild and captive red deer on the fecal microbiota can help to assess and ensure the long-term viability of this species [9]. At present, the research methods for fecal microbiota have also shifted from traditional methods to 16S rRNA gene sequencing technology, from simple microbial composition, community structure, and core microbiota research to microbial function research, which has become a hot frontier in ungulate research today [22]. The main goal of this study was to characterize the composition of the fecal microbiota of red deer of different sex and feeding plus environment. We used high-throughput 16S rRNA sequencing technology to comprehensively analyze. Thus, we hypothesized that: (1) the fecal microbiota composition and function are different between wild and captive deer; and (2) under the wild or captive environment, the microbiota diversity and evenness are different between females and males.
This study was conducted at the Gaogestai National Nature Reserve in Chifeng, Inner Mongolia (119°02′30″, 119°39′08″ E; 44°41′03″, 45°08′44″ N). The total area is 106,284 hm2. It is a typical transition zone forest-steppe ecosystem in the southern foothills of Greater Khingan Mountains, including forests, shrubs, grasslands, wetlands, and other diverse ecosystems. In February 2019, 75 line transects were randomly laid in the Gogestai protection area. Positive and reverse footprint chain tracking was carried out after the foodprints of red deer were found through line transect investigation. Disposable PE gloves were worn to collect red deer feces. While tracking the footprint chain, set 2 m × 2 m plant quadrate every 200 m to 250 m along the footprint chain, and collect all kinds of plant branches eaten by deer in the quadrate as far as possible [23]. A total of 162 fecal samples were collected and stored at −20 °C within 2 h. The feces of red deer from different areas of the Reserve were identified as coming from different individuals, and 43 feces were identified individually in the laboratory. In February 2019, the HanShan Forest Farm in Chifeng City, Inner Mongolia, China (adjacent to the Gaogestai Nature Reserve) had a total of 11 healthy adult red deer of similar age and size. Ear tags were used to differentiate each individual red deer. Through continuous observation, feces were collected immediately after excretion by different red deer individuals and stored at −20 °C. We measured crude protein, energy, neutral detergent fiber (NDF), and total non-structural carbohydrates in red deer diets.
We used a qiaamp DNA Fecal Mini-kit (QIAGEN, Hilden, Germany) to extract host deoxyribonucleic acid (DNA) from the fecal samples of red deer as previously described [24]. Microsatellite PCR technology was used with nine pairs of microsatellite primers (BM848, BMC1009, BM757, T108, T507, T530, DarAE129, BM1706, and ILST0S058) [25,26] with good polymorphism that were selected based on the research results of previous studies. These nine pairs of primers can amplify fecal DNA stably and efficiently. A fluorescence marker (TAMRA, HEX, or FAM) was added to the 5′ end of upstream primers at each site (Supplementary Table S1). Primer information, PCR amplification, and genotype identification procedures are described in the literature [27]. Multi-tube PCR amplification was used for genotyping [28], and 3–4 positive amplifications were performed for each locus to determine the final genotype [29]. The excel microsatellite toolkit [30] was used to search for matching genotypes from the data. Samples are judged to be from the same individual if all loci have the same genotype or if only one allele differs at a locus. The microsatellite data were analyzed by Cervus 3.0 software, and the genotyping was completed [31]. Male and female individuals were identified by detecting the existence of genes after the individual identification of red deer was completed. Sry gene primers (F:5′-3′ TGAACGCTTTCATTGTGTGGTC; R:5′-3′ GCCAGTAGTCTCTGTGCCTCCT) were designed, and the amplification system was determined. To minimize the occurrence of false positives or false negatives that could affect results, the Sry gene was repeated three times to expand and increase during the experiment, and samples with target bands that appeared on the second and third occasions were determined to be male [32].
