High-performance metabolic profiling of plasma from seven mammalian species for simultaneous environmental chemical surveillance and bioeffect monitoring

Abstract

High-performance metabolic profiling (HPMP) by Fourier-transform mass spectrometry coupled to liquid chromatography gives relative quantification of thousands of chemicals in biologic samples but has had little development for use in toxicology research. In principle, the approach could be useful to detect complex metabolic response patterns to toxicologic exposures and to detect unusual abundances or patterns of potentially toxic chemicals. As an initial study to develop these possible uses, we applied HPMP and bioinformatics analysis to plasma of humans, rhesus macaques, marmosets, pigs, sheep, rats and mice to determine: (1) whether more chemicals are detected in humans living in a less controlled environment than captive species and (2) whether a subset of plasma chemicals with similar inter-species and intra-species variation could be identified for use in comparative toxicology. Results show that the number of chemicals detected was similar in humans (3221) and other species (range 2537-3373). Metabolite patterns were most similar within species and separated samples according to family and order. A total of 1485 chemicals were common to all species; 37% of these matched chemicals in human metabolomic databases and included chemicals in 137 out of 146 human metabolic pathways. Probability-based modularity clustering separated 644 chemicals, including many endogenous metabolites, with inter-species variation similar to intra-species variation. The remaining chemicals had greater inter-species variation and included environmental chemicals as well as GSH and methionine. Together, the data suggest that HPMP provides a platform that can be useful within human populations and controlled animal studies to simultaneously evaluate environmental exposures and biological responses to such exposures.

Figure 1

A. Phylogenetic relationship of 7 mammalian species with associated number of chemicals detected by HPMP for each order, family and species. B. Venn diagram of the chemicals common among families. C. Hierarchical clustering analysis (HCA) of plasma metabolic profiles after variance scaling shows similarity of individuals of the same species.

Figure 2

A. Three-dimensional PCA score plot, in which the first three principal components (PCs) explained 62% of total variation, is arbitrarily rotated to visualize discrimination according to species, family and order. B. Two-dimensional PCA score plot with PC1 and PC2 corresponding to Panel D. C. 2-D PCA score plot for Sample Set 2 to evaluate whether samples from different species (human, marmoset and mouse) were discriminated according to sex. In A and B, each symbol shows a different class: diamond, primate (human, H, filled; rhesus, Rh, open; marmoset, Ma, with line); circle, artiodactyla (pig, P, filled; sheep, S, open); triangle, rotentia (rat, Ra, filled; mouse, Mo, open). In C, males, filled symbols; females open symbols; human, diamond; marmoset, square; mouse, triangle.

Figure 3

KEGG metabolic pathway matches for m/z features that are common to mammalian species. High mass accuracy m/z for 1485 common chemicals revealed 666 matches as shown in black, including matches for metabolites in 137 of the total 146 KEGG human reference metabolic pathways. Corresponding metabolite names are available at http://www.genome.jp/kegg-bin/show_pathway?13154256908831/hsa01100.args.

Figure 4

Probability-based modularity clustering of 1485 common chemicals according to intra- and inter-species variation. Module 1 (M1) includes chemicals with similar characteristics in all 7 mammalian species while Module 2 (M2) contains chemicals that have different characteristics among the species. M1 showed a preponderance of endogenous metabolites while M2 showed a preponderance of other chemicals, including known environmental chemicals. These data suggest that HPMP can be used both for surveillance of environmental exposures by focusing on chemicals in M2 and for study of biologic responses to environmental exposures by focusing on chemicals in M1.

Figure 5

The relationship between inter-species variation and within-error variation for selected chemicals in Module 1 (M1) and 2 (M2) of probability-based modularity clustering analysis shown in Fig 3. Trajectories above the diagonal line, represented by the arrows in A–C, indicate that within-species variation is large relative to interspecies variation. In contrast, trajectories below the diagonal line, represented by arrows in D–F, show that interspecies variation is large relative to within-species variation. This difference between chemicals in Module 1 and Module 2 provides a basis to use HPMP for two purposes, direct evaluation of environmental exposures (Module 2), and biological responses to environmental exposures (Module 1). A: leucine/isoleucine (M1), B: citrulline (M1), C: cystine (M1), D: glutamine (M2), E: pirimicarb (M2), F: triethylphosphate (M2).

Figure 6

Characterization of HPMP data for comparative toxicology and biomonitoring of exposures. HPMP analyses detected 3820 m/z, which reflects the total metabolome, designated here as the “pan” metabolome. The pan metabolome consists of a “core” metabolome that this is common to the 7 mammalian species and a “peri-core” metabolome that includes all of the chemicals surrounding the core metabolome. The core metabolome includes Module 1, consisting of chemicals that have inter-species variation similar to intra-species variation, and Module 2, consisting of chemicals that have greater inter-species variation than intra-species variation. Module 1 includes endogenous metabolites and has characteristics suitable for comparative studies of biologic responses to toxic exposure. Module 2 includes environmental chemicals, variable endogenous metabolites, chemicals derived from the diet, chemicals derived from the microbiome, and pharmaceuticals. Module 2 and peri-core metabolites can be used to support environment-wide association studies (EWAS) and gene-environment (G × E) studies. Identification and classification of chemicals in Module 2 and the peri-core metabolome will greatly facilitate interpretation of EWAS and G × E study results.