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dc.contributor.authorHunter, Luke
dc.contributor.authorPare, Guillaume
dc.contributor.authorVasan, Ramachandran S.
dc.contributor.authorLewis, Gregory D
dc.contributor.authorWang, Thomas Jue-Fuu
dc.contributor.authorChasman, Daniel Ian
dc.contributor.authorGerszten, Robert Edgardo
dc.contributor.authorDeo, Rahul Chandrakant
dc.contributor.authorRoth, Frederick Phillip
dc.date.accessioned2010-10-07T15:16:30Z
dc.date.issued2010
dc.identifier.citationDeo, Rahul C., Luke Hunter, Gregory D. Lewis, Guillaume Pare, Ramachandran S. Vassan, Daniel Chasman, Thomas J. Wang, Robert E. Gerszten, Frederick P. Roth. 2010. Interpreting metabolomic profiles using unbiased pathway models. PLoS Computational Biology 6(2): e1000692.en_US
dc.identifier.issn1553-734Xen_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4460857
dc.description.abstractHuman disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for “active modules”—regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.en_US
dc.description.sponsorshipMolecular and Cellular Biologyen_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofdoi:10.1371/journal.pcbi.1000692en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2829050/pdf/en_US
dash.licenseLAA
dc.subjectcomputational biologyen_US
dc.subjectmetabolic networksen_US
dc.subjectdiabetes and endocrinologyen_US
dc.subjecttype 2 diabetesen_US
dc.subjectphysiologyen_US
dc.subjectendocrinologyen_US
dc.subjectdiabetesen_US
dc.titleInterpreting Metabolomic Profiles using Unbiased Pathway Modelsen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalPLoS Computational Biologyen_US
dash.depositing.authorRoth, Frederick Phillip
dc.date.available2010-10-07T15:16:30Z
dc.identifier.doi10.1371/journal.pcbi.1000692*
dash.authorsorderedfalse
dash.contributor.affiliatedDeo, Rahul Chandrakant
dash.contributor.affiliatedLewis, Gregory
dash.contributor.affiliatedWang, Thomas Jue-Fuu
dash.contributor.affiliatedRoth, Fritz
dash.contributor.affiliatedChasman, Daniel
dash.contributor.affiliatedGerszten, Robert


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