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Interpreting Metabolomic Profiles using Unbiased Pathway Models

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 plasm...

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Autores principales: Deo, Rahul C., Hunter, Luke, Lewis, Gregory D., Pare, Guillaume, Vasan, Ramachandran S., Chasman, Daniel, Wang, Thomas J., Gerszten, Robert E., Roth, Frederick P.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2829050/
https://www.ncbi.nlm.nih.gov/pubmed/20195502
http://dx.doi.org/10.1371/journal.pcbi.1000692
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author Deo, Rahul C.
Hunter, Luke
Lewis, Gregory D.
Pare, Guillaume
Vasan, Ramachandran S.
Chasman, Daniel
Wang, Thomas J.
Gerszten, Robert E.
Roth, Frederick P.
author_facet Deo, Rahul C.
Hunter, Luke
Lewis, Gregory D.
Pare, Guillaume
Vasan, Ramachandran S.
Chasman, Daniel
Wang, Thomas J.
Gerszten, Robert E.
Roth, Frederick P.
author_sort Deo, Rahul C.
collection PubMed
description 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.
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spelling pubmed-28290502010-03-02 Interpreting Metabolomic Profiles using Unbiased Pathway Models Deo, Rahul C. Hunter, Luke Lewis, Gregory D. Pare, Guillaume Vasan, Ramachandran S. Chasman, Daniel Wang, Thomas J. Gerszten, Robert E. Roth, Frederick P. PLoS Comput Biol Research Article 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. Public Library of Science 2010-02-26 /pmc/articles/PMC2829050/ /pubmed/20195502 http://dx.doi.org/10.1371/journal.pcbi.1000692 Text en Deo et al. http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
spellingShingle Research Article
Deo, Rahul C.
Hunter, Luke
Lewis, Gregory D.
Pare, Guillaume
Vasan, Ramachandran S.
Chasman, Daniel
Wang, Thomas J.
Gerszten, Robert E.
Roth, Frederick P.
Interpreting Metabolomic Profiles using Unbiased Pathway Models
title Interpreting Metabolomic Profiles using Unbiased Pathway Models
title_full Interpreting Metabolomic Profiles using Unbiased Pathway Models
title_fullStr Interpreting Metabolomic Profiles using Unbiased Pathway Models
title_full_unstemmed Interpreting Metabolomic Profiles using Unbiased Pathway Models
title_short Interpreting Metabolomic Profiles using Unbiased Pathway Models
title_sort interpreting metabolomic profiles using unbiased pathway models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2829050/
https://www.ncbi.nlm.nih.gov/pubmed/20195502
http://dx.doi.org/10.1371/journal.pcbi.1000692
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