To understand the changes in gene expression that occur as a result of age, which might create a permissive or causal environment for age-related diseases, we produce a multi-time point age-related gene expression signature (AGES) from liver, kidney, skeletal muscle, and hippocampus of rats, comparing 6-, 9-, 12-, 18-, 21-, 24-, and 27-month-old animals. We focus on genes that changed in one direction throughout the lifespan of the animal, either early in life (early logistic changes), at mid-age (mid-logistic), late in life (late-logistic), or linearly, throughout the lifespan of the animal. The pathways perturbed because of chronological age demonstrate organ-specific and more-global effects of aging and point to mechanisms that could potentially be counter-regulated pharmacologically to treat age-associated diseases. A small number of genes are regulated by aging in the same manner in every tissue, suggesting they may be more-universal markers of aging.

We were curious to know whether examination of multiple tissues at multiple times could lead to new insights into the possible global and tissue-specific mechanisms of aging that might be causal for age-related pathologies. Thus, we studied gene expression changes with age throughout the animal’s lifespan (at 6, 9, 12, 18, 21, 24, and 27 months) in liver, gastrocnemius muscle, kidney, and hippocampus. Rats were chosen because we had previously shown that rats are an excellent model for sarcopenia—the age-related loss of skeletal muscle (). Here, we applied that experience to other tissues to develop a full multi-tissue aging signature. We discovered genes that change in common in every tissue; genes that are regulated in early logistic, mid-logistic, late-logistic, and linear fashion in particular tissues; and pathways that are regulated in multiple tissues, giving some indication of common mechanisms of aging. It is our hope that this dataset will serve as a valuable resource for further molecular insights into mechanisms of aging.

It is possible to obtain a more thorough molecular profile of aging by examining gene expression changes in multiple tissues throughout multiple time points in the lifespan of the animal and determining genes that are perturbed in a consistent direction—in other words—genes that consistently increase in expression throughout life or genes that consistently decrease in expression.

Multiple analyses of age-related changes in gene expression have been conducted in various tissues in mice () and rats (). There have been other strategies for the development of molecular signatures of aging; for example, researchers have determined DNA methylation patterns that correlate with biological age. An advantage of that method is that easily accessible tissue, such as blood, can be used to determine and cross-compare a human’s aging status ().

Aging is the strongest risk factor for many serious diseases and co-morbidities, including cancer, heart disease, kidney disease, dementia, Alzheimer’s disease, frailty, and sarcopenia (). Increasing evidence suggests that aging occurs in a regulated manner and that perturbation of discrete cell-signaling pathways can extend lifespan and delay age-related diseases and co-morbidities ().

In the gastrocnemius muscle, the MEF2 A, B, C, and D (myocyte enhancer factor-2) transcription factor-related motifs were enriched by gene promoters that were down-regulated later in life, coinciding with age-related muscle atrophy ( Tables S4 and S5 ). Interestingly, in the gastrocnemius, the MEF2A-related motif was also enriched by up-regulated gene promoters with early logistic behavior. Because early response genes assigned to this gene subset were differentially regulated between 9 and 12 months (middle age), up-regulation of factors that target the MEF2A motif may be linked with early events of muscle aging and may trigger later events of sarcopenia.

Motifs related to the inflammatory response (STAT4, STAT5, STAT1, and NF-κB) were also enriched by promoters of linearly up-regulated genes ( Table S5 ). Motifs related to STAT4, STAT5, and STAT1 were prominent in the aging kidney, and motifs related to NF-κB were prominent in the aging liver ( Table S5 ). Furthermore, in the aging kidney, the motifs for hepatocyte nuclear factors (HNF4A, HNF1, and HNF1B) were enriched by promoters of down-regulated genes ( Tables S4 and S5 ).

The motifs associated with IRF1, IRF3, and ISRE (interferon stimulated response element) were enriched by gene promoters with linear behavior in the liver and kidney, where robust age-related induction of the interferon alpha and interferon gamma signaling occurred ( Tables S4 and S5 ). The motifs associated with IRF8 were enriched in kidney and hippocampus ( Tables S4 and S5 ).

Overall, the most prominent binding motifs enriched by genes up-regulated with age were transcription factors linked with the innate immune response and inflammation. For example, the motif for the transcription factor IRF2 (interferon regulatory factor) was enriched by promoters of up-regulated genes in three tissues—liver, kidney, and hippocampus—consistent with the finding that the interferon signaling pathway was found to be up-regulated in multiple tissues coincident with age, but this motif commonality allows focus on IRF2 in particular ( Table S4 ). Genes that contributed to the identification of the IRF2 motif were characterized by linear up-regulation with aging ( Table S5 ).

