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Age and early life adversity shape heterogeneity of the epigenome across tissues in macaques.

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Title: Age and early life adversity shape heterogeneity of the epigenome across tissues in macaques.
Authors: Sadoughi, Baptiste (AUTHOR), Petersen, Rachel M. (AUTHOR), Patterson, Sam K. (AUTHOR), Slikas, Elizabeth (AUTHOR), Adjangba, Christine (AUTHOR), Ryan, Nicholas (AUTHOR), Costa, Christina E. (AUTHOR), Newman, Laura E. (AUTHOR), Watowich, Marina M. (AUTHOR), Kelsey, Cameron R. (AUTHOR), Greenier, Ashlee (AUTHOR), Goldman, Elisabeth A. (AUTHOR), Negrón-Del Valle, Josué E. (AUTHOR), Phillips, Daniel (AUTHOR), Thompson, Indya (AUTHOR), Bauman Surratt, Samuel E. (AUTHOR), González, Olga (AUTHOR), Compo, Nicole (AUTHOR), Burgos, Armando (AUTHOR), DeCasien, Alex R. (AUTHOR)
Source: Science. 6/18/2026, Vol. 392 Issue 6804, p1-15. 15p.
Subjects: DNA methylation, Epigenomics, Age, Rhesus monkeys, Physiological stress, Genetic regulation, Cellular aging
Abstract: Age and early life adversity (ELA) are key determinants of health, but whether they affect similar physiological mechanisms across tissues is unknown. We generated DNA methylation (DNAm) profiles across 14 tissues in 237 semi–free-ranging rhesus macaques with naturally occurring ELA. Age-associated DNAm was predominantly tissue dependent, yet tissue-specific epigenetic clocks showed that epigenetic aging was relatively consistent within individuals. ELA effects were adversity dependent, but each ELA exerted coordinated effects across tissues. Although ELA targeted many of the same loci as age, the directions of effects differed, which indicates that ELA does not uniformly increase epigenetic age. Instead, ELA leaves a coordinated, cross-tissue epigenetic signature that is distinct from—yet intertwined with—age-related differences, which advances our understanding of how early environments sculpt the molecular foundations of aging and disease. Editor's summary: Both aging and stressors are known to affect epigenetic marks such as DNA methylation. However, given natural variation and differences between tissues, these effects can be difficult to disentangle. Sadoughi et al. examined DNA methylation across tissues in a group of rhesus macaques from Cayo Santiago, a semi–free-ranging island population occasionally culled to prevent overpopulation. They found that early life adversity often affected the same genetic sites across tissues despite individual sources of adversity affecting different sites, whereas aging effects were more heterogenous. Even though they affected overlapping genomic targets, adversity-related changes were not consistent with "accelerated aging," suggesting that aging and adversity are likely to have distinct and intersecting effects even on the same biological processes. —Corinne Simonti INTRODUCTION: Aging is universal, yet the pace of its decline varies markedly among, and even within, individuals. Understanding the molecular basis of this aging heterogeneity—and how it is shaped by socioenvironmental conditions—is a critical challenge in geroscience, essential for identifying early vulnerabilities and factors contributing to disparities in health span and lifespan. RATIONALE: Early life adversity (ELA) is linked to age-related diseases and reduced lifespan in humans and other social mammals, but how early exposures shape aging across individuals and tissues remains unclear. We addressed this critical gap by measuring DNA methylation (DNAm)—a molecular marker that captures age-related variations and reflects environmental exposures—across multiple tissues and individuals. By pairing these molecular data with rich demographic and life history information, we examined how age and ELA predict tissue-specific methylation patterns and biological age. RESULTS: We generated a DNAm atlas across 14 tissues from 237 free-ranging rhesus macaques (n = 2485 total samples). First, we identified tissue-specific differentially methylated regions that reflect tissue-specific function and gene regulation. We found substantial age effects on DNAm across tissues. These age-associated differences generally converged toward intermediate methylation levels but exhibited marked intertissue heterogeneity in direction and magnitude. Most age effects were shared among only a few tissues, which indicates that commonly measured peripheral tissues (e.g., blood) reflect only a subset of organism-wide age-related variation. We trained tissue-specific DNAm "clocks" that accurately predicted chronological age with increased DNAm age observed in individuals with larger body mass. Tissue-specific DNAm ages were more similar within an individual than between individuals, which implies a broadly coordinated age-related