ATAC-clock: An aging clock based on chromatin accessibility

Abstract

The establishment of aging clocks highlighted the strong link between changes in DNA methylation and aging. Yet, it is not known if other epigenetic features could be used to predict age accurately. Furthermore, previous studies have observed a lack of effect of age-related changes in DNA methylation on gene expression, putting the interpretability of DNA methylation-based aging clocks into question. In this study, we explore the use of chromatin accessibility to construct aging clocks. We collected blood from 159 human donors and generated chromatin accessibility, transcriptomic, and cell composition data. We investigated how chromatin accessibility changes during aging and constructed a novel aging clock with a median absolute error of 5.27 years. The changes in chromatin accessibility used by the clock were strongly related to transcriptomic alterations, aiding clock interpretation. We additionally show that our chromatin accessibility clock performs significantly better than a transcriptomic clock trained on matched samples. In conclusion, we demonstrate that the clock relies on cell-intrinsic chromatin accessibility alterations rather than changes in cell composition. Further, we present a new approach to construct epigenetic aging clocks based on chromatin accessibility, which bear a direct link to age-related transcriptional alterations, but which allow for more accurate age predictions than transcriptomic clocks.

Changes in microglia chromatin accessibility in aged female mice

“…However, there was a higher number of distinct peaks in the younger group compared to the old, which implies that more euchromatic regions in the young became suppressed with age. This agrees with recent a finding suggesting that overall chromatin accessibility may not change with aging 90 . Instead, there are changes in chromatin accessibility at specific genomic regions, with both gains and losses observed.…”

Aging clock based on nucleosome reorganisation derived from cell‐free DNA

“…Both these predictors had similarly good performance (Pearson's correlation between predicted and chronological ages r ≈ 0.85, MSE ≈ 34), as detailed in Figure 2c,d and Supplementary Methods . The median absolute error for cfDNA‐based aging clock was 3.0 and 3.5 years for our models in Figure 2c,d correspondingly, which is comparable to methylation‐based clocks, and significantly outperforms aging clocks based on chromatin accessibility in blood cells (Morandini et al., 2023 ). Furthermore, the advantage of cfDNA‐based aging clock described here is that it does not require experiments in specific cells.…”

“…Figure 2c,d and Supplementary Methods. The median absolute error for cfDNA-based aging clock was 3.0 and 3.5 years for our models in Figure 2c,d correspondingly, which is comparable to methylationbased clocks, and significantly outperforms aging clocks based on chromatin accessibility in blood cells(Morandini et al, 2023).…”

“…Thus, studies in each of these four cohorts confirm that older people tend to have longer NRL. It is important to note that our previous finding of NRL decrease in cancer patients (Jacob et al, 2023;Piroeva et al, 2023) (Morandini et al, 2023).…”

pyaging: a Python-based compendium of GPU-optimized aging clocks

“…Addressing these challenges, I have developed , a Python-based package that acts as a comprehensive repository for various biomarkers of aging and aging clocks. offers: (i) a Python-centric approach, utilizing the versatile AnnData ( Virshup et al 2021 ) format of annotated data matrices in memory and on disk; (ii) an expanding repository, currently encompassing over 50 clocks with routine updates given new developments in the literature; (iii) clocks based on a diverse range of data types, encompassing DNA methylation ( Hannum et al 2013 , Horvath 2013 , Knight et al 2016 , Lin et al 2016 , Petkovich et al 2017 , Stubbs et al 2017 , Zhang et al 2017 , Horvath et al 2018 , Levine et al 2018 , Meer et al 2018 , Thompson et al 2018 , Lee et al 2019 , Lu et al 2019 , Zhang et al 2019 , Han et al 2020 , McEwen et al 2020 , Belsky et al 2022 , de Lima Camillo et al 2022 , Endicott et al 2022 , Higgins-Chen et al 2022 , Lu et al 2022 , Dec et al 2023 , Li et al 2023 , Lu et al 2023 , McGreevy et al 2023 , Ying et al 2024 ), transcriptomics ( Meyer and Schumacher 2021 ), histone mark ChIP-Seq ( de Lima Camillo et al 2023 ), and ATAC-Seq ( Morandini et al 2024 ); (iv) a variety of models, including linear, principal component (PC) linear models, neural networks, and automatic relevance determination (ARD) ( MacKay 2003 ) models; (v) a PyTorch-based ( Paszke et al 2019 ) backend that leverages GPU processing for enhanced inference speeds; (vi) a multi-species scope, currently covering H.sapiens , M.musculus , C.elegans , and various mammalian species.…”