| Literature DB >> 34666007 |
Evan G Williams1, Niklas Pfister2, Suheeta Roy3, Cyril Statzer4, Jack Haverty5, Jesse Ingels3, Casey Bohl3, Moaraj Hasan6, Jelena Čuklina6, Peter Bühlmann7, Nicola Zamboni6, Lu Lu3, Collin Y Ewald4, Robert W Williams3, Ruedi Aebersold8.
Abstract
We profiled the liver transcriptome, proteome, and metabolome in 347 individuals from 58 isogenic strains of the BXD mouse population across age (7 to 24 months) and diet (low or high fat) to link molecular variations to metabolic traits. Several hundred genes are affected by diet and/or age at the transcript and protein levels. Orthologs of two aging-associated genes, St7 and Ctsd, were knocked down in C. elegans, reducing longevity in wild-type and mutant long-lived strains. The multiomics data were analyzed as segregating gene networks according to each independent variable, providing causal insight into dietary and aging effects. Candidates were cross-examined in an independent diversity outbred mouse liver dataset segregating for similar diets, with ∼80%-90% of diet-related candidate genes found in common across datasets. Together, we have developed a large multiomics resource for multivariate analysis of complex traits and demonstrate a methodology for moving from observational associations to causal connections.Entities:
Keywords: GxE; aging; causal inference; gene-by-environment interaction; genetic reference population; liver; multiomics; multivariate analysis; network biology; proteomics; time course
Mesh:
Year: 2021 PMID: 34666007 PMCID: PMC8776606 DOI: 10.1016/j.cels.2021.09.005
Source DB: PubMed Journal: Cell Syst ISSN: 2405-4712 Impact factor: 10.304