pcbi-1000692-g001: Analysis flowchart for metabolic reaction network construction, active module discovery, and evaluation of active module sets for enrichment for predefined biologic pathways, enzymes/transporters, and tissue activity.

Mentions: We first built a Metabolic Reaction Network (MRN) using the 3338 metabolic reactions in Recon 1. Although Recon 1 includes most known transport reactions, the specific transporters were not always explicitly mentioned. Thus we expanded this list with 737 additional reactions explicitly modeling transport processes for the metabolites measured in this experiment (see Methods, Table S2), highlighting the relevant transporter for each reaction. We treated all reactants and product metabolites as nodes. Cellular locations were assigned to each metabolite as specified in Recon 1, and metabolites were split into multiple nodes (each corresponding to a different location). For example, five nodes in the MRN were assigned to D-Glucose, corresponding to glucose in the cytoplasmic, lysosomal, Golgi, endoplasmic reticulum and extracellular compartments. Edges were drawn between reactants and products in chemical reactions (see Methods and Figure 1) and between all substrates for each of the known enzymes or transporters catalyzing metabolic reactions (Table S2). In effect, we proceeded from a bipartite undirected graph [18], where both metabolites and proteins (enzymes/transporters) are represented as nodes, and interactions between metabolites and proteins represented as edges, to a unipartite metabolite interaction graph, where metabolites that are common substrates of enzymes or transporters were connected by edges. For those reactions where enzymes/transporters are unknown or unneeded, the corresponding reactant and product metabolites were directly connected.

Deo RC, Hunter L, Lewis GD, Pare G, Vasan RS, Chasman D, Wang TJ, Gerszten RE, Roth FP - PLoS Comput. Biol. (2010)

Bottom Line: 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.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.

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

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.