Decabromodiphenyl ethane as a neurotoxicant in Alzheimer’s disease: unraveling its mechanisms through the integration of multiple computational toxicology methods

The role of environmental pollutants as risk factors for Alzheimer’s disease (AD) and related neurodegenerative pathologies necessitates mechanistic investigation. Evidence implicates brominated flame retardants (BFRs)—decabromodiphenyl ethane (DBDPE)—in AD pathogenesis, though their molecular mechanisms remain inadequately elucidated. To address this challenge, we combined multiple cross-disciplinary methods (network toxicology, machine learning [ML], molecular docking, molecular dynamics [MD] simulations, and Mendelian randomization [MR] analysis) to systematically delineate DBDPE-induced AD pathogenesis. Initial screening of the SwissTargetPrediction database and GSE132903 dataset identified 47 overlapping DBDPE-AD targets. Subsequent protein-protein interaction (PPI) network analysis refined these to 42 high-confidence targets. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed association of core targets with metabolic pathways and neuroactive ligand-receptor interactions. Three core targets were prioritized using ML framework. Molecular docking confirmed strong binding affinities between DBDPE and the core targets. Given PLAU’s exceptional binding energy, we conducted MD simulations to validate complex stability and characterize binding-site interactions. Finally, MR analysis established causal links between PLAU and AD susceptibility. In summary, this study establishes a comprehensive theoretical framework for understanding the molecular mechanisms of DBDPE-induced AD and provides valuable insights for developing preventive and therapeutic strategies targeting AD associated with DBDPE exposure.

环境污染物作为阿尔茨海默病（Alzheimer’s disease, AD）及相关神经退行性病变的风险因子，其相关作用机制亟待开展深入探究。已有研究表明溴化阻燃剂（brominated flame retardants, BFRs）——十溴二苯乙烷（decabromodiphenyl ethane, DBDPE）——参与阿尔茨海默病的发病进程，但其具体分子机制尚未得到充分阐明。为解决这一研究难题，本研究结合多学科研究方法（网络毒理学、机器学习[machine learning, ML]、分子对接、分子动力学[molecular dynamics, MD]模拟以及孟德尔随机化[Mendelian randomization, MR]分析），系统解析DBDPE诱导阿尔茨海默病的发病机制。首先通过SwissTargetPrediction数据库与GSE132903数据集进行初步筛选，共得到47个DBDPE与AD的重叠靶点。后续通过蛋白质-蛋白质相互作用（protein-protein interaction, PPI）网络分析，将候选靶点筛选至42个高可信度靶点。随后开展基因本体论（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析，结果显示核心靶点与代谢通路及神经活性配体-受体相互作用密切相关。通过机器学习框架筛选出3个核心靶点，分子对接实验证实DBDPE与上述核心靶点之间存在较强的结合亲和力。鉴于PLAU展现出优异的结合能，本研究进一步开展分子动力学模拟以验证复合物稳定性并解析结合位点的相互作用模式。最终，孟德尔随机化分析证实PLAU与阿尔茨海默病易感性存在因果关联。综上，本研究为阐明DBDPE诱导阿尔茨海默病的分子机制构建了一套完整的理论框架，同时为针对DBDPE暴露相关阿尔茨海默病的预防与治疗策略开发提供了宝贵的研究思路。