Epigenetic signature of human immune aging in the GESTALT study

  1. Roshni Roy
  2. Pei-Lun Kuo
  3. Julián Candia
  4. Dimitra Sarantopoulou
  5. Ceereena Ubaida-Mohien
  6. Dena Hernandez
  7. Mary Kaileh
  8. Sampath Arepalli
  9. Amit Singh
  10. Arsun Bektas
  11. Jaekwan Kim
  12. Ann Z Moore
  13. Toshiko Tanaka
  14. Julia McKelvey
  15. Linda Zukley
  16. Cuong Nguyen
  17. Tonya Wallace
  18. Christopher Dunn
  19. William Wood
  20. Yulan Piao
  21. Christopher Coletta
  22. Supriyo De
  23. Jyoti Sen
  24. Nan-ping Weng
  25. Ranjan Sen
  26. Luigi Ferrucci  Is a corresponding author
  1. Laboratory of Molecular Biology and Immunology, National Institute on Aging, United States
  2. Translational Gerontology Branch, National Institute on Aging, United States
  3. Laboratory of Neurogenetics, National Institute on Aging, United States
  4. Clinical Research Core, National Institute on Aging, United States
  5. Flow Cytometry Unit, National Institute on Aging, United States
  6. Laboratory of Genetics and Genomics, National Institute on Aging, United States
  7. Laboratory of Clinical Investigation, National Institute on Aging, United States

Abstract

Age-associated DNA methylation in blood cells convey information on health status. However, the mechanisms that drive these changes in circulating cells and their relationships to gene regulation are unknown. We identified age-associated DNA methylation sites in six purified blood-borne immune cell types (naive B, naive CD4+ and CD8+ T cells, granulocytes, monocytes, and NK cells) collected from healthy individuals interspersed over a wide age range. Of the thousands of age-associated sites, only 350 sites were differentially methylated in the same direction in all cell types and validated in an independent longitudinal cohort. Genes close to age-associated hypomethylated sites were enriched for collagen biosynthesis and complement cascade pathways, while genes close to hypermethylated sites mapped to neuronal pathways. In silico analyses showed that in most cell types, the age-associated hypo- and hypermethylated sites were enriched for ARNT (HIF1β) and REST transcription factor (TF) motifs, respectively, which are both master regulators of hypoxia response. To conclude, despite spatial heterogeneity, there is a commonality in the putative regulatory role with respect to TF motifs and histone modifications at and around these sites. These features suggest that DNA methylation changes in healthy aging may be adaptive responses to fluctuations of oxygen availability.

Editor's evaluation

This fundamental work advances our understanding of chromatin changes that may be associated with aging across six distinct immune cell types. It highlights a non-uniform process of expression of aging signatures while a core signature is preserved across different cell types. The research employs solid validated and robust analysis methodologies. The findings would be of interest to researchers studying DNA methylation clock and aging biology.

https://doi.org/10.7554/eLife.86136.sa0

Introduction

Human aging is associated with site-specific changes of DNA methylation. Summary measures of DNA methylation called ‘epigenetic clocks’ are extensively used in aging research to estimate biological age (Horvath and Raj, 2018; Hannum et al., 2013; Bocklandt et al., 2011). Epigenetic clocks closely approximate chronological age and beyond age, predict adverse health conditions, including frailty (Gale et al., 2018), Alzheimer’s disease (McCartney et al., 2018), and mortality (Marioni et al., 2015; Chen et al., 2016).

Research suggest that changes in DNA methylation with aging are regulated by specific mechanisms rather than by a stochastic drift (Teschendorff et al., 2013). For example, a loss-of-function mutation in the H3K36 histone methyltransferase has been associated with epigenetic aging in mice (Martin-Herranz et al., 2019). In humans, polymorphisms in the telomerase gene (TERT) (Lu et al., 2018) and age-dependent gain of methylation in the Polycomb repressive complex 2 have been related to accelerated aging (Teschendorff et al., 2010). However, so far, no sound hypothesis exists that explains the association of DNA methylation with aging and pathology.

A main obstacle in understanding mechanisms driving age-associated changes of DNA methylation is that most human studies were performed in mixed blood cell types. The few studies that investigated select immune circulating cells failed to propose a unifying biological hypothesis explaining predictable changes of DNA methylation with aging (Dozmorov et al., 2017; Reynolds et al., 2014; Tserel et al., 2015; Bell et al., 2012; Kananen et al., 2016; Acevedo et al., 2015; Marttila et al., 2015).

We analyzed age-associated methylation in six purified blood-borne cell types sorted from peripheral blood mononuclear cells (PBMCs) from 55 donors of ages ranging from 22 to 83 years. To minimize the confounding of age-associated pre-clinical and clinical diseases, participants were ascertained to be healthy by trained health professionals according to strict clinical criteria. We looked for CpGs differentially methylated with aging in the same direction in multiple cell types. Next, in each cell type, we conducted enrichment analyses of genes close to age-associated CpGs. Finally, we looked for chromatin accessibility markers and transcription factor (TF)-binding sites close to the same age-associated CpGs. Our findings suggest that changes in methylation with aging are related to fluctuation of energetic metabolism during the life course.

Results

Age-associated methylation in individual cell types

A principal component analysis (PCA) was performed on normalized DNA methylation data for all cell types from all the 55 donors (Figure 1A and Supplementary file 1). The PCA showed that clustering by cell types was stronger than by age (Figure 1—figure supplement 1A). The genes associated with the top 500 probes corresponding to PC1, PC2, and PC3 were enriched pathways linked to innate and adaptive lineage development (Supplementary file 2).

Figure 1 with 1 supplement see all
Study design and identification of age-associated methylation probes.

(A) Study design. (B) Age-associated CpG methylation (False Discovery Rate or FDR p < 0.05) in six cell types. (C, D) SuperExactTest circular plots to show the number of age-associated hypo- and hypermethylated probes shared among different combinations of cell types (indicated by green boxes), respectively. The outermost bars show the number of probes shared among each cell-type combination (regardless of other cell types). For example, probes hypomethylated with age in B + CD4 + CD8 + gran + mono (n = 222) includes probes also hypomethylated in NK cells (n = 181) and probes not hypomethylated with age in NK cells (n = 41). Based on the exact probability distributions of multi-set intersections, all the overlaps shown are highly statistically significant (p < 10−100). (E) Graphical representation of age-associated hypomethylation in promoter region of RCAN1 in all six cell types. (F) Graphical representation of age-associated hypermethylation in promoter region of KLF14. The methylation status in peripheral blood mononuclear cell (PBMC) and buffy coat are also shown. Missing methylation data are represented in white.

Age-associated CpGs were identified through sex-adjusted beta-regression models (FDR corrected p-value <0.05). The number of hypo- or hypermethylated sites varied considerably between cell types (Figure 1B) with highest numbers in CD4+ T cells (Figure 1—figure supplement 1B and Supplementary files 3 and 4). Using a different approach of comparing between young (≤35 years, 25th percentile) and old (≥70 years, 75th percentile) individuals, we observed >90% overlap with beta-regression-derived hypomethylated sites and 70–95% overlap with hypermethylated sites in all cell types except CD8+ T cells (9–14% overlap) (Figure 1—figure supplement 1C). Having fewer old donors with CD8+ T cells may have contributed to differences (Supplementary file 1).

Like other studies, we found that a significant proportion of age-hypomethylated CpGs were in the intergenic and open sea (>4 kb from CpG island) regions while age-hypermethylated CpGs were in promoters and CpG islands (Chi sq test p < 0.001) (Figure 1—figure supplement 1D, E). Additionally, age-associated differentially methylated sites in PBMC poorly recapitulate age-dependent changes that take place in specific primary immune cells (Figure 1—figure supplement 1E, F). These findings point to a wide heterogeneity of age-differential CpG methylation across immune blood cells and suggest that studies in PBMC poorly represents the changes that take place in specific cell types with aging.

