Pan-mammalian analysis of molecular constraints underlying extended lifespan

  • Amanda Kowalczyk
    Joint Carnegie Mellon University-University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, United States
  • Raghavendran Partha
    Joint Carnegie Mellon University-University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, United States
  • Nathan L Clark
    Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States
  • Maria Chikina
    Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States

Description

<jats:p>Although lifespan in mammals varies over 100-fold, the precise evolutionary mechanisms underlying variation in longevity remain unknown. Species-specific genetic changes have been observed in long-lived species including the naked mole-rat, bats, and the bowhead whale, but these adaptations do not generalize to other mammals. We present a novel method to identify associations between rates of protein evolution and continuous phenotypes across the entire mammalian phylogeny. Unlike previous analyses that focused on individual species, we treat absolute and relative longevity as quantitative traits and demonstrate that these lifespan traits affect the evolutionary constraint on hundreds of genes. Specifically, we find that genes related to cell cycle, DNA repair, cell death, the IGF1 pathway, and immunity are under increased evolutionary constraint in large and long-lived mammals. For mammals exceptionally long-lived for their body size, we find increased constraint in inflammation, DNA repair, and NFKB-related pathways. Strikingly, these pathways have considerable overlap with those that have been previously reported to have potentially adaptive changes in single-species studies, and thus would be expected to show decreased constraint in our analysis. This unexpected finding of increased constraint in many longevity-associated pathways underscores the power of our quantitative approach to detect patterns that generalize across the mammalian phylogeny.</jats:p>

Journal

  • eLife

    eLife 9 e51089-, 2020-02-11

    eLife Sciences Publications, Ltd

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