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基于网络药理学和体外实验探讨血必净注射液治疗脓毒症相关性急性呼吸窘迫综合征的机制

[Mechanism of Xuebijing Injection in treatment of sepsis-associated ARDS based on network pharmacology and in vitro experiment].

作者信息

Ding Wei-Chao, Chen Juan, Liao Hao-Yu, Feng Jing, Wang Jing, Zhang Yu-Hao, Ji Xiao-Hang, Chen Qian, Wu Xin-Yao, Sun Zhao-Rui, Nie Shi-Nan

机构信息

Nanjing University of Chinese Medicine Nanjing 210023, China Department of Emergency Medicine, Jinling Hospital(General Hospital of Eastern Theater Command), Medical School of Nanjing University Nanjing 210002, China Department of Emergency Medicine, the Affiliated Hospital of Xuzhou Medical University Xuzhou 221002, China.

Nanjing University of Chinese Medicine Nanjing 210023, China Department of Emergency Medicine, Jinling Hospital(General Hospital of Eastern Theater Command), Medical School of Nanjing University Nanjing 210002, China.

出版信息

Zhongguo Zhong Yao Za Zhi. 2023 Jun;48(12):3345-3359. doi: 10.19540/j.cnki.cjcmm.20230202.703.

Abstract

The aim of this study was to investigate the effect and molecular mechanism of Xuebijing Injection in the treatment of sepsis-associated acute respiratory distress syndrome(ARDS) based on network pharmacology and in vitro experiment. The active components of Xuebijing Injection were screened and the targets were predicted by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). The targets of sepsis-associated ARDS were searched against GeneCards, DisGeNet, OMIM, and TTD. Weishengxin platform was used to map the targets of the main active components in Xuebijing Injection and the targets of sepsis-associated ARDS, and Venn diagram was established to identify the common targets. Cytoscape 3.9.1 was used to build the "drug-active components-common targets-disease" network. The common targets were imported into STRING for the building of the protein-protein interaction(PPI) network, which was then imported into Cytoscape 3.9.1 for visualization. DAVID 6.8 was used for Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment of the common targets, and then Weishe-ngxin platform was used for visualization of the enrichment results. The top 20 KEGG signaling pathways were selected and imported into Cytoscape 3.9.1 to establish the KEGG network. Finally, molecular docking and in vitro cell experiment were performed to verify the prediction results. A total of 115 active components and 217 targets of Xuebijing Injection and 360 targets of sepsis-associated ARDS were obtained, among which 63 common targets were shared by Xuebijing Injection and the disease. The core targets included interleukin-1 beta(IL-1β), IL-6, albumin(ALB), serine/threonine-protein kinase(AKT1), and vascular endothelial growth factor A(VEGFA). A total of 453 GO terms were annotated, including 361 terms of biological processes(BP), 33 terms of cellular components(CC), and 59 terms of molecular functions(MF). The terms mainly involved cellular response to lipopolysaccharide, negative regulation of apoptotic process, lipopolysaccharide-mediated signaling pathway, positive regulation of transcription from RNA polyme-rase Ⅱ promoter, response to hypoxia, and inflammatory response. The KEGG enrichment revealed 85 pathways. After diseases and generalized pathways were eliminated, hypoxia-inducible factor-1(HIF-1), tumor necrosis factor(TNF), nuclear factor-kappa B(NF-κB), Toll-like receptor, and NOD-like receptor signaling pathways were screened out. Molecular docking showed that the main active components of Xuebijing Injection had good binding activity with the core targets. The in vitro experiment confirmed that Xuebijing Injection suppressed the HIF-1, TNF, NF-κB, Toll-like receptor, and NOD-like receptor signaling pathways, inhibited cell apoptosis and reactive oxygen species generation, and down-regulated the expression of TNF-α, IL-1β, and IL-6 in cells. In conclusion, Xuebijing Injection can regulate apoptosis and response to inflammation and oxidative stress by acting on HIF-1, TNF, NF-κB, Toll-like receptor, and NOD-like receptor signaling pathways to treat sepsis-associated ARDS.

摘要

本研究旨在基于网络药理学和体外实验,探讨血必净注射液治疗脓毒症相关性急性呼吸窘迫综合征(ARDS)的效果及分子机制。通过中药系统药理学数据库与分析平台(TCMSP)筛选血必净注射液的活性成分并预测靶点。针对脓毒症相关性ARDS的靶点,检索GeneCards、DisGeNet、OMIM和TTD数据库。利用维生信平台映射血必净注射液主要活性成分的靶点与脓毒症相关性ARDS的靶点,绘制韦恩图以确定共同靶点。使用Cytoscape 3.9.1构建“药物-活性成分-共同靶点-疾病”网络。将共同靶点导入STRING构建蛋白质-蛋白质相互作用(PPI)网络,再导入Cytoscape 3.9.1进行可视化。利用DAVID 6.8对共同靶点进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析,然后使用维生信平台对富集结果进行可视化。选取前20条KEGG信号通路导入Cytoscape 3.9.1构建KEGG网络。最后进行分子对接和体外细胞实验以验证预测结果。共获得血必净注射液的115个活性成分和217个靶点以及脓毒症相关性ARDS的360个靶点,其中血必净注射液与该疾病共有63个共同靶点。核心靶点包括白细胞介素-1β(IL-1β)、IL-6、白蛋白(ALB)、丝氨酸/苏氨酸蛋白激酶(AKT1)和血管内皮生长因子A(VEGFA)。共注释了453个GO术语,包括361个生物过程(BP)术语、33个细胞成分(CC)术语和59个分子功能(MF)术语。这些术语主要涉及细胞对脂多糖的反应、凋亡过程的负调控、脂多糖介导的信号通路、RNA聚合酶Ⅱ启动子转录的正调控、对缺氧的反应以及炎症反应。KEGG富集分析显示85条通路。剔除疾病和通用通路后,筛选出缺氧诱导因子-1(HIF-1)、肿瘤坏死因子(TNF)、核因子-κB(NF-κB)、Toll样受体和NOD样受体信号通路。分子对接表明血必净注射液的主要活性成分与核心靶点具有良好的结合活性。体外实验证实,血必净注射液可抑制HIF-1、TNF、NF-κB、Toll样受体和NOD样受体信号通路,抑制细胞凋亡和活性氧生成,并下调细胞中TNF-α、IL-1β和IL-6的表达。综上所述,血必净注射液可通过作用于HIF-1、TNF、NF-κB、Toll样受体和NOD样受体信号通路调节细胞凋亡以及对炎症和氧化应激的反应,从而治疗脓毒症相关性ARDS。

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