The total microbial DNA of fecal samples was extracted using an E.Z.N.A® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). The DNA integrity of the extracted samples was determined by 1% agarose gel electrophoresis. Targeting a 420 bp fragment encompassing the V4-V5 region of the bacterial 16S ribosomal RNA gene was amplified by PCR using primers 515F (5′-GTG CCA GCM GCC GCG GTA A-3′) and 907R (5′-CCG TCA ATT CMT TTR AGT TT-3′). NEB 154 Q5 DNA high-fidelity polymerase (NEB, Ipswich, MA, USA) was used in PCR amplifications (Supplementary Table S1). A 1:1 mixture containing the same volume of 1XTAE buffer and the PCR products were loaded on a 2% agarose gel for electrophoretic detection. PCR products were mixed in equidensity ratios. Then, the mixture of PCR products was purified using the Quant-iTPicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). Sequencing libraries were generated using the TruSeq Nano DNA LT Library Prep kit (Illumina, San Diego, CA, USA) following the manufacturer’s recommendations, and index codes were added. The library’s quality was assessed on the Agilent 5400 (Agilent Technologies Co. Ltd., Santa Clara, CA, USA). At last, the library was sequenced on an Illumina NovaSeq 6000 platform, and 250 bp paired-end reads were generated. Microbiome bioinformatics were performed with QIIME2 2019.4 [33] with slight modification according to the official tutorials (https://docs.qiime2.org/2019.4/tutorials/ (accessed on 30 September 2022)). Briefly, raw data FASTQ files were imported into the format that could be operated by the QIIME2 system using the qiime tools import program. The DADA2 [34] process is to obtain amplified variant sequences through de-duplication. In the process, clustering is not carried out based on similarity, but only de-duplication is carried out. Demultiplexed sequences from each sample were quality filtered and trimmed, de-noised, merged, and then the chimeric sequences were identified and removed using the QIIME2 DADA2 plugin to obtain the feature table of amplicon sequence variants (ASV) [34]. The QIIME2 feature-classifier plugin was then used to align ASV sequences to a pre-trained GREENGENES 13_8 99% database (trimmed to the V4V5 around a 420bp region bound by the 515F/907R primer pair) to generate the taxonomy table [35]. In order to unify the sequence effort, samples were rarefied at a depth of 25,318 sequences per sample before alpha and beta diversity analysis. Rarefaction allows one to randomly select a similar number of sequences from each sample to reach a unified depth.
Sequence data analyses were mainly performed using QIIME2 and R software (v3.2.0). ASV-level alpha diversity indices, such as the Chao1 richness estimator and Pielou’s evenness, were calculated using the ASV table in QIIME2 [36,37], and visualized as box plots (R software, package “ggplot2”). Beta diversity analysis was performed to investigate the structural variation of microbial communities across samples using weighted or unweighted UniFrac distance metrics [38,39] and visualized via principal coordinate analysis (PCoA) (R software, package “ape”). The significance of differentiation of microbiota structure among groups was assessed by PERMANOVA (permutational multivariate analysis of variance) [40]. Random forest analysis (R software, package “randomForest”) was applied to sort the importance of microbiota with differences in abundance between groups and screen the most critical phyla and genera that lead to microbial structural differences between groups using QIIME2 with default settings [41,42]. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (Picrust2) [43] is software that predicts the functional abundance from the sequencing data of marker genes (typically 16S rRNA). An ASV’s abundance table is used for standardization, and the corresponding relationship of each ASV is compared with the Kyoto Encyclopedia of Genes and Genomes (KEGG) library to obtain the functional information and functional abundance spectrum.
A total of 22 red deer individuals were identified from 43 fecal samples, including 12 males and 10 females (Supplementary Table S2). The female captive deer were CF1, CF2, CF3, CF4, CF5, CF6, CF7, and CF8. The male captive deer were CM1, CM2, and CM3. We divided all the red deer (22 wild and 11 captive) into four groups: wild females (WF) (n = 10), wild males (WM) (n = 12), captive females (CF) (n = 8), and captive males (CM) (n = 3). The information about identification, location, sex, and diet is summarized in Supplementary Table S2.
