In this study, we found 985 genes that change expression in the cortex and the medulla of the kidney with age. Some of the genes whose transcripts increase in abundance with age are known to be specifically expressed in immune cells, suggesting that immune surveillance or inflammation increases with age. The age-regulated genes show a similar aging profile in the cortex and the medulla, suggesting a common underlying mechanism for aging. Expression profiles of these age-regulated genes mark not only age, but also the relative health and physiology of the kidney in older individuals. Finally, the set of aging-regulated kidney genes suggests specific mechanisms and pathways that may play a role in kidney degeneration with age. A study of human aging in the kidney reveals similar changes in the transcriptional profile in cortex and medulla, suggesting that a common underlying aging process is taking place This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events. As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel. For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expression in old age. Extensive studies in ed genes . Severaled genes , 2000 aned genes , 2004. Tsir2, and oxidative damage have each been shown to affect lifespan in diverse model organisms  in the renal cortex. The primary function of the tubules within the medulla is to concentrate or dilute the urine so as to maintain fluid balance. The major age-related change in kidney function is a 25% decline in the glomerular filtration rate starting at age 40 . The abiIn this study, we present a molecular portrait of the aging process in the human kidney by analyzing gene expression as a function of age on a genome-wide scale. We show that age regulation is similar in the cortex and the medulla, and that age-regulated genes in the kidney are broadly expressed. We show that the expression profiles of age-regulated genes correlate well with the morphological and physiological state of the kidney in old age. Finally, we analyze the set of age-regulated genes to identify specific metabolic processes and cellular functions that change as a function of age, and discuss their possible roles in specifying the functional lifespan of the kidney.http://genome-www5.stanford.edu/) . Using our dataset, the expression level for every gene as a function of age could be plotted. For example, the expression of CDO1 (which encodes a cysteine dioxygenase type 1 protein) tended to increase with age. There was also variation between subjects and between the cortex and the medulla . We saw large differences in expression between tissue types and between the sexes. These differences were of similar magnitude for both young and old subjects, so that aging in one tissue (or sex) typically ran parallel to aging in the other , Tissuei is one if sample i was a medulla sample (and zero for cortex), and ijɛ is a random error term. The coefficients kjβ for k = 0, 1, 2, and 3 are values to be estimated from data. Our primary interest is in βj1, which describes how quickly the expression of gene j changes with age, withβj1 = 0 for genes with no linear age relationship.In kjcan differ from zero, even when the true coefficient is zero. We judged statistical significance through p-values, where a value of pji near zero corresponds to a large absolute value |β^kj|unlikely to have arisen by chance. Such p-values do not distinguish genes that increase with age from those that decrease with age. We also use one-tailed p-values, written p˜kj, taking values near zero to be significantly decreasing trends and those near one to be significantly increasing trends  gave no reason to suspect that a curved relationship was needed. Similarly, a piecewise linear age relationship (with bends at ages 50 and 70) was not significantly better than a linear one. Large and statistically significant differences in expression were found for the two tissue types, and so the tissue type was included in p < 0.001 for both sex and age.To make p < 0.001), which is considerably greater than would be expected by chance . Of these, 742 genes increase expression with age and 243 decrease expression with age  were localized to an isolated region of the kidney. Our normal samples were obtained from the region of the kidney furthest from the carcinoma, were not directly contaminated with cancer cells, and appeared normal histologically see . This prp-value for whether renal cell carcinoma affected each of the 44,928 genes present on the Affymetrix DNA chip. The smallest p-value was 0.00013. We would expect to see almost six such p-values by chance alone. This result indicates that the presence of renal cell carcinoma does not significantly affect the expression of any gene in the normal tissue from the same kidney, compared to normal tissues taken from kidneys without renal cell carcinoma.Third, we used regression models to directly test whether our aging studies were affected by seven medical factors: renal cell carcinoma, transitional cell carcinoma, size of tumor, hypertension, systolic blood pressure, diastolic blood pressure, and diabetes mellitus. For renal cell carcinoma, we used a regression model predicting expression from age, sex, tissue type, and a zero/one variable indicating whether the sample came from a patient with renal cell carcinoma or not. The result gave a p < 0.001) age regulation using either of these models. Renal cell carcinoma does not significantly affect the age slopes for these genes . The regression slopes changed very little with and without these factors, indicating that these factors do not strongly affect our analysis of age regulation .Fourth, five of the samples were from kidneys that did not have tumors, and two of these were from donor kidneys used for transplantation that had no associated pathology at all. The expression profile from these five patients was similar to the profile from other samples used in our study. In summary, it is unlikely that these disease and medical factors have confounded our analysis of age-regulated changes in gene expression.Changes in the expression for some of the 985 age-regulated genes may directly reflect the aging process in the kidney; these genes would serve both as aging markers and provide clues about molecular mechanisms for aging in the kidney. Other changes may result from an age-related change in the relative proportion of cell types within the kidney, such as would result from increased infiltration of immune cells with age. Finally, the expression changes may reflect the downstream response of the kidney to an age-related process elsewhere, such as would result from age-related changes in blood pressure or vascular supply.Since the cortex and medulla contain different cell types and have distinct functions, it was of interest to test whether they age similarly. It is possible that the pattern of degeneration in a particular cell type reflects those metabolic pathways that are used most heavily by that cell. For example, there could be deterioration in cell adhesion in glomerular epithelial cells that form part of the filtration barrier in the cortex, while there could be an age-related decline in ion traffic or water flow across the apical or basolateral membranes of tubular epithelial cells in the medulla. Alternatively, distinct cell types could show a common pattern of age-related decline involving pathways common to all cells, such as protein synthesis and mitochondrial function. This degeneration of core cellular processes would affect every cell function, including filtration by glomerular epithelial cells and water and solute reabsorption by tubular epithelial cells.βj4 × Tissue × Age was added to the model in βj4. j4-values. Genes showing tissue-specific slopes would appear in peaks on the left and right. The figure shows neither of these peaks, indicating there is no statistically significant difference in aging between the two tissue types.To test whether age-related gene expression changes are different in cortex and medulla, we considered a model in which a term of the form To further investigate coordinate aging in the cortex and medulla, we searched for age-regulated genes in each of these tissues independently, and then tested whether age-regulated genes in one were also age-regulated in the other. Specifically, to find age-regulated genes in the cortex, we fit the modelusing the cortex samples only. To find age-regulated genes in the medulla, we fit the modelp < 0.001).using only the medulla samples. We found 634 genes in the cortex samples and 72 genes in the medulla samples that showed significant changes in expression with age (p < 0.001) in the cortex samples. If aging in the medulla were unrelated to aging in the cortex, we would expect to see a flat histogram. The actual histogram has a strong peak of genes on the right, indicating that significantly age-regulated genes in the cortex tend to also be significantly age-regulated in the medulla. Of the 634 genes that increased expression with age in the cortex, 22 also increased expression with age in the medulla, compared with the 0.6 genes expected at p = 0.001. We obtained similar results when we took the converse approach, first selecting the 72 age-regulated genes in the medulla, and then testing whether they were also age-regulated in the cortex (data not shown).Having identified age-regulated genes in the cortex, we next examined whether they were also age-regulated in the medulla. r = 0.487. Models 2 and 3 allow us to investigate whether the cortex and medulla age at the same rate as specified in model 1. For the 22 genes that are significantly age-related in both tissues, the age coefficients have a high correlation (r = 0.96), and the slopes themselves are numerically close (2 expression per year), corresponding to only a 6% divergence in expression over 50 y. Given the strong similarities in the aging profiles of these two tissue types, we are able to increase the statistical power of our analysis by pooling the cortex and medulla datasets (resulting in model 1).Next, we compared the slope of expression with respect to age in the cortex to that in the medulla C. The rely close D. We fouWe examined the list of 985 age-regulated genes, and immediately found evidence for increased expression of genes from immunocytes. Many of the 985 age-regulated genes are expressed specifically in B cells , T cells , or neutrophils  see . Nearly Immunohistochemical experiments using antibodies directed against markers specific for B cells, T cells, or neutrophils showed that the kidney