Integration of clinical data with a genome-scale metabolic model of the human adipocyte
Artikel i vetenskaplig tidskrift, 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
Författare
Adil Mardinoglu
Chalmers, Kemi- och bioteknik, Livsvetenskaper
Rasmus Ågren
Chalmers, Kemi- och bioteknik, Livsvetenskaper
C. Kampf
Uppsala universitet
A. Asplund
Uppsala universitet
Intawat Nookaew
Chalmers, Kemi- och bioteknik, Livsvetenskaper
Peter Jacobson
Göteborgs universitet
A. J. Walley
Hammersmith Hospital
P. Froguel
Hammersmith Hospital
Centre national de la recherche scientifique (CNRS)
Lena M S Carlsson
Göteborgs universitet
M. Uhlen
Alba Nova Universitetscentrum
Jens B Nielsen
Chalmers, Kemi- och bioteknik, Livsvetenskaper
Molecular Systems Biology
17444292 (eISSN)
Vol. 9 1 649- 649Infrastruktur
C3SE (-2020, Chalmers Centre for Computational Science and Engineering)
Styrkeområden
Livsvetenskaper och teknik (2010-2018)
Ämneskategorier (SSIF 2011)
Bioinformatik och systembiologi
DOI
10.1038/msb.2013.5