The wild red deer were fed on 16 species of plants in the winter. The edible plants belonged to 16 species of 16 genera and 9 families. Since the frequency of occurrence of other edible plants in red deer, such as Mongolian oak (Quercus mongolica) and Chinese maple (Acer sinensis), was less than 7%, the nutrient content of these plants was not measured. In addition, we hypothesized that they had little influence on the nutritional strategy of red deer. Therefore, the primary nutrient contents of 14 types of edible plants were determined. The food and nutritional composition of wild red deer are shown in Supplementary Table S3. When the captive red deer were fed, each type of food was fed separately at different times. The nutritional content of the primary food of captive red deer from the farm (adjacent to the Gaogestai Nature Reserve) in winter is shown in Supplementary Table S4. Only one kind of diet were provided to captive deer at each feeding time with all captive deer feeding together. Captive red deer feed on leaves and high protein given by artificial feeding. Compared with captive red deer, wild deer have a wider feeding range and no dietary limitations. Substantial differences exist between these two feeding methods.
A total of 1,561,654 high-quality sequences were obtained from the fresh winter feces of 22 wild deer and 11 captive deer. Rarefaction curves based on the Chao1 diversity index reached asymptotes at 22,500. The results showed that with the increase in amount of sequencing, the curve tended to be flat and no longer changed, indicating that the amount of sequencing in this study basically reflected the diversity of red deer fecal microbiota in this study (Supplementary Figure S1). A total of 15,228 ASVs were obtained using a 100% similarity clustering method. The WF, WM, CF, and CM groups included 3056 ASVs, 3924 ASVs, 6661 ASVs, and 1587 ASVs, respectively.
We found significant differences in fecal microbial composition between wild and captive red deer based on environment. The fecal microbial communities of four groups (WF, WM, CF, and CM) were dominated by the phyla Firmicutes and Bacteroidetes (Figure 1A). The phylum Firmicutes was the most abundant in WF (81.12 ± 2.87%), followed by WM (79.03 ± 2.19%), CF (58.24 ± 3.17%), and CM (59.66 ± 0.47%). Secondly, Bacteroidetes was abundant in WF (15.19 ± 2.09), WM (16.89 ± 2.08%), CF (33.02 ± 5.48), and CM (31.55 ± 1.61%). At the genus level, the genera from the four groups with abundance > 1% were Oscillospira, a candidate genus 5-7N15 from the family Bacteroidaceae, Ruminococcus, Roseburia, Clostridium, and Prevotella (Figure 1B and Table 1). The chao1 diversity indices demonstrate a significant difference between the WF and WM groups (p < 0.01). There was no statistically significant difference between the CF and CM groups (p > 0.05). Pieluo’s diversity index showed that no significant differences occurred between WF and WM groups (p > 0.05) or CF and CM groups (p > 0.05) (Figure 2). Wild and captive red deer also differed in beta-diversity. An PCoA plot based on the Unweighted Unifrac and Weighted Unifrac distance matrix revealed clear separation of the fecal microbiota between wild and captive red deer (Figure 3A). The results of a PCoA analysis showed that the fecal microbial structures of the CF and CM groups were more similar than those of the WF and WM communities (F = 13.82, p = 0.001; and unweighted: F = 5.983939, p = 0.001; Figure 3A; Supplementary Table S5). A random forest analysis showed that Firmicutes and Bacteroidetes were the primary microorganisms that had differences between the wild and captive populations by (an importance > 0.1) (Figure 3C, D). This analysis indicated that there were significant differences in the abundances of Firmicute and Bacteroidetes between the four groups (an importance > 0.1), which were the primary phyla that caused differences in the microbial communities between groups (Figure 3C). Ruminococcus, Treponema, Akkermansia, a candidate genus 5-7N15 belonging to family Bacteroidaceae, and a candidate genus rc4-4 belonging to family Peptococcaceae were the main genera that caused differences in microbial communities between sex and environment (importance > 0.04; Figure 3D).
