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Cell Rep. 2015 May 12;11(6):921-933. doi: 10.1016/j.celrep.2015.04.010. Epub 2015 Apr 30.

Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes.

Author information

1
Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden.
2
Centre of Inflammation and Metabolism and Centre for Physical Activity Research, Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, 2100 Copenhagen Ø, Denmark; Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, 2200 Copenhagen N, Denmark.
3
Centre of Inflammation and Metabolism and Centre for Physical Activity Research, Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
4
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden.
5
Department of Proteomics, School of Biotechnology, AlbaNova University Center, Royal Institute of Technology (KTH), 10691 Stockholm, Sweden; Science for Life Laboratory, Royal Institute of Technology (KTH), 17121 Stockholm, Sweden.
6
Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden; Science for Life Laboratory, Royal Institute of Technology (KTH), 17121 Stockholm, Sweden. Electronic address: nielsenj@chalmers.se.

Erratum in

  • Cell Rep. 2016 Feb 16;14(6):1567.

Abstract

Skeletal myocytes are metabolically active and susceptible to insulin resistance and are thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes, and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network context for the integration of high-throughput data. We generated myocyte-specific RNA-sequencing data and investigated their correlation with proteome data. These data were then used to reconstruct a comprehensive myocyte GEM. Next, we performed a meta-analysis of six studies comparing muscle transcription in T2D versus healthy subjects. Transcriptional changes were mapped on the myocyte GEM, revealing extensive transcriptional regulation in T2D, particularly around pyruvate oxidation, branched-chain amino acid catabolism, and tetrahydrofolate metabolism, connected through the downregulated dihydrolipoamide dehydrogenase. Strikingly, the gene signature underlying this metabolic regulation successfully classifies the disease state of individual samples, suggesting that regulation of these pathways is a ubiquitous feature of myocytes in response to T2D.

PMID:
25937284
DOI:
10.1016/j.celrep.2015.04.010
[Indexed for MEDLINE]
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