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Data Set Group2: EPFL/LISP BXD CD+HFD Liver Affy Mouse Gene 1.0 ST (Apr13) modify this page

Data Set: EPFL/LISP BXD CD+HFD Liver Affy Mouse Gene 1.0 ST (Aug18) RMA modify this page
GN Accession: GN857
GEO Series: GSE60149
Title: Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population
Organism: Mouse (Mus musculus, mm10)
Group: BXD
Tissue: Liver mRNA
Dataset Status: Private
Platforms: Affy Mouse Gene 1.0 ST (GPL6246)
Normalization: RMA
Contact Information
Johan Auwerx
Ecole Polytechnique Federale de Lausanne
B√Ętiment AI, Chambre 1351
Lausanne, Lausanne 1015 Switzerland
Tel. +41 216930951
Download datasets and supplementary data files

Specifics of this Data Set:
RMA Normalization CD + HFD combined


The manner by which genotype and environment affect complex phenotypes is one of the fundamental questions in biology. In this study we quantified the transcriptome, a subset of the metabolome, and using targeted proteomics, a subset of the liver proteome from 40 strains of the BXD mouse genetic reference population on two diverse diets. We discovered dozens of transcript, protein and metabolite QTLs, several of which linked to metabolic phenotypes. Most prominently, Dhtkd1 was identified as a primary regulator of 2-aminoadipate, explaining variance in fasted glucose and diabetes status in both mice and humans. We further used the integrated molecular profiles to characterize complex pathways, as illustrated with the mitochondrial unfolded protein response (UPRmt). UPRmt shows strikingly variant responses at the transcript and protein level that are remarkably conserved between C.elegans, mice, and humans. Overall, these examples demonstrate the value of an integrated multilayered omics approach to characterize complex metabolic phenotypes.

About the cases used to generate this set of data:

40 strains of the BXD family (BXD43 – BXD103) and both parental strains (C57BL/6 and DBA/2) were born and raised at the EPFL in Switzerland prior to inclusion in this study. For each strain, 10 male animals were born and then separated evenly into two cohorts at 8 weeks of age: 5 animals per strain on a chow diet (6% kcal/fat, 20% protein, 74% carbohydrate) and 5 animals per strain on high fat diet (60% kcal/fat, 20% protein, 20% carbohydrate). Animals adjusted to the diet for 8 weeks, and then an intensive phenotyping metabolic phenotyping protocol was followed from 16 to 24 weeks of age (respiration, cold tolerance, oral glucose response, VO2max exercise, voluntary exercise, basal activity). Animals were communally housed until the last 5 weeks of the experiment, when the animals could rest. Animals were fasted overnight prior to sacrifice, which occurred between 9am and 11am after isoflurane anesthesia and perfusion. The gall bladder was removed, and then the livers were immediately frozen in liquid nitrogen.

About the tissue used to generate this set of data:

Livers were later shattered in liquid nitrogen and ~100 mg fragments were taken at random from the left, right, or caudate lobes. To account for this discrepancy, all ~5 animals per cohort had their RNA prepared, and then were pooled evenly (by µg of RNA) into a single RNA sample for each cohort. These pooled RNA samples of approximately 30 µg RNA were then purified using RNEasy, then sent out for array analysis. All RIN values were > 8.0. 

About the array platform:

All 81 arrays were Affymetrix Mouse Gene 1.0 ST, run together in a single batch in March/April 2013 at the University of Tennessee Health Science Center.

About data values and data processing:

In general, the array data that we put in GeneNetwork has be logged and then z normalized, but instead of leaving the mean at 0 and the standard deviation of 1 unit, we shift up to a mean of 8 units and increase the spread by having an standard deviation of 2 units (what we call 2Z + 8 normalized data). This removes negative values from the tables.


Experiment Type:


Wu Y, Williams EG, Dubuis S, Mottis A, Jovaisaite V, Houten SM, Argmann CA, Faridi P, Wolski W, Kutalik Z, Zamboni N, Auwerx J, Aebersold R


Wu Y, Williams EG, Dubuis S, Mottis A, Jovaisaite V, Houten SM, Argmann CA, Faridi P, Wolski W, Kutalik Z, Zamboni N, Auwerx J, and Aebersold R, Multilayered genetics and omics dissection of mitochondrial activity in a mouse reference population. Cell, 158(6), 2014.

Data source acknowledgment:

The authors thank A. van Cruchten and W. Smit for the serum analysis of 2-AA. The CoLaus study received financial contributions from GlaxoSmithKline, the Faculty of Biology and Medicine of Lausanne, and the Swiss National Science Foundation (SNSF; 33CSCO-122661). The authors thank P. Vollenweider, G. Waeber, V. Mooser and D. Waterworth, Co-PIs of the CoLaus study. Special thanks to M. Bochud, Y. Barreau, M. Firmann, V. Mayor, A. Bastian, B. Ramic, M. Moranville, M. Baumer, M. Sagette, J. Ecoffey and S. Mermoud for data collection. YW was supported by the ERC (Proteomics v3.0; AdG-233226 to RA), and the LiverX program and EGW by a fellowship from the Fondation Romande pour la Recherche sur le Diabète. JA is the Nestlé Chair in Energy Metabolism. Research was supported by the EPFL, ETHZ, ERC (Sirtuins; AdG-231138 and Proteomics v3.0; AdG-233226), Velux Stiftung, LiverX and AgingX programs of the Swiss Initiative for Systems Biology (51RTP0-151019 and 2013/153), SNSF (31003A-140780, 31003A-143914, and CSRII3-136201), and the NIH (R01AG043930).

Study Id:

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GeneNetwork support from:
  • The UT Center for Integrative and Translational Genomics
  • NIGMS Systems Genetics and Precision Medicine project (R01 GM123489, 2017-2021)
  • NIDA NIDA Core Center of Excellence in Transcriptomics, Systems Genetics, and the Addictome (P30 DA044223, 2017-2022)
  • NIA Translational Systems Genetics of Mitochondria, Metabolism, and Aging (R01AG043930, 2013-2018)
  • NIAAA Integrative Neuroscience Initiative on Alcoholism (U01 AA016662, U01 AA013499, U24 AA013513, U01 AA014425, 2006-2017)
  • NIDA, NIMH, and NIAAA (P20-DA 21131, 2001-2012)
  • NCI MMHCC (U01CA105417), NCRR, BIRN, (U24 RR021760)
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