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CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

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Interpreting metabolomic profiles using unbiased pathway models.

Rahul C Deo¹, Luke Hunter, Gregory D Lewis

¹Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, United States of America.

Plos Computational Biology

|March 3, 2010

Summary

This summary is machine-generated.

Small-molecule profiling reveals how transporter activities influence metabolite levels during glucose challenges. This approach can help classify diseases by examining specific transporter functions in individuals with normal and impaired glucose tolerance.

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Area of Science:

Metabolomics
Systems Biology
Biochemistry

Background:

Human diseases exhibit heterogeneity, with similar phenotypes arising from diverse genetic and environmental factors.
Small-molecule profiling of plasma metabolites offers a non-invasive method to assess individual biologic states and address disease heterogeneity.

Purpose of the Study:

To analyze metabolite profiles from an oral glucose tolerance test (OGTT) in individuals with normal glucose tolerance (NGT) and impaired glucose tolerance (IGT).
To elucidate underlying biologic processes and identify relationships among changed metabolites using unbiased network approaches.

Main Methods:

Analysis of metabolite profiles from OGTT in 50 individuals (25 NGT, 25 IGT).
Construction of a metabolic network and identification of "active modules" enriched for metabolite level changes.
Application of hierarchical clustering and principal component analysis to group metabolites based on transporter activities.

Main Results:

Unbiased network approaches revealed concerted changes in metabolites, highlighting the role of specific solute carriers.
Changed metabolites naturally grouped according to the activities of amino acid transporters (System A and L), SLC12A6, and SLC25A13.
Individuals with IGT showed blunted glucose- and/or insulin-stimulated transporter activities compared to the NGT group.

Conclusions:

Solute carrier activities play a significant role in the physiological response to glucose challenge.
Metabolite profiling during perturbation experiments reflects specific transporter activities.
Interrogation of transporter activities via metabolite profiling may improve disease classification.