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Robust biomarkers of aging have been developed from DNA methylation in humans and more recently, in mice. This study aimed to generate a novel epigenetic clock in rats—a model with unique physical, physiological, and biochemical advantages—by incorporating behavioral data, unsupervised machine learning, and network analysis to identify epigenetic signals that not only track with age, but also relates to phenotypic aging. Reduced representation bisulfite sequencing (RRBS) data was used to train an epigenetic age (DNAmAge) measure in Fischer 344 CDF (F344) rats. This measure correlated with age at (r = 0.93) in an independent sample, and related to physical functioning (p=5.9e-3), after adjusting for age and cell counts. DNAmAge was also found to correlate with age in male C57BL/6 mice (r = 0.79), and was decreased in response to caloric restriction. Our signatures driven by CpGs in intergenic regions that showed substantial overlap with H3K9me3, H3K27me3, and E2F1 transcriptional factor binding.

Aging has been shown to be a strong driver of DNA methylation changes, leading to the development of robust biomarkers in humans and more recently, in mice. This study aimed to generate a novel epigenetic clock in rats—a model with unique physical, physiological, and biochemical advantages for studying mammalian aging. Additionally, we incorporated behavioral data, unsupervised machine learning, and network analysis to identify epigenetic signals that not only track with age, but also relate to phenotypic aging and reflect higher-order molecular aging changes. We used DNAm data from reduced representation bisulfite sequencing (RRBS) to train an epigenetic age (DNAmAge) measure in Fischer 344 CDF (F344) rats. In an independent sample of n=32 F344 rats, we found that this measure correlated with age at (r=0.93), and related to physical functioning (5.9e-3), after adjusting for age and differential cell counts. DNAmAge was also found to correlate with age in C57BL/6 mice (r=0.79), and was decreased in response to caloric restriction (CR), such that the longer the animal was on a CR diet, the greater the decrease in DNAm. We also observed resetting of DNAm when kidney and lung fibroblasts when converted to induced pluripotent stem cells (iPSCs). Using weighted gene correlation network analysis (WGCNA) we identified two modules that appeared to drive our DNAmAge measure. These two modules contained CpGs in intergenic regions that showed substantial overlap with histone marks H3K9me3, H3K27me3, and E2F1 transcriptional factor binding. In moving forward, our ability to unravel the complex signals linking DNA methylation changes to functional aging would require experimental studies in model systems in which longitudinal epigenetic changes can be related to other molecular and physiological hallmarks of aging.

Epigenetic clocks, developed using DNA methylation data, have been widely used to quantify biological aging in multiple tissues/cells. However, many existing epigenetic clocks are weakly correlated with each other, suggesting they may capture different biological processes. We utilize multi‐omics data from diverse human tissue/cells to identify shared features across eleven existing epigenetic clocks. Despite the striking lack of overlap in CpGs, multi‐omics analysis suggested five clocks (Horvath1, Horvath2, Levine, Hannum, and Lin) share transcriptional associations conserved across purified CD14+ monocytes and dorsolateral prefrontal cortex. The pathways enriched in the shared transcriptional association suggested links between epigenetic aging and metabolism, immunity, and autophagy. Results from in vitro experiments showed that two clocks (Levine and Lin) were accelerated in accordance with two hallmarks of aging—cellular senescence and mitochondrial dysfunction. Finally, using multi‐tissue data to deconstruct the epigenetic clock signals, we developed a meta‐clock that demonstrated improved prediction for mortality and robustly related to hallmarks of aging in vitro than single clocks., We compared 11 existing epigenetic clocks on the basis of their functional characteristics, transcriptional associations, and ability to capture hallmarks of aging. We then decomposed their signals and recombined them into a “meta‐clock.” This meta‐clock showed stronger prediction of all‐cause mortality than any one epigenetic clock and was able to distinguish tumor from normal tissue and capture epigenetic changes in two types of senescence (replicative and oncogene induced).