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

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. An...

Full description

Saved in:
Bibliographic Details
Published in:International journal of molecular sciences 2021-01, Vol.22 (3), p.1073
Main Authors: Kulaga, Anton Y, Ursu, Eugen, Toren, Dmitri, Tyshchenko, Vladyslava, Guinea, Rodrigo, Pushkova, Malvina, Fraifeld, Vadim E, Tacutu, Robi
Format: Article
Language:English
Subjects:
Aging
Algorithms
Animals
Bayes Theorem
Bayesian analysis
Body mass
Brain - metabolism
Cell adhesion & migration
Computational Biology
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
Metabolic rate
Metabolism
Mitochondrial DNA
Models, Genetic
Organs
Regression Analysis
Repair & maintenance
RNA-Seq
Sample size
Tissue Distribution
Transcriptome
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms22031073