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: 6950067
• 负责人: Timothy R. Zacharewski
• 金额: $ 61.71万
• 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/6950067
• 关键词: bioinformatics; biological models; biological signal transduction; blood tests; computational biology; computer data analysis; data management; dichlorodiphenyltrichloroethane; dosage; endocrine gland /system; environmental toxicology; ethinyl estradiol; female; gene environment interaction; gene expression; gene expression profiling; genistein; histopathology; laboratory rat; liver; mathematical model; metabolomics; microarray technology; nuclear magnetic resonance spectroscopy; statistics /biometry; urinalysis

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） 雌激素内分泌干扰物 (EED) 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。 它们会对健康产生许多不利影响，例如激素依赖性癌症、生殖道异常、生殖健康受损和认知能力受损。 为了充分评估合成和天然 EED 的潜在不利影响，需要在整个生物体的背景下更全面地了解它们的分子、代谢和组织水平影响。 该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、多核核磁共振波谱、数据管理和高级数据分析领域的互补多学科专业知识，阐明 EED 扰动的途径、网络和信号级联。 将通过多核（即 1H、13C、31P）NMR 评估尿液、血清和肝脏提取物中乙炔雌二醇 (EE)、金雀异黄酮 (GEN) 和邻，对'-二氯二苯基三氯乙烷 (DDT) 对代谢物水平的比较影响光谱学，并补充了来自小鼠和大鼠模型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。 所有数据都将存储和存档在 dbZach 中，这是一个符合 MIAME 标准的毒物基因组支持数据库，有助于数据分析、不同数据集的整合、研究人员之间的数据交换以及将数据存储到公共存储库中。 先进的统计方法、建模和数据集成工具（例如神经网络、数据融合和贝叶斯推理）将用于融合这些不同的数据集，以阐明对内源性雌激素反应具有重要意义的保守生物网络。 此外，与引发效应相关的 EED 扰动途径将得到进一步定义。 这些研究的结果不仅将进一步明确雌激素化合物的生理和毒性作用机制，还将证明将互补微阵列、代谢组学和组织病理学数据融合到综合计算模型中的协同作用。 当与将要开发的高级信息工具一起使用时，这种方法还将展示从所提议的创新数据管理系统中可用的所有不同数据中最大化知识提取的能力。