Disentangling age-dependent DNA methylation: deterministic, stochastic, and nonlinear

Vershinina, O. S.; Bacalini, Maria Giulia; Zaikin, Alexey; Franceschi, Claudio; Ivanchenko, Mikhail

doi:10.1038/s41598-021-88504-0

Cited by 26 publications

(22 citation statements)

References 32 publications

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“…It was suggested that 90 % of CpG sites are driven by non-stochastic genetic and environmental factors, while only 10 % are driven by biological stochastic variation 43 . Our single-cell simulation results, in contrast, are in line with a recent publication by Tarkhov et al 20 that showed increased single-cell DNA methylation level heterogeneity with age.…”

Section: Discussionmentioning

confidence: 99%

Accurate aging clocks based on accumulating stochastic variation

Schumacher

Meyer

2023

Preprint

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Aging clocks have provided one of the most significant recent breakthroughs in the biology of aging. Such clocks allow the determination of chronological and increasingly also biological age, which is prerequisite for assessing the effectiveness of interventions in the aging process and preventive treatments of age-related diseases. The most advanced aging clocks are based on age-dependent changes in DNA methylation pattern. The reproducibility of such changes over the life course has reinvigorated the debate whether a programmed process underlies aging. A programmed aging process, however, is incompatibly with the evolutionary theory of aging. Aging occurs as a consequence of a vanishing force of selective pressure post-reproduction as no fitness benefit is provided by immortality of the soma. In fact, stochastic events have been observed to increasingly occur during the aging process. Here, we test whether aging clocks could be built with entirely stochastic variation. We find that accumulating stochastic variation is sufficient to accurately predict chronological and biological age. Moreover, current aging clocks are entirely compatible with random alterations in the methylation or transcriptomic patterns. Our analysis unifies the clock measure of aging with the evolutionary theory of aging and predicts that any set of data that have a ground state at the age zero with accumulating stochastic variation could be used for building accurate aging clocks.

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Section: Discussionmentioning

confidence: 99%

Accurate aging clocks based on accumulating stochastic variation

Schumacher

Meyer

2023

Preprint

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“…In the current work, we have modelled methylation trajectories linearly. Previous studies of cultured fibroblasts [ 79 ] and cross-sectional human cohorts [ 80 ] suggest non-linear dynamics at some CpGs. Given the number of observations available per individual in the LBC cohort, non-linear trajectories cannot be fitted sufficiently robustly.…”

Section: Discussionmentioning

confidence: 99%

Local CpG density affects the trajectory and variance of age-associated DNA methylation changes

et al. 2022

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Background DNA methylation is an epigenetic mark associated with the repression of gene promoters. Its pattern in the genome is disrupted with age and these changes can be used to statistically predict age with epigenetic clocks. Altered rates of aging inferred from these clocks are observed in human disease. However, the molecular mechanisms underpinning age-associated DNA methylation changes remain unknown. Local DNA sequence can program steady-state DNA methylation levels, but how it influences age-associated methylation changes is unknown. Results We analyze longitudinal human DNA methylation trajectories at 345,895 CpGs from 600 individuals aged between 67 and 80 to understand the factors responsible for age-associated epigenetic changes at individual CpGs. We show that changes in methylation with age occur at 182,760 loci largely independently of variation in cell type proportions. These changes are especially apparent at 8322 low CpG density loci. Using SNP data from the same individuals, we demonstrate that methylation trajectories are affected by local sequence polymorphisms at 1487 low CpG density loci. More generally, we find that low CpG density regions are particularly prone to change and do so variably between individuals in people aged over 65. This differs from the behavior of these regions in younger individuals where they predominantly lose methylation. Conclusions Our results, which we reproduce in two independent groups of individuals, demonstrate that local DNA sequence influences age-associated DNA methylation changes in humans in vivo. We suggest that this occurs because interactions between CpGs reinforce maintenance of methylation patterns in CpG dense regions.

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“…When the variability in the DNA methylation intensity changes during aging, the residuals of the fitted regression function are expected to reflect this change. Previous studies have shown age-related increases in the variability of DNA methylation [ 10 , 21 – 23 ]; however, it is not clear how this variability changes with age. We, therefore, applied the DICNAP to analyze the pattern of variability functions for methylation intensity.…”

Section: Resultsmentioning

confidence: 99%

“…Human phenotype and disease risk also show different patterns of age-related changes [ 5 ], and nonlinear age-related changes in RNA and protein expression have also been reported [ 6 – 9 ]. For some DNA methylation sites, nonlinear changes in methylation intensity during aging following a power law have been reported [ 10 ]. While the patterns of aging are thought to reflect the underlying biological mechanisms, the overall landscape of nonlinear changes during aging is unknown.…”

Section: Introductionmentioning

confidence: 99%

Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging

et al. 2023

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Background Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown. Result We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age. Conclusion DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process.

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Disentangling age-dependent DNA methylation: deterministic, stochastic, and nonlinear

Cited by 26 publications

References 32 publications

Accurate aging clocks based on accumulating stochastic variation

Accurate aging clocks based on accumulating stochastic variation

Local CpG density affects the trajectory and variance of age-associated DNA methylation changes

Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging

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