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:	Rahul C Deo, Luke Hunter, Gregory D Lewis, Guillaume Pare, Ramachandran S Vasan, Daniel Chasman, Thomas J Wang, Robert E Gerszten, Frederick P Roth
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/2c252d2893e74961a368e9755e4cb1cf
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spelling	oai:doaj.org-article:2c252d2893e74961a368e9755e4cb1cf2021-11-25T05:42:37ZInterpreting metabolomic profiles using unbiased pathway models.1553-734X1553-735810.1371/journal.pcbi.1000692https://doaj.org/article/2c252d2893e74961a368e9755e4cb1cf2010-02-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20195502/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Human 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.Rahul C DeoLuke HunterGregory D LewisGuillaume PareRamachandran S VasanDaniel ChasmanThomas J WangRobert E GersztenFrederick P RothPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 2, p e1000692 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Rahul C Deo 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.
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.
format	article
author	Rahul C Deo Luke Hunter Gregory D Lewis Guillaume Pare Ramachandran S Vasan Daniel Chasman Thomas J Wang Robert E Gerszten Frederick P Roth
author_facet	Rahul C Deo Luke Hunter Gregory D Lewis Guillaume Pare Ramachandran S Vasan Daniel Chasman Thomas J Wang Robert E Gerszten Frederick P Roth
author_sort	Rahul C Deo
title	Interpreting metabolomic profiles using unbiased pathway models.
title_short	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_sort	interpreting metabolomic profiles using unbiased pathway models.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/2c252d2893e74961a368e9755e4cb1cf
work_keys_str_mv	AT rahulcdeo interpretingmetabolomicprofilesusingunbiasedpathwaymodels AT lukehunter interpretingmetabolomicprofilesusingunbiasedpathwaymodels AT gregorydlewis interpretingmetabolomicprofilesusingunbiasedpathwaymodels AT guillaumepare interpretingmetabolomicprofilesusingunbiasedpathwaymodels AT ramachandransvasan interpretingmetabolomicprofilesusingunbiasedpathwaymodels AT danielchasman interpretingmetabolomicprofilesusingunbiasedpathwaymodels AT thomasjwang interpretingmetabolomicprofilesusingunbiasedpathwaymodels AT robertegerszten interpretingmetabolomicprofilesusingunbiasedpathwaymodels AT frederickproth interpretingmetabolomicprofilesusingunbiasedpathwaymodels
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Interpreting metabolomic profiles using unbiased pathway models.

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