Histone mark age of human tissues and cells

Abstract

Background: Aging involves intricate epigenetic changes, with histone modifications playing a pivotal role in dynamically regulating gene expression. Our research comprehensively analyzes seven key histone modifications across various tissues to understand their behavior during human aging and formulate age prediction models. Results: These histone-centric prediction models exhibit remarkable accuracy and resilience against experimental and artificial noise. They showcase comparable efficacy when compared with DNA methylation age predictors through simulation experiments. Intriguingly, our gene set enrichment analysis pinpoints vital developmental pathways crucial for age prediction. Unlike in DNA methylation age predictors, genes previously recognized in animal studies as integral to aging are amongst the most important features of our models. We also introduce a pan-histone-mark, pan-tissue age predictor that operates across multiple tissues and histone marks, reinforcing that age-related epigenetic markers are not restricted to particular histone modifications. Conclusion: Our findings underscore the potential of histone marks in crafting robust age predictors and shed light on the intricate tapestry of epigenetic alterations in aging.

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.…”

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

“…Addressing these challenges, I have developed pyaging, a Python-based package that acts as a comprehensive repository for various aging clocks. pyaging offers: (1) a Python-centric approach, utilizing the versatile anndata data format; (2) an expanding repository, currently encompassing 30 clocks with an aim to include over 100; (3) clocks based on a diverse range of data types, encompassing DNA methylation, transcriptomics [7], histone mark ChIP-Seq [8], and ATAC-Seq [9]; (4) a variety of models, including linear, principal component linear models, neural networks, and automatic relevance determination models; (5) a PyTorch-based backend that leverages GPU processing for enhanced inference speeds; (6) a multi-species scope, currently covering human, various mammalian species, and C. elegans, with plans to integrate murine-specific clocks shortly.…”