Taboureau Olivier, Audouze Karine
INSERM UMR-S973, Molécules Thérapeutiques in silico, Paris, France.
University of Paris Diderot, Paris, France.
ALTEX. 2017;34(2):289-300. doi: 10.14573/altex.1607201. Epub 2016 Oct 21.
During the past decades, many epidemiological, toxicological and biological studies have been performed to assess the role of environmental chemicals as potential toxicants associated with diverse human disorders. However, the relationships between diseases based on chemical exposure rarely have been studied by computational biology. We developed a human environmental disease network (EDN) to explore and suggest novel disease-disease and chemical-disease relationships. The presented scored EDN model is built upon the integration of systems biology and chemical toxicology using information on chemical contaminants and their disease relationships reported in the TDDB database. The resulting human EDN takes into consideration the level of evidence of the toxicant-disease relationships, allowing inclusion of some degrees of significance in the disease-disease associations. Such a network can be used to identify uncharacterized connections between diseases. Examples are discussed for type 2 diabetes (T2D). Additionally, this computational model allows confirmation of already known links between chemicals and diseases (e.g., between bisphenol A and behavioral disorders) and also reveals unexpected associations between chemicals and diseases (e.g., between chlordane and olfactory alteration), thus predicting which chemicals may be risk factors to human health. The proposed human EDN model allows exploration of common biological mechanisms of diseases associated with chemical exposure, helping us to gain insight into disease etiology and comorbidity. This computational approach is an alternative to animal testing supporting the 3R concept.
在过去几十年中，已经开展了许多流行病学、毒理学和生物学研究，以评估环境化学物质作为与各种人类疾病相关的潜在毒物的作用。然而，基于化学物质暴露的疾病之间的关系很少通过计算生物学进行研究。我们开发了一个人类环境疾病网络（EDN），以探索并提出新的疾病-疾病和化学物质-疾病关系。所呈现的带评分的EDN模型是基于系统生物学和化学毒理学的整合构建的，使用了TDDB数据库中报告的化学污染物及其疾病关系的信息。由此产生的人类EDN考虑了毒物-疾病关系的证据水平，从而在疾病-疾病关联中纳入了一定程度的显著性。这样的网络可用于识别疾病之间未被表征的联系。以2型糖尿病（T2D）为例进行了讨论。此外，这个计算模型能够确认化学物质与疾病之间已知的联系（例如双酚A与行为障碍之间的联系），还能揭示化学物质与疾病之间意外的关联（例如氯丹与嗅觉改变之间的关联），从而预测哪些化学物质可能是人类健康的风险因素。所提出的人类EDN模型有助于探索与化学物质暴露相关的疾病的共同生物学机制，帮助我们深入了解疾病的病因和共病情况。这种计算方法是支持3R概念的动物试验的一种替代方法。
