Abstract:Aging is the leading risk factor for cancer. While it's been proposed that the age-related accumulation of somatic mutations drives this relationship, it is likely not the full story. Here, we show that both aging and cancer share a common epigenetic replication signature, which we modeled from DNA methylation data in extensively passaged immortalized human cells in vitro and tested on clinical tissues. This epigenetic signature of replication, termed CellDRIFT, increased with age across multiple tissues, dist… Show more
“…As anticipated, the predicted biological age of the iPSC cells was close to zero. Interestingly, the predicted biological age of the negative control cells shows a positive trend in time, consistent with the recent findings by Levine et al 41 . Figure 5 Comparing the biological age during the time-dependent differentiation process of human fibroblast cells from three donors.…”
Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.
“…As anticipated, the predicted biological age of the iPSC cells was close to zero. Interestingly, the predicted biological age of the negative control cells shows a positive trend in time, consistent with the recent findings by Levine et al 41 . Figure 5 Comparing the biological age during the time-dependent differentiation process of human fibroblast cells from three donors.…”
Ageing is often characterised by progressive accumulation of damage, and it is one of the most important risk factors for chronic disease development. Epigenetic mechanisms including DNA methylation could functionally contribute to organismal aging, however the key functions and biological processes may govern ageing are still not understood. Although age predictors called epigenetic clocks can accurately estimate the biological age of an individual based on cellular DNA methylation, their models have limited ability to explain the prediction algorithm behind and underlying key biological processes controlling ageing. Here we present XAI-AGE, a biologically informed, explainable deep neural network model for accurate biological age prediction across multiple tissue types. We show that XAI-AGE outperforms the first-generation age predictors and achieves similar results to deep learning-based models, while opening up the possibility to infer biologically meaningful insights of the activity of pathways and other abstract biological processes directly from the model.
“…Even though the role of various factors contributing to tissue DNAm changes has been extensively discussed [10][11][12][13][14] , epigenetic aging clocks are still lacking an exhaustive mechanistic explanation. The observed internal age-related changes across all cells could be confounded by multiple factors, such as changes in cell-type composition [15][16][17] and errors in DNAm maintenance during cell division and clonal expansion [18][19][20][21][22] . An important advance in this area is the development of a single-cell DNA methylation (scDNAm) clock known as scAge 23 , relying on tissue DNAm data for calibration.…”
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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