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基于网络药理学预测丹皮总苷抑制白细胞募集和血管生成治疗类风湿关节炎。

Network pharmacology-based prediction of inhibiting leukocyte recruitment and angiogenesis of total glucosides of peony against rheumatoid arthritis.

机构信息

Department of Rheumatology and Immunology, Tianjin Medical University General Hospital, Tianjin, China.

出版信息

Ann Palliat Med. 2022 Oct;11(10):3085-3101. doi: 10.21037/apm-21-2203. Epub 2022 Sep 27.

Abstract

BACKGROUND

Total glucosides of peony (TGP) is extracted from Paeonia lactiflora Pallas, which has been approved for rheumatoid arthritis (RA) treatment. There were approximately 15 monoterpene glycosides identified in TGP. Pervious researches focused on the effects of TGP and the major ingredient paeoniflorin (PF), but the functions of other monoterpene glycosides and their interactions were not clear. Network pharmacology has been one of the new strategies for multi-target drug discovery. In this study, we investigate the functions of all components of TGP and their interactions in RA treatment based on network pharmacology methods.

METHODS

The components of TGP were searched out the Web of Science, PubMed, China National Knowledge Infrastructure databases; then we identified the potential targets based of chemical similarity in the Similarity Ensemble Approach. The molecular related with RA were obtained from DrugBank, GeneCards, DisGeNET and Online Mendelian Inheritance in Man (OMIM) databases. The components-targets-disease network was constructed and analyzed with Cytoscape software; Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted with R for function analysis. The hub components-targets interactions were validated with Autodock Vina.

RESULTS

Twenty potential targets of TGP were predicted for RA treatment. The major components of TGP, PF and albiflorin (AF) had more predicted targets. Hub targets of TGP were LGALS3/9, VEGFA, FGF1, FGF2, IL-6, IL-2, SELP, PRKCA and ERAP1. These targets ameliorated RA mainly through inhibiting leukocyte recruitment and angiogenesis. Enriched pathways including VEGFR pathway, signaling by interleukins, PI3K-Akt signaling pathway, platelet activation, extracellular matrix organization, and so on. The combination of PF, AF and lactiflorin (LF) with the hub targets was further validated using docking program.

CONCLUSIONS

We investigated the comprehensive mechanism of TGP for RA treatment. We analyzed the different targets of the components in TGP and predicted the new effects of TGP on inhibiting leukocyte recruitment and angiogenesis. This study provides a better understanding of TGP on the RA treatment.

摘要

背景

白芍总苷(TGP)是从白芍中提取的,已被批准用于治疗类风湿关节炎(RA)。TGP 中大约有 15 种单萜糖苷被鉴定出来。以前的研究集中在 TGP 和主要成分芍药苷(PF)的作用上,但其他单萜糖苷的功能及其相互作用尚不清楚。网络药理学是多靶点药物发现的新策略之一。在这项研究中,我们基于网络药理学方法研究了 TGP 所有成分的功能及其在 RA 治疗中的相互作用。

方法

通过 Web of Science、PubMed、中国知网数据库搜索 TGP 的成分;然后根据化学相似性在相似性集成方法中确定潜在靶点。从 DrugBank、GeneCards、DisGeNET 和在线孟德尔遗传数据库(OMIM)中获得与 RA 相关的分子。使用 Cytoscape 软件构建和分析成分-靶标-疾病网络;使用 R 进行功能分析,进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)富集分析。使用 Autodock Vina 验证关键成分-靶标相互作用。

结果

预测了 20 个 TGP 治疗 RA 的潜在靶点。TGP 的主要成分 PF 和白芍苷(AF)有更多的预测靶点。TGP 的关键靶点是 LGALS3/9、VEGFA、FGF1、FGF2、IL-6、IL-2、SELP、PRKCA 和 ERAP1。这些靶点主要通过抑制白细胞募集和血管生成来改善 RA。富集的途径包括 VEGFR 途径、白细胞介素信号转导、PI3K-Akt 信号通路、血小板激活、细胞外基质组织等。使用对接程序进一步验证了 PF、AF 和 LF 与关键靶点的结合。

结论

我们研究了 TGP 治疗 RA 的综合机制。我们分析了 TGP 中各成分的不同靶点,并预测了 TGP 抑制白细胞募集和血管生成的新作用。本研究为更好地理解 TGP 治疗 RA 提供了依据。

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