samples contained a small proportion of immune cells (less than 1%) in sporadic clusters scattered throughout each section (data not shown). The number of immune cells varied greatly from section to section, and thus it was not possible to use immunohistochemistry to confirm a quantitative increase in the numbers of immune cells in the kidney with age.2 of the expression level for each gene, and then calculated an average expression level for the blood (five samples) and the kidney (134 samples). Of the 985 genes that change expression with age, 538 are expressed at higher levels in blood cells than in the kidney samples. Age-related changes in the RNA abundance of these genes may reflect either changes in the fraction of immune cells in the kidney or age-related changes in expression in kidney cells. The remaining 447 are expressed at higher levels in the kidney than in whole blood, and age regulation of these genes is likely to reflect expression changes in kidney cells themselves , indicating that regulation of expression due to differentiation (between the cortex and medulla) is much greater than that related to aging. This result is consistent with the idea that aging results from a slow degeneration of a core transcriptome in the cortex and the medulla of the kidney.Aging is thought to be caused by slow degeneration of the transcriptome (the entire set of genes expressed in a tissue), rather than a qualitative change in expression, as occurs during tissue specification. As such, changes in gene expression associated with aging should be less than expression differences between different types of tissues. To confirm this idea, we compared the magnitude of gene expression differences due to differentiation (cortex versus medulla) to those due to aging. We used the same approach as before to evaluate differences in expression in cortex versus medulla on a genome-wide scale. For every gene, we calculated the istogram . Genes cTo address whether different organs have distinct or common aging profiles, we analyzed whether the 447 age-regulated genes in the kidney were expressed specifically in the kidney or broadly in many tissues. If the kidney has its own specific pattern of aging, one might expect that the set of 447 aging-regulated genes would be enriched for those expressed specifically in the kidney, such as genes that have direct roles in forming the filtration barrier or in regulating ion or water reabsorption. If there is a common profile for aging shared among tissues, one might expect that most of the list of 447 aging-regulated genes would be expressed in many tissues.We determined the level of expression of the age-regulated genes in different tissues using data from a previous study reporting a genome-wide profile of gene expression in 26 different human tissues with Affymetrix gene arrays . Of the The expression levels of these 447 age-regulated genes constitute a molecular profile of aging, and we can examine the expression profile of individual patients to observe how they compare to the average for their age group. Older individuals tended to express age-induced genes at higher levels and age-repressed genes at lower levels than younger individuals. However, certain individuals had unusual expression profiles, in which genes were expressed at levels more typical of a different age group. For example, patient 81 was 78 y old but had an expression profile as though she were older see . Her kidDo the molecular gene expression profiles correlate with the physiological ages of the kidney samples? That is, does patient 81 have a kidney showing excessive age-related damage and does patient 95 have a kidney with unusually good health? To answer these questions, we determined the morphological and physiological states of the kidneys from each of the patients by examining histological stains. As people grow older, there is a general decline in the morphological appearance of the kidney: (1) the glomeruli lose their structure and their capillaries are replaced with fibrous tissue (glomerular sclerosis), (2) the tubules collapse and atrophy, and the interstitial space between them widens and scars , and (3) there is a thickening of the innermost layer of the arteriole wall due to the accumulation of hyaline material . We gave three scores to each kidney section corresponding to the appearance of the glomeruli, the tubules, and the arterioles. Scores ranged from zero for normal appearance for youthful patients to four for an advanced state of glomerular sclerosis, tubular atrophy/interstitial fibrosis, or arterial intimal hyalinosis see . We thenWe then compared the chronicity index to the gene expression profiles of the 447 age-regulated genes as a function of age see . In genemortalin-2 (which encodes Heat Shock Protein 70), which decreases expression in the kidney in old age. Heat shock proteins act as protein chaperones, and likely function to counteract cell senescence by alleviating the accumulation of damaged proteins in old cells. In human fibroblasts, overexpression of mortalin-2 extends lifespan in vitro  extends longevity, and several genes encoding heat shock proteins decrease expression in old age , all but one of which increase expression in old age see . Forty-nAnother functional group is a set of 11 genes encoding ribosomal proteins, all of which increase expression with age. Protein synthesis rates are known to decline as animals grow older, and increased expression of these ribosomal protein genes may serve to offset this.Changes in the expression of regulatory genes with age may have particularly strong effects on kidney metabolism and function, since these changes are likely to initiate cascades of changes in downstream genes. We examined our list of 447 age-regulated genes for those that are likely to function as regulatory genes. Of the 447 age-regulated genes, 15 encode transcription factors and 51 encode proteins that are part of signaling pathways.As filtration of the blood takes place in the glomerulus, age-regulated genes that are enriched in the glomerulus may be especially important for understanding how kidney function declines with age. We identified genes enriched in the glomerulus using data from a previous study, in which cDNA microarrays were used to compare expression levels in the glomeruli relative to the rest of the kidney . Of the Old age is associated with a functional decline in a myriad of molecular and cellular processes. To gain a global perspective of the diverse pathways that change with age, we performed a whole-genome analysis of gene expression as a function of age for kidney samples from 74 patients ranging in age from 27 to 92 y. Many factors affect gene expression in addition to age, including variability between individuals, between different tissues within the kidney, and between sexes. The large number of samples in our dataset provided good power for identifying age-regulated genes in noisy data despite small changes in expression, and allowed us to use a statistical linear regression model to identify 985 genes that change expression with age.The results from this work show that transcriptional differences between young and old individuals involve an accumulation of small changes in expression from many genes, rather than resulting from large expression changes in a small number of genes. This observation suggests that functional decline in old age is not the result of the complete failure of a small number of cellular processes. Rather, it is the slight weakening of many pathways that cumulatively causes a significant decrease in cell function. Studying aging by analyzing one pathway at a time is difficult, because any single pathway might show only a small change with respect to age and might contribute only a small amount to the overall functional decline in old age. By contrast, functional genomics is a powerful approach to study aging, because many genes can be simultaneously scanned in parallel for small changes in expression.(r) of the regression slopes for these 985 genes was only 0.085 between the kidney and muscle aging experiments and hence accounts for only 0.0072 of the variance between these two tissues  in which the pathology was localized and did not involve the part of the kidney sampled. Key factors from the medical record for each patient used in this study are listed in Frozen tissues were placed in cryomolds, embedded in Cryo Tissue Tek O.C.T. Compound  and cut into 4-μm sections . Sections were stained with hematoxylin and eosin, and then histologically evaluated to exclude samples showing abnormal histology. Histology slides were also marked into two main functional sections, the cortex and medulla, to help aid in accurate dissection of these two areas. We reviewed radiological findings for all tumors and histology for all slides. We excluded any cases in which radiological imaging, gross examination at the time of resection, or histological review of the removed tissue indicated that there might be tumor involvement of the normal areas. Cases with incomplete or unclear medical records were excluded from this study.Frozen kidney tissue samples were dissected into cortex and medulla sections. Portions were weighed (0.05–0.75 g), cut into small pieces on dry ice, and then placed in 1 ml of TRIzol Reagent  per 50–100 mg of tissue. The tissue was homogenized using a PowerGen700 homogenizer , and the total RNA was isolated according to the TRIzol Reagent protocol.A standard protocol designed by Affymetrix  for their HG-U133A and HG-U133B high-density oligonucleotide arrays was slightly modified by the Stanford Genome Technology Center , and all samples were processed in their facility see . Eight mhttp://genome-www5.stanford.edu/) and at the Web site http://cmgm.stanford.edu/approximatelykimlab/aging_kidney/. The Affymetrix probe IDs and the locus link IDs for the genes discussed in the paper are in Using the dChip program , microarp-values we use are based on t-tests from standard linear regression theory. Under the hypothesis H0 that kjβ = 0, the estimated coefficient β^kjis a random variable. The least squares value is a particular number, β^LSkj. The p-value measures the extent to which the least squares value is surprisingly large assuming H0 holds. Specifically, the two-tailed p-value isThe p-value we use isand the one-tailed kjis employed to test H0 against an alternative hypothesis of kjβ < 0. We use it because it distinguishes between significant increasing and significant decreasing coefficients. Under H0, the distribution of p is