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Using integrative informatics to bridge toxicogenomics and disease biology

机译：使用综合信息学桥接毒物基因组学和疾病生物学

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Traditional epidemiological methods for associating environmental exposures to human health are often limited in their ability to uncover molecular mechanisms that link molecular perturbations induced by a chemical exposure to pathophysiology. Additionally, traditional methods based on investigating exposed populations provide limited opportunities to construct predictive toxicogenomic models capable of identifying health liabilities of chemical exposures prior to observing manifestations of their potential deleterious effects in a population. The goal of this study is to develop and apply integrative informatics methodologies that incorporate existing molecular data from human diseases and chemical perturbagens to identify novel links between chemical exposures and disease pathophysiology. We developed a novel method that integrates disease molecular profiles from the NCBI Gene Expression Omnibus (GEO) with molecular signatures of chemical exposures constructed from representative data in the Comparative Toxicogenomics Database (CTD). This method employs a statistical approach to identify synergistic molecular patterns overlapping between disease states and chemical exposures, which suggest that an exposure could induce or exacerbate pathological molecular states in human tissues. We applied this method to multiple molecular profiles of breast cancer to identify chemical exposures that induce molecular patterns that are significantly synergistic with multiple representations of the disease state. Our analysis identified many chemical agents, such as Polychlorinated biphenyls (PCBs), which are known to modulate the pathophysiology of breast cancer. This study demonstrates the utility and potential for developing and applying integrative informatics approaches to enable predictive toxicogenomics and to identify putative molecular mechanisms underlying associations between chemical exposures and human disease states.
机译：将环境暴露与人类健康联系起来的传统流行病学方法通常在发现分子机制方面的能力受到限制，这些分子机制将化学暴露引起的分子扰动与病理生理联系起来。此外，基于调查暴露人群的传统方法为构建预测的毒物基因组模型提供了有限的机会，该模型能够在观察化学暴露对人群的潜在有害影响之前识别出化学暴露的健康责任。这项研究的目的是开发和应用整合信息学方法论，该方法论结合了人类疾病和化学扰动的现有分子数据，以识别化学暴露与疾病病理生理之间的新型联系。我们开发了一种新方法，该方法将NCBI基因表达综合（GEO）中的疾病分子概况与根据比较毒物基因组数据库（CTD）中的代表性数据构建的化学暴露的分子特征进行整合。该方法采用统计方法来识别疾病状态和化学暴露之间重叠的协同分子模式，这表明暴露可以诱导或加剧人体组织中的病理分子状态。我们将这种方法应用于乳腺癌的多种分子概况，以确定化学暴露可诱导与疾病状态的多种表现形式显着协同作用的分子模式。我们的分析确定了许多化学试剂，例如多氯联苯（PCBs），这些化学试剂可调节乳腺癌的病理生理学。这项研究证明了开发和应用综合信息学方法以实现预测的毒理基因组学和识别潜在的分子机制的潜在作用和潜力，这些分子机制是化学暴露与人类疾病状态之间关联的基础。

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