Aging clocks, entropy, and the limits of age-reversal

Tarkhov, Andrei E.; Denisov, Kirill A.; Федичев, П. О.

doi:10.1101/2022.02.06.479300

Cited by 17 publications

(27 citation statements)

References 74 publications

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“…1h ), thus resembling exponential-decay-like changes in the average methylation levels within the defined dynamics of DNAm changes. This is consistent with the aging-related increase in entropy metrics based on various molecular and functional readouts 12,31–34 .…”

Section: Resultssupporting

confidence: 85%

Nature of epigenetic aging from a single-cell perspective

Tarkhov

Lindstrom-Vautrin

Zhang

et al. 2022

Preprint

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Age-related changes in DNA methylation (DNAm) form the basis for the development of most robust predictors of age, epigenetic clocks, but a clear mechanistic basis for what exactly they quantify is lacking. Here, to clarify the nature of epigenetic aging, we analyzed the aging dynamics of bulk-tissue and single-cell DNAm, together with single-cell DNAm changes during early development. We show that aging DNAm changes are widespread, but are relatively slow and small in amplitude, with DNAm levels trending towards intermediate values and showing increased heterogeneity with age. By considering dominant types of DNAm changes, we find that aging manifests in the exponential decay-like loss or gain of methylation with a universal rate, independent of the initial level of DNAm. We further show that aging is dominated by the stochastic component, yet co-regulated changes are also present during both development and adulthood. We support the finding of stochastic epigenetic aging by direct single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated CpG clusters that may provide new opportunities for targeting aging and evaluating longevity interventions.

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Section: Resultssupporting

confidence: 85%

Nature of epigenetic aging from a single-cell perspective

Tarkhov

Lindstrom-Vautrin

Zhang

et al. 2022

Preprint

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“…Previous studies found an accelerated increase in DNA methylation Shannon entropy with adult age 21,33,34 , which is in line with the notion of accumulation of age-related damage. Using skin and embryo DNA methylation data, we examined the dynamics of methylation entropy in the frog.…”

Section: Profiling Dna Methylation During Aging and Embryonic Develop...supporting

confidence: 86%

Epigenetic profiling and incidence of disrupted development point to gastrulation as aging ground zero in Xenopus laevis

Zhang

Tarkhov

Ratzan

et al. 2022

Preprint

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Recent studies suggest the existence of a natural rejuvenation event during early embryonic development of mice, followed by epigenetic aging. Here, by profiling embryonic DNA methylation in the African clawed frog, Xenopus laevis, we found that the epigenetic entropy basepoint maps to the gastrulation stage of embryogenesis and corresponds to a rapid increase in embryo transcript abundance. We further developed a frog aging clock, revealing that this species epigenetically ages. Application of this clock to developmental stages identified a decrease in epigenetic age during early embryogenesis, with the minimal age reached around gastrulation. By examining individual developmental trajectories of 6,457 embryos, we found that this stage is also accompanied by a higher incidence of disrupted development. Taken together, our data point to gastrulation as a critical stage for aging and natural rejuvenation, characterized by the lowest epigenetic age, increased mortality, nadir of DNA methylation entropy and rapid increase in embryo transcript abundance, defining aging ground zero as the basepoint where rejuvenation ends and the aging process begins.

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“…The benefit may be particularly huge in studies involving large sets of longitudinal measurements but often lacking follow-up mortality and morbidity information. While hallmarks of aging in mice are correlated and primarily reversible, a large part of physiological changes associated with aging in humans is stochastic and may be thermodynamically irreversible 62 . Therefore, we expect that the systematic application of dynamic systems theory principles to biomedical data analysis will help identify actionable aging phenotypes and thus facilitate the discovery and development of anti-aging therapeutics that produce lasting rejuvenating effects.…”

Section: Discussionmentioning

confidence: 99%

Unsupervised learning of aging principles from longitudinal data

Avchaciov

Antoch

Andrianova³

et al. 2022

Nat Commun

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Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and lifespan is poorly understood. We combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the “dynamic frailty indicator” (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan. The observation of the limiting dFI was consistent with the late-life mortality deceleration. dFI changed along with hallmarks of aging, including frailty index, molecular markers of inflammation, senescent cell accumulation, and responded to life-shortening (high-fat diet) and life-extending (rapamycin) treatments.

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Aging clocks, entropy, and the limits of age-reversal

Cited by 17 publications

References 74 publications

Nature of epigenetic aging from a single-cell perspective

Nature of epigenetic aging from a single-cell perspective

Epigenetic profiling and incidence of disrupted development point to gastrulation as aging ground zero in Xenopus laevis

Unsupervised learning of aging principles from longitudinal data

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