Interpreting Metabolomic Profiles using Unbiased Pathway Models

DSpace/Manakin Repository

Interpreting Metabolomic Profiles using Unbiased Pathway Models

Citable link to this page


Title: Interpreting Metabolomic Profiles using Unbiased Pathway Models
Author: Hunter, Luke; Pare, Guillaume; Vasan, Ramachandran S.; Lewis, Gregory D; Wang, Thomas Jue-Fuu; Chasman, Daniel Ian; Gerszten, Robert Edgardo; Deo, Rahul Chandrakant; Roth, Frederick Phillip

Note: Order does not necessarily reflect citation order of authors.

Citation: Deo, 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.
Full Text & Related Files:
Abstract: 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.
Published Version: doi:10.1371/journal.pcbi.1000692
Other Sources:
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at
Citable link to this page:
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search