bioRxivEvolutionary BiologyPreprint
Epigenetic predictors of species maximum lifespan and other life history traits in mammals
C. Z. Li, A. Haghani, Q. Yan, A. T. Lu, J. Zhang, Z. Fei, J. Ernst, X. Yang, V. N. Gladyshev, K. Raj, A. Seluanov, V. Gorbunova, S. Horvath
Abstract
Maximum lifespan is an intrinsic characteristic of a biological species and is defined as the longest time an individual of a species has been reported to survive. By analyzing 15K samples derived from 348 mammalian species representing 25 taxonomic orders we previously identified CpG methylation sites associated with maximum lifespan. Here we present accurate DNA methylation-based (DNAm) predictors of maximum lifespan (r=0.89), average gestation time (r=0.96), and age at sexual maturity (r=0.85). Our DNAm maximum lifespan predictor indicates a potential innate longevity advantage for females over males in 17 mammalian species such as humans, red deer, and cattle. The DNAm maximum lifespan predictions do not vary significantly by caloric restriction and partial reprogramming. Genetic disruptions in the somatotropic axis, which includes growth hormone, IGF-1, and their related receptors, have an impact on DNAm maximum lifespan only in select mouse tissues. Cancer mortality rates in major mammalian orders show no correlation with our epigenetic estimates of life history traits. The DNAm maximum lifespan predictor does not detect variation in lifespan between individuals of the same species, such as between the breeds of dogs. We also present the first prototypes of accurate pan mammalian DNAm predictors of sex and tissue type. Collectively, our findings indicate that maximum lifespan is determined, at least in part, by an epigenetic signature that is an intrinsic property of each species and is distinct from the signatures that relate to individual lifespan, which is unaffected by interventions influencing the mortality risk of individuals.
- organism: mouse
- organism: human
- organism: multiple species
- assay: DNA methylation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: statistical model
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