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dc.contributor.authorAurich, Maike K
dc.contributor.authorPaglia, Giuseppe
dc.contributor.authorRolfsson, Óttar
dc.contributor.authorHrafnsdóttir, Sigrún
dc.contributor.authorMagnúsdóttir, Manuela
dc.contributor.authorStefaniak, Magdalena M
dc.contributor.authorPalsson, Bernhard Ø
dc.contributor.authorFleming, Ronan M T
dc.contributor.authorThiele, Ines
dc.date.accessioned2015-06-10T13:04:55Zen
dc.date.available2015-06-10T13:04:55Zen
dc.date.issued2015-06en
dc.date.submitted2015en
dc.identifier.citationMetabolomics 2015, 11 (3):603-619en
dc.identifier.issn1573-3882en
dc.identifier.pmid25972769en
dc.identifier.doi10.1007/s11306-014-0721-3en
dc.identifier.urihttp://hdl.handle.net/2336/556659en
dc.description.abstractMetabolic 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 CCRF-CEM 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.
dc.description.sponsorshipinfo:eu-repo/grantAgreement/EC/FP7/232816 Luxembourg National Research Fund (FNR) FNR/A12/01en
dc.languageENGen
dc.language.isoenen
dc.publisherSpringeren
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/232816en
dc.relation.urlhttp://dx.doi.org/10.1007/s11306-014-0721-3en
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4419158/en
dc.rightsopenAccessen
dc.subject.meshMetabolomics/methods*en
dc.titlePrediction of intracellular metabolic states from extracellular metabolomic data.en
dc.typearticleen
dc.contributor.departmentUniv Iceland, Ctr Syst Biol, Reykjavik, Iceland, Univ Luxembourg, Luxembourg Ctr Syst Biomed, Esch Sur Alzette, Luxembourg, Univ Iceland, Sch Hlth Sci, Fac Food Sci & Nutr, Reykjavik, Iceland, Univ Calif San Diego, Dept Bioengn, La Jolla, CA 92093 USAen
dc.identifier.journalMetabolomics : Official journal of the Metabolomic Societyen
dc.rights.accessOpen Access - Opinn aðganguren
html.description.abstractMetabolic 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 CCRF-CEM 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.


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