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O P E N A C C E S S S O U R C E : Cell Reports
Highlights
• The molecular footprint of aging in metabolic tissues is tissue specific
• Distinct omic layers have common functional enrichments of aging-related gene sets
• Few conserved transcription factors (TFs) may control the molecular footprint of aging
• Mendelian randomization shows evidence of these TFs’ implications in human aging
Summary
Many genes and pathways have been linked to aging, yet our understanding of underlying molecular mechanisms is still lacking. Here, we measure changes in the transcriptome, histone modifications, and DNA methylome in three metabolic tissues of adult and aged mice. Transcriptome and methylome changes dominate the liver aging footprint, whereas heart and muscle globally increase chromatin accessibility, especially in aging pathways. In mouse and human data from multiple tissues and regulatory layers, age-related transcription factor expression changes and binding site enrichment converge on putative aging modulators, including ZIC1, CXXC1, HMGA1, MECP2, SREBF1, SREBF2, ETS2, ZBTB7A, and ZNF518B. Using Mendelian randomization, we establish possible epidemiological links between expression of some of these transcription factors or their targets, including CXXC1, ZNF518B, and BBC3, and longevity. We conclude that conserved modulators are at the core of the molecular footprint of aging, and variation in tissue-specific expression of some may affect human longevity.
Graphical Abstract
Introduction
Aging is a multifactorial process characterized by the gradual decline in vitality and is accompanied by increased susceptibility to a wide range of pathologies, including cancers and neurodegenerative, cardiovascular, metabolic, muscular, and infectious diseases (Kenyon, 2010). Quantifying, analyzing, and understanding these complex processes are critical to modulate and manage its negative ramifications to increase health span.
Systems-level characterization of aging at the levels of the transcriptome and proteome have identified common pathways and signatures across species (Kenyon, 2010) as well as molecular phenomena, including genomic instability, epigenetic alterations, loss of proteostasis, and mitochondrial dysfunction (Kenyon, 2010; Riera et al., 2016). By interfering with such mechanisms, such as cytosolic or mitochondrial proteostasis, lifespan and/or health span can be prolonged in animal models (Chondrogianni et al., 2015; Houtkooper et al., 2013; Sorrentino et al., 2017; Zhang et al., 2016). However, some aging-driven changes are tissue specific, suggesting that different tissues of the same organism age differently (Schumacher et al., 2008; Ori et al., 2015). In addition, although most studies on aging signatures rely on gene expression, epigenetic alterations are a major nexus of genome stability and transcriptional control (Benayoun et al., 2015; Johnson et al., 2012; Pal and Tyler, 2016). DNA methylation levels at some CpG sites can accurately predict an individual’s chronological age (Horvath, 2013; Horvath et al., 2016), yet the functional consequence of these changes is not clear. There are also many age-associated epigenetic changes at the level of histone modifications and composition, yet their relevance in mammalian lifespan or health span remains to be established (Booth and Brunet, 2016; Morris et al., 2018; Tvardovskiy et al., 2017).
It is therefore not clear whether aging in different tissues of different organisms shares a common denominator in terms of the gene-regulatory drivers. Because of the inherent complexity of the aging process, answering this question necessitates a multi-layer and multi-tissue analysis as well as integration of data from different species. Here, we characterized the age-related epigenetic and gene expression changes in three mouse tissues (liver, heart, and quadriceps muscle). Although there are many tissue-specific molecular differences, common biological processes are affected across these layers, which are modulated by a central set of transcription factors (TFs). We extend these findings to large human population datasets, in which we identify the same gene-regulatory drivers. Finally, we establish epidemiologically relevant genetic links between specific TFs and their targets with human longevity through Mendelian randomization (MR). Thus, our integrative investigation of the gene-regulatory footprint of aging identifies regulatory drivers that are shared by different molecular layers, tissues, and species, with some of them potentially explaining genetic variation in human longevity.
