Literature DB >> 34666007

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

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.
Copyright © 2021 Elsevier Inc. All rights reserved.

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


  69 in total

1.  High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry.

Authors:  Tobias Fuhrer; Dominik Heer; Boris Begemann; Nicola Zamboni
Journal:  Anal Chem       Date:  2011-08-18       Impact factor: 6.986

2.  OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

Authors:  Hannes L Röst; George Rosenberger; Pedro Navarro; Ludovic Gillet; Saša M Miladinović; Olga T Schubert; Witold Wolski; Ben C Collins; Johan Malmström; Lars Malmström; Ruedi Aebersold
Journal:  Nat Biotechnol       Date:  2014-03       Impact factor: 54.908

3.  Quantifying and Localizing the Mitochondrial Proteome Across Five Tissues in A Mouse Population.

Authors:  Evan G Williams; Yibo Wu; Witold Wolski; Jun Yong Kim; Jiayi Lan; Moaraj Hasan; Christian Halter; Pooja Jha; Dongryeol Ryu; Johan Auwerx; Ruedi Aebersold
Journal:  Mol Cell Proteomics       Date:  2018-06-26       Impact factor: 5.911

4.  Prioritizing candidate disease genes by network-based boosting of genome-wide association data.

Authors:  Insuk Lee; U Martin Blom; Peggy I Wang; Jung Eun Shim; Edward M Marcotte
Journal:  Genome Res       Date:  2011-05-02       Impact factor: 9.043

5.  GeneNetwork: A Toolbox for Systems Genetics.

Authors:  Megan K Mulligan; Khyobeni Mozhui; Pjotr Prins; Robert W Williams
Journal:  Methods Mol Biol       Date:  2017

6.  Age and cancer risk: a potentially modifiable relationship.

Authors:  Mary C White; Dawn M Holman; Jennifer E Boehm; Lucy A Peipins; Melissa Grossman; S Jane Henley
Journal:  Am J Prev Med       Date:  2014-03       Impact factor: 5.043

7.  A repository of assays to quantify 10,000 human proteins by SWATH-MS.

Authors:  George Rosenberger; Ching Chiek Koh; Tiannan Guo; Hannes L Röst; Petri Kouvonen; Ben C Collins; Moritz Heusel; Yansheng Liu; Etienne Caron; Anton Vichalkovski; Marco Faini; Olga T Schubert; Pouya Faridi; H Alexander Ebhardt; Mariette Matondo; Henry Lam; Samuel L Bader; David S Campbell; Eric W Deutsch; Robert L Moritz; Stephen Tate; Ruedi Aebersold
Journal:  Sci Data       Date:  2014-09-16       Impact factor: 6.444

Review 8.  Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

Authors:  Jelena Čuklina; Chloe H Lee; Evan G Williams; Tatjana Sajic; Ben C Collins; María Rodríguez Martínez; Varun S Sharma; Fabian Wendt; Sandra Goetze; Gregory R Keele; Bernd Wollscheid; Ruedi Aebersold; Patrick G A Pedrioli
Journal:  Mol Syst Biol       Date:  2021-08       Impact factor: 11.429

9.  Mitonuclear protein imbalance as a conserved longevity mechanism.

Authors:  Riekelt H Houtkooper; Laurent Mouchiroud; Dongryeol Ryu; Norman Moullan; Elena Katsyuba; Graham Knott; Robert W Williams; Johan Auwerx
Journal:  Nature       Date:  2013-05-23       Impact factor: 49.962

10.  Lysosome activity is modulated by multiple longevity pathways and is important for lifespan extension in C. elegans.

Authors:  Yanan Sun; Meijiao Li; Dongfeng Zhao; Xin Li; Chonglin Yang; Xiaochen Wang
Journal:  Elife       Date:  2020-06-02       Impact factor: 8.140

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  4 in total

1.  Genetic loci and metabolic states associated with murine epigenetic aging.

Authors:  Khyobeni Mozhui; Ake T Lu; Caesar Z Li; Amin Haghani; Jose Vladimir Sandoval-Sierra; Yibo Wu; Robert W Williams; Steve Horvath
Journal:  Elife       Date:  2022-04-07       Impact factor: 8.713

2.  New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork.

Authors:  Alisha Chunduri; Pamela M Watson; David G Ashbrook
Journal:  Genes (Basel)       Date:  2022-03-29       Impact factor: 4.141

Review 3.  Longevity-Promoting Pathways and Transcription Factors Respond to and Control Extracellular Matrix Dynamics During Aging and Disease.

Authors:  Tinka Vidović; Collin Y Ewald
Journal:  Front Aging       Date:  2022-07-07

Review 4.  Perspectives for better batch effect correction in mass-spectrometry-based proteomics.

Authors:  Ser-Xian Phua; Kai-Peng Lim; Wilson Wen-Bin Goh
Journal:  Comput Struct Biotechnol J       Date:  2022-08-12       Impact factor: 6.155

  4 in total

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