Epigenetic drift underlies epigenetic clock signals, but displays distinct responses to lifespan interventions, development, and cellular dedifferentiation

Abstract

Changes in DNA methylation with age are observed across the tree of life. The stereotypical nature of these changes can be modeled to produce epigenetic clocks capable of predicting chronological age with unprecedented accuracy. Despite the predictive ability of epigenetic clocks and their utility as biomarkers in clinical applications, the underlying processes that produce clock signals are not fully resolved, which limits their interpretability. Here, we develop a computational approach to spatially resolve the within read variability or "disorder" in DNA methylation patterns and test if age-associated changes in DNA methylation disorder underlie signals comprising epigenetic clocks. We find that epigenetic clock loci are enriched in regions that both accumulate and lose disorder with age, suggesting a link between DNA methylation disorder and epigenetic clocks. We then develop epigenetic clocks that are based on regional disorder of DNA methylation patterns and compare their performance to other epigenetic clocks by investigating the influences of development, lifespan interventions, and cellular dedifferentiation. We identify common responses as well as critical differences between canonical epigenetic clocks and those based on regional disorder, demonstrating a fundamental decoupling of epigenetic aging processes. Collectively, we identify key linkages between epigenetic disorder and epigenetic clocks and demonstrate the multifaceted nature of epigenetic aging in which stochastic processes occurring at non-random loci produce predictable outcomes.

Age Prediction Using DNA Methylation Heterogeneity Metrics

“…Despite the lack of consensus on the physiological causes of aging that the epigenetic clock detects, a plethora of works evaluate stochastic changes in the methylation pattern, which is also referred to as epigenetic drift in the context of aging [18,41,42]. Therefore, metrics assessing DNA methylation disorder and heterogeneity, such as regional disorder and regional entropy, have been successfully used to predict age and assess the influence of common lifespan manipulation, development, and cellular dedifferentiation on epigenetic age estimates in mice [22].…”

“…The mean score values for each metric, both positively and negatively correlated, exhibit a quadratic relationship with chronological age (Figure 2), thereby indicating that changes in global heterogeneity occur most rapidly in youth and slow down by old age. It is noteworthy that a nonlinear change in heterogeneity during development was observed in [22], wherein global methylation disorder was investigated in aging mice. Table 1.…”

“…However, until recently, it was not clear whether such metrics were suitable for estimating chronological age since, strictly speaking, they are not directly related to the total CpG methylation level. Nevertheless, the novel results obtained on the mouse RRBS data showed that the dynamics of methylation patterns, as reflected in the form of the epigenetic "disorder" criteria, are suitable for the construction of the region-based epigenetic clocks [22]. In the present paper, we wanted to examine the ability of different heterogeneity measures to predict chronological age using the RRBS dataset from human whole blood DNA.…”

Aging clocks based on accumulating stochastic variation

“… 29 ). Moreover, ~30% of the mouse genome might be affected by age-related epigenetic disorder, which is enriched in the Petkovich clock 30 , and a clock using these biological disorder measurements could be built 31 .…”