Integration of clinical data with a genome-scale metabolic model of the human adipocyte
Journal article, 2013
We evaluated the presence/absence of proteins encoded by 14 077 genes in adipocytes obtained from different tissue samples using immunohistochemistry. By combining this with previously published adipocyte-specific proteome data, we identified proteins associated with 7340 genes in human adipocytes. This information was used to reconstruct a comprehensive and functional genome-scale metabolic model of adipocyte metabolism. The resulting metabolic model, iAdipocytes1809, enables mechanistic insights into adipocyte metabolism on a genome-wide level, and can serve as a scaffold for integration of omics data to understand the genotype-phenotype relationship in obese subjects. By integrating human transcriptome and fluxome data, we found an increase in the metabolic activity around androsterone, ganglioside GM2 and degradation products of heparan sulfate and keratan sulfate, and a decrease in mitochondrial metabolic activities in obese subjects compared with lean subjects. Our study hereby shows a path to identify new therapeutic targets for treating obesity through combination of high throughput patient data and metabolic modeling.
adipocyte
genome-scale metabolic model
flux balance analysis
obesity
proteome
Author
Adil Mardinoglu
Chalmers, Chemical and Biological Engineering, Life Sciences
Rasmus Ågren
Chalmers, Chemical and Biological Engineering, Life Sciences
C. Kampf
Uppsala University
A. Asplund
Uppsala University
Intawat Nookaew
Chalmers, Chemical and Biological Engineering, Life Sciences
Peter Jacobson
University of Gothenburg
A. J. Walley
Hammersmith Hospital
P. Froguel
Hammersmith Hospital
Centre national de la recherche scientifique (CNRS)
Lena M S Carlsson
University of Gothenburg
M. Uhlen
AlbaNova University Center
Jens B Nielsen
Chalmers, Chemical and Biological Engineering, Life Sciences
Molecular Systems Biology
17444292 (eISSN)
Vol. 9 1 649- 649Infrastructure
C3SE (Chalmers Centre for Computational Science and Engineering)
Areas of Advance
Life Science Engineering (2010-2018)
Subject Categories
Bioinformatics and Systems Biology
DOI
10.1038/msb.2013.5