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基于系统生物学和网络药理学探索青风藤治疗类风湿关节炎的活性成分及潜在作用机制

Exploring the active ingredients and potential mechanisms of action of sinomenium acutum in the treatment of rheumatoid arthritis based on systems biology and network pharmacology.

作者信息

Gong Nan, Wang Lin, An Lili, Xu YuanKun

机构信息

Graduate School, Guizhou University of Traditional Chinese Medicine, Guiyang, China.

Orthopedic Surgery, First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China.

出版信息

Front Mol Biosci. 2023 Feb 27;10:1065171. doi: 10.3389/fmolb.2023.1065171. eCollection 2023.

Abstract

To investigate and predict the targets and signaling pathways of sinomenium acutum (SA) in the treatment of rheumatoid arthritis (RA) through systems biology and network pharmacology, and to elucidate its possible mechanisms of action. We screened the active ingredients and corresponding target proteins of SA in Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Traditional Chinese Medicines Integrated Database (TCMID) and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN); and obtained the targets of rheumatoid arthritis diseases in a database of gene-disease associations (DisGeNET), Online Mendelian Inheritance in Man (OMIM) database. The two targets were mapped by Venn diagram and the intersection was taken. The intersecting targets were used to construct protein-protein interaction (PPI) network maps in the String database, and Metascape was used for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Finally, the molecular docking technique was applied to validate and further clarify the core target of SA for the treatment of rheumatoid arthritis. A total of six active ingredients and 217 potential targets were obtained after screening; 2,752 rheumatoid arthritis-related targets and 66 targets common to RA and SA. GO function and KEGG pathway enrichment analysis yielded 751 GO function entries (652 GO biological processes, 59 GO molecular functions and 40 GO cellular components) and 77 KEGG signaling pathways. It mainly involves pathways related to neural activity ligand-receptor interaction pathways, cancer pathways, calcium signaling channels, Th17 cell differentiation and others, which are mainly classified into four categories, including regulation of immunity, anti-inflammation, regulation of cell growth and apoptosis, and signaling. The molecular docking results showed that the binding energy of PTGS2, CASP3, JUN and PPARG to the key components beta-sitosterol, 16-epi-Isositsirikine, Sinomenine and Stepholidine were ≤ -6.5 kcal/mol, suggesting the existence of molecular binding sites. SA acts on key targets such as PTGS2, CASP3, JUN, and PPARG to modulate signaling pathways such as neural activity ligand-receptor interaction, cancer, calcium ion, NF-κB, and Th17 cell differentiation to regulate immunity, anti-inflammation, modulation of cell cycle, bone metabolism, and signaling for the treatment of RA. It was also confirmed that the treatment of RA with SA has multi-component, multi-target, multi-pathway and multi-mechanism characteristics.

摘要

通过系统生物学和网络药理学研究并预测青风藤(SA)治疗类风湿关节炎(RA)的靶点和信号通路,阐明其可能的作用机制。我们在中药系统药理学数据库与分析平台(TCMSP)、中药综合数据库(TCMID)和中药分子机制生物信息学分析工具(BATMAN)中筛选青风藤的活性成分和相应靶蛋白;并在基因-疾病关联数据库(DisGeNET)、人类孟德尔遗传在线(OMIM)数据库中获取类风湿关节炎疾病的靶点。将二者靶点通过韦恩图映射并取交集。将交集靶点在String数据库中构建蛋白质-蛋白质相互作用(PPI)网络图,并用Metascape进行基因本体(GO)功能注释和京都基因与基因组百科全书(KEGG)通路富集。最后,应用分子对接技术验证并进一步明确青风藤治疗类风湿关节炎的核心靶点。筛选后共得到6种活性成分和217个潜在靶点;2752个类风湿关节炎相关靶点以及RA与SA共有的66个靶点。GO功能和KEGG通路富集分析得到751个GO功能条目(652个GO生物学过程、59个GO分子功能和40个GO细胞组分)和77条KEGG信号通路。主要涉及与神经活性配体-受体相互作用通路、癌症通路、钙信号通道、Th17细胞分化等相关的通路,主要分为免疫调节、抗炎、细胞生长与凋亡调节以及信号转导四类。分子对接结果显示,PTGS2、CASP3、JUN和PPARG与关键成分β-谷甾醇、16-表异千金藤素碱、青藤碱和粉防己碱的结合能≤ -6.5 kcal/mol,表明存在分子结合位点。青风藤作用于PTGS2、CASP3、JUN和PPARG等关键靶点,调节神经活性配体-受体相互作用、癌症、钙离子、NF-κB和Th17细胞分化等信号通路,以调节免疫、抗炎、调节细胞周期、骨代谢和信号转导来治疗类风湿关节炎。还证实青风藤治疗类风湿关节炎具有多成分、多靶点、多途径和多机制的特点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a361/10009275/3b3a3949f956/fmolb-10-1065171-g001.jpg

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