Shared age-associated methylation across cell types

Only 181 age-associated hypomethylated sites and 169 hypermethylated sites were shared between all 6 cell types. These numbers increased to 776 (age-hypomethylated) and 404 (age-hypermethylated) sites in 5 or more cell types (Figure 1C, D). Thus, most age-related methylation changes are cell specific. Of note, only 10 of the sites overlap with the 359 CpGs in Horvath’s pan-tissue epigenetic clock (Horvath, 2013). Several reasons can be attributed to this poor overlap including (1) use of methylation array with about 21,369 CpGs for development of the clock in contrast to the analyses in this study based on ~850,000 CpG sites; (2) use of data from peripheral or whole blood for development these clocks in contrast to data from flow-sorted circulating immune cells in this study. While the number of shared age-hypo- or hypermethylated CpGs across cells was relatively small, it was significantly much higher than that expected based on chance alone, suggesting that common underlying epigenetic mechanisms exist across the considered cell types (Figure 1C, D). For example, CpG sites adjacent to RCAN1 (calcineurin 1) and KLF14 (Krϋppel-Like Factor 14) show similar age-associated patterns in all cell types (Figure 1E, F).

Next, we wanted to investigate whether the top age-associated genes are the ones which are shared across cell types. For this we arranged the age-associated probes with decreasing order of adjusted p-value and looked at the annotated genes to identify the top 15 genes in each cell type (Figure 2A, B, Figure 2—figure supplement 1A–E, and Supplementary file 5). THSD4 and CCDC102B were the most significant age-associated hypomethylated genes shared by five or more cell types, while ELOVL2, KLF14, LHFP14, and GPR158 were among the most significant age-hypermethylated genes in five or more cell types. This count increased to 5 and 13 genes, respectively, when the list was expanded to 50 top genes (Supplementary file 5). It is noteworthy that only 13–15% of these ‘top’ age-associated probes overlapped with the list of age-associated probes shared across cell types (181 hypomethylated and 169 hypermethylated probes). These findings suggest that most CpGs with age-associated methylation consistent across cell types undergo moderate (although significant) methylation changes with aging.

Figure 2 with 2 supplements see all
Characteristics of age-associated probes.

(A, B) Manhattan plot of age-associated hypo- and hypermethylated CpG sites in B cells, respectively. Most significant genic probes (−log padj >10) are labeled. (C) Correlation between beta-regression coefficients of age-differentially methylated CPGs in GESTALT and longitudinal InCHIANTI study. X-axis – InCHIANTI, Y-axis – B cell (C) and CD4+ T cell coefficients (D). Blue dots – age-hypomethylated CpGs, yellow triangles – age-hypermethylated CpGs. (E, F) Scatter plot of age-associated CpGs showing opposite trends in different immune cells. (E) cg27123256 (in BCL11B promoter) is hypomethylated with older age in B, monocytes, and NK while is hypermethylated with older age in CD4+ T cells. (F) cg03530364 (in FAM19A1 promoter) is hypermethylated with older age in B, granulocytes, monocytes, and NK cells while it is hypomethylated with older age in CD4+ T cells.

Longitudinal validation of age-associated CpG sites

We hypothesized that the age-associated CpGs identified across the six immune cells in this cross-sectional study would also show longitudinal changes of the size and direction predicted. We used DNA methylation data (Illumina 450K microarray on DNA from buffy coats) assessed at baseline and 9- and 13-year follow-up in 699 participants of the InCHIANTI study (Ferrucci et al., 2000). Of the 181 hypomethylated and 169 hypermethylated CpGs with age in all cell types in GESTALT, 72 and 135, respectively, were represented in the 450K microarray (Moore et al., 2016). The beta-coefficients for age of the 207 CpG probes (72 + 135) estimated from the GESTALT study and their corresponding values estimated longitudinally from the InCHIANTI study were highly and significantly correlated (hypomethylated with age CpGs: r = 0.49, p = 1.2e−09 and hypermethylated with age CpGs: r = 0.5, p = 6.9e−06 for average beta coefficients across six cell types, Figure 2C, D and Figure 2—figure supplement 2). Thus, CpGs identified as differentially methylated with aging across cell types in GESTALT also change longitudinally with aging.

Age-associated probes with opposite trends in different immune cells

Several CpGs showed significant but opposing age trends in different cell types, especially in B, CD4+ T cells, and monocytes (Figure 2—figure supplement 1F, G). For example, cg27123256 in the gene body of BCL11B was age hypomethylated in non-T cells and significantly age hypermethylated in naive CD4+ T cells (Figure 2E). Our observations implicate BCL11B in aging-related changes in naive CD4+ T cell function, distinct from its proposed role in effector cells (Tserel et al., 2015; Gray et al., 2014; Yui and Rothenberg, 2014). Conversely, cg03530364 in the body of FAM19A1 gene was hypermethylated in non-T cells but age-hypomethylated in CD4+ T cells (Figure 2F). Of note, none of these CpGs were differentially age-methylated in PBMC. Thus, opposite age-methylation trends in specific cell types may cancel each other and obscure their relevance for aging when mixed cell-type samples are assessed.

Pathway analysis of age-associated genes

Gene set enrichment analyses were performed on genes associated with at least one CpG significantly age-hypo- or hypermethylated in five or more cell types. We identified 30 pathways (q-value <0.05) (Figure 3 and Supplementary file 6). Probes commonly age-hypomethylated in five or more cell types (n = 776) pointed to genes enriched in collagen biosynthesis, complement cascade, and GTPase pathways (left-most column in bottom panel of Figure 3) that highlighted inflammatory and metabolic pathway in aging. Genes associated with shared age-hypermethylated probes (n = 404) were enriched for neural pathways previously implicated to brain aging along with G-protein-coupled receptors pathways (de Oliveira et al., 2019; Ewing et al., 2019) (left-most column in top panel of Figure 3). A recent study by Karagiannis et al. also identified neuronal genes in their PBMC aging data emphasizing a possible interlink between immune-aging and neuronal pathways (Karagiannis et al., 2023). Other key pathways are highlighted, with associated genes displayed in boxes on the right-hand side.

Pathway analysis of methylated probes.

Enrichment analysis of genes annotated to age-associated hypo- and hypermethylated CpGs in ≥5 cell types (left-most column) and in individual cell types. Red/green shades indicate enrichment scores in hyper- (red) and hypo- (green) methylated genes. Yellow indicates ambiguous pathways associated with both hypo- and hypermethylated genes in individual cell types. Not significant pathways are shown in gray. Full results in Supplementary file 6.

Functional annotation of age-associated probes

To further interrogate the relationships between DNA methylation and other epigenetic states, we mapped the methylation age-associated sites to cell-specific chromHMM-derived chromatin profiles (Ernst and Kellis, 2012). As controls, we annotated all sites in the EPIC array to the 18-state chromHMM model of respective primary cell type. Granulocytes were excluded from this analysis because reference data were not available.

Age-associated hypomethylated CpGs were significantly enriched for weak/active enhancers (yellow bar, Figure 4A) whereas, confirming previous reports, age-hypermethylated CpGs, were enriched in bivalent/polycomb regions compared to control set (brown and dark gray bars, respectively, in Figure 4A). Results for cell-type-specific analyses are shown in Figure 4B.

Figure 4 with 1 supplement see all
Functional annotation of age-associated probes along with their grouping based on sharedness.

