Cellular aging in vitro recapitulates multi-tissue epigenetic aging in vivo

Abstract

Aging is known to elicit dramatic changes to DNA methylation (DNAm). However, the causes and consequences of such alterations to the epigenome remain unclear. Therefore, the utility of biomarkers of aging based on DNAm patterns will depend on our ability to link them to cellular features, such as proliferation, differentiation, transcriptional repression/activation, senescence, and transformation. Using DNAm from serially passaged mouse embryonic fibroblasts (MEFs), we developed a predictor of replication that is able to accurately predict passage number in independent sample. Our measure, termed DNAmRep, was shown to strongly increase with age when examined in multiple tissues (blood, liver, kidney, lung and adipose). Furthermore, we observed replicative deceleration in animal undergoing caloric restriction. Upon reprogramming to iPSCs, cells derived from both lung and kidney fibroblasts exhibited resetting of our DNAmRep measure. This measure also increased in response to differentiation among B-cell populations. Enrichment analysis implicated CBX7, RNF2 and HDAC2 as potentially important transcription factors and chromatin regulators in the DNAm replication signature. Finally, DNAmRep correlated with beta-galactosidase activity in replicative senescent MEFs, but not in radiation or drug induced senescent MEFs, and was partially separated by immortalization with Large T antigen K1 mutant (LTK1), suggesting that it reflects mitotic history, rather than senescent state. Overall, this study identifies mitotically-derived alterations to the methylome, which partially underlie known epigenetic aging phenomena.

LipidClock: A Lipid-Based Predictor of Biological Age

“…Consistent with other recent developments in aging clocks, we find that dimensionality reduction by PCA prior to regression functions well in constructing aging clocks ( Minteer et al, 2020 ). One drawback of PCA and linear regression methods is that they can only capture linear dependencies.…”

“…Fig S1]. When analyzing data from aging cohorts, age is expected to be the major source of biological aging within each cohort (genotype, treatment condition) ( Tarkhov et al, 2019 ; Minteer et al, 2020 ). Following transformation of all samples into principal component space, we therefore determined the linear correlation of each PC coordinate with age in both WT and mutant cohorts ( Figure 1 ).…”

“…Aging is therefore expected to be a major source of variance when analyzing data describing a biological system across its lifespan. PCA is a technique to identify correlated features or “directions” in feature space along which covariance is large and this can be exploited in the construction of clocks and classifiers ( Tarkhov et al, 2019 ; Minteer et al, 2020 ).…”

The relationship between epigenetic age and the hallmarks of aging in human cells

“…It was particularly important to address the question of the relationship between epigenetic aging and cellular senescence, as previous reports were equivocal in their conclusions 31 , 33 , 34 . Here, using primary cells from many individual donors, the results are clear that cellular senescence, although undoubtedly a major contributor to the aging phenotype, is not associated with epigenetic aging, as measured by the Skin&blood clock.…”