Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310006, China.
Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China.
Chemosphere. 2018 Nov;210:312-319. doi: 10.1016/j.chemosphere.2018.07.023. Epub 2018 Jul 6.
Computational toxicology is widely applied to screen tens and thousands of potential environmental endocrine disruptors (EDCs) for its great efficiency and rapid evaluation in recent years. Polychlorinated biphenyls (PCBs) with 209 congeners have not been comprehensively tested for their ability to interact with the thyroid receptor (TR), which is one of the most extensively studied targets related to the effects of EDCs. In this study, we aimed to determine the thyroid-disrupting mechanisms of 209 PCBs through the combination of a novel computational ternary classification model and luciferase reporter gene assay. The reporter gene assay was performed to examine the hormone activities of 22 PCBs via TR to classify their profiles as agonistic, antagonistic or inactive. Thus, six agonists, eleven antagonists and seven inactive chemicals against TR were identified in in vitro assays. Then, six relevant variables, including molecular structural descriptors and molecular docking scores, were selected for describing compounds. Machine learning methods (i.e., linear discriminant analysis (LDA) and support vector machines (SVM)) were used to build classification models. The optimal model was obtained by using SVM, with an accuracy of 88.24% in the training set, 80.0% in the test set and 75.0% in 10-fold cross-validation. The remaining 187 PCB congeners' hormone activities were predicted using the obtained models. Out of these PCBs, the SVM model predicted 38 agonists and 81 antagonists. The findings revealed that higher chlorinated PCBs tended to have TR-antagonistic activities, whereas lower chlorinated PCBs were agonists. This study provided a reference for further exploring PCBs' thyroid effect.
近年来，计算毒理学因其在筛选成千上万种潜在环境内分泌干扰物（EDCs）方面的高效性和快速评估而得到广泛应用。具有 209 个同系物的多氯联苯（PCBs）尚未全面测试其与甲状腺受体（TR）相互作用的能力，TR 是研究与 EDCs 作用相关的最广泛的靶标之一。在这项研究中，我们旨在通过结合新型计算三元分类模型和荧光素酶报告基因检测，确定 209 种 PCBs 的甲状腺干扰机制。报告基因检测通过 TR 检查 22 种 PCBs 的激素活性，将其分类为激动剂、拮抗剂或无活性。因此，在体外试验中鉴定出六种激动剂、十一种拮抗剂和七种针对 TR 的无活性化学物质。然后，选择六个相关变量，包括分子结构描述符和分子对接分数，用于描述化合物。机器学习方法（即线性判别分析（LDA）和支持向量机（SVM））用于构建分类模型。使用 SVM 获得最佳模型，在训练集中的准确率为 88.24%，在测试集中的准确率为 80.0%，在 10 倍交叉验证中的准确率为 75.0%。使用获得的模型预测了其余 187 种 PCB 同系物的激素活性。在这些 PCB 中，SVM 模型预测了 38 种激动剂和 81 种拮抗剂。研究结果表明，高氯化 PCBs 倾向于具有 TR 拮抗剂活性，而低氯化 PCBs 则为激动剂。本研究为进一步探索 PCBs 的甲状腺作用提供了参考。