(A) ChromHMM annotation of age-associated CpGs. (B) Proportion of CpGs mapping to weak/active enhancers (left, orange box), bivalent enhancers/TSS (inset, brown box) and polycomb repressor regions (right, gray box) in age-associated hypo- (blue line), hypermethylated (red line) CpGs as compared to all MethylationEPIC CpGs (gray line). (C) DeepTools plots showing the distribution of accessible chromatin (DNase hypersensitive sites) and H3K4me1 histone mark in and around ±3 kb region of age-differentially methylated CpGs. The age-associated sites were divided into shared (blue) (common between five or more immune cells) and selective sites (green). The top row shows the pattern for age-associated hypomethylated CpGs while the bottom row is for the age-associated hypermethylated CpGs in B and CD4+ T cells.

We further mapped the profile of four epigenetic markers from the ENCODE project in and around (±3 kb) age-associated methylation sites. For B and CD4+ T cells, we observed a V-shaped peak-valley-peak pattern of DNase hypersensitivity at sites of age-associated hypomethylation, which is characteristic of promoter sites (Figure 4C; Pundhir et al., 2016). Both age-associated hypo- and hypermethylated sites showed evident H3K4me1 peaks, a marker commonly associated with active and primed enhancers (Figure 4C; Bae and Lesch, 2020). No specific trend was observed for H3K4me3 and H3K27ac (Figure 4—figure supplement 1). These patterns were highly consistent across cell types (Figure 4—figure supplement 1) and strongly suggest a functional connection between methylation and chromatin status. However, as the DHS and histone data in the ENCODE database were only available for either one of two donors (a 21-year-old male and 37-year-old female), we could not verify whether the patterns observed are stable with change in age.

Pattern of TF-binding motifs around age-associated CpGs

Specific TFs binding may induce loss of DNA methylation or bind DNA that is methylated (Medvedeva et al., 2014; Moore et al., 2013). Through our de novo HOMER analysis, we looked for TF-binding motifs in a 200-bp window around the age-associated methylated sites in each cell type. We observed that the binding motif for aryl hydrocarbon receptor nuclear translocator (ARNT, also named HIF1β) was associated with age-hypomethylated CpGs across most cell types (Figure 5A). The only exception was naive CD8+ T cells where the top enriched motif was B-cell lymphoma gene 6 (BCL6). BCL6 code for a zinc finger TF that plays a critical role in the generation of memory and effector cells in acute infection (Kim et al., 2020). Another motif associated with age-hypomethylated CpGs across most cell types was chromatin architectural protein CTCF and its closely related gene BORIS. Methylation changes at CTCF sites have been reported to reflect large-scale genome reorganization in immune cells in older individuals (van Ruiten and Rowland, 2021; Bhat et al., 2021).

Figure 5 with 1 supplement see all
Association of transcription factor (TF)-binding motifs with age-differentially methylated CpGs.

(A) Top 5 TF motifs at and around (±200 bp) of CpG sites that are hypomethylated with age. All the age-hypomethylated sites were considered for the analysis in each cell type. Recurring motifs like ARNT and CTCF/BORIS are highlighted. (B) Top 5 TF motifs at and around (±200 bp) CpG sites that are hypermethylated with age. All the age-hypermethylated sites were considered for the analysis in each cell type. Recurring motifs like REST and Sp100 are highlighted. (C) Hypoxia-centric model of age-associated sites with ARNT and REST motifs. CpG sites hypomethylated with aging across six different cell types are significantly more likely to host-binding motifs for ARNT, the core hub for the hypoxia response. On the contrary, CpG sites hypermethylated with aging are significantly more likely to host-binding motifs for REST, a hypoxia-response transcriptional repressor. On the right are selected age-associated genes that carry the motifs for ARNT or REST TFs.

Repressor Element 1-Silencing Transcription Factor (REST) was the TF motifs most frequently associated with age-hypermethylated CpGs in five of six cell types (Figure 5B). Age-hypermethylated sites in PBMCs have been previously shown to be enriched for REST, which is known to repress stress response genes and is lost in cognitive impairment and Alzheimer’s disease pathology (Yuan et al., 2015; Lu et al., 2014). The top enriched TF motif associated with age-hypermethylated sites in monocytes was Arid5A (p < 10−27) that binds to selective inflammation-related genes, such as IL6 and STAT3 and stabilize their expression (Nyati et al., 2020; Wilsker et al., 2002). We further repeated the analysis with a smaller 50 bp window size for TF motif search. Motifs for ARNT, CTCF, and REST remained the top hits in most cell types (Supplementary file 7). However, BCL6 and ARID5A were no longer the top motifs in the search indicating that motifs for these TFs appear to be farther from the age-associated CpG sites.

The recurring enrichment of ARNT and REST with age-associated CpGs observed across multiple cell types, despite relatively few shared genomic region locations, suggests a common mechanism of gene regulation. We found that only 17 and 44 age-associated hypo- and hypermethylated probes, respectively, shared ARNT or REST motifs across all cells (Figure 5—figure supplement 1A, B), suggesting these overlaps are not random and have a specific function (Figure 5—figure supplement 1A, B).

Remarkably, ARNT mRNA was significantly overexpressed in older age in three of the six cell types and REST mRNA showed a significant decrease of expression with age in most cell types (Supplementary files 8 and 9). These findings suggest that age-associated changes in expression levels of REST and ARNT can affect the epigenetic status of their target genes.

Age-related differential methylation and oxygen sensing

ARNT, REST, and BCL6, three TFs most associated with differentially methylated regions, are implicated in hypoxia response (Figure 5C). ARNT is the beta subunit of Hypoxia Factor 1 (HIF-1), which is stabilized during hypoxia and shuttled to the nucleus where it binds to DNA hypoxia-response elements and triggers a complex response that include upregulation of angiogenesis and erythropoiesis and reprogramming of energetic metabolism from oxidative phosphorylation to anaerobic glycolysis (Semenza, 2000). Hypoxia also upregulates the transcription of REST which is the master regulator of the transcriptional repression arm of the response to hypoxia. Released REST is shuttled to the nucleus where it binds to DNA and regulates approximately 20% of the hypoxia-repressed genes, including genes involved in proliferation, translation, and cell cycle progression. We identified 35 genes that were hypomethylated with aging and had close by an ARNT motif in all six cell types (Supplementary file 10). Ten of these genes (right side of Figure 5C, genes under orange headings) have been linked to hypoxia response (Craps et al., 2021; Stegmann, 1998; Chakraborty and Ain, 2017; Gusdon et al., 2012; Wang et al., 2009; Hsu et al., 2010; Pamenter et al., 2020; Pangou et al., 2016; Lazarou et al., 2013; Cai et al., 2020). Similarly, we found 26 genes with probes hypermethylated with age and with REST motif in the vicinity in all six cell types (Supplementary file 10). Four of these (right side of Figure 5C, genes under green heading) are known to be downregulated in hypoxia (Tan et al., 2012; Liu et al., 2020; Dasgupta, 2004; Carmeliet and Jain, 2011). These results strongly suggest a link between age-associated DNA methylation and oxygen sensing through putative regulation by TFs like ARNT and REST in the various immune cells.

