K. Herrick

Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.

ABSTRACT Aging is often perceived as a degenerative process resulting from random accrual of cellular damage over time. Despite this, age can be accurately estimated by epigenetic clocks based on DNA methylation profiles from almost any tissue of the body. Since such pan-tissue epigenetic clocks have been successfully developed for several different species, we hypothesized that one can build pan-mammalian clocks that measure age in all mammalian species. To address this, we generated data using 11,754 methylation arrays, each profiling up to 36 thousand cytosines in highly-conserved stretches of DNA, from 59 tissue-types derived from 185 mammalian species. From these methylation profiles, we constructed three age predictors, each with a single mathematical formula, termed universal pan-mammalian clocks that are accurate in estimating the age (r>0.96) of any mammalian tissue. Deviations between epigenetic age and chronological age relate to mortality risk in humans, mutations that affect the somatotropic axis in mice, and caloric restriction. We characterized specific cytosines, whose methylation levels change with age across most mammalian species. These cytosines are greatly enriched in polycomb repressive complex 2-binding sites, are located in regions that gradually lose chromatin accessibility with age and are proximal to genes that play a role in mammalian development, cancer, human obesity, and human longevity. Collectively, these results support the notion that aging is indeed evolutionarily conserved and coupled to developmental processes across all mammalian species - a notion that was long-debated without the benefit of this new compelling evidence. SUMMARY This study identifies and characterizes evolutionarily conserved cytosines implicated in the aging process across mammals and establishes pan mammalian epigenetic clocks.

Abstract Maximum lifespan of a species is the oldest that individuals can survive, reflecting the genetic limit of longevity in an ideal environment. Here we report methylation-based models that accurately predict maximum lifespan (r=0.89), gestational time (r=0.96), and age at sexual maturity (r=0.87), using cytosine methylation patterns collected from over 12,000 samples derived from 192 mammalian species. Our epigenetic maximum lifespan predictor corroborated the extended lifespan in growth hormone receptor knockout mice and rapamycin treated mice. Across dog breeds, epigenetic maximum lifespan correlates positively with breed lifespan but negatively with breed size. Lifespan-related cytosines are located in transcriptional regulatory regions, such as bivalent chromatin promoters and polycomb-repressed regions, which were hypomethylated in long-lived species. The epigenetic estimators of maximum lifespan and other life history traits will be useful for characterizing understudied species and for identifying interventions that extend lifespan.

Abstract Background Genetic tests for variants in MYOT (P2; rs1138656462) , FLNC (P3a; rs1139799323 or P3b; rs1142918816) and MYOZ3 (P4; rs1142544043) genes are offered commercially to diagnose myofibrillar myopathy (MFM) and type 2 polysaccharide storage myopathy (PSSM2) in Quarter Horses (QH). Objectives To determine if PSSM2‐QH has histopathological features of MFM. To compare genotype and allele frequencies of variants P2, P3, P4 between control‐QH and PSSM2‐QH diagnosed by histopathology. Study design Retrospective cross‐sectional. Methods The study includes a total of 229 healthy control‐QH, 163 PSSM2‐QH GYS1 mutation negative. Desmin stains of gluteal/semimembranosus muscle were evaluated. Purported disease alleles P2, P3a, P3b, P4 were genotyped by pyrosequencing. Genotype, allele frequency and total number of variant alleles or loci were compared between phenotypes using additive/genotypic and dominant models and quantitative effects evaluated by multivariable logistic regression. Results Histopathological features of MFM were absent in all QH. A P variant allele at any locus was not associated ( P > .05) with a histopathological diagnosis of PSSM2 and one or more P variants were common in control‐QH (57%) and PSSM2‐QH (61%). Allele frequencies (control/PSSM2) were: 0.24/0.21 (P2), 0.07/0.12 (P3a), 0.07/0.11 (P3b) and 0.06/0.08 (P4). P3a and P3b loci were not independent ( r 2 = 0.894); and not associated with PSSM2 histopathology comparing the haplotype of both P3a and P3b variants to other haplotypes. A receiver operator curve did not accurately predict the PSSM2 phenotype (AUC = 0.67, 95% CI 0.62‐0.72), and there was no difference in the total number of variant loci or total variant allele count between control‐QH and PSSM2‐QH. Main limitations P3a and P3b were not in complete linkage disequilibrium. Conclusions The P2, P3 and P4 variants in genes associated with human MFM were not associated with PSSM2 in 392 QH. Their use would improperly diagnose PSSM2/MFM in 57% of healthy QH and fail to diagnose PSSM2 in 40% of QH with histopathological evidence of PSSM2.

Correction to: Nature Aging, published online 10 August 2023. In the version of Supplementary Data initially published with this article, data were missing from Table S1.13 (Updated AnAge version in Class Mammalia), which now appear in an updated Supplementary Data file in the online version of the article.