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: 7124649
• 负责人: Timothy R. Zacharewski
• 金额: $ 56.58万
• 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/7124649
• 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） 雌激素内分泌破坏者（EED）是一组结构上多样的化合物，包括药物，饮食补充剂，工业化学物质和环境污染物。 它们可以引起许多不利的健康影响，例如激素依赖性癌症，生殖道异常，生殖适应性受损以及认知能力受损。 为了充分评估合成和天然EED的潜在不利影响，在整个生物体的背景下需要对它们的分子，代谢和组织水平效应有更全面的了解。 该协作提案将使用其团队成员在毒理学，分子生物学，内分泌学，多核NMR NMR Spectroscoppoy，数据管理和高级数据分析领域的互补多学科专业知识来阐明EED扰动的途径，网络和信号级联。 乙醇雌二醇（EE），染料黄酮（Gen）和O，P'-二氯二苯基三氯乙烷（DDT）对代谢物水平的比较影响，将在尿液，血清和肝脏提取物中评估多核（i。小鼠和大鼠模型的微阵列研究。 所有数据都将存储和存档在DBZACH，这是一个符合迈阿密的毒物基因组支持数据库，促进数据分析，不同数据集的整合，研究人员之间的数据交换以及数据沉积在公共存储库中。 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Baysean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响应的保守生物网络。 此外，将进一步定义与引起效应相关的EED扰动途径。 这些研究的结果不仅将进一步将雌激素化合物作用的生理和有毒机制定义为融合互补的微阵列，代谢组和组织病理学数据的协同作用，成为全面的整合计算模型。 当与将要开发的高级信息工具一起使用时，这种方法还将证明能够从提出的创新数据管理系统中最大化知识提取的能力。