Association with inflammatory cytokines

Low-grade inflammation has been reported to be part of healthy aging. In order to investigate whether age-related pro-inflammatory state may explain the age-related changes in methylation observed in this study, we analyzed the SomaScan protein data of seven pro-inflammatory cytokines (IL6, IL1RN, IL1A, IL1B, TNF, TNFRSF1A, and TNFRSF1B) for the same cohort of donors from Tanaka et al., 2018. For each cell type, all CpGs reported as significantly hypo- or hypermethylated with age (in beta-regression analyses adjusted for sex) were reanalyzed by incorporating data on seven pro-inflammatory cytokines (see Materials and methods for details). Briefly, by comparing a model with a cytokine as explanatory variable (CpG ~ age + sex + cytokine) with another model without it (CpG ~ age + sex), we explored the robustness of age as an explanatory variable of methylation change, as well as possible mediating effects arising from pro-inflammatory cytokines. Detailed results are provided in OSF and summary statistics for each cell type, hypo-/hypermethylation association and pro-inflammatory cytokine are provided in Supplementary file 11. By comparing the results from the two abovementioned regression models, we observed in case of hypermethylated sites in CD4 cells, the number of CpGs dropped by 10% on adding TNFRSF1A to the model, a cytokine that appears significantly associated with 7507 of those ge-associated CpGs (Column I). In addition, TNFRSF1B appears significantly associated with 9058 of the age-hypermethylated CpG sites in CD4 cells. For other cell types like B naive and monocytes, TNF-alpha was associated with 65–124 age-associated CpG sites, respectively. Fewer associations are observed for the remaining analytes. These results suggest a possible link between TNF-alpha signaling pathway, aging, and DNA methylation change in circulating immune cells.

Discussion

Novel and important conclusions arise from our observations. First, only few CpG sites are hypo- and hypermethylated with aging across all circulating cells while majority of the significant age-associated methylation changes are cell selective. Indeed, several CpGs show differential age methylation in opposite directions in different cell types and are unchanged in PBMC, suggesting that they may be missed when studying mixed cell samples. Noteworthy, age-related methylation differences in this cross-sectional study were strongly and significantly correlated with longitudinal age-associated methylation changes in an independent population.

Second, age-associated hypomethylated sites were significantly enriched for active enhancers whereas age-hypermethylated sites were enriched for bivalent/polycomb regions, confirming previous findings in whole blood (Yuan et al., 2015). Age-differential methylation coincided with specific chromatin status and histone markers patterns, suggesting that their position in proximity of promoter and active enhancer regions is connected with chromatic accessibility and potentially modulation of gene expression. Since the ENCODE data were only from two donors, it will be worthwhile to see how the histone or chromatin accessibility patterns change with age at and around these age-associated CpG sites.

Third, distinct TF-binding motifs co-localize with CpGs differentially methylated with aging despite wide variation in the distribution of such sites across cell types, suggesting a specific regulatory function. Noteworthy, the top age-associated TF identified, ARNT and REST act in coordination in hypoxia response (Cavadas et al., 2016). BCL6, another top TF-binding motif associated with age-differentially methylated CpG has also been shown to protects cardiomyocyte from damage during hypoxia (Gu et al., 2019). These findings support the hypothesis that systematic methylation changes with aging may be induced by fluctuations in oxygen availability and energy metabolism. Interestingly, the mRNA encoding ARNT significantly increases with age in all cell types except monocytes, while mRNA coding for REST declines with aging in four cell types and shows no significant change in naive CD8+ T cells and NK cells. mRNAs coding for CTCF showed strong age association across numerous cell types (Supplementary file 8). The hypothesis that oxygen sensing regulates directly or indirectly DNA methylations is consistent with studies showing that in replicating fibroblasts, biological age estimated by DNA methylation slows down under hypoxia compared to normoxia (Matsuyama et al., 2019). Further, many genes close by to ‘shared’ age-differentially methylated CpG identified in our analyses play important roles in hypoxia response (Figure 5C).

The specific mechanisms connecting age-related changes in DNA methylation in genes which also contain binding motifs the master hypoxia-response mediators remain unknown. Shahrzad et al. reported an inverse correlation between the severity of hypoxia and the degree of DNA methylation (Shahrzad et al., 2007). There is evidence that hypoxia-induced hypermethylation may be due to reduced TETs activity (Thienpont et al., 2016). Our findings add to this literature by suggesting that a direct interaction between hypoxia-related TFs and DNA methylation at specific DNA sites occur with aging, perhaps as an adaptive response triggered by fluctuations in oxygen levels that occur in many age-related conditions. This hypothesis is consistent with oxygen availability been the most important environmental factor that requires physiological adaptation during pregnancy and development and extends this concept in a life course perspective.

A limitation of this study is that we have focused on circulating cells and, therefore, our findings may not apply to age methylation in other tissues. In addition, our findings were not replicated in an independent cross-sectional study population. Despite these limitations, this study has unique features: a cohort of exceptionally healthy donors and percent methylation was assessed in specific cell types obtained by cytapheresis and sorted by using state-of-the art methods.

Conclusion

Age-associated DNA methylation profiles of the six purified primary immune cell populations in the blood show more cell specificity than sharedness. However, we observe common regulatory features with respect to TF-binding motifs and histone modifications. Based on the consistent association of these methylated sites with ARNT and REST, which are master hypoxia regulators, we hypothesize that oxygen sensing and hypoxia drive mechanisms for changes in methylation. This hypothesis should be further explored in animal models with manipulation of oxygen levels and serial measures of DNA methylation in circulating immune cells.

Materials and methods

Cohort details

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Buffy coat, PBMCs, and granulocytes were collected from Genetic and Epigenetic Signatures of Translational Aging Laboratory Testing study (GESTALT) study participants (N = 55; 34 men and 21 women; age 22–83 years) who were free of diseases (except controlled hypertension or history of cancer silent for >10 years), not on medications (except one antihypertensive drug), had no physical or cognitive impairments, non-smokers, weighed >110 lbs, had body mass index <30 kg/m2 (Roy et al., 2021; Ubaida-Mohien et al., 2019). GESTALT was approved by the institutional review board of the National Institutes of Health and participants explicitly consented to participate.

Isolation of PBMC and immune cell populations

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PBMCs were isolated from cytapheresis packs by density gradient centrifugation using Ficoll-Paque Plus. Total B, CD4+, and CD8+ T cells were enriched by negative selection using EasySep Negative Human kits specific for each cell type; monocytes were negatively enriched using ‘EasySep Human Monocyte Enrichment Kit w/o CD16 depletion’. Natural killer cells were negatively enriched by depleting PBMCs with antibodies against CD3, CD4, CD14, CD19, and Glycophorin-A in HBSS (Hanks' Balanced Salt Solution) buffer. Enriched cell populations were FACS (fluorescence-activated cell sorting) sorted by flow cytometry as per Human Immunophenotyping Consortium (HIPC) phenotyping panels (Maecker et al., 2012). Gating strategies and post-sort purity were analyzed by FlowJo software (LLC, Ashland, OR) (Roy et al., 2021). Granulocytes were positively selected from whole blood using EasySep Human Whole Blood CD66b Positive Selection Kit. Purified cells and PBMC were washed with phosphate-buffered saline, snap frozen and stored at −80°C. All sorted cells were >95% pure by flow cytometry (Roy et al., 2021).

Assessment of DNA methylation

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DNA was isolated from 1 to 2 million cells using DNAQuik DNA Extraction protocol and the Qiagen DNeasy Kit. 300 ng of DNA was treated with sodium bisulfite using Zymo EZ-96 DNA Methylation Kit. The methylation of ~850,000 CpG sites was determined using Illumina Human MethylationEPIC BeadChip, and data preanalyzed by GenomeStudio 2011.1.

Data processing and functional annotation of CpG sites

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Analyses were performed by the R minfi package (Aryee et al., 2014; Fortin et al., 2017). Probes with low detection p-values (cutoff 0.01) were filtered out (Moran et al., 2016). Data were normalized using noob and BMIQ (Liu and Siegmund, 2016), batch corrected by ComBat function (sva package), and β values were used for differential methylation analyses. Following the MethylationEPIC probe annotation (IlluminaHumanMethylationEPICanno-.ilm10b2.hg19) to the UCSC RefSeq genes (hg19), we grouped the locations into three categories: (1) promoter group – TSS 1500 (from 201 to 1500 bp upstream of TSS), TSS 200 (≤200 bp upstream of TSS), 5′UTR, first exon; (2) genebody – exons (all exons except exon1), exon intron boundary, intron and 3′UTR; and (3) intergenic probes. The first gene in the annotation package was considered. Probes were divided into three groups – within CpG islands (CGI), within CpG shore (0–2 kb from CGI), CpG shelf (2–4 kb from CGI), and open sea (>4 kb from CGI).