Metabolism was found to be the most common function prediction performed on fecal microbial communities and included the most important pathways for microbial clustering (76.67%). The second pathway of metabolism included amino acid metabolism (17.26%), carbohydrate metabolism (17.85%), metabolism of cofactors and vitamins (16.57%), and metabolism of terpenoids and polyketides (12.66%) (Figure 4A). A PCoA analysis showed that the WF and WM groups had more similar microbial function clusters (Figure 4B). It was found that there were significant differences in the three metabolic pathways of glycan biosynthesis and metabolism (GBM), energy metabolism (EM), and metabolism of other amino acids (MAA) (p < 0.05) (Figure 5).
This is the first study to apply high-throughput sequencing to describe the fecal bacterial microbiota of wild and captive red deer by sex. Analysis of the differences in fecal microbiota is a key step in releasing captive red deer to help expand the wild population. In general, the fecal bacterial microbiota of red deer was similar to that of other cervidae, such as elk (Cervus canadensis), white tailed deer (Odocoileus virginianus) [38], and white-lipped deer (Cervus albirostris) [39], at least at the bacterial phylum level, with high proportions of the phyla Firmicutes and Bacteroidetes. In the digestive tract of herbivores, the role of Firmicutes is mainly to decompose cellulose and convert it into volatile fatty acids, thereby promoting food digestion and host growth and development. The enrichment of Firmicutes plays an important role in promoting the ability of red deer to obtain abundant nutrients from food and, at the same time, affects the metabolic function of the fecal microbiota. Bacteroidetes can improve the metabolism of organisms, promote the development of the gastrointestinal immune system, participate in the body’s bile acid, protein, and fat metabolisms, and also have a certain regulatory effect on carbohydrate metabolism. It can also produce special glycans and polysaccharides, which have a strong inhibitory effect on inflammation [43]. Differences in microbiota may be explained by changes in diet. Data from previous local and overseas studies have shown that diet is the main factor affecting the gut microbiota in mammals [40]. It is likely that wild deer have a more varied diet, more than captive deer. These phyla, Firmicutes and Bacteroidetes, are involved in important processes such as food digestion, nutrient regulation and absorption, energy metabolism, and host intestinal defense against foreign pathogens [40,41,42]. Alpha diversity alterations may be attributed to differential diet or hormonal influences on the gut microbiota. Fecal microbiota richness in wild populations is higher than that in captive animals, such as the Tibetan wild ass (Equus kiang), bharal (Pseudois nayaur), Tibetan sheep (Ovis arise), and yak (Bos mutus) [44,45,46,47,48]. Nevertheless, other studies also found that captivity might increase the alpha diversity of fecal microbiota in most Cervidae compared with other animals, for example, sika deer (Genus Cervus), Père David’s (Elaphurus davidianus), and white-tailed deer (Odocoileus virginianus) [49,50]. It may be that some environmental stresses in the wild or the special structure of the stomach and intestines in these deer lead to decreased alpha diversity of fecal microbiota in wild deer [50]. This phenomenon needs further research to determine its cause. Our results showed that the richness of the fecal microbial community in wild red deer differed by sex (Figure 2). In wild deer, the microbiota diversity was higher for females than males. Microbial community alterations by sex could be attributed to hormonal [51]. The sampling time was during the gestation period of red deer. Levels of female growth hormone during pregnancy may affect the fecal microbiota. Reproductive hormones have also been associated with sex and gut microbial changes in wild animals [17,52,53]. Increased evidence indicates that sex steroid hormone levels are associated with the human gut microbiota [54,55]. Futher, Edwards et al. reported that estrogen and progesterone had an impact on gut function [56]. The captive deer also had the smallest sample size (n = 3 males and 8 