Metabolomic Assessment of Estrogenic Endocrine Disruptor

描述（由申请人提供） 雌激素内分泌干扰物（EEDs）是一组结构多样的化合物，包括药物，膳食补充剂，工业化学品和环境污染物。 它们可以引起许多不利的健康影响，如激素依赖性癌症、生殖道异常、生殖健康受损和认知能力受损。 为了全面评估合成和天然EEDs的潜在不良反应，需要在整个生物体的背景下对其分子、代谢和组织水平的影响进行更全面的了解。 这项合作提案将利用其团队成员在毒理学、分子生物学、内分泌学、多核NMR光谱学、数据管理和高级数据分析等领域的互补多学科专业知识，阐明EEDs干扰的途径、网络和信号级联。 将通过多核（i.即，1H，13 C，31 P）NMR光谱，并补充了来自小鼠和大鼠模型中正在进行的微阵列研究的组织病理学检查和基因表达数据。 所有数据将存储并存档在dbZach中，dbZach是一个符合MIAME-compliant的毒理基因组学支持数据库，有助于数据分析、整合不同数据集、研究者之间的数据交换以及将数据存入公共存储库。 先进的统计方法，建模和数据集成工具，如神经网络，数据融合和贝叶斯推理将被用来融合这些不同的数据集，以阐明保守的生物网络，是重要的内源性雌激素。 此外，EED扰动的途径与引发的影响将进一步确定。 这些研究的结果不仅将进一步定义雌激素化合物的生理和毒性作用机制，而且还将展示互补微阵列、代谢组学和组织病理学数据融合到一个全面的综合计算模型中的协同作用。 这一方法还将表明，如果与即将开发的先进信息工具一起使用，就有能力从拟议的创新数据管理系统内现有的所有不同数据中最大限度地提取知识。

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.