Given the sets of genes that were observed to be regulated by age, the next question was, what upstream mechanisms might be perturbing those genes? We, therefore, next sought to assemble the set of transcription factors that might be responsible for the gene changes, by using a program to identify transcriptional binding-site motif enrichment, known as HOMER (for Hypergeometric Optimization of Motif EnRichment) (). HOMER can be used to identify motifs that are statistically enriched in the promoter region of a given list of genes. We first applied HOMER using motifs of 6–12-bp length over a range of 1,000 bp upstream and 50 bp downstream of each transcriptional start site, using the age-regulated gene list for each tissue ( Figure 1 ). Table S4 contains the list of those enriched motifs (p value < 0.05 adjusted by the Benjamini-Hochberg procedure). In addition, we applied the HOMER program to the lists of gene subsets with linear, early, mid-, and late-logistic behaviors, here defined as “age-regulated genes” ( Table S5 ).

In analyzing the gene changes over time, we were surprised to see another characteristic emerge from the data—interestingly, aging induced an increase in the global gene expression variability ( Figure S4 ). Density plots of standard deviation variability demonstrated greater overall gene-expression variability in 27-month-old liver, gastrocnemius, and kidney, but not hippocampus, compared with samples from 6-month-old animals ( Figure S4 ). No increase in gene expression variability in the old hippocampus is consistent with a more overall muted response to aging seen in this tissue in aged SD rats ( Figure 2 D).

We also sought to determine whether there were common age-regulated genes among the multiple tissues and identified 148 genes that were common to at least three of those tissues (referred to here as the “common genes”; Table S3 ). Most of the common genes (n = 110) were up-regulated with aging consistently across tissues. Twenty-five common genes had a mixed-expression pattern, i.e., were up-regulated in some tissues and down-regulated in others. Thirteen common genes were down-regulated in at least three tissues (liver, gastrocnemius, and kidney). Interestingly, expression of 13 common genes was up-regulated with age in all four tissues, with 11 of those genes being annotated: LOC103689965, Psmb8, Gpnmb, Tspo, Irf7, Icam1, Ms4a6a, Isg15, C4a, C4b, and Cebpb ( Table 1 , and the first 13 transcripts in Table S3 ).

For each gene, the most significant adjusted p value differentiating 6 months from older ages is shown. A complete list of age-regulated genes that were in common to at least three tissues is provided in Table S3

The most prominent pathway down-regulated with aging was mitochondrial; oxidative phosphorylation, respiratory electron transport, and biological oxidation were all gradually down-regulated with age in the liver and kidney. Prominent down-regulation of oxidative phosphorylation and respiratory electron transport was seen in the gastrocnemius muscle. Age-related regulation of oxidative phosphorylation, respiratory electron transport, and biological oxidation pathways was not conspicuous in the hippocampus ( Figures 4 A and 4B). These changes were consistent with the idea that the mitochondria become less competent with age, depriving cells of critical supplies of ATP, in addition to the multitude other signals, which are mitochondrial in origin.

In addition to the pathway enrichment based on age-regulated genes described above, we also performed more global pathway-enrichment analyses comparing 6-month-old rats to older ages (i.e., 9 versus 6 months, 12 versus 6 months, etc., and ultimately 27 versus 6 months) ( Figure 1 , right side of flow chart). Mean fold change and statistical significance of genes in enriched pathways are shown in Figures 4 A and 4B , respectively. A complete list of enriched pathways based on statistical significance is provided in Figure S3 . We observed that many of these pathways were common to at least three tissues. Furthermore, although the fold change of gene expression enriched to each pathway was less in the hippocampus, compared with the liver, gastrocnemius, and kidney ( Figure 4 A), the statistical significance of the skew of the pathways toward up- or down-regulation was similar for several pathways ( Figure 4 B). Most strikingly, pathways that were related to up-regulation of inflammation were the dominant theme that we observed; for example, pathways related to the innate immune response, inflammation, and cytokine signaling were up-regulated with age in the liver, gastrocnemius, and kidney ( Figures 4 A and 4B). Age-related up-regulation of these pathways was also seen in the hippocampus, albeit with a less-dramatic increase. Pathways linked to allograft rejection and interferon alpha and gamma responses increased strongly with age in kidney, liver, and gastrocnemius muscle and were also increased in the hippocampus ( Figures 4 A and 4B). The complement pathway was also up-regulated with age in all four tissues. In addition to inflammation, other pathways of interest were perturbed; the apoptosis pathway was overall up-regulated in aging liver, gastrocnemius, and kidney and to a lesser extent in hippocampus, suggesting an overall increase in cell death occurring in tissues with age. Perhaps, this could be a consequence of the loss of growth factors or other positive anabolic signals. An increase in the apoptosis pathways was also seen in comparing young to old mice (). Metabolic pathways were generally down-regulated with age in the liver, skeletal muscle, and kidney, except for the cholesterol homeostasis pathway, which gradually increased in the liver with aging ( Figures 4 A and 4B). A decrease in metabolic pathways was also observed because of aging in mice ().

