Citation:

COLLETTE, T. W., D. R. EKMAN, A. W. GARRISON, W. M. HENDERSON, AND Q. TENG. APPLICATION OF METABOLOMICS FOR IMPROVING ECOLOGICAL EXPOSURE AND RISK ASSESSMENTS. Presented at International Science Forum on Computational Toxicology, Research Triangle Park, NC, May 21 - 23, 2007.

Impact/Purpose:

This task is divided into four major research areas: (1) Development of computational tools and databases for screening-level modeling of the environmental fate of organic chemicals; (2) Metabolism of xenobiotics: Enhancing the development of a metabolic simulator; (3) Metabonomics: The use of advanced analytical tools to identify toxicity pathways; and (4) Software infrastructure to support development and application of transformation/metabolic simulators.

For many chemicals, multiple transformation/metabolic pathways can exist. Consequently, transformation/metabolic simulators must utilize transformation rate data for prioritization of competing pathways. The prioritization process thus requires the integration of reliable rate data. When this data is absent, it is necessary to generate a database with metabolic and transformation rate constants based on: (1) experimentally measured values, including those requiring the use of advanced analytical techniques for measuring metabolic rate constants in vivo and in vitro; (2) rate constants derived from SPARC and mechanistic-based QSAR models; and (3) data mined from the literature and Program Office CBI. A long-term goal of this project is to build this database. This information will be used to enhance the predictive capabilities of the transformation/metabolic simulators. As indicated previously, exposure genomics, which provide early signs of chemical exposure based on changes in gene expression, will be used to guide chemical fate and metabolism studies. The incorporation of exposure genomics into fate studies will provide information concerning (1) the minimal concentrations at which biological events occur; and (2) the identification of biologically relevant chemicals(s) in mixtures.

The capability of categorizing chemicals and their metabolites based on toxicity pathway is imperative to the success of the CompTox Research Program. Metabonomics, which is the multi-parametric measurement of metabolites in living systems due to physiological stimuli and/or genetic modification, provides such a capability. The application of metabonomics to toxicity testing involves the elucidation of changes in metabolic patterns associated with chemical toxicity based on the measurement of component profiles in biofluids, and enables the generation of spectral profiles for a wide range of endogenous metabolites. Metabolic profiles can provide a measure of the real outcome of potential changes as the result of xenobiotic exposure.

Description:

We have developed a research program in metabolomics that involves numerous partners across EPA, other federal labs, academia, and the private sector. A primary goal is to develop metabolite-based markers that can be used by EPA in ecological exposure and risk assessments. We are focusing this program on ecologically relevant species-

in particular, small fish toxicological models. For example, to better understand the impact of endocrine-disrupting chemicals (EDCs) in small fish (fathead minnow, zebrafish), we are conducting metabolomic analyses with multiple tissues (brain, blood, liver, and gonad) and urine. We are developing hypotheses about which tissue- and biofluid-specific metabolite changes will be definitively related to exposure, based on the current understanding of modes-of-action for these chemicals. Results will allow testing of these hypotheses to refine understanding of activity, and will help ensure that molecular markers of EDC exposure are meaningful. While certain metabolites are being specifically targeted in these studies, we will also discern changes in the complete metabolic profile using nuclear magnetic resonance (NMR) and mass spectroscopic (MS) data with statistical approaches that allow capturing subtle changes in less-abundant metabolites. These data are being integrated with genomic, proteomic, and whole organism data from untreated fish and those exposed to known EDCs. Ultimately, these data are used in integrated systems biology models that link: chemical exposures and toxic modes-of-action to ecologically-relevant outcomes.

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