Identification of inhibitors from a functional food-based plant Perillae Folium against hyperuricemia via metabolomics profiling, network pharmacology and all-atom molecular dynamics simulations

作者：Chuanghai Wu, Ann Rann Wong, Qinghong Chen, Shuxuan Yang, Meiling Chen, Xiaomin Sun, Lin Zhou, Yanyan Liu, Angela Wei Hong Yang, Jianlu Bi, Andrew Hung, Hong Li, Xiaoshan Zhao · 发表于：Frontiers in Endocrinology · 年份：2024 · DOI：10.3389/fendo.2024.1320092 · 被引用次数：16 · 研究领域：Gout, Hyperuricemia, Uric Acid、Metabolomics and Mass Spectrometry Studies、Natural Products and Biological Research

Introduction: Hyperuricemia (HUA) is a metabolic disorder caused by purine metabolism dysfunction in which the increasing purine levels can be partially attributed to seafood consumption. Perillae Folium (PF), a widely used plant in functional food, has been historically used to mitigate seafood-induced diseases. However, its efficacy against HUA and the underlying mechanism remain unclear. Methods: A network pharmacology analysis was performed to identify candidate targets and potential mechanisms involved in PF treating HUA. The candidate targets were determined based on TCMSP, SwissTargetPrediction, Open Targets Platform, GeneCards, Comparative Toxicogenomics Database, and DrugBank. The potential mechanisms were predicted via Gene Ontology (GO) and Kyoto Gene and Genome Encyclopedia (KEGG) analyses. Molecular docking in AutoDock Vina and PyRx were performed to predict the binding affinity and pose between herbal compounds and HUA-related targets. A chemical structure analysis of PF compounds was performed using OSIRIS DataWarrior and ClassyFire. We then conducted virtual pharmacokinetic and toxicity screening to filter potential inhibitors. We further performed verifications of these inhibitors' roles in HUA through molecular dynamics (MD) simulations, text-mining, and untargeted metabolomics analysis. Results: We obtained 8200 predicted binding results between 328 herbal compounds and 25 potential targets, and xanthine dehydrogenase (XDH) exhibited the highest average bin...