Biological pathway enrichment for gene expression changes with age in liver, gastrocnemius muscle, kidney, and hippocampus. In both (A) and (B), each row represents a pathway, annotated on the right hand side, and each column represents a specific age comparison (i.e., 9 versus 6 months, 12 versus 6 months, etc up to 27 versus 6 months). Pathway analyses are based on n = 7–9 rats/group. Colors represent average logof the fold change of genes in the indicated pathway (A) or statistical significance of the enrichment of the pathway (B). Color transition from white to red shows up-regulation, and color transition from white to blue shows down-regulation of the pathway. The color key in (A) indicates the corresponding average logof the fold change, and the color key in (B) indicates statistical significance of logof the p value of the enrichment. Numeric annotation on the left (A and B) indicates grouping of pathways by themes: (1) inflammatory, (2) growth, (3) RNA processing, (4) DNA damage, (5) coagulation, (6) metabolic, and (7) biological oxidation. See also Figure S3

Early response pathways were enriched in the gastrocnemius muscle and were predominately driven by transcription factors FOS, JUN, and ERG1 ( Table S2 ). These early response genes are up-regulated in middle-aged rats (by 12 months old), which corresponds to ∼45 years in humans, and it is possible that these factors are responsible for initiating later events. Interestingly, transcription factors FOS, JUN, and ERG1 are activated downstream of Ras/Raf/MEK/ERK signaling, and pharmacological inhibition of this pathway has been shown to extend the lifespan in Drosophila (). In the gastrocnemius muscle, we also found up-regulated (myogenesis) and down-regulated (glucose and carbohydrate metabolism) pathways that changed with a late-logistic profile ( Table S2 ). Increase in the myogenesis pathway in late life is likely to be related to functional denervation of old myofibers as well as regenerative events in response to age-related myofiber deterioration.

We next sought to identify pathways that were enriched by genes with the linear, early, mid-, and late-logistics behavior patterns. This was achieved by performing a hypergeometric enrichment test on genes with the linear, early, mid-, and late-logistic behavior against the background of all genes that changed with age in at least one tissue ( Table S2 ). This method would identify pathways that stand out above the background of the global age-related transcriptional regulation. According to the behavior type, most of the identified pathways were “linear up-regulated” pathways, and these were seen in the kidney, liver, and hippocampus. Among the “linear up-regulated” pathways, those related to the immune response were shared between kidney and hippocampus ( Table S2 ). Pathways related to cholesterol and lipid metabolism increased linearly in the aging liver ( Table S2 ).

When analyzed by logistic or linear behavior in the liver, kidney, and hippocampus, most of the age-regulated genes changed in a linear fashion: 82.7%, 87.5%, and 86.9% of all age-regulated genes, respectively ( Figure 3 A). In contrast, most of the age-regulated genes for the gastrocnemius muscle followed a late-logistic pattern (57.8%), with 36.7% of the genes being linear ( Figure 3 A).

(A–E) Age-regulated genes were screened according to the following criteria ( Figure 1 ): a fold change of > 1.5 (in either direction); Benjamini-Hochberg-adjusted p value < 0.05 for expression regulation between 6 month and at least one older age; and a Bayesian information criterion (as specified in the Method Details ) for the linear or four-parameter logistic fit that was at least 5% improved over that of the null model. These genes were identified based on datasets from liver, gastrocnemius muscle, kidney, and hippocampus across 7 ages: 6, 9, 12, 18, 21, 24, and 27 months, n = 7–9 rats/group. Example genes demonstrating a linear (B), early logistic (C), mid-logistic (D), or late-logistic (E) patterns are included.

