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1H-NMR METABOLOMICS ANALYIS OF ZEBRAFISH (DANIO RERIO) EXPOSED TO THE ENVIRONMENTALLY-RELEVANT EDC 17 ALPHA-ETHINYLESTRADIOL (EE2)
Citation:
WHITEHEAD, T. L., D. R. EKMAN, E. J. DURHAN, K. M. JENSEN, M. D. KAHL, E. A. MAKYNEN, D. L. VILLENEUVE, AND G. T. ANKLEY. 1H-NMR METABOLOMICS ANALYIS OF ZEBRAFISH (DANIO RERIO) EXPOSED TO THE ENVIRONMENTALLY-RELEVANT EDC 17 ALPHA-ETHINYLESTRADIOL (EE2). Presented at American Chemical Society National Meeting, Atlanta, GA, March 26 - 30, 2006.
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:
Elevated levels of endocrine-disrupting chemicals (EDCs) have been reported in waterways worldwide and have been shown to affect numerous aspects of development, behavior, reproduction, and survival in various fish species. We have examined the effects of the synthetic steroid 17 alpha-ethinylestradiol (EE2) on endogenous metabolite levels in tissue and plasma from reproductively mature zebrafish (Danio rerio). EE2 is a highly specific and potent agonist of the estrogen receptor used extensively in the U.S. as an active ingredient in oral contraceptives. It has been observed in aquatic environments and is therefore considered relevant as an environmental contaminant. Fish were exposed to two concentrations of EE2 (30 or 100 ng/L) or control water for up to 96 hours in a flow-through system. Endogenous metabolites were detected in brain, liver and gonad extracts and in native plasma using 1H-NMR spectroscopy. PCA and PLS-DA techniques were employed to define metabolites responsible for class separation between control, low-dose exposure, and high-dose exposure populations. Ultimately, differential metabolic profiles will provide predictive biomarkers for risk assessment and support computational toxicology modeling efforts to characterize toxicity pathways of environmentally-relevant compounds in a systems- and population-modeling context.