School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.
School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.
Environ Int. 2021 Feb;147:106339. doi: 10.1016/j.envint.2020.106339. Epub 2021 Jan 12.
Diesel particulate matter (DPM), a major subset of urban fine particulate matter (PM2.5), raises huge concerns for human health and has therefore been classified as a group 1 carcinogen by the International Agency for Research on Cancer (IARC). However, as DPM is a complex mixture of various chemicals, understanding of DPM's toxicity mechanism remains limited. As the major exposure route of DPM is through inhalation, we herein investigated its toxicity mechanism based on the Adverse Outcome Pathway (AOP) of pulmonary fibrosis, which we previously submitted to AOPWiki as AOP ID 206 (AOP206). We first screened whether individual chemicals in DPM have the potential to exert their toxicity through AOP206 by using the ToxCast database and deep learning models approach, then confirmed this by examining whether DPM as a mixture alters the expression of the molecular initiating event (MIE) and key events (KEs) of AOP206. For identifying the activeness of the component chemicals of DPM, we used 24 ToxCast assays potentially related to AOP206 and deep learning models based on these assays, which were identified and developed in our previous study. Of the 100 individual chemicals in DPM, 34 were active in PPARγ (MIE)-related assay, of which 17 were active in one or more KEs. To further identify whether individual chemicals in DPM are related to the MIE of AOP206, we performed molecular docking simulation on PPARγ for the chemicals showing activeness. Benzo[e]pyrene, benzo[a]pyrene and other related chemicals were the most likely to bind to PPARγ. In in vitro experiments, PPARγ activity increased with exposure of the DPM mixture, and the protein expression of PPARγ (MIE), and fibronectin (AO) also tended to be increased. Overall, we have demonstrated that AOP206 can be applied to identify the toxicity pathway of DPM. Further, we suggest that applying the AOP approach using ToxCast and deep learning models is useful for identifying potential toxicity pathways of chemical mixtures, such as DPM, by determining the activity of individual chemicals.
柴油机颗粒物（DPM）是城市细颗粒物（PM2.5）的主要组成部分，对人类健康构成了巨大威胁，因此被国际癌症研究机构（IARC）列为 1 类致癌物。然而，由于 DPM 是各种化学物质的复杂混合物，其毒性机制的理解仍然有限。由于 DPM 的主要暴露途径是通过吸入，我们在此根据先前提交给 AOPWiki 的肺纤维化的不良结局途径（AOP）AOP ID 206（AOP206）来研究其毒性机制。我们首先使用 ToxCast 数据库和深度学习模型方法筛选 DPM 中的单个化学物质是否有可能通过 AOP206 发挥其毒性，然后通过检查 DPM 作为混合物是否改变 AOP206 的分子起始事件（MIE）和关键事件（KEs）的表达来确认这一点。为了确定 DPM 成分化学物质的活性，我们使用了 24 种可能与 AOP206 相关的 ToxCast 测定法和基于这些测定法的深度学习模型，这些测定法是在我们之前的研究中确定和开发的。在 DPM 中的 100 种单个化学物质中，有 34 种在与 PPARγ（MIE）相关的测定中具有活性，其中 17 种在一个或多个 KEs 中具有活性。为了进一步确定 DPM 中的单个化学物质是否与 AOP206 的 MIE 相关，我们对显示活性的化学物质对 PPARγ 进行了分子对接模拟。苯并[e]芘、苯并[a]芘和其他相关化学物质最有可能与 PPARγ 结合。在体外实验中，随着 DPM 混合物的暴露，PPARγ 活性增加，PPARγ（MIE）和纤维连接蛋白（AO）的蛋白表达也趋于增加。总的来说，我们已经证明 AOP206 可用于识别 DPM 的毒性途径。此外，我们建议使用 ToxCast 和深度学习模型的 AOP 方法通过确定单个化学物质的活性来识别 DPM 等化学混合物的潜在毒性途径是有用的。
