基于网络药理学探讨白英干预类风湿性关节炎的潜在作用机制研究

doi:10.5246/jcps.2024.06.041

摘要/Abstract

摘要：

以网络药理学和分子对接相结合的综合策略为基础, 阐明白英治疗风湿性关节炎的有效成分及其可能的作用机制。首先从文献中提取白英中的相关化合物, 通过GeneCards、DisGeNET和OMIM数据库获得RA相关靶标, 构建中药-化合物-靶标网络和蛋白-蛋白相互作用(PPI)网络, 以预测白英可能存在的关键药物靶点并确定药物靶点与化合物之间的主要结合位点及相互作用力, 然后利用对接技术对预测的候选靶点进行了分析, 最后用白英乙酸乙酯萃取物干预人类风湿关节炎滑膜成纤维细胞模型(MH7A)进行体外验证。最终筛选出41个潜在活性化合物和126个交叉药理靶标; GO富集分析显示, 基因表达的阳性调节、对缺氧的反应和凋亡过程与白英治疗RA机制密切相关; KEGG通路分析表明TNF信号通路、IL-17信号通路、PI3K-Akt信号通路、MAPK信号通路、Toll样受体信号通路与白英干预RA有关, 其作用的关键靶点可能是AKT1、TP53、VEGR、CASP3、TNF和IL6等; 分子对接分析表明, 白英中薯蓣皂苷元、士的宁、澳洲边茄碱、苦茄碱、澳洲茄胺和熊果酸等活性成分与STAT3、JUN、MAPK1、TNF、TP53、IL6、MAPK8、IL1B、MMP1和MMP3等靶点具有良好的结合能力, 提示白英中可通过调节多种信号通路和靶点从而对RA起到干预和治疗作用。体外实验表明白英乙酸乙酯萃取物可以抑制MH7A细胞增殖, 并显著降低MH7A细胞炎症因子TNF-α, IL-17, IL-1β和 IL-6的分泌。该研究增加了中药白英及其有效成分抗RA药理作用的新认识, 也为相关研究提供了新方向和新思路的参考。

关键词: 白英, 风湿性关节炎, 靶点, 网络药理, 分子对接

Abstract:

To elucidate the active ingredients and potential mechanism of Solanum lyratum in the treatment of rheumatoid arthritis (RA), we employed a comprehensive strategy that combined network pharmacology and molecular docking. First, we systematically retrieved relevant compounds from S. lyratum documented in the literature. We obtained RA-related targets by querying GeneCards, DisGeNET, and the OMIM database. Subsequently, we constructed drug-compound-target and protein-protein interaction (PPI) networks to predict the promising protein targets of S. lyratum and identify the primary interactions between these protein targets and compounds. To validate our predicted candidate targets, we employed docking techniques. Finally, we conducted an in vitro intervention and validation using the ethyl acetate extract of S. lyratum on human RA synovial fibroblasts (MH7A). Our analysis identified a total of 41 potential active compounds and 126 intersecting pharmacological targets. GO enrichment analysis revealed that positive regulation of gene expression, response to hypoxia, and apoptotic processes were closely associated with S. lyratum treatment in RA. KEGG pathway analysis suggested that the TNF signaling pathway, IL-17 signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, and Toll-Like receptor signaling pathway might play a pivotal role in S. lyratum intervention in RA. Consequently, key targets could include AKT1, TP53, VEGF, CASP3, TNF, and IL6. Molecular docking analysis indicated that diosgenin, strychnine, solamargine, solamarine, solasodine, and ursolic acid exhibited strong binding affinities with STAT3, JUN, MAPK1, TNF, TP53, IL6, MAPK8, IL1B, MMP1, and MMP3. These active compounds in S. lyratum had the potential to regulate multiple signaling pathways and target molecules, thereby exerting preventive and therapeutic effects in RA. In our in vitro experiment, we observed that the ethyl acetate extract of S. lyratum inhibited the proliferation of MH7A cells and reduced the release of cytokines. These experimental results aligned with the predictions generated through the network pharmacology approach. This study not only provided a theoretical foundation for the use of S. lyratum in the treatment of RA but also offered valuable insights for further investigations into the action mechanisms of Chinese herbal extract compounds.

Key words: Solanum lyratum, Rheumatoid arthritis, Target, Network pharmacology, Molecular docking

Supporting:

徐云玲, 贺蛟龙. 基于网络药理学探讨白英干预类风湿性关节炎的潜在作用机制研究[J]. 中国药学(英文版), 2024, 33(6): 559-570.

Yunling Xu, Jiaolong He. Underlying mechanisms of Solanum lyratum in the treatment of rheumatoid arthritis: insights from network pharmacology[J]. Journal of Chinese Pharmaceutical Sciences, 2024, 33(6): 559-570.

图/表 7

Figure 1. (A) Venn diagram of the common target; (B) Drug-components-targets network map.

Figure 2. PPI network of interaction target genes.

Figure 3. Analysis of gene function enrichment. (A) GO analysis diagram; (B) KEGG enrichment bubble diagram.

Figure 4. Thermal diagram analysis of molecular docking.

Figure 5. Simulation diagram of docking between main active components of S. lyratum and key targets.

Table 1. Cell proliferation inhibitory rate in each group (x ± s, n = 3).

Figure 6. Determination results of TNF-α, IL-17, IL-1β and IL-6 contents in culture medium in each group (x ± s, n = 3). *P < 0.05, vs. TNF-α group; **P < 0.01, vs. TNF-α group.

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