Interpreting Metabolomic Profiles using Unbiased Pathway Models
Deo, Rahul C.
Lewis, Gregory D.
Vasan, Ramachandran S.
Wang, Thomas J.
Gerszten, Robert E.
Roth, Frederick P.
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Citation (published version)Deo, Rahul C., Luke Hunter, Gregory D. Lewis, Guillaume Pare, Ramachandran S. Vasan, Daniel Chasman, Thomas J. Wang, Robert E. Gerszten, Frederick P. Roth. "Interpreting Metabolomic Profiles using Unbiased Pathway Models" PLoS Computational Biology 6(2): e1000692. (2010)
Human 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. Author Summary Human disease is complex, arising from the interaction of many genetic and environmental factors. Efforts to personalize treatment have been thwarted by "phenotypic heterogeneity", the apparent similarity of disease states with diverse underlying causes. One approach to resolve this heterogeneity is to redefine diseases on the basis of abnormal physiologic activities, which should allow grouping patients into categories with similar treatment response and prognosis. Physiologic activities can be identified and assessed through quantitative measurements of biomolecules—proteins, mRNAs, metabolites—in individual patient samples. The field of metabolomics involves the analysis of a broad array of metabolite levels from clinical fluid samples such as blood or urine and can be used to evaluate disease states. Because metabolic profiles are complex, we have taken an integrative network-based approach to understand them in terms of abnormal activities of enzymes and small molecule transporters. We have focused on the oral glucose tolerance test, used to diagnose diabetes, and have found that multiple transporters play an important role in the normal response to ingesting sugar. Many of these transporter activities are abnormal in individuals with impaired glucose tolerance and differing activities among them may reflect the diverse underlying causes and variable clinical courses of such patients.
RightsDeo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.