整合网络毒理学、分子对接与分子动力学模拟探究低氯代二苯并二噁英致抑郁症的机制

Integrating network toxicology, molecular docking and molecular dynamics simulation to explore the mechanism underlying depression induced by low?chlorinated polychlorinated dibenzo?p?dioxins

中文关键词:  网络毒理学  分子对接  分子动力学  抑郁症  多氯代二苯并二噁英  低氯代二苯并二噁英

英文关键词:network toxicology  molecular docking  molecular dynamics simulation  depression  polychlorinated dibenzo?p?dioxins (PCDDs)  low?chlorinated polychlorinated dibenzo?p?dioxins (LC?PCDDs)

中文摘要:

      背景 多氯代二苯并二噁英（PCDDs）为一类全球性环境污染物，已有多项研究提示其具备神经毒性。但现有研究多集中于2,3,7,8 - 四氯二苯并二噁英（2,3,7,8-TCDD），而环境中广泛检出的低氯代二苯并二噁英（LC?PCDDs）的致抑郁效应及分子机制仍不清楚。目的 明确LC-PCDDs是否会诱发抑郁症及相关分子机制，为环境精神毒理风险评估提供参考。方法 ①网络毒理学：以4种LC-PCDDs为研究对象，通过公共数据库预测其毒性，并分别获取LC-PCDDs与抑郁症的相关靶基因，利用Strting网站对二者的交集靶基因制作蛋白质互作网络，然后利用CytoNCA插件对PPI网络进行拓扑分析，筛选得到核心靶点。最后通过DAVID数据库将核心靶点进行基因本体论分析及通路富集分析显示。②分子对接技术：采用AutoDock Tools 1.5.7模拟分子对接，并借助PyMOL 3.1.X对对接所得的复合物构象及相互作用界面进行可视化展示。③分子动力学模拟：使用Gromacs 2025对复合物进行100 ns分子动力学模拟，蛋白质用AMBER99SB-ILDN力场，配体用GAFF2力场构建拓扑并合并，且以TIP3P水模型、0.15 M Na?/Cl?构建生理体系，经能量最小化、2 ns平衡后，进行100 ns模拟（310 K、1 bar）。结果 ①网络毒理学筛选出10个核心基因，分别是ESR1、HSP90AA1、PPARG、EGFR、MAPK1、MAPK14、AR、PGR、CDK2、TGFBR1, 且其富集于多条关键信号通路，提示LC-PCDDs可能通过信号传导异常、激素紊乱及炎症等生物过程促发抑郁症。②分子对接结果显示4种LC-PCDDs与核心靶点均具有较强结合力，其中2-MCDD与PPARG的结合亲和力最强（ΔG=-8.836 kcal/mol）。③分子动力学模拟结果示配体与蛋白的结合在分子动力学模拟中表现稳定。结论LC-PCDDs 是诱发抑郁症的重要环境风险因子，其可介导“代谢-氧化损伤-凋亡”多维调控网络参与抑郁症的发病过程。

英文摘要:

      Background Polychlorinated dibenzo?p?dioxins (PCDDs) are a class of global environmental contaminants, and multiple studies have indicated their neurotoxic potential. Existing researches mainly focus on 2,3,7,8?tetrachlorodibenzo?p?dioxin (2,3,7,8?TCDD). Nevertheless, the depression?inducing effect and underlying molecular mechanism of low?chlorinated polychlorinated dibenzo?p?dioxins (LC?PCDDs), which are widely detected in the environment, remain unclear.Objective To clarify whether LC?PCDDs can induce depression and explore its relevant molecular mechanism, so as to provide references for the risk assessment of environmental neuropsychotoxicology. Methods① Network toxicology: Four LC-PCDDs were selected as research objects. Public databases were adopted to predict their toxic effects. Target genes related to LC-PCDDs and depression were retrieved, and the overlapping targets were used to construct a protein-protein interaction (PPI) network via the STRING database. Topological analysis of the PPI network was performed using the CytoNCA plugin to screen hub targets. Gene Ontology (GO) and pathway enrichment analyses of hub targets were conducted based on the DAVID database. ② Molecular docking: AutoDock Tools 1.5.7 was used for molecular docking simulation, and PyMOL 3.1.X visualized complex conformations and interaction interfaces. ③ Molecular dynamics (MD) simulation: GROMACS 2025 was applied to perform 100 ns MD simulations of ligand-protein complexes. The AMBER99SB-ILDN force field was employed for proteins, and the GAFF2 force field for ligands. The physiological system was built with the TIP3P water model and 0.15 M Na?/Cl?. After energy minimization and 2 ns equilibration, a 100 ns production simulation was carried out at 310 K and 1 bar.Results ：① Ten hub genes were identified via network toxicology, including ESR1, HSP90AA1, PPARG, EGFR, MAPK1, MAPK14, AR, PGR, CDK2 and TGFBR1. These genes were enriched in multiple critical signaling pathways, suggesting that LC-PCDDs may promote depression via abnormal signal transduction, hormonal disturbance and inflammation. ② Molecular docking revealed that the four LC-PCDDs exhibited strong binding affinities to hub targets. Among them, 2-MCDD possessed the highest binding affinity to PPARG (ΔG = ?8.836 kcal/mol). ③ MD simulation demonstrated stable binding between ligands and proteins throughout the simulation. Conclusion：LC-PCDDs represent critical environmental risk factors for depression. They participate in the pathogenesis of depression by regulating a multi-dimensional "metabolism-oxidative damage-apoptosis" signaling network