Proteome- and Transcriptome-Driven Reconstruction of the Human Myocyte Metabolic Network and Its Use for Identification of Markers for Diabetes
Artikel i vetenskaplig tidskrift, 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.

Författare

Leif Wigge

Chalmers, Biologi och bioteknik, Systembiologi

C. Scheele

Kobenhavns Universitet

C. Broholm

Kobenhavns Universitet

Adil Mardinoglu

Chalmers, Biologi och bioteknik, Systembiologi

C. Kampf

Uppsala universitet

A. Asplund

Uppsala universitet

Intawat Nookaew

Chalmers, Biologi och bioteknik, Systembiologi

M. Uhlen

AlbaNova University Center

Kungliga Tekniska Högskolan (KTH)

B. K. Pedersen

Kobenhavns Universitet

Jens B Nielsen

Chalmers, Biologi och bioteknik, Systembiologi

Cell Reports

2211-1247 (ISSN)

Vol. 11 921-933

Ämneskategorier

Cellbiologi

Bioinformatik och systembiologi

Infrastruktur

C3SE (Chalmers Centre for Computational Science and Engineering)

Styrkeområden

Livsvetenskaper och teknik

DOI

10.1016/j.celrep.2015.04.010