females), which limited our ability to detect these differences. In this study, the functional pathway composition of wild red deer is more similar (Figure 5B), which is completely opposite to the microbial structure (Figure 3A). The change in microbial structure does not necessarily lead to the change in function, which may be due to the same function in different microbial communities [57]. In recent years, studies have shown that gut microbiota are involved in various metabolic processes such as amino acids, carbohydrates, and energy, confirming their primary role in assisting host digestion and absorption [58]. It has also been found to be involved in environmental information processing, suggesting that the gut microbiota plays an important role in facilitating acclimation to changing environments [59]. The metabolism of gut microbiota is closely related to the feeding habits of the host. In the long-term evolution process, the gut microbiota will respond to changes in diet types or specific diets by adjusting the content of certain digestive enzymes [4,60]. Studies have shown that the decrease of fecal microbial diversity can lead to a reduction in the functional microbiota, in the efficiency of the microbiota, and in the resistance to pathogen invasion [61]. The decrease in fecal microbial diversity in captive populations resulted in a decrease in functional microbiota [61]. Ruminococcaceae and Lachnospiraceae are two of the most common bacterial families within the Firmicutes phylum [62]. It has been hypothesized that they have an important role as active plant degraders [63,64]. According to our results, the level of Ruminococcaceae in the captive groups is significantly lower than that in the wild group, which could suggest that the fiber-reduced diet in captivity is modifying the ability of the fecal microbiota to degrade recalcitrant substrates such as cellulose, hemicellulose, and lignocellulose, among others, that are commonly found on the main resources of the wild red deer diet. The captive deer’s consequent reduction of diet resources might trigger the decline of important metabolic pathways associated with nutrient use [64]. 16S rRNA analysis constitutes a valuable and cost-efficient approach for surveillance and monitoring wild populations as well as captive individuals. Picrust2 prediction accuracy is dependent on the availability of closely related annotated bacterial genomes in the database and the phylogenetic distance from the reference genome. However, the prediction results are still uncertain, which does not mean that the correlation between the predicted genes and the real metagenome of the microbiota is 100% [65]. At present, due to the difficulty of cultivation, the mechanism by which some functional bacteria exert their effects remain unclear. Therefore, in the follow-up work, it is necessary to repeatedly cultivate the conditions of some intestinal anaerobic bacteria, the most extensive of which are Firmicutes and some Bacteroidetes. The microbiota was cultured in vitro by simulating the gut environment, and its functions were speculated and further verified in combination with multiple groups of studies (metagenomics, meta transcriptome, and proteome, etc.). At the same time, the unknown functional microbiota and its genome sequence information can be explored and studied. These works will help to understand the metabolic activities of the complex microbiota and further explore the host physiological processes involved in gut microbiota.
In conclusion, our study provided information on the structure and function of the fecal microbiome of red deer through the 16S rRNA gene of fecal samples. Comparing analyses identified significant variations of fecal microbiota composition and functions between captive and wild populations and also indicated that environment and sex have a great influence on these variations. These findings were of great significance for the reintroduction of captive red deer, given that the differences in fecal microbiota composition and functions between captive and wild red deer would greatly impact the ability of captive red deer to adapt to the wild environment. For further study, incorporating novel methods (e.g., transcriptome) to study the functional annotation of gene content and the functional traits of the host would be essential for better understanding the physiology and immunology of red deer. |