Results
Multi-layer Characterization of Aging Reveals Tissue-Specific Gene Regulatory Differences
Liver, heart, and quadriceps muscle were harvested, and their transcriptome, DNA methylome, and histone modification profiles were measured in adult (6 months) and old (24 months) male C57BL/6J mice, an age range comparable with 20–80 human years (Dutta and Sengupta, 2016), through genome-wide profiling of five different omic layers: the transcriptome, the DNA methylome, and three histone modifications (STAR Methods; Figure 1A). We quantified both positive (H3K27ac and H3K4me3) and negative (H3K27me3) histone marks using chromatin immunoprecipitation (ChIP) sequencing. We performed peak-based and gene-based differential binding analyses (Figure S1; STAR Methods). To simplify integration of all the layers, we chose to use the gene-based results, in which we quantified the reads in windows spanning 5 kb around gene transcription start sites (TSSs) and performed differential binding. Similarly, we performed differential methylation analyses on CpG sites and aggregated results by gene in two ways: upstream of the gene (promoter) and in the gene body (GB).
Figure 1. Tissue-Specific Multiomic Footprint of Aging
(A) General outline of the study, starting with multi-layer profiling experiments of mouse tissues followed by the identification of gene regulatory modifiers of the molecular footprint of aging. Results in the mouse were then replicated in human population data. Finally, using Mendelian randomization, genetic, tissue expression, and longevity trait data were integrated to identify candidate aging regulators of epidemiological significance.
(B) Top panel: schematic of differential analysis comparison across layers. For each measured layer, a p value threshold of 0.05 is used to define genes that go up or down. Then, the resulting set is overlapped with all other sets across layers to obtain pairwise intersections represented in the graph as circles of different sizes in the scatterplot. Bottom panel: liver, heart, and quadriceps summary of differential analysis results showing the number of genes that are either up or down upon aging for each of the layers (gray circles; size reflects number of genes). The intersection between different layers is computed and listed, as well as its significance, which is indicated by the color code (Wang et al., 2015).
More liver genes are affected in the transcriptome and methylome levels than in the histone epigenome (false discovery rate = 10%; Figure 1B; Table S1). In contrast, the heart and quadriceps aging footprints are dominated by changes in the methylome and histone modification profiles (Figure 1B). There is more DNA hyper- than hypomethylation in liver and quadriceps but not in the heart (Figure 1B).
As for the relationship between the different layers, there is a significant overlap between differentially expressed (DE) and differentially methylated (DM) GBs and promoters in the liver (Figure 1B). In addition, the overlap between DM in the GB and gene expression is directional, with more genes having increased expression having increased rather than decreased GB and promoter methylation. Compared with the GB, change in promoter methylation has a smaller overlap with expression. In heart and muscle, we observe fewer overlaps between gene expression changes and the other epigenetic layers, likely because of the smaller number of DE genes (Figure 1B). In all tissues, overlaps between DM and differential histone modifications are directional, in that many genes with increased GB methylation also have increased H3K27ac in their promoters and vice versa. In addition, the promoter DM overlaps well with that of the GB, even though these signals are traditionally regarded as having opposing effects on transcription (Jones, 2012). From our observations in these three metabolic tissues, we conclude that some omic layers are affected by age in a tissue-specific manner. Hence, the conclusions based on one tissue may not apply to another, even when exploring fundamental aspects of gene regulation and chromatin structure.
Aging Affects Similar Biological Processes at Distinct Regulatory Layers in Different Tissues
In order to gain a higher level understanding of the molecular pathways affected by aging, we performed gene set enrichment analysis (GSEA) using Gene Ontology (GO) terms of biological processes (Figure 2A). The enrichments mirror the differential analyses in that the liver transcriptome has many enrichments, whereas quadriceps and heart have enrichments mainly at the histone modification level. Within each layer, we identified many significant terms that have been previously linked to aging, including an upregulation of the immune response and downregulation of telomere-related genes, development, protein quality control, mitochondrial processes, and RNA processing (Figure 2B; Table S2). We observed no functional enrichments for changes in H3K4me3 in our data. In heart and muscle, genes with increased H3K27ac and decreased H3K27me3 are enriched for many overlapping biological processes. This emerging pattern of GO enrichments points to possible functional implications of open chromatin in aging.
Figure 2. Integrated Overview of Gene Set Enrichment Results across Biological Layers and Tissues
(A) Schematic of gene set enrichment analysis (GSEA) and integration across different layers. For each omic layer, the aging-driven log fold change is used to order genes. Then GSEA is performed using Gene Ontology biological process terms. Gene sets representing GO terms are represented as circles that are ordered on the basis of their significance and direction of enrichment, with green and red circles representing gene sets that increase and decrease with age, respectively. The size of the circle reflects the number of genes in the gene set. The overlaps between the core enrichment genes of each gene set in each layer and all other layers are computed. Links are drawn between sets of genes that have more than 20% overlap in their core enriched genes and are colored differently for each layer pair to facilitate visual discrimination.
