Conan Mael, Théret Nathalie, Langouet Sophie, Siegel Anne
Institut de Recherche en Santé, Environnement et Travail, Univ Rennes, Inserm, EHESP, IRSET, Rennes, France.
Institut de Recherche en Informatique et Systèmes Aléatoires, Univ Rennes, Inria, CNRS, IRISA, Rennes, France.
BMC Bioinformatics. 2021 Sep 21;22(1):450. doi: 10.1186/s12859-021-04363-6.
The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A[Formula: see text]C).
We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures.
Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.
肝脏在异生物（药物、污染物、农药、食品添加剂等化学物质）的代谢活化中起主要作用。在令人关注的环境污染物中，杂环芳香胺（HAA）是被国际癌症研究机构（IARC）归类为可能或很可能致癌物（2A或2B类）的异生物。关于这些HAA对人类影响的信息很少。虽然HAA是一个已鉴定出的三十多种化学物质的家族，但其中只有少数几种（MeIQx、PhIP、A[化学式：见原文]C）在人肝脏中的代谢活化及可能的DNA加合物形成已得到充分表征。
我们开发了一种建模方法，以预测异生物的所有可能代谢物以及与能够结合DNA的代谢物产生相关的酶谱。我们的代谢物预测方法依赖于从输入代谢物构建一个丰富且有注释的代谢物图谱。该流程整合了反应预测工具（SyGMa）、代谢位点预测工具（Way2Drug、SOMP和Fame 3）、一种估计异生物形成DNA加合物能力的工具（XenoSite Reactivity V1）以及基于贝叶斯框架的筛选程序。该预测流程先用咖啡因进行了评估，然后应用于HAA。该方法用于确定与源自每种能够结合DNA的HAA的代谢物最大化相关的酶谱。根据酶谱对HAA进行的分类与其化学结构一致。
总体而言，基于计算机系统生物学方法的预测毒理学模型为估计各类环境污染物的遗传毒性开辟了前景。此外，我们基于酶谱确定的方法开启了在正常和病理生理情况下预测各种易与DNA结合的异生物代谢物的可能性。
