Accurate aging clocks based on accumulating stochastic variation

Schumacher, Björn; Meyer, David H.

doi:10.21203/rs.3.rs-2351315/v1

Cited by 7 publications

(5 citation statements)

References 50 publications

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“…Although recent single‐cell RNAseq questions the existence of such an increase in transcriptional noise with age (Gems & de Magalhães, 2021) others showed that it is sufficient to predict both chronological and biological age. Overall, our data corroborate that gene expression noise occurs especially with chronological age and questions its role in the Smurf transition (Schumacher & Meyer, 2023). Old non‐Smurfs also show some of ATH1 suggesting that inflammation could precede the Smurf transition at least in old individuals although we cannot exclude that at an advanced age, the likelihood of sampling pre‐Smurf or early Smurfs is high and this signal could be due to such individuals contaminating the non‐Smurf samples.…”

Section: Discussionsupporting

confidence: 85%

Smurfness‐based two‐phase model of ageing helps deconvolve the ageing transcriptional signature

Zane,

Bouzid,

Sosa Marmol

et al. 2023

Aging Cell

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Ageing is characterised at the molecular level by six transcriptional ‘hallmarks of ageing’, that are commonly described as progressively affected as time passes. By contrast, the ‘Smurf’ assay separates high‐and‐constant‐mortality risk individuals from healthy, zero‐mortality risk individuals, based on increased intestinal permeability. Performing whole body total RNA sequencing, we found that Smurfness distinguishes transcriptional changes associated with chronological age from those associated with biological age. We show that transcriptional heterogeneity increases with chronological age in non‐Smurf individuals preceding the other five hallmarks of ageing that are specifically associated with the Smurf state. Using this approach, we also devise targeted pro‐longevity genetic interventions delaying entry in the Smurf state. We anticipate that increased attention to the evolutionary conserved Smurf phenotype will bring about significant advances in our understanding of the mechanisms of ageing.

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Section: Discussionsupporting

confidence: 85%

Smurfness‐based two‐phase model of ageing helps deconvolve the ageing transcriptional signature

Zane,

Bouzid,

Sosa Marmol

et al. 2023

Aging Cell

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show abstract

“…Interestingly, interventions decreasing it were already shown to extend lifespan in nematodes 73 . 75 76 . Old non-Smurfs also show some of ATH1 suggesting that inflammation could precede the Smurf transition at least in old individuals although we cannot exclude that at an advanced age, the likelihood of sampling pre-Smurf or early Smurfs is high and this signal could be due to such individuals contaminating the non-Smurf samples.…”

Section: Discussionmentioning

confidence: 99%

Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature

Zane

Bouzid

Besse

et al. 2022

Preprint

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Ageing is a common feature of living organisms, showing shared molecular features called hallmarks of ageing. Usually they are quantified in groups of individuals as a function of their chronological age (time passed since birth) and display continuous and progressive changes. Such approaches are based on the assumption that individuals taken at a given chronological age are biological replicates. However, even in genetically homogeneous and synchronised populations individuals do die at different chronological ages. This highlights the difference between chronological age and biological age, the latter being defined by the actual mortality risk of the organism, reflecting its physiology. The Smurf assay, previously described by Rera and colleagues, allows the identification of individuals at higher risk of death from natural causes amongst a population of a given chronological age. We found that the categorization of individuals as Smurf or non-Smurf, permits to distinguish transcriptional changes associated with either chronological or biological age. We show that transcriptional heterogeneity increases with chronological age, while four out of the six currently defined transcriptional hallmarks of ageing are associated with the biological age of individuals, i.e. their Smurf state. In conclusion, we demonstrate that studying properties of ageing by applying the Smurf classification allows us to differentiate the effect of time from the effect of a physiological response triggering an end-of-life switch (i.e. Smurf phase). More specifically, we show that the ability to isolate a pre-death phase of life in vivo enables us not only to study late life mechanisms preceding death, but also investigate early physiological changes triggering such phase. This allowed the identification of novel pro-longevity genetic interventions. We anticipate that the use of the evolutionary conserved Smurf phenotype in ageing studies will allow significant advances in our comprehension of the underlying mechanisms of ageing.

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“…To model the evolution of methylation dynamics with time, in a single CpG site, we considered a minimal model of transitions between two states. The proposed model directly relates to the early work of Markov in 1905 and was used for the first time to model the dynamics of methylation in 15 , and more recently in the context of ageing in 16 . In this model cells can be in either an unmethylated or methylated state, U and M respectively, and can transition from one state to the other at rates ν U and ν M (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…To overcome these technical limitations and the lack of biological interpretation, we developed a new model of epigenetic age acceleration based on a mathematical representation of the cellular dynamics of methylation change 15,16 . This model allows mechanistic interpretation of methylation change at CpG sites.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic inference of epigenetic age acceleration from cellular dynamics

Dabrowski

Yang

Crofts

et al. 2023

Preprint

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The emergence of epigenetic predictors was a pivotal moment in geroscience, propelling the measurement and concept of biological ageing into a quantitative era. However, while current epigenetic clocks have shown strong predictive power, they do not reflect the underlying biological mechanisms driving methylation changes with age. Consequently, biological interpretation of their estimates is limited. Furthermore, our findings suggest that clocks trained on chronological age are confounded by non-age-related phenomena.To address these limitations, we developed a probabilistic model that describes methylation transitions at the cellular level. Our approach reveals two measurable components, acceleration and bias, that directly relate to perturbations of the underlying cellular dynamics. Acceleration is the proportional increase in the speed of methylation transitions across CpG sites, whereas bias is the degree of global change in methylation affecting all CpG sites uniformly. Using data from 7,028 participants from the Generation Scotland study, we found the age acceleration parameter to be associated with physiological traits known to impact healthy ageing. Furthermore, a genome-wide association study of age acceleration identified four genomic loci previously linked with ageing.

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Accurate aging clocks based on accumulating stochastic variation

Cited by 7 publications

References 50 publications

Smurfness‐based two‐phase model of ageing helps deconvolve the ageing transcriptional signature

Smurfness‐based two‐phase model of ageing helps deconvolve the ageing transcriptional signature

Smurfness-based two-phase model of ageing helps deconvolve the ageing transcriptional signature

Probabilistic inference of epigenetic age acceleration from cellular dynamics

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