To define the behavior of age-regulated genes, we chose a strategy that would allow us to catch genes that changed over a discrete interval and then maintained their new set-point from there on. The behavior of the age-regulated genes was best fit by a linear or four-parameter logistic model, again, to determine genes that changed in either a linear pattern or in early, mid-, and late-logistic patterns, in which the point of inflection for the regulation in expression coincided with early (<12 months), mid (≥12 months and <21 months), or late (≥21 months) of life, respectively (see Figures 3 B–3E for examples). Through this analysis, we identified 1,291 age-regulated genes in the liver, 1,854 genes in the gastrocnemius muscle, 2,195 genes in the kidney, and 229 genes in the hippocampus whose expression changed in a particular direction (either upward or downward) throughout the animals’ lifespans ( Figures 2 A–2D).

There were 1,291 age-regulated genes in the liver (A); 1,854 in the gastrocnemius muscle (B); 2,195 in the kidney (C); and 229 in the hippocampus (D). In heatmaps, each row represents a single gene, and each column represents a single rat (n = 7–9 rats/group). For each gene (in each tissue), standardized expression levels were calculated by subtracting the mean expression level from each individual measurement and dividing by the standard deviation across all samples. The color key shows that the scaling of the standardized expression from low to high are colored from blue to red. Thus, gene up-regulation is shown by a transition from blue to red and down-regulation by a transition from red to blue. See also Table S1

One of the key strengths of our dataset is the multiple age time-course obtained across the lifespan of the rats from four tissues. This dataset allowed us to identify age-regulated genes and their behavior patterns in a manner distinct from multiple prior studies, in which analysis was confined to “young” versus “old” samples. Age-regulated genes were screened according to the following criteria ( Figure 1 ): a fold change of >1.5 (in either direction), Benjamini-Hochberg adjusted p value <0.05 for expression regulation between 6 month and at least one older age, and a Bayesian information criterion (as specified in the Method Details ), for the linear or four-parameter logistic fit that was at least 5% improved over that of the null model. Tracking expression levels of these genes throughout the rat lifespan allowed us to remove genes whose behavior was not consistent over time.

RNA-seq data were generated with a HiSeq 4000 platform from liver, kidney, skeletal muscle (gastrocnemius), and hippocampus of rats, comparing 6-, 9-, 12-, 18-, 21-, 24-, and 27-month-old animals. The resulting counts were first normalized with the voom method and differential expression determined with limma. Differentially expressed genes were defined by the following criteria: a fold change of > 1.5 (in either direction) and Benjamini-Hochberg-adjusted p value < 0.05 between 6 month and at least one older age. In addition to those criteria, the Bayesian information criterion of the linear or four-parameter logistic fit was compared with the null model (no relationship over time) to ultimately determine age-regulated genes. The age-regulated gene lists were used to perform pathway enrichment analyses that follow a linear pattern, or early, mid-, or late-logistic patterns and transcription-factor motif enrichment. For an alternative analysis arm, log 2 -fold changes from all detected genes were used for pathway enrichment by applying a weighted Kolmogorov-Smirnov (KS) test.

We sought to establish both tissue-specific and more global aging gene signatures, which could serve as a basis for understanding the overall aging process. We were interested in genes that changed in a particular direction (either consistently up-regulated or consistently down-regulated) throughout the lifespan of the animal. Sprague-Dawley (SD) rats live approximately 2.5–3 years under laboratory conditions (). The mortality rate of these rat cohorts at 21 months is ∼22%; at 24 months, ∼30%; and at 27 months, ∼50% (). To identify genes whose expression pattern is influenced by age, we performed gene expression profiling by RNA sequencing (RNA-seq) of liver, gastrocnemius muscle, kidney, and hippocampus from male SD rats aged 6, 9, 12, 18, 21, 24, and 27 months. The gene expression data are available at the NIH Sequence Read Archive under the BioProject accession number PRJNA516151, and Table S1 contains the log-fold change and respective p values (raw and adjusted for the false discovery rate [FDR]) for all genes in searchable Excel format. Genomic data are visually presented as Volcano plots in Figure S1 . Principal-component analysis (PCA) plots are shown in Figure S2 , demonstrating that age distinguishes the genes from the various tissues, especially at the later times. As a validation of the RNA-seq analysis, we confirmed the reproducibility of age-related changes in genes that we have previously compared between young (6 month) and old (24 month) kidneys by qRT-PCR (). Of the 28 previously validated genes, 26 were reproduced in the present study (). These include genes from the interferon gamma pathway (Cdkn1a, Psmb8, Vcam1, Cd38, Il7, Lgals3bp, Ube2l6, St8sia4, Il2rb, Isg20, Lcp2, Samhd1, C1s, Oas2, Ifitm3, and Stat4) and genes from the epithelial to mesenchymal transition pathway (Col1a1, Col1a2, Col3a1, Col4a1, Spp1, Cd44, Fn1, Pdlim4, Timp1, and Gja1).