U, and so is that of p˜. Numerically, the equationSometimes p˜holds.t-test is derived under an assumption of normally distributed errors. The data showed estimated errors with heavier than normal tails. The t-test is known to be robust against heavy-tailed errors.The A linear regression is more appropriate for these data than is an analysis of variance (ANOVA) on age groups, because the latter is aimed at piecewise constant expression patterns, and it is not plausible that expression should change sharply at a given age. A genome-wide ANOVA (data not shown) did, however, find a similar group of age-related genes. Unlike ANOVA, regression summarizes the age effect in one coefficient. This is advantageous for interpretation and for statistical power when there is little nonlinearity.p-values for all the genes. If the histogram of p˜values differed sharply from uniform, and if the smallest p-values were small compared to 1/44,928, then the coefficient was included.The decision of whether to include a variable in model 1 was based on the collection of p-value of 0.001. Such a gene list can be expected to have about 44 genes in it by chance, even if all of the coefficients are really zero. Thus, of the 985 age-related genes, it is plausible that about 44 of them are false positives. We have chosen to work with a fixed significance level, instead of attempting to fix the false discovery rate, because our test statistics are strongly correlated.Gene lists were made using a threshold 2 expression in the cortex from that in the medulla, and fit a regression of the difference versus age and sex. Such an analysis removes intra-subject correlations. There was again no evidence of genes aging differently in the two tissue types (data not shown).We were concerned that intra-subject correlations might have affected our results. For each of 59 subjects with both cortex and medulla samples, we subtracted logFigure S1Each row shows the presence of a medical or related factor. Age of patients is shown on the y-axis. Only transitional cell carcinoma showed a strong age bias. We have identified over 20 different factors that might potentially confound our study on aging, such as race, blood pressure, diabetes, and type and size of tumor adjacent to the normal section see .(221 KB PDF).Click here for additional data file.Figure S2We used regression models to directly test whether our aging studies were affected by seven medical factors: renal cell carcinoma, transitional cell carcinoma, size of tumor, hypertension, systolic blood pressure, diastolic blood pressure, or diabetes mellitus. Scatterplots show age-related slopes using a regression model that includes a term for the medical factor compared to slopes from a regression model that does not include that medical factor.p < 0.001) age regulation using either a model with a renal cell carcinoma term or without a renal cell carcinoma term. The vertical and horizontal axes show the slope from a model with and without the renal cell carcinoma term, respectively. The slopes change very little with and without the renal cell carcinoma term. As one might expect, many of the genes that are significant at the 0.001 level are just barely so. There were 866 genes significant in both models, 119 significant only when renal cell carcinoma was not in the model, and 86 significant only when renal cell carcinoma was in the model. The overall picture of age relationship changes very little whether a term for renal cell carcinoma is included in the model or not.(A) Effect of renal cell carcinoma (RCC) on age-related expression. We selected genes that showed statistically significant . We have not used false discovery rate techniques for this problem, because the age coefficients for different genes are far from independent. The scatterplot shows that transitional cell carcinoma does not affect age-related slopes very much.(B) Effect of transitional cell carcinoma (TCC) on age-related expression. Scatterplot showing age-related slopes with and without a term for transitional cell carcinoma. Transitional cell carcinoma was present in 13 patients, all of whom were old. Thus if transitional cell carcinoma affected gene expression in adjacent normal tissue, then it might bias our results on aging. (B) shows data for presence or absence of transitional cell carcinoma in the model. The gene with the smallest (C) Tumor size does not affect age regulation.(D) Hypertension (HTN) does not affect age regulation.(E) Systolic blood pressure (SBP) does not affect age regulation.(F) Diastolic blood pressure (DPB) does not affect age regulation.(G) Diabetes mellitus (DM) does not affect age regulation.(307 KB JPG).Click here for additional data file.Figure S3p < 0.004). A Pearson correlation value of 0.085 implies that 0.72% of the variance in the muscle regression coefficients is due to variance in the associated kidney regression coefficients. We note that the muscle dataset had a small sample size (n = 16), which may not be large enough to sufficiently detect similarity in age regulation with the kidney.We obtained the muscle dataset from the GEO database . To comp(59 KB XLS).Click here for additional data file.Figure S4C. elegans (D. melanogaster (We compared patterns of gene expression in the aging time course data from  elegans  and D. mnogaster  to thoseD. melanogaster. Open triangles denote age-regulated genes in humans and their orthologs in flies. Open circles denote age-regulated genes in flies and their orthologs in humans. The scatterplot shows the regression slopes from the human kidney and the fly aging datasets (r = −0.05 (p = 0.27) for human and fly, while the age-regulated fly genes paired with human orthologs show a Pearson correlation r = −0.05 (p = 0.12).(A) Regression slopes of age-regulated genes from human kidney and datasets . SpecifiC. elegans. Open circles denote age-regulated genes in humans and their orthologs in worms. Open triangles denote age-regulated genes in worms and their orthologs in humans. The scatterplot shows the regression slopes from the human kidney and C. elegans aging datasets (r = 0.05 (p = 0.54). The age-regulated worm genes paired with human orthologs show a Pearson correlation r = −0.01 (p = 0.08).(B) Regression slopes of age-regulated genes from human kidney and datasets . The ageThese results show no evidence for overlap in the aging process between different species.(509 KB PDF).Click here for additional data file.Protocol S1(40 KB DOC).Click here for additional data file.Table S1(33 KB XLS).Click here for additional data file.Table S2(13 KB XLS).Click here for additional data file.Table S3(135 KB XLS).Click here for additional data file.Table S4(75 KB XLS).Click here for additional data file.Table S5(53 KB XLS).Click here for additional data file.


Science, Stuart Kim, a Stanford developmental biologist, made the case for laying down the broad strokes of a complex physiological process before defining its mechanisms. “A powerful, top-down, holistic approach,” he wrote, “is to identify all of the components of a particular cellular process, so that one can define the global picture first and then use it as a framework to understand how the individual components of the process fit together.” To get a broad view of gene expression in the aging nematode, Kim's lab turned to DNA microarrays and functional genomics. In a new study, Kim and colleagues apply this same approach to the decidedly more complex problem of human aging and “present a molecular portrait” of the aging kidney.Four years ago in Scientists have identified a wide range of molecular pathways and mechanisms associated with aging. Many have been found in evolutionarily distant organisms, suggesting they have been conserved and could shed light on human aging. Yet other studies suggest that since few animals reach old age in the wild, any aging-related physiological changes aren't likely to impact the fitness of a population and so aren't likely to be conserved. Consequently, aging pathways in worms, for example, would have little bearing on humans. To investigate the molecular pathways associated with human aging, the authors focused on human tissue—in this case, the kidney.Kidneys came from 74 patients, ranging in age from 27 to 92. Samples were extracted from donated kidneys or “meticulously harvested” from kidneys with localized disease to ensure only normal tissue was taken. Two structures that are critical to kidney function (removing toxins from blood) were removed from each sample: the renal cortex, which filters plasma, and the medulla, which alters urine composition to maintain fluid balance. Both deteriorate with age. An extensive clinical history was noted for each sample to account for any potentially confounding medical factors.Kim and colleagues then isolated RNA transcripts from the samples to determine the activity of every gene, broken down by age and kidney section, through microarray analysis. Looking for differences in gene expression across the genome, they identified genes that showed a statistically significant change in expression as a function of age. Of 33,000 known human genes on the microarray, 985 showed age-related changes, most showing increased activity. These changes are truly age-regulated, the authors conclude, since none of the medical factors impacted the observed changes in gene expression.Although cortex and medulla have different cell types and perform different functions, their genetic aging profile was very similar, suggesting a common aging mechanism operates in both structures. In fact, these mechanisms may function broadly, as most of the age-regulated kidney genes were also active in a wide range of human tissues. Other organisms appear to lack these changes, however, prompting the authors to argue that understanding aging in humans will require human subjects.Most importantly, the genetic profile of the tissue samples correlated with the physiological and morphological decline of an aging kidney. An 81-year-old patient with an unusually healthy kidney had a molecular profile typical of someone much younger, while a 78-year-old with a damaged kidney had the profile of a much older person. Using the power of functional genomics, this study has identified a set of genes that can serve as molecular markers for various stages of a deteriorating kidney and predict the relative health of a patient compared to their age group. These gene sets can also serve as probes to shed light on the molecular pathways at work in the aging kidney, and possibly on the process of aging itself.