Definition of age-associated probes

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Age- and sex-adjusted CpG-specific beta-regressions were performed on normalized β values using the R betareg function. p-values were adjusted for multiple testing (Benjamini–Hochberg [BH] adjusted p < 0.05). Probes with FDR p < 0.05 for age and FDR p > 0.05 for sex were considered age-differentially methylated CpGs. Beta-regression estimate value was used to group the age-associated probes as hypo- (Estimateage <0) or hypermethylated (Estimateage >0). The overlap of probes across multiple combinations of the six cell types was assessed using R package SuperExactTest (v.1.1.0) (Wang et al., 2015).

Gene set enrichment analysis

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Based on the EPICarray annotation, genes were classified as differentially hypo- or hypermethylated with age. Genes with both age hypo- and hypermethylated CpGs were removed from the analysis. Enrichment analysis was performed by the tmodHGtest method in the tmod v.0.46.2 R package, comparing a foreground list of genes found in ≥5 cell types against reference gene set collections ‘Hallmarks’ and ‘Canonical Pathways’ (which includes Reactome, KEGG, WikiPathways, PID, and Biocarta gene sets) from the Molecular Signature Database MSigDB (v.7.4) (Subramanian et al., 2005).

For the gene enrichment analysis of the principal components, the top 500 CpG probes corresponding to the positive and negative directions along PC1, PC2, and PC3 were extracted and annotated to nearest gene as per the manufacturer’s annotation file. The ambiguous genes with probes associated with both positive and negative PC directions were removed from the analysis. The remaining genes were run through the abovementioned enrichment analysis pipeline. A filter based on q-value <0.05 was imposed to find the most significant pathways.

Visualization of histone peaks and DHS peaks

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Primary cell DHS and chromatin ChIP-Seq bigwig files were downloaded from ENCODE (Roy et al., 2021). DeepTools was used to visualize DHS and histone peaks in +3 kb region surrounding age-associated shared and non-shared methylated sites. For plotting purposes, the order of methylated probes was determined based on descending score of DHS peaks and followed for all histone marks (H3K4me1, H3K4me3, and H3K27ac).

Annotation of age-associated methylated probes using chromHMM

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The 18-state chromHMM models (based on 6 chromatin marks H3K4me3, H3K4me1, H3K36me3, H3K27me3, H3K9me3, and H3K27ac) for various immune cells (E032 – primary B cell, E038 – primary naive CD4+ T cells, E047 – primary naive CD8+ T cells, E029 – monocyte, E046 – NK cell) were downloaded from Roadmap epigenomics project. Bedops tool was used to map the age-associated methylated sites to the respective chromHMM profiles. All Infinium MethylationEPIC array probes were also partitioned using each of the immune cell chromHMM profiles as controls.

Prediction of de novo TF-binding motifs by HOMER

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All the age-associated methylation sites were considered for HOMER analysis. A region of ±200 bp around each age-associated methylated site was provided as input for analysis in HOMER using de novo setting (Heinz et al., 2010). As a background, we used the default background list that HOMER creates by matching the GC% in the input list. The output from the stringent de novo analysis was considered for downstream data interpretation.

InCHIANTI longitudinal study cohort

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InCHIANTI (Invecchiare in Chianti) is a population-based cohort of individuals ≥20 years old from the Chianti region of Tuscany, Italy (PMID: 11129752). The Italian National Institute of Research and Care on Aging Institutional Review Board approved the study protocol and all participants explicitly consented to participate. DNA methylation from 699 participants (1841 observations) was used for the analysis. CpG methylation of 485,577 CpGs was determined by the Illumina Infinium HumanMethylation450 BeadChip (Illumina Inc, San Diego, CA) and data processed by the R package ‘sesame’. Mean rates of change were estimated from 2 to 3 longitudinal timepoints.

RNA-Seq sample extraction, processing, and data analysis

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Total RNA was extracted from 2 × 106 cells, depleted from ribosomal RNA and 50 ng was used for cDNA synthesis and library preparation. Libraries were sequenced for 138 cycles on Illumina HiSeq 2500. After adapter removal and end trimming of raw FASTQ files, transcript abundances were quantified with reference to hg19 transcriptome using kallisto 0.44 (with options --single -l 250 -s 25). Transcripts were aggregated to genes with tximport and filtered out if less than 10 TPM were detected in more than 33% of the samples. Linear regression models (~phase + age*sex) were used on TPM normalized expression values to study expression changes of selected TFs with age. Only the regression coefficient and p-value for the three TF genes – ARNT, CTCF, and REST were used in this study.

Inflammatory cytokine analysis

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Published SomaScan protein data from the same cohort of donors used in the present study were extracted to look for age-associated changes in seven cytokines (IL6, IL1RN, IL1A, IL1B, TNF, TNFRSF1A, and TNFRSF1B) (Tanaka et al., 2018). Briefly, plasma proteomics was measured using the 1.3k SomaScan assay (SomaLogic, Boulder, CO) followed by standard quality control and normalization procedures as described in previous publications (Candia et al., 2017; Candia et al., 2022). Normalized data for seven cytokines were extracted (detailed annotation provided in Supplementary file 10). To complement the age association analysis of CpGs adjusted by sex (CpG ~ age + sex), we performed additional beta-regression analyses separately including each target pro-inflammatory cytokine as an explanatory variable in the form: CpG ~ age + sex + cytokine. Details about the cytokines are provided in OSF.

Data availability

Researchers interested in using the data from the previously published InCHIANTI study are invited to submit a proposal for consideration, for full details please see https://www.nia.nih.gov/inchianti-study. Code and data processing scripts (including a de-identified version of the GESTALT dataset) are available on OSF. DNA methylation EPIC 850k data are available at GEO under accession number GSE184269.

The following data sets were generated
    1. Roy et al
    (2023) Open Science Framework
    ID rxw6h. Epigenetic signature of human immune aging: the GESTALT study.
The following previously published data sets were used
    1. Kaileh M
    2. Roy R
    3. Ramamoorthy S
    4. Boller S
    5. Grosschedl R
    6. De S
    (2021) NCBI Gene Expression Omnibus
    ID GSE184269. Specification of human immune cell epigenetic identity by combinations of transcription factors (MethylationEPIC).

References

    1. Wilsker D
    2. Patsialou A
    3. Dallas PB
    4. Moran E
    (2002)
    ARID proteins: a diverse family of DNA binding proteins implicated in the control of cell growth, differentiation, and development
    Cell Growth & Differentiation 13:95–106.

Decision letter

  1. Gabrielle T Belz
    Reviewing Editor; University of Queensland, Australia
  2. Carlos Isales
    Senior Editor; Augusta University, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Epigenetic signature of human immune aging: the GESTALT study." for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Carlos Isales as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) A number of points require clarification as indicated by the reviewers. Please address each of these points in revision.

2) Clarify the study is a reanalysis of previously published data to shed light on new patterns.

Reviewer #1 (Recommendations for the authors):

This is an interesting study that potentially identifies a single fundamental mechanism that regulates chromatin changes in aging from stochastic changes. The notion that the methylation changes are cell selective is an interesting concept.

Building on the comments regarding circulating and tissue-resident cells, can the authors provide data to support or refute whether programs are consistent in the different compartments? It would strengthen the work to provide discussion on these points.

