Artemisinin ameliorates rheumatoid arthritis through modulation of the IL-17/PI3K/AKT signaling pathway: integrated network pharmacology, bioinformatics, and experimental validation

青蒿素 小桶 对接(动物) 计算生物学 交互网络 类风湿性关节炎 体内 转录组 自动停靠 药理学 联合疗法 转录因子 基因 信号转导 关节炎 化学 基因表达谱 网络拓扑 医学 生物 生物信息学 生物信息学
作者
Zongyuan Zhou,Le Wang,Yongzuo Li,Yue Yang,Lingyu Li,Xing Qin,Thomas Efferth,Xiaohua Lu,Zhe Qiang
出处
期刊:Frontiers in Immunology [Frontiers Media]
卷期号:17
标识
DOI:10.3389/fimmu.2026.1862104
摘要

Purpose Artemisinin regulates the immune system and is applied in RA treatment. Nevertheless, the effectiveness and potential mechanisms of artemisinin in the treatment of RA remain unclear, especially in fibroblast-like synoviocytes (FLSs). This study aimed to investigate the mechanism of artemisinin in the treatment of rheumatoid arthritis (RA) through an integrated network pharmacology and bioinformatics approach, supported by experimental validation both in MH7A cells and collagen-induced arthritis (CIA) mice. Methods SwissTargetPrediction database, TCMSP, TargetNet, SuperPred, and PharmMapper were used to identify the targets of artemisinin. RA-related differentially expressed genes (DEGs) were collected by integrating three GEO datasets. DEGs related to artemisinin were utilized to create protein-protein interaction networks and visualize them with STRING and Cytoscape. GO and KEGG functional enrichment analyses were executed. Molecular docking and molecular dynamics (MD) simulations were carried out to analyze core targets using the AutoDock Vina and Desmond software. Furthermore, CIA mouse and MH7A cells were used to validate the results obtained from network pharmacology. Results There were 73 target genes intersecting between the artemisinin targets and the DEGs. PPI network analysis and its topology indicated that AR, EGFR, JAK2, and PTGS2 had the greatest centrality. GO and KEGG enrichment analyses suggested that the candidate targets were significantly associated with the PI3K/AKT signaling pathway. Molecular docking and an in vivo study were used for further investigation. Artemisinin interacted with the cavities of AR, JAK2, EGFR, and PTGS2 with binding energies of –8.65, –8.61, –7.48, and –8.48 kcal/mol, respectively. MD simulations showed that the AR–artemisinin complex maintained persistent interactions and stable energy profiles over 20 ns. The CIA model and MH7A cell studies confirmed that artemisinin exposure inhibited the proliferation and production of IL-1β, IL-6, and IL-8 triggered by IL-17. Furthermore, artemisinin attenuated IL-17-induced AKT phosphorylation levels. Conclusion These findings suggest that artemisinin may ameliorate RA partly by suppressing IL-17-induced inflammatory activation and PI3K/AKT signaling. AR, EGFR, JAK2, and PTGS2 were prioritized as candidate hub targets.
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