We analyzed expression of 81 normal muscle samples from humans of varying ages, and have identified a molecular profile for aging consisting of 250 age-regulated genes. This molecular profile correlates not only with chronological age but also with a measure of physiological age. We compared the transcriptional profile of muscle aging to previous transcriptional profiles of aging in the kidney and the brain, and found a common signature for aging in these diverse human tissues. The common aging signature consists of six genetic pathways; four pathways increase expression with age , while two pathways decrease expression with age . We also compared transcriptional profiles of aging in humans to those of the mouse and fly, and found that the electron transport chain pathway decreases expression with age in all three organisms, suggesting that this may be a public marker for aging across species. Aging is a complex phenomenon characterized by the decay of biological function over time, eventually leading to death. High-throughput methods for examining changes in the expression of genes, such as DNA microarrays, have been successful in elucidating some of the genome-wide changes that occur with age in several human tissues. The authors profiled gene expression changes in the muscles of 81 individuals with ages spanning eight decades. They found 250 genes and 3 genetic pathways that displayed altered levels of expression in the elderly. The transcriptional profile of age-regulated genes was able to discern elderly patients with severe muscle aging from those that retained high levels of muscle function; that is, the gene expression profiles reflected physiological as well as chronological age. In order to find genetic changes that might affect most or all tissues during aging, the authors compared genome-wide profiles of aging in the muscle to those in the kidney and the brain, and found a common signature for aging shared among these three tissues consisting of six genetic pathways. One of these aging pathways (the electron transport chain pathway) is age regulated not only in humans but also in two model organisms (mice and flies), providing insights about shared age-related changes in animals with vastly different lifespans. Aging is marked by the gradual decline of a multitude of physiological functions leading to an increasing probability of death. Some aging-related changes affect one's appearance, such as wrinkled skin, whereas others affect organ function, such as decreased kidney filtration rate and decreased muscular strength. At the molecular level, we are just beginning to assemble protein and gene expression changes that can be used as markers for aging. Rather than search for molecular aging markers by focusing on only one gene or pathway at a time, an attractive approach is to screen all genetic pathways in parallel for age-related changes by using full-genome oligonucleotide chips to search for gene expression changes in the elderly. A genome-wide transcriptional profile of aging may identify molecular markers of the aging process, and would provide insight into the molecular mechanisms that ultimately limit human lifespan.INK4ap16 are molecular markers of aging, as they predict both the remaining lifespan and the physiological age of the mouse [Molecular markers of aging must reflect physiological function rather than simple chronological age because individuals age at different rates . In the he mouse –4. In thhe mouse . Whole-ghe mouse ,8, and dhe mouse , but chaIn this paper, we have performed a genome wide analysis of gene expression changes in the human skeletal muscle. As age increases, skeletal muscle degenerates, loses mass, loses total aerobic capacity, and becomes markedly weaker . One meaThe extent to which age regulation of genetic pathways is specific to a particular tissue or common across many tissues is unknown. Age regulation of gene expression between the cortex and medulla regions of the human kidney was found to be highly correlated . There wDrosophila melanogaster [Caenorhabditis elegans [Another key issue is whether there are genetic pathways that are commonly age regulated in different species with vastly different lifespans, such as human, mouse, fly, and worm. Transcriptional profiles of aging have been performed on both skeletal muscle and brains in the mouse ,15, in Dnogaster ,17, and  elegans . A compa elegans .In this work, we present a transcriptional expression profile of 81 human skeletal muscle samples as a function of age. The symporter activity, sialyltransferase activity, and chloride transport pathways all decrease expression with age in human muscle. The age-regulated genes were found to be markers of physiological age, not just chronological age. By comparing our results on aging in muscle to previous transcriptional profiles of aging in the kidney and the brain, we found a common signature for aging across different human tissues consisting of six genetic pathways that showed common patterns of age regulation in all three tissues. Finally, by comparing the signature for aging in humans to transcriptional profiles of aging in mice, flies, and worms, we found that expression of the electron transport chain decreases with age in humans, mice, and flies, constituting a public signature for aging across species with extremely different lifespans.http://www.ncbi.nlm.nih.gov/geo).In order to study the effects of aging in human muscle, we obtained 81 samples of human skeletal muscle from individuals spanning 16 to 89 y of age . Sixty-tWe used a multiple regression technique on each gene to determine how its expression changes with age, as had been done previously for age regulation in the kidney  . We analp < 0.001)  in addition to age, sex, and anatomy. We then compared the aging coefficient using this new model with the one from the original model that did not include the term. If any of the 250 genes were regulated by the medical factor and not by age per se, we would expect marked differences in the aging coefficients generated by the two multiple regression models. None of the fourteen medical factors, including hypothyroidism, had a significant effect on age regulation . Second,p < 0.001), and the large majority of these age-regulated genes change expression less than two-fold in 50 y. These results are consistent with a model in which age-related decline in cellular functions is caused by the accumulation of multiple, minute changes in the regulation of genes and pathways.In summary, we have generated a global profile of changes in gene expression during aging in human muscle . It is wCYP26B1 shows an average increase in expression of 90% in 50 y. CYP26B1 is a member of the cytochrome P450 family, which are monoxygenases used to metabolize toxic substances. Increased expression of CYP26B1 in old age could help eliminate toxins that accumulate with age.The genetic functions of many of the 250 genes shown in LASS5 decreases expression approximately 25% in 50 y. LASS5 is the human ortholog of the yeast lag1 longevity assurance gene. In yeast, lag1 expression decreases in older yeast cells [LASS5 is involved in the ceramide signaling pathway, which plays important roles on several lifespan-associated processes, such as stress resistance and apoptosis [LASS5 in old age could impair cell function by reducing ceramide signaling.st cells  similar poptosis . ReducedIn addition to searching for age regulation one gene at a time, we also screened known genetic pathways for those showing an overall change with age. With this approach, age regulation for every gene in a pathway is combined to determine whether there is an overall regulation of the entire pathway. Screening for coordinated age regulation of genetic pathways increases the sensitivity of our analysis, as the combined effects of small regulation of many genes in a pathway can be significant. For example, in a previous study of type 2 diabetes, screening genetic pathways for changes in expression provided key insights that were not possible from analyzing genes individually .We developed a variant of gene set enrichment analysis (GSEA) to determine whether a genetic pathway shows evidence for age regulation . We assap < 0.001) in this resampling. At this p value threshold, we would only expect to find 0.6 gene sets by chance alone. We also required the gene groups to show some practical significance by rejecting groups with a van der Waerden score smaller than 3.1 in absolute value. We found three pathways that passed both criteria: symporter genes, sialyltransferases, and chloride transporters showed decreasing expression with age  on average in response to increasing age. The increase is measured by a van der Waerden statistic. To judge whether a specific van der Waerden statistic is significant, we used bootstrap resampling. Each bootstrap sample was drawn by resampling the arrays and keeping the gene expression measurements linked with the age, sex, and anatomy variables. The 624 van der Waerden scores for the gene groups were recomputed for each of the 1,000 bootstrap samples. Six gene sets were found to have statistically significant van der Waerden scores (with age . Aging cSymporter genes (63 genes) and chloride transporters (35 genes) are necessary for transporting solutes during muscle contraction ; the decSome people age slowly and remain strong and fit in their 70s, whereas others age rapidly, becoming frail and susceptible to age-related disease. We wanted to determine whether the expression profile for the 250 aging-regulated genes correlated with physiological in addition to chronological aging. For example, patient V17 was 41 y old but expressed his age-regulated genes similarly to patients who were 10 to 20 y older, and we would like to determine whether this patient had poor muscle physiology for his age . ConversA simple correlation of gene expression with muscle type ratio would not be sufficient for our purposes. Such a correlation could arise simply because the gene expression and muscle type ratio are both correlated with age. Accordingly, we employed partial correlations of gene expression with muscle type ratios after adjusting for the effect of chronological age. To do this, we regressed type II/type I muscle fiber diameter ratio on age, regressed gene expression on age, and finally correlated residuals from both regressions to obtain partial correlation coefficients. The partial correlations for the 250 age-related genes are shown in p value below 0.0001 and concluded that the age-related genes are more likely than other genes to have some partial correlation with muscle diameter ratio. To illustrate this effect, we also sampled 250 genes from the genome 1,000 different times, each time counting how many had a partial correlation larger than 0.2 in absolute value. None of the samples had a count larger than 92  partial correlation was more than 0.2 . There w than 92 D.r > 0.2). We compared the distribution of partial