The interpretation of the data relies on changes in genes related to hypoxia to support that the metabolic state of the cell determines outcomes. It would be useful to test this hypothesis which would be possible in vitro to determine if programs are indeed determined by metabolic states that in vivo differ in different microenvironments, or that cells are intrinsically differentially programmed.

Although the samples were obtained from participants who were deemed 'clinically' healthy, were any finer criteria used to distinguish and cross-correlate the immune cell phenotype with the methylation changes? The identification of an inflammatory program in the data associated with aging is interesting. While it might occur that low-level inflammation is inherently associated with aging, distinguishing the two programs would be important in teasing the data apart. Thus, a current limitation of the work is the lack of cellular phenotypic and functional mapping corresponding to the molecular analysis to bring clarity to whether overlapping programs occur and inflammation contributes to driving aging normally, or whether separate programs usually occur in parallel but could overlap. Dissecting this apart further would strengthen the work.

Reviewer #2 (Recommendations for the authors):

1. The authors claimed that clustering by cell type (PC2) was stronger than by age. For the PCA, it would be great if the authors could explain what PC1 indicates and present the information about all PCs.

2. It has been reported that sex-related differences contribute to immune cell aging (Huang et al., PNAS, 2021, PMID:34385315; Marquez et al., Nature Communications, 2020, PMID: 32029736). It is unclear how sex differences affect the dataset that the authors analyzed and how sex-adjusted β-regression was conducted.

3. The authors identified 350 age-associated differentially methylated sites among all six immune cell types and compared them with other published studies. Do these sites overlap with the DNA methylation clock sites identified in PBMCs since these sites change in the same direction in separate cell populations? Also, only 10 of these 350 sites overlap with Horvath's pan-tissue epigenetic clock. Is there any factor that can explain this difference? For example, sequencing depth, coverage, etc?

4. In the legend of Figure 2, the most significant genic probes are classified as -log10(padj)<10. Did the author mean -log10(padj)>10?

5. It needs to be clarified how the top 15 genes were chosen. How many age-associated CpGs contribute to these 15 genes? Also, if more genes are considered, will more genes be shared among cell types?

6. In Figure 3C and 3D, the differences in β coefficient in the InCHIANTI study are small (scale at 0.003 and 0.006). Are the differentially methylated sites called in GESTALT identified as differentially methylated in the original InCHIANTI study?

7. In Figure 4C and Supp Figure 4, it seems both hypo- and hyper-methylated sites with age show the V-shaped pattern.

8. It is unclear how the expression levels were measured. Expression levels from qPCR or RNA-seq need to be shown in addition to Supp Table 6. RNA-seq was mentioned in the method section, and if this was performed in this study, the quality metrics for the data need to be provided and the data need to be uploaded to GEO. The current GEO number (GSE184269) provided in this manuscript is linked to a published study and does not contain RNA-seq data.

9. There are several places where the authors mentioned "data not shown". It may be better if these data could be presented in supplementary figures or tables.

Reviewer #3 (Recommendations for the authors):

Specific suggestions for Authors:

1) As mentioned in the public review, certain claims should be toned down or placed in context.

2) The authors need to make it clear in the introduction that this current study is based on the reanalysis of a data set they have published in 2021 and describe key findings and limitations of the initial study.

3) It is not clear from the methods and figure legends that motif predictions were done on DMRs that could be found in at least 5 cell types, as only loosely mentioned in the text. Please clarify this point in methods and legends.

4) Presumably, motif enrichment was performed against a genomic background. This information is not found in the methods and should be added.

5) Motif enrichment analysis was done in windows of +200bp around DMRs. Please explain why this choice was made and whether a narrower window (e.g., 100bp) would yield drastically different outcomes.

6) Can the authors speculate on why they see enrichment for neuronal and pancreas gene ontology pathways related to conserved DNA methylation changes?

7) Data should be shown in important instances like "We identified 35 genes that were hypomethylated with aging and had close by an ARNT motif in all six cell types (Data not shown). Ten of these genes (right side of Figure 5C, genes under orange headings) have been linked to hypoxia response (37-46). Similarly, we found 20 genes with probes hypermethylated with age and with REST motif in the vicinity in all six cell types (data not shown)." Please provide the information.

https://doi.org/10.7554/eLife.86136.sa1

Author response

Essential revisions:

1) A number of points require clarification as indicated by the reviewers. Please address each of these points in revision.

2) Clarify the study is a reanalysis of previously published data to shed light on new patterns.

We have addressed the points raised by the three reviewers. The data analyzed in this manuscript were collected in the context of the GESTALT study, a study of biomarkers of aging that is run in parallel to the Baltimore Longitudinal Study of Aging at the clinical site of the National Institute on Aging Intramural Research Program in Baltimore. The senior author of this manuscript designed the study and has been the π of this project from the beginning. Part of the GESTALT cohort data was previously deposited in the public domain as requested by the previous publication journal. In particular, the methylation data was previously used to explore epigenetic differences between different types of circulating immune cells in a previous published study [1]. However, methylation data was never used previously to look at association with aging, and to explore whether some CpG sites are differentially methylated with aging in the same direction across various circulating cell types. Identification of such shared CpG sites can suggest mechanisms of their agerelated changes. We note that it is common that data collected in a cohort study are used for multiple analyses that explore different hypotheses.

Reviewer #1 (Recommendations for the authors):

This is an interesting study that potentially identifies a single fundamental mechanism that regulates chromatin changes in aging from stochastic changes. The notion that the methylation changes are cell selective is an interesting concept.

Building on the comments regarding circulating and tissue-resident cells, can the authors provide data to support or refute whether programs are consistent in the different compartments? It would strengthen the work to provide discussion on these points.

The interpretation of the data relies on changes in genes related to hypoxia to support that the metabolic state of the cell determines outcomes. It would be useful to test this hypothesis which would be possible in vitro to determine if programs are indeed determined by metabolic states that in vivo differ in different microenvironments, or that cells are intrinsically differentially programmed.

Although the samples were obtained from participants who were deemed 'clinically' healthy, were any finer criteria used to distinguish and cross-correlate the immune cell phenotype with the methylation changes? The identification of an inflammatory program in the data associated with aging is interesting. While it might occur that low-level inflammation is inherently associated with aging, distinguishing the two programs would be important in teasing the data apart. Thus, a current limitation of the work is the lack of cellular phenotypic and functional mapping corresponding to the molecular analysis to bring clarity to whether overlapping programs occur and inflammation contributes to driving aging normally, or whether separate programs usually occur in parallel but could overlap. Dissecting this apart further would strengthen the work.

We thank the reviewer for the insightful suggestion. To investigate further into the role of age-associated inflammatory phenotype, we analyzed the published protein data from the same cohort of donors to look for age-associated changes in seven cytokines (IL6, IL1RN, IL1A, IL1B, TNF, TNFRSF1A, TNFRSF1B)[2]. For each cell type, all CpGs reported as significantly hypo- or hyper-methylated with age (in β-regression analyses adjusted for sex) were reanalyzed by separately adding each one of the seven pro-inflammatory cytokines Supplementary File 11. By comparing a model with a cytokine as explanatory variable (CpG ~ age + sex + cytokine) with another model without it (CpG ~ age + sex), we explored the robustness of age as explanatory variable of methylation, as well as possible mediating effects arising from pro-inflammatory cytokines.

In the Supplementary File 11, columns (C-J) show that the age association remains significant for most CpGs, even after adding one of these inflammatory mediators into the model. Interestingly we find that for age associated hypermethylated sites in CD4 cells, 10% of the CpGs associated with age show significant association with TNFRSF1A in the model which implies an association of these methylated sites or genes with the cytokine production and/or release in the circulation. The remaining columns (K-Q) show the number of CpGs for which there’s a significant association between CpG and the target analyte (in the model adjusted for age and sex). As noted above, TNFRSF1A and TNFRSF1B are associated with more than 7000 CD4 age-associated hypermethylated CpGs, suggesting an important role of age-differential methylation in the pro-inflammatory state of aging. For other cell types (most notably, B naïve and monocytes) TNF-α appears to play a role. Fewer associations are observed for the remaining analytes.