biological state across tissues. Nevertheless, within-individual DNAm age heterogeneity emerges early in life: Tissues were more dissimilar in mature compared with young individuals, which suggests that early life experiences may have lasting effects on tissue-specific aging. We identified thousands of loci differentially methylated with ELA, with the strongest ELA signal for maternal loss and in adipose tissue. Although different forms of ELA targeted largely distinct sets of CpGs (suggesting different molecular pathways), the response to any single ELA was generally similar across tissues, implying a partially coordinated organism-wide effect. ELA-associated DNAm variation was strongest in immune tissues, endocrine tissues, and tissues with long-lived cell types. Furthermore, CpG sites exhibiting tissue-dependent ELA effects were enriched near transcription start sites, which suggests that this variation in methylation likely alters tissue-specific gene regulation. Although age and ELA targeted many of the same loci—including a strong enrichment for loci linked to human aging and mortality—ELA did not consistently accelerate epigenetic age across tissues. CONCLUSION: By generating multitissue DNAm data across the life course in animals with known social histories, we reveal a fundamental contrast in epigenomic remodeling: Age-associated epigenetic variations are highly tissue dependent, whereas the molecular effect of ELA represents a more coordinated, organism-wide response. Together, these findings advance our understanding of how early environments sculpt the molecular foundations of aging and establish this comprehensive tissue atlas as a valuable resource for the scientific community. Epigenetic signatures of age and early adversity.: Age and ELA are key determinants of health span and lifespan. Their molecular signatures across tissues are compared using a multitissue DNAm atlas in semi–free-ranging rhesus macaques. Age and early adversity show overlapping but distinct epigenetic patterns: Age effects are highly tissue specific, whereas early adversity induces coordinated, cross-tissue signatures within individuals. DNAm clocks reveal moderately consistent biological ages across tissues, yet early adversity does not consistently accelerate epigenetic age. [Figure created with BioRender.com; N. Snyder-Mackler (2026), https://BioRender.com/1zie3im. DNA modified from NIAID NIH BIOART Source (NIAID Visual & Medical Arts, 10/7/2024, DNA NIAID NIH BIOART Source, bioart.niaid.nih.gov/bioart/124)] [ABSTRACT FROM AUTHOR]
Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Age and early life adversity shape heterogeneity of the epigenome across tissues in macaques.
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  Data: <searchLink fieldCode="AR" term="%22Sadoughi%2C+Baptiste%22">Sadoughi, Baptiste</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Petersen%2C+Rachel+M%2E%22">Petersen, Rachel M.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Patterson%2C+Sam+K%2E%22">Patterson, Sam K.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Slikas%2C+Elizabeth%22">Slikas, Elizabeth</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Adjangba%2C+Christine%22">Adjangba, Christine</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ryan%2C+Nicholas%22">Ryan, Nicholas</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Costa%2C+Christina+E%2E%22">Costa, Christina E.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Newman%2C+Laura+E%2E%22">Newman, Laura E.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Watowich%2C+Marina+M%2E%22">Watowich, Marina M.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kelsey%2C+Cameron+R%2E%22">Kelsey, Cameron R.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Greenier%2C+Ashlee%22">Greenier, Ashlee</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Goldman%2C+Elisabeth+A%2E%22">Goldman, Elisabeth A.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Negrón-Del+Valle%2C+Josué+E%2E%22">Negrón-Del Valle, Josué E.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Phillips%2C+Daniel%22">Phillips, Daniel</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Thompson%2C+Indya%22">Thompson, Indya</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bauman+Surratt%2C+Samuel+E%2E%22">Bauman Surratt, Samuel E.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22González%2C+Olga%22">González, Olga</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Compo%2C+Nicole%22">Compo, Nicole</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Burgos%2C+Armando%22">Burgos, Armando</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22DeCasien%2C+Alex+R%2E%22">DeCasien, Alex R.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Science%22">Science</searchLink>. 6/18/2026, Vol. 392 Issue 6804, p1-15. 15p.