correlations of the 250 age-regulated genes with type II/type I ratios to the distribution of partial correlations of the rest of the genes in the genome using nonparametric methods . This result indicates that the apparent physiological basis of our gene set is not a consequence of our having chosen 0.2 as a threshold.Our result indicating that the 250 age-regulated genes are enriched for genes regulated by type II/type I muscle fiber diameter ratio is valid even when we use other selection thresholds for muscle physiology . Aging coefficients for all genes in each of these pathways are listed in p < 0.05 in all three tissues, and hence a combined p < 1.25 × 10−4).Our initial attempt to compare transcriptional changes between tissues relied on a Venn analysis, in which we directly compared the overlap in the lists of the age-regulated genes from the three tissues. Next, we searched for a common aging signature by comparing the Pearson correlation of age regulation between two tissues. Both of these straightforward methods showed only borderline statistical evidence for similarities in aging between the three tissues , but neither is expected to be powerful. Ultimately, we compared tissues using a grouped gene analysis. Grouping genes can be more powerful if there are small but consistent effects in each of a number of genes. Furthermore, the specific biological processes associated with each genetic pathway provide insights into mechanisms of aging. We used the modified GSEA described above to analyze previously published data on age regulation in the kidney and the brain ,6. As be tissues . The cytTIMP1, which encodes tissue inhibitor of metalloproteinase 1, shows the largest increase in expression with age (average of 236% in 50 y).Increased overall expression of the extracellular matrix gene set (152 genes) with advancing age may contribute to widespread fibrosis in the elderly . FibrosiTGFB1 and FGFR1. Induction of genes in this gene set may reflect an attempt to repair tissue damage that accumulates over lifespan.The cell growth gene set (29 genes) includes genes coding for growth factors, such as Although complement activation genes (22 genes) are induced in muscle, the kidney, and the brain, they are expressed primarily in liver . TherefoCytosolic ribosomal genes include 85 genes that show a general increase in expression with age in all three tissues. This result is interesting because the rate of protein synthesis is known to decrease in old age , and yetThe chloride transport pathway is composed of 35 genes that show an overall decrease in expression with age in all three tissues. Ion transport of many types is important not only in the contraction of muscle , but als+-ATP synthase (complex V), and the uncoupling proteins. The finding that expression of genes involved in the electron transport chain decreases in old age supports the mitochondrial free-radical theory of aging [The mitochondrial electron transport chain was found to show an overall decrease in expression with age. This group contains 95 genes, including genes associated with the NADH dehydrogenase family (complex I), succinate-coenzyme Q reductase (complex II), ubiquinone-cytochrome c reductase (complex III), cytochrome c oxidase (complex IV), Hof aging , as freeThe above results show that there is common age regulation for these six genetic pathways in the kidney, muscle, and the brain. Next, we determined that there was little statistical evidence for the correlation of age regulation of individual genes in a pathway in one tissue with their age regulation in another tissue . Thus, it is unclear whether or not the same genes or different genes within a pathway show age regulation between different tissues. For example, certain genes in the electron transport pathway might be age regulated in the kidney, whereas other electron transport genes might be age regulated in the muscle.Having identified genetic pathways that are commonly age regulated in different human tissues, we next determined whether their age regulation is specific for humans (private) or whether these groups are also age regulated in other species (public). Genetic pathways that are age regulated in different species would be of particular interest because they would identify mechanisms that are inextricably related to aging, even in animals that have vastly different lifespans.D. melanogaster [C. elegans [. To examine age regulation in aging mouse kidneys, we collected a kidney sample from ten C57BL/6 mice at 1, 6, 16, and 24 mo of age for a total of 40 mouse kidney samples. RNA from each kidney was extracted, labeled with P33-dCTP, and hybridized to cDNA filter membranes comprising 16,896 cDNA clones corresponding to 11,512 unique genes. We normalized expression values using the Z-score method [We compared age regulation in humans to previously published studies of age regulation in nogaster  and C. e elegans . To exame method , and anaWe first identified orthologs of human genes in each of the other three species. Next, we determined the change in expression with respect to age for each gene in each species, using multiple regression techniques similar to the ones used for our studies of aging in human muscle . We took the six gene sets shown to be aging-regulated in diverse human tissues, and then asked whether they also showed age regulation in any of the other three species. We analyzed the expression of each of the gene sets using modified GSEA to determine whether they showed an overall bias in expression with age in each species. Extracellular matrix genes, cell growth genes, complement activation genes, cytosolic ribosomal genes, and chloride transport genes did not show age regulation in other species.Drosophila, but did not show significant age regulation in C. elegans (p < 0.02) and flies (p < 0.001) but not C. elegans. The electron transport chain gene set also shows a large van der Waerden score in mice and flies (less than −3.7). In summary, humans, mice, and flies show decreased expression of the electron transport chain during aging, defining a public pathway for aging across species with very different lifespans. In C. elegans, it is unclear whether the lack of support for age regulation of the electron transport chain pathway is because the pathway is not age regulated or because the DNA microarray experiments lack statistical power to detect age regulation.The electron transport chain genes showed a consistent overall decrease in expression with age in humans, mice, and  elegans . To showIn this study, we have generated a high-resolution transcriptional profile of aging in the human muscle. Welle et al. have previously used DNA chips to profile expression changes during aging for the human muscle ,8, and tPeople age at different rates, especially with regard to muscular aging. Some remain fit and strong, whereas other become frail and weak when they are old. The transcriptional profile for aging in this study reflects the physiological age of the subjects, as measured by muscle diameter ratio, after making an adjustment for their chronological ages. Previous work on age regulation in the kidney also identified molecular markers that could predict the physiological age of the kidney .Our results provide the some of the first evidence for a common signature of changes of gene expression in different human tissues. Specifically, we found similar patterns of age regulation for six biological pathways in the muscle, the kidney, and the brain. Previous studies found similar patterns of aging between different parts of the same tissue, but not between entirely different organs  but also to degeneration of tissue-specific functions  that rely on housekeeping pathways. By identifying a common aging signature across tissues, we can now focus on aging pathways that are general instead of tissue-specific. The common aging signature reflects the age of diverse organs, whereas genes that are age regulated in just one tissue reflect the age of that tissue. Finally, treatments or therapies that alter expression of the four common age-regulated pathways might be expected to affect diverse tissues instead of a specific tissue, and may therefore have an overall effect on longevity.Although some patterns of aging are similar between different human tissues, much of aging is tissue-specific. Decreases in expression of the sialyltransferases and symporter genes are changes specific to muscle, and do not appear to occur in either the kidney or the brain.Nearly all of the age regulation that we found is specific to humans, and does not seem to occur in old mice, flies, or worms. Thus, much of age regulation in humans is species-specific (private) rather than universal for all animals (public). This result emphasizes the importance of studying aging in humans rather than model organisms with short lifespans in order to understand how people grow old.Nevertheless, we did find one pathway that was age regulated in humans, mice, and flies. The electron transport chain gene pathway decreases expression with age in all three species. Previous studies found little or no similarity in age regulation between humans and mice  or primaC. elegans, reducing the activity of eight genes involved in the electron transport chain using RNAi increased lifespan significantly [(NDUFA10) is one of the genes showing the largest decrease in expression with age in humans, and its ortholog in worms, K04G7.4, was also found to cause one of the largest increases in lifespan using RNAi in C. elegans [In mammals, direct genetic tests of the functional relevance of reduced expression of the electron transport chain pathway on lifespan have not yet been reported. However, in ficantly ,36. A ge elegans . The genWhat types of upstream events might cause a decrease in expression of the electron transport chain pathway with age? Other mitochondrial pathways, such as the mitochondrial ribosome, do not show age regulation similar to the electron transport chain pathway. One potential cause of decreased expression of the electron transport chain pathway is that metabolism may slow in old age, resulting in reduced expression of the energy producing machinery of the cell. Another possibility is that oxidative damage to the proteins in the electron transport chain in the mitochondria may lead to reduced expression of the corresponding genes in the nucleus. The electron transport chain creates free radicals in the process of generating energy that would preferentially damage protein components of the electron transport chain .It seems unlikely that common age regulation of the electron transport chain pathway is directly due to evolutionary conservation. Events in old age are unlikely to have a significant effect on fitness of a population because old animals (such as 3-y-old mice and 80-y-old people) are a small fraction of natural populations (except in recent human history). It could be that the electron transport chain is regulated during