Citation:

CHERNEY, D., D. R. EKMAN, D. J. DIX, AND T. W. COLLETTE. RAMAN SPECTROSCOPY-BASED METABOLOMICS FOR DIFFERENTIATING TOXICITIES OF CONAZOLE FUNGICIDES. Presented at Metabolomics Society Annual Meeting, Boston, MA, June 24 - 28, 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:

Conazole fungicides are widely used both agriculturally for the protection of crops, and pharmaceutically in the treatment of topical and systemic infections. Heavy usage has created concern over the impact these compounds may have through environmental exposure to humans and other organisms. We have developed a Raman spectroscopy-based metabolomics approach, and have applied it to characterize the toxicities of three important conazole fungicides - myclobutanil, triadimefon, and propiconazole. These chemicals were chosen because they exhibit three different and known modes of toxicity. The Raman method proved capable of differentiating responses, via metabolomic analysis of urine collections, of rats exposed (individually) to these contaminants. A number of approaches were evaluated to reduce sample fluorescence to a level where adequate spectral information could be obtained. For example, a liquid-core optical fiber (or Raman waveguide) constructed of Teflon® AF allowed rapid sample photobleaching such that information-rich spectra could be acquired from urine without significant sample preparation. However, tangential flow filtration of the urine with a 500 nominal molecular weight limit filter, followed by Raman analysis with near-infrared laser excitation, proved a more robust method. Using this approach, we clearly observed changes in levels of important endogenous metabolites that were indicative of the toxic modes of action. The quality of PLS-DA models from the Raman data were comparable to those built using 1H- NMR data from the same set of urine samples. Analysis of Raman spectral changes that were related to chemical exposure yielded information that was consistent and complementary to that from NMR. With further development, the Raman approach may offer a viable metabolomics tool at a fraction of the instrumentation cost of NMR.

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