The new analysis has been added to the results and the description of the analysis has been added to the methods section.

Reviewer #2 (Recommendations for the authors):

1. The authors claimed that clustering by cell type (PC2) was stronger than by age. For the PCA, it would be great if the authors could explain what PC1 indicates and present the information about all PCs.

We performed gene set enrichment analysis with the top 500 CpG probes corresponding to PC1, PC2 and PC3. Following this, the probes were annotated to nearby genes as per manufacturer’s annotation file and the genes which had probes with both positive and negative values for a PC were excluded. With the revised manuscript, we have provided a supplementary spreadsheet with columns providing information on the associated PC component and direction. A filter with q-value<1.e-5 was imposed to highlight the most significant pathways. PC1 genes are enriched for T cell receptor and Natural killer cells -associated pathways while PC2 shows enrichment of Hallmark IL2 STAT5 signaling and Reactome innate immune system. We conclude that PC1 contributes more towards separation of CD4, CD8 and NK cells from B cells, monocytes and granulocytes while PC2 separates the innate from the adaptive cells. The details have been added to the Results section (page 4) and the Methods section (page 18) along with the addition of Supplementary File 2.

2. It has been reported that sex-related differences contribute to immune cell aging (Huang et al., PNAS, 2021, PMID:34385315; Marquez et al., Nature Communications, 2020, PMID: 32029736). It is unclear how sex differences affect the dataset that the authors analyzed and how sex-adjusted β-regression was conducted.

We performed β regression with age and sex as two variables and in order to get the probes associated with age, we only selected the probes with adjusted p value for age <0.05 and excluded the ones that were also had p<0.05 for sex (N <1000 probes in total). We have added this information in greater detail in the Methods section (page 17). We also agree with the reviewers that identifying sex-differences is an interesting avenue for future research with greater number of samples.

3. The authors identified 350 age-associated differentially methylated sites among all six immune cell types and compared them with other published studies. Do these sites overlap with the DNA methylation clock sites identified in PBMCs since these sites change in the same direction in separate cell populations? Also, only 10 of these 350 sites overlap with Horvath's pan-tissue epigenetic clock. Is there any factor that can explain this difference? For example, sequencing depth, coverage, etc?

There can be several reasons for the observation that the CpGs identified in our study show only a small overlap with those identified in Horvath’s epigenetic clock. The “Horvath clock” was developed on methylation from multiple tissues and the “Hannum clock” was developed from whole blood. The contribution of different cell types depends on their frequency, and therefore methylation sites that change with aging in cells that are present in a high proportion would be predominant on those that only exist in low proportion. Indeed, the Horvath epigenetic clock works better in the GESTALT study when the data from PBMC are considered (59 probes overlapped instead of 10). Also, since these methylation clocks are statistically derived through a regression model using 21369 probes present in 27k microarray, it is possible that neighboring CpG gets picked up for the clock or many of the 850k sites went unrepresented. We have added this explanation to the Results section on page 5.

4. In the legend of Figure 2, the most significant genic probes are classified as -log10(padj)<10. Did the author mean -log10(padj)>10?

Thank you for pointing out the error. It has been fixed in the figure legend of the revised manuscript.

5. It needs to be clarified how the top 15 genes were chosen. How many age-associated CpGs contribute to these 15 genes? Also, if more genes are considered, will more genes be shared among cell types?

We thank the reviewer for the question. As a clarification we have incorporated additional information on number of age-associated probes for each of these top 15 genes in the Supplementary File 5. In Figure 1, we have looked at the overlap of all age-associated probes. Here we wanted to focus on whether the most significant age-associated genes are shared across cell types. When we increased the selection to the top 50 genes (new data added to Supplementary File 5), the number of shared age-hypo- or hypermethylated genes increase by only 5 and 13 genes respectively. This underscores our conclusion that “most CpGs with age-associated methylation consistent across cell types undergo moderate (although significant) methylation changes with aging.” We have rephrased this part of the Results on page 6 of the revised manuscript.

6. In Figure 3C and 3D, the differences in β coefficient in the InCHIANTI study are small (scale at 0.003 and 0.006). Are the differentially methylated sites called in GESTALT identified as differentially methylated in the original InCHIANTI study?

Changes in methylation with age (per year) tend to be small although they may be highly significant. Taking that into account, both the cross-sectional and longitudinal association with aging in the InCHIANTI are consistent in direction with those detected in the GESTALT study for the “shared” age-related CpG methylation.

7. In Figure 4C and Supp Figure 4, it seems both hypo- and hyper-methylated sites with age show the V-shaped pattern.

As correctly noted by the reviewer we do observe a V pattern and strong H3K4me1 peak at age-associated hypo as well as hypermethylated sites. This implies that the age-associated sites are enriched for active and/or primed enhancers. However, as the DHS and histone data in the ENCODE database was only available for either one of 2 donors (a 21-year-old male and a 37-year-old female), we were unable to see how the patterns change with age. We have added this note to the revised manuscript in the Results section (page 9) and Discussion section (page 13).

8. It is unclear how the expression levels were measured. Expression levels from qPCR or RNA-seq need to be shown in addition to Supp Table 6. RNA-seq was mentioned in the method section, and if this was performed in this study, the quality metrics for the data need to be provided and the data need to be uploaded to GEO. The current GEO number (GSE184269) provided in this manuscript is linked to a published study and does not contain RNA-seq data.

We thank the reviewers for their valid concern. We would like to clarify that expression values of the transcription factors are obtained from RNA-Seq data. In the paper we have specifically looked into the expression of 3 selected transcription factors to look for their association with age. Towards this, we have provided an additional Supplementary File 8 with the Kallisto TPM normalized values for the three transcription factors along with the sequencing data quality metrics.

9. There are several places where the authors mentioned "data not shown". It may be better if these data could be presented in supplementary figures or tables.

These are the 3 places where we have mentioned “data not shown” and all of them have been addressed-

1.“Both age-associated hypo- and hypermethylated sites showed evident H3K4me1 peaks, a marker commonly associated with active and primed enhancers (Figure 4C) (26). No specific trend was observed for H3K4me3 and H3K27ac (data not shown)”. We have added the results for H3K4me3 and H3K27ac to the Figure 4—figure supplement 1 and edited the Results section of revised manuscript (page 9).

2.“We identified 35 genes that were hypomethylated with aging and had close by an ARNT motif in all six cell types (Data not shown)”. We have added the results for all the 35 genes with ARNT motif in the vicinity, to the Supplementary File 10 and edited in the Results section of revised manuscript (page 11).

“Similarly, we found 20 genes with probes hypermethylated with age and with REST motif in the vicinity in all six cell types (data not shown)”. We have added the results for all the 26 genes with REST motif in the vicinity, to the Supplementary File 10 and edited in the Results section of revised manuscript (page 11). We also corrected a typing error as there are 26 genes and not 20 (page 11).

Reviewer #3 (Recommendations for the authors):

Specific suggestions for Authors:

1) As mentioned in the public review, certain claims should be toned down or placed in context.

In the revised version of the paper, we have added new citations and new data to address the concern raised by the reviewer.

2) The authors need to make it clear in the introduction that this current study is based on the reanalysis of a data set they have published in 2021 and describe key findings and limitations of the initial study.