(B) Hive plot representation of the multi-layer enrichment results in the liver, heart, and quadriceps muscle. Each circle represents a Biological Process term from the Gene Ontology consortium that has an enrichment q value less than 0.1. Purple boxes list some representative gene sets that were manually selected, most of which have already been linked to aging. The signed −log10(q), which is mapped to the color, is defined as the sign of the GSEA normalized enrichment score, multiplied by −log10(q).
We explored commonalities in gene set enrichments across layers by calculating cross-layer overlaps of genes that are driving the enrichments (Figure 2B). H3K27ac and H3K27me3 share most of their enrichments in the heart, and to a lower extent in the muscle, but in an opposite direction as expected. As for the methylome, we observe enrichment in the GBs only in liver, in which we see hypermethylation of genes regulating ion transport and interleukin regulation. Furthermore, the genes driving these enrichments are upregulated at the transcriptome level, suggesting that aging leads to their GB hypermethylation and increased expression.
Interestingly, some of the same biological processes that are enriched in the liver transcriptome are also enriched at the histone modification level in heart and quadriceps, suggesting that the same biological processes may be affected by age at different gene-regulatory layers. For example, the biological process enrichment scores in H3K27ac in the heart positively correlate with those of the liver transcriptome. This correlation is even stronger than most pairwise correlations within the liver (Figure S2).
Taken together, our multi-layer and multi-tissue analysis reveals that aging affects different facets of gene regulation in a tissue-dependent manner. Whereas liver has the strongest gene expression and DNA methylation effect, heart and muscle show strong epigenetic alterations, mainly toward a gain in activating and loss of repressive marks. Even though different tissues react at different layers, the aging footprint converges on common biological processes.
The Epigenetic Footprint of Aging Is Tissue Specific
The epigenetic footprint of aging is strikingly tissue specific. For instance, liver and quadriceps have more hyper- than hypomethylated CpG sites (Figure 3A). These changes are concentrated around the TSS of genes, peaking downstream of the TSS, likely in the GB (Figure 3B). Muscle DM has a similar trend, with more diffused localization of hypermethylated sites around the TSS. The heart, on the other hand, has similar amounts of hyper- and hypomethylated CpG sites (Figures 3A and 3B). Our liver DM data correlate best with the weights associated with the CpG sites in two published epigenetic clocks, a general and a liver-specific clock (Horvath et al., 2016; Stubbs et al., 2017; Wang et al., 2017; Figure 3C; Figure S3A). The reason behind these tissue-specific correlations with the general clock is not clear and may stem from different factors. It may be that the liver’s epigenetic landscape in the ages we are measuring is indeed the most affected by age. Conversely, this can be due to the different ages at which the clocks have been derived (newborn to 41 weeks old) and the presence of more liver samples than samples from other tissues.
Figure 3. The Epigenetic Footprint of Aging Is Tissue Specific
(A) Histogram of CpG sites with a methylation difference greater than 10% in the three tissues. Aging-driven hypo- and hyper-methylated sites are colored differently. Note the greater number of hypermethylated CpG in liver and quadriceps.
(B) Metaplot of the locations of hypo- and hypermethylated CpG sites with respect to the TSS of genes showing that age-related hypermethylation is enriched around the TSS.
© Correlation between the published general epigenetic clock CpG site weights (Stubbs et al., 2017) and the methylation differences in this study (Pearson correlations: for liver, r = 0.49, n = 162, p = 5.1e−11; for heart, r = 0.096, n = 159, p = 0.23; for quadriceps, r = 0.16, n = 160, p = 0.047). The mouse general epigenetic clock is a model in which methylation of a selected set of CpG sites predicts chronological age. The weight of each CpG site determines its relative contribution to the prediction.
(D) Number of differentially bound peaks as a function of distance from the closest TSS and false discovery rate (FDR). Bars pointing downward represent peaks whose binding decreases with age. The dashed vertical red lines demarcate the 10 kb region used in the TSS-based differential binding analyses.
