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Systems proteomics of liver mitochondria function
Open accessAbstract
Expanded proteomic analysis of metabolism: Combined analysis of large data sets characterizing genes, transcripts, and proteins can elucidate biological functions and disease processes. Williams et al. report an exceptionally detailed characterization of mitochondrial function in a genetic reference panel of recombinant inbred mice. They measured the metabolic function of nearly 400 mice under various environmental conditions and collected detailed quantitative information from livers of the animals on over 25,000 transcripts. These data were integrated with quantitation of over 2500 proteins and nearly 1000 metabolites. Such analysis showed a frequent lack of correlation of transcript and protein abundance, enabled the identification of genomic variants of mitochondrial enzymes that caused inborn errors in metabolism, and revealed two genes that appear to function in cholesterol metabolism. Science , this issue p. 10.1126/science.aad0189
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“A number of studies have now shown that while levels of many proteins are strongly correlated with their transcript levels, with coincident eQTL and protein QTL (pQTL), the correlations for most protein–transcript pairs are modest [51–58].”
From Multi-omics approaches to disease (2017) · cites this paper as [57]
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“For example, analyses of clinical material from large patient cohorts42 (e.g., biomarkers, personalized medicine), association of protein abundances to genomic features using genetic reference collections or wild-type populations46 (e.g., quantitative trail locus or genome wide association studies), or large-scale perturbation screens using in vitro model systems (e.g., drug screens) are now”
From Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry (2017) · cites this paper as [46]
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“As comparative quantitative analysis of a large number of proteomes becomes accessible42, 46, 69, we can expect to see research applications where the analysis of large numbers of samples is a prerequisite.”
From Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry (2017) · cites this paper as [46]
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“Such a multilayered cross-omics approach can help elucidate complex traits associated with metabolic flexibility (302).”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [302]
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“Moreover, OXPHOS gene expression and supercomplex assembly, and PPAR and cholesterol biosynthesis pathways, which are known to influence metabolic diseases, are highly variable in the BXD population (302).”
From Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease (2018) · cites this paper as [302]
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Lamanna, A., Zhou, Z., Boussoufa, B., Pansart, E., Garcia, P., Heurtaux, T., Buttini, M., Coowar, D., Marcon, G., & Williams, E. G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.28.752290
Self-citation
Metadata
- Authors
- Williams, E. G., Wu, Y., Jha, P., Dubuis, S., Blattmann, P., Argmann, C. A., Houten, S. M., Amariuta, T., Wolski, W., Zamboni, N., Aebersold, R., & Auwerx, J.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- ETH Zurich (CH)
- Icahn School of Medicine at Mount Sinai (US)
- University of Zurich (CH)
- Corresponding author
- Aebersold, Ruedi
- Auwerx, Johan
- ORCID
- Venue
- Science
- Published
- 2016-06-10
- Volume
- 352
- Issue
- 6291
- Pages
- aad0189-aad0189
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC10859670
- OpenAlex
- W2415103133
- Cited by
- 289
- References
- 58
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 16 · 2023: 26 · 2022: 26 · 2021: 36 · 2020: 28 · 2019: 25 · 2018: 40 · 2017: 46 · 2016: 24
- FWCI
- 22.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- EPFL
- ETHZ
- ERC (AdG-670821, AdG-233226)
- Swiss Initiative for Systems Biology (51RTP0-151019, 2013/134, 2013/153)
- SNSF (31003A-140780, 31003A-143914, CSRII3-136201)
- NIH (R01AG043930)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Mitochondrial Function and Pathology
- Keywords
- liver mitochondria, mitochondrial function, hepatic metabolism, genetic variants, protein quantification, SWATH-MS, complex trait genetics, metabolome
- MeSH terms
- Animals, Cholesterol, Electron Transport Complex IV, Diet, Humans, Liver, Mice, Inbred Strains, Mitochondria, Liver, Molecular Sequence Data, Genetic Variation, Proteome, Quantitative Trait Loci, Proteomics, Mice, Metabolic Networks and Pathways, Metabolomics, Metabolome, Hep G2 Cells, Transcriptome
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