We respectfully disagree with this statement. Very often data produced by cohort studies are reanalyzed according to different hypotheses. For example, NIA maintains the Baltimore Longitudinal study of aging and more than 1500 manuscript have been published over the last 20 years. Similarly, the genetic data from the UK biobank have been used for hundreds of analyses and produced some of the most interesting works in the literature over the last 10 years. There are probably hundreds of examples, including the Framingham study, the Cardiovascular Heart Study, the Health ABC study, and the Health and Retirement survey. In none of these cases, the content of previous papers is reported in the introduction, especially when, as in this case, the results are not relevant for the analysis reported in this manuscript.

3) It is not clear from the methods and figure legends that motif predictions were done on DMRs that could be found in at least 5 cell types, as only loosely mentioned in the text. Please clarify this point in methods and legends.

Thank you for your feedback. We used all the age-associated probes for HOMER analysis and not just the ones shared across 5 or more cell types. We added the associated details of the analysis on pages 17 and 20.

4) Presumably, motif enrichment was performed against a genomic background. This information is not found in the methods and should be added.

Thank you for pointing out the missing details. We added the HOMER background associated details in the Methods section on page 19. Briefly, HOMER software creates a list of background sequences which are randomly selected from the genome and matched for GC% content to the list of target sequences. We used this background file for our motif search in HOMER.

5) Motif enrichment analysis was done in windows of +200bp around DMRs. Please explain why this choice was made and whether a narrower window (e.g., 100bp) would yield drastically different outcomes.

We appreciate a very valid point raised by Reviewer 3. We have used a 200bp window because it is the default size in HOMER software. However following Reviewer 3 comments we reanalyzed the data with +50bp window size and the results have been tabulated in the revised Supplementary File 7. We observe that the top hits across 5 of the 6 cell types continue to be ARNT and CTCF for age-hypomethylated sites and REST for age-hypermethylated sites. Of note, unlike the hits with 200bp window size, Arid5a and Bcl6 were not observed in 50bp window size indicating that these motifs are comparatively farther from the CpG sites. We have added a note about the new analysis and the results on page 10 of the revised manuscript.

6) Can the authors speculate on why they see enrichment for neuronal and pancreas gene ontology pathways related to conserved DNA methylation changes?

We appreciate the question regarding the relevance of the abovementioned pathways to our study. A recent study by Ewing et al. found CpG sites getting hypermethylated in immune cells of multiple sclerosis patients mapped to neuronal pathways where they argue that neuronal genes like GRIN, GRID and Netrin play a critical role in immune cell development as well (https://pubmed.ncbi.nlm.nih.gov/31053557/). Also, a paper by Karagiannis et al. finds neuronal genes in their PBMC aging data. Both references have been added to the Results section (page 8). However, didn’t find much with respect to pancreatic genes in the literature warranting future investigation in this direction.

7) Data should be shown in important instances like "We identified 35 genes that were hypomethylated with aging and had close by an ARNT motif in all six cell types (Data not shown). Ten of these genes (right side of Figure 5C, genes under orange headings) have been linked to hypoxia response (37-46). Similarly, we found 20 genes with probes hypermethylated with age and with REST motif in the vicinity in all six cell types (data not shown)." Please provide the information.

Thank you for the suggestions. We have added the information in Supplementary File 10 along with modifying the Results section in the revised manuscript on page 11.

https://doi.org/10.7554/eLife.86136.sa2

Article and author information

Author details

  1. Roshni Roy

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  2. Pei-Lun Kuo

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation, Formal analysis, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Julián Candia

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Contribution
    Formal analysis, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5793-8989
  4. Dimitra Sarantopoulou

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Contribution
    Formal analysis, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Ceereena Ubaida-Mohien

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Contribution
    Software, Visualization
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4301-4758
  6. Dena Hernandez

    Laboratory of Neurogenetics, National Institute on Aging, Bethesda, United States
    Contribution
    Data curation, Supervision, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Mary Kaileh

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Contribution
    Conceptualization, Resources, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2314-312X
  8. Sampath Arepalli

    Laboratory of Neurogenetics, National Institute on Aging, Bethesda, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared
  9. Amit Singh

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Contribution
    Visualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Arsun Bektas

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation, Writing – review and editing
    Competing interests
    No competing interests declared
  11. Jaekwan Kim

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared
  12. Ann Z Moore

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation, Formal analysis
    Competing interests
    No competing interests declared
  13. Toshiko Tanaka

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4161-3829
  14. Julia McKelvey

    Clinical Research Core, National Institute on Aging, Baltimore, United States
    Contribution
    Conceptualization, Data curation
    Competing interests
    No competing interests declared
  15. Linda Zukley

    Clinical Research Core, National Institute on Aging, Baltimore, United States
    Contribution
    Conceptualization, Data curation
    Competing interests
    No competing interests declared
  16. Cuong Nguyen

    Flow Cytometry Unit, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation, Methodology
    Competing interests
    No competing interests declared
  17. Tonya Wallace

    Flow Cytometry Unit, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared
  18. Christopher Dunn

    Flow Cytometry Unit, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7899-0110
  19. William Wood

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared
  20. Yulan Piao

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation, Methodology
    Competing interests
    No competing interests declared
  21. Christopher Coletta

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared
  22. Supriyo De

    Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, United States
    Contribution
    Software, Formal analysis, Methodology
    Competing interests
    No competing interests declared
  23. Jyoti Sen

    Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, United States
    Contribution
    Supervision, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  24. Nan-ping Weng

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Contribution
    Conceptualization, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  25. Ranjan Sen

    Laboratory of Molecular Biology and Immunology, National Institute on Aging, Baltimore, United States
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  26. Luigi Ferrucci

    Translational Gerontology Branch, National Institute on Aging, Baltimore, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    ferruccilu@grc.nia.nih.gov
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6273-1613

Funding

No external funding was received for this work.

Acknowledgements

This work was supported entirely by the Intramural Research Program of the National Institute on Aging. We are grateful to the GESTALT participants and the GESTALT Study Team at Harbor Hospital and NIA.

Ethics

GESTALT study was approved by the institutional review board of the National Institutes of Health. Informed consent as well as the consent to publish the data collected was obtained from every participant in the study. Since the study of gene expression and epigenetic regulation are essential aims of GESTALT, all participants were required to consent to DNA/RNA testing and storage at all visits in order to participate in the study. The GESTALT IRB approval number is 15-AG-0063.

Senior Editor

  1. Carlos Isales, Augusta University, United States

Reviewing Editor

  1. Gabrielle T Belz, University of Queensland, Australia

Version history

  1. Received: January 12, 2023
  2. Preprint posted: January 23, 2023 (view preprint)
  3. Accepted: August 16, 2023
  4. Accepted Manuscript published: August 17, 2023 (version 1)
  5. Version of Record published: September 18, 2023 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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  1. Roshni Roy
  2. Pei-Lun Kuo
  3. Julián Candia
  4. Dimitra Sarantopoulou
  5. Ceereena Ubaida-Mohien
  6. Dena Hernandez
  7. Mary Kaileh
  8. Sampath Arepalli
  9. Amit Singh
  10. Arsun Bektas
  11. Jaekwan Kim
  12. Ann Z Moore
  13. Toshiko Tanaka
  14. Julia McKelvey
  15. Linda Zukley
  16. Cuong Nguyen
  17. Tonya Wallace
  18. Christopher Dunn
  19. William Wood
  20. Yulan Piao
  21. Christopher Coletta
  22. Supriyo De
  23. Jyoti Sen
  24. Nan-ping Weng
  25. Ranjan Sen
  26. Luigi Ferrucci
(2023)
Epigenetic signature of human immune aging in the GESTALT study
eLife 12:e86136.
https://doi.org/10.7554/eLife.86136

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https://doi.org/10.7554/eLife.86136

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