Comprehensive Assessment of Endocrine Active Mixtures

说明(由申请人提供)由于接触是以混合物的形式发生的，单个化学毒性的知识通常不足以进行风险评估。为了评估混合物的影响，需要在整个有机体及其基因组的背景下，理解在分子和组织水平上发生的相加、拮抗和协同作用。这项建议将使用严格的统计学来最小化完全析因设计，以检验混合物以成分比率、组织、剂量和时间依赖的方式诱导相加、协同和拮抗相互作用的假设。基因表达和组织水平分析将用于评估与剂量和时间相关的混合物相互作用。将检测单独的化学物质及其混合物在小鼠的大脑、肝脏、乳腺、子宫和骨骼中的雌激素活性。雌激素应答基因的cDNA克隆或表达序列标签(EST)将被用来构建定制的雌激素应答cDNA/EST阵列。将确定17-β-雌二醇、染料木素、甲氧氯胺和阿特拉津对小鼠肝脏和子宫的响应的剂量和时间依赖的基因表达模式。这些化学品具有不同固定剂量比的混合物将使用射线设计进行检查，并与单个化学品的模式进行比较，以确定相互作用。子宫基因表达模式将与子宫重量的增加相关联，以确定具有更大预测价值的基因生物标志物。肝脏基因表达模式也将与小鼠HEPA 1c1c7细胞的模式进行比较，以评估体外和体内模型之间的差异。还将检查大脑、乳腺和骨骼，以调查组织特异性雌激素基因表达模式的可能性。这些数据将使用主成分分析来识别与剂量和时间相关的基因的线性组合，使用聚类分析来识别具有共同行为的基因，以及使用一般线性混合模型来识别与对照相比在统计上显著的基因表达变化。相互作用的检测和表征将使用沿固定比率射线的估计剂量响应曲线之间已建立的统计比较来确定。这项建议旨在制定创新和可信的统计战略，以严格评估由含有使用共同作用机制的成分的混合物引起的相加、协同和拮抗相互作用。这种综合的方法还将提供对与组织水平效应和生理反应的病因学相关的化学引发的分子扰动的机制的洞察。

DESCRIPTION (Provided by Applicant) Knowledge of individual chemical toxicity is often inadequate for risk assessment since exposure occurs as mixtures. To assess the effects of mixtures, an understanding of the additive, antagonistic, and synergistic interactions that occur at the molecular and tissue levels is required within the context of the whole organism and its genome. This proposal will use rigorous statistics to minimize the full factorial design to test the hypothesis that mixtures induce additive, synergistic and antagonistic interactions in a component ratio-, tissue-, dose- and time-dependent manner. Gene expression and tissue level assays will be used to assess dose- and time-dependent mixture-interactions. Individual chemicals and their mixtures will be examined for estrogenic activity in the mouse brain, liver, mammary gland, uterus and bone. cDNA clones or expressed sequence tags (ESTs) for the estrogen responsive genes will be used to construct a custom estrogen responsive cDNA/EST array. Dose- and time-dependent gene expression patterns in response to 17-beta-estradiol, genistein, methoxychlor, and atrazine will be determined in mouse liver and uterus. Mixtures with different fixed dose ratios of these chemicals will be examined using a ray design and compared to patterns for individual chemicals to identify interactions. Uterine gene expression patterns will be correlated with increases in uterine weight to identify gene biomarkers with greater predictive value. Hepatic gene expression patterns will also be compared to patterns obtained in mouse Hepa 1c1c7 cells to assess differences between in vitro and in vivo models. Brain, mammary gland, and bone will also be examined to investigate the possibility of tissue-specific estrogenic gene expression patterns. The data will be analyzed using principal components analysis to identify linear combinations of genes that are related by dose and time, cluster analysis to identify genes with common behavior, and general linear mixed model to identify statistically significant changes in gene expression relative to controls. Detection and characterization of interactions will be determined using established statistical comparisons between the estimated dose response curves along the fixed-ratio ray. This proposal aims to develop innovative and credible statistical strategies for rigorously assessing additive, synergistic and antagonistic interactions induced by mixtures containing components that use a common mechanism of action. This comprehensive approach used will also provide insights into mechanisms of chemical-initiated molecular perturbations associated with tissue level effects and the etiology of a physiological response.