Metabolomic Assessment of Estrogenic Endocrine Disruptor

雌激素内分泌干扰物的代谢组学评估

• 批准号: 7440169
• 负责人: Timothy R. Zacharewski
• 金额: $ 53.5万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-19 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7440169
• 关键词: Adverse effects; Affect; Apical; Archives; Area; Biochemical Pathway; Biological; Biological Markers; Biological Neural Networks; Cell Proliferation; Chemicals; Class; Classification; Clinical Chemistry; Cognitive; Complement; Computer Simulation; Data; Data Analyses; Data Set; Databases; Deposition; Development; Disease Progression; Dose; Endocrine Disruptors; Endocrinology; Engineering; Environmental Pollution; Estradiol; Estrogens; Funding; Future; Gene Expression; Genistein; Health; Hepatic; Histopathology; Hormones; Knowledge Extraction; Lead; Link; Lipids; Liver Extract; Machine Learning; Malignant Neoplasms; Maps; Metabolic; Metabolism; Modeling; Molecular; Molecular Biology; Molecular Profiling; Monitor; Multinuclear NMR; Mus; NMR Spectroscopy; Numbers; Organ Weight; Outcome; Pathway interactions; Pattern Recognition; Pharmacologic Substance; Physiological; Principal Investigator; Process; Rattus; Reporting; Research Design; Research Infrastructure; Research Personnel; Resources; Risk Assessment; Rodent; Sampling; Screening procedure; Serum; Signal Transduction; Spectrum Analysis; System; Techniques; Time; Tissues; Toxic effect; Toxicogenomics; Toxicology; Urine; Whole Organism; aqueous; comparative; data integration; data management; dichlorodiphenyltrichloroethane; dietary supplements; estrogenic endocrine disruptor; experience; fitness; innovation; member; metabolic abnormality assessment; metabolomics; multidisciplinary; programs; repository; reproductive; research study; response; tool

DESCRIPTION (provided by applicant) Estrogenic endocrine disruptors (EEDs) are a group of structurally diverse compounds that include pharmaceuticals, dietary supplements, industrial chemicals and environmental contaminants. They can elicit a number of adverse health effects such as hormone dependent cancers, reproductive tract abnormalities, compromised reproductive fitness, and impaired cognitive abilities. In order to fully assess the potential adverse effects of synthetic and natural EEDs, a more comprehensive understanding of their molecular, metabolic, and tissue level effects is required within the context of a whole organism. This collaborative proposal will elucidate the pathways, networks and signaling cascades perturbed by EEDs using the complementary multidisciplinary expertise of its team members in the areas of toxicology, molecular biology, endocrinology, multinuclear NMR spectroscopy, data management and advanced data analysis. The comparative effects of ethynyl estradiol (EE), genistein (GEN), and o, p'-dichlorodiphenyltrichloroethane (DDT) on metabolite levels will be assessed in urine, serum and liver extracts by multinuclear (i. e., 1H, 13C, 31P) NMR spectroscopy, and complemented with histopathology examination and gene expression data from ongoing microarray studies in both mouse and rat models. All data will be stored and archived in dbZach, a MIAME-compliant toxicogenomic supportive database that facilitates data analysis, the integration of disparate data sets, the exchange of data between investigators, and the deposition of data into public repositories. Advanced statistical approaches, modeling and data integration tools such as neural networks, data fusion, and Baysean inference will be used to fuse these disparate data sets in order to elucidate the conserved biological networks that are of importance in response to endogenous estrogens. Moreover, EED perturbed pathways associated with elicited effects will be further defined. Results from these studies will not only further define the physiologic and toxic mechanisms of action of estrogenic compounds but will also demonstrate the synergy of fusing complementary microarray, metabolomic and histopathology data into a comprehensive integrative computational model. This approach will also demonstrate the ability to maximize knowledge extraction from all disparate data available within the proposed innovative data management system when used with the advanced information tools that will be developed.

描述（由申请人提供）