Reference : Prediction of intracellular metabolic states from extracellular metabolomic data
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/17575
Prediction of intracellular metabolic states from extracellular metabolomic data
English
Aurich, Maike Kathrin mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Paglia, Guiseppe []
Rolfsson, Ottar []
Hrafnsdottir, Sigrun []
Magnusdottir, Manuela []
Stefaniak, Madgalena K. []
Palsson, Bernhard O. []
Fleming, Ronan MT mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Thiele, Ines mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2015
Metabolomics : Official journal of the Metabolomic Society
11
3
603-619
Yes
International
1573-3882
1573-3890
[en] Constraint-based modeling ; metabolomics ; metabolomics ; metabolic network ; transcriptomics
[en] Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRFCEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well-suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.
Luxembourg Centre for Systems Biomedicine (LCSB): Molecular Systems Physiology (Thiele Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Systems Biochemistry (Fleming Group)
http://hdl.handle.net/10993/17575
10.1007/s11306-014-0721-3

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