aging as an indirect consequence of regulation during development (antagonistic pleiotropy) . AlternaIt is interesting that the level of age regulation of the electron transport chain is nearly the same in each species, whereas lifespan varies greatly. Compared to humans, mice age 20- to 30-fold and flies age 400-fold more rapidly. Thus, the kinetics of the changes in gene expression for the electron transport chain genes precisely matches the difference in lifespan between species. This suggests that decreased expression of the electron transport chain pathway with age may be particularly informative as a marker of physiological aging.The muscle samples were obtained from patient biopsies collected either during surgery or in an outpatient procedure, and the medical conditions associated with each biopsy are listed in Frozen muscle samples were weighed (50–100 mg), cut into small pieces on dry ice, and then placed in 1 ml of TRIzol Reagent . The tissue was homogenized using a PowerGen700 homogenizer , and the total RNA was isolated according to the TRIzol Reagent protocol.A standard protocol designed by Affymetrix  for their HG-U133 2.0 Plus high-density oligonucleotide arrays was slightly modified by the Stanford Genome Technology Center , and all samples were processed in their facility (see Protocol S1). Eight micrograms of total RNA was used to synthesize cRNA for each sample, and 15 μg of cRNA was hybridized to each DNA chip. The samples were processed in random order with respect to age.2-transformed expression values for all subsequent analyses.We used the DChip program  to normac oxidase preparations) and measured by hand. All of the diagnostic muscle biopsies were considered, and 32 of the 81 muscle samples were sufficiently intact for measurement, the remainder being inadequately oriented for cross-sections or too small for meaningful data. Digital analysis consisted of measuring the shortest width through the approximate center of the cell. After calibration with a known length, the diameters were measured and converted to microns using SigmaScan Pro 5.0 software . Diameters were tabulated by type I and type II cell types. The counts ranged from approximately 30 cells per type to more than 100 depending on the sample size. Print analysis was by similar methodology. Raw measurements in millimeters were used to calculate the ratio of type II to type I diameters without converting to microns.Cross-sections of muscle cryosections were photographed at 200×, and the pictures were either measured digitally  or printed , iAnatomy is the anatomic location from which the muscle sample was harvested , ijɛ represents an error term, 1jβ is the change of expression with age, 2jβ is the change of expression with sex, 3jβ is the change of expression with anatomical origin of sample, and 0jβ is the regression intercept. For each gene j, we used least-squares to determine all of its coefficients, with our primary interest in the one with respect to age 1j)., indicating a significant overlap  .Here, ibution) . The 92 Second, we repeated our age analysis taking into consideration the effect of type II/type I muscle fiber diameter ratio on age regulation. To do this, we used a four-term multiple regression model that includes terms for both age and type II/type I ratio:p < 0.01) and 12,786 genes regulated by type II/type I ratio . We randomized the age variables of muscle samples 1,000 times while maintaining the sex and anatomy variables with the sample. p values in every randomization. Theory predicts, and our simulation verifies, that on average about 32 genes pass our threshold (p < 0.001) by chance. This result suggests that there are about 13% false positives in our set of 250 age-regulated genes in the muscle. In 95% of the permuted datasets, 107 or fewer genes were significant at the 0.001 level.We used a permutation analysis to simulate the number of genes that would pass our cutoff by chance . In our analysis, we have replaced the weighted Kolmogorov-Smirnov test by a weighted sum, the van der Waerden normal scores test.GSEA  uses a nG of genes to be age related than does the weighted Kolmogorov-Smirnov test. When N is large, then any small group that contains the single most age-related gene is significantly age related by the weighted Kolmogorov-Smirnov test. Such a group displays a genuine statistical significance and comprises strong evidence that F ≠ G, but isn't necessarily biologically increasing or decreasing expression as a mechanistic unit with age. For example, a group of 30 genes with two of the most age-increasing genes and 2 of the most age-decreasing genes could be found to be both an age-increasing group and also an age-decreasing group with significance, even when the other 26 genes are not particularly age related. Here it is clear that F ≠ G, but perhaps it is simply because G has higher variance than F.The van der Waerden test conforms more closely to our interpretation of what it means for a group G. This rank is the number of the original N genes with an age coefficient smaller than that of gene j. The raw van der Waerden score isTo compute the van der Waerden test, we first find the rank r(j) for every gene j ∈ Φ is the standard normal cumulative distribution function. When the N age coefficients are independent with a common continuous distribution F = G, then the distribution of Y is very nearly normally distributed with mean 0 and a variance V(Y) close to N-n. We replaced the GSEA enrichment score by the van der Waerden statistic, Z = Y/ under the null hypothesis. When distribution G is shifted left or right relative to F, then the value of Z tends to increase beyond what we would expect from the N distribution.where  null distribution to assess the significance of the enrichment score Z. The reason is that there are ordinarily correlations among the expression levels of the genes in G. When the expression levels of two genes in G are correlated, the age coefficients for those genes are correlated as well [. The value of Z can become large either because the genes are age related or because they are correlated with each other. Both may be biologically real, but the second is not an interesting finding, except possibly as confirmation that the group G is well constructed.It is better to use resampling methods instead of the N as well . It thenThe original GSEA  randomlyWe adopted instead a bootstrap approach. We resampled the data and recomputed enrichment scores, obtaining a histogram roughly centered over the observed enrichment score. If the null value (zero) is far outside the resampled histogram, then the enrichment score is statistically significant. The bootstrap approach also preserves correlations among genes as well as correlations between genes and covariates.The primary motivation for bootstrapping is the presence of covariates in our problems. Consider for example data with age, sex, and expression variables. If we permute the ages with respect to the expression data and repeat the regression, we have to decide whether the sex variable should be attached to the ages or to the expressions in the random permutation. Attaching sex to the age variables will leave us with simulated data sets in which females express Y chromosome genes as much as males. Because of such artifacts, this is not a suitable null distribution. Attaching a covariate to the expression variables is also problematic. Suppose that one of the covariates is somewhat correlated with age. The effect will be to increase the variance of the originally sampled age coefficient. In permutation samples where the covariate is attached to the expression data, it is resampled independently of age. Such independence reduces the variance of the age coefficient in the permutation data. The consequence is that the permutation-based histogram of age coefficients is then too narrow and false discoveries will result.In the bootstrap approach we generated 1,000 sample datasets. In each sample dataset we mimicked the sampling process that gave rise to the data by resampling 81 subjects from the population of 81 subjects. The resampling keeps age, expression, and all covariates of any given subject together. Bootstrap sampling mimics the random process that generated the data.We remark that both bootstrap and permutation sampling of the van der Waerden scores gave rise to Z scores that were nearly normally distributed, but not necessarily N (unpublished data). In permutation sampling, the histogram of enrichment scores tended to have means near zero, but several groups had variances larger than 1.0. In bootstrap sampling, the variances often differed from 1 and the means were usually between zero and the original enrichment score.p < 0.09, hypergeometric distribution), five aging-regulated genes in both muscle and the brain (p < 0.07), and 13 aging-regulated genes in common between the kidney and the brain (p < 0.29). There were no genes that were strongly age regulated in all three datasets. The Venn analysis approach is very interpretable but lacks power because it replaces actual measured correlations by a less informative notion of whether they are over a threshold.The most direct way to compare aging in muscle, the kidney, and the brain is via a Venn analysis: we find which genes attain a stringent significance level for each tissue and judge whether the overlap is statistically significant according to a hypergeometric distribution. We did a pairwise comparison between each tissue to find genes that are aging-regulated in both sets. There are six aging-regulated genes in both the muscle and the kidney ((r) of the resulting points (r = 0.219), and smaller but positive overlaps in aging between the muscle and the kidney (r = 0.103) or the muscle and the brain (r = 0.078).A more sensitive comparison can be based on correlating the age coefficients of genes in two tissues. We selected all genes that are age regulated in either of two tissues, plotted the age coefficient of each gene in one tissue versus that gene's coefficient in the other tissue, and computed the Pearson correlation g points . We founp value for a Pearson correlation between kidney and muscle, we used 1,000 sets of random genes. The number of genes in each set was the same as the number we used to compute the correlation in p value of 0.006 for kidney–muscle. We similarly found a p value of 0.001 for kidney–brain but only 0.058 for muscle–brain. With the possible exception of the kidney–brain pair, the age-related genes have more consistent age coefficients across tissues than randomly selected genes do.Because the genes are correlated we cannot use textbook formulas to judge the statistical significance of these Pearson scores. To get a p value of 0.078 for kidney–muscle. To save computation, we used the same set of genes in each bootstrap sample instead of making the age-related gene set vary with the sample separately. The p value for muscle–brain was 0.07 while that for kidney–brain