Proteome- and Transcriptome-Driven Reconstruction of the Human Myocyte Metabolic Network and Its Use for Identification of Markers for Diabetes
Journal article, 2015

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.

Author

Leif Wigge

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

C. Scheele

University of Copenhagen

C. Broholm

University of Copenhagen

Adil Mardinoglu

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

C. Kampf

Uppsala University

A. Asplund

Uppsala University

Intawat Nookaew

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

M. Uhlen

AlbaNova University Center

Royal Institute of Technology (KTH)

B. K. Pedersen

University of Copenhagen

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Cell Reports

2211-1247 (ISSN)

Vol. 11 6 921-933

Subject Categories

Cell Biology

Bioinformatics and Systems Biology

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Areas of Advance

Life Science Engineering (2010-2018)

DOI

10.1016/j.celrep.2015.04.010

More information

Latest update

4/10/2019