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1H-NMR METABONOMICS ANALYSIS OF SERA DIFFERENTIATES BETWEEN MAMMARY TUMOR-BEARING MICE AND HEALTHY CONTROLS
Citation:
WHITEHEAD, T. L., B. MONZAVI-KARBASSI, AND T. KIEBER-EMMONS. 1H-NMR METABONOMICS ANALYSIS OF SERA DIFFERENTIATES BETWEEN MAMMARY TUMOR-BEARING MICE AND HEALTHY CONTROLS. Metabolomics. Plenum Press, New York, NY, 1(3):230-237, (2005).
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:
Global analysis of 1H-NMR spectra of serum is an appealing approach for the rapid detection of cancer. To evaluate the usefulness of this method in distinguishing between mammary tumor-bearing mice and healthy controls, we conducted 1H-NMR metabonomic analyses on serum samples obtained from the following: 10 mice inoculated with a highly-metastatic mammary carcinoma cell line, 10 mice inoculated with a "normally" metastatic mammary carcinoma cell line, and 10 healthy controls. Following standard spectral processing and subsequent data reduction, we applied unsupervised Principal Component Analysis (PCA) to determine if unique metabolic fingerprints for different categories of metastatic breast cancer in serum exist. The PCA method correctly separated sera of tumor-bearing mice from that of normal healthy controls, as shown using the scores plot which indicated that sera classes from tumor-bearing mice did not share multivariate space with that from healthy controls. In addition, this technique was capable of distinguishing between classes of varying metastatic ability in this system. Metabolites apparently responsible for separation between diseased and healthy mice include lactate, taurine, choline, and sugar moieties. Results of this study suggest that 1H-NMR spectra of mouse serum analyzed using PCA statistical methods indicate separation of tumor-bearing mice from healthy normal controls, justifying further study of the use of 1H-NMR metabonomics for cancer detection in serum.