Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes.
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.
Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden.
Leif
L
Väremo
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.
Camilla
C
Scheele
Centre of Inflammation and Metabolism and Centre for Physical Activity Research, Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
Christa
C
Broholm
Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden.
Adil
A
Mardinoglu
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden.
Caroline
C
Kampf
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185 Uppsala, Sweden.
Anna
A
Asplund
Department of Biology and Biological Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden.
Intawat
I
Nookaew
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.
Mathias
M
Uhlén
Centre of Inflammation and Metabolism and Centre for Physical Activity Research, Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
Bente Klarlund
BK
Pedersen
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.
Jens
J
Nielsen
D000368:Aged; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015415:Biomarkers / Q000378:metabolism; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics / Q000378:metabolism; D005786:Gene Expression Regulation; D053858:Metabolic Networks and Pathways / Q000235:genetics; D008954:Models, Biological; D032342:Muscle Cells / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D020543:Proteome / Q000378:metabolism; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA; D059467:Transcriptome / Q000235:genetics
D015415:Biomarkers; D020543:Proteome