|
PMC10000043 | "Asmaa Sadat,Alshimaa M. M. Farag,Driss Elhanafi,Amal Awad,Ehab Kotb Elmahallawy,Noorah Alsowayeh,Ma(...TRUNCATED) | "Immunological and Oxidative Biomarkers in Bovine Serum from Healthy, Clinical, and Sub-Clinical Mas(...TRUNCATED) | 01-03-2023 | mastitis,E. coli,S. aureus,oxidative/antioxidant molecules,APP,inflammatory cytokines | "Simple Summary Establishing reliable biomarkers of udder bacterial infection and its bovine immune (...TRUNCATED) | "Immunological and Oxidative Biomarkers in Bovine Serum from Healthy, Clinical, and Sub-Clinical Mas(...TRUNCATED) |
|
PMC10000048 | Duy Ngoc Do,Prashanth Suravajhala | Editorial: Role of Non-Coding RNAs in Animals | 23-02-2023 | "Editorial: Role of Non-Coding RNAs in Animals\n\nThe importance of non-coding RNAs (ncRNAs), such a(...TRUNCATED) |
|||
PMC10000050 | "Rosa Marchetti,Valerio Faeti,Maurizio Gallo,Massimo Pindo,Davide Bochicchio,Luca Buttazzoni,Giacint(...TRUNCATED) | Protein Content in the Diet Influences Growth and Diarrhea in Weaning Piglets | 22-02-2023 | piglet,post-weaning diarrhea,dietary protein,fecal microbiota,feces composition | "Simple Summary Weaning (that is, removal from the sow) and the following two months are the riskies(...TRUNCATED) | "Protein Content in the Diet Influences Growth and Diarrhea in Weaning Piglets\n\nWeaning (that is, (...TRUNCATED) |
|
PMC10000055 | Xin Yang,Xuemei Wu,Shuang Huang,Qian Yao,Xi Chen,Junke Song,Yingying Fan,Guanghui Zhao | "C3a/C3aR Affects the Propagation of Cryptosporidium parvum in the Ileum Tissues of Mice by Regulati(...TRUNCATED) | 24-02-2023 | "Cryptosporidium parvum,C3a/C3aR signaling,propagation,intestinal barrier function,cell proliferatio(...TRUNCATED) | "Simple Summary The complement system plays important roles in both innate and adaptive immunity. Th(...TRUNCATED) | "C3a/C3aR Affects the Propagation of Cryptosporidium parvum in the Ileum Tissues of Mice by Regulati(...TRUNCATED) |
|
PMC10000059 | Maria Irene Pacini,Maurizio Mazzei,Micaela Sgorbini,Rossella D’Alfonso,Roberto Amerigo Papini | "A One-Year Retrospective Analysis of Viral and Parasitological Agents in Wildlife Animals Admitted (...TRUNCATED) | 04-03-2023 | wildlife,central Italy,viruses,helminths,protozoa,interspecies transmission,zoonotic pathogen | "Simple Summary In recent decades, wildlife populations in Italy have continued to expand, and some (...TRUNCATED) | "A One-Year Retrospective Analysis of Viral and Parasitological Agents in Wildlife Animals Admitted (...TRUNCATED) |
End of preview. Expand
in Dataset Viewer.
LitScan EPMC Subset
This dataset is a subset of afg1/epmc-oa-subset, which itself comes from the Europe PMC open access subset of about 5.9 million articles.
Here, we take the ~960 parquet files from the full OA subset and join them against a list of PMCIDs for articles found by LitScan, which should discuss ncRNA for the ~9.6 million IDs searched from RNAcentral. The result is a collection of just over 1 million open access fulltext articles ostensibly about ncRNA.
The primary use case for this is pre-finetuning on domain specific text. This idea of domain adaptation is similar to what NVIDIA have done with their ChipNeMo model.
We are planning to finetune some models on this dataset, probably TinyLlama, since it is quite quick to train. These will be useful for e.g. generating embeddings for RAG, or further downstream finetuning on tasks like summarisation.
Limitations
The epmc-oa-subset parquet files are parsed from JATS, which does not always go entirely to plan. As a result, there are likely to be some articles with missing text, or strange tags left in. These should be quite rare, but I can't guarantee they're not in there.
LitScan itself also has some limitations, namely that there is quite a high false positive rate for those RNA IDs that are a bit generic. This means that while most of the articles in this dataset should be focused on RNA, there will be a significant minority that are about all sorts of other things, including but not limited to: concrete, female mice, recurrent neural networks. This is a very tricky problem to solve!
- Downloads last month
- 37