Bell Shannon M, Angrish Michelle M, Wood Charles E, Edwards Stephen W
*Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee; Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711; Current Affiliation: ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), Research Triangle Park, North Carolina.
Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711;
Toxicol Sci. 2016 Apr;150(2):510-20. doi: 10.1093/toxsci/kfw017. Epub 2016 Feb 19.
Newin vitrotesting strategies make it possible to design testing batteries for large numbers of environmental chemicals. Full utilization of the results requires knowledge of the underlying biological networks and the adverse outcome pathways (AOPs) that describe the route from early molecular perturbations to an adverse outcome. Curation of a formal AOP is a time-intensive process and a rate-limiting step to designing these test batteries. Here, we describe a method for integrating publicly available data in order to generate computationally predicted AOP (cpAOP) scaffolds, which can be leveraged by domain experts to shorten the time for formal AOP development. A network-based workflow was used to facilitate the integration of multiple data types to generate cpAOPs. Edges between graph entities were identified through direct experimental or literature information, or computationally inferred using frequent itemset mining. Data from the TG-GATEs and ToxCast programs were used to channel large-scale toxicogenomics information into a cpAOP network (cpAOPnet) of over 20 000 relationships describing connections between chemical treatments, phenotypes, and perturbed pathways as measured by differential gene expression and high-throughput screening targets. The resulting fatty liver cpAOPnet is available as a resource to the community. Subnetworks of cpAOPs for a reference chemical (carbon tetrachloride, CCl4) and outcome (fatty liver) were compared with published mechanistic descriptions. In both cases, the computational approaches approximated the manually curated AOPs. The cpAOPnet can be used for accelerating expert-curated AOP development and to identify pathway targets that lack genomic markers or high-throughput screening tests. It can also facilitate identification of key events for designing test batteries and for classification and grouping of chemicals for follow up testing.
新的体外测试策略使得为大量环境化学物质设计测试组合成为可能。要充分利用这些结果，需要了解潜在的生物网络以及描述从早期分子扰动到不良后果的途径的不良后果途径（AOP）。构建正式的AOP是一个耗时的过程，也是设计这些测试组合的限速步骤。在此，我们描述了一种整合公开可用数据以生成计算预测的AOP（cpAOP）支架的方法，领域专家可以利用该支架来缩短正式AOP开发的时间。基于网络的工作流程用于促进多种数据类型的整合以生成cpAOP。通过直接实验或文献信息识别图实体之间的边，或使用频繁项集挖掘进行计算推断。来自TG-GATEs和ToxCast项目的数据被用于将大规模毒理基因组学信息导入一个cpAOP网络（cpAOPnet），该网络包含超过20000个关系，描述了化学处理、表型以及通过差异基因表达和高通量筛选靶点测量的扰动途径之间的联系。所得的脂肪肝cpAOPnet作为一种资源提供给社区。将参考化学物质（四氯化碳，CCl4）和结果（脂肪肝）的cpAOP子网与已发表的机制描述进行了比较。在这两种情况下，计算方法都近似于人工整理的AOP。cpAOPnet可用于加速专家整理的AOP开发，并识别缺乏基因组标记或高通量筛选测试的途径靶点。它还可以促进识别用于设计测试组合以及对化学物质进行分类和分组以进行后续测试的关键事件。
