Estrogen Elicited Gene Expression Network Elucidation in the Rat UterusEPA Grant Number: R831847
Title: Estrogen Elicited Gene Expression Network Elucidation in the Rat Uterus
Investigators: Zacharewski, Timothy , Chan, Christina , Harkema, Jack
Institution: Michigan State University
EPA Project Officer: Klieforth, Barbara I
Project Period: September 1, 2004 through August 31, 2007
Project Amount: $747,960
RFA: Computational Toxicology and Endocrine Disruptors: Use of Systems Biology in Hazard Identification and Risk Assessment (2004) RFA Text | Recipients Lists
Research Category: Economics and Decision Sciences , Endocrine Disruptors , Health Effects , Computational Toxicology , Health , Safer Chemicals
Systems biology involves the iterative development of strategies that integrate disparate physiological and biochemical data into computational models that are capable of predicting the biology of a cell or organism. In order to facilitate hazard identification and risk assessment, a comprehensive quantitative understanding of the molecular, cellular, physiological, and toxicological effects that are elicited following acute and chronic exposure to synthetic and natural chemicals is required within the context of the whole organism. The objective of this proposal is to develop a computational model that will identify and predict critical estrogenic endocrine disruptor elicited changes in gene expression which play a central role in the observed physiological/toxic effects based on systematic and quantitative data obtained from comparative in silico, genomic, molecular and histopathological approaches.
Gene expression and histopathological changes elicited by ethynyl estradiol, genistein, bisphenol A, o,p'-DDT will be assessed in ovariectomized immature female Sprague-Dawley rats. Dose-and time-dependent gene expression changes will be determined using customized sequence-verified cDNA rat arrays enriched with estrogen responsive genes. Significant changes in expression will be identified and weighed using Bayes and t-statistic approaches, and verified by quantitative real-time PCR (QRTPCR), western analysis, in situ hybridization and/or immunohistochemistry. Chromatin immunoprecipitation (ChIP) assays will further elucidate the estrogen receptor (ER)-mediated mechanisms of action and causal relationships between genes. In addition, histopathological assessments will be conducted to distinguish adaptive and toxic responses using various computational methods including canonical correlation, and Fisher discriminate analyses. Genetic algorithm (GA)/partial least squares (PLS) analysis will then be used to integrate this disparate data into a model that can identify the most relevant genes associated with a histopathological outcome. All data will be captured in dbZach (http://dbzach.fst.msu.edu Exit ), a MIAME-compliant toxicogenomic supportive database that facilitates data analysis, integration of disparate data, and sharing with other investigators.
The models developed will identify gene expression changes most highly associated with EED elicited histopathological uterine responses. Examination of multiple EEDs with varying potencies will also identify key regulatory nodes responsible for eliciting these responses, which could lead to the development of high throughput endocrine disruptor screening assays for chemicals in commerce. The data and resulting models can also be integrated with other algorithms (i.e. PBPK) to create a more comprehensive model of the hypothalamic-pituitary-gonadal axis.