Multiomic profiling of the liver across diets and age in a diverse mouse population

Research output: Contribution to journalJournal articleResearchpeer-review

  • Evan G. Williams
  • Pfister, Niklas Andreas
  • Suheeta Roy
  • Cyril Statzer
  • Jack Haverty
  • Jesse Ingels
  • Casey Bohl
  • Moaraj Hasan
  • Jelena Čuklina
  • Peter Bühlmann
  • Nicola Zamboni
  • Lu Lu
  • Collin Y. Ewald
  • Robert W. Williams
  • Ruedi Aebersold

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.

Original languageEnglish
JournalCell Systems
Issue number1
Pages (from-to)43-57, e1-6
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Inc.

    Research areas

  • aging, causal inference, gene-by-environment interaction, genetic reference population, GxE, liver, multiomics, multivariate analysis, network biology, proteomics, time course

ID: 304515902