Interpreting metabolomic profiles using unbiased pathway models

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.

Figure 1. 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.

Figure 2. Active Module Groups from the NGT-EMRN and NGT-CMRN.

Panels (a) and (b) correspond to NGT-EMRN and NGT-CMRN, respectively. Nodes in the AMGs correspond to metabolites in chemical reactions and edges are drawn between reactant-product pairs or shared substrates of enzymes/transporters. A gradient from gold to blue was used to denote reduced percentage change in metabolite abundance after glucose challenge. For clarity, changes were truncated at ±60%. Unmeasured nodes are shown in grey. Edges corresponding to different types of functional links between metabolites are indicated. Cellular locations for metabolites in (a) are assumed to be extracellular unless denoted by [c] for cytoplasmic. Likewise, cellular locations in (b) are assumed to be cytoplasmic unless denoted by [e] for extracellular. The lac-pyr-cit-akg group of metabolites in (a) is connected to the remainder of the set via metabolites with relative frequencies<0.20 across solutions; the same is true of the bile salts cluster in (b).

Figure 3. Proposed mechanism for coupling of methionine influx to SLC6A12 transport of glycine betaine and dimethylglycine for osmoregulation.

The connections among the 3 metabolites (and proline) in the NGT-EMRN and NGT-CMRN AMGs are shown, along with the Recon 1 betaine-homocysteine methyltransferase catalyzed reaction.

Figure 4. Hierarchical clustering of changed metabolites (FDR<0.05) in NGT Group.

Grouping is according to 1−|ρ|, where ρ is the Spearman correlation coefficient for percentage change in metabolite abundance. Metabolite clusters that correspond to established transporter activities are highlighted. Cluster I corresponds to the SLC25A13 transporter (liver variant); Cluster II corresponds to SLC6A12; Cluster III corresponds to the small aliphatic system A transport system (SLC6, SLC7 and SLC38 transporters); and cluster IV corresponds to the hydrophobic/aliphatic system L transport system (SLC6, SLC7, SLC43).

Figure 5. Hierarchical clustering of changed metabolites (FDR<0.05) in IGT Group.

Grouping is according to 1−|ρ|, where ρ is the Spearman correlation coefficient for percentage change in metabolite abundance. Metabolite clusters that correspond to established transporter activities are highlighted. Cluster numbering is as in Figure 4.

Figure 6. Principal component analysis of significantly changed metabolites (FDR<0.05) in NGT and IGT.

Panels (a) and (b) correspond to NGT and IGT, respectively. Principal component #1 largely corresponds to pathways regulated by hepatic SLC25A13 activity, including glycolysis (lac, pyr) and gluconeogenesis (ala, ser), nucleotide biosynthesis (OMP, r1p, hxan, xan, xtsn, ncam), bile salt (gchol, tdchol) and citrulline (citr) accumulation, and NAD+/NADH balance by malate shuttling (glu, akg, mal). Principal component #2 largely corresponds to System A and L amino acid transport.