In terms of histone modification changes, there are more increased than decreased H3K27ac peaks upon aging. These peaks are predominantly overlapping or within 5 kb of a TSS (Figure 3D). H3K27me3, being a broad peak, shows a more diffuse pattern with respect to TSS. However, we observe an abundance of decreased H3K27me3 peaks around the TSS in the heart and quadriceps. As for H3K4me3, we observe the greatest changes in the quadriceps, with a reduction in this mark around and downstream of the TSS.
It is not clear whether these tissue-specific differences are due to specific changes in histone modifications at certain peaks or more global changes in histone composition. To address this, we used a dataset in which histone H3 post-translational modifications were quantified using mass spectrometry in different tissues of mice from different age groups (Tvardovskiy et al., 2017; Figure S3B). Although total H3K27ac and H3K27me3 are negatively correlated in adult livers, this relationship is abolished or even reversed in old livers. Interestingly, the relative abundance of heart H3K27ac increases in old age, in contrast to the H3K27me3, which remains stable (Figure S3B). This tissue specificity is in line with the observations from our sequencing-based results and demonstrates how different tissues exhibit qualitatively distinct aging-driven epigenetic changes summarized by increased DNA methylation around promoters in liver and quadriceps and global differences in H3K27ac and H3K27me3 in heart and muscle.
TF Motif Enrichment across Layers and Tissues Identifies Candidate Central Aging Regulators
To pinpoint molecular players that may drive the gene-regulatory footprint of aging, we performed TF motif differential enrichment analysis in each of the layers using the HOCOMOCO-v10 motif database (STAR Methods; Figure 4A; Table S3). The H3K27ac and H3K27me3 TF motif enrichments show opposing signals in heart but not in quadriceps and liver (Figure 4C; Figure S4B). In addition, heart transcriptome enrichments are related negatively to that of H3K27me3 and positively to that of H3K27ac, meaning that TF motifs enriched in genes that increase in expression are also enriched in genes that have an increase in H3K27ac and a decrease in H3K27me3. In the liver, however, the transcriptome enrichments relate positively with the H3K27me3 repressive mark, which is unexpected. The observed relationship between these different layers is therefore tissue dependent. In addition, the enrichments based on changes in H3K4me3 show opposite relationships with other layers in liver versus heart and quadriceps.
Figure 4. Enrichment of TF Motifs across Different Layers and Tissues Identifies Candidate Regulators of Age-Driven Transcriptional Changes
(A) Schematic of the TF motif differential enrichment analysis. For each layer, 10 kb sequences centered around the TSS of genes that are up or down (FDR = 10%) are scanned for differential TF enrichment. In the diagram, TFx represents one of the 426 TF motifs from the mouse HOCOMOCO-v10 database (Kulakovskiy et al., 2013). TF motifs are represented as circles ordered by differential enrichment on each axis, with size and color indicating differential enrichment. Links connect the same TF across layers and are drawn only when a TF has a differential enrichment greater than 10 on the log scale. Links are colored differently for better visual discrimination.
(B) Detailed view of the differential enrichment of some top DNA-binding motifs across layers in liver and quadriceps.
© Same results as in (B) represented in a hive layout to show relationships between transcription factor enrichments across layers. To highlight the most significant TFs, links are drawn between pairs of layers only when the motif had a differential enrichment greater than 10 in at least one of the two assays. Links are colored differently for each layer pair for visual discrimination.
We used a ranking scheme to obtain the top TFs with increased or decreased age-dependent enrichment (Figure S4; STAR Methods). For example, ZIC1, ZIC2, ZIC3, KLF6, PLAG1, and IKZF1 are generally associated with genes that increase in expression or open chromatin with age, whereas HMGA1, MECP2, CXXC1, SRY, MSX2, TBP, and CDX1 are associated with genes that decrease in expression or open chromatin with age. Three of the most significant and pervasive motifs are those of three members of the zinc finger of the cerebellum (Zic) family, which have similar binding motifs and are enriched in upregulated genes in all tissues as well as genes with increases in H3K27ac in the heart and decreases in H3K27me3 in the heart and quadriceps (Figures 4B and 4C). On the opposite side, the TF motif of HMGA1 is associated with genes that are silenced or decrease in expression with age (Figures 4B and 4C). Collectively, these results raise the possibility that although aging affects the measured layers in a tissue-specific manner, the molecular landscape of aging may be modulated by common regulators.
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