Background: Since 2001, researchers have examined the human genome (G) mainly to discover causes of disease, despite evidence that G explains relatively little risk. We posit that unexplained disease risks are caused by the exposome (E; representing all exposures) and G × E interactions. Thus, etiologic research has been hampered by scientists’ continuing reliance on low-tech methods to characterize E compared with high-tech omics for characterizing G.Objectives: Because exposures are inherently chemical in nature and arise from both endogenous and exogenous sources, blood specimens can be used to characterize exposomes. To explore the “blood exposome” and its connection to disease, we sought human blood concentrations of many chemicals, along with their sources, evidence of chronic-disease risks, and numbers of metabolic pathways.Methods: From the literature we obtained human blood concentrations of 1,561 small molecules and metals derived from foods, drugs, pollutants, and endogenous processes. We mappe...

Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences. Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences.

Metabolomics answers a fundamental question in biology: How does metabolism respond to genetic, environmental or phenotypic perturbations? Combining several metabolomics assays can yield datasets for more than 800 structurally identified metabolites. However, biological interpretations of metabolic regulation in these datasets are hindered by inherent limits of pathway enrichment statistics. We have developed ChemRICH, a statistical enrichment approach that is based on chemical similarity rather than sparse biochemical knowledge annotations. ChemRICH utilizes structure similarity and chemical ontologies to map all known metabolites and name metabolic modules. Unlike pathway mapping, this strategy yields study-specific, non-overlapping sets of all identified metabolites. Subsequent enrichment statistics is superior to pathway enrichments because ChemRICH sets have a self-contained size where p-values do not rely on the size of a background database. We demonstrate ChemRICH’s efficiency on a public metabolomics data set discerning the development of type 1 diabetes in a non-obese diabetic mouse model. ChemRICH is available at www.chemrich.fiehnlab.ucdavis.edu

Small bowel transplants provide an exceptional opportunity for long-term study of the microbial ecology of the human small bowel. The ileostomy created at time of transplant for ongoing monitoring of the allograft provides access to samples of ileal effluent and mucosal biopsies. In this study, we used qPCR to assay the bacterial population of the small bowel lumen of 17 small bowel transplant patients over time. Surprisingly, the posttransplant microbial community was found to be dominated by Lactobacilli and Enterobacteria, both typically facultative anaerobes. This represents an inversion of the normal community that is dominated instead by the strictly anaerobic Bacteroides and Clostridia. We found this inverted community also in patients with ileostomies who did not receive a transplant, suggesting that the ileostomy itself is the primary ecological determinant shaping the microbiota. After surgical closure of the ileostomy, the community reverted to the normal structure. Therefore, we hypothesized that the ileostomy allows oxygen into the otherwise anaerobic distal ileum, thus driving the transition from one microbial community structure to another. Supporting this hypothesis, metabolomic profiling of both communities demonstrated an enrichment for metabolites associated with aerobic respiration in samples from patients with open ileostomies. Viewed from an ecological perspective, the two communities constitute alternative stable states of the human ileum. That the small bowel appears to function normally despite these dramatic shifts suggests that its ecological resilience is greater than previously realized.

Background: Exposure to environmental tobacco smoke (ETS) leads to higher rates of pulmonary diseases and infections in children. To study the biochemical changes that may precede lung diseases, metabolomic effects on fetal and maternal lungs and plasma from rats exposed to ETS were compared to filtered air control animals. Genome- reconstructed metabolic pathways may be used to map and interpret dysregulation in metabolic networks. However, mass spectrometry-based non-targeted metabolomics datasets often comprise many metabolites for which links to enzymatic reactions have not yet been reported. Hence, network visualizations that rely on current biochemical databases are incomplete and also fail to visualize novel, structurally unidentified metabolites.Results: We present a novel approach to integrate biochemical pathway and chemical relationships to map all detected metabolites in network graphs (MetaMapp) using KEGG reactant pair database, Tanimoto chemical and NIST mass spectral similarity scores. In fetal and maternal lungs, and in maternal blood plasma from pregnant rats exposed to environmental tobacco smoke (ETS), 459 unique metabolites comprising 179 structurally identified compounds were detected by gas chromatography time of flight mass spectrometry (GC-TOF MS) and BinBase data processing. MetaMapp graphs in Cytoscape showed much clearer metabolic modularity and complete content visualization compared to conventional biochemical mapping approaches. Cytoscape visualization of differential statistics results using these graphs showed that overall, fetal lung metabolism was more impaired than lungs and blood metabolism in dams. Fetuses from ETS-exposed dams expressed lower lipid and nucleotide levels and higher amounts of energy metabolism intermediates than control animals, indicating lower biosynthetic rates of metabolites for cell division, structural proteins and lipids that are critical for in lung development.Conclusions: MetaMapp graphs efficiently visualizes mass spectrometry based metabolomics datasets as network graphs in Cytoscape, and highlights metabolic alterations that can be associated with higher rate of pulmonary diseases and infections in children prenatally exposed to ETS. The MetaMapp scripts can be accessed athttp://metamapp.fiehnlab.ucdavis.edu.