Discussion

There are multiple definitions of aging; our interest was to determine whether we could find a reproducible pattern of gene expression changes over the lifespan of a rat in multiple tissues to establish an age-associated gene expression signature of aging (denoted AGES). The power of profiling multiple ages, rather than only two (young versus old) was that it allowed for the demonstration of several distinct patterns of gene-expression changes that may occur. Profiling several distinct organs at the same time allowed us to cross-compare the changes observed in each organ to learn which were unique and which were universal. We envision that AGES will help in research efforts to (1) identify the particular expression changes that are either diagnostic or causative of age-related disease; (2) identify changes that create an environment in which disease is more likely develop, for example, by decreasing the efficiency of DNA damage repair, by down-regulating mitochondrial genes, or by up-regulating genes whose protein products can induce inflammation-associated damage; (3) identify early triggers of the aging process; and (4) identify late-stage, disease-associated, biomarkers of aging and age-associated diseases. In addition, certain gene-expression changes may be protective or compensatory, rather than deleterious, for the aging process, and, of course, the hope is that AGES will identify pathways and mechanisms associated with disease that point to critical pathways and targets that could be considered for pharmacological intervention.

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Hubbard A. Resistance exercise reverses aging in human skeletal muscle. AGES should also help determine the relative efficacy and mechanisms by which true “anti-aging” drugs operate, by identifying that component of the signature that is counter-regulated by such a drug. For example, a drug like an antibiotic could be life-saving, but would not be expected to perturb AGES; however, a pharmacologic agent that extended lifespan in an “anti-aging” manner might be expected to counter-regulate critical components of AGES. This sort of result was seen when a rapalog, a class of drugs that has been shown to increase lifespan in many organisms (), was used in older rats, resulting in kidney protection and a reversal of a subset of age-regulated genes (). Similarly, a 6-month resistance-training program in elderly humans that improved skeletal muscle strength was accompanied by a reversal of the transcriptional signature of mitochondrial function to a younger phenotype ().

The time course used in this study demonstrated that gene-expression changes fell into several distinct classes: “early logistic,” in which the gene changed in expression relatively early in life; “mid-logistic,” in which the change happened in mid-life, and was then consistent; late-logistic, in which the changes happened at a later time point and may have been thus more coincidental with the onset of age-associated disease or pathology; and linear changes, with genes that seemed to track chronological aging faithfully and thus might be useful in understanding how particular genes “know” how old an animal is.

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et al. Short-term Low-Dose mTORC1 Inhibition in Aged Rats Counter-Regulates Age-Related Gene Changes and Blocks Age-Related Kidney Pathology. Among the pathways that were found to be linear in their behavior, most prominent were the various inflammatory pathways, including the increase in the complement pathway, and interferon signaling, most prominently in the kidney but also in other tissues ( Table S2 ). In the future, it will be interesting to see whether kidney aging is as prominent driver in other organisms as it seems to be in the SD rat. Given the prominence in this rat, it is tempting to ask whether kidney-specific perturbations would be sufficient to modulate aging in the entire organism. One hint of this was found in our recent study with a rapalog, which showed significant improvement in kidney pathology with short-term treatment in aged animals, coincident with counter-regulation of the aging pathways (). Rapalog treatment has reproducibly induced an increase in lifespan, even when given in older animals.