was 0.001. Based on these individual gene-level analyses, the age-related genes in the kidney and brain tended to be very similar. The muscle–kidney and muscle–brain comparisons were weaker.We also ran a bootstrap test of the tissue comparisons. In this test we resampled the microarray data with replacement 1,000 times. Each time we recomputed the correlations between age coefficients for genes in the kidney and muscle. In 1,000 trials we saw 39 in which the sample correlation was less than or equal to zero. After converting to a two-tailed test, this corresponds to a To test for the correlation of gene ranks between tissues within those gene sets found to be commonly age regulated in the human, we used a two-tailed Spearman correlation method to first calculate a correlation coefficient for every pairwise combination of tissues  for that age-regulated gene set . In order to test for the significance of the calculated correlations, we used a permutation-based Monte Carlo method, randomizing the ranks for each gene and tissue in the gene set and recalculating Spearman correlations 1,000 times. We found that most of the correlations between tissues were not significant .Figure S1x-axis. Sex, biopsy location, and 12 medical factors are shown in the legend. Only hypothyroidism shows any overt association with age.Each row denotes a medical or pharmaceutical factor. Age of patients is shown on the (253 KB TIF)Click here for additional data file.Figure S2y-axis) and without (x-axis) the coronary artery disease term was plotted. If coronary artery disease affected expression, we would expect a large deviation in age coefficient. No significant deviation was seen for any of the 250 age-regulated genes, indicating that coronary artery disease does not adversely affect our study of age regulation.(A) Coronary artery disease was included as an additional term in (B–L) Similar to (A) for 11 other medical factors. (B) Coronary artery disease. (C) Colorectal cancer. (D) End-stage renal disease. (E) Hyperlipidemia. (F) Hypertension. (G) Hypothyroidism. (H) Pancreatic cancer. (I) Prostate cancer. (J) Radiotherapy. (K) Statins. (L) Villous adenoma.(319 KB TIF)Click here for additional data file.Figure S3Samples are clustered on the basis of 250 age-regulated genes in muscle, shown by the top dendrogram. Columns are individual muscle samples, marked by age of the patient. Top seven rows correspond to the expression of the first seven age-regulated genes. The diagram shows anatomical, medical, and pharmaceutical factors for each patient. Each row corresponds to one medical or pharmaceutical factor.(1.2 MB TIF)Click here for additional data file.Table S1(2.1 MB XLS)Click here for additional data file.Table S2(59 KB XLS)Click here for additional data file.Table S3(63 KB XLS)Click here for additional data file.Table S4(45 KB XLS)Click here for additional data file.Table S5(16 KB XLS)Click here for additional data file.Table S6(27 KB XLS)Click here for additional data file.Table S7(32 KB XLS)Click here for additional data file.Table S8(14 KB XLS)Click here for additional data file.Table S9(101 KB XLS)Click here for additional data file.Table S10(982 KB XLS)Click here for additional data file.Table S11(15 KB XLS)Click here for additional data file.Table S12(15 KB XLS)Click here for additional data file.


No matter how healthy a life one leads, no person has managed to live much longer than a century. Even though the advances of the modern age may have extended the average human life span, it is clear there are genetic limits to longevity. One prominent theory of aging lays the blame on the accumulation of damage done to DNA and proteins by “free radicals,” highly reactive molecules produced by the metabolic activity of mitochondria. This damage is expected to reduce gene expression by damaging the DNA in which genes are encoded, and so the theory predicts that the most metabolically active tissues should show the greatest age-related reduction in gene expression. In this issue, Michael Eisen and colleagues show that the human brain follows this pattern. A similar pattern—which, surprisingly, involves different genes—is found in the brain of the aging chimpanzee.The authors compared results from three separate studies of age-related gene expression, each done on the same type of DNA microarray and each comparing brain regions in young versus old adult humans. In four different regions of the cortex (the brain region responsible for higher functions such as thinking), they found a similar pattern of age-related change, characterized by changes in expression of hundreds of genes. In contrast, expression in one non-cortical region, the cerebellum , was largely unchanged with age. In addition to confirming a prediction of the free-radical theory of aging , this is the first demonstration that age-related gene expression patterns can differ in different cells of a single organism.The authors found a similar difference in age-related patterns in the brain of the chimpanzee, with many genes down-regulated in the cortex that remained unchanged in the cerebellum. However, the set of affected cortical genes was entirely different between humans and chimps, whose lineages diverged about 5 million years ago. The explanation for this difference is unknown, but the finding highlights the fact that significant changes in gene expression patterns, and thus changes in many effects of the aging process, can accumulate over relatively short stretches of evolutionary time. These results raise a number of questions about age-related gene expression changes, including whether metabolically active non-brain tissues display similar patterns of changes, and whether the divergence between human and chimp patterns was the direct result of selection, or was an inevitable consequence of some other difference in brain evolution. The patterns seen in this study also provide a starting point for understanding the network of genetic changes in aging, and may even reveal targets for treatment of neurodegenerative diseases.


It is well established that gene expression levels in many organisms change during the aging process, and the advent of DNA microarrays has allowed genome-wide patterns of transcriptional changes associated with aging to be studied in both model organisms and various human tissues. Understanding the effects of aging on gene expression in the human brain is of particular interest, because of its relation to both normal and pathological neurodegeneration. Here we show that human cerebral cortex, human cerebellum, and chimpanzee cortex each undergo different patterns of age-related gene expression alterations. In humans, many more genes undergo consistent expression changes in the cortex than in the cerebellum; in chimpanzees, many genes change expression with age in cortex, but the pattern of changes in expression bears almost no resemblance to that of human cortex. These results demonstrate the diversity of aging patterns present within the human brain, as well as how rapidly genome-wide patterns of aging can evolve between species; they may also have implications for the oxidative free radical theory of aging, and help to improve our understanding of human neurodegenerative diseases. Transcriptional profiles in human and chimpanzee reveal a diversity of aging patterns present within the human brain, as well as how rapidly genome-wide patterns of aging can evolve between species. Despite its ubiquity and importance, aging remains a poorly understood process. This lack of understanding is due in part to the complexity of aging, which is characterized by the gradual and progressive decline of numerous physiological processes and homeostasis, eventually leading to death –6. HowevCaenorhabditis elegans belong to the insulin/IGF (insulin-like growth factor) signaling pathway, indicating possible connections to metabolism and a state of arrested development known as “dauer” [Both environmental and genetic alterations in model organisms have been found to have profound effects on aging and lifespan. In particular, dietary restriction has been found to dramatically increase the lifespan of organisms including yeast, flies, nematodes, and mammals ,5; the m “dauer” ,5. Anoth “dauer” ,3.2O2), are produced in large part by the mitochondrial electron transport chain. The free radical theory has garnered widespread support in recent years; in addition to the genetic evidence mentioned above, studies from a number of model organisms showing that decreasing ROS levels leads to an increase in lifespan indicate that ROS can strongly modulate the aging process [The free radical theory of aging was first introduced by Harman almost half a century ago . This th process –5.Exactly how macromolecules damaged by ROS may lead to aging has been studied in detail in recent years, and the human brain has been intensively examined in this regard because of its overall importance in human senescence. For example, up to one-third of the proteins in the brains of elderly individuals may be oxidatively damaged, and these damaged proteins have been shown to sometimes have diminished catalytic function ,6. One rComplementing studies of aging differences in various tissues within a single species, research into the evolution of aging has begun to shed light on the similarities and differences between species, although the expectation for how well conserved the effects of aging will be on a gene-by-gene basis is still unclear. One the one hand, if aging is largely caused by the deleterious effects of many alleles late in life—as is often the interpretation of two widely held models for the evolution of aging, known as “antagonistic pleiotropy” and “mutation accumulation” ,5,13—theOne promising approach to answering this question of evolutionary conservation lies at the level of gene expression: Do orthologous genes tend to undergo the same patterns of expression changes with age in diverse species, or can a common factor such as ROS lead to different gene expression patterns in different organisms? Using DNA microarrays, this question can now be addressed in a systematic, genome-wide manner. One such study found that a small but significant portion of aging-related gene expression changes are shared by the very distantly related nematode and fruit fly ; anotherThe human brain is of particular interest for studying the divergence in phenotypes that have changed rapidly during evolution (such as aging). Brain-specific genes have undergone accelerated evolution in the lineage leading to human since the split with chimpanzee at the levels of both protein sequence and gene expression ,17, poinIn order to test whether different regions of the human brain show similar patterns of change with age, we utilized three independently published microarray expression datasets. These were: Lu et al. , mentionTo achieve the most comprehensive picture of brain aging possible with these data, we first sought to study the patterns of