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.
Subject(s)
Gene Expression Profiling , Longevity/genetics , Machine Learning , Mammals/genetics , Aging , Algorithms , Animals , Bayes Theorem , Brain/metabolism , Computational Biology , Gene Expression , Humans , Linear Models , Liver/metabolism , Models, Genetic , RNA-Seq , Regression Analysis , Tissue Distribution , TranscriptomeABSTRACT
Aging results in various deleterious changes in the human body that may lead to loss of function and the manifestation of chronic diseases. While diseases can generally be reliably diagnosed, the aging process itself requires more sophisticated approaches to evaluate its progression. Numerous attempts have been made to establish biomarkers to quantify human aging at the cellular, tissue, and organismal level. Here, an up-to-date overview of biomarkers related to human aging with an emphasis on biomarkers that take into account different mechanisms of aging between individuals is provided. Classical discrete molecular and non-molecular biomarkers handpicked by researches on the base of their strong correlation with age, as well as emerging omics-based biomarkers, are discussed and potential future directions and developments in the field of aging assessment are outlined.
