Machine learning analysis of longevity-associated gene expression landscapes in mammals

Anton Y. Kulaga, Eugen Ursu, Dmitri Toren, Vladyslava Tyshchenko, Rodrigo Guinea, Malvina Pushkova, Vadim E. Fraifeld, Robi Tacutu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

One of the important questions in aging research is how differences in transcriptomics are associated with the longevity of various species. Unfortunately, at the level of individual genes, the links between expression in different organs and maximum lifespan (MLS) are yet to be fully understood. Analyses are complicated further by the fact that MLS is highly associated with other confounding factors (metabolic rate, gestation period, body mass, etc.) and that linear models may be limiting. Using gene expression from 41 mammalian species, across five organs, we constructed gene-centric regression models associating gene expression with MLS and other species traits. Additionally, we used SHapley Additive exPlanations and Bayesian networks to investigate the non-linear nature of the interrelations between the genes predicted to be determinants of species MLS. Our results revealed that expression patterns correlate with MLS, some across organs, and others in an organ-specific manner. The combination of methods employed revealed gene signatures formed by only a few genes that are highly predictive towards MLS, which could be used to identify novel longevity regulator candidates in mammals.

Original languageEnglish
Article number1073
Pages (from-to)1-21
Number of pages21
JournalInternational Journal of Molecular Sciences
Volume22
Issue number3
DOIs
StatePublished - 1 Feb 2021

Keywords

  • Cross-species analysis
  • Longevity
  • Mammals
  • Maximum lifespan
  • Transcriptomics

ASJC Scopus subject areas

  • Catalysis
  • Molecular Biology
  • Spectroscopy
  • Computer Science Applications
  • Physical and Theoretical Chemistry
  • Organic Chemistry
  • Inorganic Chemistry

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