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  Data: <searchLink fieldCode="DE" term="%22DNA+methylation%22">DNA methylation</searchLink><br /><searchLink fieldCode="DE" term="%22Epigenomics%22">Epigenomics</searchLink><br /><searchLink fieldCode="DE" term="%22Age%22">Age</searchLink><br /><searchLink fieldCode="DE" term="%22Rhesus+monkeys%22">Rhesus monkeys</searchLink><br /><searchLink fieldCode="DE" term="%22Physiological+stress%22">Physiological stress</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+regulation%22">Genetic regulation</searchLink><br /><searchLink fieldCode="DE" term="%22Cellular+aging%22">Cellular aging</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Age and early life adversity (ELA) are key determinants of health, but whether they affect similar physiological mechanisms across tissues is unknown. We generated DNA methylation (DNAm) profiles across 14 tissues in 237 semi–free-ranging rhesus macaques with naturally occurring ELA. Age-associated DNAm was predominantly tissue dependent, yet tissue-specific epigenetic clocks showed that epigenetic aging was relatively consistent within individuals. ELA effects were adversity dependent, but each ELA exerted coordinated effects across tissues. Although ELA targeted many of the same loci as age, the directions of effects differed, which indicates that ELA does not uniformly increase epigenetic age. Instead, ELA leaves a coordinated, cross-tissue epigenetic signature that is distinct from—yet intertwined with—age-related differences, which advances our understanding of how early environments sculpt the molecular foundations of aging and disease. Editor's summary: Both aging and stressors are known to affect epigenetic marks such as DNA methylation. However, given natural variation and differences between tissues, these effects can be difficult to disentangle. Sadoughi et al. examined DNA methylation across tissues in a group of rhesus macaques from Cayo Santiago, a semi–free-ranging island population occasionally culled to prevent overpopulation. They found that early life adversity often affected the same genetic sites across tissues despite individual sources of adversity affecting different sites, whereas aging effects were more heterogenous. Even though they affected overlapping genomic targets, adversity-related changes were not consistent with "accelerated aging," suggesting that aging and adversity are likely to have distinct and intersecting effects even on the same biological processes. —Corinne Simonti INTRODUCTION: Aging is universal, yet the pace of its decline varies markedly among, and even within, individuals. Understanding the molecular basis of this aging heterogeneity—and how it is shaped by socioenvironmental conditions—is a critical challenge in geroscience, essential for identifying early vulnerabilities and factors contributing to disparities in health span and lifespan. RATIONALE: Early life adversity (ELA) is linked to age-related diseases and reduced lifespan in humans and other social mammals, but how early exposures shape aging across individuals and tissues remains unclear. We addressed this critical gap by measuring DNA methylation (DNAm)—a molecular marker that captures age-related variations and reflects environmental exposures—across multiple tissues and individuals. By pairing these molecular data with rich demographic and life history information, we examined how age and ELA predict tissue-specific methylation patterns and biological age. RESULTS: We generated a DNAm atlas across 14 tissues from 237 free-ranging rhesus macaques (n = 2485 total samples). First, we identified tissue-specific differentially methylated regions that reflect tissue-specific function and gene regulation. We found substantial age effects on DNAm across tissues. These age-associated differences generally converged toward intermediate methylation levels but exhibited marked intertissue heterogeneity in direction and magnitude. Most age effects were shared among only a few tissues, which indicates that commonly measured peripheral tissues (e.g., blood) reflect only a subset of organism-wide age-related variation. We trained tissue-specific DNAm "clocks" that accurately predicted chronological age with increased DNAm age observed in individuals with larger body mass. Tissue-specific DNAm ages were more similar within an individual than between individuals, which implies a broadly coordinated age-related biological state across tissues. Nevertheless, within-individual DNAm age heterogeneity emerges early in life: Tissues were more dissimilar in mature compared with young individuals, which suggests that early life experiences may have lasting effects on tissue-specific aging. We identified thousands of loci differentially methylated with ELA, with the strongest ELA signal for maternal loss and in adipose tissue. Although different forms of ELA targeted largely distinct sets of CpGs (suggesting different molecular pathways), the response to any single ELA was generally similar across tissues, implying a partially coordinated organism-wide effect. ELA-associated DNAm variation was strongest in immune tissues, endocrine tissues, and tissues with long-lived cell types. Furthermore, CpG sites exhibiting tissue-dependent ELA effects were enriched near transcription start sites, which suggests that this variation in methylation likely alters tissue-specific gene regulation. Although age and ELA targeted many of the same loci—including a strong enrichment for loci linked to human aging and mortality—ELA did not consistently accelerate epigenetic age across tissues. CONCLUSION: By generating multitissue DNAm data across the life course in animals with known social histories, we reveal a fundamental contrast in epigenomic remodeling: Age-associated epigenetic variations are highly tissue dependent, whereas the molecular effect of ELA represents a more coordinated, organism-wide response. Together, these findings advance our understanding of how early environments sculpt the molecular foundations of aging and establish this comprehensive tissue atlas as a valuable resource for the scientific community. Epigenetic signatures of age and early adversity.: Age and ELA are key determinants of health span and lifespan. Their molecular signatures across tissues are compared using a multitissue DNAm atlas in semi–free-ranging rhesus macaques. Age and early adversity show overlapping but distinct epigenetic patterns: Age effects are highly tissue specific, whereas early adversity induces coordinated, cross-tissue signatures within individuals. DNAm clocks reveal moderately consistent biological ages across tissues, yet early adversity does not consistently accelerate epigenetic age. [Figure created with BioRender.com; N. Snyder-Mackler (2026), https://BioRender.com/1zie3im. DNA modified from NIAID NIH BIOART Source (NIAID Visual & Medical Arts, 10/7/2024, DNA NIAID NIH BIOART Source, bioart.niaid.nih.gov/bioart/124)] [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Science is the property of American Association for the Advancement of Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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