Machine Learning Analysis of Longevity-Associated Gene Expression Landscapes in Mammals

• Published in: International journal of molecular sciences Vol. 22; no. 3; p. 1073
• Main Authors: Kulaga, Anton Y, Ursu, Eugen, Toren, Dmitri, Tyshchenko, Vladyslava, Guinea, Rodrigo, Pushkova, Malvina, Fraifeld, Vadim E, Tacutu, Robi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.01.2021; MDPI
• Subjects: Aging; Algorithms; Animals; Bayes Theorem; Bayesian analysis; Body mass; Brain - metabolism; Cell adhesion & migration; Computational Biology; cross-species analysis; Datasets; Deoxyribonucleic acid; DNA; DNA repair; Gene Expression; Gene Expression Profiling; Genes; Gestation; Heart; Humans; Investigations; Learning algorithms; Life span; Linear Models; Liver; Liver - metabolism; Longevity; Longevity - genetics; Machine Learning; Mammals; Mammals - genetics; Mathematical models; maximum lifespan; Metabolic rate; Metabolism; Mitochondrial DNA; Models, Genetic; Organs; Regression Analysis; Repair & maintenance; RNA-Seq; Sample size; Tissue Distribution; Transcriptome; transcriptomics; transcriptomics; longevity; maximum lifespan; mammals; cross-species analysis
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms22031073

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.