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RAMAN SPECTROSCOPY-BASED METABOLOMICS FOR DIFFERENTIATING EXPOSURES TO TRIAZOLE FUNGICIDES USING RAT URINE
Citation:
CHERNEY, D., D. R. EKMAN, D. J. DIX, AND T. W. COLLETTE. RAMAN SPECTROSCOPY-BASED METABOLOMICS FOR DIFFERENTIATING EXPOSURES TO TRIAZOLE FUNGICIDES USING RAT URINE. Analytical Chemistry. American Chemical Society, Washington, DC, 79(19):7324-7332, (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:
Normal Raman spectroscopy was evaluated as a metabolomic tool for assessing the impacts of exposure to environmental contaminants, using rat urine collected during the course of a toxicological study. Specifically, one of three triazole fungicides, myclobutanil, propiconazole or triadimefon, was administered daily via oral gavage to male Sprague-Dawley rats at doses of 300, 300, or 175 mg/kg, respectively. Urine was collected from all three treatment groups and also from vehicle control rats on day six, following five consecutive days of exposure. Spectra were acquired with a CCD-based dispersive Raman spectrometer, using 785nm diode-laser excitation. To optimize the signal-to-noise ratio, urine samples were filtered through a stirred ultrafiltration cell with a 500 nominal molecular weight limit filter to remove large, unwanted urine components that can degrade the spectrum via fluorescence. However, a subsequent investigation suggested that suitable spectra can be obtained in a high-throughput fashion, with little or no Raman-specific sample preparation. For sake of comparison, a parallel 1H-NMR- based metabolomic analysis was also conducted on the unfiltered samples. Results from multivariate data analysis demonstrated that the Raman method compares favorably with NMR in regard to the ability to differentiate responses from these three contaminants.