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et al. A rat RNA-Seq transcriptomic BodyMap across 11 organs and 4 developmental stages. A follow-up question that these patterns raise is which set of genes might house the molecular signatures that induce the negative phenotypes associated with age. For example, the late-logistic genes are coincident with age-associated disease, such as sarcopenia (); however, it may be that this set of gene changes is triggered by the genes that are perturbed earlier, for example, in the mid-logistic phase. Of course, it is also possible that the genes that are regulated in a linear fashion throughout life may be the real “time-keepers” and that there is an eventual threshold that is reached by these genetic regulations, a stage that triggers the negative sequelae of age. Another rat RNA-Seq study was performed using multiple time points (); although that study focused more on the younger part of the lifespan, evaluating rats that were 2, 6, 21, and 104 weeks old, it did allow for logistic determinations of some distinct genes, although they were not called out specifically (). Determining the causative mechanisms triggering age-related gene expression will, of course, be the subject of many studies; it seems unlikely that there would be only a single inducing step, but it will still be useful to ask discrete questions, such as what is the mechanism for the induction of the dramatic increase in inflammatory gene expression seen in multiple tissues, and what is the mechanism for the decrease in mitochondria-associated gene expression?

As for the question, “does aging cause the same gene expression changes in all tissues?” there was considerable overlap in pathways perturbed by age in the kidney, liver, and gastrocnemius muscle, but it was noteworthy to see many genes that were tissue specific. This again brings up the challenge of conceiving of a single agent to broadly counter-regulate age-related gene changes, but it also highlights particular gene pathways that are common among the various tissues.

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et al. A dramatic increase of C1q protein in the CNS during normal aging. There were 13 genes regulated with age in all four tissues examined: LOC103689965, Psmb8, Gpnmb, Tspo, Irf7, Icam1, Ms4a6a, Isg15, C4b, C4a, and Cebpb, a large intergenic noncoding RNA (lincRNA) previously unannotated (ENSRNOG00000059770), and ENSRNOG00000059927, which apparently encodes for an unstudied protein whose sequence is consistent with it being an MHC1 receptor ( Table 1 ). Several of these genes have been previously shown to correlate with aging in specific tissues; however, our data shed light on a potential systemic dysregulation of these genes and their associated functions. This is highly relevant because it provides insights for the potential mechanisms underlying the overall age-associated conditions. Consistent with the finding that the complement pathway as a whole is up-regulated in aging, C4a, C4b, and LOC103689965 (C4-like) are all components of this pathway. An enhanced activation of the classical and alternative complement pathways, as well as increased concentration of complement components in serum, has been observed in older (∼62 year) individuals compared with younger (∼26 years) ones. Although this correlation needs to be validated in a longitudinal study, other studies have shown elevated levels of complement components in aged brains of mice () and humans ()—accumulation of complement member C1q occurs in areas of the brain vulnerable to neurodegeneration in aged mice, and knockout mouse models of the key components C1q and C3 display less age-related cognitive and memory decline ().

Although the data from liver, kidney, and gastrocnemius muscle indicated that many genes were regulated with age, we identified far fewer age-regulated genes in the hippocampus (229 genes). Furthermore, the response to age was muted in the hippocampus compared with the other tissues in that both the magnitude of fold changes and the statistical significance were smaller in the hippocampus compared with other tissues ( Figure S1 ). Several samples from the hippocampus are skewed, although that was not due to any overt underlying technical issue; there was no obvious biological reason for this because quality control was performed on all samples.

We have also observed this muted response in an earlier study that compared an independent cohort of 24-month-old and 6-month-old rats (data not shown). To interrogate the RNA-seq results from the hippocampus, we performed a side-by-side analysis of this earlier study and the current time-course study and identified genes that were differentially regulated with a fold change of > 1.5 (Benjamini-Hochberg-adjusted p < 0.05) between 6 and 24 months. We found 121 differentially regulated genes (108 were up-regulated, and 13 were down-regulated) in the earlier study, and 226 differentially regulated genes (210 were up-regulated, and 16 were down-regulated) in the current study, and there were 53 up-regulated genes (p < 1e−70) and 7 down-regulated genes (p < 1e−18) in common to the two studies. These results indicate that the lesser age-associated gene expression signature in the hippocampus is driven by the biology of the tissue, at least in the SD rat.

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et al. Gene expression defines natural changes in mammalian lifespan. An interesting observation that was especially evident in the latest time points was that aging induces an increase in the variability of gene expression. At the 27-month point, two different classes of animals became evident—one that was “genetically younger” than the other, because its gene expression pattern maintained a greater consistency with an earlier time, and one which was “genetically older,” because its expression pattern continued along the age-associated slope established by the earlier times. Other studies have pointed to this increased variability as well (). Potential causes include an incursion of new tissue types in organs in which they are not usually extant, for example, age-associated fibrosis is a common event, and fibroblasts would be expected to derail an organ’s gene expression signature if those cells were not normally a significant component of that tissue type. Dysregulation of heterochromatin, allowing for expression from previously silent chromosomal regions, could also contribute to this variability ().