aging in all six brain regions from Khaitovich et al. . Becausep < 0.01) Spearman rank correlation between age and expression level in frontal pole, irrespective of their fold change in expression; most of these were expected to be true positives, because only approximately 126 genes would be expected to pass this significance threshold by chance . Values of r close to one indicate good agreement between aging patterns, whereas those close to zero indicate a lack of agreement. To assess the significance of these correlations, we randomly permuted the ages of the samples, and calculated the probability of observing a random correlation as strong as that found in the real data . We note that the true similarity of aging patterns in these regions is likely to be even stronger than is indicated by the correlations because, as mentioned above, approximately 15% of our genes are expected to be false positives with no true aging-related changes. In sharp contrast to cortex, the cerebellum and caudate nucleus showed far less agreement with frontal pole (r| < 0.1 and p > 0.4 for each). These results have several implications. First, the agreement between frontal pole and four regions of cortex indicates that we were able to accurately measure the direction of gene expression changes with age for most genes, even with only three samples from each region; thus the age range, number of samples, etc., are all sufficient to reflect the pattern of gene expression changes previously reported in frontal pole [Strikingly, all four regions of cerebral cortex for which we had expression data  showed excellent agreement with the aging pattern in frontal pole . Although the cerebellum correlation is stronger here than in p = 0.17), even though the two cortex samples are both significant (p < 0.01 each). This finding supports our conclusion that cerebellum ages differently than cortex.In order to further test the similarity of aging patterns within the brain, we compared a third independent dataset to the data from Lu et al.  and Khair| < 0.5) in either dataset. This comparison showed the expected reproducibility of aging patterns across all four regions of the cortex: r > 0.76 for all four . In contrast, neither cerebellum nor caudate nucleus showed a significant correlation (r| < 0.04 and p > 0.4 for each), as expected from their lack of correlation with the frontal pole data shown in For a third test of aging patterns throughout the human brain, we determined the correlation of aging patterns between a single cortex region from Evans et al.  with allr = 0.952. The cerebellum data from Evans et al. [r = 0.964. Thus differing data quality could not explain the lack of correlation in cerebellum and caudate nucleus.The lack of correlation between the aging pattern in cerebral cortex with those in cerebellum and caudate nucleus might arise if the quality of data in the cerebellum and caudate nucleus samples was lower than that of the cortex samples from both Khaitovich et al.  and Evans et al.  was of sfewer genes changing expression with age than cortex does, and they thus lack a reproducible pattern of aging-associated gene expression changes altogether.There are two possible explanations for the difference in the aging patterns between cerebellum/caudate nucleus and cerebral cortex. One is that cerebellum and caudate nucleus have their own aging patterns distinct from that in cortex. The other possibility is that cerebellum and caudate nucleus are different from cortex because they each have far To distinguish between these possibilities, one could attempt to calculate exactly how many genes change expression with age in each region; if cerebellum and/or caudate nucleus have aging-related changes in as many (but a different set of) genes than cortex, then the number of genes identified as changing in cerebellum and/or caudate nucleus should be comparable to any region of cortex. Unfortunately, as mentioned above, there is not enough statistical power to pursue this approach, given only three samples per region .Another way to differentiate between the two possibilities listed above would be to compare two datasets of cerebellum and/or caudate nucleus aging patterns to one another. If these regions have a reproducible pattern of many genes changing expression with age , including the cerebellum–cerebellum comparison. From these results, we conclude that cerebellum has a different pattern of aging than cortex because significantly fewer genes appear to change expression with age in cerebellum.We thus expected to see a strong positive correlation between cerebellum aging patterns in our two datasets if and only if a large number of genes change expression with age in cerebellum. Because this analysis was carried out on all informative genes  for the 841 genes changing strongly with age in frontal pole, in both young (45-y-old) and old (70-y-old) samples from Khaitovich et al. . As expeIn order to study the relationship between brain aging patterns in humans and chimpanzees, we required gene expression data from the chimpanzee brain. Although four studies have already produced such data, three of these ,22,23 exr| > 0.5) present on the microarray. Although we found no significant correlation when comparing cerebellum to either cortex region (r| < 0.07 and p > 0.3 for both comparisons), we found a very strong agreement when comparing aging patterns of prefrontal cortex to anterior cingulate cortex . This result is precisely analogous to our findings in human, where the entire cerebral cortex shares a single pattern of gene expression changes with age that is not found in the cerebellum . Similar results were found when comparing chimpanzee aging changes in any brain region to the patterns from either of our other two human expression datasets [We then tested whether brain aging in chimpanzee is similar to that of human. Using the 841 genes that change expression with age in human frontal pole , we testdatasets ,20 for eGiven this difference in aging patterns between humans and chimpanzees, we examined the expression levels of the chimpanzee orthologs of the 841 human genes that change expression with age in frontal pole in order to see if the expression levels of the chimpanzee orthologs of these genes resemble young humans, old humans, or neither. To test this, we first reanalyzed the expression data by masking all microarray probes with sequence differences between humans and chimpanzees . We thenr| > 0.5) are nearly all genuinely up- or down-regulated with age. Using this list of genes with a consistent aging pattern in the two cortex regions, there were 1,252 down-regulated and 700 up-regulated genes. Note that although the false-positive rate is likely to be low, we have no way to estimate the false-negative rate, so these numbers should not be interpreted as the total number of genes changing expression with age in chimpanzee cortex. Using this list of aging-associated genes, we tested for any significant enrichments of these genes in Gene Ontology annotation categories [The high correlation of gene expression aging patterns between the two regions of chimpanzee cortex implied that the genes for which both regions show the same direction of change  and the National Institute of Mental Health (NIMH) Silvio O. Conte Center, and from ArrayExpress and GEO databases. Only the seven “Type 1” control individuals [Human microarray data was obtained from Pritzker Neuropsychiatric Disorders Research Consortium . Data were normalized using the Robust Multichip Average (RMA) method [Chimpanzee postmortem samples were obtained from Yerkes Regional Primate Center, Biomedical Primate Research Centre and Anthropologisches Institut und Museum, Universität Zürich. All individuals suffered sudden death for reasons other than their participation in this study and without any relation to the tissues used; time between death and preservation of brain tissue  did not correlate with expression of genes that changes with age. Age and sex for all individuals are listed in ) method .Among the chimpanzees used for this work, one was of indeterminate age, having been caught in the wild 40 y before its death . HoweverAdditionally, one pair of chimpanzees used were full siblings, and another pair were half siblings , which cThere are several possible artifactual explanations for our results not addressed in the main text. First, there is the possibility that gene expression changes correlated with age were caused by an unknown factor unrelated to the normal aging process. For example, if in the study by Khaitovich et al.  the 70-ySimilarly, one possible explanation for the results in If gene expression changes in primates tend to be dependent on chronological age , then the different patterns of expression changes with age seen in humans and chimpanzees could be caused by the different age ranges of the human (18–106 y) and chimpanzee (7 to approximately 45 y) samples. To control for this possibility, we truncated the data of Lu et al. ,20. TherAnother possible explanation for the difference between human and chimpanzee aging patterns B is thatOne caveat concerning our interpretation of fewer genes having aging-associated changes in expression in cerebellum than in cortex is that the two human cerebellum datasets, although both consisting of grey matter of the cerebellum, were from different regions of the cerebellum ; in these cases the test was two-sided. This randomization test is somewhat conservative; for example, analyzing the leftmost bar  of p-value of approximately 10−99, as opposed to approximately 0.015 from randomization. This large difference is due to the nonrandom structure of the expression data, which makes it more likely to observe strong correlations than would be expected in a set of random data. Finally, we note that our finding of significant agreement between aging patterns in datasets containing various numbers of microarrays does not imply that these numbers of microarrays will always yield sufficient power to find such a correlation if one exists.All correlation coefficients reported here were calculated by Spearman rank correlation, a nonparametric method that is robust to the presence of any outliers. The correlation coefficients from comparisons of aging profiles between two tissues or species as in  and 4 weTable S1p-value cutoff for each GO group is 0.005.“All detected” is the number of genes on the microarray that had an annotation in each of the three Gene Ontology (GO) categories. “Detected/group” is the number of genes on the microarray that belong to the specific GO group listed. “All selected” is the number of genes up-regulated with age in chimpanzee cortex that have annotations in each of the three GO categories. “Selected/group” is the number of genes up-regulated with age in chimpanzee cortex that belong to the specific GO group listed. The uncorrected (21 KB XLS).Click here for additional data file.Table S2(18 KB XLS).Click here for additional data file.