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Kim S.K. The Inflammatory Transcription Factors NFκB, STAT1 and STAT3 Drive Age-Associated Transcriptional Changes in the Human Kidney. Because aging is the biggest risk factor for the most serious diseases that affect humans, including cancer, heart disease, dementia, sarcopenia and frailty, and chronic kidney disease, it seems plausible that the gene-changes associated with aging could contribute to the onset of those conditions, either by creating a permissive environment for further pathology or by directing the causing aspects of those diseases. Pathways that were found in common to all four tissues included the inflammatory pathways, especially interferon alpha- and interferon gamma-responsive genes, JAK-STAT-activated genes, and TNF-alpha-induced genes; those were up-regulated in all tissues and most strongly in the kidney. From the clinical perspective, it is noteworthy that up-regulation of gene pathways induced by inflammatory cytokines and interferon gamma is also a characteristic to the human kidney aging ().

White et al. (2015) White R.R.

Milholland B.

MacRae S.L.

Lin M.

Zheng D.

Vijg J. Comprehensive transcriptional landscape of aging mouse liver. Pathways that were down-regulated in liver, gastrocnemius muscle, and kidney included mitochondrial pathways: oxidative phosphorylation and respiratory electron transport pathways. That highlights, again, the point that aging bay be, in part, due to a loss of mitochondrial competence, a point that has been made many times in the literature. Here, we show that a dramatic decline in mitochondrial competence-associated pathways occurs later in life, meaning that one might hope that intervention rather late in life could still be quite helpful, before the onset in the dramatic downturn in mitochondrial competence.studied aging liver in the mouse, with an N of 3 young versus 3 old animals. In that study, they too found an increase in interferon pathway genes, indicating the robustness of this change.

Benayoun et al., 2019 Benayoun B.A.

Pollina E.A.

Singh P.P.

Mahmoudi S.

Harel I.

Casey K.M.

Dulken B.W.

Kundaje A.

Brunet A. Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses. Benayoun et al., 2019 Benayoun B.A.

Pollina E.A.

Singh P.P.

Mahmoudi S.

Harel I.

Casey K.M.

Dulken B.W.

Kundaje A.

Brunet A. Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses. De Cecco et al., 2019 De Cecco M.

Ito T.

Petrashen A.P.

Elias A.E.

Skvir N.J.

Criscione S.W.

Caligiana A.

Brocculi G.

Adney E.M.

Boeke J.D.

et al. L1 drives IFN in senescent cells and promotes age-associated inflammation. A search for transcription factor motifs in the promoter regions of the genes that were regulated by aging causes further focus on interferon signaling ( Table S4 ). Enhanced use of the IRF2 motif was found to be significantly associated with three tissues: kidney, liver, and hippocampus. Several other interferon response factor motifs—IRF3, IRF8, IRF1, and ISRE—were associated with aging in liver and kidney. This repeated finding of interferon gamma and alpha signaling increasing in age, in multiple tissues, using multiple modes of analysis, does cause particular focus on those pathways, leading one to ask whether discrete inhibition of interferon signaling in aged animals might benefit age-related disorders. The finding also leads one to ask for the mechanism causing this reproducible increase. Interferon signaling up-regulation was also seen in a recent paper that looked at aging in mice (). In that study, the up-regulation of the interferon response pathway with age was accompanied by increased transcription and chromatin remodeling at specific endogenous retroviral sequences (). Another mechanism that could explain an up-regulation of the interferon pathway is the increased transcription of repetitive elements, including long interspersed nuclear elements (LINE−1), seen as a result of aging (). These LINE−1s could cause double-stranded RNA production, giving cells the impression they are being infected by viral agents.

We hope to continue to build on AGES by profiling more organs and cell types, building on the current AGES. Of course, this will be a long and continuous task, given the multiple additional organs that could be eventually added to the signature, including heart, lungs, hematopoietic and lymphoid cells, blood vessels, sensory organs, etc. In addition, with the advent of single-cell RNA profiling and given a desire for increasing granularity to help tease out the mechanisms that might bridge gene changes between early, mid-, and late logistic genes, the idea of a truly complete AGES seems daunting indeed. For now, the current version should provide a rich resource to inspire new questions for further follow-up and study.